Systems Biology for Oncology - Eduati lab

Predictive biomarkers from functional analysis of static data

• Integration of static bulk RNAseq data with different sources of prior knowledge to derive a quantitative characterisation of different facets of the TME that is predictive of response to immune checkpoint inhibitors. Tool: EaSIeR. Paper: Lapuente-Santana et al, Patterns, 2021).
• Inference of personalised (ensembles of) cell-cell interaction networks from bulk RNA-seq data and prior knowledge using random graph models. Tool: RaCInG. Paper: van Santvoort et al, bioRxiv, 2023).

Spatial multicellular models

• Development of computational approaches to quantitatively characterise spatial localization of cell types in the tumor microenvironment using radiogenomics approaches, i.e., combining imaging features and gene expression.
• Development of agent-based models to study how interactions between different cells in the TME coordinate tumour development and response to treatment. Models: Prostate cancer development, Prostate cancer androgen deprivation therapy. Papers: Passier et al, Cancer Research Communications, 2023, van Genderen et al, npj Systems Biology and Applications, 2024).

Diagnostic and prognostic biomarkers from liquid biopsies

• Development of machine learning models to use tumour markers (TMs) from liquid biopsies such as circulating tumour DNA (ctDNA) and proteins in the blood for diagnosis and monitoring of lung cancer. Model: Diagnostic lung cancer model. Paper: Visser et al, Lung cancer, 2023).

Mechanistic models from functional perturbation data

• Development of microfluidics technology for high-throughput perturbation screening of live cells from cancer patient biopsies. Paper: Eduati et al, Nature Communications, 2018).
• Inference predictive personalised mathematical models of intracellular signalling pathways based on logic ordinary differential equations to understand cellular mechanisms mediating differential drug response in patients. Paper: Eduati et al, Molecular Systems Biology, 2020).

Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties

M. van Genderen^$, J. Kneppers^$, A. Zaalberg^$, E. Bekers, A. M. Bergman^#, W. Zwart^#, F. Eduati^#. npj Systems Biology and Applications. , 2023. (^$ co-first authors, ^# co-last authors)

Mathematically mapping the network of cells in the tumor microenvironment

M. van Santvoort, Ó. Lapuente-Santana, F. Finotello, P. van der Hoorn^#, F. Eduati^#. bioRxiv. 2023. (^# co-last authors)

Exploring the onset and progression of prostate cancer through a multicellular agent-based model

M. Passier, M. van Genderen, A. Zaalberg, J. Kneppers, E. Bekers, A. M. Bergman^#, W. Zwart^#, F. Eduati^#. Cancer Research Communications. CRC-23-0097, 2023. (^# co-last authors)

Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer

E. Visser^$, S. A.A.M. Genet^$, R. P.P.A. de Kock, B. E.E.M. van den Borne, M. Youssef-El Soud, H. N.A. Belderbos, G. Stege, M. E.A. de Saegher, S. C. van 't Westeinde, L. Brunsveld, M. A. C. Broeren, D. van de Kerkhof, B. A.L.M. Deiman, F. Eduati^#, V. Scharnhorst^#. Lung Cancer. 178, 28-36, 2023. (^$ co-first authors, ^# co-last authors)

Technological and computational advances driving high-throughput oncology

L. Kolmar^$, A. Autour^$, X. Ma, B. Vergier, F. Eduati^#, C. A. Merten^#. Trends in Cell Biology, 32(11), 847-961, 2022. (^$ co-first authors, ^# co-last authors)

Interpretable systems biomarkers predict response to immune-checkpoint inhibitors

Ó. Lapuente-Santana, M. van Genderen, P. A. J. Hilbers, F. Finotello, F. Eduati. Patterns, 2(8), 100293. 2021.

Toward Systems Biomarkers of Response to Immune Checkpoint Blockers

Ó. Lapuente-Santana, F. Eduati. Frontiers in Oncology, 10:1027. 2020.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

F. Eduati, P. Jaaks, J. Wappler, T. Cramer, C. A. Merten, M. J. Garnett, J. Saez-Rodriguez. Molecular Systems Biology, 16(2),e8664. 2020.

A microfluidics platform for combinatorial drug screening on cancer biopsies.

F. Eduati^$, R. Utharala^$, D. Madhavan, U.P. Neumann, T. Longerich, T. Cramer, J. Saez- Rodriguez^#., C. Merten^#. Nature Communications, 9(1):2434, 2018. (^$ co-first authors, ^# co-last authors)

Multi-Omics Profiling of the Tumor Microenvironment: Paving the Way to Precision Immuno-Oncology.

F. Finotello, F. Eduati. Frontiers in Oncology, 8, 1–9. 2018.

Drug resistance mechanisms in colorectal cancer dissected with cell type-specific dynamic logic models.

F. Eduati, V. Doldàn-Martelli, B. Klinger, T. Cokelaer, A. Sieber, F. Kogera, M. Dorel, M. J. Garnett, N. Blüthgen^#, J. Saez-Rodriguez^#. Cancer Research, 77(12):3364-3375, 2017. (^# co-last authors)

Prediction of human population responses to toxic compounds by a collaborative competition.

F. Eduati^$, L. M. Mangravite^$, T. Wang*, H. Tang*, J. C. Bare, R. Huang, T. Norman, M. Kellen, M. P. Menden, J. Yang, X. Zhan, R. Zhong, G. Xiao, M. Xia, N. Abdo, O. Kosyk, The NIEHS-NCATS-UNC DREAM Collaboration, S. Friend, A. Dearry, A. Simeonov, R. R. Tice, I. Rusyn, F. A. Wright, G. Stolovitzky, Y. Xie, and J. Saez-Rodriguez. Nature Biotechnology, 733(9):933-940, 2015. (^$ co-first authors)