RESEARCH CENTRE

Inria Centre
at Université Côte d’Azur

2023
ACTIVITY REPORT

IN PARTNERSHIP WITH:

INSERM, Aix-Marseille Université, CNRS,
CAC4 MARSEILLE - Institut
Paoli-Calmettes

Project-Team

COMPO

COMPutational pharmacology and clinical
Oncology

IN COLLABORATION WITH: Centre de Recherche en Cancérologie de
Marseille

DOMAIN

Digital Health, Biology and Earth

THEME

Computational Neuroscience and
Medicine

Contents

Project-Team COMPO

1 Team members, visitors, external collaborators

2 Overall objectives

1

2

4

3 Research program

4
4
3.1 Scientific context and motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5
3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
3.3 Mathematical methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
3.3.1 Mechanistic modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
3.3.2 Statistical modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
3.4 Experimental therapeutics in oncology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
3.5 Axis 1: Quantitative modeling for personalized clinical oncology . . . . . . . . . . . . . . . .
3.6 Axis 2: Individualizing anticancer drugs dosing regimen . . . . . . . . . . . . . . . . . . . . .
9
3.7 Axis 3: Optimizing combinatorial strategies with immune checkpoint inhibitors . . . . . . . 10
3.8 Mid-term objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.8.1 Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.8.2 Pharmacological and clinical oncology . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.9 Long-term objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.9.1 Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.9.2 Pharmacological and clinical oncology . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4 Application domains

13

5 Social and environmental responsibility

14
5.1 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

6 Highlights of the year

14
6.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

7 New software, platforms, open data

15
7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
7.1.1 compo.EDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
7.1.2 compo.NLME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
7.1.3 ml.tidy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
7.1.4 stats_pioneer
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
7.1.5 nlml_onco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
7.1.6 metamats
7.1.7 metamatsModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
7.1.8 SChISModeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

8 New results

21

8.1 Predicting survival and trial outcome in non-small cell lung cancer integrating tumor and

blood markers kinetics with machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

8.2 Mechanistic modeling of metastatic relapse in early breast cancer to investigate the biolo-

gical impact of prognostic biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

8.3 Computational markers for personalized prediction of outcomes in non-small cell lung

cancer patients with brain metastases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

8.4 Machine-learning and mechanistic modeling of primary and metastatic breast cancer

growth after neoadjuvant targeted therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
8.5 A comparison of machine learning algorithms for survival with missing data . . . . . . . . . 24
8.6 Pharmacokinetic Modeling for drug delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
8.7 Pancreatic ductal adenocarcinoma ubiquitination profiling reveals specific prognostic and

theranostic markers

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

8.8 Killing a fly with a sledgehammer: Atezolizumab exposure in real-world lung cancer patients 25
8.9 Harnessing tumor immunity with cytotoxics: T cells monitoring in mice bearing lung

tumors treated with anti-VEGF and pemetrexed-cisplatin doublet . . . . . . . . . . . . . . . 26

8.10 Cytidine deaminase status as a marker of response to azacytidine treatment in MDS and

AML patients.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

8.11 Determination of 5-azacitidine in human plasma by LC-MS/MS: application to pharma-

cokinetics pilot study in MDS/AML patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

9 Bilateral contracts and grants with industry

28
9.1 Research contracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
9.2 Clinical trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

10 Partnerships and cooperations

10.1 International research visitors

40
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
10.1.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
10.1.2 Visits to international teams
10.2 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
10.3 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

11 Dissemination

46
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
11.1.2 Journal
11.1.3 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
11.1.4 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
11.1.5 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
11.2.2 Teaching administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
11.2.3 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
11.2.4 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

12 Scientific production

52
12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
12.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
12.3 Cited publications

Project COMPO

1

Project-Team COMPO

Creation of the Project-Team: 2021 May 01

Keywords

Computer sciences and digital sciences

A3.1.1. – Modeling, representation

A3.3.2. – Data mining

A3.3.3. – Big data analysis

A3.4.1. – Supervised learning

A3.4.2. – Unsupervised learning

A3.4.5. – Bayesian methods

A6.1.1. – Continuous Modeling (PDE, ODE)

A9.2. – Machine learning

Other research topics and application domains

B1.1.8. – Mathematical biology

B2.2.3. – Cancer

B2.4.1. – Pharmaco kinetics and dynamics

B2.4.2. – Drug resistance

2

Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Sebastien Benzekry [Team leader, INRIA, Researcher, HDR]

• Joseph Ciccolini [Team leader, AMU APHM, PU-PH, HDR]

Faculty Members

• Dominique Barbolosi [AMU, Professor, HDR]

• David Boulate [APHM, Professor, PUPH, Thoracic surgeon, HDR]

• Raphaelle Fanciullino [AMU APHM, Associate Professor, MCU-PH, HDR]

• Florence Gattacceca [AMU, Associate Professor, HDR]

• Laurent Greillier [AMU, Professor, PU-PH, Head of the Pneumology, allergology and oncology

department, HDR]

• Athanassios Iliadis [AMU, Emeritus, HDR]

• Xavier Muracciole [APHM, Hospital Staff, Radiotherapist]

• Anne Rodallec [AMU, Associate Professor]

• Sebastien Salas [APHM, PU-PH, Medical oncologist, HDR]

Post-Doctoral Fellows

• Paul Dufosse [AMU, Post-Doctoral Fellow]

• Patricia Piris [CNRS, University Paris-Sud, from Mar 2023]

PhD Students

• Celestin Bigarre [INRIA]

• Mohamed Boussena [AMU, from Dec 2023]

• Mathilde Dacos [APHM]

• Anthéa Deschamps [AMU]

• Hafida Hamdache [INSERM, from Oct 2023]

• Mourad Hamimed [AMU]

• Clemence Marin [APHP]

• Linh Nguyen Phuong [AMU]

• Loic Osanno [APHM]

• Jessica Ou [AMU]

• Guillaume Sicard [APHM]

3

Project COMPO

Technical Staff

• Anastasiia Bakhmach [Inserm, Engineer, from Oct 2023]

• Mohamed Boussena [INRIA, Engineer, from Jun 2023 until Nov 2023]

• Sarah Giacometti [AMU, Technician]

• Melanie Karlsen [INSERM, Engineer, until Apr 2023]

• Sophie Marolleau [AMU, Engineer]

• Andréa Vaglio [AMU, Engineer, from Nov 2023]

Interns and Apprentices

• Anastasiia Bakhmach [GRENOBLE INP, Intern, from Feb 2023 until Jul 2023]

• Nastasia Cellier [INRIA, Intern, from Apr 2023 until May 2023]

• Tabea Gnade [Centrale Marseille, Intern, from Apr 2023 until Sep 2023]

• Govind Kallee [APHM, Intern]

• Mariam Mahdjoub [INRIA, Intern, from Mar 2023 until Sep 2023]

• Carla Perez [ERC]

• Dorian Protzenko [APHM, Intern]

• Antonin Ronda [APHM, Intern]

• Ruben Taieb [ENS DE LYON, Intern, from Oct 2023]

Administrative Assistant

• Sandrine Boute [INRIA]

Visiting Scientists

• Mahsa Dehghani Soufi [Univ Tabriz , from Oct 2023]

• Ana Homsek [University of Belgrade, from Oct 2023 until Oct 2023]

• Zeyad Ibrahim [Perkins Institute Perth Australia , from Oct 2023 until Oct 2023]

External Collaborators

• Alice Daumas [AMU, from Mar 2023]

• Nausicaa Malissen [APHM, Dermatologist, onco-dermatology department]

• Quentin Marcou [AMU, from Oct 2023]

• Laetitia Padovani [APHM, PU-PH, Head of the radiotherapy department, HDR]

• Amelie Pouchin [CHU LA TIMONE, from Jul 2023]

4

Inria Annual Report 2023

Figure 1: Mechanistic and statistical modeling for pharmacological and clinical oncology. To account for
the increasing dimension of the quantitative data able to feed mechanistic models, we propose to combine
methods from machine learning and mechanistic modeling. The link between high-dimensional baseline
data (demographic, clinical, pathological, molecular, or biological variables) and model parameters could
be achieved with ML algorithms. Longitudinal data (e.g., tumor size measurements, pharmacokinetics,
immune-monitoring, seric biomarkers, or circulating DNA), possibly in large dimension as well, are
incorporated using mixed-effects statistical modeling, in which dimension reduction techniques are
coupled with mixed-effects modeling. Time-to-event data could also be modeled with a mechanistic basis
(instead of biologically agnostic survival analysis based on, e.g., Cox regression), using adapted, survival
statistical methods. Once identified, the mechanistic models can be used to optimize the scheduling
regimen for clinical trials or treatment individualization.

2 Overall objectives

We aim to optimize therapeutic approaches (i.e., controlling toxicities while ensuring a maximal efficacy)
in oncology using mechanistic and statistical modeling (see Figure 1). These therapeutic approaches
include immunotherapy, radiotherapy, chemotherapy, targeted therapies and their planification: com-
binations, sequences, intensification – densification, dosing and scheduling. Specifically, our research
will be organized along three main axes:

1. Quantitative modeling for personalized clinical oncology.

2. Individualizing anticancer drugs regimen.

3. Optimizing combinatorial strategies with immune checkpoint inhibitors.

Of note, in the Research Priorities document released by the American Society of Clinical Oncology
in February 2021, “Developing and Integrating Artificial Intelligence in Cancer Research”, “Identifying
Strategies That Predict Response and Resistance to Immunotherapies” and “Optimizing Multimodality
Treatment for Solid Tumors” are listed as top-priorities, which fit quite well with our 3 axes.

3 Research program

3.1 Scientific context and motivations

The project-team is based upon the development of model-driven clinical oncology as a means to optim-
ize anticancer therapies. Despite continuous efforts to make available novel drugs beyond traditional

Project COMPO

5

cytotoxic chemotherapy (i.e., oral targeted therapies, biologics, immune checkpoint inhibitors), pro-
gnosis of outcome remains poor for many cancers. The dosing regimen of anticancer drugs given today
remains largely empirical, because dose-finding studies are often performed using outdated, sub-optimal
protocols (such as modified-Fibonacci dose-ranging protocols) or because concomitant administration
is the rule when combining several drugs. Consequently, clinical oncologists struggle to refine the way
they use the anticancer agents made at their disposal. For instance, it took several years of bedside
practice to understand that paclitaxel in breast cancer patients should not be administrated using the
officially approved 150 mg/m² every 3 weeks scheduling, but rather with an alternate 75 mg/m² weekly
dosing [105]. Similarly, multi-targets sunitinib is now given on a 50 mg two-weeks on / one-week off
basis, rather than the officially approved four-weeks on / two-weeks off schedule [93]. Elsewhere, several
combinatorial strategies trials have failed to yield convincing results, mostly because of the lack of a
strong rationale regarding the best way to sequence treatments [86]. Globally, clinical oncology today is
still all about finding the best way to treat patients ensuring an optimal efficacy / safety balance.

After having long been limited to cytotoxic chemotherapy (in addition to surgery and radiotherapy),
the arsenal of anti-cancer agents has dramatically increased over the last two decades. Indeed, major
advances in the understanding of cancer biology that occurred in 1) the discovery and quantification of
(epi)genetic alterations leading to targeted therapy and 2) the realization of the importance of the non-
cancer cell components of tumors, i.e., the tumor micro-environment and tumor immunity, have helped
to identify novel targets. Drugs targeting the tumor vasculature (e.g., first-in-class bevacizumab, approved
in the mid-2000’s [74]) or tumor immunity (e.g., immune checkpoint inhibitors (ICI) such as first-in-class
ipilimumab, approved in the early 2010’s [103]) represent groundbreaking innovations in oncology. ICIs
in particular are considered as game-changing drugs because diseases with once dismal prognosis (e.g.,
metastatic melanoma, non-small cell lung cancer (NSCLC), kidney cancer or head and neck cancer [125])
now show 20-40% of 5-years survival. Nevertheless, these impressive results are limited to a minority of
patients in a limited number of cancers. In addition, no validated biomarker predictive of response has
yet been identified, thus highlighting how early prediction of response and probability of future relapse
are a critical, unmet medical need. The encouraging yet still insufficient clinical results of ICIs have
led current clinical oncology to consider combinations of such immunotherapies with preexisting anti-
cancer modalities: radiation therapy [112], cytotoxic [99], targeted [117] or anti-angiogenic therapies [124].
However, the near-infinite possibilities of combinations in terms of sequencing, dosing and scheduling
challenge the ability of classical trial-and-error methods to find appropriate modes of combination [87].
In addition, day-to-day clinical decisions made by oncologists are based on a large amount of in-
formation, coming from: 1) their own knowledge integrating years of clinical practice combined to
updated literature and 2) objective data coming from multiple sources (demographic data, radiology,
functional imaging, molecular biology, histology, biomarkers, blood counts, etc. . . ). The large amount of
clinical and biological data generated now in clinical oncology is not properly analyzed, because of the
lack of appropriate models to picture the complexity of longitudinal observations. Oncologists lack a
comprehensive framework and numerical software that could support decision of therapeutic strategy
(e.g., to treat or not? to what extent? with what treatment (surgery, radiotherapy, systemic therapy)? in
what order? etc.), especially when their time dedicated to examination of a given patient case is limited
(e.g., in multidisciplinary meetings (RCP), or individual consultations). Furthermore, modeling is the only
way to retrieve similar characteristics from very different experimental conditions and clinical protocols.

To address these major issues, our project-team aims at:

• guiding anticancer therapy by developing patient-specific predictive models (individual level);

• better designing clinical trials, in particular regarding combinatorial trials (population level).

3.2 Data

We use non-clinical and clinical data related with the pharmacology of anti-cancer agents and medical
monitoring of the disease status. The former includes pharmacokinetics (drug levels in plasma (patients)
and full body pharmacokinetics (animal models)), pharmacodynamics (efficacy, safety), pharmacogen-
etics (i.e., constitutional genetic polymorphisms affecting drug transport and metabolizing enzymes),
pharmacogenomics (i.e., molecular and genetic alterations affecting tumor cells). The latter include
demographics, anatomical imaging (e.g., tumor sizes derived from CT scan or MRI), functional imaging

6

Inria Annual Report 2023

(e.g., positron emission tomography), histopathology quantifications, biological variables (such as kidney
and liver functions or blood counts), immuno-monitoring data (flow cytometry) or cell free DNA. We
will especially rely on real-world data (also termed fragmentary data) collected from patient routine
monitoring by our members with hospital activity.

Experimental data are generated by the experimental wet-lab group (involving COMPO team mem-
bers: AR, RF, JC), relying on state-of-the-art experimental pharmacokinetic laboratory fully equipped
to perform in vitro and in vivo explorations of drug metabolism, pharmacokinetics and experimental
therapeutics in oncology, including bioanalytical support and fluorescence/bioluminescence monitoring
in rodents with highly specialized staff. Clinical pharmacokinetics and pharmacogenetics data are gener-
ated by the clinical pharmacology group (JC, RF), relying on the expertise of the clinical pharmacokinetics
laboratory of the La Timone University Hospital of Marseille, an FDA-labelled, ISO15189-labelled facility
with state-of-the-art bioanalytical resources to assay any kind of drugs or drug metabolites in patients.
Specific data regarding cancer biology and pharmacodynamics (immunomonitoring, pharmacogenom-
ics) are generated in collaboration with other CRCM teams.

Clinical data and additional biomarker data are collected from either clinical trials or real-world
studies performed by hospital pharmacists and oncologists of the joint-team (RF, JC, SS, LG, XM) and
their residents, or by other medical oncologist partners. We have strong collaborations and ongoing
projects with pediatrics (Pr N. Andre), hematology (Dr G. Venton), nuclear medicine (Pr D. Taïeb) and
radiotherapy (Pr L. Padovani). Importantly, the project-team is located near the INCa-labeled center for
early clinical trials (CLIP2), thus facilitating the data collection and later, the implementation of modeling
approaches in early clinical trials.

In addition, we also rely on publicly available data from online databases such as the TCGA (genomic

data), the TCIA (imaging data) or data from clinical trials.

3.3 Mathematical methodology

Our primary objective is centered on the improvement of therapeutic strategies in oncology. Nevertheless,
this brings novel methodological challenges requiring developments at the formal level within the
generic field of modeling biological and pharmaco-patho-physiological systems. Difficulties to take
into account include: the longitudinal profile of the quantities of interest; measurement uncertainty
(requiring statistical considerations); difficulties in sampling the real processes leading to scarcity of the
observed data and large inter-individual variability. Many specific problems in life science systems are
very different from that encountered from physical modeling in industrial applications (e.g., mechanical
engineering or energy).

To summarize our methodology, we are interested in modeling the dynamics of pharmaco-oncological
processes (mechanistic modeling) and their inter-individual variability (statistical modeling). Our inten-
ded methodological contributions are: 1) to invent novel mechanistic models for complex physiological
processes able to describe the effect of therapeutic intervention, 2) to design appropriate statistical
frameworks for parameter estimation and description of inter-individual variability, 3) to test and validate
the models against experimental and clinical data, 4) to combine state-of-the art machine learning (ML)
methods with mechanistic models to integrate large dimension data.

3.3.1 Mechanistic modeling

Mechanistic models are defined here as mathematical constructs that describe physiological variables
(e.g., plasma drug concentration, tumor size, or biomarkers) and their dynamics based on physical and
biological principles (e.g., law of mass conservation). They describe the time profiles of the variables of
interest by means of ordinary or partial differential equations (ODEs and PDEs, respectively) and are thus
deterministic.

The main challenge of the modeling exercise is to find the appropriate balance between the degree
of integration of biological phenomena (model complexity) and granularity of the data available (i.e.,
sampling time resolution, observed variables, spatio-temporal or only temporal measurements) ensuring
the feasibility of parameter estimation. Indeed, cancer biology is extremely complex, involving processes
at multiple temporal and spatial scales (intra- and inter-cellular, tissular, organism). It is thus tempting to
build intricate models integrating as many phenomena as possible. Along these lines, the last decades

Project COMPO

7

have witnessed the proliferation of multiple such complex models. We see two shortcomings to this
approach. First, in contrast with models of physical phenomena, the parameters of biological models
are often not directly measurable and thus have to be estimated from fitting the models to experimental
or clinical data. Therefore, their number has to be commensurate to the available data in order to
ensure identifiability. Unfortunately, many complex models from mathematical oncology have too many
parameters to be reasonably identified and have thus had a limited application in terms of biological
insights or clinical applications. Second, complex, multiscale models are characterized by a reductionist
point-of-view whereby general phenomena could be explained by decomposing them into elementary
pieces. However, corresponding elementary experiments would not be suitable for quantification of
the several homeostatic mechanisms involved in the whole real process. Thereby, we do not adhere to
this reductionist vision and for modeling purposes we rather adopt a holistic approach considering the
process as an indivisible whole.

To avoid the above-mentioned caveats, our methodology always starts from: 1) a clinically relevant

medical problem but more importantly 2) the data available to build models.

In several instances, the mechanistic models are ordinary differential equations (ODEs). This is the
case for the simplest type of experimental data that we generate, i.e., tumor growth kinetics. Departing
from previous works establishing models for untreated experimental growth, we are now actively engaged
into designing pharmacokinetics (PK)/ pharmacodynamics (PD) models of the effect of multiple therapies.
These models have to account for the specificities of the drug delivery (e.g., nanoparticles), the biological
effect of the treatments (e.g., cytotoxics, antiangiogenics or immunotherapies) and resistance to the
therapy (either innate or acquired). The resulting models are novel nonlinear ODEs that need to be
validated against the data and, when necessary, theoretically studied for their qualitative behavior.
With the advent of immunotherapies, there has been a regain of interest to modeling tumor-immune
interactions. Again, despite a wide literature on the subject, very few models have been validated against
empirical data. A methodological objective is to establish and validate such models, including effect of
immunotherapies.

Description of other phenomena are more adapted to partial differential equations (PDE) models.
For instance, following an approach initiated by Iwata et al. [108], structured PDE models can be written
for description of a population of metastases (see 4.3.2). Indeed, at the organism scale, cancer diseases
are often characterized by a generalized (metastatic) state. However, few modeling efforts are currently
focused on this aspect. The only validated models in large cohorts for systemic disease concern the sum
of largest diameters as defined by the RECIST criteria [83]. We aim to go beyond this state of the art
by: 1) providing models of coupled tumor growth with interactions (and quantification of inter-lesion
variability) [80, 81] and more importantly 2) developing models accounting not only for growth of the
tumors, but also dissemination (birth of new lesions).

To date, most of the available and collectable clinical data about tumor growth and response to therapy
consist of scalar data, often even limited to lesion diameters or sum of diameters. This is why we primarily
focus our efforts on developing kinetic models of such data, the novelty coming from integrating other
longitudinal biological data (e.g., from blood counts). Nevertheless, imaging data are now increasingly
accessible and recent advances in image analysis allow the automatic segmentation of lesions make it
possible to quantify the spatial shape and texture of tumors without a prohibitive cost for radiologists.
This opens the way to develop spatially distributed PDE models of tumor dynamics. The existing models
have largely remained unconfronted to data, apart from notable exceptions from the Inria MONC [90]
and EPIONE [88] teams, as well as the Swanson [75] and Yankeelov [104] groups. Radiomics approaches
quantifying heterogeneity in the images could bring additional information. We will rely on existing or
establish collaborations with other dedicated Inria teams (EPIONE, MONC) for such purpose. COMPO
would ideally bridge the gap between clinical studies and the Inria ecosystem. Finally, PDE models are
also well adapted to describe intra-tumor drug penetration and we have recently developed such models
for description of intra-tumor fluid flow and transport of antibody nano-conjugates [126].

3.3.2 Statistical modeling

Statistical models are defined here as mathematical constructs that describe the stochastic sources of
variability in the data. They comprise both: 1) classical statistical models defining the functional and
probabilistic relationship explicitly and 2) machine learning (ML) algorithms highly based on the data

8

Inria Annual Report 2023

alone (e.g., tree-based models and associated ensemble methods or support vector machines) [101]. We
use such models for the following purposes: defining appropriate frameworks for parameter estimation;
quantitative testing of biological hypothesis; addressing interindividual variability (using nonlinear
mixed-effects (NLME) modeling); and building predictive models.

NLME – also termed the population approach in PK/PD modeling, or hierarchical modeling [110]
– consists in assuming a statistical distribution of the parameters of the structural (often mechanistic)
model, in order to describe longitudinal observations within a population of individuals. Instead of
estimating individual parameters on a subject per subject basis – leading to identifiability issues in
sparse data situations characteristic of longitudinal measurements in oncology – all data can be pooled
together and a joint likelihood is obtained. Likelihood maximization becomes more complex than
for classical nonlinear regression, nevertheless this problem has already been addressed by means of
algorithms such as the deterministic first-order conditional expansion (FOCE) algorithm [111] or the
stochastic approximation of the expectation-maximization algorithm (SAEM) [92]. These algorithms are
implemented in widely used software in the PMX community such as NONMEM® (Icon) or Monolix®
(Lixoft), or R packages (e.g., saemix or nlme). Once the population distribution is estimated, empirical
Bayes estimates (EBEs) can be derived for estimation of individual parameters. We also use the language
Stan that implements state-of-the art Bayesian methods [84].

