Bonnie E Gould Rothberg
Yale University, USA
Title: Custom AmpliSeqâ„¢ targeted resequencing to identify novel clincopathologic correlates in early-stage lung adenocarcinoma: A yale lung cancer biorepository study
Biography
Biography: Bonnie E Gould Rothberg
Abstract
The 5-year survival for Stages I/II lung adenocarcinoma (L-ADC) (60,500 US cases annually) following curative-intent complete resection and, where indicated, adjuvant chemotherapy, ranges from 36% (Stage II) to only 80% (Stage IA). Although evaluation of EGFR and KRAS somatic mutations in early-stage L-ADCs is now common, clinicopathologic correlates and associations with prognosis are incompletely understood and the correlates of less common driver mutations and frequent passenger mutations (e.g., STK11, p53) are only emerging. To better characterize the clinicopathologic correlates of L-ADC somatic mutations, we have coupled an ongoing prospective cohort study of early-stage L-ADC patients treated with curative-intent surgery at Yale-New Haven Hospital with a robust, novel next-generation DNA sequencing and analysis pipeline. Eligible participants are enrolled into the Yale Lung Cancer Biorepository, a Biobank that couples biospecimen best practices for both fresh and formalin-fixed materials with comprehensive clinico-epidemiologic annotations for each participant both at intake and at regular follow-up. Genomic DNA from both tumor and germline is prepared using the Ambion RecoverAll DNA preparation kits and quantified using an RNAse P standard. Ten ng is subsequently used for each of 3 pools from a customly constructed Ion Torrent AmpliSeqTM panel that targets 93 genes where the published literature supports a significant role for somatic mutations in L-ADC biology. Following emulsion PCR and sequencing on the Ion Torrent PGMTM next-generation DNA sequencer, resulting BAM files are processed through a novel front-end bioinformatics pipeline that specifically adjudicates each sequencing run to identify and eliminate from subsequent analyses runs not meeting specific qualifying criteria. Next, variants are aligned from matched germline and tumor samples, and, among tumor-specific variants, the non-synonymous coding mutations are further pursued, being organized according to functional roles within genes and pathways with these summary data arrayed into a data matrix for subsequent statistical analysis. This talk will focus on our uniquely comprehensive method, while highlighting results from our preliminary analyses.