Abstract A23: Rare variants in the FAT1 gene may predispose to familial colorectal cancer
Bibliographic record
Abstract
Abstract Introduction: Population- and family-based studies suggest germline predisposition in up to 35% of colorectal cancer (CRC) cases. Known genetic factors, including three highly penetrant Mendelian cancer syndromes and approximately 45 low penetrant alleles, account for less than 15% of all CRC and only a small proportion of early onset CRC. Identifying the genes responsible for the “missing” CRC predisposition will inform prevention and management. Methods: Our discovery cohort were selected to enrich for likely heritable susceptibility and consisted of 127 CRC cases from 86 pedigrees selected from Canadian and Australian CRC Family Registries (CCFRs) that met the Familial Colorectal Cancer Type X definition (satisfy Amsterdam I Criteria and lack other features of known cancer syndromes). Germline DNA from multiple CRC–affected individuals per pedigree underwent whole exome sequencing. The two replication cohorts were composed of pedigrees with multiple generations affected by CRC, one an Australian cohort of 166 families from which multiple affected and unaffected members were genotyped for rare (<0.01 MAF) germline likely deleterious variants in FAT1, the other a Polish cohort of 496 probands that underwent targeted next generation sequencing of FAT1. Alignment and variant calling followed the Genome Analysis Tool Kit “best practices”, and ANNOVAR was used for functional effect, allele frequency and intolerance score annotations. Variant filtering and statistical analysis were done in R (version 3.0). A hemizygous transgenic mouse model of our top genetic candidate was developed and exposed to intra-peritoneal azoxymethane (AOM) to induce colonic dysplasia. Necropsies were performed at 6 months of age, and the colons underwent gross and histologic inspection. This work was supported by grant UM1 CA167551 from the National Cancer Institute (NCI) to the Colon Cancer Family Registry. Outcomes: Using filter-based approaches in our discovery cohort, we identified rare, non-silent variants in FAT1 that co-segregated with CRC in Ontario (Canadian), Newfoundland (Canadian) and Australian pedigrees, including one stopgain variant that was found in all 4 affected of one Ontario pedigree and in 2 of 6 unaffected. Somatic loss of heterozygosity of FAT1 was seen in available FFPE tumors from 3 Ontario and 4 Australian pedigrees by either Sanger sequencing or microarray. Modified segregation analysis of genotyped multi-case families from the Australian CCFR demonstrated an overall increased risk for CRC in FAT1 carriers (HR 1.44, 95%CI 1.05-1.97), with greater hazard ratios (>2.5) for individual FAT1 variants identified in sufficient numbers of families. Deep sequencing in 496 Polish probands identified 4 additional rare truncating FAT1 variants. Using the ExAC database as controls, variant-level association analyses of the Polish cohort cases revealed two strongly predisposing FAT1 rare missense variants, namely c.T9440G (OR 7.53, 95%CI 4.1-12.8, Fisher's exact test, q < 0.05 by false discovery rates) and c.G3067A (OR 10.8, 95%CI 3.3-26.8, q < 0.05). Gene-level association testing was also significant for increased CRC risk when considering all rare, non-silent FAT1 variants (OR 2.2, 95%CI 1.8-2.6, q < 0.05). 30 FAT1 hemizygous and 28 wild type mice on a CD1 background, balanced for sex and weight, did not show differences in number or greatest size of aberrant crypts, adenoma or adenocarcinoma. Conclusions: We have shown that rare germline variants in FAT1 may predispose to CRC. Discovery of such causal genes and associated pathways will improve our understanding of early onset CRC for both familial and sporadic cases. Stratifying CRC risk by familial susceptibility genes should foster tailored, cost-effective primary and secondary prevention strategies. Citation Format: Ashton A. Connor, Jordan Lerner-Ellis, Mohammad R. Akbari, Cezary Cybulski, J Lubinski, Caroline Badouel, Helen McNeill, James G. Dowty, Mark Clendenning, Daniel D. Buchanan. Rare variants in the FAT1 gene may predispose to familial colorectal cancer. [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer: From Initiation to Outcomes; 2016 Sep 17-20; Tampa, FL. Philadelphia (PA): AACR; Cancer Res 2017;77(3 Suppl):Abstract nr A23.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".