Abstract 98: The somatic mutational landscape of the mitochondrial genome in prostate cancer: evaluation of clinical impact
Bibliographic record
Abstract
Abstract Prostate cancer remains the most prevalent and second most lethal non-skin cancer in men. Whole genome studies have provided important insights into specific driver genes, however most of these studies have not assessed one key portion of the genome: the mitochondrial genome. To gain a complete understanding of the most commonly-diagnosed sub-groups of prostate cancer: low- and intermediate-risk localized disease, we surveyed the mitochondrial genomes from next-generation sequencing (NGS) data of over 300 tumour samples from prostate cancer patients. These samples were mainly from prostate cancer patients with clinical Gleason Scores of 3+3, 3+4 and 4+3. All had at least 5 years of follow-up data (median > 8 years), allowing identification of clinical associations with identified somatic mutations via Cox Proportional Hazards modeling and machine-learning. Recurrent somatic mutations in mtDNA were identified, and these were associated with clinical outcomes. One third of patients were found to have a somatic mtDNA mutation. These mutations appear to be associated with age of patient. The mtDNA region with the majority of mutations was the regulatory D-loop region, although certain proteins had high numbers of mutations. Those somatic mutations occurring within the coding regions in general were nonsynonymous. Specific identified candidate somatic mutations were validated via Sanger sequencing. Clinical associations between somatic were also integrated with existing copy-number alteration (CNA) biomarkers using machine learning methods to evaluate performance. mtDNA mutations were also compared to identified aberrations (CNA, PGA, SNVs) within the nuclear genome to determine correlations between the two genomes, in addition to other somatic mutations or altered-expression in nuclear-encoded mitochondrial proteins. Taken together, these data demonstrate a key role for mitochondrial mutations in driving prostate cancer. Citation Format: Julia F. Hopkins, Veronica Y. Sabelnykova, John Watson, Lawrence E. Heisler, Junyan Zhang, Michael Fraser, Theodorus van der Kwast, Robert G. Bristow, Paul C. Boutros. The somatic mutational landscape of the mitochondrial genome in prostate cancer: evaluation of clinical impact. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 98.
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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 | 0.002 |
| 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) | 0.003 | 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".