Abstract B069: Drug response variability between luminal and basal prostate cancer tumors
Bibliographic record
Abstract
Abstract Introduction: Prostate cancer (PCa) is a genomically heterogeneous disease that has been subtyped into molecularly distinct subtypes. Over the past several years, multiple drugs have demonstrated improvements in overall survival in men with advanced prostate cancer, prompting further investigations of these drugs. However, characterizing drug response in the localized disease setting and in the context of PCa molecular subtypes needs further investigation. Here in a large prospective cohort of men with adverse pathology we explore the heterogeneity of patient drug response between basal, luminal, and neuroendocrine prostate cancer subtypes. Methods: Whole-transcriptome RNA expression profiles of 9,640 radical prostatectomy (RP) samples from prospective use of the Decipher test were obtained from the GRID registry database. Patients were subtyped into basal, luminal A, luminal B, and neuroendocrine subtypes using the PAM50 and small cell gene expression signatures. Drug response scores (DRS) predicting patient sensitivity for 89 oncology drugs were determined using in vitro drug sensitivity and microarray data from the NCI-60 panel. Pearson’s chi squared test was used to determine significant differences in drug sensitivity among PCa subtypes. Results: Applying the subtype signatures to the cohort, we classified 43% of samples as basal, 26% as luminal A, 30% as luminal B, and 2% as neuroendocrine. DRS was highly variable across the subtypes. Basal tumors showed a distinct drug response profile where basal tumors were more sensitive to kinase inhibitors (e.g., cabozantanib, dasatnib, erlotinib), mTOR inhibitors (e.g., everolimus, temsirolumus), DNA repair inhibitors (e.g., olaparib, mitoxantrone), and alkylating (e.g., cisplatin, carboplatin) chemotherapy. Luminal A and B were more sensitive to steroid inhibition (e.g., abiraterone, tamoxifen) and anti-microtuble (e.g., docetaxel, paclitaxel, vinorelbine) chemotherapy, whereas neuroendocrine had highest DRS for antiproliferative agents (e.g., mitomycin, cytabarine, carmustine, topotecan), Significant differences in average DRS scores in subtypes were observed for all 89 drugs (p<0.001). Conclusion: Prostate cancer subtypes in the localized disease setting have distinct drug response profiles, suggesting that subtyping and DRS scores may be useful for selecting candidates for systemic therapy trials. Citation Format: Robert Den, Jonathan Lehrer, Mandeep Takhar, Mohammed Alshalalfa, Nicholas Erho, Elai Davicioni, Felix Feng. Drug response variability between luminal and basal prostate cancer tumors [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B069.
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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 | 0.000 |
| 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".