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Discovering the causes of cancer and the means of prevention
 

Harnessing Rare Somatic Variants for Tumor Classification - Shen

Biostatistics Branch Seminar Series

December 3, 2019 | 10:30 AM – 11:30 AM

NCI Shady Grove Seminar Room 5E032/034 Rockville

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Speaker

Ronglai Shen, Ph.D.
Associate Member, Department of Epidemiology and Biostatistics, 
Memorial Sloan-Kettering Cancer Center

Abstract

In recent years there has been extensive investigation of the mutational landscape of the cancer genome from large-scale sequencing studies. However, the bulk of the attention has focused on major cancer genes, and especially the hotspot mutations in these genes at which mutations occur frequently. However, the vast majority of somatic mutations occur at “rare” genetic loci. Of the 1,788,153 distinct mutations that were observed in the 10,295 tumors in the Cancer Genome Atlas (TCGA) study over 92% were singletons, i.e. mutations observed in only one tumor. Moreover, when new tumors are sequenced, on average over 60% of mutations observed are mutations that were not observed in TCGA. To date investigators have mostly ignored this “hidden iceberg” of potential information. Our study is motivated by the believe that at least a portion of these rare mutations contain important information that could be harnessed for clinical purposes. We draw upon statistical methodology that has been developed in other fields of study, notably in species estimation in ecology, and word frequency estimation in computational linguistics to extract information about these “rare” mutations from existing databases with a view to classify the site of origin of a tumor,  an important clinical challenge for cancers where the primary site is unknown or when the cancer is detected in a cell-free DNA sequencing based screening test.

**The mission of the Biostatistics Branch (BB) is to be an outstanding biostatistics unit that can contribute to the understanding of cancer etiology and to improve public health by the development and application of quantitative methods.  The BB Investigators develop statistical methods and data resources to strengthen observational studies, intervention trials, and laboratory investigations of cancer.**