Departing from a general distribution of the parameters (often assumed log-normal) with quantified
but unexplained interindividual variability, covariates are incorporated to explain this variability and
build predictive models. This is traditionally done by means of linear models (possibly up to a functional
transformation). However, with the increase in number of such covariates, the traditional tools and al-
gorithms are limited. We thus develop advanced covariate models in NLME incorporating ML algorithms.
Such methods require novel contributions. A possible lead is to first identify the EBEs and then use ML
algorithms to predict these from the covariates [118]. In other cases, ensemble models could be built
from the heterogeneous sources of data, integrating one sub-model from EBEs identified from early data.
Another, more challenging, avenue would be to adapt the parameter estimation algorithms like SAEM to
include ML models in the covariate part.

In addition, because few data have been available longitudinally so far (i.e., small number of quantities
measured at each time point), the current use of NLME relies on models with a small number of output
variables. In this respect, modern clinical oncology studies bring new modeling and statistical challenges
because many more quantitative data are collected at each time point (e.g., hundreds of variables from
immuno-monitoring or possibly tens of thousands from circulating DNA, or radiomics features from
imaging). Defining high-dimensional ODE models describing all the physiologically meaningful variables
becomes intractable, therefore new methods are required. A possible avenue is to have a sequential
approach, using first ML methods to reduce the dimension, then model the reduced number of variables.
Another, more challenging, avenue would be to perform the two tasks (dimension reduction and temporal
modeling) at the same time, and include this in an NLME framework for population estimation. The first
part could be done using tools from unsupervised learning such as auto-encoders.

Following the availability of longitudinal tumor measurements, recent developments in the field
of NLME have concerned joint modeling [94]. This consists in modeling the longitudinal kinetics of a
biomarker (e.g., tumor size) together with censored time-to-event data (e.g., overall survival) in a single
step. Promising results have been obtained so far and we intend to develop methods beyond the state-of-
the art in this area. This includes, in connection with above: 1) extension to models with emergence of
new metastases, 2) integration of high-dimensional covariates and 3) high-dimensional longitudinal data.
This Bayesian integration of data for updated survival predictions could lead to high impact results, as
demonstrated by a recent publication in Cell [109].

Finally, we intend to bring the use of established ML tools to address concrete clinical problems
emerging from the data collected in routine or clinical trials. Indeed, such data is so far analyzed using
traditional statistical methods. ML algorithms could bring added value for predicting efficacy or toxicity
from demographic, clinical and biological data.

3.4 Experimental therapeutics in oncology

The project-team is based upon generating experimental and clinical data to identify and test the models,
and to provide proof-of-concept studies so as to validate the model-based dosing and scheduling prior

Project COMPO

9

to transposing them in patients. Historically, experimental therapeutics in oncology has relied on a
wide variety of in vitro and in vivo models mimicking human cancer disease. In oncology, hundreds
of in vitro models using cancer cell lines cultivated following 2D or 3D (spheroids) fashion, plus more
sophisticated models with cancer cells enriched with fibroblasts or endothelial cells [119], eventually
leading to complex organoids [98]. Similarly, almost all kind of tumors can be tested in vivo, mostly
in small rodents. In oncology, in vivo models are mostly based upon xenografting human tumors
from established cell lines or from patient biopsies (patient-derived xenografts or PDX) so as to better
mimic human pharmacology when testing active compounds next. To achieve this, several strains of
immune-compromised mice have been successfully developed. Because immune checkpoint inhibitors
do not exert direct anti-proliferative activity on cancer cells but are rather expected to harness tumor
immunity, human xenografts in immuno-compromised mice is not anymore a suitable model. This has
led investigators to shift towards immune-competent syngeneic mice models. Non-clinical experiments
with drug candidates in immunotherapy mostly focus on deciphering the pharmacology of the targeted
pathways, assessing the cytokine release potential, studying receptor occupancy, by using models the
most likely to mimic tumor immunity in human. More sophisticated animal models such as human
knock-in mice, immuno-avatar, hemato-lymphoid humanized mice or immune-PDX mice have been
developed [122] (i.e., allowing to test immune checkpoint inhibitors in mice models combining human
xenograft with relevant, humanized immunity and stroma cells) have been made available as well.
Beyond generating data on efficacy such as reduction in primary tumor mass or metastatic spreading,
experimental models help providing as well in depth knowledge on human and animal target cells, in vitro
and in vivo concentration-effect studies, search for biomarkers, plus the most comprehensive knowledge
on animal vs. human differences on dose – exposure – effects relationships and finally drug distribution
throughout the body, target expression, affinity of target-binding and intrinsic efficacy, duration and
reversibility of the effects. In particular, animal drug metabolism and pharmacokinetics (i.e., exploration
of liver metabolism and distribution / absorption processes using in vitro or in vivo dedicated models)
help understanding the disposition and distribution of the drug in the body throughout time, especially
its ability to target tumor tissues (i.e., in vivo distribution in tumor-bearing mice) and help understanding
sources of pharmacokinetic variability. All this information requires state-of-the-art techniques for
measuring drugs and drug metabolites into biological fluids in tissues, such as fluorescence-imaging,
high-performance liquid chromatography or liquid-chromatography-mass spectrometry bioanalysis.
Our team has proven track records in the field of experimental therapeutics in oncology, with two PhDs
on developing anticancer nanoparticles in breast and colorectal cancer [97, 121] plus experiments on
model-driven way to combine anti-angiogenics with cytotoxics in breast and lung cancers [107, 114,
123], model-driven determination of alternate dosing in neuroblastoma, or methodological studies on
monitoring tumor growth [115].

3.5 Axis 1: Quantitative modeling for personalized clinical oncology

The different steps of patient therapeutic management by clinicians consist mostly of: diagnosis, estima-
tion of the extension of the disease, choice of therapy and evaluation of the therapy (efficacy, toxicity).
This axis is specifically concerned with such clinical problems, apart from the pharmacological aspects
addressed in the other axes.

In this axis, we aim to develop mathematical and statistical models and methods able to process
this information to bring added value by inferring hidden parameters and provide simulations and
predictions about the past and future behavior of the disease.

In the short-term (4 years), our research projects are: (1) modeling large-scale longitudinal data from
immuno-oncology for prediction of response to immune checkpoint inhibition (QUANTIC and TGI-ML
projects), (2) developing clinically relevant mathematical models of metastasis and (3) modeling the
kinetics of clinical biomarkers.

3.6 Axis 2: Individualizing anticancer drugs dosing regimen

This axis fits within the “population approach” introduced in the 80’s by L. Sheiner 121 and aims at
gathering exhaustive information about the multiple sources for variability in response in patients
(including but not limited to drug-drug interactions, pharmacogenetics, and cormorbidities affecting

10

Inria Annual Report 2023

renal and liver functions), build specific mechanistic models including relevant covariates and determine
the PK/PD relationships of drugs used in oncology-hematology or for treating solid tumors. This covers
cytotoxics, oral targeted therapies, biologics or immune checkpoint inhibitors. The overall goal is to
achieve precision and personalized drug administration, i.e., the right dosing and scheduling regimen for
the right patient.

In the short-term (4 years), our research projects will be focused on the following objectives: 1) predict
response or toxicity variability dependent on pharmacogenetic (PGx) and pharmacogenomic data, 2)
assess PMX of anticancer agents such as biologics, including ICI, and 3) develop physiologically-based
PK models of nanoparticles distribution. Together, these objectives will allow to gain insights in the
variability in drug response that depends on PK (1, 2 and 3) or germinal genetic alterations (1).

3.7 Axis 3: Optimizing combinatorial strategies with immune checkpoint inhibitors

Our hypothesis is that so many attempts to combine drugs fail not because the underlying pharmacolo-
gical concepts are wrong (such as immunogenic cell death triggered by cytotoxics or radiation therapy, or
increase in T cells infiltration with anti-angiogenics) but because these combinations probably require
fine tuning in terms of dosing, scheduling and sequencing, whereas in practice all the drugs are given
the same day. The goal of this second axis is therefore to shift from current empirical and suboptimal
combinatorial regimen to model-informed designs to best combine drugs and therapeutic approaches
so as to maximize efficacy while controlling toxicities. To do so, we will rely on our pioneering work
about model-driven scheduling in early phase trials for combination of cytotoxic agents in metastatic
breast cancer (MODEL1 trial) [102, 113] and metronomic vinorelbine in lung cancers (MetroVino trial)
[78, 96]. Leveraging the unique multidisciplinary aspect of our team, we implement a fully translational
approach going from experimental therapeutics, PMX and quantitative systems pharmacology, to clinical
trials either in early (phase I/II) or late (phase III) settings. Of note, our group has already an expert-
ise in developing mathematical models determining the best sequencing between chemotherapy and
anti-angiogenics.

In the short-term (4 years), our research projects will be focused on providing model-informed designs

for combining ICI with: (1) cytotoxics, (2) an experimental immunoliposome and (3) radiotherapy.

3.8 Mid-term objectives

3.8.1 Modeling

We plan to achieve two main things on the modeling side: 1) the development of effective numerical
tools (either as web applications or as part of simulation software) and 2) the empirical validation of the
models.

For 1), this includes a tool able to predict response to ICI monotherapy in NSCLC from baseline and
early response data . We will work with our industrial partners (in particular, HalioDX from the PIONeeR
consortium), to transfer the tool for commercial use. Second, we plan to have a validated numerical tool
able to predict metastatic relapse from clinical biomarkers at diagnosis, using our mechanistic model.
This model will integrate the effect of adjuvant therapy (hormonotherapy or cytotoxic therapy) and will be
able to simulate the long-term impact of alternative treatments (e.g., number of cycles to be administered
to prevent distant relapse). It will have been validated from our local databases and will be implemented
in a clinically-usable online tool such as PREDICT [127]. The main difference with this tool will be the
ability to mechanistically simulate the effect of therapy. We plan to have initiated a larger initiative at
the national or European level to collect large data bases, validate further the predictive power of the
model, and refine its structure if required. We will also extend this tool to other pathologies that share
the same problematic (diagnosis at early-stage, important probability of future distant relapse) such as
kidney cancer, following our initial work from preclinical data [76, 79, 91].

The pharmacometric models that will have been developed will also be implemented as clinically
effective dose adaptation numerical tools directly usable to personalize the dose and scheduling of
multiple anti-cancer agents not only by the clinicians and pharmacists from our group, but also by others,
at least at a regional level.

Project COMPO

11

For 2), our strategy of validation is the following. First, during the development phase, a proportion of
the dataset (usually, 30%) is left aside unused for establishment of the model and initial calibration, and
then used as a test set. When the sample size is too small (n ≤ 100 patients), only (nested) cross-validation
is employed to assess the predictive power of the model. Evaluation metrics will be the classical ones,
adapted to the task (classification, regression or survival regression) and include discrimination and
calibration. In addition, specific methods will be used when in the context of mixed-effects approach (e.g.,
visual predictive checks for evaluation at the population level). The second step consists in evaluating
the predictive power of the models in retrospective, external data sets. We have for instance initiated
a collaboration with Dr C. Scherer (Clermont-Ferrand) to validate our metastatic prediction model on
an external database of 3061 patients. The third step is to validate the added value of the model-based
approach compared with the standard of care and is a long-term rather than mid-term objective.

3.8.2 Pharmacological and clinical oncology

In axis 2, in addition to standard drugs, developing tools for similarly better understanding the sources of
therapeutic and PK variability and understanding the PK/PD relationships of cell therapy in oncology
such as CAR-T cell therapies, is a challenging task. The challenge with CAR-T cells is that first, developing
bioanalytical tools to monitor them in patients is not trivial, and second, little but nothing is known
regarding their PK properties and possible sources impacting on PK/PD relationships such as disease
status or immune status of the patient. We aim at developing both a platform to monitor CAR-T cells
and future cell therapies in plasma and mechanistic models to describe the disposition of these new
therapies in the body.

The nanoPBPK model will be extrapolated to humans and used to determine the specifications of an
optimal nanosystem in order to penetrate solid tumors such as pancreatic tumors. The rationally designed
nanosystem will be evaluated in vitro and in vivo in order to validate the approach. The nanoPBPK model
will be interfaced to become a software and be shared with the scientific community. To achieve this
goal, a partnership with ESQlabs, the company developing the opensource PBPK platform PKSim, has
already been approved by both sides. The nanoPBPK model will be combined with pharmacodynamic
modeling describing the effect of the loaded anticancer drug on tumor growth and metastases spread, on
the immune system, and on dose-limiting toxicities.

Our mid-term objective in axis 3 is to assist the design of scheduling regimen for combinatorial
treatments in early phase clinical trials, which represent an important clinical challenge of the next 10
years. To do so, we will benefit from our close connection to the INCa-labeled AP-HM’s center for early
phase clinical trials (CLIP2). Our aim is to design model-based, individualized and adaptive scheduling
regimen that depend on the monitoring of the disease evolution. We plan to run phase I/II trials based on
the model recommendations. Depending on our achievements and success in phase I/II trials our mid-
term goal would be to lead a prospective, randomized, phase III trial comparing a model-based adaptive
regimen to the standard of care for combination of immune checkpoint inhibition with chemotherapy
and/or anti-angiogenic and targeted therapy. According to our team expertise, the target malignancies
would be primarily lung cancer (LG) and head and neck cancer (SS).

3.9 Long-term objectives

At long-term, we globally wish to have established a worldwide leader position in the fields of quantitative
mathematical oncology and PMX, as well as the pharmacokinetics of nanoparticles. We hope that
this would translate into the achievement of three goals: (1) the development of software effectively
used for clinical decision-making and dosing adjustment (estimated achievable), (2) the initiation of
prospective, phase III clinical trials comparing model-guided therapy versus standard of care (highly
challenging), (3) clinical trials of nanosystems designed by our group (estimated achievable). In addition,
we foresee several avenues both in terms of modeling opportunities and applications.

3.9.1 Modeling

Our short-term program is devoted to the development of new models and their confrontation to
empirical data. The mid-term program will focus on the validation and refinement of these models. In

12

Inria Annual Report 2023

the long-term, we foresee that this will bring novel questions in terms of mathematical analysis of the
models. For instance, metastatic modeling will establish validated models for tumor-tumor interactions,
including immune-mediated interactions. In turn, this leads to nonlinear, size-structured, renewal
PDEs. Study of the asymptotic behavior of such equations is non-trivial.

More generally, we expect that physiologically structured PDEs (psPDE) can become relevant to
practical modeling in oncology, from two types of data: flow cytometry and single-cell sequencing.
Flow cytometry is currently becoming of increasing relevance to characterize multiple populations of
cells, for instance in the context of immuno-oncology. In the QUANTIC project (10.2), we are starting to
interact with such data, only by means of scalar quantities so far. However, the structure of this data is to
have, for each cell, a quantitative measure (e.g., a surface marker). Measuring these in a population of
millions or billions of cells makes it adapted to modeling by such psPDE. Similarly, single-cell sequencing
is a technique by which every cell of a population (e.g., in a tumor) is sequenced, thus having mutation
information. In turn, this allows to quantify subclones in the population. Such data has already generated
fascinating results, for instance in the study of metastatic development theories [100, 106]. Although
evolutionary modeling is a wide field with established groups (M. Nowak or F. Michor in Harvard, C. Curtis
in Stanford, T. Graham at the CRUK), few groups are modeling dynamical data at single-cell resolution. To
this regard, the theoretical work initiated by J. Clairambault and B. Perthame (Inria MAMBA) suggesting
to use psPDEs to model evolution in cancer cell populations could be appropriate [85]. Parameter
estimation in such models is a challenging task [95] and data assimilation from flow cytometry or single-
cell sequencing data sets would represent an important avenue. Dynamical data can be provided by
circulating tumor DNA and we have already initiated contacts with an important clinical and biological
study in Marseille on this topic (the SCHISM study, PIs: SS and F. Fina). The recent developments of
technology enabling spatial resolution of single-cell sequencing also paves the way to exciting avenues in
terms of modeling [116].

We will also build models that can optimize the effectiveness of treatments incorporating new criteria
(other than the evolution of tumor mass) of diagnostic and therapeutic evaluation, especially those we
have forged around the information provided by functional imaging (T80) computational algorithm time
at which 80% of FDG is metabolized [77, 89, 120].

3.9.2 Pharmacological and clinical oncology

A general, challenging, long-term objective, would be to run prospective clinical trials in which a model-
informed arm would be compared to the standard of care. In the model-informed arm, therapeutic
decision would be based on the recommendation of the model. This applies to the models developed in
all axes. For instance, in breast cancer, the number of cycles of chemotherapy would be adapted based
on the model indication (axis 1), decision of the maximum tolerated dose in the treatment of leukemia
patients would be based on the PGx/PK/PD model (axis 2) or the combination scheduling regimen would
be given by model calculations (axis 3).

Regarding nanosystems initially designed by our group based on the nanoPBPK modeling, they
will also be tested in early clinical trials. Our group will drive the design of these based on simulations
performed with the nanoPBPK and pharmacodynamic model, in order to guarantee the highest chances of
success while ensuring patients safety. In particular, nanoparticles specifically transporting cytoxics could
replace standard systemic myelo-ablation in hematopoietic stem cell transplantation, a risky strategy with
frequent life-threatening, when not lethal, toxicities. Because of the fully controlled distribution phase
in the body, nanoparticles encapsulating several drugs could thus be implemented in the preparative
regimens for allogeneic stem cell transplantation in leukemia or myeloid malignancies.

In addition, several groups predict that in addition to standard drugs or biologics, or rising gene
therapy and cell therapy strategies, new devices such as nanobots will be developed to treat cancers.
Nanobots are entities which are not designed to interact with standard pharmacological targets or
genes like current anticancer treatments, but could fix the cancer cell, either by providing a missing
protein, or ultimately trigger a mechanic cell-death using radiation, thermal wave, or by disrupting cell
membrane. These new entities should exhibit totally new pharmacokinetics, because they are unlikely
to be metabolized in the liver or to be cleared by the kidney or the biliary tract. Therefore, new models
for PK/PD should be developed, because neither behavior in the body nor intrinsic mechanisms of
action are known yet. In addition, the issue of nanosafety with such devices will be particularly critical

Project COMPO

13

and will require extensive PMX resources, to predict long-term effects or to keep under control the
mechanism of action. Should such nanobots be developed, the COMPO joint-team should develop
specific resources to monitor their fate in the body, specific resources to write equations describing
nanobots/body, nanobots/immune system, and nanobots/cancer cells interactions, plus new global
models encapsulating all the interactions, and pharmacodynamics impact of such devices.

Depending on the expertise we gain in the PK/PD knowledge of those cell therapies as part of the
mid-term objectives, optimizing combinatorial strategies to such cell therapies will be another long-term
objective.

Last, pharmaco-economic studies including impact of quality of life, increase in both tolerability and
efficacy will be performed to determine whether PMX-based dosing is a cost-saving strategy. For instance,
by refining the scheduling of immune checkpoint inhibitors, such as determining, using modeling and
simulation strategies, when the plasma concentration of the drug reaches the threshold in trough levels
necessary to ensure maximal target engagement, it will be possible to customize the frequency of the
administrations, with possible strong impact on treatment costs. By using real-life data, we propose
to use dedicated models to quickly define alternate and more appropriate dosing and/or scheduling
with newly marketed anticancer agents, either chemotherapy, targeted therapies or biologics including
immune checkpoint inhibitors.

4 Application domains

The COMPO research team’s projects all focus on a serial of complementary and inter-related domains
described in an itemized fashion below:

• Health: all the models to be developed within the framework of the COMPO team are related to

improving healthcare;

• Cancer: in particular, the models will be developed to address specific issues related to cancerous

diseases;

• Precision Medicine: in particular, in cancer the developed models will be part of the implementa-

tion of precision medicine in oncology focusing on the following items;

• Combinatorial regimen: developing model-informed strategies to determine the optimal mod-
alities when combining several treatments altogether. With the increasingly diversified arsenal
of therapeutic approaches to treat cancers (surgery, radiotherapy, chemotherapy, targeted and
anti-angiogenic therapy and immunotherapy), defining optimal combination protocols is highly
challenging [87]. This spans the issues of sequencing, scheduling and dosing of those therapies,
which are to date largely addressed using a trial-and-error approaches. Consequently, too many
combinatorial trials fail, and attrition rate with combinatorial immunotherapy is now a rising issue
in clinical oncology and we hypothesize that extensive modeling and pharmacometrics could help
refining the way anticancer drugs are combined;

• Tools for decision-making: developing model-informed strategies to forecast clinical outcome,
i.e., during clinical trials. Assessing the predictive power of markers not only at baseline but also
in their change over time is a current challenge. The information available, on the basis of which
decision is made, includes clinico-demographic variables, classical biomarkers (e.g., blood counts,
thyroglobulin, lactate dehydrogenase levels, etc...) but also an increasing amount of data from
other sources (e.g., immuno-monitoring, anatomical functional imaging or genomics) that require
state-of-the-art modeling to analyze extremely dense and longitudinal data;

• Adaptive dosing strategies: developing model-informed strategies to customize dosing so as to
ensure an optimal toxicity-efficacy ratio. All anticancer agents are approved upon registration trials
performed in highly selected patients (i.e., with controlled lifestyle, little comorbidities, controlled
polymedication and restricted range of age), thus smoothing the interindividual variability. In real-
world practice however, patients are all heavily co-treated with a wide variety of other drugs plus
herbal medications, likely to interact with drug metabolism and transport, and are frequently older

14

Inria Annual Report 2023

than in clinical trials. In addition, genetic polymorphisms affecting genes coding for drug transport
proteins or drug-metabolizing enzymes in the liver, or transcriptional factors can impact as well on
dose-exposure relationships. Consequently, standard dosing may not be suitable in non-standard
patients to reach the adequate drug exposure levels associated with optimal toxicity/efficacy
balance;

• Nanomedicines: developing model-informed strategies to conceptualize drug-loaded nanoparticles
likely to improve the toxicity-efficacy ratio over conventional treatments. As of today, the biod-
istribution phase of anticancer agents is totally aspecific, making "on-taget off-site" actions an
issue because it is associated with drug-related side effects affecting healthy tissues. Nanoparticles
present unique features likely to deliver specifically a high amount of payload directly on a tumor
site, thus improving the antiproliferative action while sparing healthy tissues. In addition, nano-
particles are expected to reshape the tumor micro-environment, making them good candidates to
be further associated with immunotherapy (see Combinatorial Regimen above).

