3.1 Scientific context and motivations

The project-team is based upon the development of model-driven clinical oncology as a means to optimize anticancer therapies. Despite continuous efforts to make available novel drugs beyond traditional cytotoxic chemotherapy (i.e., oral targeted therapies, biologics, immune checkpoint inhibitors), prognosis of outcome remains poor for many cancers. The dosing regimen of anticancer drugs given today remains largely empirical, because dose-finding studies are often performed using outdated, sub-optimal protocols (such as modified-Fibonacci dose-ranging protocols) or because concomitant administration is the rule when combining several drugs. Consequently, clinical oncologists struggle to refine the way they use the anticancer agents made at their disposal. For instance, it took several years of bedside practice to understand that paclitaxel in breast cancer patients should not be administrated using the officially approved 150 mg/m² every 3 weeks scheduling, but rather with an alternate 75 mg/m² weekly dosing 90. Similarly, multi-targets sunitinib is now given on a 50 mg two-weeks on / one-week off basis, rather than the officially approved four-weeks on / two-weeks off schedule 76. Elsewhere, several combinatorial strategies trials have failed to yield convincing results, mostly because of the lack of a strong rationale regarding the best way to sequence treatments 70. Globally, clinical oncology today is still all about finding the best way to treat patients ensuring an optimal efficacy / safety balance.

After having long been limited to cytotoxic chemotherapy (in addition to surgery and radiotherapy), the arsenal of anti-cancer agents has dramatically increased over the last two decades. Indeed, major advances in the understanding of cancer biology, including: 1) the discovery and quantification of (epi)genetic alterations leading to targeted therapy and 2) the realization of the importance of the non-cancer cell components of tumors, i.e., the tumor micro-environment and tumor immunity, have helped to identify novel targets. Drugs targeting the tumor vasculature (e.g., first-in-class bevacizumab, approved in the mid-2000’s 58) or tumor immunity (e.g., immune checkpoint inhibitors (ICI) such as first-in-class ipilimumab, approved in the early 2010’s 88) represent groundbreaking innovations in oncology. ICIs in particular are considered as game-changing drugs because diseases with once dismal prognosis (e.g., metastatic melanoma, non-small cell lung cancer (NSCLC), kidney cancer or head and neck cancer 110) now show 20-40% of 5-years survival. Nevertheless, these impressive results are limited to a minority of patients in a limited number of cancers. In addition, no validated biomarker predictive of response has yet been identified, thus highlighting how early prediction of response and probability of future relapse are a critical, unmet medical need. The encouraging yet still insufficient clinical results of ICIs have led current clinical oncology to consider combinations of such immunotherapies with preexisting anti-cancer modalities: radiation therapy 97, cytotoxic 84, targeted 102 or anti-angiogenic therapies 109. However, the near-infinite possibilities of combinations in terms of sequencing, dosing and scheduling challenge the ability of classical trial-and-error methods to find appropriate modes of combination 69.

In addition, day-to-day clinical decisions made by oncologists are based on a large amount of information, coming from: 1) their own knowledge integrating years of clinical practice combined to updated literature and 2) objective data coming from multiple sources (demographic data, radiology, functional imaging, molecular biology, histology, biomarkers, blood counts, etc…). The large amount of clinical and biological data generated now in clinical oncology is not properly analyzed, because of the lack of appropriate models to picture the complexity of longitudinal observations. Oncologists lack a comprehensive framework and numerical software that could support decision of therapeutic strategy (e.g., to treat or not? to what extent? with what treatment (surgery, radiotherapy, systemic therapy)? in what order? etc.), especially when their time dedicated to examination of a given patient case is limited (e.g., in multidisciplinary meetings (RCP), or individual consultations). Furthermore, modeling is the only way to retrieve similar characteristics from very different experimental conditions and clinical protocols.

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

• guiding anticancer therapy by developing patient-specific predictive models (individual level);
• better designing clinical trials, in particular regarding combinatorial trials (population level).

3.2 Data

We use non-clinical and clinical data related with the pharmacology of anti-cancer agents and medical monitoring of the disease status. The former includes pharmacokinetics (drug levels in plasma (patients) and full body pharmacokinetics (animal models)), pharmacodynamics (efficacy, safety), pharmacogenetics (i.e., constitutional genetic polymorphisms affecting drug transport and metabolizing enzymes), pharmacogenomics (i.e., molecular and genetic alterations affecting tumor cells). The latter include demographics, anatomical imaging (e.g., tumor sizes derived from CT scan or MRI), functional imaging (e.g., positron emission tomography), histopathology quantifications, biological variables (such as kidney and liver functions or blood counts), immuno-monitoring data (flow cytometry) or cell free DNA. We will especially rely on real-world data (also termed fragmentary data) collected from patient routine monitoring by our members with hospital activity.

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

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

In addition, we also rely on publicly available data from online databases such as the TCGA (genomic data), the TCIA (imaging data) or data from clinical trials.

3.3 Mathematical methodology

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

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

3.4 Experimental therapeutics in oncology

The project-team is based upon generating experimental and clinical data to identify and test the models, and to provide proof-of-concept studies so as to validate the model-based dosing and scheduling prior to transposing them in patients. Historically, experimental therapeutics in oncology has relied on a wide variety of in vitro and in vivo models mimicking human cancer disease. In oncology, hundreds of in vitro models using cancer cell lines cultivated following 2D or 3D (spheroids) fashion, plus more sophisticated models with cancer cells enriched with fibroblasts or endothelial cells 104, eventually leading to complex organoids 83. Similarly, almost all kind of tumors can be tested in vivo, mostly in small rodents. In oncology, in vivo models are mostly based upon xenografting human tumors from established cell lines or from patient biopsies (patient-derived xenografts or PDX) so as to better mimic human pharmacology when testing active compounds next. To achieve this, several strains of immune-compromised mice have been successfully developed. Because immune checkpoint inhibitors do not exert direct anti-proliferative activity on cancer cells but are rather expected to harness tumor immunity, human xenografts in immuno-compromised mice is not anymore a suitable model. This has led investigators to shift towards immune-competent syngeneic mice models. Non-clinical experiments with drug candidates in immunotherapy mostly focus on deciphering the pharmacology of the targeted pathways, assessing the cytokine release potential, studying receptor occupancy, by using models the most likely to mimic tumor immunity in human. More sophisticated animal models such as human knock-in mice, immuno-avatar, hemato-lymphoid humanized mice or immune-PDX mice have been developed 107 (i.e., allowing to test immune checkpoint inhibitors in mice models combining human xenograft with relevant, humanized immunity and stroma cells) have been made available as well. Beyond generating data on efficacy such as reduction in primary tumor mass or metastatic spreading, experimental models help providing as well in depth knowledge on human and animal target cells, in vitro and in vivo concentration-effect studies, search for biomarkers, plus the most comprehensive knowledge on animal vs. human differences on dose – exposure – effects relationships and finally drug distribution throughout the body, target expression, affinity of target-binding and intrinsic efficacy, duration and reversibility of the effects. In particular, animal drug metabolism and pharmacokinetics (i.e., exploration of liver metabolism and distribution / absorption processes using in vitro or in vivo dedicated models) help understanding the disposition and distribution of the drug in the body throughout time, especially its ability to target tumor tissues (i.e., in vivo distribution in tumor-bearing mice) and helps understanding sources of pharmacokinetic variability. All this information requires state-of-the-art techniques for measuring drugs and drug metabolites into biological fluids in tissues, such as fluorescence-imaging, high-performance liquid chromatography or liquid-chromatography-mass spectrometry bioanalysis. Our team has proven track records in the field of experimental therapeutics in oncology, with two PhDs on developing anticancer nanoparticles in breast and colorectal cancer 81, 106 plus experiments on model-driven way to combine anti-angiogenics with cytotoxics in breast and lung cancers 92, 99, 108, model-driven determination of alternate dosing in neuroblastoma, or methodological studies on monitoring tumor growth 100.

