2023Activity reportProject-TeamCOMPO

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 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) 79, 80 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 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 algorithms 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.

3.8.1 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, 81, 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.

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 $\le$ 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).

3.9.1 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 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 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.

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
• 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.
  • 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.

  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
• 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

7.1.6 metamats

• 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 $N\left(t\right)$ (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

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 objective 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

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 ($\alpha$) and dissemination ($\mu$). 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 $\alpha$, $\mu$ or both and (ii) generate an optimal candidate model for DMFS prediction.

Results

We found that Ki67 and Thymidine Kinase-1 were associated with $\alpha$ and nodal status and Plasminogen Activator Inhibitor-1 with $\mu$. 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.

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 $TBM$ .

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 $T_{BM}$ and estimate the amount and sizes of (visible and invisible) BMs, as well as their future behavior. These two key computational markers are $\alpha$ , the proliferation rate of a single tumor cell; and $\mu$, 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 $T_{BM}$ for 20 patients. Parameters $\alpha$ and $\mu$ 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.

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 postsurgical 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 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.

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.

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.

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 ubiquitination 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 ubiquitination 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 chemotherapies thereby allowing the personalization of treatments.

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.

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.

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, combination 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.

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.

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.

10.1.1 Visits of international scientists

10.1.2 Visits to international teams

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:
  $\simeq$ 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:
  $\simeq$ 10 M€, ANR
• Principal investigator:
  D. Boulate (COMPO)
• COMPO seniors:
  D. Barbolosi, S. Benzekry

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).

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: pharmacokinetics 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

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 immunotherapy 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)

TORNADO

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

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)

11.1.1 Scientific events: organisation

11.1.2 Journal

11.1.3 Invited talks

• S. Benzekry
  • October, 2023. Workshop for young researchers in Cancer, invited as keynote speaker, Cancéropôle 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.
  • 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 Pharmacology" XIeme Journées Pharmacologiques de L'Hôpital Cochin, Paris France.
  • November, 2023. "Anti-Body Drug Conjugates: which clinical perspectives in digestive oncology?" 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 melanoma". 4ème Journées Horizon Mélanome Paris, France
  • September, 2023."Difficulties in implementing TDM in oncology: another French paradox.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
  • June-July, 2023 (2 sessions per week during 8 weeks). Invited instructor for the online "Summerschool PBPK SimulationsPlus".
• L. Greillier
  • June 2023, "Real-life efficacy of nivolumab plus ipilimumab in untreated, unresectable malignant 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

11.2.1 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.
• J. Ciccolini Lectures at Aix Marseille univ in: MSc (2d year) in Oncology, MSc (2d year) in Oncogenetics, 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 Metronomics. 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, Romania, 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
• 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 combined to machine learning for prediction of response and survival following immunotherapy, 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 programs 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

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 University), 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, SimulationsPlus, Paris, Jan 2023

