Molecular and clinical characterization of 1,577 primary prostate cancer tumors to reveal novel clinical and biological insights into its subtypes.
Bibliographic record
Abstract
9 Background: Prostate cancer molecular subtypes based on ETS gene fusions and SPINK1 were originally identified through outlier gene expression profiling analysis. Such molecular subtypes may have utility in disease stratification and clonality assessment, complementing available purely prognostic tests. Hence, we determined the analytical validity of molecular subtyping in a large sample of PCa treated with radical prostatectomy. Methods: We analyzed Affymetrix Human Exon 1.0ST GeneChip expression profiles for 1,577 patients from 8 radical prostatectomy (RP) cohorts. Multi-feature random forest classifiers and outlier analysis were used to define microarray-based molecular subtypes. Results: A random forest (RF) classifier was trained and validated to predict ERG fusion status using a subset with known ERG rearrangement status defined by FISH, achieving >95% sensitivity and specificity in the validation subset. Less frequent rearrangements involving other ETS genes or SPINK1 over-expression were predicted based on gene expression outlier analysis. Across cohorts, 45%, 9% 8% and 38% of PCa were classified as ERG+, ETS+, SPINK+, and Triple Negative, respectively. Global gene expression analysis shows that the four subtypes could be collapsed into three entities (ERG+, ETS+ and SPINK+/Triple Negative) based on expression patterns and clinical characteristics similarity. A series of multivariable analyses further revealed, ERG+ to be associated with lower pre PSA and Gleason scores but more likely to have EPE and occur in patients with European American ancestry compared to the ETS+, SPINK+/Triple Negative tumors (p<0.001). In contrast, patients with ETS+ were more likely to have SVI compared to both ERG+ and SPINK/Triple Negative (p=0.01), while SPINK+/Triple Negative had higher Gleason scores and were more likely to occur in African Americans (p<0.001). Conclusions: The Decipher platform can accurately determine ERG rearrangement status and PCa molecular subtypes. Inclusion of molecular subtyping, such as m-ERG status, may enable additional precision medicine opportunities in prognostic tests
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".