ICBP Centers for Cancer Systems Biology

The Broad Institute/Dana-Farber Cancer Institute
Principal Investigator: Todd Golub, M.D.
Website: http://www.broadinstitute.org/science/programs/cancer/icbp/broad-institute-icbp

The Broad Center for Cancer Systems Biology has the overall goal of developing computational models that predict genome-wide essentiality based on the molecular characteristics of the tumor. A critical goal in cancer research is to accurately predict essential genes/proteins across a diversity of tumor subtypes. Such a capability would allow for i) the elucidation of the molecular targets against which therapeutics should be developed, and ii) the identification of specific patient populations likely to respond to such targeted interventions. Accomplishing this goal will require developing a deep understanding of the cellular circuitry of tumor cells — the details of which are increasingly recognized to depend on the genetic subtype of the tumor.

A number of technology developments over the past several years are now making it possible to seriously take on this goal. For example, the ability to perform genome-wide RNAi screens now makes it possible to systematically perturb the cancer genome, thereby identifying those genes that are essential in a given experimental system. In addition, it is becoming increasingly possible to perform extensive molecular characterization of tumor samples and model systems — including the patterns of gene copy number, gene expression, somatic mutation, tyrosine phosphorylation, etc., which might serve as predictive features of essentiality.

Major obstacles currently prevent rapid progress toward the goal of predicting essentiality: First, there is a lack of data of sufficient scope (i.e., genome-wide RNAi screening data and extensive molecular characterization) and scale (i.e., data spanning large numbers of cell lines that adequately represent the true genetic diversity of cancer). Second, there is a lack of a computational framework proven to be successful for the integrative challenge of generating predictive models of cellular phenotypes based on molecular features of the cell.

Our Center aims to overcome these obstacles, with the ultimate goal of developing predictive models that accurately identify the “Achilles’ heels” of tumors of different genotypes. We are focusing on lung cancer and melanoma because i) there is great unmet medical need, ii) there exist appropriate experimental systems, iii) there is deep expertise of Broad investigators to these diseases, and iv) the focus on two cancer types will allow us to determine to what extent the predictive “rules” that govern the behavior of one tumor type are applicable to the other.

Columbia University

Principal Investigator:  Andrea Califano, Ph.D.

Website: http://magnet.c2b2.columbia.edu/
               http://hiccc.columbia.edu/?page=education/cbtp

Georgetown University

Principal Investigator:  Robert Clarke, Ph.D.

Website: http://lombardi.georgetown.edu/breastcancer/ccsb

Massachusetts Institute of Technology (MIT)
Principal Investigator: Doug Lauffenburger, Ph.D.
Website: http://web.mit.edu/icbp

The research in the MIT Integrative Cancer Biology Program is organized into three projects, each involving scientists working at different levels of analysis - cancer biology, cell biology and computational modeling. The members of each project interact closely to integrate these different approaches to understand the underlying molecular and cellular processes that govern each process and to develop testable models to drive future understanding, analysis, and intervention into those processes and their malfunctions in cancer.

The focus of the MIT Integrative Cancer Biology Program is to advance the state of the art in cancer biology beyond the analysis of data to the construction of quantitative, predictive and testable, computational and mathematical models based on that data.

The MIT ICBP center complements those of other efforts within the ICBP program and the larger NCI through its focus on three thrust areas that cover three key processes involved in cancer progression (i) mitogenic signaling networks, (ii) DNA repair, and (iii) migration signaling networks. The MIT ICBP center also has a strong emphasis on producing the next generation of young investigators trained in interdisciplinary investigations of cancer.

Memorial Sloan-Kettering Cancer Center

Principal Investigator:  Chris Sander, Ph.D.

Website: http://www.mskcc.org/mskcc/html/11655.cfm

Methodist Hospital Research Institute

Principal Investigator:  Steve Wong, Ph.D.

Website: http://www.methodisthealth.com/tmhri.cfm?id=39163

Oregon Health & Science University
Principal Investigator: Joe W. Gray, Ph.D.
Website:

The goal of the Integrative Cancer Biology Program (ICBP) Award entitled "Systems based predictions of responses to cancer therapy" is to develop experimental and computational strategies to predict individual responses to therapies targeted along the Raf-MEK-ERK signaling pathway. The overall thrust of this program is the integration of experimental and computational approaches towards the understanding of cancer biology using both 2D and 3D cell culture models. While experimental data are supported by cell-based assays and expression data, in silico models leverage deterministic and probabilistic techniques to support the integrative cancer biology program.