International journals

• 13 articleD.D. Abbot, A.A. Bikfalvi, A.A.L. Bleske-Rechek, W.W. Bodmer, P.P. Boghossian, C.C.M. Carvalho, J.J. Ciccolini, J.J.A. Coyne, J.J. Gauss, P.P.M.W. Gill, S.S. Jitomirskaya, L.L. Jussim, A.A.I. Krylov, G.G.C. Loury, L.L. Maroja, J.J.H. Mcwhorter, S.S. Moosavi, P. N.P. Nayna Schwerdtle, J.J. Pearl, M.M.A. Quintanilla-Tornel, H. S.H.F. Schaefer Iii, P.P.R. Schreiner, P.P. Schwerdtfeger, D.D. Shechtman, M.M. Shifman, J.J. Tanzman, B.B.L. Trout, A.A. Warshel and J.J.D. West. In Defense of Merit in Science.Journal of Controversial Ideas31April 2023, 1HALDOI
• 14 articleS.Sébastien Benzekry, P.Pirmin Schlicke, A.Alice Mogenet, L.Laurent Greillier, P.Pascale Tomasini and E.Eléonore Simon. Computational markers for personalized prediction of outcomes in non-small cell lung cancer patients with brain metastases.Clinical and Experimental MetastasisDecember 2023HALDOIback to text
• 15 articleC.Célestin Bigarré, F.François Bertucci, P.Pascal Finetti, G.Gaëtan Macgrogan, X.Xavier Muracciole and S.Sébastien Benzekry. Mechanistic modeling of metastatic relapse in early breast cancer to investigate the biological impact of prognostic biomarkers.Computer Methods and Programs in Biomedicine231April 2023, 107401HALDOIback to text
• 16 articleE.Edith Borcoman, A.Ana Lalanne, J.-P.Jean-Pierre Delord, P.Philippe Cassier, F.Frédéric Rolland, S.Sébastien Salas, J.-M.Jean-Marc Limacher, O.Olivier Capitain, O.Olivier Lantz, C.Christina Ekwegbara, E.Emmanuelle Jeannot, J.Joanna Cyrta, C.Carine Tran-Perennou, Z.Zahra Castel-Ajgal, G.Grégoire Marret, E.Eliane Piaggio, M.Maud Brandely, A.Annette Tavernaro, H.Hakim Makhloufi, K.Kaidre Bendjama and C.Christophe 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.European Journal of Cancer191September 2023, 112981HALDOI
• 17 articleF.Florian Chatelet, F. R.François Régis Ferrand, S.Sarah Atallah, J.Juliette Thariat, F.François Mouawad, N.Nicolas Fakhry, O.Olivier Malard, C.Caroline Even, E.Erwan de Monès, E.Emmanuelle Uro-Coste, N.Nazim Benzerdjeb, S.Stéphane Hans, S.Sylvie Testelin, O.Olivier Mauvais, D.Diane Evrard, V.Vianney Bastit, S.Sébastien Salas, F.Florent Espitalier, M.Marion Classe, L.Laurence Digue, M.Mélanie Doré, S.Stéphanie Wong, C.Charles Dupin, F.France Nguyen, J.Jeremie Bettoni, A.Ariane Lapierre, E.Emilien Colin, P.Pierre Philouze, S.Sébastien Vergez, B.Bertrand Baujat, P.Philippe Herman, B.Benjamin Verillaud, J.-M.Jean-Marc Constans, A.Alexandre Coutte and B.Bernard 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.European Journal of Cancer185May 2023, 11-27HALDOI
• 18 articleJ.Joseph Ciccolini and G.Gerard Milano. Immune check points in cancer treatment: current challenges and perspectives.British Journal of Cancer1299October 2023, 1365-1366HALDOI
• 19 articleM.Melanie Donnette, M.Mourad Hamimed, J.Joseph Ciccolini, G.Guillaume Sicard, F.Florian Correard, L.Laure Farnault, L.L'Houcine Ouafik, G.Geoffroy Venton and R.Raphaëlle Fanciullino. Cytidine deaminase status as a marker of response to azacytidine treatment in <scp>MDS</scp> and <scp>AML</scp> patients.British Journal of Haematology2034November 2023, 625-636HALDOI
• 20 articleM.Melanie Donnette, L.Loic Osanno, M.Madeleine Giocanti, G.Geoffroy Venton, L.Laure Farnault, Y.Yael Berda-Haddad, R.Régis Costello, S.Solas Caroline, L.L.’houcine Ouafik, J.Joseph Ciccolini and R.Raphaëlle Fanciullino. Determination of 5-azacitidine in human plasma by LC–MS/MS: application to pharmacokinetics pilot study in MDS/AML patients.Cancer Chemotherapy and Pharmacology913March 2023, 231-238HALDOI
