Biology subjects

Chatterjee, N.

Publications and source records attributed to Chatterjee, N..

10 recordsLinked to original sources

bioRxiv2018

Comparative validation of breast cancer risk prediction models and projections for future risk stratification

BackgroundWell-validated risk models are critical for risk stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) tool for comparative model validation of five-year risk of invasive breast cancer in a prospective cohort, and to make projections for population risk stratification.\n\nMethodsPerformance of two recently developed models, iCARE-BPC3 and iCARE-Lit, were compared with two established models (BCRAT, IBIS) based on classical risk factors in a UK-based cohort of 64,874 women (863 cases) aged 35-74 years. Risk projections in US White non-Hispanic women aged 50-70 years were made to assess potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).\n\nResultsThe best calibrated models were iCARE-Lit (expected to observed number of cases (E/O)=0.98 (95% confidence interval [CI]=0.87 to 1.11)) for women younger than 50 years; and iCARE-BPC3 (E/O=1.00 (0.93 to 1.09)) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify ~500,000 women at moderate to high risk (>3% five-year risk). Additional information on MD and a PRS based on 172 variants is expected to increase this to ~3.6 million, and among them, ~155,000 invasive breast cancer cases are expected within five years.\n\nConclusionsiCARE models based on classical risk factors perform similarly or better than BCRAT or IBIS. Addition of MD and PRS can lead to substantial improvements in risk stratification. Independent prospective validation of integrated models is needed prior to clinical evaluation risk stratified breast cancer screening and prevention.

epidemiology↗

bioRxiv2018

A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics

AO_SCPLOWBSTRACTC_SCPLOWCancers are routinely classified into subtypes according to various features, including histopathological characteristics and molecular markers. Previous genome-wide association studies have reported heterogeneous associations between loci and cancer subtypes. However, it is not evident what is the optimal modeling strategy for handling correlated tumor features, missing data, and increased degrees-of-freedom in the underlying tests of associations. We propose to test for genetic associations using a mixed-effect two-stage polytomous model score test (MTOP). In the first stage, a standard polytomous model is used to specify all possible sub-types defined by the cross-classification of the tumor characteristics. In the second stage, the subtype-specific case-control odds ratios are specified using a more parsimonious model based on the case-control odds ratio for a baseline subtype, and the case-case parameters associated with tumor markers. Further, to reduce the degrees-of-freedom, we specify case-case parameters for additional exploratory markers using a random-effect model. We use the Expectation-Maximization (EM) algorithm to account for missing data on tumor markers. Through simulations across a range of realistic scenarios and data from the Polish Breast Cancer Study (PBCS), we show MTOP outperforms alternative methods for identifying heterogeneous associations between risk loci and tumor subtypes. The proposed methods have been implemented in a user-friendly and high-speed R statistical package called TOP (https://github.com/andrewhaoyu/TOP).

genetics↗

bioRxiv2018

Synthetic essentiality of metabolic regulator PDHK1 in PTEN-deficient cells and cancers

PTEN is a tumor suppressor that is often inactivated in cancer and possesses both lipid and protein phosphatase activities. We report the metabolic regulator PDHK1 (pyruvate dehydrogenase kinase1) is a synthetic-essential gene in PTEN-deficient cancer and normal cells. The predominant mechanism of PDHK1 regulation and dependency is the PTEN protein phosphatase dephosphorylates NF{kappa};B activating protein (NKAP) and limits NF{kappa}B activation to suppress expression of PDHK1, a NF{kappa}B target gene. Loss of the PTEN protein phosphatase upregulates PDHK1 to drive aerobic glycolysis and induce PDHK1 cellular dependence. PTEN-deficient human tumors harbor increased PDHK1, which is a biomarker of decreased patient survival, establishing clinical relevance. This study uncovers a PTEN-regulated signaling pathway and reveals PDHK1 as a potential target in PTEN-deficient cancers.\n\nSIGNIFICANCEThe tumor suppressor PTEN is widely inactivated in cancers and tumor syndromes. PTEN antagonizes PI3K/AKT signaling via its lipid phosphatase activity. The modest success of PI3K/AKT inhibition in PTEN-deficient cancer patients provides rationale for identifying other vulnerabilities in PTEN-deficient cancers to improve clinical outcomes. We show that PTEN-deficient cells are uniquely sensitive to PDHK1 inhibition. PTEN and PDHK1 co-suppression reduced colony formation and induced cell death in vitro and tumor regression in vivo. PDHK1 levels were high in PTEN-deficient patient tumors and associated with inferior patient survival, establishing clinical relevance. Our study identifies a PTEN-regulated signaling pathway linking the PTEN protein phosphatase to the metabolic regulator PDHK1 and provides a mechanistic basis for PDHK1 targeting in PTEN-deficient cancers.