5 Social and environmental responsibility

5.1 Impact of research results

Due to its unique composition including medical oncologists, clinical pharmacologists and mathematical
modelers, COMPO is at stake with important social challenges: oncology healthcare and innovation in
drug development. The software and results developed by COMPO are devoted to these challenges and
aim to be directly used by medical and pharmaceutical oncologists or by the biotech and pharmaceutical
industry to help drug development and biomarker discovery.

To give a few examples:

• the KineticsPro software historically developed by Pr Iliadis is used daily by pharmacists to indi-

vidually adapt the dose of anti-cancer drugs (e.g., for cisplatin or busulfan)

• the compoEDA package is used by physicians to produce automated statistical reports, helping to

analyze the data collected for specific medical questions.

• the nlml_onco software is developed in collaboration with the industry (Roche / Genentech) to
help anticipate the results of phase 3 clinical trials using machine learning models trained on the
results of earlier (phase 2) clinical trials [59]-

• COMPO is in charge of the biostatistical, machine learning and mechanistic modeling analysis of
the large-scale PIONeeR RHU project to identify biomarker signatures predictive of the resistance
to immunotherapy in lung cancer

• the LUCA-pi RHU, led by COMPO member Pr Boulate, will conduct research to implement lung

cancer screening in France (currently not performed)

6 Highlights of the year

• Organisation of the first Mathematics and Medicine workshop in presence of high-impact clinical
oncologists, including Prs F. Barlesi, F. Andre and E. Deutsch, respectively CEO, head of research
and chair of the radiation therapy department, Gustave Roussy Institute.

• The LUCA-pi project (Lung cancer prevention and interception), led by COMPO member Pr D.
Boulate was laureate of the sixth RHU call (large national initiatives for clinical research, 10M€
budget, 5 years duration)

• COMPO is part of the South-ROCK consortium, a Lyon-Marseille integrated research center for
pediatric oncology, that has been laureate of the PEDIACRIEIX national cancer institute call (total
funding 6M€, 5 years duration)

Project COMPO

15

• Pr L. Greillier has been promoted to Head of the Department of Pneumology, Allergology and
Oncology at APHM (Assistance Publique - Hopitaux de Marseille), elected President of the French
group of thoracic oncology (GFPC), and elected General Secretary of the Oncology Group of the
French Respiratory Society (GOLF).

• Dr R. Fanciullino has been elected Chief-Coordinator for the Clinical Pharmacy Group of the Société

Française des Pharmaciens Oncologues (SFPO).

• COMPO has joined the PETRA consortium (réseau PrEclinique et TRAnslationnel de recherche en

neuro-oncologie ) supported by the Canceropôle PACA.

• Two Ph.D. students at COMPO have succesfully defended their PhD’s in 2023: Guillaume Sicard
(May 2023, supervisors Joseph Ciccolini and Raphaelle Fanciullino) and Mourad Hamimed (October
2023, supervisors Joseph Ciccolini and Nicolas André).

6.1 Awards

• Mathilde Dacos : Best Poster Presentation Award, XIVth SFPO Meeting Nancy France (supervisor:

R. Fanciullino)

7 New software, platforms, open data

7.1 New software

7.1.1 compo.EDA

Name: Exploratory Data Analysis for Clinical Oncology Data

Keywords: Clinical oncology, Data visualization, Biostatistics, Statistical analysis

Scientific Description: This library implements as an R package:

• Exploratory analysis:

– Clinical characteristics table

– Kaplan-Meier estimation of the progression-free and overall survival

– Clinical and biological features distribution

• Classification analysis:

– Univariate and multivariate logistic regression

– Odds ratio

– Area under ROC curve

– t test / chi2 test

• Survival analysis:

– Univariate and multivariate Cox regression

– Hazard ratio

– Area under ROC curve

– log-rank test

• Data visualization:

– Correlation plots (Pearson correlation)

– Volcano plots (p-value and adjusted p-value)

– Boxplots (quantitative features) and barplots (qualitative feaures)

– Kaplan-Meier curves

– Automatic comprehensive and customizable statistical reports

16

Inria Annual Report 2023

Functional Description: The package compoEDA aims to provide a comprehensive exploratory analysis
of data from clinical studies in oncology. These studies commonly investigate biological markers
able to reveal and distinguish different tumor profiles, in order to early adapt the therapeutic
strategy for patients.

The objective of this software is to provide a simplified tool for both computational scientists and
clinical researchers to easily generate agraphical results and automatic reports containing the
following analyses:

• Overview and visualization of clinical data and biological markers

• Overview and visualization of clinical data and biological markers

• Univariate and multivariate classification analysis (logistic regression)

• Univariate and multivariate survival analysis (Cox regression, Kaplan-Meier analysis)

• Correlation analysis

• Statistical tests

• Visualization of markers (boxplots, barplots, volcano plots, forest plots ...).

Release Contributions: First release

News of the Year:

• Creation of the package

• Improvement and prettification of the generation of data plots (boxplots, barplots, ...)

• Automation of the results generation: classification and survival analysis

• Automation, refactor and improvements of automatic statistical reports

URL: https://team.inria.fr/compo/

Contact: Sebastien Benzekry

Participants: Sebastien Benzekry, Linh Nguyen Phuong, Celestin Bigarre, Paul Dufosse, Melanie Karlsen

7.1.2 compo.NLME

Keywords: Nonlinear mixed effects models, Biostatistics, Statistics, Cache, Cancer

Scientific Description: Available features:

• Structural models

– constant

– linear

– double exponential

– double exponential with dropout

– hyperbolic

• preprocess blood marker datasets

• preprocess tumor kinetics datasets

• fit NLME models using monolix API

• post-process of results

Available data:

• Tumor Kinetics with dropout data. A simulated dataset of tumor kinetics following the
double-exponential model, with parameters obtained from (Benzekry et al., PAGE 20, 2022),
which deals with the RECIST-based sum of largest diameters (SLD, in mm) of lung cancer
treated with immune-checkpoint blockade (anti-PDL1 drug atezolizumab). Dropout was also
simulated using a Weibull survival model.

Project COMPO

17

• Tumor and Blood marker Kinetics with dropout data. A simulated dataset of joint tumor
and blood markers (albumin C-reactive protein, lactate dehydrogenase, neutrophils) kinetics
following the models and parameters established in (Benzekry et al., PAGE 20, 2022). These
are monitoring data during immune-checkpoint blockade (anti-PDL1drug atezolizumab) in
lung cancer. Dropout was also simulated using a Weibull survival model.

Functional Description: This R package implements a framework to work with Non-linear Mixed effects
models in the context of clinical oncology to predict relapse and survival using longitudinal data.

News of the Year: Development of the initial version of the package.

Publications: hal-04375606, hal-03921394

Contact: Sebastien Benzekry

Participants: Sebastien Benzekry, Celestin Bigarre, Ruben Taieb

7.1.3 ml.tidy

Name: Machine learning with tidymodels

Keywords: Survival analysis, Machine learning, Data analysis, Oncology

Scientific Description: This software maximizes the use of the R package tidymodels.

Functional Description: This package provides multiple functions to perform machine learning analysis
using the ‘tidymodels‘ framework. Tasks include: feature selection, plot feature importances, train,
corss-validate or apply supervised machine learning algorithms (classification or survival analyses),
evaluate metrics of predictive performances, compute learning curves.

Initial development was part of the ‘stats_pioneer‘ package (also called ‘pioneerPackage‘) and
‘ml.tidy‘ evolved as a standalone package only in February 2023.

News of the Year:

• Added "LASSO" as an option to compute incremential model by varying the

penalization value lambda in a logistic LASSO regression

• Fixing one hot encoding for non-dichotomic variables that are qualitative

• Creating vignettes (long form HTML guide) for ML classification and survival algorithms

• Cleaning documentation and description of function in package and formatting for better

readability

URL: https://gitlab-int.inria.fr/compo/ml.tidy

Publications: hal-03478003, hal-03926538, hal-03928784

Authors: Sebastien Benzekry, Melanie Karlsen, Mohamed Boussena

Contact: Sebastien Benzekry

7.1.4 stats_pioneer

Name: Statistical analysis and reporting of the PIONeeR study

Keywords: PIONeeR, Statistical analysis, Biostatistics

Functional Description: This software was built to analyse the PIONeeR (Precision Immuno-Oncology
for advanced Non-small cell lung cancer patients with PD-(L) 1 ICI Resistance) data. PIONeeR
is a prospective, multicenter study with primary objective being to validate the existence of a
hypothetical immune profile explaining resistance to immunotherapy in non-small cell lung cancer
patients.

18

Inria Annual Report 2023

It initially integrated preprocessing, exploratory data analysis, visualization, statistical analysis,
feature selection, machine learning and results generation and reporting. Since, exploratory data
analysis, visualization and statistical analysis have been promoted to the COMPO-level ‘compoEDA‘
package and feature selection and machine learning to the COMPO-level ‘ml.tidy‘ package.

This software corresponds to the very first step of the data analysis, which is the preprocessing, and
the very last: generation of results. Some of its functions aim at:

• preprocessing the data (creation of clinical variables, dictionary, outcome variables, data

monitoring and correcctions, treatment of the variables types)

• generating the tools to load the data and metadata

• computing statistical tests, logistic or Cox regression, or performing a correlation analysis

• visualising the data (boxplots, barplots, survival curves, ROC curves, volcano plots)

• providing detailed and interactive statistical reports on the data

• automating the production of these reports using Gitlab CI/CD

News of the Year: Two releases of the analyses of the data locks 3 and 4. Analysis of data lock 5 ongoing

(60%). Analyses performed included:

• Preprocess:

– monitoring and corrections

– multiple data and metadata wrangling and organization

• Improved statistical analysis reports:

– refactor and improvement of multiple visualizations

– patient history app displays longitudinal markers

– factorization of automatic reports, then promoted to the ‘compoEDA‘ COMPO-wise

package

– extensive univariate statistical analysis of all biomarkers at screening (436 variables)

– shiny apps for patient history and variable summary

• Continuous integration / continuous development:

– refactor

– extensions

– centralized website for sharing the results with the partners

• Feature selection:

– clean and reproducible versions of the feature selection stability report: ‘reports/technical_reports/supervised/feature_selection/signature_volatility‘

– benchmark of FS methods

– unsupervised analysis of BOLASSO-filtered variables (clustering)

– multiple signature defined and investigated

• Machine learning (ML) for prediction of early progression in 2+ line patients:

– clean Rmd ML reports

– clean low-NA dataset

– classical ML algorithms benchmark

- Addition and development of a python package python-based analyses:

• Reporting:

– scripts for reproducible Figures of three ongoing papers

– slide decks for the two biannual general assembly meetings

URL: https://gitlab-int.inria.fr/pioneer/pioneer

Publications: hal-03926538, hal-03928784

Project COMPO

19

Authors: Sebastien Benzekry, Melanie Karlsen, Kokou Atsou, Paul Dufosse, Celestin Bigarre, Mohamed

Boussena, Andréa Vaglio

Contact: Sebastien Benzekry

Partners: Veracyte, Innate pharma, APHM

7.1.5 nlml_onco

Name: Nonlinear mixed-effects modeling and machine learning for oncology

Keywords: Nonlinear mixed effects models, Machine learning, Oncology, Survival analysis

Scientific Description:

• Exploratory data analysis

• Automated mixed-effects modeling analysis

• Preprocess

• Analysis of high dimensional transcriptomic data

• Dimension reduction

• Feature selection

• Survival machine learning algorithms

• Applications (+ 3,000 patients)

– individual predictions of survival

– predictions of hazard ratios for applications in drug development, for both monotherapy

and combinatorial treatments

Functional Description: This software analyses multiple data arising from clinical oncology (routine

care and clinical trials). This data can be of two types:

• Static (e.g., baseline features), from clinical, biological, molecular (e.g., transcriptomic or

mutation data)

• Longitudinal (multiple time points per individual): tumor kinetics, biomarkers. The second
type is modeled using the framework of nonlinear mixed-effects modeling. All features are
then analyzed using data science techniques (preprocess, feature selection, machine learning
algorithms), in order to predict survival outcome.

Release Contributions:

• Established the first clean version of the software with multiple use cases

• Checked that all runs and produces the expected outputs

• Separated code from input (data) and output

News of the Year:

• New visualization feature

• Code refactor

• Fixed minor bugs

• Extensive use for generation of results

URL: https://gitlab.inria.fr/benzekry/nlml_onco

Publications: hal-04375606, hal-03921394, hal-03935768, hal-03935786, hal-03935812

Contact: Sebastien Benzekry

Participant: Sebastien Benzekry

Partner: Roche

20

7.1.6 metamats

Inria Annual Report 2023

Keywords: Statistical modeling, Tumor growth

Functional Description: This R package is the implementation of a general framework to build and use
models of the metastatic process based on the initial model of Iwata et al. (2000). The family of
model that can be built describe the metastatic disease with a partial differential equation (pde) on
the size structured distribution of the tumors. These models have three components, a function
that characterize the growth of the primary tumor, a function that characterize the growth of the
metastases, and a dissemination function that decribes how new metastases are produced.

Release Contributions: Features:

• Model structure

• Direct computation of N (t ) (C++)

• Individual fit of cumulative size distribution (direct only)

• Many diagnostic plots

News of the Year: Development of the first version of the sofware and several updates.

This sofware was used and will be described in a work to be published in 2024. The application was
the use of this model to describe the dynamics of the brain metastasis disease in small cell lung
cancer patient with or without prophylactic cranial irradiation (PCI) and study the impact of PCI
on the overall survival of the patients as well as the progression of the brain disease.

URL: https://gitlab.inria.fr/cbigarre/metamats

Contact: Celestin Bigarre

Participants: Sebastien Benzekry, Celestin Bigarre

7.1.7 metamatsModels

Keyword: Tumor growth

Functional Description: A collection of models implementation for the metamats R package

Release Contributions: Available models:

• Gompertz growth model (alpha, mu parametrization)

• Gompertz growth model (alpha, K parametrization)

• Gompertz with dormancy growth model (alpha, mu, tau parametrization)

• Proportional dissemination model

• Power dissemination model (mu, gamma parametrization)

• Iwata model object (identical gompertz growth, proportional dissemination)

• Benzekry model object (identical gompertz growth with dormance, power diss)

News of the Year: Development of the first version of the sofware and several updates.

This sofware is works in combinaison with the metamats R package and was used and will be
described in a work to be published in 2024 (see the BIL entry of metamats R package for more
details).

Contact: Celestin Bigarre

Participants: Celestin Bigarre, Sebastien Benzekry

Project COMPO

21

7.1.8 SChISModeling

Name: SChISM modeling

Keywords: SChISM, Statistical analysis, Biostatistics

Scientific Description:

• Preprocess

• Exploratory data analysis

• Classification analysis (logistic regression)

• Survival analysis (Cox regression)

• Mixed-effects modeling analysis

Functional Description: SChISModeling aims to analyze SChISM data (Size CfDNA Immunotherapies
Signature Monitoring). SChISM is a clinical study that introduces an innovative approach to
quantify circulating free DNA in cancer patients treated with immunotherapy. The study’s object-
ive is to early predict response to immunotherapy in patients at an advanced/metastatic stage
according to these quantitative cfDNA data.

This software corresponds to the very first step of the data analysis, which is the statistical analysis.
Some of its functions aim at:

• preprocessing the data (creation of clinical variables, dictionary, outcome variables, clinical

biomarkers, treatment of the variables types)

• computing statistical tests, logistic or Cox regression, performing a correlation analysis

• visualizing the data (boxplots, barplots, survival curves, ROC curves, volcano plots)

• providing detailed and interactive statistical reports on the data

News of the Year:

• Preprocess

• Exploratory data analysis

• Classification analysis (logistic regression)

• Survival analysis (Cox regression)

• Mixed-effects modeling analysis

Contact: Sebastien Benzekry

Participants: Sebastien Benzekry, Sebastien Salas, Linh Nguyen Phuong

8 New results

8.1 Predicting survival and trial outcome in non-small cell lung cancer integrating

tumor and blood markers kinetics with machine learning

Participants:

Sébastien Benzekry, Mélanie Karlsen, Célestin Célestin, Abdessa-
mad El Kaoutari, Rene Bruno (Roche Genentech), Ales Neubert (Roche
pRED), François Mercier (Roche Genentech), Martin Stern (Roche
pRED), Bruno Gomes (Roche pRED), Suresh Vatakuti (Roche pRED),
Peter Curle (Roche pRED), Candice Jamois (Roche pRED).

Funding and data: Roche pRED
Publication: Submitted to Clinical Cancer Research (preprint in medrxiv [59]), Oral communications

[33, 35, 34, 36, 37]

Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine
learning integration. We introduce a novel kinetics-machine learning (kML) model that integrates

22

Inria Annual Report 2023

baseline markers, tumor kinetics and four on-treatment simple blood markers (albumin, CRP, lactate
dehydrogenase and neutrophils).

Developed for immune-checkpoint inhibition (ICI) in non-small cell lung cancer on three phase 2
trials (533 patients), kML was validated on the two arms of a phase 3 trial (ICI and chemotherapy, 377
and 354 patients). It outperformed the current state-of-the-art for individual predictions with a test set
c-index of 0.790, a 12-months survival accuracy of 78.7% and a hazard ratio of 25.2 (95% CI: 10.4 – 61.3,
p < 0.0001) to identify long-term survivors.

Critically, kML predicted the success of the phase 3 trial using only 25 weeks of on-study data (pre-
dicted HR = 0.814 (0.64 – 0.994) versus final study HR = 0.778 (0.65 – 0.931)). Our model constitutes a
valuable approach to support personalized medicine and drug development.

8.2 Mechanistic modeling of metastatic relapse in early breast cancer to investigate

the biological impact of prognostic biomarkers

Participants: Célestin Bigarré, François Bertucci (Paoli Calmettes Institute), Pas-
cal Finetti (Paoli Calmettes Institute), Gaëtan Macgrogan (Bergonié
Institute), Xavier Muracciole, Sébastien Benzekry.

Funding: Inria-Inserm Phd Grant
Publication: Published in ”Computer Methods and Programs in Biomedicine” [15] and AACR com-

munication in 2022 [82]

Background and Objective Estimating the risk of metastatic relapse is a major challenge to decide
adjuvant treatment options in early-stage breast cancer (eBC). To date, distant metastasis-free survival
(DMFS) analysis mainly relies on classical, agnostic, statistical models (e.g., Cox regression). Instead, we
propose here to derive mechanistic models of DMFS.

Methods The present series consisted of eBC patients who did not receive adjuvant systemic therapy
from three datasets, composed respectively of 692 (Bergonié Institute), 591 (Paoli-Calmettes Institute,
IPC), and 163 (Public Hospital Marseille, AP-HM) patients with routine clinical annotations. The last
dataset also contained expression of three non-routine biomarkers. Our mechanistic model of DMFS
relies on two mathematical parameters that represent growth (α) and dissemination (µ). We identified
their population distributions using the mixed-effects modeling. Critically, we propose a novel variable
selection procedure allowing to: (i) identify the association of biological parameters with either α, µ or
both and (ii) generate an optimal candidate model for DMFS prediction.

Results We found that Ki67 and Thymidine Kinase-1 were associated with α and nodal status and
Plasminogen Activator Inhibitor-1 with µ. The predictive performances of the model were excellent in
calibration but moderate in discrimination, with c-indices of 0.72 (95% confidence interval [0.48, 0.95],
AP-HM), 0.63 ([0.44, 0.83], Bergonié) and 0.60 (95% CI [0.54, 0.80], IPC).

Conclusions Overall, we demonstrate that our novel method combining mechanistic and advanced
statistical modeling is able to unravel the biological roles of clinicopathological parameters from DMFS
data.

8.3 Computational markers for personalized prediction of outcomes in non-small

cell lung cancer patients with brain metastases

Participants:

Sébastien Benzekry, Pirmin Schlicke (TU München), Alice Mo-
genet (AP-HM), Laurent Greillier, Pascale Tomasini (AP-HM), Ele-
onore Simon (AP-HM).

Project COMPO

23

Data: early-stage NSCLC patients with brain metastases, Multidisciplinary oncology and therapeutic

innovation department, APHM

Publication: [14]

Background Intracranial progression after curative treatment of early-stage non-small cell lung cancer
(NSCLC) occurs from 10 to 50% and is difficult to manage, given the heterogeneity of clinical presentations
and the variability of treatments available.

Objective The objective of this study was to develop a mechanistic model of intracranial progression to
predict survival following a firstbrain metastasis (BM) event occurring at a time T B M .

Methods Data included early-stage NSCLC patients treated with a curative intent who had a BM as
the first and single relapse site (N = 31). We propose a mechanistic mathematical model able to derive
computational markers from primary tumor and BM data at TB M and estimate the amount and sizes of
(visible and invisible) BMs, as well as their future behavior. These two key computational markers are α ,
the proliferation rate of a single tumor cell; and µ, the per day, per cell, probability to metastasize. The
predictive value of these individual computational biomarkers was evaluated.

Results The model was able to correctly describe the number and size of metastases at TB M for 20
patients. Parameters α and µ were significantly associated with overall survival (OS) (HR 1.65 (1.07–2.53)
p = 0.0029 and HR 1.95 (1.31–2.91)p = 0.0109, respectively). Adding the computational markers to the
clinical ones significantly improved the predictive valueof OS (c-index increased from 0.585 (95% CI
0.569–0.602) to 0.713 (95% CI 0.700–0.726), p < 0.0001). We demonstrated that our model was applicable
to brain oligoprogressive patients in NSCLC and that the resulting computational markers had predictive
potential.

Conclusion This may help lung cancer physicians to guide and personalize the management of NSCLC
patients with intracranial oligoprogression.

8.4 Machine-learning and mechanistic modeling of primary and metastatic breast

cancer growth after neoadjuvant targeted therapy

Participants:

Sébastien Benzekry, Michalis Mastri, Chiara Nicolò, John ML Ebos.

Data: Preclinical data of primary tumor and metastatic growth in 128 mice.
Publication: under revision at PLoS Computational Biology [60]

Background Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink
tumors prior to surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity,
and suppression of distant (occult) metastatic disease. Yet such trials are rarely preceded by preclinical
testing involving surgery.

Objective Here we used a mouse model of spontaneous metastasis after surgical removal to develop
a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine
kinase inhibitor (RTKI).

Methods Longitudinal data consisted of measurements of presurgical primary tumor size and post-
surgical metastatic burden in 128 mice (104 for model training, 24 for validation), following variable
neoadjuvant treatment schedules over a 14-day period. A nonlinear mixed-effects modeling approach
was used to quantify inter-animal variability. Machine learning algorithms were applied to investigate the
significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included

24

Inria Annual Report 2023

circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor
cells) as well as immunohistochemical tumor proteins (CD31 and Ki67).

