Integrating Tumor Sequencing Into Clinical Practice for Patients With Mismatch Repair-Deficient Lynch Syndrome Spectrum Cancers
Bibliographic record
Abstract
INTRODUCTION: Uninformative germline genetic testing presents a challenge to clinical management for patients suspected to have Lynch syndrome, a cancer predisposition syndrome caused by germline variants in the mismatch repair (MMR) genes or EPCAM. METHODS: Among a consecutive series of MMR-deficient Lynch syndrome spectrum cancers identified through immunohistochemistry-based tumor screening, we investigated the clinical utility of tumor sequencing for the molecular diagnosis and management of suspected Lynch syndrome families. MLH1-deficient colorectal cancers were prescreened for BRAF V600E before referral for genetic counseling. Microsatellite instability, MLH1 promoter hypermethylation, and somatic and germline genetic variants in the MMR genes were assessed according to an established clinical protocol. RESULTS: Eighty-four individuals with primarily colorectal (62%) and endometrial (31%) cancers received tumor-normal sequencing as part of routine clinical genetic assessment. Overall, 27% received a molecular diagnosis of Lynch syndrome. Most of the MLH1-deficient tumors were more likely of sporadic origin, mediated by MLH1 promoter hypermethylation in 54% and double somatic genetic alterations in MLH1 (17%). MSH2-deficient, MSH6-deficient, and/or PMS2-deficient tumors could be attributed to pathogenic germline variants in 37% and double somatic events in 28%. Notably, tumor sequencing could explain 49% of cases without causal germline variants, somatic MLH1 promoter hypermethylation, or somatic variants in BRAF. DISCUSSION: Our findings support the integration of tumor sequencing into current Lynch syndrome screening programs to improve clinical management for individuals whose germline testing is uninformative.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".