Abstract 3509: Racial variation in molecularly-defined prostate cancer subtypes
Bibliographic record
Abstract
Abstract Background: Socioeconomic, environmental, and healthcare utilization factors are likely drivers of the persistent prostate cancer disparities between African-American (AA) and European-American (EA) men. Tumor molecular heterogeneity may also contribute, and Eurocentric studies and initiatives have the potential to widen disparities through the development of prognostic signatures and targeted therapeutics that do not account for genetic diversity. Methods: The Decipher Genomics Resource Information Database (GRID) contains tumor mRNA expression and clinical data generated through use of the Decipher test to predict prostate cancer prognosis. We matched 426 AA and 426 EA patients with localized prostate cancer using a propensity score accounting for age and tumor clinicopathological factors. We then applied five validated prostate cancer molecular subtype classifiers by Alshalalfa et al (Neuroendocrine, Adenocarcinoma), Kamoun et al (S1-S3), Tomlins et al(ERG+, ETS+, SPINK1+, ERG-/ETS-/SPINK1-), You et al (PCS1-PCS3), and Zhang et al (Basal, Luminal) to assign tumor subtypes. Heterogeneity in subtype frequency by self-identified race (SIR) was evaluated using chi-squared tests. Differences in subtype prognostic value by SIR were evaluated in logistic regression models using a high Decipher tumor genomic risk score of ≥0.6 as a surrogate for higher risk of metastases. Results: AA men were more likely to have a Decipher score ≥0.6 than EA men (25.6% vs. 20.0%, p<0.001). Subtypes reflecting SPINK1 overexpression were more frequent among AA men, while subtypes reflecting the presence of ERG or ETS fusions were more common among EA men (all p<0.001). The distribution of Basal vs. Luminal tumors did not differ by SIR (p=0.19), nor did Neuroendocrine vs. Adenocarcinoma (p=0.14). Across SIR groups, the ERG+, Basal, PCS1, and Neuroendocrine tumors were the most likely to have high Decipher scores, while the S2 subtype was associated with a lower Decipher score. However, associations between subtypes and the Decipher score differed by SIR for three of five classifiers. The ERG+ subtype (relative to ERG-/ETS-/SPINK1-) was associated with a higher risk of metastases in AA men (OR=3.18 95% CI 1.59-6.37), but not in EA men (OR=0.69, 95% CI 0.39-1.24, p-het=0.002). A similar pattern was observed in the PCS3 subtype, which is also characterized by the presence of ERG or ETS fusions (p-het=0.003). The hypothesized low-risk S2 subtype was associated with lower risk of metastases (relative to S1) among EA men (OR=0.31, 95% CI 0.15-0.61), but not among AA men (OR=0.99, 95% CI 0.39-2.49, p-het=0.001). The Zhang (p-het=0.36) and Alshalalfa (p-het=0.85) classifiers did not show heterogeneous associations between subtype and Decipher score by SIR. Conclusions: Prostate cancer molecular subtype distributions differed by SIR, with AA men generally more likely to have aggressive subtypes across classification schemes. Furthermore, AA and EA had a heterogeneous risk of metastases (defined by Decipher genomic risk score) for several subtypes. Further research is needed to better define subtyping classifiers and the prognostic value thereof in AA men. Citation Format: Kevin H. Kensler, Mohamed Alshalalfa, Brandon A. Mahal, Yang Liu, Elai Davicioni, Shivanshu Awasthi, Kosj Yamoah, Timothy R. Rebbeck. Racial variation in molecularly-defined prostate cancer subtypes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3509.
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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 | 0.001 | 0.002 |
| 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) | 0.005 | 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".