Abstract IA006: Overcoming complex polyclonality for accurate clinical genotyping in metastatic prostate cancer
Bibliographic record
Abstract
Abstract Background: De novo metastatic castration-sensitive prostate cancer (mCSPC) is highly aggressive, but the lack of routine tumor tissue in this setting hinders genomic stratification and jeopardizes precision oncology efforts. Accurate molecular profiling at diagnosis is important for future genomics-informed risk stratification strategies and biomarker-guided treatment. Currently, it is unclear the extent that intrapatient tumor heterogeneity impacts clinical cancer genotyping. Methods: We performed genomic profiling of 607 synchronous primary foci, metastatic lesions, and plasma cell-free DNA from a rare clinical trial cohort of 43 patients with de novo mCSPC who underwent radical prostatectomy at diagnosis. Surgery is not currently standard practice in this disease setting. All samples were subjected to targeted DNA sequencing using a bespoke prostate cancer-specific panel and a subset were also subjected to whole-exome sequencing. Results: Sequencing-derived tissue tumor fraction was highly heterogeneous between samples, and was below 40% across all foci in approximately 20% of patients, potentially precluding routine detection of key classes of clinically-relevant biomarkers. In samples with high tumor fraction, the genomic landscape of mCSPC closely resembled metastatic castration-resistant prostate cancer. In same-patient samples, intra-prostate heterogeneity in mutation, copy number, and whole-genome duplication was pervasive and affected tumor suppressor genes including PTEN, TP53, and RB1. Phylogenetic modeling demonstrated additional complexity in several patients driven by polyclonal metastatic seeding from the reservoir of primary populations. While the metastatic clones were often identified in the primary site, frequent discordance between select primary foci and synchronous metastases in clinically-relevant genes, plus highly variable per-sample tumor fraction, resulted in false genotyping of the dominant disease, when relying on a single tissue focus. However, in silico modeling demonstrated that analysis of multiple prostate diagnostic biopsy cores can rescue misassigned somatic genotypes. Conclusions: Our work reveals extensive polyclonality that undermines standard precision genotyping in de novo mCSPC, nominates practical strategies for improved biomarker profiling and genomics-informed risk stratification and offers deep biological insight into the relationship between primary and untreated metastases. Citation Format: Alexander W. Wyatt, Andrew J. Murtha, Evan Warner, Kim Van der Eecken, Edmond M. Kwan, Cameron Herberts, Joonatan Sipola, Sarah W.S. Ng, Xinyi E. Chen, Nicolette M. Fonseca, Elie Ritch, Elena Schönlau, Cecily Q. Bernales, Gráinne Donnellan, Kevin Beja, Amanda Wong, Sofie Verbeke, Nicolaas Lumen, Jo Van Dorpe, Bram De Laere, Matti Annala, Gillian Vandekerkhove, Piet Ost. Overcoming complex polyclonality for accurate clinical genotyping in metastatic prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr IA006.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".