Circulating tumor (ct)-DNA alterations in urothelial/bladder cancer (UC/BC): Updates on a dynamic genomic landscape.
Bibliographic record
Abstract
4534 Background: Cell-free ctDNA may be potentially actionable, may have prognostic/predictive role and evolve after therapy. We updated our analysis of our retrospective study to shed light on UC/BC biology. Methods: Patients (pts) with UC/BC with ctDNA analysis for potentially actionable alterations using Guardant360 were identified. A 70-gene ctDNA next generation sequencing panel from a CLIA-licensed, CAP-accredited laboratory (Guardant Health, Inc.) offers complete exon sequencing for 29 cancer genes, critical exons in 39 genes and amplifications (16 genes), fusions (6 genes) and indels (3 genes) harvested from 10 mL of peripheral blood. Descriptive statistics were used. Results: There were 246 pts with 276 samples. At least 1 alteration was detected in 249 (90%) samples. Median age at time of ctDNA collection was 67 years (39-85), 78% men, median number of alterations per sample was 3.5 (1-35) most pts had MIBC. In MIBC pts, the most common alterations at the 1st ctDNA sample were in TP53 (52%), PI3KCA (18%) ARID1A (17%), FGFR2 (15%), MET & NF1 (14%), EGFR (13%), BRAF (12%), FGFR3 (11%), RAF1 (10%), BRCA1 & CCNE1 (9%). In MIBC pts, the most common genes with increased copy number were RAF1 & CCNE1 (8%), ERBB2 & PI3KCA (7%), EGFR, BRAF, FGFR1, MYC (each 5%), MET (4%), KRAS (3%). Most common altered pathways included TP53 signaling (56%), RAS/RAF/MEK/ERK (51%), RTK (48%), cell cycle (38%), FGFR family (34%), DNA damage response (25%), PI3KCA/AKT/mTOR (23%) and chromatin remodeling (17%). Interestingly, FGFR3 and RAS alterations were mutually exclusive in most cases, but each may co-occur with TP53alterations. 54 serial ctDNA samples from 24 pts (18 pts with 2 samples; 6 pts with 3 samples) revealed persistent, lost and new gene alterations. Conclusions: ctDNA was detected in 90% of pts and alterations were similar to those previously seen in UC tumor tissue. Tumor heterogeneity, interim therapy, genomic instability and clonal evolution can explain differences in serial samples. Correlation assessment with prior therapies and outcomes is being pursued to inform trial designs. Prospective validation, assessment of ctDNA concordance with tumor tissue DNA, and evaluation of clinical utility is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".