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Abstract B113: Prevalence of germline BRCA and mismatch repair (MMR) gene mutations in pancreatic cancer

2015· article· en· W761243188 on OpenAlexaffabout
Iris Selander, Robert C. Grant, Ashton A. Connor, Shamini Selvarajah, Ayelet Borgida, Laurent Briollais, Jordan Lerner‐Ellis, Spring Holter, Steven Gallinger

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of TorontoLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsMSH6MSH2Germline mutationPancreatic cancerMLH1MedicineCancerCHEK2OncologyGermlineMUTYHInternal medicineGeneticsMutationBiologyDNA mismatch repairGeneColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Background: Germline mutations of BRCA1, BRCA2, MLH1, MSH2, and MSH6 are known to increase risk of pancreatic cancer. However, the prevalence of pathogenic germline mutations of these genes among pancreatic cancer patients is unknown. Accurately characterizing the frequencies of mutations of these genes will inform patient selection for future gene discovery studies and, clinically, foster screening strategies and tailored treatments. Objective: To determine the prevalence of pathogenic germline mutations of BRCA1, BRCA2, MLH1, MSH2, and MSH6 in a cohort of pancreatic cancer patients, and to determine the association between mutation carrier status and personal and family history of cancer. Methods: The Ontario Pancreas Cancer Study (OPCS) enrolls all consenting participants with a diagnosis of pancreatic cancer into a province-wide electronic pathology database. A cross-sectional sample of 300 patients from 715 OPCS probands enrolled between April 2003 and August 2012 were randomly selected from three strata based on a family history of pancreatic, breast, and ovarian cancer. Targeted next-generation sequencing was successfully performed on germline DNA from 291 of the 300 selected probands. Mutations were classified as benign, of unknown significance, or pathogenic by literature and database review. Pathogenic mutations were confirmed with Sanger sequencing. Prevalence estimates representing the whole OPCS were estimated using the Horvitz-Thompson estimator. Univariate analysis determined association between carrier status and clinical covariates, and regression analysis for overall survival. Results: A total of 7 pathogenic germline mutations were identified: 1 BRCA1, 2 BRCA2, 1 MLH1, 2 MSH2, and 1 MSH6. The estimated prevalence of pathogenic mutations in BRCA1 and BRCA2 among probands in this OPCS series was 1.1% (95% confidence interval: 0.1-2.0%); in the mismatch repair genes MLH1, MSH2, and MSH6 it was 1.5% (0.3-2.6%). Both a personal history of colorectal cancer and a first-degree relative with colorectal cancer, breast cancer, or melanoma were significantly associated (p<0.001 and p<0.01, respectively) with MMR mutations. There were no significant differences in tumor location within the pancreas (head/uncinate versus body/tail), clinical nodal status, rates of resection, age at diagnosis, BMI, history of smoking, history of pancreatitis, family history of pancreatic cancer, family history of colorectal cancer, or differences in survival between the MMR or sporadic cohorts. Conclusion: This study is the first to quantify population-based prevalences of germline mutations of BRCA1, BRCA2, MLH1, MSH2, and MSH6 in pancreatic cancer. Surprisingly, the prevalence of pathogenic germline mutations of the MMR genes in pancreatic cancer is higher than expected, and is comparable to that of BRCA1 and BRCA2. The prevalence of the MMR germline mutations is also comparable to the prevalence of MMR germline mutations in colorectal cancer cohorts. Relatives who carry MMR gene mutations can benefit from tailored cancer prevention strategies; thus mutational analysis of MLH1, MSH2, and MSH6 should be included in molecular genetic testing and counseling strategies for pancreatic cancer patients, especially those with a family history of malignancy. Translational studies will follow to determine if stratifying pancreatic cancer risk by familial susceptibility genes, as in colorectal cancer, fosters tailored and cost-effective primary and secondary prevention strategies for carriers. Moreover, unique chemotherapy sensitivity and resistance patterns would be expected for MMR-associated pancreatic cancer, as is emerging for BRCA-associated pancreatic cancer. Citation Format: Iris Selander, Robert Grant, Ashton Connor, Shamini Selvarajah, Ayelet Borgida, Laurent Briollais, Jordan Lerner-Ellis, Spring Holter, Steven Gallinger. Prevalence of germline BRCA and mismatch repair (MMR) gene mutations in pancreatic cancer. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Innovations in Research and Treatment; May 18-21, 2014; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2015;75(13 Suppl):Abstract nr B113.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.059
GPT teacher head0.386
Teacher spread0.327 · 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
Published2015
Admission routes2
Has abstractyes

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