Results Our simulations showed that neoadjuvant RTKI treatment inhibits primary tumor growth
but has little efficacy in preventing (micro)-metastatic disease progression after surgery. Surprisingly,
machine-learning algorithms demonstrated only limited predictive power of tested biomarkers on the
mathematical parameters. These results suggest that presurgical modeling might be an effective tool to
screen biomarkers prior to clinical trial testing.

Conclusion Mathematical modeling combined with artificial intelligence techniques represent a novel
platform for integrating preclinical surgical metastasis models in outcome prediction of neoadjuvant
treatment.

8.5 A comparison of machine learning algorithms for survival with missing data

Participants:

Paul Dufossé, Sébastien Benzekry.

Funding: Laënnec Institute.
Publication: [39]
Survival analysis is an essential tool for the study of health data. An inherent component of such
data is the presence of missing values. In recent years, researchers proposed new learning algorithms for
survival tasks based on neural networks. Here, we studied the predictive performance of such algorithms
coupled with different methods for handling missing values on simulated data that reflect a realistic
situation, i.e., when individuals belong to unobserved clusters. We investigated different patterns of
missing data. The results show that, without further feature engineering, no single imputation method is
better than the others in all cases. The proposed methodology can be used to compare other missing
data patterns and/or survival models. The Python code is accessible via the package survivalsim.

8.6 Pharmacokinetic Modeling for drug delivery

Participants:

Anne Rodallec
, Sophie Marolleau
, Sébastien Benzekry
.

Data: PK and PD data on breast cancer bearing mice, Paris-Sud.

Background Commercial Paclitaxel (PTX) formulations such as Taxol are associated with adverse drug
toxicities related to the added emulsionizer. Therefore, PTX formulations have been explored as they
could increase efficacy and have improved pharmacokinetic (PK) properties. Recently our partners have
developed a new polymer prodrug of PTX for which we proposed that metronomic dosing could result in
an effective and tolerable anticancer treatment that is simultaneously convenient for patients as it allows
for subcutaneous (SC) administration.

Objective The objective of this study was to develop a PK/PD model of a novel polymer prodrug
subcutaneously injectable to predict the best administration scheme.

Methods We developed a population PK model and calibrated it with experimental data of a novel
PTX-Polymer from a separate study using Monolix software.

Project COMPO

25

Results The model was able to correctly describe treatment pharmacokinetic and efficacy profiles.
Pharmacokinetic was described using a two-compartment model with an additional compartment
for the polymer groupe. We selected the reduced Gompertz model as the optimal model to represent
unperturbed tumor growth using available pharmacodynamic (PD) data. Efficacy was best described
using Simeoni model. We discovered that adding drug resistance of the treatment efficacy was necessary
to correctly fit the data. Our simulations show that administrating the prodrug daily is a promising
strategy for further preclinical studies.

Conclusion We developed a PK/PD model describing preclinical data of a novel anticancer polymer
prodrug and validated that metronomic dosing could result in greater efficacy.

8.7 Pancreatic ductal adenocarcinoma ubiquitination profiling reveals specific pro-

gnostic and theranostic markers

Participants:

Samad El Katouary, Joseph Ciccolini
.

Background and objective Pancreatic ductal adenocarcinoma (PDAC) has been widely studied at
multiomics level. However, little is known about its specific ubiquitination, a major post-translational
modification (PTM). As PTMs regulate the final function of any gene, we decided to establish the ubiquit-
ination profiles of 60 PDAC.

Methods We used specific proteomic tools to establish the ubiquitin dependent proteome (ubiquitinome)
of frozen PDXs (Patients’ derived xenographs). Then, we performed bioinformatics analysis to identify
the possible associations of these ubiquitination profiles with tumour phenotype, patient survival and
resistance to chemotherapies. Finally, we used proximity ligation assays (PLA) to detect and quantify the
ubiquitination level of one identified marker.

Results We identified 38 ubiquitination site profiles correlating with the transcriptomic phenotype
of tumours and four had notable prognostic capabilities. Seventeen ubiquitination profiles displayed
potential theranostic marker for gemcitabine, seven for 5-FU, six for oxaliplatin and thirteen for irinotecan.
Using PLA, we confirmed the use of one ubiquitination profile as a drug-response marker, directly on
paraffin embedded tissues, supporting the possible application of these biomarkers in the clinical setting.

Conclusion These findings bring new and important insights on the relationship between ubiquitina-
tion levels of proteins and different molecular and clinical features of PDAC patients. Markers identified
in this study could have a potential application in clinical settings to help to predict response to chemo-
therapies thereby allowing the personalization of treatments.

8.8 Killing a fly with a sledgehammer: Atezolizumab exposure in real-world lung

cancer patients

Participants:

Sophie Marolleau, Mourad Hamimed, Joseph Ciccolini
.

Background Atezolizumab is an anti-PDL1 approved for treating lung cancer. A threshold of 6 µg/mL in
plasma has been associated with target engagement. The extent to which patients could be overexposed
with the standard 1200 mg q3w dosing remains unknown.

26

Inria Annual Report 2023

Patients and Methods Here, we monitored atezolizumab peak and trough levels in 27 real-world
patients with lung cancer as part of routine therapeutic drug monitoring. Individual pharmacokinetic (PK)
parameters were calculated using a population approach and optimal dosing-intervals were simulated
with respect to the target trough levels.

Results No patient had plasma levels below 6 µg/mL. The results showed that the mean trough level
after the first treatment was 78.3 ± 17 µg/mL, that is, 13 times above the target concentration. The overall
response rate was 55.5 percent. Low-grade immune-related adverse events was observed in 37 percent of
patients. No relationship was found between exposure metrics of atezolizumab (i.e., minimum plasma
concentration, maximum plasma concentration, and area under the curve) and pharmacodynamic end
points (i.e., efficacy and toxicity). Further simulations suggest that the dosing interval could be extended
from 21 days to 49 up to 136 days (mean: 85.7 days, i.e., q12w), while ensuring plasma levels still above
the 6 µg/mL target threshold. This observational, real-world study suggests that the standard 1200 mg
q3w fixed-dose regimen of atezolizumab results in significant overexposure in all the patients. This was
not associated with increased side effects. As plasma levels largely exceed pharmacologically active
concentrations, interindividual variability in PK parameters did not impact efficacy.

Conclusion Our data suggest that dosing intervals could be markedly extended with respect to the
target threshold associated with efficacy.

8.9 Harnessing tumor immunity with cytotoxics: T cells monitoring in mice bearing

lung tumors treated with anti-VEGF and pemetrexed-cisplatin doublet

Participants: Guillaume Sicard, Dorian Protzenko, Sarah Giacometti, Joseph Cic-

colini, Raphaelle Fanciullino
.

Background Successful immunotherapy is restricted to some cancers only, and combinatorial strategies
with other drugs could help to improve their efficacy. Here, we monitor T cells in NSCLC model after
treatment with cytotoxics (CT) and anti-vascular endothelial growth factor (VEGF) drugs, to understand
when immune checkpoint inhibitors should be best associated next.

Methods
In vivo study was performed on BALB/c mice grafted with KLN205 cells. Eight treatments
were tested including control, cisplatin and pemetrexed as low (LD CT) and full (MTD CT) dose as single
agents, flat dose anti-VEGF and the association anti-VEGF + CT. Full immunomonitoring was performed
by flow cytometry on tumor, spleen and blood over 3 weeks.

Results
Immunomodulatory effect was dependent upon both treatments and time. In tumors, combin-
ation groups shown numerical lower Treg cells on Day 21. In spleen, anti-VEGF and LD CT group shown
higher CD8/Treg ratio on Day 7; on Day 14, higher T CD4 were observed in both combination groups.
Finally, in blood, Tregs were lower and CD8/Treg ratio higher, on Day 14 in both combination groups. On
Day 21, CD4 and CD8 T cells were higher in the anti-VEGF + MTD CT group.

Conclusions Anti-VEGF associated to CT triggers notable increase in CD8/Tregs ratio. Regarding the
scheduling, a two-week delay after using anti-VEGF and CT could be the best sequence to optimize
antitumor efficacy.

8.10 Cytidine deaminase status as a marker of response to azacytidine treatment in

MDS and AML patients.

Project COMPO

27

Participants: Mélanie Donnette, Mourad Hamimed, Guillaume Sicard, Joseph Cic-

colini, Raphaelle Fanciullino
.

Background Azacitidine (Aza) is a mainstay of treatment for patients with acute myeloid leukaemia
(AML) ineligible for induction chemotherapy and other high-risk myelodysplastic syndromes (MDS).
Only half of patients respond, and almost all will eventually relapse. There are no predictive markers of
response to Aza. Aza is detoxified in the liver by cytidine deaminase (CDA).

Patients and Methods Here, we investigated the association between CDA phenotype, toxicity and
efficacy of Aza in real-world adult patients.

Results Median overall survival (OS) was 15 months and 13 months in AML and high-risk MDS patients
respectively. In addition, our data suggest that delaying Aza treatment was not associated with lack of
efficacy and should not be considered a signal to switch to an alternative treatment. Half of the patients
had deficient CDA activity (i.e. <2 UA/mg), with a lower proportion of deficient patients in MDS patients
(34 percent) compared to AML patients (67 percent). In MDS patients, CDA deficiency correlated with
longer landmark OS (14 vs. 8 months; p = 0.03), but not in AML patients.

Conclusions Taken together, our data suggest that CDA is an independent covariate and may therefore
be a marker for predicting clinical outcome in MDS patients treated with Aza.

8.11 Determination of 5-azacitidine in human plasma by LC-MS/MS: application to

pharmacokinetics pilot study in MDS/AML patients

Participants:

Loic Osanno, Mélanie Donnette, Joseph Ciccolini, Raphaelle Fan-
ciullino
.

Background Azacitidine (Vidaza®, AZA) is a mainstay for treating acute myeloid leukemia (AML) in
patients unfit for standard induction and other myelodysplastic syndromes (MDS). However, only half of
the patients usually respond to this drug and almost all patients will eventually relapse. Predictive markers
for response to AZA are yet to be identified. AZA is metabolized in the liver by a single enzyme, cytidine
deaminase (CDA). CDA is a ubiquitous enzyme coded by a highly polymorphic gene, with subsequent
great variability in resulting activities in the liver. The quantitative determination of AZA in plasma is
challenging due the required sensitivity and because of the instability in the biological matrix upon
sampling, possibly resulting in erratic values.

Methods We have developed and validated following EMA standards a simple, rapid, and cost-effective
liquid chromatography-tandem mass spectrometry method for the determination of azacitidine in
human plasma.

Results After a simple and rapid precipitation step, analytes were successfully separated and quantitated
over a 5-500 ng/mL range. The performance and reliability of this method were tested as part of an
investigational study in MDS/AML patients treated with standard azacitidine (75 mg/m2 for 7 days a
week every 28 days).

Conclusion Overall, this new method meets the requirements of current bioanalytical guidelines and
could be used to monitor drug levels in MDS/AML patients.

28

Inria Annual Report 2023

9 Bilateral contracts and grants with industry

Participants:

Sebastien Benzekry,
Laurent Greillier, Anne Rodallec, Sebastien Salas.

Joseph Ciccolini, Raphaelle Fanciullino,

9.1 Research contracts

kML 2.0

• Partner: Genentech

• Title: Kinetics - machine learning modeling for prediction of outcome in oncology

• Funding: 25 k€

• Duration: Oct 2023 - Apr 2024

• Principal investigator: S. Benzekry

NanoImmuno

• Partner: Institut Roche France and Genentech US

• Title: NanoImmuno: a nanoparticle to harness tumor immunity in her2+ breast cancer

• Funding: 80 k€

• Duration: Jan 2020 - March 2023

• Principal investigator: Raphaelle Fanciullino (Senior), Anne Rodallec (Junior)

9.2 Clinical trials

CetuxiMAX

• Registration: NCT4218136

• Partner: Merck Serono

• Title: Maximizing Cetuximab efficacy in head and neck cancer patients through PK/PD modeling

• Funding: 40 k€

• Duration: 2020 - 2024

• Principal investigator: Sébastien Salas

IMHOTEP

• Registration: NCT04795661

• Partner: INCa

• Title: Immunotherapy in MSI/dMMR Tumors in Perioperative Setting

• Funding: 800 k€

• Duration: 2023 - 2027

• COMPO investigator: Joseph Ciccolini

MOIO

Project COMPO

29

• Registration: NCT05078047

• Partner: BMS and INCa

• Title: Study Comparing the Standard Administration of IO Versus the Same IO Administered Each 3

Months in Patients With Metastatic Cancer in Response After 6 Months of Standard IO

• Funding: 500 k€

• Duration: 2020-2024

• Principal investigator: Gwenaelle Gravis (IPC)

• COMPO investigator: Joseph Ciccolini

PEMBOV

• Registration: NCT03596281

• Partner: INCa

• Title: Pembrolizumab in Combination With Bevacizumab and Pegylated Liposomal Doxorubicin in

Patients With Ovarian Cancer (PEMBOV)

• Funding: 700 k€

• Duration: 2020-2023

• Principal investigator: Judith Mitchels (IGR)

• COMPO investigator: Joseph Ciccolini

REZOLVE

• Registration: ANZGOG-1101

• Partner: Sydney Medical Center Australia

• Title: Pembrolizumab in Combination With Bevacizumab and Pegylated Liposomal Doxorubicin in

Patients With Ovarian Cancer (PEMBOV)

• Funding: Aus$ 800k

• Duration: 2018-2024

• Principal investigator: Sonia Yip (Sydney University)

• COMPO investigator: Joseph Ciccolini

DROP

• Registration: NCT03257969

• Partner: INCa

• Title: Drug Related problems in Oncology Practice

• Funding: 400 k€

• Duration: 2018 - 2023

• COMPO investigator: Raphaelle Fanciullino

VIDAZAML

• Registration: EudraCT 2020-A02042-37

30

Inria Annual Report 2023

• Partner: APHM and CAL Nice

• Title: Role of CDA and dCK metabolic enzymes in clinical response of patients treated with azaci-

tidine for blood disorders

• Funding: 80 k€ (APHM)

• Duration: 2021-2023

• Principal investigator: Geoffroy Venton - COMPO Investigator: Raphaelle Fanciullino

VENETACIBLE

• Registration: AORC 2023-AO1546-39

• Partner: CHU Nice

• Title: Personalizing Venetoclax dosing in AML patients non-eligible for HD chemotherapy as first

line treatment.

• Funding: 30 k€ (Nice University Hospitals)

• Duration: 2023-2024

• COMPO Investigator: Raphaelle Fanciullino

Morpheus-Lung

• Registration: Eudract 2017-001267-21 / NCT03337698

• Partner: Hoffmann-La Roche

• Title: A Study to Evaluate Efficacy and Safety of Multiple Immunotherapy-Based Treatment Com-

binations in Patients with Metastatic Non-Small Cell Lung Cancer

• Duration: 2018-2023

• COMPO investigator: Laurent Greillier

PDC-LUNG-101

• Registration: Eudract 2018-002382-19 / NCT03970746

• Partner: PDC*line Pharma SAS

• Title: Safety, immunogenicity and preliminary clinical activity study of PDC*lung01 cancer vaccine

in NSCLC

• Duration: 2022-2024

• Principal investigator: Johan Vansteenkiste, Prof KU Leuven

• COMPO investigator: Laurent Greillier

NCT03708328

• Registration: Eudract 2018-000982-35 / NCT03708328

• Partner: Hoffmann-La Roche

• Title: A Dose Escalation and Expansion Study of RO7121661, a PD-1/TIM-3 Bispecific Antibody, in

Participants With Advanced and/or Metastatic Solid Tumors

• Duration: 2018-2023

• COMPO investigator: Laurent Greillier

Project COMPO

PROPEL

31

• Registration: Eudract 2019-003474-35 / NCT03138889

• Partner: Nektar Therapeutics

• Title: Bempegaldesleukin and Pembrolizumab With or Without Chemotherapy in Locally Advanced

or Metastatic Solid Tumors

• Duration: 2017-2024

• COMPO investigator: Laurent Greillier

TRIDENT-1

• Registration: Eudract 2016-003616-13 / NCT03093116

• Partner: Turning Point Therapeutics, Inc.

• Title: A Study of Repotrectinib (TPX-0005) in Patients With Advanced Solid Tumors Harboring ALK,

ROS1, or NTRK1-3 Rearrangements

• Duration: 2017-2023

• Principal investigator: Shanna Stopatschinskaja, Dan Liu

• COMPO investigator: Laurent Greillier

NCT04721015

• Registration: Eudract 2020-004953-57 / NCT04721015

• Partner: AbbVie

• Title: Study of Intravenous (IV) ABBV-637 Alone or in Combination With IV Docetaxel/Osimertinib

to Assess Adverse Events and Change in Disease Activity in Adult Participants With Relapsed/Refractory
(R/R) Solid Tumors

• Duration: 2021-2026

• COMPO investigator: Laurent Greillier

PIONeeR

• Registration: NCT03493581

• Partner: APHM, HalioDx, Innate Pharma, AMU

• Title: Precision Immuno-Oncology for Advanced Non-small Cell Lung Cancer Patients With PD-1

ICI Resistance

• Duration: 2018-2024

• Principal investigator: Laurent Greillier, Jean-Olivier Arnaud

ELDERLY

• Registration: NCT03977194

• Partner: Intergroupe Francophone de Cancerologie Thoracique

• Title: Atezolizumab in Elderly Patients With Advanced Non-Small-Cell Lung Cancer and Receiving

Carboplatin Paclitaxel Chemotherapy

• Duration: 2019-2023

32

Inria Annual Report 2023

• Principal investigator: Elisabeth QUOIX, Céline MASCAUX

• COMPO investigator: Laurent Greillier

NCT01817192

• Registration: NCT01817192

• Partner: Razor Genomics

• Title: Adjuvant Chemotherapy in Patients With Intermediate or High Risk Stage I or Stage IIA

Non-squamous Non-Small Cell Lung Cancer

• Duration: 2020-2025

• Principal investigator: David R Spigel, Sarah Cannon

• COMPO investigator: Laurent Greillier

SAVIMMUNE

• Registration: NCT04108026

• Partner: Intergroupe Francophone de Cancerologie Thoracique

• Title: Immunotherapy in Patient With Poor General Condition

• Duration: 2020-2023

• Principal investigator: Valérie GOUNANT, Michael DURUISSEAUX

• COMPO investigator: Laurent Greillier

NCT04042558

• Registration: NCT04042558

• Partner: Centre Francois Baclesse

• Title: A Study Evaluating Platinum-Pemetrexed-Atezolizumab (+/-Bevacizumab) for Patients With
Stage IIIB/IV Non-squamous Non-small Cell Lung Cancer With EGFR Mutations, ALK Rearrange-
ment or ROS1 Fusion Progressing After Targeted Therapies

• Duration: 2019-2024

• COMPO investigator: Laurent Greillier

NIPINEC

• Registration: NCT03591731

• Partner: Intergroupe Francophone de Cancerologie Thoracique, Federation Francophone de Can-

cerologie Digestive, GERCOR - Multidisciplinary Oncology Cooperative Group

• Title: Nivolumab +/- Ipilimumab in Patients With Advanced, Refractory Pulmonary or Gastroen-

teropancreatic Poorly Differentiated Neuroendocrine Tumors (NECs)

• Duration: 2019-2023

• Principal investigator: Nicolas GIRARD, Thomas WALTER

• COMPO investigator: Laurent Greillier

RESILIENT

• Registration: NCT03088813

Project COMPO

• Partner: Ipsen

33

• Title: Study of Irinotecan Liposome Injection (ONIVYDE®) in Patients With Small Cell Lung Cancer

• Duration: 2018-2022

• COMPO investigator: Laurent Greillier

Canopy-A

• Registration: NCT03447769

• Partner: Novartis Pharmaceuticals

• Title: Study of Efficacy and Safety of Canakinumab as Adjuvant Therapy in Adult Subjects With
Stages AJCC/UICC v. 8 II-IIIA and IIIB (T>5cm N2) Completely Resected Non-small Cell Lung

• Duration: 2018-2027

• COMPO investigator: Laurent Greillier

NCT04350463

• Registration: NCT04350463

• Partner: Celgene

• Title: A Safety and Efficacy Study of CC-90011 in Combination With Nivolumab in Subjects With

Advanced Cancers

• Duration: 2020-2024

• COMPO investigator: Laurent Greillier

TROPION-LUNG01

• Registration: NCT04656652

• Partner: Daiichi Sankyo, Inc., AstraZeneca

• Title: Study of DS-1062a Versus Docetaxel in Previously Treated Advanced or Metastatic Non-small

Cell Lung Cancer Without Actionable Genomic Alterations

• Duration: 2020-2024

• COMPO investigator: Laurent Greillier

MERMAID-1

• Registration: NCT04385368

• Partner: AstraZeneca

• Title: Phase III Study to Determine the Efficacy of Durvalumab in Combination With Chemotherapy

in Completely Resected Stage II-III Non-small Cell Lung Cancer (NSCLC)

• Duration: 2020-2026

• Principal investigator: Solange Peters, Charles Swanton

• COMPO investigator: Laurent Greillier

NCT03840902

• Registration: NCT03840902

34

Inria Annual Report 2023

• Partner: EMD Serono Research and Development Institute, Inc., Merck KGaA

• Title: M7824 With cCRT in Unresectable Stage III Non-small Cell Lung Cancer (NSCLC)

• Duration: 2019-2023

• COMPO investigator: Laurent Greillier

CARMEN-LC03

• Registration: NCT04154956

• Partner: Sanofi

• Title: SAR408701 Versus Docetaxel in Previously Treated, Carcinoembryonic Antigen-related Cell
Adhesion Molecule 5 (CEACAM5) Positive Metastatic Non-squamous Non-small Cell Lung Cancer
Patients

• Duration: 2020-2024

• COMPO investigator: Laurent Greillier

SKYSCRAPER-03

• Registration: NCT04513925

• Partner: Hoffmann-La Roche

• Title: A Study of Atezolizumab and Tiragolumab Compared With Durvalumab in Participants With

Locally Advanced, Unresectable Stage III Non-Small Cell Lung Cancer (NSCLC)

• Duration: 2020-2027

• COMPO investigator: Laurent Greillier

NCT03899155

• Registration: NCT03899155

• Partner: Bristol-Myers Squibb

• Title: Pan Tumor Nivolumab Rollover Study

• Duration: 2019-2025

NCT03798535

• Registration: NCT03798535

• Partner: AstraZeneca

• Title: First Real-world Data on Unresectable Stage III NSCLC Patients Treated With Durvalumab

After Chemoradiotherapy

• Duration: 2018-2023

• COMPO investigator: Laurent Greillier

IMbrella A

• Registration: NCT03148418

• Partner: Hoffmann-La Roche

• Title: A Study in Participants Previously Enrolled in a Genentech- and/or F. Hoffmann-La Roche

Ltd-Sponsored Atezolizumab Study

Project COMPO

35

• Duration: 2017-2030

• COMPO investigator: Laurent Greillier

BEAT-meso

• Registration: NCT03762018

• Partner: European Thoracic Oncology Platform, Hoffmann-La Roche

• Title: Bevacizumab and Atezolizumab in Malignant Pleural Mesothelioma

• Duration: 2019-2024

• Principal investigator: Enriqueta Felip, Sanjay Popat

• COMPO investigator: Laurent Greillier

SPECTA

• Registration: NCT02834884

• Partner: European Organisation for Research and Treatment of Cancer - EORTC

• Title: Screening Cancer Patients for Efficient Clinical Trial Access

• Duration: 2017-2026

• Principal investigator: Vassilis Golfinopoulos

• COMPO investigator: Laurent Greillier

TROPION-Lung05

• Registration: EudraCT 2020-002774-27

• Partner: Daiichi Sankyo, Inc.

