Abstract 5397: Characterizing the genomic landscape of bladder cancer with circulating tumor DNA
Bibliographic record
Abstract
Abstract Background: Targeted agents are emerging as promising treatment options for patients with metastatic bladder cancer (BCa). However, since the genomic landscape of BCa is highly heterogeneous, future clinical trial success will likely depend upon tumor molecular stratification and therefore require a practical method for somatic genome analysis. Circulating cell-free tumor DNA (ctDNA) collected from peripheral blood has been established in several major solid malignancies as a minimally-invasive tool to profile the tumor genome but is under-explored in BCa. Goal: Our aim was to identify the spectrum of clinically-informative genomic alterations detectable in plasma cell-free DNA (cfDNA) collected from patients with BCa. Patient cohort and methodology: We recruited 45 patients with BCa, 14 with localized disease and 31 with metastatic disease, and collected whole blood samples for isolation of cfDNA. Isolation of plasma cfDNA was performed using the QIAAmp Circulating Nucleic Acid Kit, while germline control DNA was extracted from buffy coat using the DNEasy Kit (Qiagen). A custom targeted next-generation sequencing (NGS) strategy was employed to detect BCa- specific somatic alterations in ctDNA; samples underwent deep (>500X) targeted DNA sequencing using the NimbleGen SeqCap EZ Choice system and Illumina technology. Our custom panel included 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 oncogenes (e.g. RAS/RAF, EGFR, ERBB2, PPARG, FGFR3). Results: The majority of patients with metastatic BCa (18/31 patients, 24/43 samples) had robust evidence of somatic alterations and therefore ctDNA. Our findings were consistent with the known landscape of BCa, including mutations in TP53 and ARID1A (and other chromatin modifiers), hotspot activating mutations in PIK3CA, as well as typical copy number changes such as focal amplifications of ERBB2, KRAS, and CCNE1. In addition, we identified complex gene rearrangements including in one case an activating FGFR3 gene fusion. Some samples had evidence of very high mutation rates, indicative of somatic ‘hypermutation’. Interestingly, in contrast to the metastatic setting, 85% of patients with localized BCa had no evidence of ctDNA, despite elevated cfDNA yields in some patients. Conclusion: The majority of metastatic BCa patients have high levels of ctDNA suitable for standard targeted sequencing approaches, while patients with localized muscle-invasive tumors appear not to harbor ctDNA at fractions greater than 1-2%. The robust detection of so-called ‘actionable’ copy number alterations, mutations and rearrangements in ctDNA provides an unparalleled opportunity for practical molecular stratification of patients in clinical trials of novel targeted agents. Citation Format: Gillian R. Vandekerkhove, Tilman Todenhöfer, Matti Annala, Werner J. Struss, Kevin Beja, Amanda Wong, Scott North, Bernie Eigl, Peter Black, Alexander W. Wyatt. Characterizing the genomic landscape of bladder cancer with circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5397. doi:10.1158/1538-7445.AM2017-5397
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".