RESUMEN
The complex interaction between cancer cells and the microenvironment plays an essential role in all stages of tumourigenesis. Despite the significance of this interplay, alterations in protein composition underlying tumour-stroma interactions are largely unknown. The aim of this study was to identify stromal proteins with clinical relevance in non-small cell lung cancer (NSCLC). A list encompassing 203 stromal candidate genes was compiled based on gene expression array data and available literature. The protein expression of these genes in human NSCLC was screened using the Human Protein Atlas. Twelve proteins were selected that showed a differential stromal staining pattern (BGN, CD99, DCN, EMILIN1, FBN1, PDGFRB, PDLIM5, POSTN, SPARC, TAGLN, TNC and VCAN). The corresponding antibodies were applied on tissue microarrays, including 190 NSCLC samples, and stromal staining was correlated with clinical parameters. Higher stromal expression of CD99 was associated with better prognosis in the univariate (p = 0.037) and multivariate (p = 0.039) analysis. The association was independent from the proportion of tumour stroma, the fraction of inflammatory cells and clinical and pathological parameters like stage, performance status and tumour histology. The prognostic impact of stromal CD99 protein expression was confirmed in an independent cohort of 240 NSCLC patients (p = 0.008). Furthermore, double-staining confocal fluorescence microscopy showed that CD99 was expressed in stromal lymphocytes as well as in cancer-associated fibroblasts. Based on a comprehensive screening strategy the membrane protein CD99 was identified as a novel stromal factor with clinical relevance. The results support the concept that stromal properties have an important impact on tumour progression.
Asunto(s)
Antígenos CD/metabolismo , Biomarcadores de Tumor , Carcinoma de Pulmón de Células no Pequeñas/metabolismo , Moléculas de Adhesión Celular/metabolismo , Neoplasias Pulmonares/metabolismo , Células del Estroma/metabolismo , Antígeno 12E7 , Antígenos CD/genética , Carcinoma de Pulmón de Células no Pequeñas/genética , Carcinoma de Pulmón de Células no Pequeñas/mortalidad , Moléculas de Adhesión Celular/genética , Progresión de la Enfermedad , Perfilación de la Expresión Génica , Humanos , Estimación de Kaplan-Meier , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/mortalidad , Pronóstico , Transporte de Proteínas , Proteoma , Microambiente TumoralRESUMEN
We describe a bioinformatic tool, Tumor Aberration Prediction Suite (TAPS), for the identification of allele-specific copy numbers in tumor samples using data from Affymetrix SNP arrays. It includes detailed visualization of genomic segment characteristics and iterative pattern recognition for copy number identification, and does not require patient-matched normal samples. TAPS can be used to identify chromosomal aberrations with high sensitivity even when the proportion of tumor cells is as low as 30%. Analysis of cancer samples indicates that TAPS is well suited to investigate samples with aneuploidy and tumor heterogeneity, which is commonly found in many types of solid tumors.