Integrated somatic subtypes of localized intermediate-risk prostate cancer.
Bibliographic record
Abstract
e560 Background: Approximately two thirds of intermediate risk prostate cancer patients are over- or under- treated because we cannot correctly prognose this risk group; therefore we require novel biomarkers to better direct patient therapies and avoid subjecting patients to side effects without benefit. One reason genomic biomarkers are not currently used in clinical settings is because they are notoriously difficult to validate in follow-up studies. Furthermore, the lack of clear prostate cancer subtypes prevents the development of subtype specific biomarkers as is standard practice in breast cancer. We aim to improve biomarker validation rates by defining prostate cancer subtypes that can be used to create subtype specific biomarkers. Methods: First, we assess large scale genomic patterns using whole genome sequencing and methylation data and create integrative subtypes for intermediate risk prostate cancer. Second, we assess associations between specific aberrations and subtypes, and determine whether certain types of molecular aberrations are more important background aberrations for subtype specific biomarker development. Finally, we assess biases in prognostic performance of the published Lalonde biomarker between groups associated with patient subtypes to show that subtype aware biomarkers are necessary. Results: We demonstrate that the Lalonde biomarker is biased by the cohorts’ proportion of TMPRSS2-ERG (T2E) aberrations illustrating the need to develop different biomarkers for patients with T2E and patients without T2E. Further, we suggest integrative subtypes can be used to select patients with similar genomic profiles for biomarker analysis to improve biomarker validation rates. Conclusions: This analysis provides direct guidance for future biomarker development and addresses an important barrier to clinical use of genomic biomarkers for prostate cancer.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".