Outcomes for patients with Lynch Syndrome in Manitoba.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».