Generative models
Does modelling how drugs change gene expression help predict response?
Open access · cc by · source: Europe PMC
A variational autoencoder that learns both how drugs shift gene expression and whether cells respond predicted drug sensitivity better than standard classifiers for over half of the drugs tested.
Study at a glance
- Design
- Computational / modelling — Dr.VAE fitted separately for each of 26 drugs on CTRPv2 sensitivity and CMap-L1000 perturbation data; 100 train/validation/test splits; compared with ridge logistic regression, random forest, RBF-SVM, SSVAE, PCA and PertVAE embeddings
- N
- 26 drugs evaluated; cell-line counts vary by drug (up to 855 cell lines with viability data, up to 60 with perturbation data), so no single N
- Population
- Cancer cell lines from the CTRPv2 drug-sensitivity screen and the LINCS CMap-L1000 perturbation screen
- Outcome
- Test AUROC and AUPR for classifying cell lines as drug responders or non-responders
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
Dr.VAE beat all three standard classifiers on at least 14 of the 26 drugs, tied on nine, and lost on only three. It significantly beat the perturbation-free SSVAE on 9 drugs and lost on one. Swapping the learned perturbation function for an identity lowered performance on 16 drugs, but most of the gain seemed to come from a better latent representation. Standard classifiers also did better on an unsupervised VAE embedding than on PCA.
Methodology
The authors built Dr.VAE, a deep generative model that compresses gene expression into a low-dimensional latent space, learns how a drug moves a cell line within that space, and predicts whether the cell line responds. It was trained on drug-sensitivity labels plus before-and-after-treatment expression profiles, separately for 26 FDA-approved drugs, over 100 data splits. They compared it with ridge logistic regression, random forests and SVMs, and ran ablations: a VAE without perturbation data (SSVAE), Dr.VAE with its perturbation function replaced by an identity, and classifiers fitted on unsupervised PCA or VAE embeddings.
Limitations
Everything is in cancer cell lines, so it says nothing directly about predicting response in patients. Only gene expression was used, and perturbations were taken only at 6 hours at one dose, which the authors note may miss slower feedback effects (as with fluvastatin). For some drugs perturbation data existed for very few cell lines, and improvements over baselines were often a few percentage points of AUROC.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
A generative latent space can improve prediction even when the modelled mechanism adds less.
Dr.VAE, which models drug-induced expression changes in a latent space, beat standard classifiers on at least 14 of 26 drugs and lost on three, though much of the gain seemed to come from a better latent representation.
Evidence for the claim as stated.
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