3.5 Axis 1: Quantitative modeling for personalized clinical oncology

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

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

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

3.6 Axis 2: Individualizing anticancer drugs dosing regimen

This axis fits within the “population approach” introduced in the 80’s by L. Sheiner 121 and aims at gathering exhaustive information about the multiple sources for variability in response in patients (including but not limited to drug-drug interactions, pharmacogenetics, and cormorbidities affecting renal and liver functions), build specific mechanistic models including relevant covariates and determine the PK/PD relationships of drugs used in oncology-hematology or for treating solid tumors. This covers cytotoxics, oral targeted therapies, biologics or immune checkpoint inhibitors. The overall goal is to achieve precision and personalized drug administration, i.e., the right dosing and scheduling regimen for the right patient.

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

3.7 Axis 3: Optimizing combinatorial strategies with immune checkpoint inhibitors

Our hypothesis is that so many attempts to combine drugs fail not because the underlying pharmacological concepts are wrong (such as immunogenic cell death triggered by cytotoxics or radiation therapy, or increase in T cells infiltration with anti-angiogenics) but because these combinations probably require fine tuning in terms of dosing, scheduling and sequencing, whereas in practice all the drugs are given the same day. The goal of this second axis is therefore to shift from current empirical and suboptimal combinatorial regimen to model-informed designs to best combine drugs and therapeutic approaches so as to maximize efficacy while controlling toxicities. To do so, we will rely on our pioneering work about model-driven scheduling in early phase trials for combination of cytotoxic agents in metastatic breast cancer (MODEL1 trial) 87, 98 and metronomic vinorelbine in lung cancers (MetroVino trial) 62, 80. Leveraging the unique multidisciplinary aspect of our team, we implement a fully translational approach going from experimental therapeutics, PMX and quantitative systems pharmacology, to clinical trials either in early (phase I/II) or late (phase III) settings. Of note, our group has already an expertise in developing mathematical models determining the best sequencing between chemotherapy and anti-angiogenics.

In the short-term (4 years), our research projects will be focused on providing model-informed designs for combining ICI with: (1) cytotoxics, (2) an experimental immunoliposome and (3) radiotherapy.

3.8 Mid-term objectives

3.9 Long-term objectives

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

5.1 Awards

Coline Sentis received the bronze medal for her MD thesis: “Descriptive and prognosis value of a mechanistic mathematical model of metastatic development in high-risk neuroblastoma" (Supervisors: N. André and S. Benzekry). Associated publication: 15

Clemence Marin received the 2d prize for the young Investigator Award for her presentation "A Multiplex Mass Spec Method for Assaying mAbs Plasma Levels in Patients with Cancer", GPCO-Unicancer, Dijon Nov. 2021 (supervisor: J. Ciccolini). Associated publication: 41

6.1 New software

6.2 New platforms

7.1 Pharmacometrics modeling coupled with machine learning for early prediction of overall survival following atezolizumab monotherapy in non-small cell lung cancer

Participants: Sébastien Benzekry, Mélanie Karlsen, Abdessamad El Kaoutari, Suresh Vatakuti, Peter Curle, Candice Jamois.

Funding and data: Roche pRED

Publication: Abstract sent to the world conference on pharmacometrics 2022.

BACKGROUND: Pharmacometrics (PMx) and Machine Learning (ML) approaches could help to predict survival based on patients’ and disease characteristics and early response to treatment.

METHODS: We developed a predictive algorithm of survival following atezolizumab (ATZ) therapy based on the combination of nonlinear mixed-effects modeling (NLME) and ML. The data consisted of three phase 2 trials (training set: 862 patients) and one phase 3 trial (test set: 553 patients). Longitudinal data included tumor kinetics (5,570 data points) and four pharmacodynamic (PD) markers: neutrophils, C-reactive protein, lactate deshydrogenase and albumin (61,296 data points). Baseline data included: clinical factors (p = 73 variables), transcriptomic data (p = 58,311 transcripts) and mutation data (p = 395 genes). Double-exponential and hyperbolic models were used for NLME and a random survival forest for ML. Cross-validation was used to assess the predictive performances of the model (c-index, landmark time classification metrics and calibration curves).

SOFTWARE: This project led to the development of the software nlml_onco. It is an R package that implements the following tasks: 1) preprocess of the data (including missing data imputation and scaling), 2) NLME fit and individual empirical Bayes estimations (possibly using an external prior), 3) dimensionality reduction, 4) feature selection (selected among 5 methods), 5) ML (train, cross-validation or predict) and 6) post-processing. In addition, the software allows data truncation either after completion of a number of cycles or a given study duration. As output, the software generates either individual- or study-level predictions

RESULTS: The best models were double-exponential (TK, neutrophils, CRP, LDH) and hyperbolic (albumin) models for NLME and a random survival forest for ML. A minimal ML model was derived that contains 11 routine clinical variables (CRP, heart rate, neutrophils-to-lymphocyte ratio, neutrophils, lymphocytes-to-leucocytes ratio, hepatic metastases, ECOG, PDL1, hemoglobin, baseline tumor size and LDH), together with 3 TK and 9 PD model derived parameters obtained from Bayesian estimation. Predictive power was significantly improved when using the PD data compared with models based either on clinical variables or TK parameters for both discrimination (c-index = 0.83 ± 0.02 vs 0.72 ± 0.04 vs 0.72 ± 0.02 in cross-validation, 0.79 vs 0.69 vs 0.70 in test, 1-year AUC = 0.92 ± 0.02 vs 0.80 ± 0.05 vs 0.81 ± 0.05) and calibration metrics.

CONCLUSION: Our novel prediction algorithm was able to predict accurately survival following atezolizumab monotherapy, both at the study and individual levels.

7.2 Comprehensive biomarkers analysis to explain resistances to PD1/L1 ICIs: The Precision Immuno-Oncology for advanced Non-Small CEll Lung CancER (PIONeeR) trial.

Participants: Laurent Greillier, Florence Monville, Vanina Leca, Frédéric Vely, Stéphane Garcia, Joseph Ciccolini, Florence Sabatier, Gilbert Ferrani, Nawel Boudai, Lamia Ghezali, Marcellin Landri, Clémence Marin, Mourad Hamimed, Laurent Arnaud, Arnaud Boyer, Mélanie Karlsen, Kevin Atsou, Pauline Fleury, Clarisse Audigier Valette, Stéphanie Martinez, Hervé Pégliasco, Louisiane Lebas, Patricia Barré, Sarah Zahi, Ahmed Frika, Pierre Bory, Maryannick Le Ray, Lilian Laborde, Virginie Martin, Richard Malkoun, Marie Roumieux, Julien Mazières, Maurice Perol, Eric Vivier, Sébastien Benzekry, Jacques Fieschi, Fabrice Barlési.

Funding : This work is supported by French National Research Agency (ANR-17-RHUS-0007, a partnership of AMU, APHM, AstraZeneca, Centre Léon Bérard, CNRS, Veracyte, ImCheck Therapeutics, Innate Pharma, Inserm, Institut Paoli Calmettes and sponsored by AP HM and initiated by Marseille Immunopole. It is also supported by Plan Cancer Inserm grant number 19CM148-00 as part of the QUANTIC project.

