ArticlePLoS computational biology2020
Tissue-guided LASSO for prediction of clinical drug response using preclinical samples.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 52 citations in OpenAlex.
- Tissue-aware elastic net decomposition reveals shared and lineage-specific drug response biomarkers.bioRxiv : the preprint server for biology · 2026Article
- MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling.BMC biology · 2025Article
- Fast-adapting graph neural network with prior knowledge for drug response prediction across preclinical and clinical data.Journal of pharmaceutical analysis · 2025Article
- DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.Journal of pharmaceutical analysis · 2025Article
- Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.Interdisciplinary sciences, computational life sciences · 2025Article
- Out of distribution learning in bioinformatics: advancements and challenges.Briefings in bioinformatics · 2025Review
- Anticancer Monotherapy and Polytherapy Drug Response Prediction Using Deep Learning: Guidelines and Best Practices.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Transfer learning of condition-specific perturbation in gene interactions improves drug response prediction.Bioinformatics (Oxford, England) · 2024Article
- TheMarker: a comprehensive database of therapeutic biomarkers.Nucleic acids research · 2024Article
- Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model.Scientific reports · 2023Article
- Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL.Genomics, proteomics & bioinformatics · 2023Article
- Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?Bioinformatics (Oxford, England) · 2023Article
- MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores.Bioinformatics (Oxford, England) · 2023Article
- NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data.BMC medicine · 2022Article
- Tissue-specific identification of multi-omics features for pan-cancer drug response prediction.iScience · 2022Article
- Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics.Current genomics · 2022Article
- An overview of machine learning methods for monotherapy drug response prediction.Briefings in bioinformatics · 2022Article
- Representation of molecules for drug response prediction.Briefings in bioinformatics · 2022Review
- Dissecting the Genome for Drug Response Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Exploring Machine Learning Techniques to Predict the Response to Omalizumab in Chronic Spontaneous Urticaria.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients. We designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples' tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide better prediction performance. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs' mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.