Clonal hematopoiesis in metastatic urothelial and renal cell carcinoma
Bibliographic record
Abstract
Clonal hematopoiesis (CH) is an age-related expansion of white blood cell (WBC) progenitors linked to risk of hematological malignancy. Patients with cancer have increased CH prevalence compared to healthy populations, but the characteristics and relevance of CH in advanced urological cancers are unknown. We interrogated CH and circulating tumor DNA (ctDNA) in 299 patients with metastatic urothelial or renal cell carcinoma using error-corrected targeted sequencing of matched WBC DNA and plasma cell-free DNA (cfDNA). 73% of patients carried CH variants at ≥0.25% allele frequency, with 13% exhibiting large CH populations marked by variants ≥10%. CH presence, clone size, and genotype did not impact patient survival. However, CH variants frequently affected solid cancer driver genes and were not individually discriminable from ctDNA variants based on cfDNA features including fragment length. In contrast, matched WBC DNA sequencing to ≥25% of cfDNA depth sufficiently resolved CH from ctDNA variants. Serial profiling revealed ctDNA and CH temporal dynamics including treatment-related expansion of PPM1D-mutated CH clones following platinum chemotherapy. Our data reveal the molecular landscape of CH in urological cancers and suggest that CH interferes in clinical ctDNA genotyping. We urge test providers to comprehensively filter CH from ctDNA results using matched WBC sequencing and propose a cost-effective framework for its integration into existing plasma-only assays.
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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 | 0.002 |
| 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) | 0.000 | 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".