Abstract 5447: A microRNA signature for the classification of renal cell carcinoma subtypes
Bibliographic record
Abstract
Abstract Introduction: Renal cell carcinoma (RCC) accounts for 90% of all kidney cancers. It comprises a heterogeneous group of renal tumors with distinct genetic and molecular characteristics including clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC). The differential diagnosis of RCC subtypes relies on distinct morphology which is not always accurate. Accurate classification of RCC subtypes is critical since each exhibits different clinical behaviour, prognosis and response to therapy. The purpose of this study is to determine whether a limited number of miRNAs can classify RCC subtypes with high accuracy. Experimental Design: We extracted RNA from 90 formalin-fixed paraffin-embedded (FFPE) tissues including 27 clear cell RCC, 29 papillary RCC, 19 chromophobe RCC, 4 unclassified RCC tumors and 11 oncocytomas. We measured the absolute expression of six miRNAs by qRT-PCR. Receiver operator characteristic curves were constructed and the area under the curve (AUC) was calculated to assess diagnostic performance. We also tested miRNA expression by in situ hybridization (ISH) in an independent set of ninety-eight FFPE renal tumors. Results: We developed a two-step miRNA classifier. In the first step, expressions of selected miRNAs were found to discriminate clear cell RCC and papillary RCC from chromophobe RCC and renal oncocytoma. Two miRNAs were able to discriminate clear cell RCC and papillary RCC from chromophobe RCC and oncocytoma. miR-221 was significantly overexpressed in chromophobe RCC and oncocytoma compared to clear cell RCC and papillary RCC (4.49-fold change, p= 6.398e-010) and was able to discriminate between the two groups (AUC: 0.9637, 95% CI: 0.9132 to 1.014, p<0.0001). In the second step, the absolute expression of two miRNAs could distinguish clear cell RCC from papillary RCC (10.4-fold change, p = 1.243e-013). Moreover, an additional two miRNAs could differentiate chromophobe RCC from renal oncocytoma (3.30- fold change, p = 1.751e-006). In situ hybridization revealed that miRNAs display a nuclear staining pattern that was able to distinguish clear cell RCC from papillary RCC (p<0.001) and chromophobe RCC from renal oncocytoma (p =0.009). Conclusion: miRNA expressions were able to distinguish between RCC subtypes and renal oncocytoma. miRNA assessment by in situ hybridization is a clinically useful diagnostic tool that can complement current methods for RCC classification. Citation Format: Ashley Di Meo, Mereet Hanna, Rola Saleeb, Samantha Wala, Adriana Krizova, Manal Gabril, Haiyan Zhai, Maria Pasic, Andrew Evans, Fadi Brimo, George Yousef. A microRNA signature for the classification of renal cell carcinoma subtypes [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 5447. doi:10.1158/1538-7445.AM2017-5447
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".