Genomic biomarkers to guide precision radiotherapy in prostate cancer
Bibliographic record
Abstract
Our ability to prognosticate the clinical course of patients with cancer has historically been limited to clinical, histopathological, and radiographic features. It has long been clear however, that these data alone do not adequately capture the heterogeneity and breadth of disease trajectories experienced by patients. The advent of efficient genomic sequencing has led to a revolution in cancer care as we try to understand and personalize treatment specific to patient clinico-genomic phenotypes. Within prostate cancer, emerging evidence suggests that tumor genomics (e.g., DNA, RNA, and epigenetics) can be utilized to inform clinical decision making. In addition to providing discriminatory information about prognosis, it is likely tumor genomics also hold a key in predicting response to oncologic therapies which could be used to further tailor treatment recommendations. Herein we review select literature surrounding the use of tumor genomics within the management of prostate cancer, specifically leaning toward analytically validated and clinically tested genomic biomarkers utilized in radiotherapy and/or adjunctive therapies given with radiotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".