Abstract 4420: Elevated glycolytic gene signature in patient-derived neuroendocrine prostate cancer xenograft models and its clinical relevance
Bibliographic record
Abstract
Abstract Neuroendocrine prostate cancer (NEPC) is a highly aggressive subtype of prostate cancer (PCa) that is becoming increasingly common in the clinic. While the vast majority of PCa presents as androgen-dependent adenocarcinoma, recent uses of increasingly potent therapeutics targeting the androgen receptor signaling axis has resulted in the promotion of NEPC transdifferentiation as a mechanism of treatment resistance. Unfortunately, there is currently no effective treatment option for NEPC. Altered cancer metabolism is now recognized as a hallmark of cancer and a crucial factor for promoting tumour growth and spread. In particular, altered glucose metabolism and the resultant acidification of the tumor microenvironment via increased lactic acid production has been shown to play an important role in multiple cancer-promoting processes, including tissue invasion/metastasis, angiogenesis, and suppression of local anticancer immunity. While increased glycolysis is not generally considered a phenomenon relevant to primary treatment-naive PCa, we have recently demonstrated its relavance to castration-resistant prostate cancer (CRPC) and thus suspect that it is also relevant to the more aggressive NEPC. Our laboratory has also developed a number of unique serially transplantable patient-derived xenograft (PDX) models of NEPC that are histologically highly similar to the donor tissues and retain important genetic and epigenetic features. In particular, we have developed the first spontaneous NEPC transdifferentiation model in the field (LTL331/331R). The gene expression profiles of these NEPC PDX models were compared to that of PCa adenocarcinoma PDX models. To determine whether certain metabolic pathway alterations were specific to NEPC, genes from a number of key metabolic pathways were compiled and overall pathway scores were generated using average expression z-scores. Furthermore, publically available gene expression data from NEPC patient tumors were used to validate our findings. From our analysis, we found that genes in the glycolysis pathway were signficiantlly upregulatied in both our PDX models and also in patient NEPC samples. Of particular interest is the upregulation of genes involved in the production and secretion of lactic acid, such as LDHA and MCT4. As such, our results suggest that elevated glycolysis and production of lactic acid could be a clinically important NEPC phenotype. Furthermore, the inhibition of glycolysis and particularly the inhibition of lactic acid secretion via MCT4 could be a potentially viable therapeutic strategy for NEPC. Citation Format: Stephen Y. Choi, Susan L. Ettinger, Dong Lin, Hui Xue, Robert H. Bell, Fan Mo, Michael Pollak, Colin C. Collins, Yuzhuo Wang. Elevated glycolytic gene signature in patient-derived neuroendocrine prostate cancer xenograft models and its clinical relevance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4420. doi:10.1158/1538-7445.AM2017-4420
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".