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Sean Tavtigian

Associate Professor of Oncological Sciences

B.S. Pomona College

Ph.D. California Institute of Technology

Research

References

Sean Tavtigian's Email

Sean Tavtigian's PubMed Literature Search

Research

We are interested in genetic susceptibility to common cancers such as breast cancer, colon cancer, and prostate cancer. Within this general area, we are interested in questions such as: "Can we identify new high-risk or intermediate-risk susceptibility genes?", "Can we better characterize the mutation profile of known genes, especially the intermediate-risk susceptibility genes?", and "Can we become more effective at classifying the many 'variants of unknown significance' that are observed during the clinical testing of established high-risk cancer susceptibility genes?". We approach these questions with a combination of bioinformatics and genomics, and much of our work takes place in the context of collaborative international consortia.

Historically, most of the known high-risk cancer susceptibility genes (including, for example, APC, BRCA1, BRCA2, MLH1, and MSH2) were found either by linkage analysis/ positional cloning or by mutation screening of established high-risk susceptibility genes' biochemical pathway "nearest-neighbors". While the linkage analysis/ positional cloning approach is nearly obsolete, massively parallel sequencing enables a number of new strategies for gene identification. One of these is whole-exome mutation screening in pedigrees as a method to identify relatively high-risk susceptibility genes. Another is biochemical pathway-based mutation screening in a case-control format as a method to identify intermediate-risk susceptibility genes. We are pursuing breast cancer genetics projects in both of these areas and are likely to expand to prostate cancer or colon cancer projects in the near future. Independent to which specific cancer we work with, the massively parallel sequencing is going to identify enormous numbers of rare sequence variants. Most of these will be neutral with respect to disease risk, and it will take a concentrated bioinformatics effort to identify bona fide susceptibility genes, and the pathogenic sequence variants that they harbor.

Clinical mutation screening of high-risk cancer susceptibility genes such as BRCA1, BRCA2, MLH1, and MSH2 will often find clearly pathogenic mutations, providing very useful information for the clinical management of high-risk patients and their close relatives. However, about 10% of patients who undergo mutation screening are found to carry an unclassified sequence variant (UV). Observations of UVs are problematic for clinical mutation screening services, for clinical cancer genetics, and for the patients. We have developed a bioinformatics method, called the "integrated evaluation", for analysis and eventual classification of UVs. Currently, the method is applicable to UVs in the breast cancer susceptibility genes BRCA1 and BRCA2. We are working to improve the method, to extend it to other susceptibility genes, and to create databases that will disseminate classification results to clinical cancer geneticists throughout the world. A particularly satisfying aspect of this work is that it archetypically translational: reclassification of clinically observed sequence variants is immediately applicable to patient management.

References