Biology subjects

Cheng, F.

Publications and source records attributed to Cheng, F..

5 recordsLinked to original sources

bioRxiv2018

Multi-Center Study of Resectable Lung Lesions by Ultra-Deep Sequencing of Targeted Genes in Plasma Cell-Free DNA to Assess Nodule Malignancy and Detect Lung Cancers

BACKGROUNDEarly detection of lung cancer to allow curative treatment remains challenging. Cell-free circulating tumor DNA (ctDNA) analysis may aid in malignancy assessment and early cancer diagnosis of lung nodules found in screening imagery.\n\nMETHODSThe multi-center clinical study enrolled 192 patients with operable occupying lung diseases. Plasma ctDNA, white blood cell genomic DNA (gDNA) and tumor tissue gDNA of each patient were analyzed by ultra-deep sequencing to an average of 35,000X of the coding regions of 65 lung cancer-related genes.\n\nRESULTSThe cohort consists of a quarter of benign lung diseases and three quarters of cancer patients with all histopathology subtypes. 64% of the cancer patients is at Stage I. Gene mutations detection in tissue gDNA and plasma ctDNA results in a sensitivity of 91% and specificity of 88%. When ctDNA assay was used as the test, the sensitivity was 69% and specificity 96%. As for the lung cancer patients, the assay detected 63%, 83%, 94% and 100%, for Stage I, II, III and IV, respectively. In a linear discriminant analysis, combination of ctDNA, patient age and a panel of serum biomarkers boosted the overall sensitivity to 80% at a specificity of 99%. 29 out of the 65 genes harbored mutations in the lung cancer patients with the largest number found in TP53 (30% plasma and 62% tumor tissue samples) and EGFR (20% and 40%, respectively).\n\nCONCLUSIONPlasma ctDNA was analyzed in lung nodule assessment and early cancer detection while an algorithm combining clinical information enhanced the test performance.

clinical trials↗

bioRxiv2018

Comparative whole-genome analysis reveals genetic adaptation of the invasive pinewood nematode

Genetic adaptation to new environments is essential for invasive species. To explore the genetic underpinnings of invasiveness of a dangerous invasive species, the pinewood nematode (PWN) Bursaphelenchus xylophilus, we analysed the genome-wide variations of a large cohort of 55 strains isolated from both the native and introduced regions. Comparative analysis showed abundant genetic diversity existing in the nematode, especially in the native populations. Phylogenetic relationships and principal component analysis indicate a dominant invasive population/group (DIG) existing in China and expansion beyond, with few genomic variations. Putative origin and migration paths at a global scale were traced by targeted analysis of rDNA sequences. A progressive loss of genetic diversity was observed along spread routes. We focused on variations with a low frequency allele (<50%) in the native USA population but fixation in DIG, and a total of 25,992 single nuclear polymorphisms (SNPs) were screened out. We found that a clear majority of these fixation alleles originated from standing variation. Functional annotation of these SNP-harboured genes showed that adaptation-related genes are abundant, such as genes that encode for chemoreceptors, proteases, detoxification enzymes, and proteins involved in signal transduction and in response to stresses and stimuli. Some genes under positive selection were predicted. Our results suggest that adaptability to new environments plays essentially roles in PWN invasiveness. Genetic drift, mutation and strong selection drive the nematode to rapidly evolve in adaptation to new environments, which including local pine hosts, vector beetles, commensal microflora and other new environmental factors, during invasion process.

genomics↗

bioRxiv2018

Identifying associations in dense connectomes using structured kernel principal component regression

A powerful and computationally efficient multivariate approach is proposed here, called structured kernel principal component regression (sKPCR), for the identification of associations in the voxel-level dense connectome. The method can identify voxel-phenotype associations based on the voxels whole-brain connectivity pattern, which is applicable to detect linear and non-linear signals for both volume-based and surface-based functional magnetic resonance imaging (fMRI) data. For each voxel, our approach first extracts signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations via a general linear model. The method derives its power by appropriately modelling the spatial structure of the data. Simulations based on dense connectome data have shown that our method can accurately control the false-positive rate, and it is more powerful than many state-of-the-art approaches, such as the connectivity-wise general linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). To demonstrate the utility of our approach in real data analysis, we apply these methods to identify voxel-wise difference between schizophrenic patients and healthy controls in two independent resting-state fMRI datasets. The findings of our approach have a better between-sites reproducibility, and a larger proportion of overlap with existing schizophrenia findings. Code for our approach can be downloaded from https://github.com/weikanggong/vBWAS.

neuroscience↗

bioRxiv2017

Gene markers for exon capture and phylogenomics in ray-finned fishes

Gene capture coupled with the next generation sequencing has become one of the favorable methods in subsampling genomes for phylogenomic studies. Many target gene markers have been developed in plants, sharks, frogs, reptiles and others, but few have been reported in the ray-finned fishes. Here, we identified a suite of \"single-copy\" protein coding sequence (CDS) markers through comparing eight fish genomes, and tested them empirically in 83 species (33 families and 11 orders) of ray-finned fishes. Sorting through the markers according to their completeness and phylogenetic decisiveness in taxa tested resulted in a selection of 4,434 markers, which were proven to be useful in reconstructing phylogenies of the ray-finned fishes at different taxonomic level. We also proposed a strategy of refining baits (probes) design a posteriori based on empirical data. The markers that we have developed may fill a gap in the tool kit of phylogenomic study in vertebrates.

evolutionary biology↗

bioRxiv2016

Statistical testing and power analysis for brain-wide association study

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression [Cheng et al., 2015a,b, 2016], the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method considerably reduces the computational complexity of a permutation-or simulation-based approach, thus, it can efficiently tackle large datasets with ultra-high resolution images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods failed. A software package is available at https://github.com/weikanggong/BWAS.

neuroscience↗