Publication: Abstract sent to the AACR conference 2022.

BACKGROUND: Resistance to PD1/L1 immune checkpoint inhibitors (ICIs) in advanced NSCLC patients is observed in about 80% of individuals with no robust predictive biomarker yet. The PIONeeR trial (NCT03493581) aims to predict such resistances through a comprehensive multiparametric biomarkers analysis.

METHODOLOGY: Among the >300 advanced NSCLC patients (pts) recruited in PIONeeR, we focused on the first 137 $\ge$ 2nd line ECOG PS0-1 patients treated with single-agent nivolumab, pembrolizumab or atezolizumab, with good performance status. Tumor tissue was collected at baseline and Pts were systematically re-biopsied at 6 weeks, and blood-sampled every cycle throughout the 24 weeks post C1D1. Response to PD1/L1 ICIs was assessed by RECIST 1.1 every 6 weeks.

Immune contexture was characterized in tumor and blood of each pt through FACS for circulating immune cell subtypes quantification, dual- and multiplex IHC / digital pathology to quantify activating & suppressive immune cells infiltrating the tumor, standardized methods for endothelial activation assessment, blood soluble factors dosage and tumor & blood WES (TMB and other). ICI plasma levels and pharmacokinetics parameters were also monitored, leading to 331 measured biomarkers in addition to routine clinical parameters. Multivariable (MV) logistic regression was used to examine the association of each biomarker (controlled by sex, age, smoking status, histological type & PD-L1 TC expression) with the risk of Early Progression (EP), i.e. within 3.5 months of treatment. Multivariable Cox regression analysis was conducted for association with PFS and OS.

SOFTWARE: This project is associated to the development of the stats_pioneer software.

RESULTS: Overall, the 137 pts were mainly male (64%), smokers (92%) and <70yrs (68%). Tumors were mainly non-squamous (79%) with >1% PDL1+ TC in 36% of the cases. 21% were still on treatment at data cut-off. Archived samples were available for 109 patients (80%) at inclusion and re-biopsy was available in 52.9% of these cases. The median follow up was 19.8 months, 22.5% of pts did not progress at data cut-off, and 85 patients present with EPs.

Tumor Cytotoxic T-cells density, especially PD1+ were lower in EP (MV OR=0.45, p=0.022); conversely, higher proportions of activated cytotoxic T-cells were observed among circulating lymphocytes in EP (MV OR=3.8, p<0.001). Among other biomarkers, Tregs (MV OR=0.44, p=0.018), 3 NK sub-populations (MV OR $\le$ 0.44, p<0.05), albumin (MV OR 0.4, p<0.01) and PDL1 TC % (MV OR=0.27, p<0.01) were decreased whereas alkaline phosphatase was increased (OR=3, p=0.018). A large inter-pt variability (i.e., >65%) was observed in plasma exposures for all ICIs, with 8-10% of pts displaying trough levels below the threshold associated with target engagement. Data will be presented through unsupervised clustering algorithms and dedicated multi-modal supervised learning methods. Changes observed after 6 weeks of treatment will be analyzed to further investigate the drugs mechanisms of action.

CONCLUSION: The PIONeeR trial provides with the 1st comprehensive biomarkers’ analysis of to establish predictive models of resistance in advanced NSCLC pts treated with PD1/L1 ICIs and highlights how tumor and circulating biomarkers are complementary.

7.3 Machine Learning for Prediction of Immunotherapy Efficacy in Non-Small Cell Lung Cancer from Simple Clinical and Biological Data

Participants: Sébastien Benzekry, Mathieu Granjeon, Mélanie Karlsen, Maria Alexa, Isabella Bicalho-Frazeto, Solène Chaleat, Pascale Tomasini, Dominique Barbolosi, Fabrice Barlési, Laurent Greillier.

Publication: Cancers14

Funding : This work is supported by Plan Cancer Inserm grant number 19CM148-00 as part of the QUANTIC project.

Simple Summary: We studied determinants of response to immune-checkpoint inhibition in ad-vanced non-small cell lung cancer patients. Specifically, we evaluated the association with responseof multiple simple pre-treatment blood markers available from routine examination. We first usedclassical statistical tools and then developed a machine learning algorithm for individual predictions.We obtained a 69% accuracy. Hemoglobin levels and performance status were the strongest predic-tors. Neutrophil-to-lymphocyte ratio was also associated with outcome. A benchmark of 8 machinelearning models also evidenced that the best model performed almost equally well than a logisticregression (basic statistical learning model).

Keywords:blood counts; lung cancer; response; survival; prediction; machine learning

BACKGROUND: Immune checkpoint inhibitors (ICIs) are now a therapeutic standard inadvanced non-small cell lung cancer (NSCLC), but strong predictive markers for ICIs efficacy arestill lacking. We evaluated machine learning models built on simple clinical and biological data toindividually predict response to ICIs.

METHODS: Patients with metastatic NSCLC who received ICIin second line or later were included. We collected clinical and hematological data and studied theassociation of this data with disease control rate (DCR), progression free survival (PFS) and overallsurvival (OS). Multiple machine learning (ML) algorithms were assessed for their ability to predictresponse.

RESULTS: Overall, 298 patients were enrolled. The overall response rate and DCR were 15.3% and 53%, respectively. Median PFS and OS were 3.3 and 11.4 months, respectively. In multivariable analysis, DCR was significantly associated with performance status (PS) and hemoglobin level(OR 0.58,p< 0.0001; OR 1.8,p< 0.001). These variables were also associated with PFS and OS and ranked top in random forest-based feature importance. Neutrophil-to-lymphocyte ratio was also associated with DCR, PFS and OS. The best ML algorithm was a random forest. It could predict DCR with satisfactory efficacy based on these three variables. Ten-fold cross-validated performances were: accuracy 0.68 ± 0.04, sensitivity 0.58 ± 0.08; specificity 0.78 ± 0.06; positive predictive value 0.70 ± 0.08; negative predictive value 0.68 ± 0.06; AUC 0.74 ± 0.03. Conclusion: Combination of simple clinical and biological data could accurately predict disease control rate at the individual level.

7.4 A mechanistic model for prediction of metastatic relapse in early-stage breast cancer using routine clinical features

Participants: Célestin Bigarré, Pascal Finetti, François Bertucci, Dominique Barbolosi, Xavier Muracciole, Sébastien Benzekry.

Authors: Célestin Bigarré, Pascal Finetti, François Bertucci, Dominique Barbolosi, Xavier Muracciole, Sébastien Benzekry

Funding: Inria-Inserm Phd Grant

Publication: Abstract sent to the AACR Anual meeting 2022.

BACKGROUND: Estimation of the risk of metastatic relapse is a major challenge to decide treatment options for early-stage breast cancer patients. To date, metastatic free survival (MFS) analysis mainly relies on classical – agnostic – statistical models (e.g., Cox regression). Instead, we propose to derive mechanistic models to predict MFS.

METHODS: The data consisted of patients who did not receive adjuvant systemic therapy from two databases: one (data 1, N = 163) with routine clinical features and the PAI-1 and uPA biomarkers and two (data 2, N = 692) with 11 routine clinical features. The mathematical models are based on a partial differential equation describing a size-structured population of metastases. They predict MFS from the size of the tumor at diagnosis and two mathematical parameters, $\alpha$ and $\mu$ describing respectively the tumor growth speed and metastatic dissemination potential. Using mixed-effects modeling, the population distributions of $\alpha$ and $\mu$ were assumed to be lognormal and to depend on routine clinical variables, whereas the observation error was assumed lognormal on the time-to-relapse. Variable selection consisted first in a univariate Wald test for all covariates with effect either on $\alpha$ or $\mu$. We then used a backward elimination procedure. Significance of the covariates in the final model was assessed by a multivariable Wald test. Concordance indexes (c-index) were computed to assess the predictive power in cross-validation procedures as well as test sets.

