Abstract 3315: Evaluating biomarker potential of germline genomic factors for predicting clinical outcomes in prostate cancer
Bibliographic record
Abstract
Abstract Prostate cancer is the second-most diagnosed cancer and the second leading cause of cancer death in American men. Early detection is common, but is followed by the more challenging task of prognosing a highly variable clinical course. Current clinical risk-assessment strategies such as serum abundance of prostate specific antigen (PSA), tumor size & extent, and tumor grade based on biopsy are highly imprecise: over a third of patients are over-treated. An improved method of risk stratification may lie in hereditary factors. Prostate cancer is one of the most strongly inherited (h2 = 57%), with accumulating evidence associating rare variants, common variants, and genetic ancestry to clinical outcomes. We have performed germline sequencing on blood from thousands of patients diagnosed with localized prostate cancer and with extensive follow-up data. We quantify the interactions of rare and common variants, and demonstrate that germline features provide insights into patient outcomes and optimal management strategies. Citation Format: Nicole Zeltser, Kathleen E. Houlahan, Sarah M. Al-Hiyari, Stefan E. Eng, Yash Patel, Takafumi N. Yamaguchi, Shu Tao, Rong Rong Huang, Robert E. Reiter, Huihui Ye, Adam S. Kinnaird, Paul C. Boutros. Evaluating biomarker potential of germline genomic factors for predicting clinical outcomes in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3315.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".