detecting-somatic-genetic-alterations-tumor-specimens-exon-capture

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Efforts  to detect and investigate key oncogenic mutations have proven valuable to  facilitate the appropriate treatment for cancer patients. The ...

Memorial Sloan-Kettering Cancer Center

Less than 20 years ago, the von Hippel-Lindau (VHL) gene was discovered and associated with sporadic renal cell carcinoma (RCC). Since then, researchers and clinicians have labored to better understand the biology driving RCC tumor progression and provide means to predict patient survival and response to therapy. Studies surrounding VHL inactivation and downstream effects continue to provide insights into these areas. Besides studies of this primary pathway, cytogenetic studies, gene expression analyses, tissue microarrays, serum proteomics, genomic resequencing, and microRNA profiling have yielded greater understanding of RCC biology and clinical presentation, and have led to a rich understanding of the heterogeneity of this disease. We review the current state of research investigations into the molecular biology of RCC, and discuss the applications to currently used clinical prognostic nomograms.

Renal cell carcinoma: where will the state-of-the-art lead us?

Clear cell renal cell carcinoma (ccRCC) is the predominant RCC subtype, but even within this classification, the natural history is heterogeneous and difficult to predict. A sophisticated understanding of the molecular features most discriminatory for the underlying tumor heterogeneity should be predicated on identifiable and biologically meaningful patterns of gene expression. Gene expression microarray data were analyzed using software that implements iterative unsupervised consensus clustering algorithms to identify the optimal molecular subclasses, without clinical or other classifying information. ConsensusCluster analysis identified two distinct subtypes of ccRCC within the training set, designated clear cell type A (ccA) and B (ccB). Based on the core tumors, or most well-defined arrays, in each subtype, logical analysis of data (LAD) defined a small, highly predictive gene set that could then be used to classify additional tumors individually. The subclasses were corroborated in a validation data set of 177 tumors and analyzed for clinical outcome. Based on individual tumor assignment, tumors designated ccA have markedly improved disease-specific survival compared to ccB (median survival of 8.6 vs 2.0 years, P = 0.002). Analyzed by both univariate and multivariate analysis, the classification schema was independently associated with survival. Using patterns of gene expression based on a defined gene set, ccRCC was classified into two robust subclasses based on inherent molecular features that ultimately correspond to marked differences in clinical outcome. This classification schema thus provides a molecular stratification applicable to individual tumors that has implications to influence treatment decisions, define biological mechanisms involved in ccRCC tumor progression, and direct future drug discovery.

Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns.

Advances in basic research will enhance prognosis, diagnosis, and treatment of renal cancer patients.

Basic research in kidney cancer.

Clear cell renal cell carcinoma (ccRCC) displays molecular and histologic heterogeneity. Previously described subsets of this disease, ccA and ccB, were defined based on multigene expression profiles, but it is unclear whether these subgroupings reflect the full spectrum of disease or how these molecular subtypes relate to histologic descriptions or gender.

A. Rose Brannon

Department of Pathology

Memorial Sloan-Kettering Cancer Center

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

A. Rose Brannon

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Pathology

Memorial Sloan-Kettering Cancer Center

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Department of Pathology