Abstract 3625: Clonal architecture and evolution of treatment-resistant prostate cancer via deep whole-genome ctDNA sequencing
Bibliographic record
Abstract
Abstract Background: Circulating tumor DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring. However, integration of ctDNA tests into clinical management is critically impeded by the poor understanding of the distinct somatic populations comprising bulk ctDNA—including their relationship to synchronous metastatic tissue, and their temporal dynamics during standard-of-care treatment. Prior approaches relying on targeted and/or low-resolution techniques (e.g. targeted exon sequencing, low-pass (shallow) whole-genome sequencing; WGS) do not permit comprehensive dissection of clonal architecture and unbiased analysis of putative resistance mechanisms. Methods: We performed deep WGS on serial plasma ctDNA (median depth: 185×) and synchronous metastatic tissue biopsies with high tumor purity from 35 patients with metastatic castration-resistant prostate cancer. We developed a subclonal reconstruction algorithm optimized for our data enabling resolution of per-patient evolutionary histories and ctDNA clonal composition. ctDNA nucleosome footprinting was used to infer mRNA abundance in synchronously biopsied metastases and androgen receptor (AR) transcription factor activity at 3224 AR binding sites (ARBS). Results: We comprehensively assess all classes of genomic alterations and demonstrate that ctDNA harbors greater populational heterogeneity than metastatic tissue (p<0.001). The evolutionary histories of ctDNA populations indicate frequent whole-genome doubling and attenuation of C>T aging-associated mutation signature during subclonal differentiation. Although driver alterations were largely concordant between tissue and ctDNA, each individual metastasis contributed only a minor share of total ctDNA (average ctDNA contribution: 17%). By comparing serial ctDNA before and after clinical progression on potent AR pathway inhibitors, we reveal population restructuring converging solely on AR copy augmentation as the dominant genomic driver of acquired treatment-resistance. Nucleosome depletion at transcription start-sites is highly correlated with same-patient metastatic tissue mRNA abundance, indicating that ctDNA fragmentomics can recapitulate transcriptomic patterns in metastatic lesions. Most ctDNA samples exhibited strong ARBS nucleosome depletion which correlated with AR gene copy number (R=0.36, p=0.003). Finally, serial ctDNA nucleosome profiling at ARBS revealed adaptive transcriptomic resistance to AR pathway inhibitors, including lineage switch to a neuroendocrine-like (AR-low) state. Conclusions: We show that the populations comprising ctDNA are typically complex and more heterogeneous than those found in bulk WGS of a synchronous metastasis. Our work advocates for liquid biopsy as a comprehensive multi-omic discovery tool for cancers with high ctDNA fractions. Citation Format: Cameron Herberts, Matti Annala, Joonatan Sipola, Sarah W. Ng, Xinyi E. Chen, Anssi Nurminen, Olga Korhonen, Aslı D. Munzur, Kevin Beja, Elena Schönlau, Cecily Q. Bernales, Elie Ritch, Jack V. Bacon, Nathan A. Lack, Matti Nykter, Rahul Aggarwal, Eric J. Small, Martin E. Gleave, David A. Quigley, Felix Y. Feng, Kim N. Chi, Alexander W. Wyatt. Clonal architecture and evolution of treatment-resistant prostate cancer via deep whole-genome ctDNA sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3625.
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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 | 0.000 |
| 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) | 0.001 | 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".