• 21 articleA.Abdessamad El Kaoutari, N.Nicolas Fraunhoffer, S.Stéphane Audebert, L.Luc Camoin, Y.Yolande Berthois, O.Odile Gayet, J.Julie Roques, M.Martin Bigonnet, C.Claire Bongrain, J.Joseph Ciccolini, J.Juan Iovanna, N.Nelson Dusetti and P.Philippe Soubeyran. Pancreatic ductal adenocarcinoma ubiquitination profiling reveals specific prognostic and theranostic markers.EBioMedicine92June 2023, 104634HALDOI
• 22 articleM.-C.Marie-Christine Etienne-Grimaldi, N.Nicolas Pallet, V.Valérie Boige, J.Joseph Ciccolini, L.Laurent Chouchana, C.Chantal Barin-Le Guellec, A.Aziz Zaanan, C.Céline Narjoz, J.Julien Taieb, F.Fabienne Thomas and M.-A.Marie-Anne Loriot. Current diagnostic and clinical issues of screening for dihydropyrimidine dehydrogenase deficiency.European Journal of Cancer181March 2023, 3-17HALDOI
• 23 articleG.Gwenaelle Gravis, P.Patricia Marino, D.Daniel Olive, F.Frederique Penault-Llorca, J.-P.Jean-Pierre Delord, C.Clotilde Simon, A.Assia Lamrani-Ghaouti, R.Renaud Sabatier, J.Joseph Ciccolini and J.-M.Jean-Marie 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.BMC Cancer231December 2023, 393HALDOI
• 24 article M.Mourad Hamimed, R.Raynier Devillier, P.-J.Pierre-Jean Weiller, C.Clémence Marin, J.-M.Jean-Marc Schiano, N.Nawel Belmecheri, M.-C.Marie-Christine Etienne-Grimaldi, J.Joseph Ciccolini and S.Samia Harbi. Life-threatening toxicities upon Pembrolizumab intake: could pharmacokinetics be the bad guy? Cancer Chemotherapy and Pharmacology November 2023 HAL DOI
• 25 articleK.Kevin Harrington, R.Robert Ferris, M.Maura Gillison, M.Makoto Tahara, A.Athanasios Argiris, J.Jérôme Fayette, M.Michael Schenker, Å.Åse Bratland, J.John Walker, P.Peter Grell, C.Caroline Even, C.Christine Chung, R.Rebecca Redman, A.Alexandre Coutte, S.Sébastien Salas, C.Cliona Grant, S.Sergio de Azevedo, D.Denis Soulières, A.Aaron Hansen, L.Li Wei, T. A.Tariq Aziz Khan, K.Karen Miller-Moslin, M.Mustimbo Roberts and R.Robert 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.JAMA oncology96June 2023, 779HALDOI
• 26 articleS.Sophie Marolleau, A.Alice Mogenet, C.Clara Boeri, M.Mourad Hamimed, J.Joseph Ciccolini and L.Laurent Greillier. Killing a fly with a sledgehammer: Atezolizumab exposure in real‐world lung cancer patients.CPT: Pharmacometrics and Systems Pharmacology1211November 2023, 1795-1803HALDOI
• 27 articleP.Patricia Piris, D.Duje Buric, T.Toshihide Yamasaki, P.Paul Huchedé, M.Maïlys Rossi, M.Mélanie Matteudi, M.-P.Marie-Pierre Montero, a.anne rodallec, R.Romain Appay, C.Christine Roux, S.Sébastien Combes, E.Eddy Pasquier, M.Marie Castets, N.Nicolas André, P.Paul Brémond and M.Manon Carré. Conditional generation of free radicals by selective activation of alkoxyamines: towards more effective and less toxic targeting of brain tumors.Chemical Science14292023, 7988-7998HALDOI
• 28 articleB.B. Royer, M.M. Launay, J.J. Ciccolini, L.L. Derain, F.F. Parant, F.F. Thomas and J.J. Guitton. Impact of renal impairment on dihydropyrimidine dehydrogenase (DPD) phenotyping.ESMO Open83June 2023, 101577HALDOI
• 29 articleS.Sebastien Salas, G.Guillaume Economos, D.Damien Hugues, E.Elise Gilbert, D.Dominique Gracia, P.Philippe Poulain, C.Christine Mateus, E.Elsa Collet, B.Brigitte Planchet-Barraud, A.Andre Colpaert, É.Élise Perceau-Chambard, L. Y.Laurent Yves Calvel, C.Cecile Franck, D.Donatien Mallet, K.Karine Baumstarck and A.Adrien Evin. Legalisation of euthanasia and assisted suicide: advanced cancer patient opinions – cross-sectional multicentre study.BMJ Palliative and supportive care13August 2023, e1335-e1341HALDOI
• 30 articleG.G. Sicard, D.D. Protzenko, S.S. Giacometti, F.F. Barlési, J.J. Ciccolini and R.R. Fanciullino. Harnessing tumor immunity with cytotoxics: T cells monitoring in mice bearing lung tumors treated with anti-VEGF and pemetrexed-cisplatin doublet.British Journal of Cancer1299October 2023, 1373-1382HALDOI

