Protein expression based multimarker analysis of breast cancer samples

Department of Biostatistics, UCLA, Los Angeles, CA 90095, USA.
BMC Cancer (Impact Factor: 3.36). 06/2011; 11(1):230. DOI: 10.1186/1471-2407-11-230
Source: PubMed


Tissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.
We apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.
We find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.
We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.

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Available from: Angela Presson, Aug 28, 2015
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    • "Breast cancer represents a complex and heterogeneous disease that comprises distinct pathologies, histological features , and clinical outcome. The status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor type 2 (HER2) has been used as predictive markers to identify a high-risk phenotype and for selection of the most efficient therapies [2] [3] Triple-negative breast cancer (TNBC) is a subtype characterized by the lack of ER, PR, and HER2 expression and is associated with younger age at diagnosis and often occurs in African-American, premenopausal, and overweighed women (particularly with abdominal obesity) [4]. It represents approximately 12–17% of all breast cancers [5] and encompasses a heterogeneous group of tumors including, but not "
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    ABSTRACT: Triple negative breast cancer (TNBC) is a relevant subgroup of neoplasia which presents negative phenotype of estrogen and progesterone receptors and has no overexpression of the human epidermal growth factor 2 (HER2). FOXP3 (forkhead transcription factor 3) is a marker of regulatory T cells (Tregs), whose expression may be increased in tumor cells. This study aimed to investigate a polymorphism (rs3761548) and the protein expression of FOXP3 for a possible involvement in TNBC susceptibility and prognosis. Genetic polymorphism was evaluated in 50 patients and in 115 controls by allele-specific PCR (polymerase chain reaction). Protein expression was evaluated in 38 patients by immunohistochemistry. It was observed a positive association for homozygous AA (OR = 3.78; 95% CI = 1.02-14.06) in relation to TNBC susceptibility. Most of the patients (83%) showed a strong staining for FOXP3 protein in the tumor cells. In relation to FOXP3-positive infiltrate, 47% and 58% of patients had a moderate or intense intratumoral and peritumoral mononuclear infiltrate cells, respectively. Tumor size was positively correlated to intratumoral FOXP3-positive infiltrate (P = 0.026). In conclusion, since FOXP3 was positively associated with TNBC susceptibility and prognosis, it seems to be a promising candidate for further investigation in larger TNBC samples.
    BioMed Research International 04/2014; 2014(6):341654. DOI:10.1155/2014/341654 · 3.17 Impact Factor
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    ABSTRACT: Molecular classification of breast cancer (BC) identified diverse subgroups that encompass distinct biological behavior and clinical implications, in particular in relation to prognosis, spread, and incidence of recurrence. Basal-like breast cancers (BLBC) compose up to 15% of BC and are characterized by lack of estrogen receptor (ER), progesterone receptor (PR), and HER-2 amplification with expression of basal cytokeratins 5/6, 14, 17, epidermal growth factor receptor (EGFR), and/or c-KIT. There is an overlap in definition between triple-negative BC and BLBC due to the triple-negative profile of BLBC. Also, most BRCA1-associated BCs are BLBC, triple negative, and express basal cytokeratins (5/6, 14, 17) and EGFR. There is a link between sporadic BLBC (occurring in women without germline BRCA1 mutations) with dysfunction of the BRCA1 pathway. Despite the molecular and clinical similarities, these subtypes respond differently to neoadjuvant therapy. BLBCs are associated with an aggressive phenotype, high histological grade, poor clinical behavior, and high rates of recurrences and/or metastasis. Their molecular features render these tumors especially refractory to anti-hormonal-based therapies and the overall prognosis of this subset remains poor. In this article, the molecular profile, genomic, and epigenetic characteristics as well as BRCA1 pathway dysfunction, clinicopathological behavior, and therapeutic options in BLBC are presented, with emphasis on the discordant findings in current literature.
    Breast Cancer Research and Treatment 01/2012; 134(1):21-30. DOI:10.1007/s10549-011-1934-z · 3.94 Impact Factor
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    ABSTRACT: Object: Meningiomas are among the most common primary adult brain tumors. Although typically benign, roughly 2%-5% display malignant pathological features. The key molecular pathways involved in malignant transformation remain to be determined. Methods: Illumina expression microarrays were used to assess gene expression levels, and Illumina single-nucleotide polymorphism arrays were used to identify copy number variants in benign, atypical, and malignant meningiomas (19 tumors, including 4 malignant ones). The authors also reanalyzed 2 expression data sets generated on Affymetrix microarrays (n = 68, including 6 malignant ones; n = 56, including 3 malignant ones). A weighted gene coexpression network approach was used to identify coexpression modules associated with malignancy. Results: At the genomic level, malignant meningiomas had more chromosomal losses than atypical and benign meningiomas, with average length of 528, 203, and 34 megabases, respectively. Monosomic loss of chromosome 22 was confirmed to be one of the primary chromosomal level abnormalities in all subtypes of meningiomas. At the transcriptome level, the authors identified 23 coexpression modules from the weighted gene coexpression network. Gene functional enrichment analysis highlighted a module with 356 genes that was highly related to tumorigenesis. Four intramodular hubs within the module (GAB2, KLF2, ID1, and CTF1) were oncogenic in other cancers such as leukemia. A putative meningioma tumor suppressor MN1 was also identified in this module with differential expression between malignant and benign meningiomas. Conclusions: The authors' genomic and transcriptome analysis of meningiomas provides novel insights into the molecular pathways involved in malignant transformation of meningiomas, with implications for molecular heterogeneity of the disease.
    Neurosurgical FOCUS 12/2013; 35(6):E3. DOI:10.3171/2013.10.FOCUS13326 · 2.11 Impact Factor
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