SOFTWARE: The model was implemented as an R package using optimized C++ code for the mechanistic part and enabling parallelization, resulting in a 4-fold reduction of the computing compared to a former python implementation.

RESULTS: The model selection procedure on data 1 revealed an association of PAI1 and age with $\mu$, and of Estrogen receptor level with alpha, consistent with the established biological link between PAI-1 and tumor invasiveness. The resulting model achieved good prediction performances with a c-index of 0.72 in 5 folds cross-validation. Using only routine clinical markers, the algorithm on data 2 selected a model with the grade and Progesterone Receptors in $\alpha$, Ki67 and node status in $\mu$. These were able to adequately describe the data on calibration plots but performed poorly in prediction with a cross validated c-index of 0.60.

CONCLUSION: Mechanistic modeling was able to unravel the biological and predictive role of PAI-1 but showed limited predictive power when using only on routine clinical features.

7.5 Development and Validation of a Prediction Model of Overall Survival in High-Risk Neuroblastoma Using Mechanistic Modeling of Metastasis

Participants: Sébastien Benzekry, Coline Sentis, Carole Coze, Laëtitia Tessonnier, Nicolas André.

Publication: JCO: Clinical Cancer Informatics15

Prognosis of high-risk neuroblastoma (HRNB) remains poor despite multimodal therapies. Better prediction of survival could help to refine patient stratification and better tailor treatments. We established a mechanistic model of metastasis in HRNB relying on two processes: growth and dissemination relying on two patient-specific parameters: the dissemination rate μ and the minimal visible lesion size Svis. This model was calibrated using diagnosis values of primary tumor size, lactate dehydrogenase circulating levels, and the meta-iodobenzylguanidine International Society for Paediatric Oncology European (SIOPEN) score from nuclear imaging, using data from 49 metastatic patients. It was able to describe the data of total tumor mass (lactate dehydrogenase, R2 > 0.99) and number of visible metastases (SIOPEN, R2 = 0.96). A prediction model of overall survival (OS) was then developed using Cox regression. Clinical variables alone were not able to generate a model with sufficient OS prognosis ability (P = .507). The parameter μ was found to be independent of the clinical variables and positively associated with OS (P = .0739 in multivariable analysis). Critically, addition of this computational biomarker significantly improved prediction of OS with a concordance index increasing from 0.675 (95% CI, 0.663 to 0.688) to 0.733 (95% CI, 0.722 to 0.744, P < .0001), resulting in significant OS prognosis ability (P = .0422).

7.6 Machine learning and mechanistic modeling for the prediction of Overall Survival on the basis of first line tumor kinetics in Head and Neck Squamous Cell Carcinoma

Participants: Kevin Atsou, Anne Auperin, Joël Guigay, Sébastien Salas, Sébastien Benzekry.

Funding: This work is supported by Plan Cancer Inserm grant number 19CM148-00 as part of the QUANTIC project.

Publication: to be submitted

BACKGROUND: The prediction of Overall Survival and Response to 2nd line treatment is a major challenge in treatment of Head and Neck Squamous Cell Carcinoma (HNSCC). Existing prediction algorithms are only based on baseline features and depend on mere survival analysis statistical tools which are agnostic. Here we used tumor kinetics measured by the Sum of the Largest Diameters (SLD) of the lesions in the patients during the first line to provide interesting predictive metrics for Overall Survival.

METHODS: The data we used comes from a multicentre, open-label, randomised, phase 2 trial, done in 68 centres in France, Spain and Germany aiming to compare the efficacy and safety of the TPEx(platinum-docetaxel-cetuximab) regimen against the standard of care regimen: EXTREME (platinum-fluorouracil-cetuximab). 528 patients were included in the clinical trial and the data used in our analysis consist of 526 patients with 38 baseline clinical features. First, a mechanistic model of tumor growth inhibition was fitted to a training data (70% of the full data) using mixed-effects modeling. Then, different machine learning algorithms for survival analysis (such as Random Survival Forest, DeepSurv, Cox-Lasso, …) were assessed on the training data set for their ability to predict the Overall Survival of the patients. Subsequently, the best performing model on the training set was used to predict the overall survival on a test data set (30 % of the full data).

SOFTWARE: The model was implemented as an R package using keras and pytorch for the implementation of some machine learning algorithms for survival analysis.

RESULTS: Using a mechanistic mathematical model, we were able to fit the clinical data of SLD measurements. The fitted mathematical model revealed a significant association between the treatment arms and the tumor growth rate. Which were not possible with a mere Cox regression analysis. The Random Survival Forest Algorithm combined with the mechanistic model was able to predict the Overall Survival with a C-index of 0.75 in 10-folds cross-validation on the train set and 0.67 on the test set. Likewise, the most impacting parameters for the prediction of OS are the parameters resulting from the mathematical model describing the kinetics of tumors (the tumor growth rate and Time to Growth after shrinkage).

CONCLUSION: By using only the first line SLD measurements, the proposed model could be used to predict the Survival of patients as a surrogate of their response to specific first line treatments.

7.7 Pharmacogenetics of cytarabine in AML patients

Participants: Mélanie Donnette, Joseph Ciccolini, Geoffroy Venton, Regis Costello, Laure Flarnault, Raphaelle Fanciullino.

Funding and data: AORC Junior, APHM

Publication: 8, 30, 78

BACKGROUND: Cytarabine is a mainstay to treat AML patients and erratic pharmacokinetics could explain unpredctible clinical outcome, including lethal toxicities.

METHODS: This is a clinical trial registred as EudraCT 2017-A00070-54 (PI: Dr Raphaelle Fanciullino) aiming at studying the pharmacokinetics of adult AML patients treated with the standard 7+3 induction and consolidation regimen. Additional PGx testing on CDA and CdK enzymes are performed.

SOFTWARE: PK modeling and PK/PGx/PD relationships are investigated on MonoLix (Lixoft France).

RESULTS: Results showed that incidence of CDA deficiency is significantly higher in patients with blood disorders as compared with patients with solid tumors or patients with no cancers. CDA deficiency was associated with marked over-exposure un cytarabine, a decrease in metabolization, and a trend towards more frequent and more severe toxicities, including lethal toxicities.

CONCLUSION: Prior search for the CDA deficiency syndrome could help to customize cytarabine dosing to limit the risk of life-threatening toxicities.

7.8 Pharmacogenetics of CPX-351 in AML patients

Participants: Mélanie Donnette, Mourad Hamimed, Geoffroy Venton, Laure Flarnault, Joseph Ciccolini, Raphaelle Fanciullino.

Funding and data: AORC Junior, APHM

Publication: 31, abstract submitted to AACR 2022

BACKGROUND: CPX-351 is a liposomal combination between daunorubicin and cytarabine approved in AML patients. Little is known about the impact of CDA gene polymorphism on the fate and of CPX-351 and resulting clinical outcome.

METHODS: This is an ancillary study to the clinical trial registered as EudraCT 2017-A00070-54 (PI: Dr Raphaelle Fanciullino) on a subset of adult AML patients treated with CPX-351. Full PK and PGx investigatiosn were performed.

