Abstract 2464: Papillary renal cell carcinoma, proposal of a new classification system based on integrated molecular, histological and clinical analysis
Bibliographic record
Abstract
Abstract Background: Papillary Renal Cell Carcinoma (PRCC) is divided into histological subtypes 1 and 2. Type 2 is known to have worse clinical behavior. A number of PRCC cases (~ 50%), fail to meet all reported morphological criteria for either type, hence are best characterized as PRCC not otherwise specified (NOS). There are yet no reliable markers to resolve the PRCC NOS category. That in turn reflects the clinical dilemma of how to manage these patients. Experimental Design: PRCC patient cohort of 115 cases was selected for the study. Cases were subtyped histologically into PRCC types 1, 2 and NOS. Potentially distinguishing markers ABCC2, CA9, SAll4, and BCL2 selected from our previous genomic analysis, were assessed by immunohistochemistry (IHC). A total of 24 cases were further selected for molecular analysis using miRNA expression and copy number variation (CNV). Univariate and multivariate survival analysis were performed using Log rank test and cox proportionate hazards. Results: Markers ABCC2, CA9 exhibited distinct staining patterns between the two classic PRCC subtypes; and successfully classified many of the PRCC NOS (45%) cases. Moreover, immunomarkers revealed a third distinct subtype of PRCC (35% of the PRCC cohort). Molecular testing using miRNA expression and CNV analysis confirmed the presence of three distinct molecular signatures corresponding to the 3 subtypes. On univariate analysis DFS was significantly enhanced in the type1 versus 2& 3 (p value 0.047). PRCC subtyping retained significance on multivariate analysis (p value 0.025, HR:6, 95% CI 1.25 to 32.2) . Conclusion: We propose a new classification system of PRCC integrating morphological, immunophenotypical, and molecular analysis. Our classification reveals a 3rd PRCC subtype that was not previously described. This subtype has overlapping morphology of with PRCC types 1 and 2, hence would be subtyped as PRCC NOS in the current classification. Molecularly PRCC type 3 has a distinct signature and clinically it behaves similar to PRCC type 2. The new classification stratifies PRCC patients into clinically relevant subgroups and has significant future implications on the management of PRCC. Citation Format: Rola Saleeb, Mina Farag, Fadi Brimo, Fabio Rotondo, Pamela Plant, George Yousef. Papillary renal cell carcinoma, proposal of a new classification system based on integrated molecular, histological and clinical analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2464. doi:10.1158/1538-7445.AM2017-2464
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".