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Protein expression based multimarker analysis of breast cancer samples.

ABSTRACT: BACKGROUND: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. METHODS: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-?1, and TGF ? 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. RESULTS: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. CONCLUSIONS: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.

SUBMITTER: Presson AP

PROVIDER: S-EPMC3142534 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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Protein expression based multimarker analysis of breast cancer samples.

Presson Angela P AP Yoon Nam K NK Bagryanova Lora L Mah Vei V Alavi Mohammad M Maresh Erin L EL Rajasekaran Ayyappan K AK Goodglick Lee L Chia David D Horvath Steve S

BMC cancer 20110608

<h4>Background</h4>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.<h4>Methods</h4>We apply weighted correlation network ana ...[more]

PMID: 21651811

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Project description:Expression and differential expression analysis of breast cancer patient samples and normal samples from breast reduction operations. Fresh frozen tumor biopsies from early breast cancer cases were collected from 920 patients included in the Oslo Micrometastasis (MicMa) Study -- Oslo I from various hospitals between 1995 and 1998 (Naume et al. "Presence of bone marrow micrometastasis is associated with different recurrence risk within molecular subtypes of breast cancer." Mol Oncol 2007, 1: 160-171; Wiedswang et al. "Detection of isolated tumor cells in bone marrow is an independent prognostic factor in breast cancer." J Clin Oncol 2003, 21: 3469-3478.). Breast tissue samples from breast reduction operations were provided from the Colosseum Clinic, Oslo in co-operation with Akershus University Hospital, Lørenskog and are referred to as normal tissue. Expression and differential expression was assessed by using an Agilent custom microarray (244K, nONCOchip). The custom array contains probes for genomic regions that have been found to be differentially expressed (i) throughout cell cycle progression, (ii) in response to the anti-proliferative and pro-apoptotic p53 pathway, and (iii) the anti-apoptotic and pro-proliferative STAT-3 pathway by employing TAS (Kampa et al. "Novel RNAs identified from an in-depth analysis of the transcriptome of human chromosomes 21 and 22". Genome Research, 14:331-42, 2004). In addition, the Agilent custom array (244K) interrogates probes for genomic regions predicted to contain a conserved secondary structure identified by RNAz (Washietl et al. "Fast and reliable prediction of noncoding RNAs." Proc Natl Acad Sci USA. 102:2454-9, 2005.) or Evofold (Pedersen et al. "Identification and classification of conserved RNA secondary structures in the human genome." PLoS Comput Biol. 2:e33, 2006.), as well as known non-coding RNAs from public databases, and the Agilent mRNA probe set 014850. We analyzed 5 to 6 arrays each for breast cancer patient samples and normal samples.

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Protein expression based multimarker analysis of breast cancer samples.

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Protein expression based multimarker analysis of breast cancer samples.

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