Abstract 1224: Deep targeted tumor sequencing of colorectal cancer cases to study associations of molecular subtypes with clinical, genetic, and lifestyle risk factors
Bibliographic record
Abstract
Abstract Colorectal cancer (CRC), a common malignancy, is a biologically heterogeneous disease. Next-generation sequencing (NGS) has enabled CRC characterization by identifying somatically mutated genes which now allow us to better define colorectal tumor subtypes (e.g. by mutated pathways). However, the relationship of such CRC subtypes to patient survival and genetic and lifestyle risk factors has not been comprehensively studied. To identify somatic mutations in CRC cases, we designed a targeted AmpliSeq panel of CRC related genes and genomic regions informed by whole exome sequencing data from ~1,200 CRC cases. The sequencing was conducted on Illumina HiSeq 2500 with a mean coverage of 740x and 240x for DNA extracted from FFPE tumor tissues and matched normal samples, respectively. Strelka, MuTect, VarDict, and Varscan2 were used to identify somatic single nucleotide variants and indels. Sanger sequencing was performed to validate a subset of variants. To date, we have sequenced ~2,400 CRC tumors and matched control tissues from four studies participating in the Colon Cancer Family Registry (CCFR) and Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). In most tumors, we identified non-silent mutations in genes belonging to the WNT (77%), p53 (44%), IGF2/PI3K (22%), RTK-RAS (47%), and TGF-beta (26%) signaling pathways. Among the 15% of tumors that could be classified as hypermutated, based on the number of mutations, 39% exhibited non-silent mutations in MLH1, MLH3, MSH2, MSH6, and PMS2 and 41% exhibited non-silent mutations in POLE and POLD1. In a subset of studies with available survival data, we used Cox regression to assess the association of hypermutation status and the presence of non-silencing mutations in key signaling pathways with overall (OS) and disease-specific (DSS) survival. OS and DSS were significantly more favorable in cases with hypermutated vs. non-hypermutated CRC (HR=0.77, 95% CI: 0.60-0.98, p=0.04 and HR=0.35, 95% CI: 0.22-0.57, p=2x10-5, respectively); these associations were most pronounced for POLE/POLD1 mutated hypermutated CRC (HR=0.69, 95% CI: 0.46-1.02, p=0.06, HR=0.21, 95% CI: 0.08-0.56, p=2x10-3, respectively). There was no significant association of mutations in WNT, p53, IGF2/PI3K, RTK-RAS, or TGF-beta pathways with survival (p>0.05). The comprehensive molecular characterization of this large panel of CRC cases will support further studies of molecular subtypes of CRC with clinical, lifestyle, and environmental factors. A better understanding of molecular mechanisms of CRC will be valuable in developing strategies for prevention, diagnosis, and treatment of this life-threatening disease. Citation Format: Syed Zaidi, Amanda Phipps, Tabitha Harrison, Catherine Grasso, Robert Steinfelder, Quang Trinh, Charles Connolly, Barbara Banbury, Adilya Rafikova, Philipp Hofer, Stefanie Brezina, Marios Giannakis, Xinmeng Jasmine Mu, Michael Quist, Charles Fuchs, Levi Garraway, Li Hsu, Lincoln Stein, Andrea Gsur, Shuji Ogino, Steven Gallinger, Polly Newcomb, Peter Campbell, Wei Sun, Thomas Hudson, Ulrike Peters. Deep targeted tumor sequencing of colorectal cancer cases to study associations of molecular subtypes with clinical, genetic, and lifestyle risk factors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1224.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".