Deep learning transcriptomic model for prediction of pan-drug chemotherapeutic sensitivity
Bibliographic record
Abstract
The emergence of precision oncology approaches has begun to inform clinical decision-making in diagnostic, prognostic, and treatment contexts. High-throughput technology has enabled machine learning algorithms to use the molecular characteristics of tumors to generate personalized therapies. However, precision oncology studies have yet to develop a predictive biomarker incorporating pan-cancer gene expression profiles to stratify tumors into similar drug sensitivity profiles. Here we show that a neural network with ten hidden layers accurately classifies pancancer cell lines into two distinct chemotherapeutic response groups based on a pan-drug dataset with 89.0% accuracy (AUC = 0.904). Using unsupervised clustering algorithms, we found a cohort of cell line gene expression data from the Genomics of Drug Sensitivity in Cancer could be clustered into two response groups with significant differences in pan-drug chemotherapeutic sensitivity. After applying the Boruta feature selection algorithm to this dataset, a deep learning model was developed to predict chemotherapeutic response groups. The model’s high classification efficacy validates our hypothesis that cell lines with similar gene expression profiles present similar pan-drug chemotherapeutic sensitivity. This finding provides evidence for the potential use of similar combinatorial biomarkers to select potent candidate drugs that maximize therapeutic response and minimize the cytotoxic burden. Future investigations should aim to recursively subcluster cell lines within the response clusters defined in this study to provide a higher resolution of potential patient response to chemotherapeutics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".