Evaluating the utility of tumour mutational signatures for identifying hereditary colorectal cancer and polyposis syndrome carriers
Notice bibliographique
Résumé
ABSTRACT Objective Germline pathogenic variants (PVs) in the DNA mismatch repair (MMR) genes and in the base excision repair gene MUTYH underlie hereditary colorectal cancer (CRC) and polyposis syndromes. We evaluated the robustness and discriminatory potential of tumour mutational signatures in CRCs for identifying germline PV carriers. Design Whole exome sequencing of formalin-fixed paraffin embedded (FFPE) CRC tissue was performed on 33 MMR germline PV carriers, 12 biallelic MUTYH germline PV carriers, 25 sporadic MLH1 methylated MMR-deficient CRCs (MMRd controls) and 160 sporadic MMR-proficient CRCs (MMRp controls) and included 498 TCGA CRC tumours. COSMIC V3 single base substitution (SBS) and indel (ID) mutational signatures were assessed for their ability to differentiate CRCs that developed in carriers from non-carriers. Results The combination of mutational signatures SBS18 and SBS36 contributing >30% of a CRC’s signature profile was able to discriminate biallelic MUTYH carriers from all other non-carrier control CRCs with 100% accuracy (area under the curve (AUC) 1.0). SBS18 and SBS36 were associated with specific MUTYH variants p.Gly396Asp (p=0.025) and p.Tyr179Cys (p=5×10 −5 ), respectively. The combination of ID2 and ID7 could discriminate the 33 MMR PV carrier CRCs from the MMRp control CRCs (AUC 0.99), however, SBS and ID signatures, alone or in combination, could not provide complete discrimination (AUC 0.79) between CRCs from MMR PV carriers and sporadic MMRd controls. Conclusion Assessment of SBS and ID signatures can discriminate CRCs from biallelic MUTYH carriers and MMR PV carriers from non-carriers with high accuracy, demonstrating utility as a potential diagnostic and variant classification tool. SIGNIFICANCE OF THE STUDY What is already known about this subject? Identifying carriers of pathogenic variants (PVs) in moderate/high-risk colorectal cancer (CRC) and polyposis susceptibility genes has clinical relevance for diagnosis, targeted screening and prevention strategies, prognosis, and treatment options. However, challenges still remain in the identification of carriers and the classification of rare variants in these genes. Previous studies have identified tumour mutational signatures that result from defective DNA repair including DNA mismatch repair (MMR) deficiency and base excision repair defects, DNA repair mechanisms that underlie the common hereditary CRC and polyposis syndromes but their diagnostic utility in CRC is unknown. What are the new findings? Single base substitution (SBS)-related mutational signatures derived from whole exome sequencing of formalin-fixed paraffin embedded (FFPE)-derived CRC tissue DNA can effectively discriminate CRCs that developed in biallelic MUTYH PV carriers from CRC-affected non-carriers. CRCs that develop in MMR PV carriers (Lynch syndrome) can be effectively differentiated from sporadic MMR-proficient CRC by a combination of indel (ID) signatures, but the SBS and ID tumour mutational signatures are less effective at discriminating Lynch syndrome-related CRC from sporadic MMR-deficient CRC resulting from MLH1 gene promoter hypermethylation. The SBS and ID mutational signatures associated with biallelic MUTYH PV carriers and MMR PV carriers are robust to changes in experimental settings. We demonstrate the optimal experimental settings for calculating mutational signatures and define thresholds that optimise sensitivity and specificity for classifying CRC aetiology as hereditary or non-hereditary. How might it impact on clinical practice in the foreseeable future? Deriving SBS- and ID-related mutational signatures from CRCs can identify carriers of PVs in hereditary CRC and polyposis susceptibility genes. The application of mutational signatures has the potential to improve the diagnosis of hereditary CRC and aid in variant classification, leading to improved clinical management and CRC prevention.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».