cancer biology↗

bioRxiv2018

Mendelian Randomization Analysis Using Mixture Models (MRMix) for Genetic Effect-Size-Distribution Leads to Robust Estimation of Causal Effects

We propose a novel method for robust estimation of causal effects in two-sample Mendelian randomization analysis using potentially large number of genetic instruments. We consider a \"working model\" for bi-variate effect-size distribution across pairs of traits in the form of normal-mixtures which assumes existence of a fraction of the genetic markers that are valid instruments, i.e. they have only direct effect on one trait, while other markers can have potentially correlated, direct and indirect effects, or have no effects at all. We show that model motivates a simple method for estimating causal effect ({theta}) through a procedure for maximizing the probability concentration of the residuals, [Formula], at the \"null\" component of a two-component normal-mixture model. Simulation studies showed that MRMix provides nearly unbiased or/and substantially more robust estimates of causal effects compared to alternative methods under various scenarios. Further, the studies showed that MRMix is sensitive to direction and can achieve much higher efficiency (up to 3-4 fold) relative to other comparably robust estimators. We applied the proposed methods for conducting MR analysis using largest publicly available datasets across a number of risk-factors and health outcomes. Notable findings included identification of causal effects of genetically determined BMI and ageat-menarche, which have relationship among themselves, on the risk of breast cancer; detrimental effect of HDL on the risk of breast cancer; no causal effect of HDL and triglycerides on the risk of coronary artery disease; a strong detrimental effect of BMI, but no causal effect of years of education, on the risk of major depressive disorder.

genetics↗

bioRxiv2017

Heritability Informed Power Optimization (HIPO) Leads to Enhanced Detection of Genetic Associations Across Multiple Traits

Genome-wide association studies have shown that pleiotropy is a common phenomenon that can potentially be exploited for enhanced detection of susceptibility loci. We propose heritability informed power optimization (HIPO) for conducting powerful pleiotropic analysis using summary-level association statistics. We find optimal linear combinations of association coefficients across traits that are expected to maximize non-centrality parameter for the underlying test statistics, taking into account estimates of heritability, sample size variations and overlaps across the traits. Simulation studies show that the proposed method has correct type I error, robust to population stratification and leads to desired genome-wide enrichment of association signals. Application of the proposed method to publicly available data for three groups of genetically related traits, lipids (N=188,577), psychiatric diseases (Ncase=33,332, Ncontrol=27,888) and social science traits (N ranging between 161,460 to 298,420 across individual traits) increased the number of genome-wide significant loci by 12%, 200% and 50%, respectively, compared to those found by analysis of individual traits. Evidence of replication is present for many of these loci in subsequent larger studies for individual traits. HIPO can potentially be extended to high-dimensional phenotypes as a way of dimension reduction to maximize power for subsequent genetic association testing.

genetics↗

bioRxiv2017

Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits and implications for the future

