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Labs

BayesMendel

The BayesMendel working group is dedicated to the development of methodologies, models, and open source software for predicting who may carry a cancer susceptibility gene. We use statistical ideas that go back to Bayes and genetic models that go back to Mendel.

Scott Carter Lab

Dr. Carter works closely with Boston area physicians to design and execute studies of cancer initiation, drug resistance, and metastasis using genomics technology applied to cancer-tissue specimens collected at various stages of disease progression. Dr. Carter has developed several novel computational methods in order to analyze these datasets and make inferences about clonal evolution underlying cancer progression. He has also developed software tools that are significantly increasing the impact of his work by making those methods available to the broader research community. These tools include HAPSEG, ABSOLUTE, CapSeg, Allelic CapSeg, and Phylogic.

Genomics has revolutionized biology, but not in the ways that many of us initially envisioned. While the reference genome sequences and the catalogues of genes that genome projects have provided are useful starting points for understanding the basis for development and disease, the tools and technology spawned by the genome project have had a far greater impact. Our group focuses on the application of functional genomics techniques including microarrays, proteomics, metabolomics, and other high-throughput approaches and the development of computational approaches in support of these studies to develop a comprehensive view of human diseases including cancer. Our goal is to develop software, databases, and bioinformatics techniques that will allow the development of new diagnostics and a more complete understanding of the cellular networks that are mechanistically responsible for diseases. Our commitment is to make those tools widely and freely available to the research community to enable research beyond our own.

In our lab, we combine experiments and theory to understand the complex dynamics of cell state transitions in development and in disease. To do so, we expand and combine emerging experimental techniques. For example, we use time-lapse microscopy to track the lineage history of individual cells as they divide, followed by single-molecule imaging to readout the expression levels of multiple genes in the same cells. We also use synthetic biology to engineer novel genetic circuits that can retain the history of major transitions in each cell. One of our favorite model systems is mouse embryonic stem (ES) cells in culture, which can rehash slices of development on a dish, providing us with a rich playground.

At the same time, we also develop new theoretical frameworks to decipher the data generated in our experiments: for example, methods for inferring dynamics from single-cell lineage trees with endpoint gene expression measurements. Often inspired by concepts in statistical physics, we have used ideas from geometry, critical phenomena, and at times number theory. Ultimately, we seek to find general theoretical principles to understand the stochastic processes and collective dynamics that emerge in populations of proliferating cells.

The unprecedented advancements in digital technology during the second half of the 20th century has produced a measurement revolution that is transforming science. In biomedical research, the Genomics revolution is being driven by new technologies that permit us to observe molecular entities analogous to identifying microorganisms and other breakthroughs permitted by the invention of the microscope. Choice examples of these technologies are next generation sequencing (NGS) and microarrays.

Scientific fields that have traditionally relied upon simple data analysis techniques have been turned on their heads by these technologies. Interpreting information extracted from these massive and complex datasets requires sophisticated statistical methodology as one can easily be fooled by patterns arising by chance or systematic errors that are hard to detect.

Rafael Irizarry’s lab is interested in the development of statistical tools that help researchers better interpret their data. The lab disseminates these tools through open source that is available for free online. This software has tens of thousands of users and the scientific publications in which these methods are highly cited.

The Knowledge Systems Group is an applied genomics software and data sciences group, focused on enabling cancer genomics research and precision cancer medicine at Dana-Farber Cancer Institute.

We are currently focused on four areas of research and development: 1) we collaborate with MSKCC on the continued development of the cBioPortal for Cancer Genomics, an open source platform for mining cancer genomics data sets; 2) we are actively collaborating with translational researchers and clinicians to mine genomic data generated by Dana-Farber's enterprise sequencing project -- now one of the largest databases of genomic data linked to detailed clinical data; 3) we are building new genomic tools for clinicians, including a new clinical trial matching platforms for algorithmically matching genomic profiles to open clinical trials; 4) we are helping to build new cloud-based platforms for securely sharing genomic data across multiple cancer centers, while preserving patient confidentiality.

Our research focuses on algorithm development and integrative mining from high throughput data to understand gene regulation in cancer biology. We have developed a number of widely used algorithms for transcription factor motif finding, ChIP-chip / ChIP-seq / DNase-seq / CRISPR screen data analysis. Through integrating genome-wide transcription factor binding, chromatin dynamics, gene expression profiles, and chemical and functional screens, we try to model the specificity and function of transcription factors, chromatin regulators, RNA binding proteins, kinases, and lncRNAs in tumor development, progression, drug response and resistance.

The research of our lab focuses on the evolutionary dynamics of cancer. Cancer emerges due to an evolutionary process in somatic tissue. The fundamental laws of evolution can best be formulated as exact mathematical equations. Therefore, the process of cancer initiation and progression is amenable to mathematical investigation. Current areas of research include cancer stem cells, evolution of drug resistance, and the dynamics of metastasis formation.

Program for Innovations in Therapeutic and Biomarker Development

The mission of the Program for Innovations in Therapeutic and Biomarker Development (PITBD) is to generate novel approaches for the development of therapeutics and biomarkers through preclinical and clinical trial designs, pipeline modeling and analysis, and advancement in regulatory science and policy.

The development and translation of effective precision medicines requires generation of information regarding both efficacious therapeutics and putative biomarkers that predict that efficacy. The exponential increase in potential biomarker information that is available for any given patient or disease, including genomic biomarkers, has created challenges with respect to the best methods to generate meaningful information to support both the scientific development and regulatory decision-making that is required to bring new medicines to clinic. The PITBD consists of both an internal program that develops new approaches for information generation to support precision-medicine development as well as an external facing component that serves as a nidus to bring together expertise from multiple fields to support the development of precision  medicines and serve as an intellectual collaborator for the early concept development of clinical trials.

The overall goal of our work in the Sander Lab is to solve biological problems using quantitative methods from bioinformatics, statistical physics, data sciences, statistics, computer science, and mathematics. We apply these computational methods to build predictive network models of molecular and cell-cell interactions, to support cancer precision medicine, and to make discoveries in structural and evolutionary biology.

The main research interests of our lab are to develop systems biology approaches for characterizing cell states and understanding the regulatory circuitry. To this end, we take advantage of the existing large amount of omic-level datasets and develop computational methods to analyze and integrate various genomic data types, with the focus on chromatin dynamics and single-cell analysis. In collaboration with experimental biologists and clinical scientists, we have applied these computational approaches to study stem cell and cancer biology. We are excited to be involved in the International Human Cell Atlas Initiative.

Cheng-Zhong Zhang Lab

Cheng-Zhong Zhang Lab

Cheng-Zhong Zhang obtained a Ph.D. in Chemical Engineering with a minor in Physics from Caltech. He received postdoctoral training in single-molecule biophysics in Timothy Springer’s laboratory at Harvard Medical School and subsequently worked on single-cell genomics and cancer biology in Matthew Meyerson’s laboratory at the Broad Institute of Harvard and MIT and at Dana-Farber Cancer Institute. He combines single-cell genomic analysis and cell biology to study genome evolution and phenotypic variation. Currently he focuses on the mechanisms and the transcriptional consequences of chromosomal abnormalities, such as rearrangement and aneuploidy, and how such events may promote tumor development. 

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