Identification of Hypermutation and Defective Mismatch Repair in ctDNA from Metastatic Prostate Cancer
Bibliographic record
Abstract
Abstract Purpose: DNA mismatch repair defects (MMRd) and tumor hypermutation are rare and under-characterized in metastatic prostate cancer (mPC). Furthermore, because hypermutated MMRd prostate cancers can respond to immune checkpoint inhibitors, there is an urgent need for practical detection tools. Experimental Design: We analyzed plasma cell-free DNA-targeted sequencing data from 433 patients with mPC with circulating tumor DNA (ctDNA) purity ≥2%. Samples with somatic hypermutation were subjected to 185 × whole-exome sequencing and capture of mismatch repair gene introns. Archival tissue was analyzed with targeted sequencing and IHC. Results: Sixteen patients (3.7%) had somatic hypermutation with MMRd etiology, evidenced by deleterious alterations in MSH2, MSH6, or MLH1, microsatellite instability, and characteristic trinucleotide signatures. ctDNA was concordant with mismatch repair protein IHC and DNA sequencing of tumor tissue. Tumor suppressors such as PTEN, RB1, and TP53 were inactivated by mutation rather than copy-number loss. Hotspot mutations in oncogenes such as AKT1, PIK3CA, and CTNNB1 were common, and the androgen receptor (AR)-ligand binding domain was mutated in 9 of 16 patients. We observed high intrapatient clonal diversity, evidenced by subclonal driver mutations and shifts in mutation allele frequency over time. Patients with hypermutation and MMRd etiology in ctDNA had a poor response to AR inhibition and inferior survival compared with a control cohort. Conclusions: Hypermutated MMRd mPC is associated with oncogene activation and subclonal diversity, which may contribute to a clinically aggressive disposition in selected patients. In patients with detectable ctDNA, cell-free DNA sequencing is a practical tool to prioritize this subtype for immunotherapy. See related commentary by Schweizer and Yu, p. 981
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".