ArticleBMC genomics2016
Outlier analysis of functional genomic profiles enriches for oncology targets and enables precision medicine.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Functional Depth Biomarkers Distinguish Lung Squamous Cell Carcinoma from Lung Adenocarcinoma.Research square · 2026Article
- Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning.Cell genomics · 2024Article
- ACE Phenotyping in Human Blood and Tissues: Revelation of ACE Outliers and Sex Differences in ACE Sialylation.Biomedicines · 2024Article
- YAP1 Expression in SCLC Defines a Distinct Subtype With T-cell-Inflamed Phenotype.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2021Article
- Detecting EGFR mutations and ALK/ROS1 rearrangements in non-small cell lung cancer using malignant pleural effusion samples.Thoracic cancer · 2019Article
- Characterization of Alternative Splicing Events in HPV-Negative Head and Neck Squamous Cell Carcinoma Identifies an Oncogenic DOCK5 Variant.Clinical cancer research : an official journal of the American Association for Cancer Research · 2018Article
- A Novel Functional Splice Variant ofCancer research · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenome-scale functional genomic screens across large cell line panels provide a rich resource for discovering tumor vulnerabilities that can lead to the next generation of targeted therapies. Their data analysis typically has focused on identifying genes whose knockdown enhances response in various pre-defined genetic contexts, which are limited by biological complexities as well as the incompleteness of our knowledge. We thus introduce a complementary data mining strategy to identify genes with exceptional sensitivity in subsets, or outlier groups, of cell lines, allowing an unbiased analysis without any a priori assumption about the underlying biology of dependency.
resultsGenes with outlier features are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, despite no a priori knowledge being used to drive the analysis. Identification of exceptional responders (outliers) may not lead only to new candidates for therapeutic intervention, but also tumor indications and response biomarkers for companion precision medicine strategies. Several tumor suppressors have an outlier sensitivity pattern, supporting and generalizing the notion that tumor suppressors can play context-dependent oncogenic roles.
conclusionsThe novel application of outlier analysis described here demonstrates a systematic and data-driven analytical strategy to decipher large-scale functional genomic data for oncology target and precision medicine discoveries.
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