Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance

Emad, Amin; Cairns, Junmei; Kalari, Krishna R.; Wang, Liewei; Sinha, Saurabh

doi:10.1101/090027

Cited by 13 publications

(20 citation statements)

References 82 publications

(97 reference statements)

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“…To develop a gene signature corresponding to reactive stroma in the FinHer HER2‐positive cohort (HER2STROMA—Supporting Information Table S1), we used a method called ProGENI, implemented as part of the KnowEnG analytical platform (http://www.knoweng.org). ProGENI is a gene prioritization method that combines information on “omics” profiles of samples with a network of gene–gene interactions to improve the accuracy of prioritization.…”

Section: Methodsmentioning

confidence: 99%

Reactive stroma and trastuzumab resistance in HER2‐positive early breast cancer

Sonnenblick

Salmon‐Divon

Salgado

et al. 2020

Intl Journal of Cancer

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We investigated the value of reactive stroma as a predictor for trastuzumab resistance in patients with early HER2-positive breast cancer receiving adjuvant therapy. The pathological reactive stroma and the mRNA gene signatures that reflect reactive stroma in 209 HER2-positive breast cancer samples from the FinHer adjuvant trial were evaluated. Levels of stromal gene signatures were determined as a continuous parameter, and pathological reactive stromal findings were defined as stromal predominant breast cancer (SPBC; ≥50% stromal) and correlated with distant disease-free survival. Gene signatures associated with reactive stroma in HER2-positive early breast cancer (N = 209) were significantly associated with trastuzumab resistance in estrogen receptor (ER)-negative tumors (hazard ratio [HR] = 1.27 p interaction = 0.014 [DCN], HR = 1.58, p interaction = 0.027 [PLAU], HR = 1.71, p interaction = 0.019 [HER2STROMA, novel HER2 stromal signature]), but not in ERpositive tumors (HR = 0.73 p interaction = 0.47 [DCN], HR = 0.71, p interaction = 0.73 [PLAU], HR = 0.84; p interaction = 0.36 [HER2STROMA]). Pathological evaluation of HER2-positive/ER-negative tumors suggested an association between SPBC and trastuzumab resistance. Reactive stroma did not correlate with tumor-infiltrating lymphocytes (TILs), and the expected benefit from trastuzumab in patients with high levels of TILs was pronounced only in tumors with low stromal reactivity (SPBC <50%). In conclusion, reactive stroma in HER2-positive/ER-negative early breast cancer tumors may predict resistance to adjuvant trastuzumab therapy. † A.S., M.S.-D. and R.S. contributed equally to this work Additional Supporting Information may be found in the online version of this article.

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Section: Methodsmentioning

confidence: 99%

Reactive stroma and trastuzumab resistance in HER2‐positive early breast cancer

Sonnenblick

Salmon‐Divon

Salgado

et al. 2020

Intl Journal of Cancer

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“…We made available as an online resource all (TF, Drug) associations validated using siRNA or overexpression experiments in this study as well as those found to be similarly validated in our survey of the literature (Supplemental Note S1-S2), the GENMi study (Hanson et al 2015), and a related work that performs the same experimental validations (Emad et al 2017). This resource is available at veda.…”

Section: Database Of (Tf Drug) Associationsmentioning

confidence: 89%

“…On a relatively minor note, the above methods for reconstructing context-specific transcriptional regulatory networks require data on discrete cellular conditions (disease versus nondisease state, for instance) and are thus distinct from pGENMi, which focuses on interindividual continuous variation in cellular phenotype. TF associations with drugs can also be inferred from general studies that prioritize genes related to drug response based on prior functional networks (Morrison et al 2005;Chen et al 2012;Emad et al 2017) or based on observed or imputed gene expression alone (Barretina et al 2012;Basu et al 2013;Gusev et al 2016;Rees et al 2016). These approaches are not focused on identifying regulators of phenotype based on cis-regulatory evidence.…”

Section: Discussionmentioning

confidence: 99%

Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation

et al. 2018

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Recent studies have analyzed large-scale data sets of gene expression to identify genes associated with interindividual variation in phenotypes ranging from cancer subtypes to drug sensitivity, promising new avenues of research in personalized medicine. However, gene expression data alone is limited in its ability to reveal -regulatory mechanisms underlying phenotypic differences. In this study, we develop a new probabilistic model, called pGENMi, that integrates multi-omic data to investigate the transcriptional regulatory mechanisms underlying interindividual variation of a specific phenotype-that of cell line response to cytotoxic treatment. In particular, pGENMi simultaneously analyzes genotype, DNA methylation, gene expression, and transcription factor (TF)-DNA binding data, along with phenotypic measurements, to identify TFs regulating the phenotype. It does so by combining statistical information about expression quantitative trait loci (eQTLs) and expression-correlated methylation marks (eQTMs) located within TF binding sites, as well as observed correlations between gene expression and phenotype variation. Application of pGENMi to data from a panel of lymphoblastoid cell lines treated with 24 drugs, in conjunction with ENCODE TF ChIP data, yielded a number of known as well as novel (TF, Drug) associations. Experimental validations by TF knockdown confirmed 41% of the predicted and tested associations, compared to a 12% confirmation rate of tested nonassociations (controls). An extensive literature survey also corroborated 62% of the predicted associations above a stringent threshold. Moreover, associations predicted only when combining eQTL and eQTM data showed higher precision compared to an eQTL-only or eQTM-only analysis using pGENMi, further demonstrating the value of multi-omic integrative analysis.

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“…This process was repeated 200 times, each time using a distinct random partition of data. The Cox regression model using EMAT labels along with clinical parameters provided the best predictions (Figure 2A-C bottom panels), evaluated using a one-sided Wilcoxon signed rank test on paired C-index values of the compared methods as well as another measure called percentage of improved folds (PIF) (Emad, Cairns et al, 2017) defined as percent of the partitions in which one class of features outperforms another class.…”

Section: Figurementioning

confidence: 99%

An epithelial-mesenchymal-amoeboid transition gene signature reveals subtypes of breast cancer progression and metastasis

Emad

T²,

et al. 2017

Preprint

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Cancer cells within a tumor are known to display varying degrees of metastatic propensity but the molecular basis underlying such heterogeneity remains unclear. We analyzed genome-wide gene expression data obtained from primary tumors of lymph node-negative breast cancer patients using a novel metastasis biology-based Epithelial-Mesenchymal-Amoeboid Transition (EMAT) gene signature, and identified subtypes associated with distinct prognostic profiles. EMAT subtype status improved prognosis accuracy of clinical parameters and statistically outperformed traditional breast cancer intrinsic subtypes even after adjusting for treatment variables. Additionally, analysis of 3D spheroids from an in vitro isogenic model of breast cancer progression reveals that EMAT subtypes display progression from premalignant to malignant and pre-invasive to invasive cancer. EMAT classification is a biologically informed method to assess metastasis risk in early stage, lymph node-negative breast cancer patients.

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Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance

Abstract: Background: Identification of genes whose basal mRNA expression predicts the sensitivity of

Cited by 13 publications

References 82 publications

Reactive stroma and trastuzumab resistance in HER2‐positive early breast cancer

Reactive stroma and trastuzumab resistance in HER2‐positive early breast cancer

Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation

An epithelial-mesenchymal-amoeboid transition gene signature reveals subtypes of breast cancer progression and metastasis

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