Circulating tumor DNA in patients with metastatic urothelial cancer: concordance of genomic findings with matched tissue biopsies.
Bibliographic record
Abstract
e16036 Background: Patients (pts) with metastatic urothelial cancer (mUC) now have access to many different treatment options. This creates an incentive for molecular profiling of their tumors, with the aim of developing biomarkers. Genomic profiling may leverage the presence of circulating tumor DNA (ctDNA), but it has not been shown whether this recapitulates the findings from tissue samples. Methods: Whole blood samples were collected for next-generation sequencing of leukocyte and cell-free DNA (cfDNA). Deep targeted sequencing was performed across a UC-specific custom 50-gene panel (median depth of 986x). Matched archival tissue was profiled using the same assay for 65 pts. Results: Between 11/2011 and 12/2017, 90 pts developed mUC (87 evaluable). Baseline characteristics: median age 67, 83% male, 14% upper-tract disease, 17% stage IV at initial presentation. Treatments delivered: 76% platinum-based chemotherapy, 47% PD-1/PD-L1 inhibitor. At a median follow-up of 12.8 mo., 45% remain alive. We found ctDNA fractions above 2% in at least one blood collection in 81% of cases. For 17 pts, matched metastatic biopsies and cfDNA collection were available; in those cases 82% of coding somatic mutations identified in tissue were independently detected in cfDNA. Half of discordant findings could be attributed to low ctDNA fraction. Most (89%) mutations detected in primary tissue (cystectomy or nephrectomy) were present in later cfDNA collections. ctDNA detected mutations in TP53 and ARID1A in 64% and 29% of pts, respectively. A tumor mutation burden ≥25 mutation per Mb was observed in 27% of cases. Conclusions: There is a high concordance between genomic findings from ctDNA and matched tissue of pts with mUC. This supports the use of liquid biopsies to study potential biomarkers in this disease.
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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 | 0.003 |
| 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".