Development and Validation of an Intratumor Heterogeneity–Based Prognostic Model for Clear Cell Renal Cell Carcinoma
Bibliographic record
Abstract
PURPOSE: Clear cell renal cell carcinoma (ccRCC) is characterized by marked intratumor heterogeneity (ITH), which contributes to therapeutic resistance and poor clinical outcomes. We aimed to develop a robust prognostic model for stratifying patients with ccRCC on the basis of ITH. METHODS: RNA-seq data from 522 patients with ccRCC in TCGA-KIRC were analyzed using the DEPTH algorithm to quantify ITH, with external validation in the E-MTAB-1980 cohort (N = 101). Differentially expressed genes between high and low DEPTH tumors were identified, and a machine learning framework was applied to develop the ITHscore. The ITHscore was compared with other published signatures in literature for ccRCC. RESULTS: ) was selected to compose the ITHscore, showing high accuracy in the development (5-year AUC = 0.957) and in the validation cohorts (5-year AUC = 0.82). The ITHscore had the best performance across all 45 retrieved signatures in both development and validation data sets. High-ITHscore tumors exhibited immunosuppressive microenvironments and were associated with immune checkpoint blockade (ICB) resistance signatures. The ITHscore was significantly associated with poor overall survival in five distinct tumor types across a meta-analysis of 104 independent data sets comprising 18,004 patients. CONCLUSION: We developed and validated the ITHscore, a three-gene expression-based model with superior prognostic performance in ccRCC. The ITHscore reflects key features of aggressiveness in tumor biology, including immune evasion and ICB resistance. Its minimal gene set and consistent performance across data sets support its potential for clinical implementation in ccRCC stratification.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".