Abstract 4346: Prognostic gene signature for intermediate risk prostate cancer
Bibliographic record
Abstract
Abstract The over treatment of prostate cancer patients is a significant concern, as recent clinical trials suggest that many patients are over treated which can lead to significant patient morbidity. Although the Gleason score is a powerful predictor of lethal or indolent disease, a significant proportion (∼40%) of men present with early stage Gleason score (GS) 7 tumors, for whom prognosis is variable. The goal of this study is to develop and optimize a robust prognostic gene signature that can be utilized on formalin fixed paraffin embedded (FFPE) core biopsy tumor material to better classify patients with intermediate risk, GS 7 tumors into good and poor outcome groups. Three gene signatures were derived from publicly available gene expression profiles of the Swedish Watchful Waiting cohort. The Genomic Grade Index consisted of the top 25 molecular signatures discriminating between high (8, 9 & 10) and low (≤ 6) GS tumors. The Lethal Gene Score consisted of the top 25 molecular signatures discriminating between lethal and indolent disease within GS 7 tumors only. A network-based gene signature consisted of 88 genes which accurately stratified GS 7 patients into high risk and low risk groups, resembling the survival curves of high GS and low GS patients. The prognostic capacity of the combined gene signature was tested in silico on the gene expression profiles of the Mayo cohort. Results demonstrated the gene signature's highly robust capacity for differentiating low risk and high risk patients within GS 7 patients. The NanoString nCounter System will be used to quantify mRNA from prostate FFPE blocks to assess the expression of the 138 prognostic genes. 156 archived prostate tumor blocks will be collected from intermediate risk, GS 7 patients enrolled in the 2005 PR5 prostate trial, which also collected 12 years of clinical follow-up information. Results will be correlated with biochemical (PSA) failure rates and overall survival. In short, our findings provide proof-of-principle that through the use of gene signatures it is possible to separate prostate cancer patients of intermediate risk into good and poor outcome groups. Furthermore, they also identify multiple gene candidates whose expression could likely be formulated into a clinically applicable assay, the implementation of which could serve to stratify prostate cancer patients with tumors of intermediate risk into more accurate high and low risk groups. Citation Format: Brian Li, Robin Hallet, Ying Wu, Greg Pong, John Hassell, Sebastien Hotte, Mark Levine, Himansu Lukka, Anita Bane. Prognostic gene signature for intermediate risk 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 4346. doi:10.1158/1538-7445.AM2015-4346
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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 | 0.001 | 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) | 0.001 | 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".