• Title: Phase 2 Study of DS-1062a in Advanced or Metastatic Non-small Cell Lung Cancer with

Actionable Genomic Alterations

• Starting year: 2021

• COMPO investigator: Laurent Greillier

SKYSCRAPER-06

• Registration: NCT04619797

• Partner: Hoffmann-La Roche

• Title: A Study of Tiragolumab in Combination With Atezolizumab Plus Pemetrexed and Car-
boplatin/Cisplatin Versus Pembrolizumab Plus Pemetrexed and Carboplatin/Cisplatin in Par-
ticipants With Previously Untreated Advanced Non-Squamous Non-Small Cell Lung Cancer

• Duration: 2020 - 2025

• COMPO investigator: Laurent Greillier

PERSEE

• Registration: EudraCT 2020-002626-86

• Partner: CHRU de Brest

36

Inria Annual Report 2023

• Title: A trial comparing the pembrolizumab platinum based chemotherapy combination with
pembrolizumab monotherapy in first line treatment of non small-cell lung cancer (NSCLC) patients

• Starting year: 2020

• Principal investigator: Renaud Descourt, Chantal Decroisette, Christos Chouaid

• COMPO investigator: Laurent Greillier

SAPPHIRE

• Registration: EudraCT 2019-001043-41

• Partner: Mirati Therapeutics, Inc.

• Title: A Randomized Phase 3 Study of Sitravatinib in Combination with Nivolumab Versus Docetaxel
in Patients with Advanced Non-Squamous Non-Small Cell Lung Cancer with Disease Progression
On or After Platinum-Based Chemotherapy and Checkpoint Inhibitor Therapy

• Starting year: 2020

• COMPO investigator: Laurent Greillier

Nivothym

• Registration: EudraCT 2015-005504-28

• Partner: European Organisation for Research and Treatment of Cancer

• Title: Single-arm, multicenter, phase II study of immunotherapy in patients with type B3 thymoma

and thymic carcinoma previously treated with chemotherapy

• Starting year: 2017

• Principal investigator: Nicolas Girard, Solange Peters

• COMPO investigator: Laurent Greillier

ELEVATE HNSCC

• Registration: NCT04854499

• Partner: Gilead Sciences

• Title: Study of Magrolimab Combination Therapy in Patients With Head and Neck Squamous Cell

Carcinoma

• Duration: 2021-2-25

• COMPO investigator: Sébastien Salas

ROMANE

• Registration: ML43492

• Partner: ROCHE

• Title: Retrospective observational study of patients with locally advanced squamous cell carcinoma

of the head and neck

• Starting year: 2023

• COMPO investigator: Sébastien Salas

XRAY VISION

Project COMPO

37

• Registration: NCT05386550

• Partner: EMD Serono Research & Development Institute, Inc.

• Title: Phase III Xevinapant (Debio 1143) and Radiotherapy in Resected LA SCCHN, High Risk,

Cisplatin-ineligible Participants

• Duration: 2022-2030

• COMPO investigator: Sébastien Salas

Iintune-1

• Registration: NCT04420884

• Partner: Takeda

• Title: A Study of Dazostinag as Single Agent and Dazostinag in Combination With Pembrolizumab

in Adults With Advanced or Metastatic Solid Tumors

• Duration: 2020-2026

• COMPO investigator: Sébastien Salas

AHEAD-MERIT

• Registration: NCT04534205

• Partner: BioNTech SE

• Title: A Clinical Trial Investigating the Safety, Tolerability, and Therapeutic Effects of BNT113 in
Combination With Pembrolizumab Versus Pembrolizumab Alone for Patients With a Form of Head
and Neck Cancer Positive for Human Papilloma Virus 16 and Expressing the Protein PD-L1

• Duration: 2021-2028

• COMPO investigator: Sébastien Salas

CODEBREAK IGR

• Registration: NCT05631249

• Partner: Gustave Roussy, Cancer Campus, Grand Paris

• Title: Sotorasib in Previously Treated Locally Advanced or Metastatic NSCLC Subjects With Mutated

KRAS p.G12C

• Duration: 2022-2026

• COMPO investigator: Laurent Greillier

ADAPTABLE

• Registration: NCT05781308

• Partner: Intergroupe Francophone de Cancerologie Thoracique

• Title: Combination of Paclitaxel-bevacizumab ± Atezolizumab in Patients With Advanced NSCLC

Progressing After Immunotherapy & Chemotherapy

• Duration: 2023-2026

• COMPO investigator: Laurent Greillier

MATISSE

38

Inria Annual Report 2023

• Registration: NCT05742607

• Partner: Innate Pharma

• Title: IPH5201 and Durvalumab in Patients With Resectable Non-Small Cell Lung Cancer

• Duration: 2023-2026

• COMPO investigator: Laurent Greillier

DESTINY-Lung04

• Registration: NCT05048797

• Partner: AstraZeneca

• Title: A Study to Investigate the Efficacy and Safety of Trastuzumab Deruxtecan as the First Treat-
ment Option for Unresectable, Locally Advanced/Metastatic Non-Small Cell Lung Cancer With
HER2 Mutations

• Duration: 2021-2027

• COMPO investigator: Laurent Greillier

KRYSTAL-12

• Registration: NCT04685135

• Partner: Mirati Therapeutics Inc.

• Title: Phase 3 Study of MRTX849 (Adagrasib) vs Docetaxel in Patients With Advanced Non-Small

Cell Lung Cancer With KRAS G12C Mutation

• Duration: 2021-2024

• COMPO investigator: Laurent Greillier

PECATI

• Registration: NCT04710628

• Partner: MedSIR

• Title: Combination of Pembrolizumab and Lenvatinib, in Pre-treated Thymic CArcinoma paTIents

• Duration: 2021-2026

• COMPO investigator: Laurent Greillier

A2A-005

• Registration: NCT05403385

• Partner: iTeos Belgium SA

• Title: Study of Inupadenant (EOS100850) With Chemotherapy as Second Line Treatment for Non-

squamous Non-small Cell Lung Cancer

• Duration: 2022-2025

• COMPO investigator: Laurent Greillier

Delivir

• Registration: NCT05788926

Project COMPO

39

• Partner: Transgene

• Title: A Clinical Trial of TG6050 in Patients With Metastatic Non-Small Cell Lung Cancer

• Duration: 2023-2025

• COMPO investigator: Laurent Greillier

BEAMION-Lung 1

• Registration: NCT04886804

• Partner: Boehringer Ingelheim

• Title: A Study to Test Different Doses of BI 1810631 in People With Different Types of Advanced

Cancer (Solid Tumours With Changes in the HER2 Gene)

• Duration: 2021-2027

• COMPO investigator: Laurent Greillier

ELEVATE Lung & UC

• Registration: NCT04827576

• Partner: Gilead Sciences

• Title: Study of Magrolimab in Patients With Solid Tumors

• Duration: 2021-2025

• COMPO investigator: Laurent Greillier

IDE397-001

• Registration: NCT04794699

• Partner: IDEAYA Biosciences

• Title: Study of IDE397 in Participants With Solid Tumors Harboring MTAP Deletion

• Duration: 2021-2024

• COMPO investigator: Laurent Greillier

CA099-003

• Registration: NCT05407675

• Partner: Bristol-Myers Squibb

• Title: A Study to Evaluate the Safety and Tolerability of BMS-986408 Alone and in Combination

With Nivolumab or Nivolumab and Ipilimumab in Participants With Advanced Solid Tumors

• Duration: 2022-2027

• COMPO investigator: Laurent Greillier

40

Inria Annual Report 2023

10 Partnerships and cooperations

10.1 International research visitors

10.1.1 Visits of international scientists

Mahsa Dehgani

Status PhD student

Institution of origin: Tabriz University of Medical Sciences

Country: Iran

Dates: Oct - Dec 2023

Mobility program/type of mobility: Research stay

Ana Homsek

Status Assistant Professor

Institution of origin: School of Pharmacy of Belgrade

Country: Serbia

Dates: Oct - Oct 2023

Mobility program/type of mobility: PROGRAMME DE RECHERCHES PHC - Pavle Savic France-Serbie:

Bioanalysis and Pharmacometrics of Pembrolizumab in NSCLC.

Zeyab Ibrahim

Status Senior Scientist

Institution of origin: Perkins Institute, Perth Australia West University

Country: Australia

Dates: Oct - Oct 2023

Mobility program/type of mobility: Winston Churchill Grant - implementing PGx in Oncology

10.1.2 Visits to international teams

Research stays abroad: Anne Rodallec

Visited institution: Departement of pharmaceutics, Bonn University

Country: Germany

Dates: Sept 203 - Aug 2024

Context of the visit: Research collaboration

Mobility program/type of mobility: Research Stay

Project COMPO

41

10.2 National initiatives

QUANTIC

Title: QUANTitative modeling combined to statistical learning to understand and predict resistance to

Immune-checkpoint inhibition in non-small cell lung Cancer

Partner Institutions:

• AP-HM, Marseille

• Inserm, Marseille

• Aix-Marseille University, Marseille

• Veracyte, Marseille

• InnatePharma, Marseille

Date/Duration: 2019 - 2023

Funding: 338 k€, Inserm Plan Cancer MIC

Principal investigator: S. Benzekry

COMPO members involved: L. Greillier, M. Karlsen, P. Dufosse.

PIONeeR - biomarkers

Title: Precision Immuno-Oncology for advanced Non-small cell lung cancer patients with PD-1 ICI

Resistance

Partner Institutions:

• AP-HM, Marseille

• 13 national cancer centers, including Centre Lyon Bérard and IUCT in Toulouse

• Marseille Immmunopôle (immuno-monitoring platform)

• Vasculo-monitoring platform of AP-HM

• Inserm

• Aix-Marseille University

• Veracyte, Marseille

• InnatePharma, Marseille

Date/Duration: 2018 - 2024

Funding: ≃ 8 M€, ANR

Principal investigator: F. Barlesi (IGR)

COMPO members involved: S. Benzekry, J. Ciccolini, L. Greillier, P. Dufosse, M. Boussena, M. Hamimed,

M. Karlsen, S. Marolleau, C. Marin, A. Vaglio.

LUCA-pi RHU

Title: Lung cancer prevention and interception

Partner Institutions:

• Gustave Roussy Institute (G. Kroemer, L. Zitvogel)

• Therapanacea (N. Paragios)

• CIML (P. Milpied)

Date/Duration: 2023 - 2028

Funding: ≃ 10 M€, ANR

Principal investigator: D. Boulate (COMPO)

COMPO seniors: D. Barbolosi, S. Benzekry

42

Inria Annual Report 2023

DIGPHAT PEPR Digital Health

Title: Digital pharmacological twin

Partner Institutions:

• JB Woillard (CHU Limoges)

• C. Battail (CEA Grenoble)

• M. Ursino + S. Zohar (HEKA, Inria, Paris)

• J. Josse (PREMEDICAL, Inria, Montpellier)

• E. Chatelut + M. White-Koning (IUCT, Toulouse)

Date/Duration: 2023 - 2028

Funding: Total 1.8 M€, COMPO 251k€

Principal investigator: JB Woillard (CHU Limoges)

COMPO senior: S. Benzekry.

COMPO junior: A. Bakhmach

COPYCAT

Title: Combining Organoid technology with Mathematics to develop innovative models mimicking

tumor cellular heterogeneity and plasticity for pediatric oncology

Partner Institutions:

• L. Broutier (CRCL, Inserm, CNRS, UCBL, Lyon)

• E. Pasquier (CRCM)

• R. Mounier (INMG, Inserm, CNRS, UCBL, Lyon)

Date/Duration: 2023 - 2027

Funding: Total 922k€, COMPO 115k€ (INCa)

Principal investigator: L. Broutier (CRCL, Inserm, CNRS, UCBL, Lyon)

COMPO members involved: S. Benzekry.

PhD H. Hamdache

Title: Improving therapeutic efficacy and managing side effects and sequelae in pediatric cancer through

improved and personalized nutritional programs using computer simulations

Partner Institutions:

• V. Pancaldi (IUCT, Inserm, Toulouse)

Date/Duration: 2023 - 2026

Funding: 120k€, Inria-Inserm PhD grant

Principal investigator: V. Pancaldi (IUCT, Inserm, Toulouse)

COMPO members involved: S. Benzekry (co-supervisor).

43

Project COMPO

South-ROCK

Title: South-research on cancer for kids

Partner Institutions: 28 constitutive teams

• P. Mehlen (Centre Léon Bérard + Hospices Civils de Lyon)

• E. Pasquier (CRCM, Marseille)

• M. Castets (CRCL, Lyon)

Date/Duration: 2023 - 2028

Funding: Total 2 M€

Principal investigators: P. Mehlen (CLB + HCL, Lyon), E. Pasquier (CRCM, Marseille), M. Castets (CRCL,

Lyon)

COMPO seniors: S. Benzekry, F. Gattacceca, J.Ciccolini

THERMONANO

Title: Nanoassemblies for the subcutaneous self-administration of anticancer drugs

Partner Institution:

• Institut Galien Paris-Saclay (UMR CNRS 8612)

Date/Duration: 2019 - 2024

Funding: 1.8 M€, ERC

Principal investigator: J. Nicolas (Institut Galien, Paris-Sud)

COMPO members involved: A. Rodallec, S. Benzekry, S. Marolleau.

PEMBOV

Title: Pembrolizumab in Combination With Bevacizumab and Pegylated Liposomal Doxorubicin in

Patients With Ovarian Cancer

Partner Institution:

• Institut Gustave Roussy (IGR)

Date/Duration: 2021-2024

Funding: 300 K€, INCa

Principal investigator: J. Michels (IGR)

COMPO members involved: J. Ciccolini, M. Hamimed

PEMEBRAIN

Title: Intrathecal administration of low dose Pemetrexed in patients with brain metastasis: pharmacokin-

etics exploration

Partner Institution:

• Assistance Publique Hôpitaux de paris(APHP)

Date/Duration: 2022-2023

Funding: APHP

Principal investigator: P. Decq (APHP)

COMPO members involved: J. Ciccolini, C. Boéri

44

Inria Annual Report 2023

10.3 Regional initiatives

SChISM

Title: Size CfDNA Immunotherapies Signature Monitoring

Partner Institutions:

• APHM

• Adelis (JC Garcia, F. Ginot),

• ID-Solutions Oncology (F. Fina)

• XPOP Inria (M. Lavielle)

Date/Duration: 2022 - 2025

Funding: 120k€, Institut Laënnec

Principal investigator: S. Salas (clinical, COMPO), S. Benzekry (modeling, COMPO)

COMPO junior members: L. Nguyen

Brain meta SCLC

Title: Modeling the impact of prophylactic whole brain radiotherapy to prevent brain metastases in SCLC

Partner Institutions:

• APHM (L. Padovani)

Date/Duration: 2023 - 2024

Funding: 100k€, Inria-Inserm PhD grant

Principal investigator: S. Benzekry (COMPO)

COMPO senior members: X. Muracciole

COMPO junior members: C. Bigarré, A. Daumas

PhD M. Boussena

Title: Machine learning methods for clinical oncology data: application to the prediction of immuno-

therapy response in lung cancer

Partner Institutions:

• J. Josse (PREMEDICAL, Inria)

• GFPC

Date/Duration: 2023 - 2026

Funding: 120k€, Institut Laënnec

Principal investigator: S. Benzekry (COMPO)

COMPO senior: L. Greillier

COMPO junior: M. Boussena (PhD)

Project COMPO

TORNADO

45

Title: TOols for Rational NAnoDrugs Optimization: development of a physiologically-based pharma-

cokinetic model for nanomedicines

Partner Institutions:

• P. Garrigue (CERIMED, Marseille)

• S. Legeay (MINT, Univ Angers)

• E. Roger (MINT, Univ Angers)

Date/Duration: 2021 - 2024

Funding: 120k€, PhD grant AMU

Principal investigator: F. Gattacceca (COMPO)

COMPO junior: J. Ou (PhD)

PKID

Title: Population pharmacokinetic approach centered on the patient: a patient pharmacokinetic ID

from one drug to predict the PK of the next one

Partner Institutions:

• R. Guilhaumou (APHM, Marseille)

Date/Duration: 2021 - 2024

Funding: APHM (hospital intern)

Principal investigator: F. Gattacceca (COMPO)

COMPO junior: A. Deschamps (PhD)

PICOMALE

Title: Phase I Clinical trials Oncology & Machine Learning

Partner Institutions:

• N. André (APHM, CRCM)

• M. Ouladsine (LIS, AMU)

Date/Duration: 2023 - 2026

Funding: Total 100k€, COMPO 33k€

Principal investigator: N. André (clinical, APHM)

COMPO senior: S. Benzekry

COMPO junior: C. Bigarré (PhD)

COMPLICITY

Title: COMPutationaL tools for NanoBooster In Cancer ImmunoTherapY

Partner Institution:

• Pancreas Cancer Team (CRCM)

Date/Duration: 2022-2024

Funding: 25 K€ (Amidex), 30K€ (ARC), 2K€(DAAD)

Principal investigator: A. Rodallec (CRCM)

COMPO members involved: A. Rodallec, F. Gattacceca, C. Perez

46

Inria Annual Report 2023

PREVALUNG ETOILE

Title: Structuration d’un programme de dépistage des cancers du poumon incluant un phénotypage
clinique, radiologique et biologique utile au développement d’outils de prédiction individualisée
du risque.

Partner Institution:

• APHM

Date/Duration: 2023

Funding: 15 K€, Amidex

Principal investigator: D. Boulate (COMPO)

COMPO senior: D. Barbolosi

COMPO junior: A. Bakhmach

kML-m

Title: kinetics-Machine Learning for advanced melanoma treated with immunotherapy

Partner Institutions:

• N. Malissen, J. Forestier (APHM)

Date/Duration: 2023 - 2024

Funding: No dedicated funding

Principal investigators: N. Malissen (clinical, APHM) S. Benzekry (modeling, COMPO)

COMPO junior: A. Daumas (M2 intern)

11 Dissemination

Participants: Dominique Barbolosi, Sebastien Benzekry, David Boulate, Joseph Cic-
colini, Raphaelle Fanciullino, Florence Gattacceca, Laurent Greillier,
Xavier Muracciole, Anne Rodallec, Sebastien Salas.

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

General chair, scientific chair

• D. Barbolosi and X. Muracciole organized the first Mathematics and Medicine workshop in Ajaccio.
It was a large success and was held in presence of national leaders in clinical oncology, including
Prs F. Barlesi, F. Andre and E. Deutsch, respectively CEO, head of research and chair of the radiation
therapy department, Gustave Roussy Institute.

• J. Ciccolini

– Secretary to the PAMM-EORTC European group.

– Board Member of the GPCO-Unicancer group.

– Board Member of the Copil H&N Taskforce group.

– Co-Head of the Translational Research and Innovative Treatment (Translate-IT) Department

of the Centre de Recherche en Cancérologie de Marseille (CRCM).

Project COMPO

47

– Member of the "TDM in Oncology" Committee for the organization of the International
Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT) which ran
its 21th International Congress in Oslo, Norway, Sept 24-27 2023 (850 attendees, worldwide
audience).

• F. Gattacceca

– President of the GEPK (Group of Lecturers in PK) and organized the 2023 annual meeting,

Lille, France, July 3-4, 2023.

– Member of the scientific committee and chair of the workshop "First in human dose selection"
organized by GMP (Group of Metabolism and Pharmacokinetics), Paris, France, Oct 17 2023.

– Founding member and the secretary of the "Treatment individualization joint GMP-SFPT
(French Society of Pharmacology and Therapeutics) commission" and co-organized and
chaired the commission workshop "Therapeutic Monitoring and treatment Individualisation"
with GMP, Paris, France, Oct 18 2023.

11.1.2 Journal

Member of the editorial boards

• S. Benzekry, Mathematical Biosciences

• J. Ciccolini, Cancer Chemotherapy & Pharmacology (Springer)

• F. Gattacceca, Cancer Chemotherapy & Pharmacology (Springer)

• J. Ciccolini, Guest Editor Themed Issue British Journal of Cancer (Nature Publishing Group)

• L. Greillier, Associate Editor at Respiratory Medicine & Research (French Society of Respiratory

Diseases)

Reviewer - reviewing activities

• S. Benzekry: Cancer Letters

• J. Ciccolini: Cancer Chemotherapy & Pharmacology, British Journal of Cancer, Clin Pharmacol
Ther, Clin Pharmacol Ther PSP, British Journal of Clinical Pharmacology, Cancer Treatment Review,
Frontiers in Pharmacology, Frontiers in Immunology, Cancers, Cancer Letters, Pharmaceutics,
Oncotarget, Journal of Chromatography B, ESMO open, Fundamental and Clinical Pharmacology,
Breast Cancer: Targets & Therapy, Journal of the National Comprehensive Cancer Network.

• R. Fanciullino: Cancer Chemotherapy & Pharmacology, Pharmaceutical Research, Leukemia

• F. Gattacceca: Cancer Chemotherapy & Pharmacology, Annales Pharmaceutiques Françaises

• L. Greillier, Respiratory Medicine & Research

11.1.3 Invited talks

• S. Benzekry

– October, 2023. Workshop for young researchers in Cancer, invited as keynote speaker, Can-

céropôle PACA, Porquerolles.

– July, 2023. SMB congress (Society of Mathematical Biology). Talk within the mini-symposium
"modeling approaches used to study cancer treatment." organized by K. Wilkie and G. Powathil.
L. Nguyen et al., S. Benzekry, Mechanistic modeling of the longitudinal tumor and biological
markers combined with quantitative cell-free DNA. Ohio State University, OH, USA.

– June, 2023. Annual meeting of the APHM center for early phase clinical trials in oncology,

Marseille, France.

48

Inria Annual Report 2023

– June, 2023. Annual congress of the French group of clinical pneumo-oncology (GPCO), Toulon,

France.

– June, 2023. Annual congress of the French society of pharmacology and therapeutics (SFPT),

Limoges, France.

