Bibliographic record
Abstract
Introduction: Genomic and immune analyses in sarcomatoid/rhabdoid renal cell carcinoma (S/R RCC) have been limited to bulk tumor analysis and thus lack cellular resolution and spatial perspective.Herein, we use in situ whole transcriptome profiling (WTP) to define molecular differences between tumor regions with and without S/R features, aiming to identify molecular markers of S/R tumors that could lead to better diagnosis or treatments.Methods: All patients who underwent surgical excision of RCC at the MUHC between 2010 and 2020 were screened by a uropathologist, and histologically defined regions of S/R, ccRCC, papillary, chromophobe RCC, and benign kidney were selected to construct tissue microarrays (TMAs).Whole-exome sequencing (WES) and Compartment-guided spatial WTP were applied for gene and transcriptome analysis.Results: Our cohort included 56 RCC patients and their TMAs, consisting of 403 cores representing patient-matched tumor areas with and without S/R features.For WES, 47 patients were used to identify copy number variations (CNVs) analysis.Four hundred cores of 55 patients were used for WTP and five groups of clustered with 2000 highly variable genes (HVGs) were constructed.The most variable genes of each tumor type were identified by using digital spatial transcriptome profiling.Whole-exome sequencing was used to identify mutational patterns of tumor cells using a list of specific genes of interest.Conclusions: According to current and ongoing results, WES and compartmentguided WTP should be used to generate an unprecedented resolution to the molecular and genomic characteristics of S/R RCC tumors and tumor microenvironment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.364 | 0.249 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".