National journals

• 31 articleJ.Joseph Ciccolini. Antibody-drug Conjugates: pharmacokinetics and pharmacodynamics perspective.Correspondances en onco-théranosticXII2May 2023, 56-59HAL

Invited conferences

• 32 inproceedingsS.Sébastien Benzekry. Artificial intelligence in healthcare data warehouses.Annual congress of the French group of clinical pneumo-oncology (GPCO)Toulon, FranceJune 2023HAL
• 33 inproceedingsS.Sébastien Benzekry. Integrative kinetics and machine learning modeling for prediction of outcome following immunotherapy in lung cancer.CARe graduate school workshopToulouse (FRANCE), FranceJanuary 2023HALback to text
• 34 inproceedingsS.Sébastien Benzekry. Integrative kinetics and machine learning modeling for prediction of outcome following immunotherapy in lung cancer.SMAI congress (French Society of Applied and Industrial Mathematics)Pointe à Pitre, FranceMay 2023HALback to text
• 35 inproceedingsS.Sébastien Benzekry. Integrative kinetics and machine learning modeling for prediction of outcome followingimmunotherapy in lung cancer.Annual congress of the French society of pharmacology and therapeutics (SFPT)Limoges, FranceJune 2023HALback to text
• 36 inproceedingsS.Sébastien Benzekry. Integrative kinetics and machine learning modeling for prediction ofoutcome following immunotherapy in lung cancer.Workshop for young researchers in CancerPorquerolles, FranceOctober 2023HALback to text
• 37 inproceedingsS.Sébastien Benzekry. Using statistical learning for clinical trials.Annual meeting of the APHM center for early phase clinical trials in oncologyMarseille, FranceJune 2023HALback to text

International peer-reviewed conferences

• 38 inproceedingsM.Mathilde Dacos, G.Guillaume Sicard, B.Benoit Immordino, S.Sarah Giacometti, J.Joseph Ciccolini, A.Anne Rodallec and R.Raphaelle Fanciullino. Nanoliposomes harness tumor immunity in breast cancer models.Cancer researchAACR 2023 Annual Meeting83Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts)7_SupplementOrlando (FL), United StatesApril 2023, 1857-1857HALDOI
• 39 inproceedingsP.Paul Dufossé and S.Sébastien Benzekry. A comparison of machine learning algorithms for survival with missing data.54es Journées de Statistique de la SFdS54es Journées de StatistiqueBruxelles (BEL), Belgium2023HALDOIback to text
• 40 inproceedingsJ.Judith Michels, M.Mourad Hamimed, Y.Yves Menu, C.Corinne Balleyguier, F.François Ghiringhelli, J.-S.Jean-Sebastien Frenel, C.Catherine Genestie, B.Benoit You, A.Anne Floquet, L.Lauriane Eberst, A.Angelo Paci, R.Rastilav Bahleda, P.Patricia Pautier, E.Emeline Colomba, F.Fanny Pommeret, A.Alexandra Leary, A.Aurelien Marabelle, A.Aurelie Bardet, C.Caroline Brard and J.Joseph Ciccolini. Bevacizumab exposure and tumor response in patients with platinum resistant epithelial ovarian cancer..Journal of Clinical Oncology2023 ASCO Annual Meeting4116_supplChicago (Ill.), United StatesJune 2023, 3093-3093HALDOI
• 41 inproceedingsJ.Jessica Ou, B.Béatrice Louis, L.Laure Balasse, T.Tom Roussel, L.Ling Peng, B.Benjamin Guillet, P.Philippe Garrigue and F.Florence Gattacceca. Development of a physiologically-based pharmacokinetic model to understand and predict the disposition of gallium-68 radiolabeled-dendrimers in vivo.PAGE. Abstracts of the Annual Meeting of the Population Approach Group in Europe. ISSN 1871-6032.PAGE 2023 - Population approach group Europe meeting31A Coruña, SpainJune 2023, Abstr 10514HAL
• 42 inproceedingsG.Guillaume Sicard, S.Sarah Giacometti, F.Fabrice Barlesi, R.Raphaelle Fanciullino and J.Joseph Ciccolini. Pemetrexed-cisplatin-bevacizumab combo in lung cancer models: impact on tumor immunity.Cancer researchAARC 2023 annual meeting -83Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts)7_SupplementOrlando (FL), United StatesApril 2023, 1856-1856HALDOI