– May, 2023. SMAI congress (French Society of Applied and Industrial Mathematics). Talk within
the mini-symposium "Modélisation Mathématique du cancer et de son environnement"
organized by F. Hubert and M. Mezache. Pointe à Pitre, France.

– January, 2023. CARe graduate school workshop, Numerical and technical tools to better

investigate metabolism, inflammation and cancer. Toulouse, France.

• J. Ciccolini

– November, 2023. "Anti-Body Drug Conjugates: Why « Magic Bullet » is not « Magic Pharmaco-

logy" XIeme Journées Pharmacologiques de L’Hôpital Cochin, Paris France.

– November, 2023. "Anti-Body Drug Conjugates: which clinical perspectives in digestive onco-

logy?" Cours Saint Paul Oncologie Digestive Nice France.

– November, 2023. "ADCs: Pharmacokinetics & dosing considerations".XIXeme Journées du

GPCO-Unicancer Strasbourg, France.

– November, 2023. "Anti-PARP: Aspects PK et PK/PD quelle place pour le TDM?" XIXèmes

Journées du GPCO-Unicancer Strasbourg, France.

– October, 2023. "Cancer de la prostate : implications PK et PK/PD d’un antagoniste oral de la
LHRH".14ème Congrès de la SFPO - société Française des Pharmaciens Oncologues Nancy,
France

– September, 2023. "BRAF-i plus anti-MEK combo: targeting MAP-kinase in metastatic melan-

oma". 4ème Journées Horizon Mélanome Paris, France

– September, 2023."Difficulties in implementing TDM in oncology: another French para-
dox.IATDMCT 2023 - 21st Congress of the International Association of Therapeutic Drug
Monitoring & Clinical Toxicology Oslo (Norway), Norway

– July, 2023. "Antibody-Drug Conjugates ; beyond the hype, which pharmacology in breast

cancer?" Séminaire annuel du Cancéropôle PACA Frejus, France

– June, 2023. "TDM of oral targeted therapies in metastatic melanoma." Pierre Fabre Symposium

Avène, France.

– May, 2023. "Unlocking the potential of ADCs for HER2 signaling pathway."" Daïchi Sankyo

Breast Symposium Toulon, France

– April, 2023. "BRAF-inhibitors in mCRC: pharmacology and PK/PD perspectives". Aquitaine

Gastro Annual Meeting Bordeaux, France.

– March, 2023. "From HER2+ to HER2-low: specificity of the new generation ADCs". 9ème

Congrès du Monaco Age Oncology (MAO) Monaco.

– January, 2023. "PKPD with Immunotherapy".7e Journées Scientifiques Immunité & Cancer

(JSIC) Paris, France

– January, 2023. "TDM in oncology: application to breast cancer patients." 22ème Cours Saint

Paul Sein & Cancer Gynécologiques Cannes, France

– January, 2023. "Interaction of PGxs with Pharmacometric and modelling platform" APSA-

ASCEPT 2022 JOINT conference Perth Western Australia.

• R. Fanciullino

– December, 2023. "Médicament anti-cancéreux et intégration des données vie réelle". Meeting

de la Société Française de Pharmacie Clinique (SFPC); Lyon France.

• F. Gattacceca

Project COMPO

49

– June-July, 2023 (2 sessions per week during 8 weeks). Invited instructor for the online "Sum-

merschool PBPK SimulationsPlus".

• L. Greillier

– June 2023, "Real-life efficacy of nivolumab plus ipilimumab in untreated, unresectable ma-
lignant pleural mesotheliomas: results of French early access program". 16th International
Conference of the International Mesothelioma Interest Group, Lille, France.

11.1.4 Scientific expertise

• S. Benzekry: COFECUB program

• J. Ciccolini: INCa Scientific Committee (PHRC-K program, Reviewer)

• J. Ciccolini: ITMO Cancer (Board Member)

• J. Ciccolini: H&N Copil Unicancer (Board Member)

• J. Ciccolini: Israeli Ministry of Innovation, Science and Technology: Grant Application (Reviewer)

• J. Ciccolini: Scientific Board for the Groupe Francophone des Myelodysplasies (GFM): trial GFM-

ONUVEN-MDS (Reviewer).

• J. Ciccolini: Conseil Scientifique Régional du Languedoc Roussillon de la Ligue contre le cancer

(Reviewer)

• J. Ciccolini: Selection Scientific Committee, Tenured Track Assistant-Professor Application, College
of Veterinary Sciences, Faculty of Animal Husbandry and Veterinary Sciences, Peshawar, Pakistan
(Reviewer).

• R. Fanciullino: Member of the Scientific Committee for Association pour la Recherche contre le

Cancer (ARC) Scientific Committee CN5 (Reviewer)

• F. Gattacceca: Scientific expert at ANSM (Agence Nationale de Sécurité du Médicament), member

of the permanent scientific committee "Quality and Safety of drugs".

• L. Greillier: Scientific coordinator for the national recommendations on lung cancer management

at the National Institute of Cancer.

• L. Greillier: Scientific expert at the National Institute of Cancer.

11.1.5 Research administration

• A. Rodallec: Website and Social Media coordinator for the CRS benelux Local Chapter

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

• S. Benzekry: M2 Biologie Santé – Parcours IA biomarqueurs (6h). M2 "Pharmacokinetics" (6h).

• S. Benzekry: CESU "Fundamentals for modeling and simulation in pharmacokinetics/pharmacodynamics"

(6h).

• C. Bigarré: CESU "Fundamentals for modeling and simulation in pharmacokinetics/pharmacodynamics"

(12h). MSc (2nd y) "Pharmacokinetics" (12h).

• A. Rodallec: lectures in MSc in Pharmacokinetics, MSc in Digipharm, MSc in Innovative Diagnostic
and therapeutic Drug Products, DESU "Advances courses in pharmacometrics", DES in animal
experiments, Pharm.D. studies (2d, 3d, 4th and 6th year), -> 190 h a year + additional lectures at
University of Paris Saclay.

50

Inria Annual Report 2023

• J. Ciccolini Lectures at Aix Marseille univ in: MSc (2d year) in Oncology, MSc (2d year) in Oncogen-
etics, MSc (2d year) in Pharmacokinetics, MSc (2d year) in Digipharm, MSc (1st year) in Drugs &
Health Products, D.U. in Animal Experiments, D.U. in Genetic Councelling, Master Class in Lung
Cancer, Pharm.D. studies (2d, 3d, 4th and 6th year) -> 190 h a year.

• Teaching outside of AMU: additional lectures in pharmacokinetics at Université Catholique de
Lyon, University of Amsterdam NL, University of Pisa Italy, and the International School of Met-
ronomics. Lectures for Cours National Approfondissement DES Oncologie Médicale and Phase
d’Approfondissement Docteur Junior, Paris Saclay.

• R. Fanciullino: Lectures in: MSc (2d year) in Pharmacokinetics, MSc (2d year) in Digipharm, CESU
in Oncogeriatry, DES in PK Variability, Pharm.D. studies (3d, 4th and 5th year) -> 190 h a year .

• F. Gattacceca: lectures at Aix-Marseille University school of pharmacy (305h), teaching in other

universities (50h: Nîmes, Angers, Montpellier): 90% at a post-graduate level.

• F. Gattacceca: CIVIS International Summerschool "Drug Design and Discovery", Bucharest, Ro-

mania, July 2023 (Lectures and Hands-on, 10h)

• L. Greillier: M2 Recherche clinique et Simulation en Santé.

• L. Greillier: oncology and pulmonology for 3rd – 11 year medical students.

• X. Muracciole: DIU radio-urology for resident medical student.

• X. Muracciole: DCIU radio surgery for resident medical student

• S. Salas: Medical study/initial training. Seminary: palliative care Therapeutic module: pains

Oncodigestive module Cancerology (44h), for 3rd - 6th year medical students

• S. Salas: Medical, paramedical study. DU Supportive care in oncology and palliative medicine.
DU Wounds and healing. DIU Supportive care in oncology and palliative medicine. Master’s in
Advance Practice Nursing: Cancerology module, General module, pains. CEU Service providers at
home, Home-based cancer care. DU Ambulatory shift,Oncology module. (37h)

11.2.2 Teaching administration

• J. Ciccolini: Founder and Head, Digipharm MSc degree (Aix Marseille Univ).

• F. Gattacceca

– Director of the Master Program “Pharmacokinetics”.

– Director of the international University Diploma: “Modeling and simulation: population

approaches in pharmacokinetics/pharmacodynamics”

– Founder and Director of the international University Diploma: “Modeling and simulation:

physiologically-based pharmacokinetic modeling for pharmacology and toxicology”

– Member of the national reflection committee for the industry pharmacy studies

– Member of the training steering committee of ICI (Immunology Cancer Institute)

11.2.3 Supervision

• Postdoc

– S. Benzekry

* P. Dufossé, QUANTIC, funding Laënnec Institute

– A. Rodallec

* R. Passannante, Thermonano
* P. Piris, Thermonano

Project COMPO

51

• PhD students

– S. Benzekry

* C. Bigarré, 2020 - 2024, Mathematical modeling for prediction of metastatic relapse in

breast cancer, co-supervision X. Muracciole, funding Inria – Inserm

* L. Nguyen Phuong, 2022 - 2025, SChISM, Mechanistic modeling of circulating DNA com-
bined to machine learning for prediction of response and survival following immunother-
apy, co-supervision S. Salas, funding Amidex ICI (Institute for Cancer Immunotherapy)
* H. Hamdache, 2023 - 2026, Improving therapeutic efficacy and managing side effects
and sequelae in pediatric cancer through improved and personalized nutritional pro-
grams using computer simulations, co-supervision V. Pancaldi (IUCT, Inserm, Toulouse),
funding Inria–Inserm.

– J. Ciccolini

* M. Hamimed, Provin & Ovima trials, co-supervision N.André, funding AMU.
* G. Sicard, SMART A*MIDEX project, co-supervision R. Fanciullino, funding Amidex

Foundation.

* C. Marin, CetuxIMAX, funding APHM & Merck Serono

– F. Gattacceca

* A. Deschamps, 2019-2024, funding APHM, co-supervision R. Guilhaumou (APHM)
* J. Ou, 2021-2024, funding AMU doctoral school 62, co-supervision P. Garrigue (AMU)
* S. Benamara, 2023-2026, funding CIFRE, co-supervision D. Teutonico (Sanofi)

– R. Fanciullino

* M. Dacos, Nanimmuno funding APHM & Institut Roche
* L. Osanno, Venaza project funding APHM

• Engineers

– S. Benzekry

* M. Boussena, QUANTIC
* M. Karlsen, QUANTIC
* A. Vaglio, PIONeeR

– J. Ciccolini

* S. Marolleau, PIONeeR
* C. Perez, PIONeeR

• Interns (Master 2)

– S. Benzekry

* A. Bakhmach, Ground-truth assessment of feature selection methods from real datasets,

Erasmus Mundus Master: Be In Precision Medicine

* T. Gnade, Survival analysis: Adapting Loss Functions for Cox Regression with Neural

Networks, M2 IAAA, Centrale Marseille

– R. Fanciullino

* A. Ronda, Predicting MTX toxicity, APHM

– J. Ciccolini

* D. Protzenko, Machine Learning for HD Busulfan, APHM
* G. Kallee, DPD UM status and treatment failure upon 5-FU, APHM

– A. Rodallec

* C. Perez, immunomodulation of nanoparticles, AMU

– F. Gattacceca

* M. Mahdjoub, Multi-compartmental model for Lipid nanocapsules biodistribution, AMU

52

Inria Annual Report 2023

11.2.4 Juries

• Sebastien Benzekry

– Reviewer of the PhD of M. Pere, Université Côte d’Azur, Sophia Antipolis

– Reviewer of the PhD of P. Chassonnery, Université Toulouse 3 Paul Sabatier

– Member of the follow-up PhD committee of 3 students

• Joseph Ciccolini: President of Pham.D. Thesis, School of Pharmacy of Marseille (approx. 30

thesis/year)

• Joseph Ciccolini: Jury of Ph.D. Defense : Ph.D. Reviewer for the Simon Verdez (Bourgogne Univer-
sity), Arthur Geraud-Cremieux (Université Paris Saclay) and Christophe Maritaz (Université Paris
Saclay); President for Aurélien Millet (Université of Lyon).

• Joseph Ciccolini: Member of the Scienfic Committee Evaluation for the Ph.D. of Magdallena

Centanni, Upssala University, Sweden (supervisor: Mats Karlsson and Lena Friberg).

• Anne Rodallec: Jury of Pharm.D. Thesis, School of Pharmacy of Marseille (approx. 5 thesis/year)

• Raphaelle Fanciullino: Jury of Pharm.D. Thesis, School of Pharmacy of Marseille (approx. 10
thesis/year) Jury of DES Hospital Pharmacist thesis, School of Pharmacy of Marseille (approx.
10/year).

• Raphaelle Fanciullino: Reviewer for the Habilitation à Diriger les recherches (HDR) of Litaty

M’Batchi (Montpellier University).

• Sebastien Salas: Jury of the HDR of Matthieu Frasca

• Sebastien Salas: President of M.D. Thesis, School of Medicine of Marseille (2 thesis)

• L. Greillier: President or member of the Jury (3 theses in medicine).

• F. Gattacceca: Reviewer of 1 HDR (A. Marsot, University of Montréal, Paris Oct 2023). Reviewer of
5 PhD (C. Pouzin, Paris, Jan 2023; R. Garreau, Lyon, Sept 2023; S. Baklouti, Toulouse, Oct 2023; S.
Lobet, Tours, Oct 2023; F-C. Chao, Saclay, Dec 2023). Jury of 1 HDR (L. Mbatchi, Montpellier, June
2023).

• F. Gattacceca: Scientific supervisor of a PhD in VAE (validation of experience). M. le Merdy, Simula-

tionsPlus, Paris, Jan 2023

12 Scientific production

12.1 Major publications

[1] D. Barbolosi, J. Ciccolini, B. Lacarelle, F. Barlési and N. André. ‘Computational oncology — math-
ematical modelling of drug regimens for precision medicine’. In: Nature Reviews Clinical Oncology
13.4 (Apr. 2016), pp. 242–254. DOI: 10.1038/nrclinonc.2015.204. URL: https://hal.inria
.fr/hal-03513035.

[2] F. Barlesi, L. Greillier, F. Monville, C. Audigier-Valette, S. Martinez, N. Cloarec, S. van Hulst, L.
Odier, F. Vely, L. Juquel, L. Arnaud, S. Bokobza, M. Hamimed, M. Karlsen, P. Dufossé, A. Pouchin,
L. Ghezali, M. Le Ray, J. Fieschi-Meric and S. Benzekry. ‘3MO - Comprehensive biomarkers
(BMs) analysis to predict efficacy of PD1/L1 immune checkpoint inhibitors (ICIs) in combination
with chemotherapy: a subgroup analysis of the Precision Immuno-Oncology for advanced Non-
Small CEll Lung CancER (PIONeeR) trial’. In: Annals of Oncology. 2022 ESMO Immuno-Oncology
Congress. Vol. 16. suppl_1. Geneva, Switzerland, 2022. URL: https://hal.science/hal-03928
784.

Project COMPO

53

[3] S. Benzekry. ‘Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in
Oncology’. In: Clinical Pharmacology and Therapeutics (18th June 2020). DOI: 10.1002/cpt.1951.
URL: https://hal.inria.fr/hal-02916941.

[4] S. Benzekry, M. Grangeon, M. Karlsen, M. Alexa, I. Bicalho-Frazeto, S. Chaleat, P. Tomasini, D.
Barbolosi, F. Barlési and L. Greillier. ‘Machine Learning for Prediction of Immunotherapy Efficacy
in Non-Small Cell Lung Cancer from Simple Clinical and Biological Data’. In: Cancers 13.24 (Dec.
2021), p. 6210. DOI: 10.3390/cancers13246210. URL: https://inria.hal.science/hal-03
478003.

[5] S. Benzekry, M. Karlsen, C. Bigarré, A. E. Kaoutari, B. Gomes, M. Stern, A. Neubert, R. Bruno, F.
Mercier, S. Vatakuti, P. Curle and C. Jamois. Predicting survival and trial outcome in non-small cell
lung cancer integrating tumor and blood markers kinetics with machine learning. 26th Sept. 2023.
DOI: 10.1101/2023.09.26.23296135. URL: https://inria.hal.science/hal-04375606.

[6] C. Bigarré, F. Bertucci, P. Finetti, G. Macgrogan, X. Muracciole and S. Benzekry. ‘Mechanistic
modeling of metastatic relapse in early breast cancer to investigate the biological impact of
prognostic biomarkers’. In: Computer Methods and Programs in Biomedicine 231 (Apr. 2023),
p. 107401. DOI: 10.1016/j.cmpb.2023.107401. URL: https://inria.hal.science/hal-04
008520.

[7]

J. Ciccolini, D. Barbolosi, C. Meille, C. Serdjebi, S. Giacometti, L. Padovani, E. G. Pasquier and N. G.
André. ‘Pharmacokinetics and Pharmacodynamics-Based Mathematical Modeling Identifies an
Optimal Protocol for Metronomic Chemotherapy’. In: Cancer Research 77.17 (2017), pp. 4723–4733.
DOI: 10.1158/0008-5472.CAN-16-3130. URL: https://hal.archives-ouvertes.fr/hal-
01789578.

[8]

J. Ciccolini and G. Milano. ‘Women at a Disadvantage in Fluorouracil Treatment’. In: JAMA
oncology 2.6 (1st June 2016), p. 829. DOI: 10.1001/jamaoncol.2016.1086. URL: https://hal
.inria.fr/hal-03513066.

[9] R. Fanciullino, L. Farnault, M. Donnette, D.-C. Imbs, C. Roche, G. Venton, Y. Berda-Haddad,
V. Ivanov, J. Ciccolini, L. Ouafik, B. Lacarelle and R. Costello. ‘CDA as a predictive marker for
life-threatening toxicities in patients with AML treated with cytarabine’. In: Blood Advances 2.5
(28th Feb. 2018), pp. 462–469. DOI: 10.1182/bloodadvances.2017014126. URL: https://hal-
amu.archives-ouvertes.fr/hal-01770057.

[10] F. Ferrer, R. Fanciullino, G. Milano and J. Ciccolini. ‘Towards Rational Cancer Therapeutics: Optim-
izing Dosing, Delivery, Scheduling, and Combinations’. In: Clinical Pharmacology and Therapeut-
ics 108.3 (Sept. 2020), pp. 458–470. DOI: 10.1002/cpt.1954. URL: https://hal.archives-ouv
ertes.fr/hal-03464922.

[11] D.-C. Imbs, R. El Cheikh, A. Boyer, J. Ciccolini, C. Mascaux, B. Lacarelle, F. Barlesi, D. Barbolosi
and S. Benzekry. ‘Revisiting bevacizumab + cytotoxics scheduling using mathematical modeling:
proof of concept study in experimental non-small cell lung carcinoma’. In: CPT: Pharmacometrics
and Systems Pharmacology (2018), pp. 1–9. DOI: 10.1002/psp4.12265. URL: https://inria.h
al.science/hal-01624423.

[12] C. Nicolò, C. Périer, M. Prague, C. Bellera, G. Macgrogan, O. Saut and S. Benzekry. ‘Machine
Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast
Cancer’. In: JCO Clinical Cancer Informatics 4 (Sept. 2020), pp. 259–274. DOI: 10.1200/CCI.19.0
0133. URL: https://hal.archives-ouvertes.fr/hal-02942177.

12.2 Publications of the year

International journals

[13] D. Abbot, A. Bikfalvi, A. Bleske-Rechek, W. Bodmer, P. Boghossian, C. Carvalho, J. Ciccolini, J.
Coyne, J. Gauss, P. Gill, S. Jitomirskaya, L. Jussim, A. Krylov, G. Loury, L. Maroja, J. Mcwhorter,
S. Moosavi, P. N. Schwerdtle, J. Pearl, M. Quintanilla-Tornel, H. S. Iii, P. Schreiner, P. Schwerdtfeger,
D. Shechtman, M. Shifman, J. Tanzman, B. Trout, A. Warshel and J. West. ‘In Defense of Merit in

54

Inria Annual Report 2023

Science’. In: Journal of Controversial Ideas 3.1 (28th Apr. 2023), p. 1. DOI: 10.35995/jci03010001.
URL: https://hal.science/hal-04352297.

[14] S. Benzekry, P. Schlicke, A. Mogenet, L. Greillier, P. Tomasini and E. Simon. ‘Computational
markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain
metastases’. In: Clinical and Experimental Metastasis (20th Dec. 2023). DOI: 10.1007/s10585-0
23-10245-3. URL: https://hal.science/hal-04360487.

[15] C. Bigarré, F. Bertucci, P. Finetti, G. Macgrogan, X. Muracciole and S. Benzekry. ‘Mechanistic
modeling of metastatic relapse in early breast cancer to investigate the biological impact of
prognostic biomarkers’. In: Computer Methods and Programs in Biomedicine 231 (Apr. 2023),
p. 107401. DOI: 10.1016/j.cmpb.2023.107401. URL: https://inria.hal.science/hal-04
008520.

[16] E. Borcoman, A. Lalanne, J.-P. Delord, P. Cassier, F. Rolland, S. Salas, J.-M. Limacher, O. Capitain, O.
Lantz, C. Ekwegbara, E. Jeannot, J. Cyrta, C. Tran-Perennou, Z. Castel-Ajgal, G. Marret, E. Piaggio,
M. Brandely, A. Tavernaro, H. Makhloufi, K. Bendjama and C. Le Tourneau. ‘Phase Ib/II trial of
tipapkinogene sovacivec, a therapeutic human papillomavirus16-vaccine, in combination with
avelumab in patients with advanced human papillomavirus16-positive cancers’. In: European
Journal of Cancer 191 (Sept. 2023), p. 112981. DOI: 10 . 1016 / j . ejca . 2023 . 112981. URL:
https://hal.science/hal-04382182.

[17] F. Chatelet, F. R. Ferrand, S. Atallah, J. Thariat, F. Mouawad, N. Fakhry, O. Malard, C. Even, E. de
Monès, E. Uro-Coste, N. Benzerdjeb, S. Hans, S. Testelin, O. Mauvais, D. Evrard, V. Bastit, S. Salas,
F. Espitalier, M. Classe, L. Digue, M. Doré, S. Wong, C. Dupin, F. Nguyen, J. Bettoni, A. Lapierre,
E. Colin, P. Philouze, S. Vergez, B. Baujat, P. Herman, B. Verillaud, J.-M. Constans, A. Coutte
and B. Devauchelle. ‘Survival outcomes, prognostic factors, and effect of adjuvant radiotherapy
and prophylactic neck dissection in salivary acinic cell carcinoma: A prospective multicenter
REFCOR study of 187 patients’. In: European Journal of Cancer 185 (May 2023), pp. 11–27. DOI:
10.1016/j.ejca.2023.02.020. URL: https://u-picardie.hal.science/hal-04044097.

