Grants


 
HealthHolland CellCircuits (Heath Holland) (In collaboration with Thijn Brummelkamp) A genomics compendium to identify therapeutic targets. This project exploits Phenosaurus, a large collection of ~130 haploid screens generated in collaboration with Scenic Biotech BV to construct a map of cellular regulation (the cell’s circuitry) and aims to discover novel therapeutic targets. (2020 – 2023)
KWF Logo Cetuximab response prediction (Dutch Cancer Society) (Incollaboration with Connie Jimenez, Livio Trusolino and Henk Verheul) Phosphoproteomics and integrative analysis to enable precision medicine for anti-EGFR therapy in colorectal cancer. Constructing computational models to predict Cetuximab response in RAS wild type patients based on (phopspho)proteome, DNAseq and RNAseq data collected longitudinally in PDX models and patient samples (2020 – 2023)
Genmab.svg HexaBodyR-DR5/DR5 biomarkers (Genmab BV) Indentification of biomarkers and predictors of response to the HexaBodyR-DR5/DR5 therapy based on DNAseq, RNAseq and response data collected for a collection of cell lines and PDX models. (2018 – 2022)
KWF Logo Intermediate phenotypes (Dutch Cancer Society) In this high risk project we are recording intermediate phenotypes (percentage of cells exposed to a drug, changes in molecular profiles etc.) based on on-treatment samples from lung, colorectal and melanoma patients that have undergone targeted combination treatment. The aim is to investigate the behavior of these intermediate phenotypes and to explore the possibilities to link them to patient response (2018 – 2022)
ZonMw COMPUTE CANCER: CO-data Moderated Prediction Underpinning Therapy personalization and Early diagnostics of CANCER (ZonMW) (In collaboration with Mark vd Wiel (UAMC), Marcel Reinders and Marco Loog (TUDelft)) In this project, we explore the application of weighted learning and transfer learning to construct improved predictors of drug response. To this end we employ either other related data sources from patients to guide predictor construction (weighted learning) or cell line drug response data (transfer learning) (2017 – 2023).
Eracosysmed EraCoSysMed (European Union) COLOSYS: A systems approach to preventing drug resistance in colon cancer. Constructing computational models based on experimetal data and knowledge to better understand resistance mechanisms in colon cancer and devise ways to circumvent these. (2016 - 2019)
KWF Logo Dutch Cancer Society (Jonkers, Altelaar, Wessels) Combatting therapy resistance by integrating genomic, transcriptomic and proteomic data from mouse models of invasive lobular breast carcinoma. Integrative modeling of proteomic, DNA and RNA profiles of mouse tumors to unravel resitance mechanisms to FGFR2 and mTOR inhibitors. (2017-2021)
CGCnl2 Netherlands Organization for Scientific Research (Cancer Genomics NL Consortium) In silico modelling of response and resistance mechanisms in cancer. (2013-2018)
KWF Logo Dutch Cancer Society and AACR (SU2C) Dream Team Award Tumour organoids: a new pre-clinical model for drug sensitivity analysis. Constructing computational models to predict combinatorial therapy response from molecular data and drug screens in organoids. (2014 – 2017)
ERC logo ERC COMBATCANCER: Combination therapies for personalized cancer medicine Employing in vitro, in vivo models and computational approaches to develop combination therapies to overcome resistance in lung, colorectal, breast an melanoma. (2013 – 2019)
 NWO logo Netherlands Organization for Scientific Research (NWO) Gravitation award: In silico modelling of response and resistance mechanisms in cancer Construcnting in silico models of signaling pathways in breast and colorectal cancer from data derived from organoid cultures. (2013 – 2018)
SU2C SU2C/AACR Prospective Use of DNA-Guided Personalized Cancer Treatment (CPCT). Constructing computational models to predict therapy response in neo-adjuvant patients from capture sequencing data. (2013–2016)