Abstract PO-004: Intra-individual heterogeneity of prostate cancer gene expression signatures of luminal and basal subtypes: Implications for selection of biopsy cores for genomic testing
Bibliographic record
Abstract
Abstract Background: Prostate cancer (PrCa) heterogeneity is a well-recognized challenge for the development of molecular biomarker tests for clinical applications. Prostate biopsy is a critical point for diagnosis and treatment planning; biopsy procedures typically produce 12-16 cores and multiple cancer-positive cores are common. For the commercial prognostic gene expression tests, current recommendations are to test only the highest grade core with the highest % tumor involvement. More recently, gene expression tests that classify PrCa according to luminal and basal subtypes have been proposed as predictors of response to specific therapeutic interventions. In PrCa, Luminal B (LumB) appears to be the most aggressive subtype and most sensitive to ADT while the Basal subtype includes a subset of PrCa’s that are relatively insensitive to ADT therapy (PMID: 28494073, 31515456). Clinical trials are underway to assess whether molecular stratification by luminal and basal signatures will identify subsets of PrCa patients who will derive the greatest benefit from ADT. In this pilot study, we compare the results of testing all biopsy cores with sufficient tumor vs testing only the highest grade/% core in the detection of PrCa luminal and basal subtypes. Study design: Gene expression profiles were obtained from 238 cancer-positive biopsy cores from 83 treatment-naïve PrCa patients. Areas of cancer delineated by the pathologist were macrodissected from formalin-fixed paraffin embedded sections and the RNA analyzed using an established clinical grade microarray platform (PMID: 23826159). Luminal A (LumA), LumB and Basal gene expression signatures were obtained using the PAM50 gene expression classifier (PMID: 19204204). Results: Of the cancers in the 238 biopsy cores, 106 were classified as LumA, 81 as LumB and 51 as Basal by the PAM50 classifier. As expected, the LumB cores had a higher proportion of higher grade cancer (grade group 2+) than the LumA and Basal cores (59% vs 27% and 24%, respectively, P < 0.01). Of the 55 patients with multiple cancer positive cores, 31 (56%) showed more than one luminal/basal subtype and 6 had at least one core of each of the three subtypes. Overall, at least one core tested positive for the LumA, LumB and Basal signatures in biopsies from 47, 37 and 34 patients, respectively. If only the core with the highest grade/% core had been tested, 12 patients would have had undetected LumA, 4 undetected LumB and 14 undetected Basal cancer foci, potentially confounding clinical trials seeking to develop biomarker guided interventions. Conclusion: PrCa heterogeneity may require that the current practice of testing only the biopsy core with the highest grade/% cancer be expanded for luminal/basal subtyping. Citation Format: Sandra M. Gaston, Sanoj Punnen, Elai Davicioni, Seagle Liu, Oleksandr N. Kryvenko, Benjamin O. Spieler, Alan Pollack, Radka Stoyanova. Intra-individual heterogeneity of prostate cancer gene expression signatures of luminal and basal subtypes: Implications for selection of biopsy cores for genomic testing [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-004.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".