Abstract 3746: Proteomic analysis of laser capture microdissected cells from basal, Her 2 positive and luminal A human breast tumors

Abstract

Abstract The analysis of tissue is often confounded by the heterogenous nature, small size of biopsies, and focal presence of tumors. The use of laser capture microdissection (LCM) allows for isolation of nearly pure cell populations enhancing specificity of subsequent analyses. A method developed in our laboratory facilitating the removal of adhered cells utilizing SDS-PAGE followed by in-gel digestion and tandem mass spectrometry was applied to LCM-acquired cells from Basal, Her 2 positive and Luminal A breast tumor tissues. Cells were LCM-acquired from frozen tissue sections corresponding to Basal, Her 2 positive or Luminal A human breast tumor tissue. The sample set consisted of 3 tumors per subgroup with 3 caps per tumor, for a total of 27 caps each containing approximately 10,000 cells corresponding to 3-5 μg protein. The thermoplastic membranes containing the captured cells were peeled from the cap and suspended in SDS loading buffer and gently heated for 10 minutes. The solubilized proteins were electrophoresed 2 cm into a 10-20% Tricine gel followed by in-gel trypsin digestion and peptide extraction. Tryptic peptides were analyzed by reversed-phase (RP) liquid chromatography-tandem mass spectrometry (LC-MS-MS) on a Thermo Orbitrap instrument. Following LC/MS/MS analysis of the LCM digests, tandem spectra were searched against the human IPI database using the search algorithm, Myrimatch, and the search results filtered using IDPicker. A spectral counting approach was applied to the data-dependent data to compare quantitative differences of proteins identified. A total of 91646 spectra were confidently identified which corresponded to 1671 protein groups across all biological and technical replicates. An average of 545, 543, and 610 protein groups were identified in the basal, Her 2 positive, and Luminal A subtypes, respectively, from an equivalent of roughly 800 cells. Spectral count differences between datasets between normal and tumor samples were analyzed using both a Quasi-likelihood Poisson regression model, developed in-house, in addition to using the Limma package in Bioconductor. Using the criteria of a False Discovery Rate (FDR) < 0.05, a total of 90 proteins showed statistically significant differences in expression among the breast tumor subtypes. A hierarchical clustering of these proteins was performed, creating a heat map representing the gene expression patterns of these proteins. As expected, ERBB2 was identified in the Her 2 positive tumor tissue samples; however, this protein was not identified in either the Basal or Luminal A samples. The proteins identified which were found to distinguish each tissue subtype are currently being investigated for their biological relevance in breast cancer pathways and as possible prognostic and predictive biomarkers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3746.