- OVERALL COMPONENT We will establish a Center for Cancer Systems Pharmacology (CSP Center) that constructs and applies network-level computational models to understand mechanisms of drug response, resistance and toxicity for targeted small molecule drugs and immune checkpoint inhibitors (ICIs). We hypothesize that improved understanding of fundamental cell signaling pathways and interactions between cancer and immune cells will result in greater efficacy while minimizing toxicity. Intrinsic and acquired drug resistance pose the primary challenges to broader application of all cancer therapies. By systematically dissecting how resistance to targeted therapies and ICIs arises, we aim to understand and overcome resistance mechanisms using new drugs or drug combinations, while simultaneously predicting and balancing potential toxicities. These goals will be accomplished by translating findings from the bedside to the bench and then back to the bedside focusing on melanoma, a type of cancer in which both ICIs and targeted drugs are effective, and triple negative breast cancer (TNBC) and brain cancers (GBM) for which ICIs are not approved but where sporadic responses have been observed. We will develop, validate and apply innovative pharmacological concepts and instantiate these in practical form using computational models. Such models will explicitly consider the impact of mutations, phenotypic variability, cell-to-cell interaction and the composition of the tumor microenvironment in mechanisms of action of sequential or simultaneous combinations of targeted drugs and ICIs. Hypothesis generation will focus on deep phenotyping of patient-derived specimens followed by hypothesis testing in pre- clinical settings using complementary multi-omic and computational methods. We will also create and distribute new measurement and software methods to promote systems pharmacology in other areas of cancer biology.
Aim 1 will establish an Administrative Core to oversee and coordinate all center activities.
Aim 2 will establish a Systems Pharmacology Core to coordinate experimental and computational resources for proteomic, transcriptomic, metabolomic and imaging assays across all three Projects.
Aim 3 will establish an Outreach core that promotes training via a website and seminars and ensures curation and distribution of Center data according to FAIR standards.
Aim 4 (Project 1) will develop multi-scale computational models of adaptive drug resistance in melanoma that capture and ultimately explain the wide diversity of changes in cell states associated with resistance to RAF/MEK inhibitors.
Aim 5 (Project 2) will measure and model the tumor microenvironment before and during treatment, and at the time of drug resistance using a range of innovative, highly-multiplexed assays for malignant and non-malignant cells.
Aim 6 (Project 3) will measure and model cell type-specific metabolic, signaling, and transcriptional mechanisms that contribute to the efficacy of ICI combinations, in order to develop improved therapeutic strategies for patients unresponsive to monotherapy.

Public Health Relevance

Targeted drugs and, more recently, therapeutic antibodies that inhibit immune checkpoint regulators to augment the body?s own defenses against cancer, are among the most promising approaches to treating cancer. However, the majority of cancer patients derive no long-term benefit from such drugs and even those who do may suffer from serious adverse effects. Systematic study of therapeutic and adverse drug responses using diverse experimental technologies and the latest data science and machine learning methods is essential for bringing about the next wave of precision cancer medicine.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Specialized Center--Cooperative Agreements (U54)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Salnikow, Konstantin
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Harvard Medical School
Schools of Medicine
United States
Zip Code