Abstract 5221: Linking the molecular profile of colorectal tumors to germline genetic and environmental risk factors
Bibliographic record
Abstract
Abstract The completion of The Cancer Genome Atlas (TCGA) project for colorectal cancer (CRC) has enabled a new, focused phase of sequencing tumor samples for which genome-wide genetic, epidemiological, clinical and lifestyle data have been collected. By combining somatic mutational profiles with these aforementioned data, we may identify and report effective prevention and treatment approaches for a broader population of individuals. The advent of targeted deep sequencing approaches using DNA obtained from formalin fixed paraffin embedded tissues has made possible the genetic characterization of the large numbers of patients needed to make such an effort relevant. As a first step, we describe a custom gene panel designed from large-scale studies for targeted deep sequencing, and its application to over 4,200 CR tumors, collected by the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). This study is designed to identify recurrent somatic mutations and copy number alterations (CNAs) for association testing with germline genetic and lifestyle and environmental risk factors for CRC, and thereby identify relevant approaches to impact cancer prevention. The targeted CRC panel includes 205 genes. Genes were primarily selected as significantly mutated genes identified from the Nurses’ Health Study and Health Professional's Follow-up Study (n = 700), and TCGA (n = 525). We also included 15 genes associated with high penetrance germline mutations and augmented the list to include genes in pathways of somatically altered genes, identified by literature review, from public databases and known to be associated with loss of heterozygosity. For these 205 genes, amplification primers were designed to include all coding regions of transcripts that are listed in the UCSC Genome Browser. For regions with CNAs, the TCGA dataset was analyzed to include regions with greater than or equal to 2 copy focal gains or losses that were found in more than 4 or 3 tumors, respectively. Candidate genes in regions with significant CNAs from the TCGA CRC publication (Nature 2012) were also included. For CNAs, 6 to 12 amplicons were designed for each of the 56 selected regions (32 gains and 24 losses). Additional target regions include: 1) 25 microsatellite loci previously used to identify defective DNA mismatch repair and 212 homoploymer repeats. These include microsatellite loci recommended by the NCI Consensus Panel for identifying MSI; 2) amelogenin (for gender); and 3) Fusobacterium to detect a putative CRC-associated pathogen in tumor biopsies. At the AACR annual meeting, we expect to present results for deep sequencing (∼1,000X) of the first 1,000 CR tumors, including any preliminarily identified pathways and subtypes that may provide the basis for association testing with germline genetic and lifestyle and environmental risk factors needed for inferring better approaches to prevention and treatment of CRC. Citation Format: Syed H. Zaidi, Catie Grasso, Jasmine Mu, Eve Shinbrot, Marios Giannakis, Charles Connolly, Ivan Borozan, Hermann Brenner, Peter Campbell, Andrew Chan, Jenny Chang-Claude, Mengmeng Du, Vincent Ferretti, Amy French, Charles Fuchs, Steven Gallinger, Levi Garraway, Andrea Gsur, Marc Gunter, Tabitha Harrison, Michael Hoffmeister, Li Hsu, Wen-Yi Huang, Jeroen Huyghe, Mathieu Lemire, Elaine Mardis, John McPherson, Polly Newcomb, Lincoln Stein, Wei Sun, Lee Timms, Quang Trinh, David Wheeler, Christina Yung, Niha Zubair, Shuji Ogino, Stephen Thibodeau, Ulrike Peters, Thomas Hudson. Linking the molecular profile of colorectal tumors to germline genetic and environmental risk factors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5221.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".