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Record W7161756731 · doi:10.82308/39156

Contribution of known and novel DNA repair genes to pancreatic cancer susceptibility

2018· dissertation· en· W7161756731 on OpenAlexaboutno aff
Alyssa Smith

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsPALB2Pancreatic cancerGeneGermline mutationDNA repairPopulationGenetic testingCancerGermline

Abstract

fetched live from OpenAlex

Pancreatic adenocarcinoma (PAC) is a deadly malignancy that most commonly presents at a late stage and is ultimately refractory to systemic therapies. Even more devastating is the familial clustering of PAC (and other cancers) that is observed in 10-15% of cases. These kindreds, however, represent an opportune subset of patients for studies aimed at early detection and precision oncology strategies. While a fraction of the observed familial clustering of PAC is attributable to inherited (i.e., germline) genetic mutations in known PAC susceptibility genes (e.g., BRCA1 and BRCA2), the genetic causes for the overwhelming majority of familial PAC (~85%) remain undefined. The rapid lethality of PAC has hindered the collection of DNA, tumour specimens, and high-quality clinical and epidemiologic data that are critical for genetic studies of PAC. Herein, we demonstrate that a rapid ascertainment methodology, as used by the Quebec Pancreas Cancer Study (QPCS), a prospective clinic-based PAC research registry that was established in our lab in 2012, leads to high participation rates and allows for the collection of rare "high-risk" kindreds for studies of PAC heredity. Motivated by recent studies by our group and others suggesting that PAC associated with germline mutations in homology-directed DNA repair (HDR) genes have increased sensitivities to DNA damaging agents (e.g., platinums) and poly(ADP-ribose) polymerase inhibitors, we combined the resources of the QPCS and the Ontario Pancreas Cancer Study to define the prevalence of germline mutations in 4 known HDR-implicated PAC susceptibility genes (BRCA1, BRCA2, PALB2 and ATM) among 150 consecutive PAC cases with French-Canadian ancestry – a population known to harbour founder (i.e., recurrent) mutations in these genes – and 236 cases unselected for ancestry. Using clinical data collected by these registries, we provide supporting evidence for the role of precision therapy in this PAC subtype, and make recommendations for reflex genetic testing that can be easily applied in the routine management of PAC. To elucidate the unexplained majority of familial PAC, we used next-generation sequencing to interrogate the germline exomes of 109 PAC cases from 93 "high-risk" kindreds and used a filter-based approach to identify several candidate PAC susceptibility genes involved in DNA repair. Most notable are FAN1, NEK1 and RHNO1, which each harboured pathogenic mutations in 3 kindreds (3.2%) and demonstrated segregation with PAC in 2 kindreds. The identification of several low prevalence candidate genes, rather than a single major gene in our large case series highlights the likely heterogeneity of PAC heredity. Adverse survival was observed in early stage PAC cases with germline mutations in DNA repair genes, pointing to a hypothesis that these cases may represent a distinct clinical subtype with selective drug sensitivities. Overall, this thesis aims to characterize the contribution of both known and novel germline genetic causes of PAC and supports the notion that distinct genetic subtypes of PAC may benefit from targeted therapies, highlighting the limitations of the current "one-size-fits-all" approach to PAC treatment.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.277
Teacher spread0.269 · 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 designBench or experimental
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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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