Sage Bionetworks

Principal Investigator:  Stephen Friend, Ph.D.

Website: http://www.sagebase.org/research/index.php

St. Elizabeth’s Medical Center/Tufts University

Principal Investigator:  Lynn Hltaky, Ph.D.

Website: http://www.cancer-systems-biology.org

Stanford University School of Medicine
Principal Investigator: Sylvia Plevritis, Ph.D.
Website: http://ccsb.stanford.edu

The Stanford Center for Systems Biology of Cancer (CCSB) aims to discover molecular mechanisms underlying cancer progression by studying cancer as a complex biological system that is driven, in part, by impaired differentiation. Increasing evidence indicates that many cancers, like normal tissue, are composed of a hierarchy of cells at different stages of differentiation, and that the disease is maintained by a self-renewing subpopulation. Our overarching goal is to provide a better understanding of the self-renewing properties of cancer that will enable us to identify molecular therapeutic targets and strategies to eradicate this disease, or to maintain it in a nonlethal state. Our biological projects are integrated with novel computational techniques, designed to dissect processes and causal factors underlying impaired differentiation as a driver of cancer progression in several hematologic malignancies. In order to identify mechanistic underpinnings of cancer progression, a network-based and multiscale viewpoint is mandatory. Increasingly, diseases such as cancer are recognized as resulting from disruption in the coordinated performance of a complex biological system. This systems biology viewpoint necessitates the incorporation of high throughput, high dimensional data, and development of computational methods specifically geared to its analysis. We aim to develop three essential and interlocking requirements for a comprehensive systems analysis of cancer. First, powerful methods are required to infer molecular regulatory networks that drive phenotypic processes such as differentiation. Second, computational approaches are needed that can identify and isolate underlying patterns of progression in cancer, which can then be related to underlying regulatory networks.

Third, executable models are desirable so that it is possible to pose hypothetical “what if” questions to predict how, for example, a targeted intervention might affect the subsequent course of disease. These computational approaches are applied to the study of differentiation in AML, Follicular Lymphoma and T-ALL. Our integrative approach will enable us to ascertain differences between these hematologic malignancies, and commonalities, which may generalize to other cancers.

University of Texas Health Science Center, San Antonio
Principal Investigator: Tim H-M Huang, Ph.D.
Website:  http://www.uthscsa.edu

The San Antonio-IU ICBP site is focused on understanding the epigenomic changes associated with hormone resistant cancers and the development of therapies for tumor resistance, recurrence and metastasis. We hypothesis that DNA methylation signatures can be used for predictive prognostic testing in order to better tailor patient treatments.

Our center assembles an integrated team of scientists to uncover how the epigenome interacts with the genome in the genesis and the progression of human cancers, at both the global level and the single gene level, to provide opportunities for personalized medicine in cancer prevention and recurrence. The Center uses cutting-edge next generation sequencing technologies coupled with novel bioinformatics and computationalapproaches to unravel the role of epigenetic alternations in human cancers and their microenvironment in the dysregulated cellular and molecular functionsobserved in this disease. The team systematically collects information on the cancer epigenome and provides informatics tools to the ICBP and research community using visualization of data through web portals to facility viewing and data mining.  

Vanderbilt University Medical Center
Principal Investigator: Vito Quaranta, M.D.
Website: http://vicbc.vanderbilt.edu/ccsb

Cancer is a multi-factorial disease too complex for intuitive understanding. Its outcome is the result of a complex interplay between conflicting factors, which are specific to different cancers, and different patients. Thus, cancer is particularly suited for integrative approaches, such as mathematical modeling. The Vanderbilt Integrative Cancer Biology Center (VICBC) fuses several disciplines (BioMathematics, Cancer Biology, Training, Bioengineering and Cancer Imaging) in the quest to develop mathematical models of Cancer Invasion that should enable a rational approach for accurate diagnostic staging and therapeutic targeting of cancer. Our focus is on the parameterization of the Mathematical Models of Cancer (i.e., hybrid discrete continuous) at the cellular, multi-cellular and organ biological scales. This approach fits well with that of other ICBP Centers that focus on molecular and sub-cellular scales.