Dissecting the genomic landscape of metastatic bladder cancer using circulating tumor DNA.
Bibliographic record
Abstract
378 Background: To date, only limited data are available on genomic alterations present in metastatic lesions of patients with metastatic urothelial carcinoma (mUC). The constant release of cell free DNA (cfDNA) from tumor cells into the peripheral blood stream may provide a valuable source for detection of cancer associated somatic copy number variations and mutations. The aim of this project was to evaluate the feasibility of detecting clinically relevant genomic alterations in mUC by next-generation sequencing (NGS) of circulating cfDNA. Methods: Peripheral blood (20ml) was collected from 15 patients with mUC before chemotherapy. Plasma cfDNA was isolated using the QIAAmp Circulating Nucleic Acid Kit. Germline control DNA was extracted from peripheral blood mononuclear cells (PBMCs) using the DNEasy Kit (Qiagen). We performed deep (>500X) targeted DNA sequencing on cfDNA and germline DNA using a custom NimbleGen SeqCap EZ Choice Library and Illumina sequencers. Our custom panel included 50 bladder cancer-relevant genes, including key tumor suppressors (e.g. TP53, RB1, CDKN2A), cell cycle drivers (e.g. CCND1, CCNE1), DNA repair genes (e.g. ATM, BAP1, ERCC2), PI3K pathway genes (e.g. PIK3CA, PTEN, AKT1), and other oncogenic pathways (e.g. RAS/RAF, EGFR, ERBB2, PPARG, FGFR3). Results: Somatic mutations and/or copy number changes were detected in 14/15 samples, with a median variant allele frequency >5%. The overall mutational landscape was highly consistent with bladder cancer, including mutations in TP53 and ARID1A (and other chromatin modifiers), as well as hotspot activating mutations in PIK3CA. Typical copy number changes, including focal amplifications of ERBB2, KRAS, and CCNE1 were detected. We identified complex gene rearrangements, including an activating FGFR3 fusion in one case. Extended analyses with a larger patient population are currently pending. Conclusions: Non-invasive characterization of genomic changes in peripheral blood of patients with mUC using NGS of plasma cfDNA is feasible in a high proportion of patients. Analysis of cfDNA might therefore provide an improved understanding of genomic changes in mUC and could be developed as a biomarker to help guide the use of targeted therapies.
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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".