Biology subjects

Fan, C.-C.

Publications and source records attributed to Fan, C.-C..

6 recordsLinked to original sources

bioRxiv2020

Epigenetic therapy remodels the immune synaptic cytoskeleton to potentiate cancer susceptibility to γδ T cells

{gamma}{delta} T cells are a distinct subgroup of T cells that bridge the innate and adaptive immune systems and can attack cancer or virus-infected cells in an MHC-unrestricted manner. Despite its antitumor ability in both autologous and allogeneic settings, earlier trials of adoptive {gamma}{delta} T cell transfer in solid tumors had limited success due to limitations in cell expansion and the lack of a strategy to modulate tumor lytic interactions between {gamma}{delta} T and cancer cells. Here, we show through quantitative surface proteomics and gene enrichment analyses that DNA methyltransferase inhibitors (DNMTis) upregulate multiple surface molecules related to {gamma}{delta} T cell activation in cancer cells. DNMTi treatment of human lung cancer potentiates tumor lysis by ex vivo-expanded {gamma}{delta} T cells using a clinical-grade expansion protocol developed by our team to enrich for the V{delta}1 subset while preserving their antitumor effector functions. Mechanistically, DNMTis enhance immune synapse formation and stabilize the synaptic cleft to facilitate {gamma}{delta} T-mediated tumor lysis. Through integrated analysis of RNA-seq, DNA methylation, and ATAC-seq, we demonstrate that depletion of DNMTs induces coordinated pattern alterations of immune synaptic-cytoskeletal networks at the cancer side of the immune synapse. In addition, single-cell mass cytometry reveals enrichment of polyfunctional {gamma}{delta} T subsets by DNMTis. Combined DNMTi and adoptive {gamma}{delta} T transfer in a mouse lung cancer model offers a significant survival benefit. Consistently, the DNMTi-associated cytoskeleton signature identifies a subset of lung cancer patients with improved survival. Our results demonstrate that epigenetic mechanisms are crucial for cytoskeletal remodeling in cancer to potentiate immune attack and support a combinatorial strategy of DNMTis and {gamma}{delta} T cell-based immunotherapy in lung cancer management. One Sentence SummaryDNA methyltransferase inhibitors potentiate the killing of lung cancer by {gamma}{delta} T cells through remodeling cytoskeletal-immune synaptic networks.

cancer biology↗

bioRxiv2020

Fluid and crystallised intelligence are associated with distinct regionalisation patterns of cortical morphology

Cognitive performance in children is predictive of academic and social outcomes; therefore, understanding neurobiological mechanisms underlying individual differences in cognition during development may be important for improving quality of life. The belief that a single, psychological construct underlies many cognitive processes is pervasive throughout society. However, it is unclear if there is a consistent neural substrate underlying many cognitive processes. Here we show that a distributed configuration of cortical surface area and apparent thickness, when controlling for global imaging measures, is differentially associated with cognitive performance on different types of tasks in a large sample (N=10,145) of 9-11 year old children from the Adolescent Brain and Cognitive DevelopmentSM (ABCD) study. The minimal overlap in these regionalization patterns of association has implications for competing theories about developing intellectual functions. Surprisingly, not controlling for sociodemographic factors increased the similarity between these regionalization patterns. This highlights the importance of understanding the shared variance between sociodemographic factors, cognition and brain structure, particularly with a population-based sample such as ABCD.

neuroscience↗

bioRxiv2019

RICOPILI: Rapid Imputation for COnsortias PIpeLIne

MotivationGenome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open sourced Perl-based pipeline was developed to address the challenges of rapidly processing large scale multi-cohort GWAS studies including quality control, imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing (HPC) environments.\n\nSummaryRICOPILI was created as the Psychiatric Genomics Consortium (PGC) pipeline for GWAS and has been adopted by other users. The pipeline features i) technical and genomic quality control in case-control and trio cohorts ii) genome-wide phasing and imputation iv) association analysis v) meta-analysis vi) polygenic risk scoring and vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all of the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work.\n\nAvailability and ImplementationRICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home\n\nContactsripke@broadinstitute.org\n\nSupplementary informationSupplementary data are available.

genetics↗

bioRxiv2018

MODE for detecting and estimating genetic causal variants

Determining the genetic causal variants and estimating their effect sizes are considered to be correlated but independent problems. Fine-mapping studies often rely on the ability to integrate useful functional annotation information into genome wide association univariate/multivariate analysis. In the present study, by modeling the probability of a SNP being causal and its effect size as a set of correlated Gaussian/non-Gaussian random variables, we design an optimization routine for simultaneous fine-mapping and effect size estimation. The algorithm is released as an open source C package MODE.\n\nAvailability and Implementation: http://sites.google.com/site/sundarvelkur/mode\n\nContact: amdale@ucsd.edu, svelkur@ucsd.edu

bioinformatics↗

bioRxiv2017

Estimating inflation in GWAS summary statistics due to variance distortion from cryptic relatedness

Cryptic relatedness is inherently a feature of large genome-wide association studies (GWAS), and can give rise to considerable inflation in summary statistics for single nucleotide polymorphism (SNP) associations with phenotypes. It has proven difficult to disentangle these inflationary effects from true polygenic effects. Here we present results of a model that enables estimation of polygenicity, mean strength of association, and residual inflation in GWAS summary statistics. We show that there is substantial residual inflation in recent large GWAS of height and schizophrenia; correcting for this reduces the number of independent genome-wide significant loci from the reported values of 697 for height and 108 for schizophrenia to 368 and 61, respectively. In contrast, a larger GWAS of educational attainment shows no residual inflation. Additionally, we find that height has a relatively low polygenicity, with approximately 8k SNPs having causal association, more than an order of magnitude less than has been reported. The residual inflation in GWAS summary statistics can be corrected using the standard genomic control procedure with the estimated residual inflation factor.

genomics↗

bioRxiv2017

Estimating Degree Of Polygenicity, Causal Effect Size Variance, And Confounding Bias In GWAS Summary Statistics

Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from an extensive reference panel, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from [~=] 2 x 10-5 to [~=] 4 x 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics. Author SummaryThere are ~10 million common variants in the genome of humans with European ancestry. For any particular phenotype a number of these variants will have some causal effect. It is of great interest to be able to quantify the number of these causal variants and the strength of their effect on the phenotype. Genome wide association studies (GWAS) produce very noisy summary statistics for the association between subsets of common variants and phenotypes. For any phenotype, these statistics collectively are difficult to interpret, but buried within them is the true landscape of causal effects. In this work, we posit a probability distribution for the causal effects, and assess its validity using simulations. Using a detailed reference panel of ~11 million common variants - among which only a small fraction are likely to be causal, but allowing for non-causal variants to show an association with the phenotype due to correlation with causal variants - we implement an exact procedure for estimating the number of causal variants and their mean strength of association with the phenotype. We find that, across different phenotypes, both these quantities - whose product allows for lower bound estimates of heritability - vary by orders of magnitude.

genomics↗