Dataset Information

Comprehensive expression profiling of tumor cell lines identifies molecular signatures of melanoma progression.

ABSTRACT:

Background

Gene expression profiling has revolutionized our ability to molecularly classify primary human tumors and significantly enhanced the development of novel tumor markers and therapies; however, progress in the diagnosis and treatment of melanoma over the past 3 decades has been limited, and there is currently no approved therapy that significantly extends lifespan in patients with advanced disease. Profiling studies of melanoma to date have been inconsistent due to the heterogeneous nature of this malignancy and the limited availability of informative tissue specimens from early stages of disease.

Methodology/principle findings

In order to gain an improved understanding of the molecular basis of melanoma progression, we have compared gene expression profiles from a series of melanoma cell lines representing discrete stages of malignant progression that recapitulate critical characteristics of the primary lesions from which they were derived. Here we describe the unsupervised hierarchical clustering of profiling data from melanoma cell lines and melanocytes. This clustering identifies two distinctive molecular subclasses of melanoma segregating aggressive metastatic tumor cell lines from less-aggressive primary tumor cell lines. Further analysis of expression signatures associated with melanoma progression using functional annotations categorized these transcripts into three classes of genes: 1) Upregulation of activators of cell cycle progression, DNA replication and repair (CDCA2, NCAPH, NCAPG, NCAPG2, PBK, NUSAP1, BIRC5, ESCO2, HELLS, MELK, GINS1, GINS4, RAD54L, TYMS, and DHFR), 2) Loss of genes associated with cellular adhesion and melanocyte differentiation (CDH3, CDH1, c-KIT, PAX3, CITED1/MSG-1, TYR, MELANA, MC1R, and OCA2), 3) Upregulation of genes associated with resistance to apoptosis (BIRC5/survivin). While these broad classes of transcripts have previously been implicated in the progression of melanoma and other malignancies, the specific genes identified within each class of transcripts are novel. In addition, the transcription factor NF-KB was specifically identified as being a potential "master regulator" of melanoma invasion since NF-KB binding sites were identified as consistent consensus sequences within promoters of progression-associated genes.

Conclusions/significance

We conclude that tumor cell lines are a valuable resource for the early identification of gene signatures associated with malignant progression in tumors with significant heterogeneity like melanoma. We further conclude that the development of novel data reduction algorithms for analysis of microarray studies is critical to allow for optimized mining of important, clinically-relevant datasets. It is expected that subsequent validation studies in primary human tissues using such an approach will lead to more rapid translation of such studies to the identification of novel tumor biomarkers and therapeutic targets.

SUBMITTER: Ryu B

PROVIDER: S-EPMC1895889 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The identification of novel tumor-specific markers may improve understanding of melanoma progression and prognostic accuracy. Whole genome expression profiling of 46 primary melanomas, 12 metastases, and 16 normal skin samples using Affymetrix U133 PLUS 2.0 array generated gene lists including both known and new melanoma genes. The molecular genetic alterations contributing to the pathogenesis of melanoma are incompletely defined and few independent prognostic features have been identified beyond the pathologic characteristics of the primary tumor. Early stage melanoma is frequently curable, in contrast to the inferior prognosis of melanomas with regional lymph nodes involvement and the incurability of distant metastatic disease. The identification of novel tumor-specific markers may improve our understanding of melanoma progression and prognostic accuracy. To find differentially expressed genes that can distinguish melanoma from normal tissue, we performed a whole genome expression profiling of 46 primary melanoma samples, 12 regional or distant metastases, and 16 normal skin samples using Affymetrix U133 2.0 Plus array. Our study generated lists of differentially expressed genes in melanoma and identified novel prognostic marker HMGA2. It is a novel, highly overexpressed melanoma gene associated with poor prognosis. The overexpression of HMGA2 is strongly associated with regional and distant metastases and serves as an independent predictor of disease-free survival and overall survival in melanoma. Melanoma samples were obtained through an IRB-approved protocol using informed consent at the University of Michigan Multidisciplinary Melanoma Clinic. A portion of pigmented lesions clinically suspicious for melanoma and known melanoma were sampled by punch biopsy at the time of excision. The punch biopsy was bisected; half was sent to for clinical diagnosis and the other half along with adjacent normal skin when available immediately snap-frozen in liquid nitrogen. Snap-frozen tissue was embedded in OCT (TissueTek) followed by frozen sectioning and H&E slide preparation. The slides were evaluated by dermatopathologist who identified areas with greater than 70% tumor cellularity, which were sampled for RNA extraction. Primary melanomas and melanoma metastases were derived from different patients. Metastatic samples included lymph nodes (n=8), subcutaneous soft tissue (n=2), spleen (n=1), and small intestine (n=1).

Project description:Summary: Breast cancer cell lines have been used widely to investigate breast cancer pathobiology and new therapies. Breast cancer is a molecularly heterogeneous disease, and it is important to understand how well and which cell lines best model that diversity. In particular, microarray studies have identified molecular subtypes (luminal A, luminal B, ERBB2-associated, basal-like and normal-like) with characteristic gene-expression patterns and underlying DNA copy number alterations (CNAs). Here, we studied a collection of breast cancer cell lines to catalog molecular profiles and to assess their relation to breast cancer subtypes. Whole-genome DNA microarrays were used to profile gene expression and CNAs in a collection of 52 widely-used breast cancer cell lines, and comparisons were made to existing profiles of primary breast tumors. Hierarchical clustering was used to identify gene-expression subtypes, and Gene Set Enrichment Analysis (GSEA) to discover biological features of those subtypes. Genomic and transcriptional profiles were integrated to discover within high-amplitude CNAs candidate cancer genes with coordinately altered gene copy number and expression. Transcriptional profiling of breast cancer cell lines identified one luminal and two basal-like (A and B) subtypes. Luminal lines displayed an estrogen receptor (ER) signature and resembled luminal-A/B tumors, basal-A lines were associated with ETS-pathway and BRCA1 signatures and resembled basal-like tumors, and basal-B lines displayed mesenchymal and stem-cell characteristics. Compared to tumors, cell lines exhibited similar patterns of CNA, but an overall higher complexity of CNA (genetically simple luminal-A tumors were not represented), and only partial conservation of subtype-specific CNAs. We identified 80 high-level DNA amplifications and 13 presumptive homozygous deletions, and the resident genes with concomitantly altered gene-expression, highlighting known and novel candidate breast cancer genes. Overall, breast cancer cell lines were genetically more complex than tumors, but retained expression patterns with relevance to the luminal-basal subtype distinction. The compendium of molecular profiles defines cell lines suitable for investigations of subtype-specific pathobiology, biomarkers and therapies, and provides a resource for discovery of new breast cancer genes. HEEBO oligonucleotide microarrays from the Stanford Functional Genomics Facility were used to perform gene expression profiling of 50 human breast epithelial cell lines, in comparison to a universal RNA reference. Expression data were analyzed by hierarchical clustering to identify subgroups, and gene set enrichment analysis to identify subgroup-specific gene pathways.