Abstract C68: Global transcriptome sequencing of ethnically diverse formalin-fixed patient samples identifies biomarkers of recurrence in prostate cancer
Bibliographic record
Abstract
Abstract Prostate cancer remains the second leading cause of cancer death in American men, but biomarkers that can predict outcome following treatment are urgently needed to identify patients with aggressive disease. In an effort to identify biomarkers of recurrence, we have performed global RNA-sequencing on 106 formalin-fixed, paraffin-embedded (FFPE) prostatectomy samples from 100 patients at three independent sites, and identified a new set of biomarkers of biochemical recurrence composed of a 24-gene panel including 22 protein-coding genes and two non-coding genes. We observed excellent correlation between TaqMan and RNAseq values, as well as for RNAseq between replicate libraries. We validated this 24-gene panel on an independent publicly available dataset of 140 patients and this new panel outperformed previously published markers based on cell proliferation gene sets. In addition, we have identified genes that are differentially expressed between African-American and Caucasian prostate cancer patients, and mitochondrial SNPs that are associated with both race and outcome. We observed a number of genes relevant to prostate cancer biology including ETV5, ZEB1, ZEB2, B2M, FYN, and miR-183 that were differentially expressed between African-American and Caucasian patients. These genes may play a role in the disparities observed in African-American patients who have significantly worse outcomes relative to Caucasian patients with prostate cancer. Citation Format: Jianpeng Xu, Qi Long, Adeboye O. Osunkoya, Soma Sannigrahi, Brent A. Johnson, Wei Zhou, Theresa Gillespie, Jong Y. Park, Robert K. Nam, Linda Sugar, Aleksandra Stanimirovic, Arun K. Seth, John A. Petros, Carlos S. Moreno. Global transcriptome sequencing of ethnically diverse formalin-fixed patient samples identifies biomarkers of recurrence in prostate cancer. [abstract]. In: Proceedings of the Sixth AACR Conference: The Science of Cancer Health Disparities; Dec 6–9, 2013; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2014;23(11 Suppl):Abstract nr C68. doi:10.1158/1538-7755.DISP13-C68
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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) | 0.001 | 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 | 0.001 |
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".