[18]

J. Ciccolini and G. Milano. ‘Immune check points in cancer treatment: current challenges and
perspectives’. In: British Journal of Cancer 129.9 (26th Oct. 2023), pp. 1365–1366. DOI: 10.1038/s
41416-023-02478-6. URL: https://hal.science/hal-04352278.

[19] M. Donnette, M. Hamimed, J. Ciccolini, G. Sicard, F. Correard, L. Farnault, L. Ouafik, G. Venton
and R. Fanciullino. ‘Cytidine deaminase status as a marker of response to azacytidine treatment in
<scp>MDS</scp> and <scp>AML</scp> patients’. In: British Journal of Haematology 203.4 (Nov.
2023), pp. 625–636. DOI: 10.1111/bjh.19096. URL: https://hal.science/hal-04352285.

[20] M. Donnette, L. Osanno, M. Giocanti, G. Venton, L. Farnault, Y. Berda-Haddad, R. Costello, S.
Caroline, L. Ouafik, J. Ciccolini and R. Fanciullino. ‘Determination of 5-azacitidine in human
plasma by LC–MS/MS: application to pharmacokinetics pilot study in MDS/AML patients’. In:
Cancer Chemotherapy and Pharmacology 91.3 (Mar. 2023), pp. 231–238. DOI: 10.1007/s00280-0
23-04505-y. URL: https://hal.science/hal-04352302.

[21] A. El Kaoutari, N. Fraunhoffer, S. Audebert, L. Camoin, Y. Berthois, O. Gayet, J. Roques, M. Bi-
gonnet, C. Bongrain, J. Ciccolini, J. Iovanna, N. Dusetti and P. Soubeyran. ‘Pancreatic ductal
adenocarcinoma ubiquitination profiling reveals specific prognostic and theranostic markers’. In:
EBioMedicine 92 (June 2023), p. 104634. DOI: 10.1016/j.ebiom.2023.104634. URL: https://h
al.science/hal-04352295.

[22] M.-C. Etienne-Grimaldi, N. Pallet, V. Boige, J. Ciccolini, L. Chouchana, C. Barin-Le Guellec, A.
Zaanan, C. Narjoz, J. Taieb, F. Thomas and M.-A. Loriot. ‘Current diagnostic and clinical issues of
screening for dihydropyrimidine dehydrogenase deficiency’. In: European Journal of Cancer 181
(Mar. 2023), pp. 3–17. DOI: 10.1016/j.ejca.2022.11.028. URL: https://inria.hal.scienc
e/hal-03934173.

Project COMPO

55

[23] G. Gravis, P. Marino, D. Olive, F. Penault-Llorca, J.-P. Delord, C. Simon, A. Lamrani-Ghaouti, R.
Sabatier, J. Ciccolini and J.-M. Boher. ‘A non-inferiority randomized phase III trial of standard
immunotherapy by checkpoint inhibitors vs. reduced dose intensity in responding patients with
metastatic cancer: the MOIO protocol study’. In: BMC Cancer 23.1 (Dec. 2023), p. 393. DOI: 10.11
86/s12885-023-10881-8. URL: https://amu.hal.science/hal-04109560.

[24] M. Hamimed, R. Devillier, P.-J. Weiller, C. Marin, J.-M. Schiano, N. Belmecheri, M.-C. Etienne-
Grimaldi, J. Ciccolini and S. Harbi. ‘Life-threatening toxicities upon Pembrolizumab intake: could
pharmacokinetics be the bad guy?’ In: Cancer Chemotherapy and Pharmacology (13th Nov. 2023).
DOI: 10.1007/s00280-023-04611-x. URL: https://hal.science/hal-04352277.

[25] K. Harrington, R. Ferris, M. Gillison, M. Tahara, A. Argiris, J. Fayette, M. Schenker, Å. Bratland,
J. Walker, P. Grell, C. Even, C. Chung, R. Redman, A. Coutte, S. Salas, C. Grant, S. de Azevedo,
D. Soulières, A. Hansen, L. Wei, T. A. Khan, K. Miller-Moslin, M. Roberts and R. Haddad. ‘Efficacy
and Safety of Nivolumab Plus Ipilimumab vs Nivolumab Alone for Treatment of Recurrent or
Metastatic Squamous Cell Carcinoma of the Head and Neck’. In: JAMA oncology 9.6 (1st June 2023),
p. 779. DOI: 10.1001/jamaoncol.2023.0147. URL: https://hal.science/hal-04360480.

[26] S. Marolleau, A. Mogenet, C. Boeri, M. Hamimed, J. Ciccolini and L. Greillier. ‘Killing a fly with a
sledgehammer: Atezolizumab exposure in real-world lung cancer patients’. In: CPT: Pharmacomet-
rics and Systems Pharmacology 12.11 (Nov. 2023), pp. 1795–1803. DOI: 10.1002/psp4.13063.
URL: https://hal.science/hal-04352276.

[27] P. Piris, D. Buric, T. Yamasaki, P. Huchedé, M. Rossi, M. Matteudi, M.-P. Montero, a. rodallec
anne, R. Appay, C. Roux, S. Combes, E. Pasquier, M. Castets, N. André, P. Brémond and M. Carré.
‘Conditional generation of free radicals by selective activation of alkoxyamines: towards more
effective and less toxic targeting of brain tumors’. In: Chemical Science 14.29 (2023), pp. 7988–7998.
DOI: 10.1039/d3sc01315d. URL: https://hal.science/hal-04344737.

[28] B. Royer, M. Launay, J. Ciccolini, L. Derain, F. Parant, F. Thomas and J. Guitton. ‘Impact of renal
impairment on dihydropyrimidine dehydrogenase (DPD) phenotyping’. In: ESMO Open 8.3 (June
2023), p. 101577. DOI: 10.1016/j.esmoop.2023.101577. URL: https://hal.science/hal-0
4352292.

[29]

S. Salas, G. Economos, D. Hugues, E. Gilbert, D. Gracia, P. Poulain, C. Mateus, E. Collet, B. Planchet-
Barraud, A. Colpaert, É. Perceau-Chambard, L. Y. Calvel, C. Franck, D. Mallet, K. Baumstarck and
A. Evin. ‘Legalisation of euthanasia and assisted suicide: advanced cancer patient opinions – cross-
sectional multicentre study’. In: BMJ Palliative and supportive care 13 (3rd Aug. 2023), e1335–
e1341. DOI: 10.1136/spcare-2022-004134. URL: https://hal.science/hal-04382168.

[30] G. Sicard, D. Protzenko, S. Giacometti, F. Barlési, J. Ciccolini and R. Fanciullino. ‘Harnessing tumor
immunity with cytotoxics: T cells monitoring in mice bearing lung tumors treated with anti-VEGF
and pemetrexed-cisplatin doublet’. In: British Journal of Cancer 129.9 (26th Oct. 2023), pp. 1373–
1382. DOI: 10.1038/s41416-023-02350-7. URL: https://hal.science/hal-04352289.

National journals

[31]

J. Ciccolini. ‘Antibody-drug Conjugates: pharmacokinetics and pharmacodynamics perspective’.
In: Correspondances en onco-théranostic. La Révolution des ADC XII.2 (May 2023), pp. 56–59. URL:
https://hal.science/hal-04371424.

Invited conferences

[32] S. Benzekry. ‘Artificial intelligence in healthcare data warehouses’. In: Annual congress of the
French group of clinical pneumo-oncology (GPCO). Toulon, France, June 2023. URL: https://in
ria.hal.science/hal-04388413.

[33] S. Benzekry. ‘Integrative kinetics and machine learning modeling for prediction of outcome
following immunotherapy in lung cancer’. In: SMAI congress (French Society of Applied and
Industrial Mathematics). Pointe à Pitre, France, May 2023. URL: https://inria.hal.science
/hal-04388478.

56

Inria Annual Report 2023

[34] S. Benzekry. ‘Integrative kinetics and machine learning modeling for prediction of outcome fol-
lowing immunotherapy in lung cancer’. In: CARe graduate school workshop. Toulouse (FRANCE),
France, Jan. 2023. URL: https://inria.hal.science/hal-04388750.

[35] S. Benzekry. ‘Integrative kinetics and machine learning modeling for prediction of outcome fol-
lowingimmunotherapy in lung cancer’. In: Annual congress of the French society of pharmacology
and therapeutics (SFPT). Limoges, France, June 2023. URL: https://inria.hal.science/hal-
04388499.

[36]

[37]

S. Benzekry. ‘Integrative kinetics and machine learning modeling for prediction ofoutcome follow-
ing immunotherapy in lung cancer’. In: Workshop for young researchers in Cancer. Porquerolles,
France, Oct. 2023. URL: https://inria.hal.science/hal-04388572.

S. Benzekry. ‘Using statistical learning for clinical trials’. In: Annual meeting of the APHM center
for early phase clinical trials in oncology. Marseille, France, June 2023. URL: https://inria.hal
.science/hal-04388605.

International peer-reviewed conferences

[38] M. Dacos, G. Sicard, B. Immordino, S. Giacometti, J. Ciccolini, A. Rodallec and R. Fanciullino.
‘Nanoliposomes harness tumor immunity in breast cancer models’. In: Cancer research. AACR
2023 Annual Meeting. Vol. 83. Proceedings of the American Association for Cancer Research
Annual Meeting 2023; Part 1 (Regular and Invited Abstracts) 7_Supplement. Orlando (FL), United
States, 4th Apr. 2023, pp. 1857–1857. DOI: 10.1158/1538-7445.AM2023-1857. URL: https://h
al.science/hal-04352700.

[39] P. Dufossé and S. Benzekry. ‘A comparison of machine learning algorithms for survival with
missing data’. In: 54es Journées de Statistique de la SFdS. 54es Journées de Statistique. Bruxelles
(BEL), Belgium, 2023. DOI: 10.48550/arXiv.2303.13590. URL: https://hal.science/hal-0
4384220.

[40]

[41]

J. Michels, M. Hamimed, Y. Menu, C. Balleyguier, F. Ghiringhelli, J.-S. Frenel, C. Genestie, B. You, A.
Floquet, L. Eberst, A. Paci, R. Bahleda, P. Pautier, E. Colomba, F. Pommeret, A. Leary, A. Marabelle,
A. Bardet, C. Brard and J. Ciccolini. ‘Bevacizumab exposure and tumor response in patients
with platinum resistant epithelial ovarian cancer.’ In: Journal of Clinical Oncology. 2023 ASCO
Annual Meeting. Vol. 41. 16_suppl. Chicago (Ill.), United States, 1st June 2023, pp. 3093–3093. DOI:
10.1200/JCO.2023.41.16_suppl.3093. URL: https://hal.science/hal-04352637.

J. Ou, B. Louis, L. Balasse, T. Roussel, L. Peng, B. Guillet, P. Garrigue and F. Gattacceca. ‘De-
velopment of a physiologically-based pharmacokinetic model to understand and predict the
disposition of gallium-68 radiolabeled-dendrimers in vivo’. In: PAGE. Abstracts of the Annual
Meeting of the Population Approach Group in Europe. ISSN 1871-6032. PAGE 2023 - Population
approach group Europe meeting. Vol. 31. A Coruña, Spain, 27th June 2023, Abstr 10514. URL:
https://hal.science/hal-04268794.

[42] G. Sicard, S. Giacometti, F. Barlesi, R. Fanciullino and J. Ciccolini. ‘Pemetrexed-cisplatin-bevacizumab

combo in lung cancer models: impact on tumor immunity’. In: Cancer research. AARC 2023 annual
meeting -. Vol. 83. Proceedings of the American Association for Cancer Research Annual Meeting
2023; Part 1 (Regular and Invited Abstracts) 7_Supplement. Orlando (FL), United States, 4th Apr.
2023, pp. 1856–1856. DOI: 10.1158/1538-7445.AM2023-1856. URL: https://hal.science/h
al-04352713.

Conferences without proceedings

[43]

[44]

J. Ciccolini. ‘ADCs: Pharmacokinetics & dosing considerations’. In: XIXeme Journées du GPCO-
Unicancer. Strasbourg, France, 17th Nov. 2023. URL: https://hal.science/hal-04352797.

J. Ciccolini. ‘Anti-Body Drug Conjugates: which clinical perspectives in digestive oncology?’ In:
2ème Cours Saint Paul Cancers de l’Appareil digestif. Nice, France, 18th Nov. 2023. URL: https:
//hal.science/hal-04352814.

Project COMPO

57

[45]

[46]

[47]

[48]

[49]

[50]

[51]

[52]

[53]

[54]

[55]

[56]

J. Ciccolini. ‘Anti-Body Drug Conjugates: Why « Magic Bullet » is not « Magic Pharmacology »’. In:
XIeme Journées Pharmacologiques de Cochin. Paris, France, 30th Nov. 2023. URL: https://hal
.science/hal-04356309.

J. Ciccolini. ‘Anti-PARP: Aspects PK et PK/PD quelle place pour le TDM?’ In: XIXèmes Journées du
GPCO-Unicancer. Strasbourg, France, 17th Nov. 2023. URL: https://hal.science/hal-04352
786.

J. Ciccolini. ‘Antibody-Drug Conjugates ; beyond the hype, which pharmacology in breast cancer?’
In: Séminaire annuel du Cancéropôle PACA. Frejus, France, 3rd July 2023. URL: https://hal.sc
ience/hal-04352852.

J. Ciccolini. ‘BRAF-i plus anti-MEK combo: targeting MAP-kinase in metastatic melanoma’. In:
4ème Journées Horizon Mélanome. Paris, France, 30th Sept. 2023. URL: https://hal.science
/hal-04352834.

J. Ciccolini. ‘BRAF-inhibitors in mCRC: pharmacology and PK/PD perspectives’. In: Aquitaine
Gastro Annual Meeting. Bordeaux, France, 1st Apr. 2023. URL: https://hal.science/hal-043
52744.

J. Ciccolini. ‘Cancer de la prostate : implications PK et PK/PD d’un antagoniste oral de la LHRH’.
In: 14ème Congrès de la SFPO - société Française des Pharmaciens Oncologues. Nancy, France,
11th Oct. 2023. URL: https://hal.science/hal-04352755.

J. Ciccolini. ‘Difficulties in implementing TDM in oncology: another French paradox’. In: IATDMCT
2023 - 21st Congress of the International Association of Therapeutic Drug Monitoring & Clinical
Toxicology. Oslo (Norway), Norway, 24th Sept. 2023. URL: https://hal.science/hal-0435272
5.

J. Ciccolini. ‘From HER2+ to HER2-low: specificity of the new generation ADCs’. In: 9ème Congrès
du Monaco Age Oncology (MAO). Monaco, Monaco, 22nd Mar. 2023. URL: https://hal.scienc
e/hal-04352893.

J. Ciccolini. ‘PK/PD with Immunotherapy’. In: 7e Journées scientifiques Immunité & Cancer. Paris,
France, 30th Jan. 2023. URL: https://hal.science/hal-04352913.

J. Ciccolini. ‘TDM in oncology: application to breast cancer patients’. In: 22ème Cours Saint Paul
Sein & Cancer Gynécologiques. Cannes, France, 21st Jan. 2023. URL: https://hal.science/ha
l-04352931.

J. Ciccolini. ‘TDM of oral targeted therapies in metastatic melanoma’. In: Pierre Fabre Symposium.
Avène, France, 27th June 2023. URL: https://hal.science/hal-04354962.

J. Ciccolini. ‘Unlocking the potential of ADCs for HER2 signaling pathway’. In: Daïchi Sankyo
symposium. Toulon (83000), France, 4th May 2023. URL: https://hal.science/hal-0435294
1.

[57] L. Nguyen Phuong, L. Greillier, C. Gaudy, J.-L. Deville, J.-C. Garcia, F. Fina, S. Salas and S. Benzekry.
‘Mechanistic modeling of the longitudinal tumor and biological markers combined with quantitat-
ive cell-free DNA’. In: Annual Meeting of the Society for Mathematical Biology. Columbus (Ohio),
United States, 16th July 2023. URL: https://inria.hal.science/hal-04388020.

Reports & preprints

[58] S. Benzekry, P. Schlicke, P. Tomasini and E. Simon. Mechanistic modeling of brain metastases in
NSCLC provides computational markers for personalized prediction of outcome. 12th Jan. 2023.
DOI: 10.1101/2023.01.10.23284189. URL: https://hal.science/hal-03935739.

[59] S. Benzekry, M. Karlsen, C. Bigarré, A. E. Kaoutari, B. Gomes, M. Stern, A. Neubert, R. Bruno, F.
Mercier, S. Vatakuti, P. Curle and C. Jamois. Predicting survival and trial outcome in non-small cell
lung cancer integrating tumor and blood markers kinetics with machine learning. 26th Sept. 2023.
DOI: 10.1101/2023.09.26.23296135. URL: https://inria.hal.science/hal-04375606.

58

Inria Annual Report 2023

[60]

S. Benzekry, M. Mastri, C. Nicolò and J. M. Ebos. Machine-learning and mechanistic modeling of
primary and metastatic breast cancer growth after neoadjuvant targeted therapy. 23rd Feb. 2023.
DOI: 10.1101/2023.02.22.529613. URL: https://hal.science/hal-04384182.

[61] D. Boulate and D. Barbolosi. Prevalung étoile. 5th Aug. 2021. URL: https://inria.hal.science

/hal-03926628.

[62] Y. Shi, M. Dolan, M. Mastri, A. Mckenery, J. W. Hill, A. Dommer, S. Benzekry, M. Long, S. Abrams,
I. Puzanov and J. M. Ebos. Acquired resistance to PD-L1 inhibition is associated with an enhanced
type I IFN-stimulated secretory program in tumor cells. 1st July 2021. DOI: 10.1101/2021.07.01
.450417. URL: https://inria.hal.science/hal-04387866.

Other scientific publications

[63] M. Dacos, E. Diroff, S. Giacometti, L. Osanno, J. Ciccolini and R. Fanciullino. ‘Liposomal for-
mulation of docetaxel: biodistribution and immunomodulatory benefit in HER2 breast cancer
treatment’. In: SFNano 2023 Annual Meeting. Montpellier, France, 4th Dec. 2023. URL: https://h
al.science/hal-04356233.

[64] M. Dacos, E. Diroff, S. Giacometti, L. Osanno, J. Ciccolini and R. Fanciullino. ‘Pharmacokinetics of
an anticancer drug: liposomal formulation of docetaxel’. In: Congrès 2023 de la SFPO - 14èmes
Journées Nationales Actualités en Oncologie. Nancy, France, 2023. URL: https://hal.science
/hal-04356213.

[65] M. Dacos, G. Sicard, D. Protzenko, S. Giacometti, J. Ciccolini and R. F. Smartc. ‘Nanoliposomes
harness tumor immunity in breast cancer model’. In: Séminaire Annuel du Cancéropôle PACA.
Fréjus St Raphael, France, 3rd July 2023. URL: https://hal.science/hal-04356150.

[66] A. Deschamps, L. Velly, E. Jouve, R. Guilhaumou and F. Gattacceca. ‘Population pharmacokinetic
model of cefotaxime encompassing time-varying physiopathology: exploration of intra-individual
variability of the renal function’. In: GMP symposium 2023. Paris, France, 18th Oct. 2023. URL:
https://hal.science/hal-04386141.

[67] L. Osanno, L. Farnault, J. Colle, G. Venton and R. Fanciullino. ‘Study of the pharmacokinetic
variability of azacytidine’. In: Séminaire annuel du Canceropôle PACA. Fréjus St Raphael, France,
3rd July 2023. URL: https://hal.science/hal-04355717.

[68]

J. Ou, B. Louis, L. Balasse, T. Roussel, L. Peng, B. Guillet, P. Garrigue and F. Gattacceca. ‘Physiologically-
based pharmacokinetic modelling of dendrimer nanoparticles to unravel structure – PK paramet-
ers relationships’. In: GMP Symposium 2023. Paris, France, 18th Oct. 2023. URL: https://hal.sc
ience/hal-04344099.

[69] D. Protzenko, R. Devillier, B. Bouchacourt, L. Bourguignon and J. Ciccolini. ‘Développement d’un
modèle d’intelligence artificielle pour la prédiction de l’exposition au Busulfan et l’individualisation
des posologies’. In: Congrès 2023 de la SFPO - 14èmes Journées Nationales Actualités en Oncologie.
Nancy, France, 11th Oct. 2023. URL: https://hal.science/hal-04365394.

[70] D. Protzenko, R. Devillier, B. Bouchacourt, L. Bourguignon and J. Ciccolini. ‘Maitrise de l’exposition
au Busulfan à travers le développement d’un modèle prédictif de la survie’. In: Congrès 2023 de la
SFPO - 14èmes Journées Nationales Actualités en Oncologie. Nancy, France, 11th Oct. 2023. URL:
https://hal.science/hal-04365392.

[71] D. Protzenko, R. Devillier, B. Bouchacourt, L. Bourguignon and J. Ciccolini. ‘Utilisation de l’IA pour
une meilleur compréhension des mécanismes de rechute dans les greffes CSH conditionnées par
Busulfan haute dose’. In: Congrès 2023 de la SFPO - 14èmes Journées Nationales Actualités en
Oncologie. Nancy, France, 2023. URL: https://hal.science/hal-04365399.

[72] D. Protzenko, K. Hammami and G. Sicard. ‘Développement et utilisation d’un algorithme pour
automatiser la standardisation des posologies en URC’. In: Congrès 2023 de la SFPO - 14èmes
Journées Nationales Actualités en Oncologie. Nancy, France, 11th Oct. 2023. URL: https://hal
.science/hal-04365397.

Project COMPO

59

[73] A. Ronda, L. Bourguignon, F. Correard, J. Ciccolini and R. Fanciullino. ‘High Dose Methotrexate :
covariates of interest to anticipate response and toxicities’. In: Séminaire annuel du Cancéropôle
PACA. Fréjus St Raphael, France, 3rd July 2023. URL: https://hal.science/hal-04355698.

12.3 Cited publications

[74] K. Afrasiabi, M. E. Linskey and Y.-H. Zhou. ‘Exploiting Cancer’s Tactics to Make Cancer a Manage-
able Chronic Disease’. In: Cancers 12.66 (June 2020), p. 1649. DOI: 10.3390/cancers12061649.

[75]

a. L. Baldock, R. C. Rockne, a. D. Boone, M. L. Neal, A. Hawkins-Daarud, D. M. Corwin, C. A. Bridge,
L. a. Guyman, A. D. Trister, M. M. Mrugala, J. K. Rockhill and K. R. Swanson. ‘From patient-specific
mathematical neuro-oncology to precision medicine.’ In: Front Oncol 3.April (Jan. 2013), pp. 62–
62.

