Abstract PR08: The genomic landscape of metastatic urothelial carcinoma from circulating tumor DNA
Bibliographic record
Abstract
Abstract The recent expansion of treatment options for patients with metastatic urothelial carcinoma (mUC) has emphasized the need for molecular biomarkers in this setting. However, knowledge of the somatic genome landscape in mUC is limited; large-scale sequencing efforts have relied on primary tumor tissue from muscle-invasive bladder cancer (MIBC), revealing a molecularly heterogeneous disease characterized in particular by high somatic mutation rates. Putative prognostic and predictive biomarkers described from primary tissue need validation in metastatic tumors. Given the challenge of obtaining metastatic tissue, we sought to utilize circulating tumor DNA (ctDNA) to characterize the somatic landscape in mUC. We collected 162 whole-blood samples from 90 mUC patients. Targeted next-generation sequencing was performed on cell-free DNA (cfDNA) and matched leukocyte DNA utilizing a custom 50-gene panel. Somatic alteration frequencies in our metastatic cohort were compared to those reported for primary MIBC by TCGA (Cell 2017, n=412), with data obtained via cBioPortal. Our cohort of 90 mUC patients included 14% with upper tract disease. The median cfDNA sequencing depth was 986x, with ctDNA detectable in at least one blood collection for 81% (73/90) of mUC patients. In 10/73 patients, the estimated tumor mutation burden was ≥30 mutations per Mb. TP53 was the most frequently mutated gene (46/73 ctDNA-positive patients). Chromatin modifiers were also frequently altered: ARID1A 27%, and 25% for KDM6A and KMT2D. Genes mutated in the PI3K pathway included PIK3CA (22%), PTEN (5.5%), and PIK3R1 (1.4%). FGFR3 mutations were identified in 8.2% of patients. ERBB2 mutations were present in 12.3% of patients, and amplification was detected in eight patients. Thus, the metastatic somatic landscape closely resembles primary disease; however, comparison to TCGA dataset revealed mutations in TP53 and FGFR1 were enriched for in the metastatic setting (TP53: 63.0% vs. 48.1%, p = 0.022; FGFR1: 5.5% vs. 1.5%, p = 0.049; two-sided Fisher's exact test). Profiling of ctDNA from a large mUC cohort suggests a relatively similar somatic landscape to primary MIBC. However, alterations in key driver genes are potentially over- or underrepresented in metastatic disease, which has prognostic implications. In mUC, ctDNA profiling is a powerful alternative to metastatic tissue biopsy. This abstract is also being presented as Poster B19. Citation Format: Gillian R. Vandekerkhove, Matti Annala, Jean-Michel Lavoie, Nora Sundahl, Simon Walz, Takeshi Sano, Andrew Murtha, Tilman Todenhöfer, Piet Ost, Kim N. Chi, Peter C. Black, Bernhard Eigl, Alexander W. Wyatt. The genomic landscape of metastatic urothelial carcinoma from circulating tumor DNA [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr PR08.
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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 | 0.000 | 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) | 0.003 | 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".