Abstract 3500: Refining diagnosis of renal cell carcinoma subtypes through single-cell resolution transcriptomic signatures
Bibliographic record
Abstract
Abstract A remaining challenge in the field of renal cell carcinoma (RCC) is diagnosis of rare or mixed subtypes, which hampers the delivery of the most effective healthcare to patients. While a new classification scheme, integrating molecular characteristics of tumors, is emerging, it requires a deep understanding of the tumor heterogeneity and etiopathogenesis of each subtype. We have generated and analyzed single-cell transcriptome profiles of 38 human RCCs and normal kidneys 16 RCCs with diverse histological subtypes and established gene signatures specific to each cell type. We have applied these gene signatures to bulk RNA-sequencing data of 1000 RCC tumors with different histological subtypes and normal kidneys from The Cancer Genome Atlas (TCGA) to characterize different cancer types and cellular origins. Unsupervised clustering analysis of TCGA data shows diagnostic discrepancies for 9.3% cases and subclassifies each major subtype (clear cell, papillary and chromophobe) into clusters with differential clinical outcomes.[KM1]. The pathological marker expression in misdiagnosed tumors is consistent with clustering-based classification, not their original diagnosis. Notably, using unique and subtype-specific gene signature patterns, we identified clear cell papillary renal cell tumor (ccpRCT), which is a newly classified benign entity and the main tumor type representing the mis-diagnosed tumors. These observations have further been confirmed by reviewing histological images of the tumors by an experienced genitourinary pathologist. Interestingly, our gene signatures reveal that ccpRCT does not originate from proximal tubule cells unlike clear cell and papillary RCCs. Taken together, our approach suggests a potential new classification method connected to clinical behaviors and drug responses based on overall transcriptomic differences. Citation Format: Minjun Kim, Ariel Madrigal, Zohreh Mehrjoo, Kate I. Glennon, Madeleine Arseneault, Morag Park, Fadi Brimo, Simon Tanguay, Hamed S. Najafabadi, Yasser Riazalhosseini. Refining diagnosis of renal cell carcinoma subtypes through single-cell resolution transcriptomic signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3500.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".