Genomic concordance between profiling of circulating tumor DNA (ctDNA) and matched tissue in metastatic urothelial carcinoma.
Bibliographic record
Abstract
457 Background: Biomarkers are urgently needed to facilitate tumor molecular stratification in metastatic urothelial carcinoma (mUC), thus potentially enabling patient selection for targeted- and immuno-therapies. We aimed to assess concordance for clinically-relevant driver gene alterations between same-patient tumor tissue and ctDNA. Methods: Whole blood samples were collected from 90 mUC patients (162 samples in total) for next-generation sequencing of cell-free DNA (cfDNA) and leukocyte DNA. Deep targeted sequencing was performed across a custom 50 bladder cancer gene panel (median cfDNA depth of 986x). Matched archival primary tissue and/or metastatic tissue biopsy was available from 65 patients, and profiled using the same assay. Results: 81% of mUC patients (73/90) had ctDNA fractions above 2% in at least one blood collection (median ctDNA fraction 22%). A high tumor mutation burden (≥25 mutations per Mb) was observed in ctDNA from 20 patients (27%). From ctDNA, TP53 and ARID1A were mutated in 64% and 29% of patients, respectively. Tissue from distant metastatic lesions was available from 17 patients; 82% (62/76) of coding somatic mutations identified were independently detected in the matched ctDNA sample; however, 7/14 discordant calls were attributable to the paired sample having a low ctDNA fraction. Similarly, 89% (88/99) of coding somatic mutations detected in archival primary tissue (cystectomy or nephrectomy) were present in later cfDNA collections. Sequencing multiple sites from archival cystectomies revealed spatially and genomically distinct subclones in 2/4 cases. Conclusions: In mUC, tumor tissue and ctDNA demonstrate remarkably high concordance; our findings support the use of either approach in the characterization of truncal driver gene alterations.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 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".