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Record W2594298541 · doi:10.1158/1538-7445.crc16-ia27

Abstract IA27: Understanding more about risk and prognostic factors: Lessons from the Colon Cancer Family Registry

2017· article· en· W2594298541 on OpenAlexaboutno aff
Polly A. Newcomb

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsnot available
Fundersnot available
KeywordsColorectal cancerMedicinePenetranceCancerLynch syndromeFamily historyOncologyFamilial adenomatous polyposisCancer registryDiseaseEpidemiologyInternal medicineGeneticsBiologyGeneDNA mismatch repair

Abstract

fetched live from OpenAlex

Abstract Background: Colorectal cancer (CRC) is a heterogeneous disease with etiology associated with genetic and epidemiologic factors. Family studies have served as the cornerstone of genetic research on CRC. Studies of multiple-case families led to the discovery of the best characterized CRC susceptibility syndromes: hereditary nonpolyposis colorectal cancer (HNPCC; hereafter called Lynch syndrome) and familial adenomatous polyposis (FAP). The primary genetic bases of these dominant, relatively high-penetrance syndromes were identified. However, somatic and epigenetic alterations in these same genes are involved in sporadic CRC as well. Although these changes are established causes of CRC, many important questions remain, specifically the relation of these genetic and also environmental risk factors in relation to CRC survival. The Colon Cancer Family Registry (CCFR) is an NCI-supported international resource established in 1997 to support interdisciplinary studies on the genetic, somatic, environmental, behavioral, and lifestyle risk factors for CRC. Case subjects with incident colon or rectal cancer were identified via regional, state, and provincial cancer registries in the U.S., Canada and Australasia. Now in its 18th year, the CCFR is the largest registry of colorectal cancers world-wide. In this study, we have now leveraged this cohort to examine the epidemiology, genetics, and molecular characteristics of outcomes after a diagnosis of colorectal cancer. Methods: Over 10,000 participants with incident CRC were recruited into the CCFR between 1998 and 2007 across six international sites; nearly 3,500 deaths have occurred. Each center had slight differences in design and sampling schemes, which ensured a resource that covers the continuum of disease risk for CRC. Risk-factor data, blood and tumor samples, and medical records were collected from participants using standardized protocols. Participants continue to be followed every 5 years for changes in common risk factors, surveillance, and new cancers. Vital status, including cause of death, is recorded. Tumors have been characterized for microsatellite instability (MSI) status, protein expression for mismatch repair mutations (MLH1, MSH2, MSH6 and PMS2), and for point mutations in BRAF V600E and KRAS codons 12 and 13. CpG Island Methylator Phenotype (CIMP) was measured in tumors demonstrating microsatellite instability and the somatic silencing of MLH1 due to DNA methylation was tested in tumors showing loss of MLH1 protein expression. Germline testing was conducted for MutYH mutations in all cases and for MMR gene mutations for all cases showing a loss of gene expression in the respective protein. Genome-wide association studies (GWAS) will soon be completed on all cases. Analyses have been conducted using the CCFR case population as a whole, or using site subsets based on appropriate design and inclusion criteria. Results: Several factors known to be associated with a reduced risk of colorectal cancer, such as physical activity and the use of non-steroidal anti-inflammatory drugs (NSAIDs), were also associated with improved survival, while other such factors, including hormone replacement therapy, were not. Elevated pre-diagnostic BMI was associated with increased risk but modestly elevated post-diagnostic BMI was associated with improved survival. A history of smoking was associated with both increased risk and worse survival. Family history of colorectal cancer, which has consistently been associated with increased risk of disease, was not found to be associated with survival. Similarly, we found in a screen of 18 genetic susceptibility loci, one identified locus in SMAD7 was also associated with survival, but others were not. Tumor microsatellite instability was strongly associated with improved survival. Cases with CIMP positive tumors and/or mutations in BRAF, and/or KRAS were each associated with higher mortality, particularly when those characteristics were combined, where a two-fold increase was observed in cases MSS, CIMP+, and positive for BRAF when compared to cases with MSS tumors and negative for all other markers; a 70% lower mortality was observed in cases with tumors characterized as MSI-high, non-CIMP, and negative for mutations in BRAF and KRAS. Conclusions: The CCFR has shed light on many environmental factors, genetics and tumor characteristics that are related to both incidence and survival. Information gained from studies using this resource provides insight into the biology of this common cancer and importantly may help target messaging on prevention, inform the development of interventions, or tailor recommendations for CRC survivorship care. The greater scientific community has access to this rich resource. Citation Format: Polly A. Newcomb. Understanding more about risk and prognostic factors: Lessons from the Colon Cancer Family Registry. [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 IA27.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.093
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.093
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0010.002
Scholarly communication0.0040.011
Open science0.0030.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.300
GPT teacher head0.476
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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