ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways

Shen, Ying; Rahman, Mumtahena; Piccolo, Stephen R.; Gusenleitner, Daniel; El-Chaar, Nader N.; Cheng, Luis; Monti, Stefano; Bild, Andrea; Johnson, W. Evan

doi:10.1093/bioinformatics/btv031

Cited by 27 publications

(27 citation statements)

References 38 publications

(37 reference statements)

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“…Past attempts to overcome these challenges have relied on the premise that particular genes are expressed in only a single cell type, which does not represent the general biological case (Gaujoux and Seoighe, 2012). A more recent approach, conceptually akin to the methods developed here, uses SnS priors to reduce the mathematical ambiguities inherent in matrix factorization and has recently been applied to the identification of genetic disease pathways (Shen et al , 2015), albeit without addressing cell type diversity.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons

Gabitto

Pakman

Bikoff

et al. 2016

Cell

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SUMMARY Documenting the extent of cellular diversity is a critical step in defining the functional organization of tissues and organs. To infer cell type diversity from partial or incomplete transcription factor expression data we devised a sparse Bayesian framework that is able to handle estimation uncertainty, and can incorporate diverse cellular characteristics to optimize experimental design. Focusing on spinal V1 inhibitory interneurons, for which the spatial expression of 19 transcription factors has been mapped, we infer the existence of approximately 50 candidate V1 neuronal types, many of which localize in compact spatial domains in the ventral spinal cord. We have validated the existence of inferred cell types by direct experimental measurement, establishing this Bayesian framework as an effective platform for cell type characterization in the nervous system and elsewhere.

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Section: Discussionmentioning

confidence: 99%

Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons

Gabitto

Pakman

Bikoff

et al. 2016

Cell

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“…A formal definition of the ASSIGN model and software implementation was reported previously [58]. RNA-Seq data from HMECs overexpressing GFP control were compared to HMECs overexpressing AKT1 , IGF1R , BAD , HER2 , KRAS (G12V) , RAF1 , and EGFR.…”

Section: Methodsmentioning

confidence: 99%

Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes

et al. 2017

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BackgroundThe growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns.MethodsNovel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways (HER2, IGF1R, AKT1, EGFR, KRAS (G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines.ResultsApplication of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways (“the survival phenotype”) or the EGFR, KRAS (G12V), RAF1, and BAD pathways (“the growth phenotype”). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies.ConclusionsGene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0429-x) contains supplementary material, which is available to authorized users.

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“…A formal definition of the ASSIGN model and software implementation was previously described [52]. RNA-Seq data from HMECs overexpressing GFP control were compared to HMECs overexpressing KRAS (G12V), RAF1, and EGFR.…”

Section: Methodsmentioning

confidence: 99%

“…To further explore pathway activation in relation to mutational status, we measured pathway activity and mutational status for key RAS pathway components EGFR, KRAS, and RAF across 8 different cancers in TCGA [6] which express varying levels of KRAS, EGFR , or BRAF mutations. Specifically, we used our previously generated gene expression signatures that measure the activity of the EGFR, KRAS, and RAF1 pathway components using our published pathway modeling toolkit, Adaptive Signature Selection and InteGratioN ( ASSIGN) [46,52,53] (see methods section). Unsupervised hierarchical clustering of the pathway activity estimates for all cancer types and pathway signatures revealed distinct patterns of pathway activation across cancer types (Figure 2).…”

Section: Genomics Provides Insight Into the Ras Pathwaymentioning

confidence: 99%

The value of genomics in dissecting the RAS-network and in guiding therapeutics for RAS-driven cancers

Shrestha

MacNeil

McQuerry

et al. 2016

Seminars in Cell & Developmental Biology

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The rise in genomic knowledge over the past decade has revealed the molecular etiology of many diseases, and has identified intricate signaling network activity in human cancers. Genomics provides the opportunity to determine genome structure and capture the activity of thousands of molecular events concurrently, which is important for deciphering highly complex genetic diseases such as cancer. In this review, we focus on genomic efforts directed towards one of cancer’s most frequently mutated networks, the RAS pathway. Genomic tools such as gene expression signatures and assessment of mutations across the RAS network enable the capture of RAS signaling complexity. Due to this high level of interaction and cross-talk within the network, efforts to target RAS signaling in the clinic have generally failed, and we currently lack the ability to directly inhibit the RAS protein with high efficacy. We propose that the use of expression data can identify effective treatments that broadly inhibit the RAS network as this approach measures pathway activity independent of mutation status or any single mechanism of activation. Here we review the genomic studies that map the complexity of the RAS network in cancer, and that show how genomic measurements of RAS pathway activation can identify effective RAS inhibition strategies. We also address the challenges and future directions for treating RAS-driven tumors. In summary, genomic assessment of RAS signaling provides a level of complexity necessary to accurately map the network that matches the intricacy of RAS pathway interactions in cancer.

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ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways

Abstract: Software for our approach is available for download at: http://www.bioconductor.org/packages/release/bioc/html/ASSIGN.html and https://github.com/wevanjohnson/ASSIGN.

Cited by 27 publications

References 38 publications

Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons

Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons

Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes

The value of genomics in dissecting the RAS-network and in guiding therapeutics for RAS-driven cancers

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