Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Transcription profiling of human malignant or benign breast cancer tumors to identify genes that present a differential expression between the two types of lesion

ABSTRACT: Background. Although gene expression arrays have been used to generate molecular predictors of relapse and drug sensitivity in breast cancer, no large study describes genes and exons differentially expressed between breast cancer and benign lesions.

Methods. One hundred and sixty-five tumour samples were obtained by fine needle aspiration (FNA). cDNA were hybridized on Splice ArrayTM. A nearest centroid prediction rule was developed to classify lesions as malignant or benign on a training set, and its performance evaluated on an independent validation set. A two-way ANOVA model was used to identify probesets that present a differential expression between cancer and benign lesions while adjusting for scan dates. P-values were adjusted for False Discovery Rate.

Findings. Overall 120 breast cancers and 45 benign lesions were included in the study. A 1228-probeset molecular classifier for breast cancer diagnosis was generated from the training set (n=94). This signature accurately classified all samples (100% accuracy, 95% exact CI: 96-100%). In the validation set (n=71), the molecular predictor accurately classified 68 out of 71 tumours (96%, 95% CI: 88-99%). When the 165 samples were taken into account, 37 858 exon-probesets (5.4%) and 3733 genes (20%) were found to be differentially expressed between malignant and benign conditions (adjusted p-value<0.05). Pathway analyses showed that genes involved in spliceosome assembly were significantly enriched in malignant condition (permutation p=0.002). In the same population of 165 samples, 956 exon-probesets presented both a higher intensity and higher splice index in breast cancer, although located on unchanged genes.

Interpretation. The present study provides a thorough description of differentially expressed exons between breast cancer and benign lesions, and emphasizes the contribution of spliceosome and alternative transcripts to the molecular portrait of breast malignancy. This allowed the development of a molecular classifier for breast cancer diagnosis using FNA.

ORGANISM(S): Homo sapiens

DISEASE(S): benign lesion

SUBMITTER: Philippe Dessen

PROVIDER: E-TABM-609 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Background Breast cancer patients who present in the early stage of disease are affected by metastasis to the axillary group of lymph nodes. The first among this group that is affected is called as sentinel lymph node, and metastasis to this lymph node is crucial for the staging of cancer and the quality of surgical intervention. Sentinel Lymph Node Biopsy (SLNB) that is currently used to assess lymph node metastasis is neither sensitive, nor specific, is time-consuming, thereby necessitating the identification of novel biomarkers that can flag sentinel lymph node metastasis. Methods Breast cancer patients were screened, and those with early stage were recruited in the study. Surgical resection of the breast was followed by identification of sentinel lymph nodes by methylene fluorescent technique. Histo-pathology of fresh frozen section biopsy was used as the gold standard to assign the clinical phenotypes of metastatic (SLNM+) and non-metastatic sentinel lymph nodes (SLNM-). Discovery phase of the experiment included isobaric Tags for Relative and Absolute Quantitation (iTRAQ) technique comprising of six comparative experiments coupled with mass spectrometric analysis on Orbitrap Fusion to identify differentially expressed proteins on Proteome Discoverer 2.4. Functional enrichment and pathway analyses of differentially regulated genes was carried out in DAVID functional annotation tool. Validation was done by ELISA and protein concentrations were used to estimate the ROC for computing diagnostic parameters. Results Based on MS/MS spectra there were 2396 unique protein groups and 81 differentially expressed proteins comparing SLNB + and SLNB -. Nineteen proteins up-regulated, and eight proteins that were down regulated in SLNB+ as compared to SLNB-. Bioinformatic analysis showed the implication of extra cellular matrix proteins and ECM-receptor interaction pathways to be implicated in lymph node metastasis. ELISA confirmed the up-regulation of caveolin 1, collagen α-1, desmin, fibrillin-1, and microfibrillar associated glycoprotein 4 in metastatic, as compared to non-metastatic lymph nodes. These proteins are known to be integral in tumorogenesis, cell proliferation, invasion, cell survival and anti-apoptosis. These proteins have 80%-100%, of sensitivity and specificity to differentiate the two clinical phenotypes. Conclusion Identified extra cellular matrix protein biomarkers have requisite diagnostic parameters to be developed as a translational tool to assess the status of sentinel lymph nodes during mastectomy procedure to guide surgical therapy of axillary lymph nodes in early breast oncology.

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Transcription profiling of human malignant or benign breast cancer tumors to identify genes that present a differential expression between the two types of lesion

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