Integrative molecular characterization of sarcomatoid and rhabdoid renal cell carcinoma (S/R RCC) to reveal potential determinants of poor prognosis and response to immune checkpoint inhibitors (ICI).
Bibliographic record
Abstract
715 Background: S/R RCC are highly aggressive tumors but recent pilot clinical data have suggested that these tumors respond well to ICI. Our aim was to perform integrative molecular characterization of S/R RCC tumors in order to characterize potential features that underlie their poor prognosis and responses to ICI. Methods: We compared genomic (1), transcriptomic (2) and immune microenvironment (3) data between S/R and non-S/R tumors. (1) S/R patients from 3 cohorts [N = 209]: The Cancer Genome Atlas [TCGA], CheckMate 010/025 & panel sequencing from Dana-Farber/Harvard Cancer Center [DF/HCC]. (2) RNA-seq on S/R from 2 cohorts [N = 98]: TCGA & CheckMate 010/025. (3) Immunofluorescence for CD8+ T cells [N = 17] & Immunohistochemistry for PD-L1 expression on tumor cells [N = 118] from CheckMate 010/025. Overall Response Rate (ORR), Progression Free Survival (PFS), and Overall Survival (OS) in S/R RCC was compared between ICI and non-ICI in clinical cohorts (Table). Results: S/R tumors were significantly enriched in mutations in BAP1, NF2, RELN, and MUTYH, deletions of CDKN2A/B & amplifications of EZH2 (q < 0.05) compared to non-S/R tumors. Gene Set Enrichment Analysis showed upregulation of epithelial-mesenchymal transition, immune pathways, and proliferation programs compared to non-S/R tumors in both RNA-seq cohorts independently (q < 0.25). S/R tumors exhibited greater infiltration by CD8+ T cells at the tumor margin (p = 0.048) and PD-L1 expression on tumor cells (43.2% vs 21.0%, p < 0.01) compared to non-S/R. S/R had improved ORR, PFS, and OS on ICI vs. non-ICI (Table). Conclusions: S/R RCC tumors have distinctive molecular features that may account for their association with poor prognosis and outcomes on ICI.[Table: see text]
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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) | 0.002 | 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".