Deciphering drug response and phenotypic heterogeneity of cancer cells using gene ensembles of regulatory units defined by chromatin domains
Bibliographic record
Abstract
Abstract The effect of co-localization of genes in the topologically associated domains (TADs) and their activity as a regulatory unit in cancer samples and cells, together with drug-response, needs comprehensive analysis. Here, we analyzed the activity of TADs using cancer-cell transcriptomes along with chromatin-interaction and epigenome profiles to understand their relationship with drug-response. Our analysis of 819 cancer cell-line transcriptomes revealed that their response to multiple drugs was more correlated with the activity of individual TADs than genes. Applying our approach to 9014 cancer patients’ data (20 different cancer types) also revealed a higher association between survival and the activity of thousands of individual TADs in comparison to their genes. CRISPR-mediated knock-out of regulatory sites inside a TAD associated with cisplatin-response of oral cancer cells and discovery of primate-specific gain of synteny of genes within a TAD containing EGFR gene and its contribution towards cancer malignancy demonstrate greater utility of TAD-activity based analysis.
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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 | 0.000 | 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) | 0.001 | 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".