Summary-level statistics from genome-wide association studies are now widely used to estimate heritability and co-heritability of traits using the popular linkage-disequilibrium-score (LD-score) regression method. We develop a likelihood-based approach for analyzing summary-level statistics and external LD information to estimate common variants effect-size distributions, characterized by proportion of underlying susceptibility SNPs and a flexible normal-mixture model for their effects. Analysis of summary-level results across 32 GWAS reveals that while all traits are highly polygenic, there is wide diversity in the degrees of polygenicity. The effect-size distributions for susceptibility SNPs could be adequately modeled by a single normal distribution for traits related to mental health and ability and by a mixture of two normal distributions for all other traits. Among quantitative traits, we predict the sample sizes needed to identify SNPs which explain 80% of GWAS heritability to be between 300K-500K for some of the early growth traits, between 1-2 million for some anthropometric and cholesterol traits and multiple millions for body mass index and some others. The corresponding predictions for disease traits are between 200K-400K for inflammatory bowel diseases, close to one million for a variety of adult onset chronic diseases and between 1-2 million for psychiatric diseases.

genetics↗

bioRxiv2017

MicroRNA perturbation of the MRN complex buffers DNA damage response from VEGF signaling

MicroRNAs contribute to biological robustness by buffering cellular processes from external perturbations. Here we report an unexpected link between DNA damage response and angiogenic signaling that is buffered by two distinct microRNAs. We demonstrate that genotoxic stress-induced miR-494 and miR-99b inhibit the DNA repair machinery by targeting the MRE11a-RAD50-NBN (MRN) complex. Functionally, gain and loss of function experiments show that miR-494 and miR-99b affect telomerase activity, activate p21 and Rb pathways and diminish angiogenic sprouting in vitro and in vivo. Genetic and pharmacological disruption of VEGFR-2 signaling and the MRN complex reveal a surprising co-dependency of these pathways in regulating endothelial senescence and proliferation. Vascular-targeted delivery of miR-494 decreases both growth factor-induced and tumor angiogenesis in mouse models. Mechanistically, disruption of the MRN complex induced CD44, a known driver of senescence and regulator of VEGF signaling in addition to suppressing IL-13 a stimulator of VEGF signaling. Our work identifies a putative miR-facilitated mechanism by which endothelial cells can be insulated against VEGF signaling to facilitate the onset of senescence and highlight the potential of targeting DNA repair to disrupt pathological angiogenesis.

cancer biology↗

bioRxiv2017

dMARCH8, a Drosophila ubiquitin ligase, regulates polymodifications of tubulin in the spermiogenic pathway

Ciliary stability and function are regulated by the covalent addition of polyglutamate and polyglycine chains to axonemal tubulin subunits. The Drosophila gene CG13442 encodes a predicted ubiquitin E3 ligase involved in the regulation of tubulin glycylation and glutamylation. Homologous to mammalian MARCH8, CG13442/dMARCH8 is required for male fertility. Sperm in dMARCH8 mutant testes appear to undergo a normal individualization process but fail to be transferred to the seminal vesicle. This phenotype is very similar to that of mutants in the Ntl gene, shown here to be a glycine transporter using a [3H]glycine uptake assay. Mutations in dMARCH8 are associated with a reduction of both polyglutamylation and polyglycylation of sperm tubulin. Polyglutamylation of tubulin is significantly increased in the Ntl- background, and recovers to wild-type levels in the Ntl--dMARCH8- double mutant background, indicating that glycine and glutamate compete for some common site(s) on tubulin molecules in this system. By analogy to the regulation of the mammalian glycine transporter GlyT2 through ubiquitin-mediated trafficking between the plasma membrane and endosome, dMARCH8 may target Ntl and glutamate transporters, or other upstream regulators of these proteins.

cell biology↗

bioRxiv2017

Simplified Power Calculations for Aggregate-level Association Tests Provide Insights to Challenges for Rare Variant Association Studies

