Biology subjects

Clish, C. B.

Publications and source records attributed to Clish, C. B..

39 records · Page 3Linked to original sources

bioRxiv2019

Circulating Lysophosphatidylcholines, Phosphatidylcholines, Ceramides, and Sphingomyelins and Ovarian Cancer Risk: a 23-year Prospective Study

BackgroundExperimental evidence supports a role of lipid dysregulation in ovarian cancer progression and metastasis. We estimated associations with ovarian cancer risk for circulating levels of four lipid groups measured 3-23 years before diagnosis.\n\nMethodsAnalyses were conducted among cases (N = 252) and matched controls (N = 252) from the Nurses Health Studies. We used logistic regression adjusting for risk factors to investigate associations of lysophosphatidylcholines (LPC), phosphatidylcholines (PC), ceramides (CER), and sphingomyelins (SM) with ovarian cancer risk overall and by histotype. A Bonferroni adjusted p-value threshold of 0.0125 (0.05/4; 4 measured lipid groups) was used to evaluate statistical significance. Odds ratios (OR; 10th to the 90th percentile) and 95% confidence intervals of ovarian cancer risk were estimated.\n\nResultsC16:0 SM, C18:0 SM, C16:0 CER and SM sum were significantly positively associated with ovarian cancer risk, with ORs ranging from 1.95-2.10, with stronger ORs for postmenopausal women (2.02-3.22). ORs were generally similar for serous/poorly differentiated and endometrioid/clear cell tumors, although most did not meet the Bonferroni-adjusted p-value for significance. C18:1 LPC and the ratio of LPC to PC were significantly inversely, while C18:0 SM was significantly positively, associated with risk of endometrioid/clear cell tumors.\n\nConclusionElevated levels of circulating SMs 3-23 years before diagnosis were associated with increased risk of ovarian cancer, regardless of histotype, with stronger associations among postmenopausal women. Prospective and experimental studies are required to validate our findings and understand the role of lipid dysregulation, SMs in particular, in ovarian carcinogenesis.

epidemiology↗

bioRxiv2018

netome: a computational framework for metabolite profiling and omics network analysis

SummaryAdvances in metabolomics technologies have enabled comprehensive analyses of associations between metabolites and human disease and have provided a means to study biochemical pathways and processes in detail using model systems. Liquid chromatography tandem mass spectrometry (LC-MS) is an analytical technique commonly used by metabolomics labs to measure hundreds of metabolites of known identity and thousands of \"peaks\" from yet to be identified compounds that are tracked by their measured masses and chromatographic retention times. netome is a computational framework that provides tools for analyzing processed LC-MS data. In this framework, we develop and provide various computational resources including individual software modules to inspect and adjust trends in raw data, align unknown peaks between separately acquired data sets, and to remove redundancies in nontargeted LC-MS data arising from multiple ionization products of a single metabolite. These tools are deployed through computing resources such as web servers and virtual machines with detailed documentation in order to support researchers.\n\nAvailability and implementationnetome is publicly available with extensive documentation and support via issue tracker at https://broadinstitute.github.io/netome under the MIT license. netome includes a set of computational methods that have been designed to execute quality control and post-raw data processing tasks for metabolomics data (e.g. scaling and clustering metabolite abundances), as well as statistical association testing in a network manner (e.g., testing relationship between metabolites and microbes). Each individual tool is available with source code, workshop-oriented documentation which includes instructions for installation and using tools with demonstration examples, and a web server with all services. We also provide a complete image of the netome package with all pre-installed dependencies and support for Google Compute Engine and Amazon EC2. All tools and related services are maintained, and upon new developments, new modules will be added to the environment.\n\nContactrah@broadinstitute.org, clary@broadinstitute.org\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

HIF-2α drives an intrinsic vulnerability to ferroptosis in clear cell renal cell carcinoma

Kidney cancers are characterized by extensive metabolic reprogramming and resistance to a broad range of anti-cancer therapies. By interrogating the Cancer Therapeutics Response Portal compound sensitivity dataset, we show that cells of clear-cell renal cell carcinoma (ccRCC) possess a lineage-specific vulnerability to ferroptosis that can be exploited by inhibiting glutathione peroxidase 4 (GPX4). Using genome-wide CRISPR screening and lipidomic profiling, we reveal that this vulnerability is driven by the HIF-2-HILPDA pathway by inducing a polyunsaturated fatty acyl (PUFA)-lipid-enriched cell state that is dependent on GPX4 for survival and susceptible to ferroptosis. This cell state is developmentally primed by the HNF-1{beta}-1-Acylglycerol-3-Phosphate O-Acyltransferase 3 (AGPAT3) axis in the renal lineage. In addition to PUFA metabolism, ferroptosis is facilitated by a phospholipid flippase TMEM30A involved in membrane topology. Our study uncovers an oncogenesis-associated vulnerability, delineates the underlying mechanisms and suggests targeting GPX4 to induce ferroptosis as a therapeutic opportunity in ccRCC.\n\nHIGHLIGHTSO_LIccRCC cells exhibit strong susceptibility to GPX4 inhibition-induced ferroptosis\nC_LIO_LIThe GPX4-dependent and ferroptosis-susceptible state in ccRCC is associated with PUFA-lipid abundance\nC_LIO_LIThe HIF-2-HILPDA axis promotes the selective deposition of PUFA-lipids and ferroptosis susceptibility\nC_LIO_LIAGPAT3 selectively synthesizes PUFA-phospholipids and primes renal cells for ferroptosis\nC_LI

cancer biology↗