SOFTWARE: PK modeling and PK/PGx/PD relationships are investigated on MonoLix (Lixoft France).

RESULTS: Results showed that surprisingly, CDA deficiency has strong impact on the PK of liposomal CPX-351. CDA-deficient patients had a trend towards decreased clearance, sharp overexposure and non-hematological toxicities when treated with CPX-351.

CONCLUSION: Unexpectedly, CDA deficiency could have a deleterious impact on the clinical outcome of patients treated with liposomal CPX-351.

7.9 Implementing adaptive dosing strategy with targeted therapies in mRCC patients

Participants: Florent Ferrer, Jean-Laurent Deville, Jonathan Chauvin, Gwenaelle Gravis, Joseph Ciccolini.

Funding and data: Canceropole Emergence, APHM and Institut Paoli Calmettes

Publication: 33, 82, Abstract submitted to AACR 2022

BACKGROUND: oral targeted therapies (i.e., sunitinib, cabozantinib) are pivotal drugs to treat metastatic renal cell carcinoma currently administered as flat dosing. To what extent adaptive dosing strategies could help improving clinical outcome is yet to be established.

METHODS: This is a real-world, observational study in mRCC patients from the APHM and the IPC cancer Center. Data from routine therapeutic drug monitoring of sunitinib and cabozantinib were used retrospectively in an adaptive dosing procedure implemented in Monolix. Models recommendation were compared with real-world data regarding efficacy and safety.

SOFTWARE: PK modeling and adaptive dosing algorithm was performed on MonoLix (Lixoft France)

RESULTS: Results show that model recommendations would have allowed to quickly reduce the dosing based upon early TDM sampling, thus avoiding the occurrence of severe toxicities in mRCC patients. Similarly, the model would have proposed to increase dosing in most patients for which progressive dosing was observed, thus suggesting that efficacy of the treatment could have been improved using model-based dosing.

CONCLUSION: This study calls for the implementation of prospective adaptive dosing with sunitinib and cabozantinib in mRCC patients.

7.10 Cetuximax: maximizing Cetuximab efficacy in head and neck cancer patients through PK/PD modeling

Participants: Clémence Marin, Mourad Hamimed, Joseph Ciccolini, Sébastien Salas.

Funding and data: Merck Serono, APHM, GPCO-Unicancer network

Publication: IATDMCT, Rome, 2021 and Abstract submitted to AACR 2022

BACKGROUND: anti-EGFR cetuximab is a pivotal mAb in head and neck cancer patients in association with chemotherapy. Therapeutic window of cetuximab and possible room for adaptive dosing strategies are unknown.

METHODS: In this multicentric clinical trial registered as NCT4218136 (P.I.: Pr Sebastien Salas), pharmacokinetics and pharmacodynamics of cetuximab will be both assessed so as to help determining the metrics (e.g., trough levels, Cmax, AUC) associated with both efficacy and toxicity of cetuximab in head and neck cancer patients. Once the therapeutic window is established, an adaptive dosing algorithm will be propose to customize drug dosing and/or scheduling.

SOFTWARE: PK/PD modeling and adaptive dosing algorithm will be performed on MonoLix (Lixoft France)

RESULTS: Early Results suggest that 34 µg/ml could be the threshold associated with efficacy upon Recist criteria. Conversely, it is not possible yet to identify exposure levels associated with severe toxicities. A large inter-individual variability in PK parameters and exposure levels has been observed (i.e., > 60%), calling for developing therapeutic drug monitoring strategies to check exposure levels.

CONCLUSION: the early results of this study confirm that PK/PD relationships with cetuximab plus large variability observed in exposure levels warrants the need for customizing dosing to ensure a maximum efficacy.

7.11 BUSEQ

Participants: Joseph Ciccolini, Sabine Furst, Didier Blaise, Raynier Devillier.

Funding and data: IPC and CHU Nice

Publication: ASH Meeting, 2019

BACKGROUND: High dose busulfan (HD Busulfan) is the backbone for bone marrow transplant conditioning but the best way to schedule its administration remains unknown. HD Busulfan is associated with up to 30% of toxic-death.

METHODS: In this multicentric clinical trial registered NCT 2020-003392-17 (P.I.: Pr Rainier Devillier IPC), pharmacokinetics and pharmacodynamics of HD Busulfan administered in a sequential fashion are investigated. Dosing is customized using a pop-PK approach so as to reach a predefined target exposure. Treatment-unrelated mortality.

SOFTWARE: PK modeling and adaptive dosing algorithm will be performed on KineticPro (MIPPS, France).

RESULTS: Preliminary results show that pop-PK identification of individual parameters after the first injection of HD Busulfan allows to customize the dosing in an iterative fashion to target a global AUC defined as the exposure to be reached.

CONCLUSION: PK-guided dosing of HD Busulfan is feasible and allows to target accurately a predefined exposure level.

8.1 Research contracts

D-LIGHT

• Partner: Roche
• Title: Tumor growth inhibition modeling coupled with machine learning for early response prediction and identification of common features of response, to optimize combination therapies in Oncology
• Funding: 76.5 k€
• Duration: Jan 2021 - Jan 2022
• Principal investigator: S. Benzekry

NanoImmuno

• Partner: Institut Roche France and Genentech US
• Title: NanoImmuno: a nanoparticle to harness tumor immunity in her2+ breast cancer
• Funding: 80 k€
• Duration: Jan 2020 - March 2023
• Principal investigator: Raphaelle Fanciullino (Senior), Anne Rodallec (Junior)

8.2 Clinical trials

Cetuximax

• Registration: NCT4218136
• Partner: Merck Serono
• Title: Maximizing Cetuximab efficacy in head and neck cancer patients through PK/PD modeling
• Funding: 40 k€
• Duration: 2020 - 2023
• Principal investigator: Sébastien Salas

MOVIE

• Registration: NCT03518606
• Partner: Astra Zeneca and INCa
• Title: Metronomic Oral Vinorelbine Plus Anti-PD-L1/Anti-CTLA4 ImmunothErapy in Patients With Advanced Solid Tumours
• Funding: 500 k€
• Duration: 2019-2022
• Principal investigator: Anthony Goncalves (IPC)
• Compo investigator: Joseph Ciccolini

MOIO

• Registration: NCT05078047
• Partner: BMS and INCa
• Title: Study Comparing the Standard Administration of IO Versus the Same IO Administered Each 3 Months in Patients With Metastatic Cancer in Response After 6 Months of Standard IO
• Funding: 500 k€
• Duration: 2020-2024
• Principal investigator: Gwenaelle Gravis (IPC)
• Compo investigator: Joseph Ciccolini

CytaPGx

• Registration: EudraCT 2017-A00070-53
• Partner: APHM and CAL Nice
• Title: Constitutive biomarkers in AML patients treated with cytarabine
• Funding: 80 k€ (APHM)
• Duration: 2018-2021
• Principal investigator: Raphaelle Fanciullino

VIDAZAML

• Registration: EudraCT 2020-A02042-37
• Partner: APHM and CAL Nice
• Title: Role of CDA and dCK metabolic enzymes in clinical response of patients treated with azacitidine for blood disorders
• Funding: 80 k€ (APHM)
• Duration: 2021-2023
• Principal investigator: Geoffroy Venton - Compo Investigator: Raphaelle Fanciullino

Morpheus-Lung

• Registration: Eudract 2017-001267-21 / NCT03337698
• Partner: Hoffmann-La Roche
• Title: A Study to Evaluate Efficacy and Safety of Multiple Immunotherapy-Based Treatment Combinations in Patients with Metastatic Non-Small Cell Lung Cancer
• Duration: 2018-2023
• Compo investigator: Laurent Greillier