Conferences without proceedings

• 43 inproceedingsJ.Joseph Ciccolini. ADCs: Pharmacokinetics & dosing considerations.XIXeme Journées du GPCO-UnicancerStrasbourg, FranceNovember 2023HAL
• 44 inproceedingsJ.Joseph Ciccolini. Anti-Body Drug Conjugates: Why « Magic Bullet » is not « Magic Pharmacology ».XIeme Journées Pharmacologiques de CochinParis, FranceNovember 2023HAL
• 45 inproceedings J.Joseph Ciccolini. Anti-Body Drug Conjugates: which clinical perspectives in digestive oncology? 2ème Cours Saint Paul Cancers de l'Appareil digestif Nice, France November 2023 HAL
• 46 inproceedings J.Joseph Ciccolini. Anti-PARP: Aspects PK et PK/PD quelle place pour le TDM? XIXèmes Journées du GPCO-Unicancer Strasbourg, France November 2023 HAL
• 47 inproceedings J.Joseph Ciccolini. Antibody-Drug Conjugates ; beyond the hype, which pharmacology in breast cancer? Séminaire annuel du Cancéropôle PACA Frejus, France July 2023 HAL
• 48 inproceedingsJ.Joseph Ciccolini. BRAF-i plus anti-MEK combo: targeting MAP-kinase in metastatic melanoma.4ème Journées Horizon MélanomeParis, FranceSeptember 2023HAL
• 49 inproceedingsJ.Joseph Ciccolini. BRAF-inhibitors in mCRC: pharmacology and PK/PD perspectives.Aquitaine Gastro Annual MeetingBordeaux, FranceApril 2023HAL
• 50 inproceedingsJ.Joseph Ciccolini. 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 OncologuesNancy, FranceOctober 2023HAL
• 51 inproceedingsJ.Joseph Ciccolini. Difficulties in implementing TDM in oncology: another French paradox.IATDMCT 2023 - 21st Congress of the International Association of Therapeutic Drug Monitoring & Clinical ToxicologyOslo (Norway), NorwaySeptember 2023HAL
• 52 inproceedingsJ.Joseph Ciccolini. From HER2+ to HER2-low: specificity of the new generation ADCs.9ème Congrès du Monaco Age Oncology (MAO)Monaco, MonacoMarch 2023HAL
• 53 inproceedingsJ.Joseph Ciccolini. PK/PD with Immunotherapy.7e Journées scientifiques Immunité & CancerParis, FranceJanuary 2023HAL
• 54 inproceedingsJ.Joseph Ciccolini. TDM in oncology: application to breast cancer patients.22ème Cours Saint Paul Sein & Cancer GynécologiquesCannes, FranceJanuary 2023HAL
• 55 inproceedingsJ.Joseph Ciccolini. TDM of oral targeted therapies in metastatic melanoma.Pierre Fabre SymposiumAvène, FranceJune 2023HAL
• 56 inproceedingsJ.Joseph Ciccolini. Unlocking the potential of ADCs for HER2 signaling pathway.Daïchi Sankyo symposiumToulon (83000), FranceMay 2023HAL
• 57 inproceedingsL.Linh Nguyen Phuong, L.Laurent Greillier, C.Caroline Gaudy, J.-L.Jean-Laurent Deville, J.-C.Jean-Charles Garcia, F.Frédéric Fina, S.Sébastien Salas and S.Sébastien Benzekry. Mechanistic modeling of the longitudinal tumor and biological markers combined with quantitative cell-free DNA.Annual Meeting of the Society for Mathematical BiologyColumbus (Ohio), United StatesJuly 2023HAL

