ArticleBriefings in bioinformatics2022
A cross-study analysis of drug response prediction in cancer cell lines.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed.
- Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts.iScience · 2026Article
- Convergent neuroimmune signaling underlying rapid antidepressant response to ketamine and psychedelics.Molecular psychiatry · 2026Article
- Harmonization and integration of pharmacogenomics screens.Bioinformatics (Oxford, England) · 2026Article
- DiSPA: differential substructure-pathway attention for drug response prediction.Bioinformatics (Oxford, England) · 2026Article
- Critical evaluation of drug response prediction models with DrEval.Nature communications · 2026Article
- Monotherapy cancer drug-blind response prediction is limited to intraclass generalization.PLoS computational biology · 2026Article
- Monotherapy cancer drug-blind response prediction is limited to intraclass generalization.PLoS computational biology · 2026Article
- BKDRP: a biological knowledge-driven approach for drug response prediction using multi-omics data in cancer cell lines.BMC bioinformatics · 2026Article
- A Pan-Cancer Ex Vivo Drug Screen Atlas for Functional Precision Oncology.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis.Briefings in bioinformatics · 2026Article
- PKIDB-informed molecular profiling improves reproducible prediction of cancer kinase-inhibitor response.Frontiers in pharmacology · 2026Article
- Genomic innovations in cancer prevention, diagnosis, prognosis and precision therapeutics.Frontiers in genetics · 2026Review
- A novel explainable AI for revealing determinants of cancer drug response through integrative multi-omics analysis.Frontiers in oncology · 2026Article
- Article
- Three-Dimensional Tumor Spheroids of Pancreatic and Colorectal Cancer Cell Lines for High-Throughput Screening of Drug Candidates.ACS omega · 2025Article
- MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling.BMC biology · 2025Article
- Advances and Challenges in Drug Screening for Cancer Therapy: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2025Review
- Pharmacogenomic Drug-Target Network Analysis Reveals Similarity Profiles Among FDA-Approved Cancer Drugs.Pharmaceutics · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Quantitative Assessment of Biological Dynamics with Aggregate Data.Bulletin of mathematical biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
Abstract
To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.
Indexed as
Identifiers
What OpenQuestion holds
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.