Metabolic heterogeneity signature of primary treatment-naïve prostate cancer
Bibliographic record
Abstract
// Dong Lin 1,2,* , Susan L. Ettinger 1,* , Sifeng Qu 1,2 , Hui Xue 2 , Noushin Nabavi 1,2 , Stephen Yiu Chuen Choi 1,2 , Robert H. Bell 1 , Fan Mo 1 , Anne M. Haegert 1 , Peter W. Gout 2 , Neil Fleshner 3 , Martin E. Gleave 1 , Michael Pollak 4 , Colin C. Collins 1 and Yuzhuo Wang 1,2 1 The Vancouver Prostate Centre, Vancouver General Hospital and Department of Urologic Sciences, The University of British Columbia, Vancouver, British Columbia, Canada 2 Department of Experimental Therapeutics, BC Cancer Research Centre, Vancouver, British Columbia, Canada 3 Division of Urology, University of Toronto, Department of Urology, University Health Network, Princess Margaret Hospital, Toronto, Ontario, Canada 4 Lady Davis Research Institute and McGill University, Montreal, Quebec, Canada * Co-first Authors Correspondence to: Yuzhuo Wang, email: // Colin C. Collins, email: // Michael Pollak, email: // Keywords : prostate cancer; tumour heterogeneity; metabolic heterogeneity; patient-derived xenografts Received : January 20, 2017 Accepted : January 25, 2017 Published : February 09, 2017 Abstract To avoid over- or under-treatment of primary prostate tumours, there is a critical need for molecular signatures to discriminate indolent from aggressive, lethal disease. Reprogrammed energy metabolism is an important hallmark of cancer, and abnormal metabolic characteristics of cancers have been implicated as potential diagnostic/prognostic signatures. While genomic and transcriptomic heterogeneity of prostate cancer is well documented and associated with tumour progression, less is known about metabolic heterogeneity of the disease. Using a panel of high fidelity patient-derived xenograft (PDX) models derived from hormone-naïve prostate cancer, we demonstrated heterogeneity of expression of genes involved in cellular energetics and macromolecular biosynthesis. Such heterogeneity was also observed in clinical, treatment-naïve prostate cancers by analyzing the transcriptome sequencing data. Importantly, a metabolic gene signature of increased one-carbon metabolism or decreased proline degradation was identified to be associated with significantly decreased biochemical disease-free patient survival. These results suggest that metabolic heterogeneity of hormone-naïve prostate cancer is of biological and clinical importance and motivate further studies to determine the heterogeneity in metabolic flux in the disease that may lead to identification of new signatures for tumour/patient stratification and the development of new strategies and targets for therapy of prostate cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".