Reports & preprints

• 58 miscS.Sébastien Benzekry, M.Mélanie Karlsen, C.Célestin Bigarré, A. E.Abdessamad El Kaoutari, B.Bruno Gomes, M.Martin Stern, A.Ales Neubert, R.Rene Bruno, F.François Mercier, S.Suresh Vatakuti, P.Peter Curle and C.Candice Jamois. Predicting survival and trial outcome in non-small cell lung cancer integrating tumor and blood markers kinetics with machine learning.September 2023HALDOIback to textback to text
• 59 miscS.Sébastien Benzekry, M.M. Mastri, C.C. Nicolò and J. M.J. Ml Ebos. Machine-learning and mechanistic modeling of primary and metastatic breast cancer growth after neoadjuvant targeted therapy.February 2023HALDOIback to text
• 60 miscS.Sebastien Benzekry, P.Pirmin Schlicke, P.Pascale Tomasini and E.Eleonore Simon. Mechanistic modeling of brain metastases in NSCLC provides computational markers for personalized prediction of outcome.January 2023HALDOI
• 61 miscD.David Boulate and D.Dominique Barbolosi. Prevalung étoile.August 2021HAL
• 62 miscY.Yuhao Shi, M.Melissa Dolan, M.Michalis Mastri, A.Amber Mckenery, J. W.James W Hill, A.Adam Dommer, S.Sébastien Benzekry, M.Mark Long, S.Scott Abrams, I.Igor Puzanov and J. M.John M.L. Ebos. Acquired resistance to PD-L1 inhibition is associated with an enhanced type I IFN-stimulated secretory program in tumor cells.July 2021HALDOI

Other scientific publications

• 63 inproceedingsM.Mathilde Dacos, E.Erwann Diroff, S.Sarah Giacometti, L.Loic Osanno, J.Joseph Ciccolini and R.Raphaëlle Fanciullino. Liposomal formulation of docetaxel: biodistribution and immunomodulatory benefit in HER2 breast cancer treatment.SFNano 2023 Annual MeetingMontpellier, FranceDecember 2023HAL
• 64 inproceedingsM.Mathilde Dacos, E.Erwann Diroff, S.Sarah Giacometti, L.Loic Osanno, J.Joseph Ciccolini and R.Raphaëlle Fanciullino. Pharmacokinetics of an anticancer drug: liposomal formulation of docetaxel.Congrès 2023 de la SFPO - 14èmes Journées Nationales Actualités en OncologieNancy, France2023HAL
• 65 inproceedingsM.Mathilde Dacos, G.Guillaume Sicard, D.Dorian Protzenko, S.Sarah Giacometti, J.Joseph Ciccolini and R. F.Raphaelle Fanciullino Smartc. Nanoliposomes harness tumor immunity in breast cancer model.Séminaire Annuel du Cancéropôle PACAFréjus St Raphael, FranceJuly 2023HAL
• 66 inproceedingsA.Anthea Deschamps, L.Lionel Velly, E.Elisabeth Jouve, R.Romain Guilhaumou and F.Florence Gattacceca. Population pharmacokinetic model of cefotaxime encompassing time-varying physiopathology: exploration of intra-individual variability of the renal function.GMP symposium 2023Paris, FranceOctober 2023HAL
• 67 inproceedingsL.Loïc Osanno, L.Laure Farnault, J.Julien Colle, G.Geoffroy Venton and R.Raphaelle Fanciullino. Study of the pharmacokinetic variability of azacytidine.Séminaire annuel du Canceropôle PACAFréjus St Raphael, FranceJuly 2023HAL
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