Novel RB1-Loss Transcriptomic Signature Is Associated with Poor Clinical Outcomes across Cancer Types
Bibliographic record
Abstract
Abstract Purpose: Rb-pathway disruption is of great clinical interest, as it has been shown to predict outcomes in multiple cancers. We sought to develop a transcriptomic signature for detecting biallelic RB1 loss (RBS) that could be used to assess the clinical implications of RB1 loss on a pan-cancer scale. Experimental Design: We utilized data from the Cancer Cell Line Encyclopedia (N = 995) to develop the first pan-cancer transcriptomic signature for predicting biallelic RB1 loss (RBS). Model accuracy was validated using The Cancer Genome Atlas (TCGA) Pan-Cancer dataset (N = 11,007). RBS was then used to assess the clinical relevance of biallelic RB1 loss in TCGA Pan-Cancer and in an additional metastatic castration-resistant prostate cancer (mCRPC) cohort. Results: RBS outperformed the leading existing signature for detecting RB1 biallelic loss across all cancer types in TCGA Pan-Cancer (AUC, 0.89 vs. 0.66). High RBS (RB1 biallelic loss) was associated with promoter hypermethylation (P = 0.008) and gene body hypomethylation (P = 0.002), suggesting RBS could detect epigenetic gene silencing. TCGA Pan-Cancer clinical analyses revealed that high RBS was associated with short progression-free (P < 0.00001), overall (P = 0.0004), and disease-specific (P < 0.00001) survival. On multivariable analyses, high RBS was predictive of shorter progression-free survival in TCGA Pan-Cancer (P = 0.03) and of shorter overall survival in mCRPC (P = 0.004) independently of the number of DNA alterations in RB1. Conclusions: Our study provides the first validated tool to assess RB1 biallelic loss across cancer types based on gene expression. RBS can be useful for analyzing datasets with or without DNA-sequencing results to investigate the emerging prognostic and treatment implications of Rb-pathway disruption. See related commentary by Choudhury and Beltran, p. 4199
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 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".