Predicting transcriptional interactions with GrnPFN

Abstract Combinatorial therapies offer opportunities to improve treatment efficacy and overcome therapeutic resistance, but their rational design requires understanding how combined interventions interact beyond their individual effects. Combined genetic and chemical perturbations can elicit nonadditive transcriptional responses, yet the vast number of possible combinations limits experimental characterization. Predicting these interactions is particularly challenging when few or no combinations have been measured. Here we present GrnPFN, a prior-data fitted network that predicts nonadditive transcriptional responses to combinations from single-perturbation profiles. Pretrained on simulated screens generated from random gene-regulatory networks, GrnPFN incorporates measured combinations through in-context learning without retraining. We evaluated GrnPFN in single-cell screens spanning CRISPR activation, RNA knockdown and drug combinations, using separate cells to construct model inputs, training targets and test targets. Without measured combinations in the target screen, GrnPFN outperformed gradient boosting trained on other screens. With limited combination measurements, it outperformed kernel ridge regression fitted to the same measured pairs. These advantages extended to an additional screen combining CRISPR interference with a drug. Our results demonstrate that pretraining on simulated regulatory networks enables prediction of nonadditive transcriptional interactions across perturbation modalities when experimental measurements are scarce.