Abstract 5342: Somatic bi-allelic loss of TSC genes in eosinophilic solid and cystic renal cell carcinoma (ESC RCC)
Bibliographic record
Abstract
Abstract Renal cell carcinoma (RCC) subtypes with overlapping histomorphologic features pose diagnostic challenges. For instance, a unique category of sporadic renal tumors with eosinophilic cytoplasm and solid and cystic growth pattern (ESC RCC) may mimic a RCC subtype usually encountered in patients with germline aberrations of tuberous sclerosis complex (TSC) genes (TSC RCC). Here, we used next-generation sequencing (NGS) technology to interrogate the clinicopathologic and molecular profiles of ESC RCC tumors. Mutational and copy number analysis of NGS data from ESC RCC tumors revealed a somatic bi-allelic loss of TSC family genes, specifically TSC1 or TSC2, in six out of seven profiled cases. However, the corresponding background kidney showed only wild type alleles, thus excluding any germline involvement and differentiating ESC RCC from TSC RCC. Furthermore, bi-allelic loss of the TSC genes occurred in a mutually exclusively manner in this cohort. Our study clarifies the molecular identity of ESC RCC, and can thus guide future therapeutic strategies and provide a basis for the revision of current RCC classification. Citation Format: Nicole D. Lee, Pankaj Vats, Xuhong Cao, Fengyun Su, Robert Lonigro, Kumpati Premkumar, Kiril Trpkov, Jesse K. McKenney, Rohit Mehra, Saravana M. Dhanasekaran, Arul M. Chinnaiyan. Somatic bi-allelic loss of TSC genes in eosinophilic solid and cystic renal cell carcinoma (ESC RCC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5342.
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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 | 0.000 |
| 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".