[76] E. Baratchart, S. Benzekry, A. Bikfalvi, T. Colin, L. S. Cooley, R. Pineau, E. J. Ribot, O. Saut and W.
Souleyreau. ‘Computational Modelling of Metastasis Development in Renal Cell Carcinoma’. In:
PLoS Comput Biol 11.11 (Nov. 2015). 00007, e1004626. DOI: 10.1371/journal.pcbi.1004626.

[77] D. Barbolosi, S. Hapdey, S. Battini, C. Faivre, J. Mancini, K. Pacak, B. Farman-Ara and D. Taïeb.
‘Determination of the unmetabolized 18F-FDG fraction by using an extension of simplified kinetic
analysis method: clinical evaluation in paragangliomas’. In: Medical & biological engineering &
computing 54.1 (Jan. 2016), pp. 103–111. DOI: 10.1007/s11517-015-1318-3.

[78] F. Barlesi, D.-C. Imbs, P. Tomasini, L. Greillier, M. Galloux, A. Testot-Ferry, M. Garcia, X. Elharrar,
A. Pelletier, N. André, C. Mascaux, B. Lacarelle, R. E. Cheikh, R. Serre, J. Ciccolini and D. Barbolosi.
In: Oncotarget 8.29 (July 2017), pp. 47161–47166. DOI: 10.18632/oncotarget.17562.

[79]

S. Benzekry, A. Tracz, M. Mastri, R. Corbelli, D. Barbolosi and J. M. L. Ebos. ‘Modeling Spontaneous
Metastasis following Surgery: An In Vivo-In Silico Approach’. In: Cancer Research 76.3 (Feb. 2016).
00000, pp. 535–547. DOI: 10.1158/0008-5472.CAN-15-1389.

[80] S. Benzekry, A. Gandolfi and P. Hahnfeldt. ‘Global Dormancy of Metastases Due to Systemic
Inhibition of Angiogenesis’. In: PLoS ONE 9.1 (Jan. 2014). 00019, e84249–11. DOI: 10.1371/journ
al.pone.0084249.

[81] S. Benzekry, C. Lamont, D. Barbolosi, L. Hlatky and P. Hahnfeldt. ‘Mathematical Modeling of
Tumor-Tumor Distant Interactions Supports a Systemic Control of Tumor Growth.’ In: Cancer
Research 77.18 (Sept. 2017), pp. 5183–5193. DOI: 10.1158/0008-5472.CAN-17-0564.

[82] C. Bigarré, P. Finetti, F. Bertucci, X. Muracciole and S. Benzekry. ‘A mechanistic model for prediction
of metastatic relapse in early-stage breast cancer using routine clinical features’. In: AACR. Vol. 82.
12. New Orléans, United States, Apr. 2022, p. 2737. DOI: 10.1158/1538- 7445.AM2022- 2737.
URL: https://inria.hal.science/hal-03752407.

[83] R. Bruno, D. Bottino, D. P. de Alwis, A. T. Fojo, J. Guedj, C. Liu, K. R. Swanson, J. Zheng, Y. Zheng
and J. Y. Jin. ‘Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor
Dynamic Models.’ In: Clinical cancer research : an official journal of the American Association for
Cancer Research (Dec. 2019), pp. 1–22. DOI: 10.1158/1078-0432.CCR-19-0287.

[84] B. Carpenter, A. Gelman, M. D. Hoffman, D. Lee, B. Goodrich, M. Betancourt, M. Brubaker, J.
Guo, P. Li and A. Riddell. ‘Stan: A Probabilistic Programming Language’. In: Journal of Statistical
Software 76.1 (Jan. 2017), pp. 1–32. DOI: 10.18637/jss.v076.i01.

[85] R. H. Chisholm, T. Lorenzi, A. Lorz, A. K. Larsen, L. N. de Almeida, A. Escargueil and J. Clairambault.
‘Emergence of drug tolerance in cancer cell populations: an evolutionary outcome of selection,
nongenetic instability, and stress-induced adaptation’. In: Cancer Research 75.6 (Mar. 2015),
pp. 930–939. DOI: 10.1158/0008-5472.CAN-14-2103.

[86]

J. Ciccolini, D. Barbolosi, N. André, S. Benzekry and F. Barlesi. ‘Combinatorial immunotherapy
strategies: most gods throw dice, but fate plays chess’. In: Annals of Oncology 30.11 (Nov. 2019),
pp. 1690–1691. DOI: 10.1093/annonc/mdz297.

60

Inria Annual Report 2023

[87]

J. Ciccolini, D. Barbolosi, N. André, F. Barlesi and S. Benzekry. ‘Mechanistic Learning for Combinat-
orial Strategies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigators?’
In: JCO Precision Oncology 4 (May 2020), pp. 486–491. DOI: 10.1200/PO.19.00381.

[88] O. Clatz, M. Sermesant, P.-Y. Bondiau, H. Delingette, S. Warfield, G. Malandain and N. Ayache.
‘Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with
biomechanical deformation’. In: IEEE Transactions on Medical Imaging 24.10 (Oct. 2005), pp. 1334–
1346. DOI: 10.1109/TMI.2005.857217.

[89] E. Colard, S. Delcourt, L. Padovani, S. Thureau, A. Dumouchel, P. Gouel, J. Lequesne, B. F. Ara,
P. Vera, D. Taïeb, I. Gardin, D. Barbolosi and S. Hapdey. ‘A new methodology to derive 3D kinetic
parametric FDG PET images based on a mathematical approach integrating an error model of
measurement’. In: EJNMMI Research 8 (Nov. 2018). DOI: 10.1186/s13550-018-0454-9. URL:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6238015/.

[90] T. Colin, A. Iollo, D. Lombardi and O. Saut. ‘Prediction of the Evolution of Thyroidal Lung Nodules

Using a Mathematical Model.’ In: 82 (Jan. 2010), pp. 37–38.

[91] L. S. Cooley, J. Rudewicz, W. Souleyreau, K. Clarke, F. Falciani, M. Dufies, S. Verbeke, A. Emanuelli,
S. Benzekry, C. Taing, E. J. Ribot, S. Miraux, D. Lambrechts, E. Modave, R. Pineau, M.-A. Derieppe,
D. Ambrosetti, J.-C. Bernhard, A. Ravaud, S. Négrier, J.-M. Ferrero, G. Pagès, M. Nikolski and
A. Bikfalvi. ‘A multi-layered systems approach for renal cell carcinoma’. In: bioRxiv (Jan. 2020),
p. 2020.01.13.904235. DOI: 10.1101/2020.01.13.904235.

[92] B. Delyon, M. Lavielle and E. Moulines. ‘Convergence of a stochastic approximation version of
the EM algorithm’. In: The Annals of Statistics 27.1 (Mar. 1999). Zbl: 0932.62094, pp. 94–128. DOI:
10.1214/aos/1018031103.

[93] H. Deng, M. Li, Q. Wu, L. Wang, Z. Hong, F. Yi, Y. Wei and W. Zhang. ‘A 2/1 Sunitinib Dosing Sched-
ule Provides Superior Antitumor Effectiveness and Less Toxicity Than a 4/2 Schedule for Metastatic
Renal Cell Carcinoma: A Systematic Review and Meta-Analysis’. In: Frontiers in Oncology 10 (2020),
p. 313. DOI: 10.3389/fonc.2020.00313.

[94] S. Desmée, F. Mentré, C. Veyrat-Follet, B. Sébastien and J. Guedj. ‘Using the SAEM algorithm for
mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and
survival in prostate cancer patients’. In: Biometrics 73.1 (Mar. 2017), pp. 305–312. DOI: 10.1111/b
iom.12537.

[95] M. Doumic, B. Perthame and J. P. Zubelli. ‘Numerical solution of an inverse problem in size-
structured population dynamics’. In: Inverse Problems 25.4 (Apr. 2009), pp. 045008–045008.

[96] X. Elharrar, D. Barbolosi, J. Ciccolini, C. Meille, C. Faivre, B. Lacarelle, N. André and F. Barlesi.
‘A phase Ia/Ib clinical trial of metronomic chemotherapy based on a mathematical model of
oral vinorelbine in metastatic non-small cell lung cancer and malignant pleural mesothelioma:
rationale and study protocol’. In: BMC cancer 16.1 (2016). 00000, p. 278. DOI: 10.1186/s12885-0
16-2308-z.

[97] R. Fanciullino, S. Giacometti, C. Mercier, C. Aubert, C. Blanquicett, P. Piccerelle and J. Ciccolini.
‘In vitro and in vivo reversal of resistance to 5-fluorouracil in colorectal cancer cells with a novel
stealth double-liposomal formulation’. In: British Journal of Cancer 97.7 (Oct. 2007), pp. 919–926.
DOI: 10.1038/sj.bjc.6603970.

[98] F. Foglietta, R. Canaparo, G. Muccioli, E. Terreno and L. Serpe. ‘Methodological aspects and
pharmacological applications of three-dimensional cancer cell cultures and organoids’. In: Life
Sciences 254 (Aug. 2020), p. 117784. DOI: 10.1016/j.lfs.2020.117784.

[99] L. Gandhi. ‘Pembrolizumab plus Chemotherapy in Metastatic Non-Small-Cell Lung’. In: Cancer. N

Engl J Med 378.2078–2092 (2018).

Project COMPO

61

[100] G. Gundem, P. Van Loo, B. Kremeyer, L. B. Alexandrov, J. M. C. Tubio, E. Papaemmanuil, D. S.
Brewer, H. M. L. Kallio, G. Högnäs, M. Annala, K. Kivinummi, V. Goody, C. Latimer, S. O’Meara,
K. J. Dawson, W. Isaacs, M. R. Emmert-Buck, M. Nykter, C. Foster, Z. Kote-Jarai, D. Easton, H. C.
Whitaker, I. P. U. Group, D. E. Neal, C. S. Cooper, R. A. Eeles, T. Visakorpi, P. J. Campbell, U.
McDermott, D. C. Wedge and G. S. Bova. ‘The evolutionary history of lethal metastatic prostate
cancer’. In: Nature 520.7547 (Apr. 2015), pp. 353–357. DOI: 10.1038/nature14347.

[101] T. Hastie, R. Tibshirani and J. Friedman. ‘The elements of statistical learning: data mining, infer-

ence, and prediction, Springer Series in Statistics’. In: (2009).

[102] E. Hénin, C. Meille, D. Barbolosi, B. You, J. Guitton, A. Iliadis and G. Freyer. ‘Revisiting dosing
regimen using PK/PD modeling: the MODEL1 phase I/II trial of docetaxel plus epirubicin in
metastatic breast cancer patients’. In: Breast Cancer Research and Treatment 156.2 (Mar. 2016),
pp. 331–341. DOI: 10.1007/s10549-016-3760-9.

[103] A. Hoos, R. Ibrahim, A. Korman, K. Abdallah, D. Berman, V. Shahabi, K. Chin, R. Canetta and
R. Humphrey. ‘Development of ipilimumab: contribution to a new paradigm for cancer immuno-
therapy’. In: Seminars in Oncology 37.5 (Oct. 2010), pp. 533–546. DOI: 10.1053/j.seminoncol.2
010.09.015.

[104] D. A. Hormuth, A. M. Jarrett, X. Feng and T. E. Yankeelov. ‘Calibrating a Predictive Model of Tumor
Growth and Angiogenesis with Quantitative MRI’. In: Annals of Biomedical Engineering 47.7 (July
2019), pp. 1539–1551. DOI: 10.1007/s10439-019-02262-9.

[105] G. N. Hortobagyi and F. A. Holmes. ‘Single-agent paclitaxel for the treatment of breast cancer: an

overview’. In: Seminars in Oncology 23.1 Suppl 1 (Feb. 1996), pp. 4–9.

[106] Z. Hu, J. Ding, Z. Ma, R. Sun, J. A. Seoane, J. Scott Shaffer, C. J. Suarez, A. S. Berghoff, C. Cremolini,
A. Falcone, F. Loupakis, P. Birner, M. Preusser, H.-J. Lenz and C. Curtis. ‘Quantitative evidence for
early metastatic seeding in colorectal cancer’. In: Nature Genetics 51.77 (July 2019), pp. 1113–1122.
DOI: 10.1038/s41588-019-0423-x.

[107] D.-C. Imbs, R. El Cheikh, A. Boyer, J. Ciccolini, C. Mascaux, B. Lacarelle, F. Barlesi, D. Barbolosi
and S. Benzekry. ‘Revisiting Bevacizumab + Cytotoxics Scheduling Using Mathematical Modeling:
Proof of Concept Study in Experimental Non-Small Cell Lung Carcinoma.’ In: CPT: Pharmacomet-
rics & Systems Pharmacology 7.1 (Jan. 2018), pp. 42–50. DOI: 10.1002/psp4.12265.

[108] K. Iwata, K. Kawasaki and N. Shigesada. ‘A Dynamical Model for the Growth and Size Distribution
of Multiple Metastatic Tumors’. In: Journal of Theoretical Biology 203.2 (Mar. 2000), pp. 177–186.
DOI: 10.1006/jtbi.2000.1075.

[109] D. M. Kurtz, M. S. Esfahani, F. Scherer, J. Soo, M. C. Jin, C. L. Liu, A. M. Newman, U. Dührsen,
A. Hüttmann, O. Casasnovas, J. R. Westin, M. Ritgen, S. Böttcher, A. W. Langerak, M. Roschewski,
W. H. Wilson, G. Gaidano, D. Rossi, J. Bahlo, M. Hallek, R. Tibshirani, M. Diehn and A. A. Alizadeh.
‘Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction’. In:
Cell 178.3 (July 2019), 699–713.e19. DOI: 10.1016/j.cell.2019.06.011.

[110] M. Lavielle. Mixed Effects Models for the Population Approach. CRC Press, July 2014.

[111] M. L. Lindstrom and D. M. Bates. ‘Nonlinear mixed effects models for repeated measures data.’ In:

Biometrics 46.3 (Sept. 1990), pp. 673–687.

[112] S. M. McBride, E. J. Sherman, C. J. Tsai, S. S. Baxi, J. Aghalar, J. Eng, W. I. Zhi, D. C. McFarland,
L. S. Michel, D. Spielsinger, Z. Zhang, J. Flynn, L. Dunn, A. L. Ho, N. Riaz, D. G. Pfister and N. Y. Lee.
‘A phase II randomized trial of nivolumab with stereotactic body radiotherapy (SBRT) versus
nivolumab alone in metastatic (M1) head and neck squamous cell carcinoma (HNSCC).’ In:
Journal of Clinical Oncology 36.15_suppl (May 2018), pp. 6009–6009. DOI: 10.1200/JCO.2018.3
6.15_suppl.6009.

[113] C. Meille, D. Barbolosi, J. Ciccolini, G. Freyer and A. Iliadis. ‘Revisiting Dosing Regimen Using
Pharmacokinetic/Pharmacodynamic Mathematical Modeling: Densification and Intensification
of Combination Cancer Therapy’. In: Clinical Pharmacokinetics 55.8 (Aug. 2016). 00000, pp. 1015–
1025. DOI: 10.1007/s40262-016-0374-7.

62

[114]

Inria Annual Report 2023

S. Mollard, J. Ciccolini, D.-C. Imbs, R. E. Cheikh, D. Barbolosi and S. Benzekry. ‘Model driven op-
timization of antiangiogenics + cytotoxics combination: application to breast cancer mice treated
with bevacizumab + paclitaxel doublet leads to reduced tumor growth and fewer metastasis’. In:
Oncotarget 8.14 (Feb. 2017), pp. 23087–23098. DOI: 10.18632/oncotarget.15484.

[115] S. Mollard, R. Fanciullino, S. Giacometti, C. Serdjebi, S. Benzekry and J. Ciccolini. ‘In Vivo Biolu-
minescence Tomography for Monitoring Breast Tumor Growth and Metastatic Spreading: Com-
parative Study and Mathematical Modeling’. In: Scientific Reports 6 (Nov. 2016). 00000, p. 36173.
DOI: 10.1038/srep36173.

[116] R. Moncada, D. Barkley, F. Wagner, M. Chiodin, J. C. Devlin, M. Baron, C. H. Hajdu, D. M. Simeone
and I. Yanai. ‘Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals
tissue architecture in pancreatic ductal adenocarcinomas’. In: Nature Biotechnology 38.33 (Mar.
2020), pp. 333–342. DOI: 10.1038/s41587-019-0392-8.

[117] R. J. Motzer, K. Penkov, J. Haanen, B. Rini, L. Albiges, M. T. Campbell, B. Venugopal, C. Kollmanns-
berger, S. Negrier, M. Uemura, J. L. Lee, A. Vasiliev, W. H. Miller, H. Gurney, M. Schmidinger,
J. Larkin, M. B. Atkins, J. Bedke, B. Alekseev, J. Wang, M. Mariani, P. B. Robbins, A. Chudnovsky,
C. Fowst, S. Hariharan, B. Huang, A. di Pietro and T. K. Choueiri. ‘Avelumab plus Axitinib versus
Sunitinib for Advanced Renal-Cell Carcinoma’. In: New England Journal of Medicine 380.12 (Mar.
2019), pp. 1103–1115. DOI: 10.1056/NEJMoa1816047.

[118] C. Nicolò. ‘Mathematical modelling of neoadjuvant antiangiogenic therapy and prediction of
post-surgical metastatic relapse in breast cancer patients’. PhD thesis. Université de Bordeaux,
Oct. 2019. URL: https://tel.archives-ouvertes.fr/tel-02560543.

[119] A. Noël, M. Jost and E. Maquoi. ‘Matrix metalloproteinases at cancer tumor–host interface’. In:
Seminars in Cell & Developmental Biology. Matrix Metalloproteinases 19.1 (Feb. 2008), pp. 52–60.
DOI: 10.1016/j.semcdb.2007.05.011.

[120] L. Padovani, A. Baret, J. Ciccolini, D. Taieb, F. Bardia, L. Teissonnier, X. Muracciole, F. Barlesi and
D. Barbolosi. ‘An alternative parameter for early forecasting clinical response in NSCLC patients
during radiotherapy: proof of concept study’. In: The British Journal of Radiology 89.1062 (June
2016), p. 20160061. DOI: 10.1259/bjr.20160061.

[121] A. Rodallec, G. Sicard, S. Giacometti, M. Carré, B. Pourroy, F. Bouquet, A. Savina, B. Lacarelle, J.
Ciccolini and R. Fanciullino. ‘From 3D spheroids to tumor bearing mice: efficacy and distribution
studies of trastuzumab-docetaxel immunoliposome in breast cancer’. In: International Journal of
Nanomedicine 13 (2018), pp. 6677–6688. DOI: 10.2147/IJN.S179290.

[122] M. F. Sanmamed, C. Chester, I. Melero and H. Kohrt. ‘Defining the optimal murine models to
investigate immune checkpoint blockers and their combination with other immunotherapies’.
In: Annals of Oncology: Official Journal of the European Society for Medical Oncology 27.7 (2016),
pp. 1190–1198. DOI: 10.1093/annonc/mdw041.

[123] B. K. Schneider, A. Boyer, J. Ciccolini, F. Barlesi, K. Wang, S. Benzekry and J. P. Mochel. ‘Optimal
Scheduling of Bevacizumab and Pemetrexed/cisplatin Dosing in Non-Small Cell Lung Cancer’. In:
CPT: pharmacometrics & systems pharmacology (Apr. 2019). DOI: 10.1002/psp4.12415.

[124] M. A. Socinski, R. M. Jotte, F. Cappuzzo, F. Orlandi, D. Stroyakovskiy, N. Nogami, D. Rodríguez-
Abreu, D. Moro-Sibilot, C. A. Thomas, F. Barlesi, G. Finley, C. Kelsch, A. Lee, S. Coleman, Y. Deng,
Y. Shen, M. Kowanetz, A. Lopez-Chavez, A. Sandler, M. Reck and I. S. Group. ‘Atezolizumab for
First-Line Treatment of Metastatic Nonsquamous NSCLC.’ In: New England Journal of Medicine
378.24 (June 2018), pp. 2288–2301. DOI: 10.1056/NEJMoa1716948.

[125]

J. Tang, A. Shalabi and V. M. Hubbard-Lucey. ‘Comprehensive analysis of the clinical immuno-
oncology landscape.’ In: Ann. Oncol. 29.1 (Jan. 2018), pp. 84–91. DOI: 10.1093/annonc/mdx755.

[126] C. Vaghi. ‘Mathematical modeling of antibody nanoconjugates transport in tumors’. PhD thesis.

Bordeaux University, Dec. 2020.

[127] G. C. Wishart, E. M. Azzato, D. C. Greenberg, J. Rashbass, O. Kearins, G. Lawrence, C. Caldas and
P. D. P. Pharoah. ‘PREDICT: a new UK prognostic model that predicts survival following surgery
for invasive breast cancer.’ In: Breast cancer research 12.1 (Jan. 2010), R1. DOI: 10.1186/bcr2464.
<scratchpad>
- Main topic: Inria Centre at Université Côte d’Azur research activity report for 2023
- Key points:
  - The COMPO project aims to optimize therapeutic approaches in oncology using mechanistic and statistical modeling
  - The project focuses on three main axes: quantitative modeling for personalized clinical oncology, individualizing anticancer drugs dosing regimen, and optimizing combinatorial strategies with immune checkpoint inhibitors
  - The team has made significant progress in developing models and tools for predicting patient outcomes, including survival and trial outcome prediction in non-small cell lung cancer
  - The project has also explored the use of machine learning algorithms for survival analysis with missing data and pharmacokinetic modeling for drug delivery
- Structure: The document is organized into sections on research activity, activity report, project team, and new software, platforms, and open data
- Potential artifacts to ignore: web-related elements, such as links and HTML tags
</scratchpad>

<final_summary>
The COMPO project at Inria Centre at Université Côte d’Azur has made significant progress in 2023, focusing on optimizing therapeutic approaches in oncology through mechanistic and statistical modeling. The project aims to improve patient outcomes by developing models and tools for personalized clinical oncology, individualized anticancer drug dosing, and optimized combinatorial strategies with immune checkpoint inhibitors. The team has achieved notable results in predicting patient outcomes, including survival and trial outcome prediction in non-small cell lung cancer, and has explored the use of machine learning algorithms for survival analysis with missing data and pharmacokinetic modeling for drug delivery. The project's findings have the potential to improve cancer treatment and patient care, and its research activity will continue to advance the field of personalized medicine in oncology.
The COMPO project at Inria Centre at Université Côte d’Azur has made significant progress in 2023, focusing on optimizing therapeutic approaches in oncology through mechanistic and statistical modeling. The project aims to improve patient outcomes by developing models and tools for personalized clinical oncology, individualized anticancer drug dosing, and optimized combinatorial strategies with immune checkpoint inhibitors. The team has achieved notable results in predicting patient outcomes, including survival and trial outcome prediction in non-small cell lung cancer, and has explored the use of machine learning algorithms for survival analysis with missing data and pharmacokinetic modeling for drug delivery. The project's findings have the potential to improve cancer treatment and patient care, and its research activity will continue to advance the field of personalized medicine in oncology.