ABSTRACT
PURPOSE: As massively parallel sequencing is increasingly being used for clinical decision making, it has become critical to understand parameters that affect sequencing quality and to establish methods for measuring and reporting clinical sequencing standards. In this report, we propose a definition for reduced coverage regions and describe a set of standards for variant calling in clinical sequencing applications. METHODS: To enable sequencing centers to assess the regions of poor sequencing quality in their own data, we optimized and used a tool (ExCID) to identify reduced coverage loci within genes or regions of particular interest. We used this framework to examine sequencing data from 500 patients generated in 10 projects at sequencing centers in the National Human Genome Research Institute/National Cancer Institute Clinical Sequencing Exploratory Research Consortium. RESULTS: This approach identified reduced coverage regions in clinically relevant genes, including known clinically relevant loci that were uniquely missed at individual centers, in multiple centers, and in all centers. CONCLUSION: This report provides a process road map for clinical sequencing centers looking to perform similar analyses on their data.
Subject(s)
Exome Sequencing/methods , Sequence Analysis, DNA/methods , Whole Genome Sequencing/methods , Base Sequence , Chromosome Mapping , Exome , Genome, Human , High-Throughput Nucleotide Sequencing/methods , Humans , Sequence Analysis, DNA/standards , SoftwareABSTRACT
Despite recent developments in treatment strategies, castration-resistant prostate cancer (CRPC) is still the second leading cause of cancer-associated mortality among American men, the biological underpinnings of which are not well understood. To this end, we measured levels of 150 metabolites and examined the rate of utilization of 184 metabolites in metastatic androgen-dependent prostate cancer (AD) and CRPC cell lines using a combination of targeted mass spectrometry and metabolic phenotyping. Metabolic data were used to derive biochemical pathways that were enriched in CRPC, using Oncomine concept maps (OCM). The enriched pathways were then examined in-silico for their association with treatment failure (i.e., prostate specific antigen (PSA) recurrence or biochemical recurrence) using published clinically annotated gene expression data sets. Our results indicate that a total of 19 metabolites were altered in CRPC compared to AD cell lines. These altered metabolites mapped to a highly interconnected network of biochemical pathways that describe UDP glucuronosyltransferase (UGT) activity. We observed an association with time to treatment failure in an analysis employing genes restricted to this pathway in three independent gene expression data sets. In summary, our studies highlight the value of employing metabolomic strategies in cell lines to derive potentially clinically useful predictive tools.
Subject(s)
Metabolomics , Orchiectomy , Prostatic Neoplasms/metabolism , Cell Line, Tumor , Chromatography, Liquid , Gene Expression , Glucuronosyltransferase/metabolism , Humans , Male , Mass Spectrometry , Prostatic Neoplasms/enzymology , Prostatic Neoplasms/geneticsABSTRACT
Metabolic pathway reprogramming is a hallmark of cancer cell growth and survival and supports the anabolic and energetic demands of these rapidly dividing cells. The underlying regulators of the tumor metabolic program are not completely understood; however, these factors have potential as cancer therapy targets. Here, we determined that upregulation of the oncogenic transcriptional coregulator steroid receptor coactivator 2 (SRC-2), also known as NCOA2, drives glutamine-dependent de novo lipogenesis, which supports tumor cell survival and eventual metastasis. SRC-2 was highly elevated in a variety of tumors, especially in prostate cancer, in which SRC-2 was amplified and overexpressed in 37% of the metastatic tumors evaluated. In prostate cancer cells, SRC-2 stimulated reductive carboxylation of α-ketoglutarate to generate citrate via retrograde TCA cycling, promoting lipogenesis and reprogramming of glutamine metabolism. Glutamine-mediated nutrient signaling activated SRC-2 via mTORC1-dependent phosphorylation, which then triggered downstream transcriptional responses by coactivating SREBP-1, which subsequently enhanced lipogenic enzyme expression. Metabolic profiling of human prostate tumors identified a massive increase in the SRC-2-driven metabolic signature in metastatic tumors compared with that seen in localized tumors, further implicating SRC-2 as a prominent metabolic coordinator of cancer metastasis. Moreover, SRC-2 inhibition in murine models severely attenuated the survival, growth, and metastasis of prostate cancer. Together, these results suggest that the SRC-2 pathway has potential as a therapeutic target for prostate cancer.