Abstract 731: Integrative genomic analysis to identify emergent enzalutamide resistance mechanisms in castration-resistant prostate cancer
Bibliographic record
Abstract
Abstract Background: Multiple lines of evidence demonstrate that castration-resistant prostate cancers (CRPCs) remain reliant on androgens that activate the androgen receptor. Treatment with the novel anti-androgen enzalutamide improves progression-free survival and overall survival in CRPC patients; however, nearly 50% of patients never respond, and progression is universal (Beer, 2014, Scher, 2012). Mechanisms of enzalutamide resistance are largely unknown and few treatments exist for enzalutamide-resistant CRPC. Recent work demonstrates that CRPC tumors harbor countless genomic aberrations that control many hallmarks of cancer (Grasso, 2012, Hanahan and Weinberg, 2011). Based on our prior work (Heiser, 2012, Vaske, 2010), we hypothesize that these aberrations operate in concert to drive enzalutamide resistance and influence specific cancer hallmarks. Methods: We performed genomic studies using paired enzalutamide-sensitive and resistant LNCaP cell models. After transcriptional and copy number profiling, we performed an integrative pathway-informed PARADIGM analysis to identify differentially regulated cellular networks (Heiser, 2012, Vaske, 2010). These large-scale networks underwent regression analysis to identify sub-networks associated with acquired resistance. Genes residing within significant sub-networks were nominated for functional validation studies with RNAi or existing therapeutic compounds that impinge upon significant sub-networks in resistant models. Results: We used PARADIGM to compare the genomic alterations between the parental and enzalutamide-resistant cell line models and identified critical deregulated networks that may be targeted therapeutically. Currently, we are applying this same PARADIGM analysis to additional model systems and metastatic patient tumors obtained prior to treatment and at the time of disease progression through a West Coast Dream Team prospective enzalutamide clinical trial. Conclusions: PARADIGM integrative genomic analysis identifies specific sub-networks that contribute to enzalutamide resistance. A predicted outcome of our efforts is the development of rationally designed clinical trials with specific enzalutamide drug combinations in distinct molecular subsets of CRPC patients in the near-term. Citation Format: Josha Woodward, Carly King, Daniel Coleman, Robert Lisac, Jacob Schwartzman, Nicholas Wang, Martin Gleave, Joe Gray, George Thomas, Tomasz M. Beer, Katy Van Hook, Robert Baertsch, Ted Goldstein, Josh Stuart, Lina Gao, Joshua Urrutia, Laura Heiser, Joshi J. Alumkal. Integrative genomic analysis to identify emergent enzalutamide resistance mechanisms in castration-resistant prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 731. doi:10.1158/1538-7445.AM2015-731
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".