Evaluating the utility of tumour mutational signatures for identifying hereditary colorectal cancer and polyposis syndrome carriers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".