ArticleGenome research2014
Linking signaling pathways to transcriptional programs in breast cancer.
Article in Genome research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 68 citations in OpenAlex.
- Trial
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- COVID-19db linkage maps of cell surface proteins and transcription factors in immune cells.Journal of medical virology · 2023Article
- Linking Expression of Cell-Surface Receptors with Transcription Factors by Computational Analysis of Paired Single-Cell Proteomes and Transcriptomes.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.Nucleic acids research · 2022Article
- Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Expression and prognostic potential of TMEM204: a pan-cancer analysis.International journal of clinical and experimental pathology · 2022Article
- SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators.Nucleic acids research · 2021Article
- Integrative network analysis identifies potential targets and drugs for ovarian cancer.BMC medical genomics · 2020Article
- Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data.Genome biology · 2020Article
- Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks.mSystems · 2020Article
- KnockTF: a comprehensive human gene expression profile database with knockdown/knockout of transcription factors.Nucleic acids research · 2020Article
- Investigating the effect of dependence between conditions with Bayesian Linear Mixed Models for motif activity analysis.PloS one · 2020Article
- m1A Regulated Genes Modulate PI3K/AKT/mTOR and ErbB Pathways in Gastrointestinal Cancer.Translational oncology · 2019Article
- Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers.Nature communications · 2019Article
- An integrative method to predict signalling perturbations for cellular transitions.Nucleic acids research · 2019Article
- The Proliferative and Apoptotic Landscape of Basal-like Breast Cancer.International journal of molecular sciences · 2019Review
- Oncogenic MSH6-CXCR4-TGFB1 Feedback Loop: A Novel Therapeutic Target of Photothermal Therapy in Glioblastoma Multiforme.Theranostics · 2019Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Cancer cells acquire genetic and epigenetic alterations that often lead to dysregulation of oncogenic signal transduction pathways, which in turn alters downstream transcriptional programs. Numerous methods attempt to deduce aberrant signaling pathways in tumors from mRNA data alone, but these pathway analysis approaches remain qualitative and imprecise. In this study, we present a statistical method to link upstream signaling to downstream transcriptional response by exploiting reverse phase protein array (RPPA) and mRNA expression data in The Cancer Genome Atlas (TCGA) breast cancer project. Formally, we use an algorithm called affinity regression to learn an interaction matrix between upstream signal transduction proteins and downstream transcription factors (TFs) that explains target gene expression. The trained model can then predict the TF activity, given a tumor sample's protein expression profile, or infer the signaling protein activity, given a tumor sample's gene expression profile. Breast cancers are comprised of molecularly distinct subtypes that respond differently to pathway-targeted therapies. We trained our model on the TCGA breast cancer data set and identified subtype-specific and common TF regulators of gene expression. We then used the trained tumor model to predict signaling protein activity in a panel of breast cancer cell lines for which gene expression and drug response data was available. Correlations between inferred protein activities and drug responses in breast cancer cell lines grouped several drugs that are clinically used in combination. Finally, inferred protein activity predicted the clinical outcome within the METABRIC Luminal A cohort, identifying high- and low-risk patient groups within this heterogeneous subtype.
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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.