DNA methylation signature to define cell ontogeny of renal cell carcinomas.
Bibliographic record
Abstract
536 Background: DNA methylation is a heritable covalent modification that is developmentally regulated and is critical in tissue-type definition. Although genotype-phenotype correlations have been described for different subtypes of renal cell carcinoma (RCC), it is unknown if DNA methylation profiles correlate with morphological or ontology based phenotypes. Here we test the hypothesis that DNA methylation signatures can discriminate between putative precursor cells in the nephron. Methods: We performed deep profiling of DNA methylation in diverse histopathological RCC subtypes and validated DNA methylation and transcriptome signatures in The Cancer Genome Atlas Clear Cell and Chromophobe Renal Cell Carcinoma Datasets. Results: Our data provide the first mapping of methylome epi-signature and indicates that RCC subtypes can be grouped into two major epi-clusters: C1 which encompasses clear cell RCC, papillary RCC, mucinous and spindle cell carcinomas and translocation RCC; C2 which comprises oncocytoma and chromophobe RCC. Interestingly, C1 epi-cluster displayed three fold more hypermethylation as compared to C2 epi-cluster. Of note, differentially methylated regions between C1 and C2 epi-clusters occur in gene bodies and intergenic regions, instead of gene promoters. Transcriptome analysis of C1 epi-cluster suggests a functional convergence on Polycomb targets, whereas C2 epi-cluster displays DNA methylation defects. Furthermore, we find that our epigenetic ontogeny signature is associated with worse outcomes of patients with clear-cell RCC. Conclusions: Taken together, our data defines the epi-clusters that can discriminate between distinct RCC subtypes and for the first time, to our knowledge, define the epigenetic basis for proximal versus distal tubule derived kidney tumors.
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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 | 0.001 |
| 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) | 0.001 | 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".