ArticleNature communications2022
An integral genomic signature approach for tailored cancer therapy using genome-wide sequencing data.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- NFATC2 target gene signature correlates with immune checkpoint blockade resistance in melanoma.American journal of cancer research · 2025Article
- Tumor-Associated Antigen Burden Correlates with Immune Checkpoint Blockade Benefit in Tumors with Low Levels of T-cell Exhaustion.Cancer immunology research · 2024Article
- iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.BMC bioinformatics · 2024Article
- Omics-based molecular classifications empowering in precision oncology.Cellular oncology (Dordrecht, Netherlands) · 2024Review
- Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future.Future oncology (London, England) · 2024Review
- iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.Research square · 2023Article
- An integral genomic signature approach for tailored cancer therapy using genome-wide sequencing data.Nature communications · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Low-cost multi-omics sequencing is expected to become clinical routine and transform precision oncology. Viable computational methods that can facilitate tailored intervention while tolerating sequencing biases are in high demand. Here we propose a class of transparent and interpretable computational methods called integral genomic signature (iGenSig) analyses, that address the challenges of cross-dataset modeling through leveraging information redundancies within high-dimensional genomic features, averaging feature weights to prevent overweighing, and extracting unbiased genomic information from large tumor cohorts. Using genomic dataset of chemical perturbations, we develop a battery of iGenSig models for predicting cancer drug responses, and validate the models using independent cell-line and clinical datasets. The iGenSig models for five drugs demonstrate predictive values in six clinical studies, among which the Erlotinib and 5-FU models significantly predict therapeutic responses in three studies, offering clinically relevant insights into their inverse predictive signature pathways. Together, iGenSig provides a computational framework to facilitate tailored cancer therapy based on multi-omics data.
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Registered trials
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