Integrative Multi-omics and Supervised Learning Identifies an Epithelial Signature for Radiotherapy Response in Colorectal Cancer
Bibliographic record
Abstract
ABSTRACT Colorectal cancer (CRC) is the third most diagnosed cancer globally, accounting for 9.6% of all cancer cases, and the second leading cause of cancer deaths. Radiotherapy is a common treatment, but can demonstrate dangerous side effects and varying patient outcomes that reflects on CRC cancer heterogeneity at genetic, epigenetic, transcriptomics and proteomic levels. Identifying biomarkers capable of effectively predicting CRC patient responses to radiotherapy remains paramount, and an unmet need. We channeled both unsupervised and supervised approaches to assess radiotherapy response for 233 patients of the S:CORT Consortium. Splitting the cohort, we first integrated matched RNA, CNA, mutation, and methylation profiles of 117 patients using multi-omics factor analysis (MOFA). We identified a new radiotherapy signature of 101 biomarkers associated with patients who demonstrate a complete response to radiotherapy, and validated the signature using a random forest classifier on the internal validation dataset, and an independent testing cohort. Our signature effectively predicted treatment outcomes, achieving 89% accuracy with strong discriminatory performance (ROC_AUC = 0.85; PR_AUC = 0.71) to differentiate patients with complete response to radiotherapy compared to incomplete responders. Assessing human and murine scRNAseq datasets underscores that the signature is predominantly expressed in CRC epithelial cells, which underpin CRC heterogeneity and cellular diversity. Our identified signature enables pre-treatment identification of CRC patients that are unlikely to achieve a complete response to radiotherapy, thereby sparing these patients from unnecessary radiation exposure and off-target damage effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".