Multi-objective domain adaptation for pan-cancer transfer of pharmacogenomic drug response to patient tumours
- 1University of Eastern Finland
Abstract
Large-scale pharmacogenomic screens provide rich drug-sensitivity measurements in cancer cell lines, but transferring these measurements to patient tumours remains limited by biological heterogeneity and cross-domain distribution shift. Existing machine learning approaches, including adversarial autoencoder and domain adaptation, address this gap by learning shared latent representations between preclinical models and patient samples. However, alignment without explicit biological constraints can produce unreliable representations when cancer lineage structure and clinically relevant patient heterogeneity are not preserved. Here, we introduce MODAE+, a pan-cancer multi-objective deep learning domain adaptation framework that jointly balances domain alignment, deconfounding, drug-response prediction, survival prediction, and cancer-lineage classification across 9,696 TCGA tumours, 1,293 CCLE cancer cell lines, and CTRP sensitivity profiles for 544 compounds in 738 matched cell lines. This multi-objective design improved cross-domain alignment while preserving cancer-lineage organization and patient heterogeneity. For drugs shared between the pharmacogenomic screen and TCGA treatment records, MODAE+ predicted sensitivity was significantly associated with overall survival among patients who received each drug in 46.3% of evaluated cancer-drug pairs by log-rank testing and 41.5% by multivariable Cox analysis. Biological validation across four curated drug-gene resources identified significant expression differences exceeding 1.5-fold for 34.9-64.4% of drugs in pan-cancer analyses, based on the lowest-p-value annotated gene per drug. Drug-class-specific patterns involved intended molecular targets for targeted therapies and predominantly transport, metabolism, detoxification and resistance-related genes for classical chemotherapies. As an additional validation, we derived pretreatment responder-nonresponder gene-expression signatures from 13 external breast cancer datasets and evaluated their concordance with MODAE+ predictions across 69 signature-drug comparisons in TCGA patients treated with paclitaxel or doxorubicin. Consensus signature scores showed Spearman correlations of 0.791 with predicted paclitaxel sensitivity and 0.724 with predicted doxorubicin sensitivity, averaging 0.758 across the two drugs. Together, these complementary analyses provide supporting evidence for the clinical relevance and biological consistency of MODAE+ predictions, suggesting their potential to identify drug-sensitive tumour subgroups and generate hypotheses about mechanisms of drug resistance.
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BibTeX
@misc{fortino2026multiobjective,
title = {{Multi-objective domain adaptation for pan-cancer transfer of pharmacogenomic drug response to patient tumours}},
author = {V. Fortino and T. J. Rintala},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.04.764164},
url = {https://www.biorxiv.org/content/10.64898/2026.11.04.764164v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Multi-objective domain adaptation for pan-cancer transfer of pharmacogenomic drug response to patient tumours
AU - V. Fortino
AU - T. J. Rintala
PY - 2026
DA - 2026/11/04
PB - bioRxiv
DO - 10.64898/2026.11.04.764164
UR - https://www.biorxiv.org/content/10.64898/2026.11.04.764164v1
AB - Large-scale pharmacogenomic screens provide rich drug-sensitivity measurements in cancer cell lines, but transferring these measurements to patient tumours remains limited by biological heterogeneity and cross-domain distribution shift. Existing machine learning approaches, including adversarial autoencoder and domain adaptation, address this gap by learning shared latent representations between preclinical models and patient samples. However, alignment without explicit biological constraints can produce unreliable representations when cancer lineage structure and clinically relevant patient heterogeneity are not preserved. Here, we introduce MODAE+, a pan-cancer multi-objective deep learning domain adaptation framework that jointly balances domain alignment, deconfounding, drug-response prediction, survival prediction, and cancer-lineage classification across 9,696 TCGA tumours, 1,293 CCLE cancer cell lines, and CTRP sensitivity profiles for 544 compounds in 738 matched cell lines. This multi-objective design improved cross-domain alignment while preserving cancer-lineage organization and patient heterogeneity. For drugs shared between the pharmacogenomic screen and TCGA treatment records, MODAE+ predicted sensitivity was significantly associated with overall survival among patients who received each drug in 46.3% of evaluated cancer-drug pairs by log-rank testing and 41.5% by multivariable Cox analysis. Biological validation across four curated drug-gene resources identified significant expression differences exceeding 1.5-fold for 34.9-64.4% of drugs in pan-cancer analyses, based on the lowest-p-value annotated gene per drug. Drug-class-specific patterns involved intended molecular targets for targeted therapies and predominantly transport, metabolism, detoxification and resistance-related genes for classical chemotherapies. As an additional validation, we derived pretreatment responder-nonresponder gene-expression signatures from 13 external breast cancer datasets and evaluated their concordance with MODAE+ predictions across 69 signature-drug comparisons in TCGA patients treated with paclitaxel or doxorubicin. Consensus signature scores showed Spearman correlations of 0.791 with predicted paclitaxel sensitivity and 0.724 with predicted doxorubicin sensitivity, averaging 0.758 across the two drugs. Together, these complementary analyses provide supporting evidence for the clinical relevance and biological consistency of MODAE+ predictions, suggesting their potential to identify drug-sensitive tumour subgroups and generate hypotheses about mechanisms of drug resistance.
ER -