PDC-LUNG-101

• Registration: Eudract 2018-002382-19 / NCT03970746
• Partner: PDC*line Pharma SAS
• Title: Safety, immunogenicity and preliminary clinical activity study of PDC*lung01 cancer vaccine in NSCLC
• Duration: 2022-2024
• Principal investigator: Johan Vansteenkiste, Prof KU Leuven
• Compo investigator: Laurent Greillier

NCT03840915

• Registration: Eudract 2018-004040-28 / NCT03840915
• Partner: EMD Serono Research and Development Institute, Inc., Merck KGaA
• Title: A Phase Ib/II, Open-Label Study of M7824 in Combination With Chemotherapy in Participants With Stage IV Non-small Cell Lung Cancer
• Duration: 2019-2022
• Compo investigator: Laurent Greillier

NCT03708328

• Registration: Eudract 2018-000982-35 / NCT03708328
• Partner: Hoffmann-La Roche
• Title: A Dose Escalation and Expansion Study of RO7121661, a PD-1/TIM-3 Bispecific Antibody, in Participants With Advanced and/or Metastatic Solid Tumors
• Duration: 2018-2023
• Compo investigator: Laurent Greillier

PROPEL

• Registration: Eudract 2019-003474-35 / NCT03138889
• Partner: Nektar Therapeutics
• Title: Bempegaldesleukin and Pembrolizumab With or Without Chemotherapy in Locally Advanced or Metastatic Solid Tumors
• Duration: 2017-2024
• Compo investigator: Laurent Greillier

TRIDENT-1

• Registration: Eudract 2016-003616-13 / NCT03093116
• Partner: Turning Point Therapeutics, Inc.
• Title: A Study of Repotrectinib (TPX-0005) in Patients With Advanced Solid Tumors Harboring ALK, ROS1, or NTRK1-3 Rearrangements
• Duration: 2017-2023
• Principal investigator: Shanna Stopatschinskaja, Dan Liu
• Compo investigator: Laurent Greillier

NCT04721015

• Registration: Eudract 2020-004953-57 / NCT04721015
• Partner: AbbVie
• Title: Study of Intravenous (IV) ABBV-637 Alone or in Combination With IV Docetaxel/Osimertinib to Assess Adverse Events and Change in Disease Activity in Adult Participants With Relapsed/Refractory (R/R) Solid Tumors
• Duration: 2021-2026
• Compo investigator: Laurent Greillier

PIONeeR

• Registration: NCT03493581
• Partner: APHM, HalioDx, Innate Pharma, AMU
• Title: Precision Immuno-Oncology for Advanced Non-small Cell Lung Cancer Patients With PD-1 ICI Resistance
• Duration: 2018-2022
• Principal investigator: Laurent Greillier, Jean-Olivier Arnaud

ELDERLY

• Registration: NCT03977194
• Partner: Intergroupe Francophone de Cancerologie Thoracique
• Title: Atezolizumab in Elderly Patients With Advanced Non-Small-Cell Lung Cancer and Receiving Carboplatin Paclitaxel Chemotherapy
• Duration: 2019-2023
• Principal investigator: Elisabeth QUOIX, Céline MASCAUX
• Compo investigator: Laurent Greillier

NCT01817192

• Registration: NCT01817192
• Partner: Razor Genomics
• Title: Adjuvant Chemotherapy in Patients With Intermediate or High Risk Stage I or Stage IIA Non-squamous Non-Small Cell Lung Cancer
• Duration: 2020-2025
• Principal investigator: David R Spigel, Sarah Cannon
• Compo investigator: Laurent Greillier

SAVIMMUNE

• Registration: NCT04108026
• Partner: Intergroupe Francophone de Cancerologie Thoracique
• Title: Immunotherapy in Patient With Poor General Condition
• Duration: 2020-2023
• Principal investigator: Valérie GOUNANT, Michael DURUISSEAUX
• Compo investigator: Laurent Greillier

NCT04042558

• Registration: NCT04042558
• Partner: Centre Francois Baclesse
• Title: A Study Evaluating Platinum-Pemetrexed-Atezolizumab (+/-Bevacizumab) for Patients With Stage IIIB/IV Non-squamous Non-small Cell Lung Cancer With EGFR Mutations, ALK Rearrangement or ROS1 Fusion Progressing After Targeted Therapies
• Duration: 2019-2024
• Compo investigator: Laurent Greillier

NIPINEC

• Registration: NCT03591731
• Partner: Intergroupe Francophone de Cancerologie Thoracique, Federation Francophone de Cancerologie Digestive, GERCOR - Multidisciplinary Oncology Cooperative Group
• Title: Nivolumab +/- Ipilimumab in Patients With Advanced, Refractory Pulmonary or Gastroenteropancreatic Poorly Differentiated Neuroendocrine Tumors (NECs)
• Duration: 2019-2023
• Principal investigator: Nicolas GIRARD, Thomas WALTER
• Compo investigator: Laurent Greillier

RESILIENT

• Registration: NCT03088813
• Partner: Ipsen
• Title: Study of Irinotecan Liposome Injection (ONIVYDE®) in Patients With Small Cell Lung Cancer
• Duration: 2018-2022
• Compo investigator: Laurent Greillier

Canopy-A

• Registration: NCT03447769
• Partner: Novartis Pharmaceuticals
• Title: Study of Efficacy and Safety of Canakinumab as Adjuvant Therapy in Adult Subjects With Stages AJCC/UICC v. 8 II-IIIA and IIIB (T>5cm N2) Completely Resected Non-small Cell Lung
• Duration: 2018-2027
• Compo investigator: Laurent Greillier

NCT04350463

• Registration: NCT04350463
• Partner: Celgene
• Title: A Safety and Efficacy Study of CC-90011 in Combination With Nivolumab in Subjects With Advanced Cancers
• Duration: 2020-2024
• Compo investigator: Laurent Greillier

TROPION-LUNG01

• Registration: NCT04656652
• Partner: Daiichi Sankyo, Inc., AstraZeneca
• Title: Study of DS-1062a Versus Docetaxel in Previously Treated Advanced or Metastatic Non-small Cell Lung Cancer Without Actionable Genomic Alterations
• Duration: 2020-2024
• Compo investigator: Laurent Greillier

MERMAID-1

• Registration: NCT04385368
• Partner: AstraZeneca
• Title: Phase III Study to Determine the Efficacy of Durvalumab in Combination With Chemotherapy in Completely Resected Stage II-III Non-small Cell Lung Cancer (NSCLC)
• Duration: 2020-2026
• Principal investigator: Solange Peters, Charles Swanton
• Compo investigator: Laurent Greillier

NCT03840902

• Registration: NCT03840902
• Partner: EMD Serono Research and Development Institute, Inc., Merck KGaA
• Title: M7824 With cCRT in Unresectable Stage III Non-small Cell Lung Cancer (NSCLC)
• Duration: 2019-2023
• Compo investigator: Laurent Greillier

CARMEN-LC03

• Registration: NCT04154956
• Partner: Sanofi
• Title: SAR408701 Versus Docetaxel in Previously Treated, Carcinoembryonic Antigen-related Cell Adhesion Molecule 5 (CEACAM5) Positive Metastatic Non-squamous Non-small Cell Lung Cancer Patients
• Duration: 2020-2024
• Compo investigator: Laurent Greillier

