Identify functional vulnerabilities at early and advanced cancer stages and model cell population dynamics during treatment

Host laboratory and collaborators

Elisabeth Remy / I2M / elisabeth.remy@univ-amu.fr

Flavio Maina / IBDM / flavio.maina@univ-amu.fr


Promising frameworks emerge from computational biology and deep learning, designed to analyse patient -omics data and screen outcomes for precision medicine. Among them, “synthetic lethality and rescue-mediated precision oncology via the transcriptome” (SELECT) is a novel framework that identifies and uses genetic interactions of drug targets to predict the response to cancer therapies [1]. Few examples have documented the superiority of SELECT compared with other transcriptomic-based predictors. Using liver cancer as a biological system [2,3], we propose to adapt SELECT for predicting vulnerabilities across heterogeneous populations of patients, and functionally test them in complementary biological models. Merging SELECT with UPMaBoSS, a framework for modelling populations of interacting cells [4], we will simulate cell population dynamics to predict and follow the behaviour of heterogeneous cell subgroups and the emergence of new subtypes.


synthetic lethal interactions, prediction of vulnerabilities, modelling heterogeneous populations, in vitro/ex vivo/in vivo biological models, liver organoids


1. Using SELECT, identify “predicted vulnerabilities” in patients, focussing on synthetic lethal/rescue interactions, by analysing multiple transcriptomics databases from patients and mouse models (at early and advance stages).

2. Functionally test predicted vulnerabilities, longitudinally, in complementary in vitro/ex vivo/in vivo models.

3. Integrate genetic interactions identified by SELECT in an explanative dynamical model and perform stochastic simulations of cell population dynamics (using UPMaBoSS) to document the emergence of population subtypes.

Expected profile

The candidate should have knowledge in computational biology and mathematical modelling for the analysis of transcriptomics and prediction of genetic interactions. He/she should be able to exploit frameworks like those mentioned in the objectives, adapting them to specific biological systems and questions. Previously acquired interdisciplinary experience combining mathematics and biology will be particularly considered.
Candidate biologists interested in applying mathematical modelling will also be considered.

In the case of an existing project, please explain the links between the two projects (5 lines)

This project has been designed to exploit two powerful frameworks recently published, SELECT and UPMaBoSS, merging the expertise of Maina lab, on signalling and vulnerabilities identifications from screen studies, and of the Remy lab, on mathematical modelling and prediction. Through this project, a) identification of predicted vulnerabilities (with Remy lab expertise) will be tested in a variety of biological models (Maina lab interests), while b) powerful frameworks will be integrated to follow dynamics of heterogeneous populations (Remy lab interests), whose robustness will be experimentally documented (Maina lab expertise).

This new project emerges from a fruitful collaboration between the two labs during the past year and half on a project that aims to contextualise signalling fluctuations in resilient versus vulnerable biological systems exploiting proteomic data.

2 to 5 references related to the project

1) Lee SL. Et al. Synthetic lethality-mediated precision oncology via the tumor transcritprome. Cell 2021.
2) Cassol F. et al. Tracking dynamics of spontaneous tumours in mice using Photon Counting Computed Tomography. iScience 2019.
3) Fan Y. et al. A Phosphokinome-based screen uncovers new drug synergies for cancer driven by liver-specific gain of non-oncogenic RTKs. Hepatology 2017.
4) Stoll et al. UPMaBoSS: a novel framework for dynamic cell population modelling. Frontiers 2022.

3 main publications from each PI over the last 5 years

Elisabeth Remy

- L. Cantini, P. Zakeri, C. Hernandez, A. Naldi, D. Thieffry, E. Remy, A. Baudot. Benchmarking joint multi-omics dimensionality reduction approaches for cancer study. Nature Communications 12, 124 (2021). PMID: 33402734.
- L. Hérault, M. Poplineau, A. Mazuel, N. Platet, É. Remy, and E Duprez. Single-cell RNA-seq reveals a concomitant delay in differentiation and cell cycle of aged hematopoietic stem cells. BMC biology, 19(1) :1–20, (2021). PMID: 33526011.
- L. Pio-Lopez, A. Valdeolivas, L. Tichit, É. Remy, and A. Baudot. Multiverse: a multiplex and multiplex-heterogeneous network embedding approach. Scientific Reports, 11, (2021). PMID: 33888761.


Fabio Maina

- Arechederra M., Bazai S., Abdouni A., Sequera C., Mead T.J., Richelme S., Daian F., Audebert S., Dono R., Lozano A., Gregoire D., Hibner U., Allende D., Apte S.S., Maina F. ADAMTSL5 is an epigenetically activated gene that confers tumorigenic properties and drug resistance in hepatocellular carcinoma. Journal of Hepatology 2021, 74(4):893-906. PMID: 33197513.
- Fan Y., Bazai S.K., Daian F., Arechederra M., Richelme S., Temiz N.A., Yim A., Habermann B.H., Dono R., Largaespada D.A., Maina F. Evaluating the landscape of gene cooperativity with RTKs in liver tumorigenesis. Journal of Hepatology 2019, 70(3): 470-482. PMID: 30529386.
- Arechederra M., Daian F., Yim A., Sehrish S.K., Richelme S., Dono R., Saurin A.J., Habermann B.H., Maina F. Hypermethylation of gene body CpG islands predicts high dosage of functional oncogenes in liver cancer. Nature Communications 2018, 9(1):3164. PMID: 30089774.