*** a.westerman@amsterdamumc.nl ***

Identifying the relation between therapy combination efficacy and tumor heterogeneity for brain cancer

The brain tumor glioblastoma is currently a non-curable disease. Because mono-therapies have a limited efficacy, combinations of therapies might be more effective. However, the selection of effective drug combinations is not a trivial task since hundred thousand combinations can be made with clinically approved drugs. By using different bioinformatics strategies, we have been able to predict and validate drug-target interactions (i.e. synergy). By using synergistic drug combinations and/or multi-target drugs, we have a powerful methodology to enable multi-drug therapy and this enables identification of biomarkers that predict synergy-sensitivity.

Ongoing projects

AI-IMPACT (Health Holland funded): Drug discovery of multi-target (polypharmacological) kinase inhibitors Kinase inhibitors as being used in the clinic commonly target multiple kinase proteins. We have shown that high efficacies of  kinase inhibitors in laboratory experiments can be explained by their ability to inhibit multiple target at once (also called poly-pharmacology). To interface multi-target drugs to tumor vulnerabilities, we have generated a target predictor for 100,000 kinase inhibitors. Based on the availability of 3000 drug-kinase structures present in the KLIFS database (www.klifs.vu-compmedchem.nl), we developed a convolutional neural network prediction model to predict the target fingerprint of these kinase inhibitors using 270,000 compound ligand measurements. This prediction model will enable us to match bioactivity (target) fingerprints to personalized (vulnerability) fingerprints and design optimal compounds that can reach the brain. 

THE TOXICITY ATLAS (Health Holland funded): Balancing therapy efficacy and adverse events of multitarget therapies Therapy combinations with desirable efficacies might not be easily translated for clinical use given the potential toxic effect of drug combinations. Using a bioinformatic approach, we provide a rationale for selecting therapy combinations aimed to provide an optimal balance between efficacy and side effects. This is expected to enable further implementation of personalized combination therapies in the clinic. Our approach, called the toxicology atlas, forms a global representation of different responses of the human body to FDA approved drugs. This representation will guide us to which vulnerabilities such as additive toxicity have to be avoided.

Attacking Glioblastoma Heterogeneity using Macrophage Metabolic Rewiring and Targeted Therapy (NWO funded Open Competition Domain Science – XL call, consortia). We propose to attack glioblastoma heterogeneity by rewiring the metabolism of macrophages in the tumor micro-environment as well as the tumor cells directly. The team will target metabolic vulnerabilities using novel nanoparticle tools to cross the blood-brain barrier. This Facilitated drug delivery is expected to render tumor cells more susceptible to specific combination therapies, tailored to each glioblastoma patient specifically. The teams will use complementary model systems (zebrafish, mouse, and human) and innovative chemistry to uncover precise mechanistic insights of glioblastoma malignancy with an outlook to create a translational path to patients.

GENE-ATLAS: Predicting tumor evolution Intratumoral heterogeneity plays a dominant role in tumor evolution and is considered the major cause therapy resistance. We performed a comprehensive analysis of 16 different tumor types of 10,000 patients of whole-exome sequencing and copy number variation (CNV), obtained from cBioPortal for Cancer Genomics. This showed that multiple tumor driving events in the same gene are commonly found in 5% of the tumors. We found that these patients have higher mutation rates on chromosome where the recurrent mutation is localized. We also found that recurrent mutations of oncogenic drivers is linked to more dependency on these genes and accompanied by commonly occuring co-mutations. Based on this information, we have developed a prediction model which can predict the likelyhood that a therapy resistance causing mutation is present in that tumor.

Team

• Laurine Wedekind (Lab/grant manager)
• Ammarina Beumer Chuwonpad (postdoctoral fellow)
• Olivier Bequignon (postdoctoral fellow)
• Jiahang Su (postdoctoral Fellow)
• George Kanev (postdoctoral fellow)
• Megan Houweling (PhD student)
• Asli Kucukosmanoglu (PhD student)
• Fleur Cornelissen (PhD student)
• Silvia Scoarta (PhD student)
• Yoran Broersma (PhD student)
• Xiangming Cai (PhD student)
• Anna Giczewska (PhD student)

Other Scientific roles

• Lecturer Bioinformatics at Amsterdam University College (VU/UvA)
• Ad hoc reviewer for scientific journals: Bioinformatics, BJC Pharmacology, British Journal of Cancer, Cancer Biology and Therapy, Cancer Cell, Cancer Discovery, Cancer Drug Resistance, Cancer Reports, Cell Reports, Cells, Clinical Cancer Research, CNS Oncology, iScience, Molecular biosystems, Nature communications, Neuro Oncology, Oncogenesis, Oncotarget, Oncotargets and therapy, Science Advances, Scientific reviews
• Ad hoc reviewer of grant proposals: EU-ERC, CRUK, ISF, UKRI
• AACR Member
• Dr. Bart Westerman reports a relationship with Health~Holland that includes: funding grants. Dr. Bart Westerman received a public-private partnership fund from Health Holland on a peer-reviewed projects where Medstone B.V., IOTA Pharmaceuticals Ltd and NTRC Therapeutics B.V. are the private parties and contributors to the project.
• Supervised over 100 student projects and theses

Alumni

• Ravi Narayan, PhD student, now Medical Advisor Europe at Genmab
• Cyrillo Brahm, PhD student, now MD in residence
• Rogier Dik, Research Staff Scientist, now Medical Science Liaison at Abbvie