SKYSCRAPER-03

• Registration: NCT04513925
• Partner: Hoffmann-La Roche
• Title: A Study of Atezolizumab and Tiragolumab Compared With Durvalumab in Participants With Locally Advanced, Unresectable Stage III Non-Small Cell Lung Cancer (NSCLC)
• Duration: 2020-2027
• Compo investigator: Laurent Greillier

NCT03899155

• Registration: NCT03899155
• Partner: Bristol-Myers Squibb
• Title: Pan Tumor Nivolumab Rollover Study
• Duration: 2019-2025

NCT03798535

• Registration: NCT03798535
• Partner: AstraZeneca
• Title: First Real-world Data on Unresectable Stage III NSCLC Patients Treated With Durvalumab After Chemoradiotherapy
• Duration: 2018-2023
• Compo investigator: Laurent Greillier

IMbrella A

• Registration: NCT03148418
• Partner: Hoffmann-La Roche
• Title: A Study in Participants Previously Enrolled in a Genentech- and/or F. Hoffmann-La Roche Ltd-Sponsored Atezolizumab Study
• Duration: 2017-2030
• Compo investigator: Laurent Greillier

BEAT-meso

• Registration: NCT03762018
• Partner: European Thoracic Oncology Platform, Hoffmann-La Roche
• Title: Bevacizumab and Atezolizumab in Malignant Pleural Mesothelioma
• Duration: 2019-2024
• Principal investigator: Enriqueta Felip, Sanjay Popat
• Compo investigator: Laurent Greillier

SPECTA

• Registration: NCT02834884
• Partner: European Organisation for Research and Treatment of Cancer - EORTC
• Title: Screening Cancer Patients for Efficient Clinical Trial Access
• Duration: 2017-2026
• Principal investigator: Vassilis Golfinopoulos
• Compo investigator: Laurent Greillier

TROPION-Lung05

• Registration: EudraCT 2020-002774-27
• Partner: Daiichi Sankyo, Inc.
• Title: Phase 2 Study of DS-1062a in Advanced or Metastatic Non-small Cell Lung Cancer with Actionable Genomic Alterations
• Starting year: 2021
• Compo investigator: Laurent Greillier

NeoCOAST

• Registration: EudraCT 2018-002932-26
• Partner: MedImmune, LLC, a wholly owned subsidiary of AstraZeneca PLC
• Title: Neoadjuvant Durvalumab Alone or in Combination with Novel Agents in Resectable Non-Small Cell Lung Cancer
• Duration: 2019 - 2021
• Compo investigator: Laurent Greillier

PIVOT-02

• Registration: EudraCT 2016-003543-11
• Partner: Nektar Therapeutics
• Title: A study to investigate the use of the study drug NKTR-214 in combination with other approved treatments for solid tumours
• Duration: 2016 - 2021
• Compo investigator: Laurent Greillier

SKYSCRAPER-06

• Registration: NCT04619797
• Partner: Hoffmann-La Roche
• Title: A Study of Tiragolumab in Combination With Atezolizumab Plus Pemetrexed and Carboplatin/Cisplatin Versus Pembrolizumab Plus Pemetrexed and Carboplatin/Cisplatin in Participants With Previously Untreated Advanced Non-Squamous Non-Small Cell Lung Cancer
• Duration: 2020 - 2025
• Compo investigator: Laurent Greillier

PERSEE

• Registration: EudraCT 2020-002626-86
• Partner: CHRU de Brest
• Title: A trial comparing the pembrolizumab platinum based chemotherapy combination with pembrolizumab monotherapy in first line treatment of non small-cell lung cancer (NSCLC) patients
• Starting year: 2020
• Principal investigator: Renaud Descourt, Chantal Decroisette, Christos Chouaid
• Compo investigator: Laurent Greillier

SAPPHIRE

• Registration: EudraCT 2019-001043-41
• Partner: Mirati Therapeutics, Inc.
• Title: A Randomized Phase 3 Study of Sitravatinib in Combination with Nivolumab Versus Docetaxel in Patients with Advanced Non-Squamous Non-Small Cell Lung Cancer with Disease Progression On or After Platinum-Based Chemotherapy and Checkpoint Inhibitor Therapy
• Starting year: 2020
• Compo investigator: Laurent Greillier

Nivothym

• Registration: EudraCT 2015-005504-28
• Partner: European Organisation for Research and Treatment of Cancer
• Title: Single-arm, multicenter, phase II study of immunotherapy in patients with type B3 thymoma and thymic carcinoma previously treated with chemotherapy
• Starting year: 2017
• Principal investigator: Nicolas Girard, Solange Peters
• Compo investigator: Laurent Greillier

9.1 Visits of international scientists

9.2 International collaborations

REZOLVE TRIAL

• Registration: ANZGOG11-01
• Partner: Sydney University, Australia
• Title: Phase 2 study of intraperitoneal Bevacizumab in patients with refractory ovarian cancer
• Funding: 40 k€
• Duration: 2020-2022
• Principal investigator: Sonia Yip (Sydney) - COMPO investigator: Joseph Ciccolini , Clémence Marin, Mourad Hamimed

TAX TRIAL

• Registration: ongoing
• Partner: EORTC, Belgium
• Title: Trastuzumab-deruxtecan in older patients with Her2+ breast cancer
• Funding: 62 k€
• Duration: 2022-2024
• Principal investigator: Hans Wielder (Luven) - COMPO investigator: Joseph Ciccolini , Mourad Hamimed

9.3 National initiatives

9.4 Regional initiatives

SMART project

• Partner: A*MIDEX
• Title: Optimizing pemetrexed-cisplatin-bevacizumab dosing and scheduling with anti-PD1 in NSCLC models
• Funding: 350 k€
• Duration: 2018-2022
• Principal investigator: Fabrice Barlesi (IGR) COMPO investigator: Joseph Ciccolini, Guillaume Sicard, Raphaelle Fanciullino

Cardiotoxicity of immuno-oncology drugs

• Partner: Maison du Coeur, PRT-K (INCa)
• Title: Myocardites induites par les immunothérapies anticancéreuses : approche translationnelle des mécanismes physiopathologiques et des facteurs pronostiques
• Funding: 47 k€ + 250 K€
• Duration: 2021-2023
• Principal investigator: Franck Thuny (APHM) COMPO investigator: Joseph Ciccolini, Raphaelle Fanciullino

ETP Program

• Partner: APHM
• Title: Therapeutic Education of the patient in oncology- hematology
• Funding: none (APHM own resources)
• Duration: 2018-present
• Principal investigator: Raphaelle Fanciullino

