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Cancer Genomics

Research Summary

Todd Golub's research is focused on the use of genomic approaches to study cancer biology and cancer medicine.

Our research is based on the premise that extraordinary insights into the molecular basis of cancer can be obtained by taking global views of the genomes of patient-derived tumor samples and model systems. We are committed to taking global views of cancer genomes that are not constrained by prior assumptions about the nature of cancer pathogenesis, and we are striving to use genomic information to understand divergent responses to treatment.

Genomic Characterization of Cancer
While much is known about the pathogenesis of cancer, much remains a mystery. Our approach has been to take unbiased views of cancer, striving to develop classification systems based on underlying molecular features. In the late 1990’s we discovered that it was feasible to classify human cancers (acute leukemias, in particular) solely on their patterns of gene expression. That work was extended to molecular classification of lymphomas, brain tumors (in particular, the discovery of a subset of childhood medulloblastomas that are driven by the sonic hedgehog pathway), and other solid tumors. In addition, in collaboration with Tyler Jacks (HHMI, Massachusetts Institute of Technology) and Robert Horvitz (HHMI, MIT), we showed that small, non-coding RNAs (miRNAs) could also classify human cancers, and suggested that dysregulation of miRNAs may also contribute to pathogenesis. Most recently, the laboratory has focused on massively parallel genome sequencing methods to systematically characterize the somatic genomes of human cancers. For example, our group described the genomic landscape of multiple myeloma, and developed analytical methods that remain in active use throughout the cancer research community.

Discovering Cancer Vulnerabilities
In addition to describing the molecular drivers of cancer, our laboratory is attempting to use the tools of genomics to discover the vulnerabilities of cancer that may identify new therapeutic strategies. For example, we demonstrated the feasibility of using RNA interference methods to discover disease genes (e.g. the RPS14 gene in myelodysplastic syndrome) and to pinpoint an erythroid differentiation defect as a predictor of response to the drug thalidomide. Similarly, we have shown that the anti-apoptotic protein MCL1 represents a vulnerability in certain tumors defined by a particular expression pattern of anti-apoptotic factors. Work with collaborators is now under way to systematically identify all of the vulnerabilities in common cancers, thereby creating a Cancer Dependency Map.

Genomic Approaches to Therapeutic Discovery
More recently, we have extended our search from genetic vulnerabilities to pharmacologic vulnerabilities that could serve as starting point for new therapeutic discovery. We have developed two powerful approaches. The first, the Connectivity Map, involves the systematic measurement of gene expression signatures following perturbation of cells with small-molecules and genetic perturbations. By creating signatures of disease states, drug treatment, and genetic perturbation, it should become possible to establish connections between, for example, a disease state and a drug. The Connectivity Map has now grown from a pilot of around 500 signatures to well over 2 million signatures, and was the inspiration for a new NIH program known as LINCS, that now serves as a resource for the entire research community.

Our lab has also developed new methods for the rapid testing of drug sensitivity in cancer. By inserting molecular “barcodes” into cancer cell lines, the lines can be pooled, and then treated with drugs or “tool compounds.” Barcodes that drop out following treatment indicate cell lines that were particularly sensitive to treatment. Using this approach, which we termed PRISM, researchers can now rapidly screen for compounds that kill subsets of cancer cells, and then develop predictors of drug sensitivity based on the molecular features of the tumor (e.g. pattern of mutation or gene expression).

Last, our laboratory is studying mechanisms by which tumors develop drug resistance. In particuilar, we are studying the role of the tumor microenvironment in mediating drug resistance. For example, in melanoma, we showed that tumor-associated fibroblasts can secrete hepatocyte growth factor (HGF), which is the ligand for the MET receptor, whose activation can lead to resistance to RAF inhibitors in patients with BRAF-mutant melanoma. In more recent work, we have shown that the microbiome can also contribute to drug resistance. Future work will focus on how to use this understanding of drug resistance to develop rational drug combinations that are effective in mitigating the emergence of resistance.

This research is also supported by grants from the National Institutes of Health.

As of May 3, 2016

Scientist Profile

Investigator
Dana-Farber Cancer Institute
Cancer Biology, Genetics