Abstract PR09: The prognostic landscape of genes and infiltrating immune cells across human cancers
Bibliographic record
Abstract
Abstract Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing datasets are fragmented and difficult to analyze systematically. We present a pan-cancer resource and comprehensive meta-analysis of expression signatures from ~18,000 human tumors with overall survival outcomes across 39 malignancies. While a third of prognostic genes were cancer-specific, a FOXM1 regulatory network is a major predictor of adverse outcomes, while favorably prognostic genes largely reflect tumor-associated leukocytes. Using a novel computational approach, we enumerated leukocyte subsets in bulk tumor transcriptomes, revealing complex novel malignancy-specific associations between 22 distinct leukocytes and cancer survival. Tumor-associated neutrophil-like polymorphonuclear cell and plasmacytic cells emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. Our results introduce new analytical tools for delineating prognostic genes and leukocytes within and across cancers, and shed light on the impact of tumour heterogeneity on cancer outcomes, with applications for discovering novel biomarkers and therapeutic targets Citation Format: Andrew J. Gentles, Aaron M. Newman, Chih Long Liu, Scott V. Bratman, Weiguo Feng, Dongkyoon Kim, Viswam S. Nair, Xu Yue, Amanda Khuong, Chuong D. Hoang, Maximilian Diehn, Robert B. West, Sylvia K. Plevritis, Ash A. Alizadeh. The prognostic landscape of genes and infiltrating immune cells across human cancers. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR09.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".