Despite tremendous advances in our understanding of cancer pathogenesis, the treatment of individual patients with either conventional chemotherapy or targeted agents remains highly empiric. Current efforts to predict drug efficacy are generally focused on genetic and transcriptional markers of pathway activation or drug binding, such as resistance mutations that sterically hinder small molecule binding or activate parallel or orthogonal signaling pathways. These markers exist in a very small fraction of all cancers, such that most patients are treated with little or no understanding of whether they will respond to an individual therapy. This results in many patients receiving ineffective and/or unnecessarily toxic therapies. There is a desperate need to change this paradigm. The ideal for characterizing therapeutic sensitivity would allow for: realtime decision making, identification of rare subpopulations with therapeutic resistance, analysis of very small samples (e.g. MRD), and maintains viability individual cells for downstream assays to characterize phenotypic, genotypic, transcriptional and other determinants of sensitivity. The overall goal of our project is to address this need using new strategies for predicting therapeutic response in which paired phenotypic and genomic properties are measured at the singlecell level. Phenotypic properties will include both physical parameters (e.g. mass, mass accumulation rate) and molecular markers (e.g. protein secretion, surface immunophenotype) that are rapidly affected by effective therapeutics and precede longer term phenotypes (e.g. loss of viability). Because these properties are measured for each single cell, clonal architectures based on therapeutic response will be established across each tumor sample by incorporating molecular and physical parameter data from large numbers of cells. In settings of deep treatment response, pretreatment and MRD samples will be compared to define the effects of therapy on clonal architecture. The cells that exhibit particular functional properties (e.g. phenotypic nonresponders) will be isolated and analyzed for genomic determinants of these properties. These data will then be incorporated into mathematical models to design and optimize therapeutic approaches that overcome the heterogeneity within individual tumors responsible for treatment failure. By pursuing this approach, our center will establish a framework that enables an iterative cycle between novel singlecell measurements from clinicallyrelevant specimens and computational approaches that result in testable predictions.
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Dr. Scott Manalis (Lead) has an undergraduate and doctorate degree in physics and applied physics, respectively, and his faculty appointment is in the departments of biological and mechanical engineering and an intramural member of MIT’s cancer center (Koch Institute for Integrative Cancer Research). He was the PI of MIT’s PSOC for Single Cell Dynamics in Cancer from 2012-2016 and a leader of a project and core within the center since it started in 2009.
Dr. Douglas Lauffenburger is a biological engineer, formally educated in chemical engineering but with research program focused on quantitative, multi-variate studies of cell biology since beginning his academic faculty career in 1979. He is an affiliate member of the Koch Institute for Integrative Cancer Research, and has served as PI of the NCI-funded MIT Integrative Cancer Biology Program for the period 2007-2014.
Dr. William Hahn is a medical oncologist and professor of medicine at Harvard Medical School who has extensive experience in the genomic characterization and functional genomic analysis of cancers in vitro and in vivo. He is currently the Chief of the Division of Molecular and Cellular Oncology and Chair of the Executive Committee for Research at DFCI. Dr. Hahn is also an Institute Member of the Cancer Program at the Broad Institute.
Dr. Alex K. Shalek received training in physics, mathematics and chemistry, and his faculty appointment is in the Institute for Medical Engineering and Science and the department of Chemistry. He is also an Associate Member of the Broad and Ragon Institutes, where he has additional labs and access to an array of cutting-edge equipment, platforms and approaches.
Dr. David Weinstock is an oncologist at DFCI, Associate Professor at Harvard Medical School and Associate Member of the Broad Institute.
Dr. Christopher Love has an undergraduate and doctoral degree in physical chemistry, and his faculty appointment is in the department of chemical engineering. He is an intramural member of the Koch Institute for Integrative Cancer Research and an associate member at both the Broad Institute and Ragon Institute.
Ms. Kristen Stevenson is a biostatistician and has worked for the past 10 years at DFCI in research of hematologic malignancies primarily including myelodysplastic syndrome, chronic lymphocytic leukemia, and acute lymphoblastic leukemia.
Project 1. Systematic discovery of cell-intrinsic mechanisms of cancer drug resistance
We aim to utilize high precision single-cell growth measurements together with single-cell RNA-Seq (scRNA-Seq) to profile the intrinsic factors that inform the responses of individual cancer cells to therapeutic interventions.
PIs: Manalis (lead) and Lauffenburger (co-lead), Shalek, Weinstock and Hahn
Project 2. Systematic discovery of cell-extrinsic mechanisms of cancer drug resistance
We aim to utilize high precision single-cell growth measurements and single-cell RNA-Seq (scRNA-Seq) together with nanowells to systematically examine how the extracellular factors present in leukemias and colon and pancreatic cancers influence drug responses.
PIs: Shalek (lead), Hahn and Love (co-leads), Weinstock, Lauffenburger and Manalis
Core 1: Biospecimens and Patient-derived xenografts
The overarching goal of Core 1 is to provide the infrastructure and professional expertise needed to bank primary leukemia, colon, and pancreatic cancer specimens, establish patient-derived xenograft (PDX), organoid, and cell models, and make these patient-derived resources available to all Program investigators, thereby facilitating the translational and laboratory-based research performed by CSBC investigators in Projects 1 and 2.
PIs: Hahn (lead) and Weinstock (co-lead)
Core 2: Computational Analysis Core
The Computational Analysis Core (Core 2) will support Projects 1 & 2 by bringing necessary bioinformatics and computational methods to bear on the questions addressed by each project.
PIs: Lauffenburger (lead), Shalek and Stevenson
Education and Outreach Core
The mentorship of current and future trainees who can tackle cancer-related problems with computational systems biology approaches is an integral part of fulfilling our commitment to catalyze and generate new bodies of knowledge and fields of cancer study.
The overall mission of the Administrative Unit is to provide oversight and coordination on administrative and fiscal aspects of the MIT/DFCI CSBC.
For more information visit MIT – CSBC online.