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Outcomes for patients with Lynch Syndrome in Manitoba.

2023· article· en· W4379346636 on OpenAlexafffundabout
Sangyang Jia, Harminder Singh, Heidi Rothenmund, Allison Feely, Oliver Bucher, Christina Kim

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsCancerCare ManitobaUniversity of Manitoba
FundersCancerCare Manitoba Foundation
KeywordsLynch syndromeMedicineMSH6PMS2MSH2MLH1CancerInternal medicineOncologyColorectal cancerEndometrial cancerCancer registryGynecologyDNA mismatch repair

Abstract

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e22548 Background: Lynch Syndrome (LS) is the leading cause of hereditary colorectal cancer (CRC) and is also associated with an increased risk of extracolonic cancers including endometrial, ovarian, upper gastrointestinal tract and genitourinary malignancies. Since 2013 in Manitoba, Canada, all CRC surgical specimens in patients ≤70 undergo reflex screening for the mismatch repair (MMR) proteins (MLH1, MSH2, MSH6, PMS2) via immunohistochemistry. Since 2016, all endometrial cancers (EC) in patients ≤60 undergo similar reflex screening. The aim of this study was to examine the demographics, treatments and outcomes of patients with LS in Manitoba who have had a cancer diagnosis. Methods: Patients with pathogenic/likely pathogenic (P/LP) LS gene variants in Manitoba from 1999 were identified using records from the Program of Genetics and Metabolism. Those with a cancer diagnosis were identified using the Manitoba Cancer Registry (MCR). Non-melanomatous skin cancers and in-situ cancers were excluded. Descriptive statistics were used to report patient characteristics, LS gene variants, cancer diagnoses, treatments and outcomes. A survival analysis was undertaken using a matched cohort of patients with a diagnosis of CRC from 2004 to 2021 to compare overall survival (OS) between those with and without LS gene variants. A landmark survival analysis was performed to compare OS between those who had CRC before or after LS diagnosis. Results: 311 individuals with P/LP LS gene variants (96% pathogenic) and a record in the MCR were identified. The most common gene was MLH1 (33%), followed by MSH2 (29%), MSH6 (20%), PMS2 (16%) and EPCAM (2%). Most (72%) LS diagnoses occurred after 2014. There were 310 cancer diagnoses. The most common cancers amongst patients with LS were CRC (56%), EC (21%), urinary tract (5%), and ovarian (4%). Most cancer diagnoses (56%) occurred between 40-59 years old; 52% had stage I-II disease; 89% underwent surgery, 17% radiation & 40% received systemic therapy. Of 12 patients diagnosed with cancer at < 30 years old, 7 (58%) carried an MLH1 variant and 5 (42%) an MSH2 variant. 75 patients had ≥2 cancer diagnoses, 27 with an MLH1 variant (range 2-4); 30 MSH2 (2-5), 7 MSH6 (2-3), 9 PMS2 (2-3), 2 EPCAM (2-3). Using a matched CRC cohort to compare OS between those with and without LS, controlling for age, stage, sex, year of diagnosis, income quintile and treatment received, LS diagnosis was associated with a trend towards lower risk of death (HR 0.456, 95% CI 0.205-1.012, p = 0.053). There was no difference in OS according to whether a CRC diagnosis occurred pre- or post- LS diagnosis (p = 0.120). Conclusions: In this population-based study, the most common cancer diagnoses in patients with LS were CRC and EC. Many patients were diagnosed at a young age & experienced multiple cancer diagnoses. More LS diagnoses occurred after 2014, supporting the role of reflex tumor testing. Amongst those diagnosed with CRC, LS is associated with a trend towards improved OS.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.398
Threshold uncertainty score0.800

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.139
GPT teacher head0.465
Teacher spread0.326 · 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
Published2023
Admission routes3
Has abstractyes

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