10.1 Promoting scientific activities

10.2 Teaching - Supervision - Juries

10.3 Popularization

11.1 Major publications

• 1 articleD.Dominique Barbolosi, J.Joseph Ciccolini, B.Bruno Lacarelle, F.Fabrice Barlési and N.Nicolas André. Computational oncology — mathematical modelling of drug regimens for precision medicine.Nature Reviews Clinical Oncology134April 2016, 242-254
  HALDOI
• 2 articleS.Sébastien Benzekry. Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology.Clinical Pharmacology and TherapeuticsJune 2020
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• 3 articleS.Sébastien Benzekry, C.Clare Lamont, D.Dominique Barbolosi, L.Lynn Hlatky and P.Philip Hahnfeldt. Mathematical modeling of tumor-tumor distant interactions supports a systemic control of tumor growth.Cancer Research2017
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• 4 articleS.Sébastien Benzekry, A.Amanda Tracz, M.Michalis Mastri, R.R. Corbelli, D.Dominique Barbolosi and J. M.John M.L. Ebos. Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach.Cancer Research763February 2016, 535 - 547
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• 5 articleJ.Joseph Ciccolini, D.Dominique Barbolosi, C.Christophe Meille, C.Cindy Serdjebi, S.Sarah Giacometti, L.Laetitia Padovani, E. G.Eddy G Pasquier and N. G.Nicolas G André. Pharmacokinetics and Pharmacodynamics-Based Mathematical Modeling Identifies an Optimal Protocol for Metronomic Chemotherapy.Cancer Research77172017, 4723-4733
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• 6 articleJ.Joseph Ciccolini and G.Gérard Milano. Women at a Disadvantage in Fluorouracil Treatment.JAMA oncology26June 2016, 829
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• 7 articleX.Xavier Elharrar, D.Dominique Barbolosi, J.Joseph Ciccolini, C.Christophe Meille, C.Christian Faivre, B.Bruno Lacarelle, N.Nicolas André and F.Fabrice Barlesi. A phase Ia/Ib clinical trial of metronomic chemotherapy based on a mathematical model of oral vinorelbine in metastatic non-small cell lung cancer and malignant pleural mesothelioma: rationale and study protocol.BMC Cancer161December 2016, 278
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• 8 articleR.Raphaelle Fanciullino, L.Laure Farnault, M.Mélanie Donnette, D.-C.Diane-Charlotte Imbs, C.Catherine Roche, G.Geoffroy Venton, Y.Yael Berda-Haddad, V.Vadim Ivanov, J.Joseph Ciccolini, L.L’Houcine Ouafik, B.Bruno Lacarelle and R.Régis Costello. CDA as a predictive marker for life-threatening toxicities in patients with AML treated with cytarabine.Blood Advances25February 2018, 462 - 469
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• 9 articleF.Florent Ferrer, R.Raphaelle Fanciullino, G.Gérard Milano and J.Joseph Ciccolini. Towards Rational Cancer Therapeutics: Optimizing Dosing, Delivery, Scheduling, and Combinations.Clinical Pharmacology and Therapeutics1083September 2020, 458-470
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• 10 articleC.Chiara Nicolò, C.Cynthia Périer, M.Mélanie Prague, C.Carine Bellera, G.Gaëtan Macgrogan, O.Olivier Saut and S.Sébastien Benzekry. Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer.JCO Clinical Cancer Informatics4September 2020, 259-274
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11.2 Publications of the year

11.3 Other

11.4 Cited publications

• 58 articleK.Kambiz Afrasiabi, M. E.Mark E. Linskey and Y.-H.Yi-Hong Zhou. Exploiting Cancer’s Tactics to Make Cancer a Manageable Chronic Disease.Cancers1266Jun 2020, 1649
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• 59 articlea. L.a L Baldock, R. C.R C Rockne, a. D.a D Boone, M. L.M L Neal, A.A Hawkins-Daarud, D. M.D M Corwin, C. A.C A Bridge, L. a.L a Guyman, A. D.A D Trister, M. M.M M Mrugala, J. K.J K Rockhill and K. R.K R Swanson. From patient-specific mathematical neuro-oncology to precision medicine..Front Oncol3AprilJan 2013, 62–62
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• 60 articleE.Etienne Baratchart, S.Sébastien Benzekry, A.Andreas Bikfalvi, T.Thierry Colin, L. S.Lindsay S. Cooley, R.Raphäel Pineau, E. J.Emeline J Ribot, O.Olivier Saut and W.Wilfried Souleyreau. Computational Modelling of Metastasis Development in Renal Cell Carcinoma.PLoS Comput Biol111100007Nov 2015, e1004626
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• 61 articleD.Dominique Barbolosi, S.Sebastien Hapdey, S.Stephanie Battini, C.Christian Faivre, J.Julien Mancini, K.Karel Pacak, B.Bardia Farman-Ara and D.David Taïeb. Determination of the unmetabolized 18F-FDG fraction by using an extension of simplified kinetic analysis method: clinical evaluation in paragangliomas.Medical & biological engineering & computing541Jan 2016, 103–111
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• 62 articleF.Fabrice Barlesi, D.-C.Diane-Charlotte Imbs, P.Pascale Tomasini, L.Laurent Greillier, M.Melissa Galloux, A.Albane Testot-Ferry, M.Mélanie Garcia, X.Xavier Elharrar, A.Annick Pelletier, N.Nicolas André, C.Céline Mascaux, B.Bruno Lacarelle, R. E.Raouf El Cheikh, R.Raphaël Serre, J.Joseph Ciccolini and D.Dominique Barbolosi. Oncotarget829Jul 2017, 47161–47166
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• 63 articleS.Sébastien Benzekry, A.Alberto Gandolfi and P.Philip Hahnfeldt. Global Dormancy of Metastases Due to Systemic Inhibition of Angiogenesis.PLoS ONE9100019Jan 2014, e84249-11
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• 64 articleS.Sébastien Benzekry, C.Clare Lamont, D.Dominique Barbolosi, L.Lynn Hlatky and P.Philip Hahnfeldt. Mathematical Modeling of Tumor-Tumor Distant Interactions Supports a Systemic Control of Tumor Growth..Cancer Research7718Sep 2017, 5183–5193
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• 65 articleS.Sebastien Benzekry, A.Amanda Tracz, M.Michalis Mastri, R.Ryan Corbelli, D.Dominique Barbolosi and J. M.John M. L. Ebos. Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach.Cancer Research76300000Feb 2016, 535–547
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• 66 articleR.Rene Bruno, D.Dean Bottino, D. P.Dinesh P de Alwis, A. T.Antonio T Fojo, J.Jérémie Guedj, C.Chao Liu, K. R.Kristin R Swanson, J.Jenny Zheng, Y.Yanan Zheng and J. Y.Jin Y Jin. Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models..Clinical cancer research : an official journal of the American Association for Cancer ResearchDec 2019, 1–22
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• 67 articleB.Bob Carpenter, A.Andrew Gelman, M. D.Matthew D. Hoffman, D.Daniel Lee, B.Ben Goodrich, M.Michael Betancourt, M.Marcus Brubaker, J.Jiqiang Guo, P.Peter Li and A.Allen Riddell. Stan: A Probabilistic Programming Language.Journal of Statistical Software761Jan 2017, 1–32
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• 68 articleR. H.Rebecca H. Chisholm, T.Tommaso Lorenzi, A.Alexander Lorz, A. K.Annette K. Larsen, L. N.Luís Neves de Almeida, A.Alexandre Escargueil and J.Jean Clairambault. Emergence of drug tolerance in cancer cell populations: an evolutionary outcome of selection, nongenetic instability, and stress-induced adaptation.Cancer Research756Mar 2015, 930–939
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• 69 articleJ.Joseph Ciccolini, D.Dominique Barbolosi, N.Nicolas André, F.Fabrice Barlesi and S.Sébastien Benzekry. Mechanistic Learning for Combinatorial Strategies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigators?JCO Precision Oncology4May 2020, 486–491
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• 70 articleJ.J. Ciccolini, D.D. Barbolosi, N.N. André, S.S. Benzekry and F.F. Barlesi. Combinatorial immunotherapy strategies: most gods throw dice, but fate plays chess.Annals of Oncology3011Nov 2019, 1690–1691
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• 71 articleO.O. Clatz, M.M. Sermesant, P.-Y.P.-Y. Bondiau, H.H. Delingette, S.S.K. Warfield, G.G. Malandain and N.N. Ayache. Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation.IEEE Transactions on Medical Imaging2410Oct 2005, 1334–1346
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