Genome-wide association studies are now shifting focus from analysis of common to uncommon and rare variants with an anticipation to explain additional heritability of complex traits. As power for association testing for individual rare variants may often be low, various aggregate level association tests have been proposed to detect genetic loci that may contain clusters of susceptibility variants. Typically, power calculations for such tests require specification of large number of parameters, including effect sizes and allele frequencies of individual variants, making them difficult to use in practice. In this report, we approximate power to varying degree of accuracy using a smaller number of key parameters, including the total genetic variance explained by multiple variants within a locus. We perform extensive simulation studies to assess the accuracy of the proposed approximations in realistic settings. Using the simplified power calculation methods, we then develop an analytic framework to obtain bounds on genetic architecture of an underlying trait given results from a genome-wide study and observe important implications for the completely lack of or limited number of findings in many currently reported studies. Finally, we provide insights into the required quality of annotation/functional information for identification of likely causal variants to make meaningful improvement in power of subsequent association tests. A shiny application, Power Analysis for GEnetic AssociatioN Tests (PAGEANT), in R implementing the methods is made publicly available.

bioinformatics↗

bioRxiv2016

Retrotransposon Activation Contributes to Neurodegeneration in a DrosophilaTDP-43 Model of ALS.

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two incurable neurodegenerative disorders that exist on a symptomological spectrum and share both genetic underpinnings and pathophysiological hallmarks. Functional abnormality of TAR DNA-binding protein 43 (TDP-43), an aggregation-prone RNA and DNA binding protein, is observed in the vast majority of both familial and sporadic ALS cases and in [~]40% of FTLD cases, but the cascade of events leading to cell death are not understood. We have expressed human TDP-43 (hTDP-43) in Drosophila neurons and glia, a model that recapitulates many of the characteristics of TDP-43-linked human disease including protein aggregation pathology, locomotor impairment, and premature death. We report that such expression of hTDP-43 impairs small interfering RNA (siRNA) silencing, which is the major post-transcriptional mechanism of retrotransposable element (RTE) control in somatic tissue. This is accompanied by de-repression of a panel of both LINE and LTR families of RTEs, with somewhat different elements being active in response to hTDP-43 expression in glia versus neurons. hTDP-43 expression in glia causes an early and severe loss of control of a specific RTE, the endogenous retrovirus (ERV) gypsy. We demonstrate that gypsy causes the degenerative phenotypes in these flies because we are able to rescue the toxicity of glial hTDP-43 either by genetically blocking expression of this RTE or by pharmacologically inhibiting RTE reverse transcriptase activity. Moreover, we provide evidence that activation of DNA damage-mediated programmed cell death underlies both neuronal and glial hTDP-43 toxicity, consistent with RTE-mediated effects in both cell types. Our findings suggest a novel mechanism in which RTE activity contributes to neurodegeneration in TDP-43-mediated diseases such as ALS and FTLD.\n\nAUTHOR SUMMARYFunctional abnormality of TAR DNA-binding protein 43 (TDP-43), an aggregation-prone RNA and DNA binding protein, is observed in the vast majority of both familial and sporadic ALS cases and in [~]40% of FTLD cases, and mutations in TDP-43 are causal in a subset of familial ALS cases. Although cytoplasmic inclusions of this mostly nuclear protein are a hallmark of the disease, the cascade of events leading to cell death are not understood. We demonstrate that expression of human TDP-43 (hTDP-43) in Drosophila neurons or glial cells, which results in toxic cytoplasmic accumulation of TDP-43, causes broad expression of retrotransposons. In the case of glial hTDP-43 expression, the endogenous retrovirus (ERV) gypsy causally contributes to degeneration because inhibiting gypsy genetically or pharmacologically is sufficient to rescue the phenotypic effects. Moreover, we demonstrate that activation of DNA damage-mediated programmed cell death underlies hTDP-43 and gypsy mediated toxicity. Finally, we find that hTDP-43 pathology impairs small interfering RNA silencing, which is an essential system that normally protects the genome from RTEs. These findings suggest a novel mechanism in which a storm of retrotransposon activation drives neurodegeneration in TDP-43 mediated diseases such as ALS and FTLD.

neuroscience↗