Dataset Information

Reverse phase protein array identification of triple-negative breast cancer subtypes and comparison with mRNA molecular subtypes.

ABSTRACT: Reverse phase protein array (RPPA) analysis, allows investigation of potential targets at the functional protein level,. We identified TNBC subtypes at the protein level using RPPA and compared them with mRNA molecular subtypes (TNBCtype, TNBCtype-4, and PAM50) that is unique in its availability of both RPPA and mRNA analyses.We classified the samples from 80 TNBC patients using both k-means and hierarchical consensus clustering analysis and performed Ingenuity Pathway Analysis. We also investigated whether we could reproduce the mRNA molecular subtypes using the RPPA dataset.Both clustering methods divided all samples into 2 clusters that were biologically the same. The top canonical pathways included inflammation, hormonal receptors, and MAPK signaling pathways for the first cluster ["inflammation and hormonal-related (I/H) subtype"] and the GADD45, DNA damage, and p53 signaling pathways for the second cluster ["DNA damage (DD)-related subtype"]. Further k-means cluster analysis identified 5 TNBC clusters. Comparison between sample classification using the 5 RPPA-based k-means cluster subtypes and 6 gene-expression-based TNBCtype molecular subtypes showed significant association between the 2 classifications (p = 0.017).The I/H and DD subtypes identified by RPPA advance our understanding of TNBC's heterogeneity from the functional proteomic perspective.

SUBMITTER: Masuda H

PROVIDER: S-EPMC5642571 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Reverse phase protein array identification of triple-negative breast cancer subtypes and comparison with mRNA molecular subtypes.

Masuda Hiroko H Qi Yuan Y Liu Shuying S Hayashi Naoki N Kogawa Takahiro T Hortobagyi Gabriel N GN Tripathy Debu D Ueno Naoto T NT

Oncotarget 20170731 41

<h4>Background</h4>Reverse phase protein array (RPPA) analysis, allows investigation of potential targets at the functional protein level,. We identified TNBC subtypes at the protein level using RPPA and compared them with mRNA molecular subtypes (TNBCtype, TNBCtype-4, and PAM50) that is unique in its availability of both RPPA and mRNA analyses.<h4>Methods</h4>We classified the samples from 80 TNBC patients using both k-means and hierarchical consensus clustering analysis and performed Ingenuity ...[more]

PMID: 29050296

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Project description:PurposeImmune checkpoint blockade (ICB) therapy has transformed the treatment of triple-negative breast cancer (TNBC) in recent years. However, some TNBC patients with high programmed death-ligand 1 (PD-L1) expression levels develop immune checkpoint resistance. Hence, there is an urgent need to characterize the immunosuppressive tumor microenvironment and identify biomarkers to construct prognostic models of patient survival outcomes in order to understand biological mechanisms operating within the tumor microenvironment.Patients and methodsRNA sequence (RNA-seq) data from 303 TNBC samples were analyzed using an unsupervised cluster analysis approach to reveal distinctive cellular gene expression patterns within the TNBC tumor microenvironment (TME). A panel of T cell exhaustion signatures, immunosuppressive cell subtypes and clinical features were correlated with the immunotherapeutic response, as assessed according to gene expression patterns. The test dataset was then used to confirm the occurrence of immune depletion status and prognostic features and to formulate clinical treatment recommendations. Concurrently, a reliable risk prediction model and clinical treatment strategy were proposed based on TME immunosuppressive signature differences between TNBC patients with good versus poor survival status and other clinical prognostic factors.ResultsSignificantly enriched TNBC microenvironment T cell depletion signatures were detected in the analyzed RNA-seq data. A high proportion of certain immunosuppressive cell subtypes, 9 inhibitory checkpoints and enhanced anti-inflammatory cytokine expression profiles were noted in 21.4% of TNBC patients that led to the designation of this group of immunosuppressed patients as the immune depletion class (IDC). Although IDC group TNBC samples contained tumor-infiltrating lymphocytes present at high densities, IDC patient prognosis was poor. Notably, PD-L1 expression was relatively elevated in IDC patients that indicated their cancers were resistant to ICB treatment. Based on these findings, a set of gene expression signatures predicting IDC group PD-L1 resistance was identified then used to develop risk models for use in predicting clinical therapeutic outcomes.ConclusionA novel TNBC immunosuppressive tumor microenvironment subtype associated with strong PD-L1 expression and possible resistance to ICB treatment was identified. This comprehensive gene expression pattern may provide fresh insights into drug resistance mechanisms for use in optimizing immunotherapeutic approaches for TNBC patients.

Project description:BACKGROUND:Understanding mechanisms underlying specific chemotherapeutic responses in subtypes of cancer may improve identification of treatment strategies most likely to benefit particular patients. For example, triple-negative breast cancer (TNBC) patients have variable response to the chemotherapeutic agent cisplatin. Understanding the basis of treatment response in cancer subtypes will lead to more informed decisions about selection of treatment strategies. METHODS:In this study we used an integrative functional genomics approach to investigate the molecular mechanisms underlying known cisplatin-response differences among subtypes of TNBC. To identify changes in gene expression that could explain mechanisms of resistance, we examined 102 evolutionarily conserved cisplatin-associated genes, evaluating their differential expression in the cisplatin-sensitive, basal-like 1 (BL1) and basal-like 2 (BL2) subtypes, and the two cisplatin-resistant, luminal androgen receptor (LAR) and mesenchymal (M) subtypes of TNBC. RESULTS:We found 20 genes that were differentially expressed in at least one subtype. Fifteen of the 20 genes are associated with cell death and are distributed among all TNBC subtypes. The less cisplatin-responsive LAR and M TNBC subtypes show different regulation of 13 genes compared to the more sensitive BL1 and BL2 subtypes. These 13 genes identify a variety of cisplatin-resistance mechanisms including increased transport and detoxification of cisplatin, and mis-regulation of the epithelial to mesenchymal transition. CONCLUSIONS:We identified gene signatures in resistant TNBC subtypes indicative of mechanisms of cisplatin. Our results indicate that response to cisplatin in TNBC has a complex foundation based on impact of treatment on distinct cellular pathways. We find that examination of expression data in the context of heterogeneous data such as drug-gene interactions leads to a better understanding of mechanisms at work in cancer therapy response.

Dataset Information

Reverse phase protein array identification of triple-negative breast cancer subtypes and comparison with mRNA molecular subtypes.

Publications

Reverse phase protein array identification of triple-negative breast cancer subtypes and comparison with mRNA molecular subtypes.

Similar Datasets

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