Biology subjects

Qian, Z.

Publications and source records attributed to Qian, Z..

4 recordsLinked to original sources

bioRxiv2018

SCDT: Detecting CNVs of low chimeric ratio in cf-DNA

MotivationSequencing of cell-free DNA (cf-DNA) has enabled Noninvasive Prenatal Testing (NIPT) and\"liquid biopsy\" of cancers. However, while the aneuploidy and point mutations were focused on by most of NITP and liquid biopsy studies, detecting sub-chromosome CNVs that affect a few to dozens of megabases was rarely reported, likely attributable to the difficulty in accurately identifying them, especially for those present in a small fraction of cf-DNA.\n\nResultsWe developed a somatic CNV detection tool (SCDT), for detecting sub-chromosome CNVs in cf-DNA using whole genome sequencing (WGS) data or off-target reads in target sequencing data. Additional to using control samples for correcting genome position specific bias, two GC correction steps were performed, which regressed GC content of DNA fragments and that of genome bins, respectively. After GC correction, the coefficients of variation of copy ratios approximated the lower boundary of theoretical values, suggesting removing of almost all systematic errors. Finally, CNVs were detected by a piecewise least squares fitting based segmentation algorithm, which outperformed other segmentation methods. We applied SCDT on simulated and real maternal plasma samples, and target cf-DNA sequencing of 118 normal individuals and 240 cancer patients, and demonstrated high sensitivity and specificity.\n\nAvailabilitySCDT is available at https://github.com/Martiantian/Somatic_cnv_detect_tool.\n\nContactzhuhongmei@genomics.cn\n\nSupplementary InformationSupplementary data are available at Bioinformatics online

bioinformatics↗

bioRxiv2018

AnatomyNet: Deep 3D Squeeze-and-excitation U-Nets for fast and fully automated whole-volume anatomical segmentation

PurposeRadiation therapy (RT) is a common treatment for head and neck (HaN) cancer where therapists are often required to manually delineate boundaries of the organs-at-risks (OARs). The radiation therapy planning is time-consuming as each computed tomography (CT) volumetric data set typically consists of hundreds to thousands of slices and needs to be individually inspected. Automated head and neck anatomical segmentation provides a way to speed up and improve the reproducibility of radiation therapy planning. Previous work on anatomical segmentation is primarily based on atlas registrations, which takes up to hours for one patient and requires sophisticated atlas creation. In this work, we propose the AnatomyNet, an end-to-end and atlas-free three dimensional squeeze-and-excitation U-Net (3D SE U-Net), for fast and fully automated whole-volume HaN anatomical segmentation.\n\nMethodsThere are two main challenges for fully automated HaN OARs segmentation: 1) challenge in segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and 2) training model with inconsistent data annotations with missing ground truth for some anatomical structures because of different RT planning. We propose the AnatomyNet that has one down-sampling layer with the trade-off between GPU memory and feature representation capacity, and 3D SE residual blocks for effective feature learning to alleviate these challenges. Moreover, we design a hybrid loss function with the Dice loss and the focal loss. The Dice loss is a class level distribution loss that depends less on the number of voxels in the anatomy, and the focal loss is designed to deal with highly unbalanced segmentation. For missing annotations, we propose masked loss and weighted loss for accurate and balanced weights updating in the learning of the AnatomyNet.\n\nResultsWe collect 261 HaN CT images to train the AnatomyNet, and use MICCAI Head and Neck Auto Segmentation Challenge 2015 as the benchmark dataset to evaluate the performance of the AnatomyNet. The objective is to segment nine anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right, parotid gland left, parotid gland right, submandibular gland left, and submandibular gland right. Compared to previous state-of-the-art methods for each anatomy from the MICCAI 2015 competition, the AnatomyNet increases Dice similarity coefficient (DSC) by 3.3% on average. The proposed AnatomyNet takes only 0.12 seconds on average to segment a whole-volume HaN CT image of an average dimension of 178 x 302 x 225. All the data and code will be availablea.\n\nConclusion1We propose an end-to-end, fast and fully automated deep convolutional network, AnatomyNet, for accurate and whole-volume HaN anatomical segmentation. The proposed Anato-myNet outperforms previous state-of-the-art methods on the benchmark dataset. Extensive experiments demonstrate the effectiveness and good generalization ability of the components in the AnatomyNet.

bioengineering↗

bioRxiv2018

Inherited DNA Repair Defects in Colorectal Cancer

Colorectal cancer (CRC) heritability has been estimated to be around 30%. However, mutations in the known CRC susceptibility genes explain CRC risk in under 10% of the cases. Germline mutations in DNA-repair genes (DRGs) have recently been reported in CRC but their contribution to CRC risk is largely unknown. We evaluated the gene-level germline mutation enrichment of 40 DRGs in 680 unselected CRC individuals compared to 27728 ancestry-matched cancer-free adults. Significant findings were then examined in independent cohorts of 1661 unselected CRC cases and 1456 early-onset CRC cases. Of 680 individuals in the discovery set, 31 (4.56%) individuals harbored germline pathogenic mutations in known CRC susceptibility genes while another 33 (4.85%) individuals had DRG mutations that have not been previously associated with CRC risk. Germline pathogenic mutations in ATM and PALB2 were enriched in both the discovery (OR= 2.81; P= 0.035 and OR= 4.91; P= 0.024, respectively) and validation sets (OR= 2.97; Adjusted P= 0.0013 and OR= 3.42; Adjusted P= 0.034, for ATM and PALB2 respectively). Biallelic loss of ATM was evident in all cases with matched tumor profiling. CRC cases also had higher rates of actionable mutations in the HR pathway that can substantially increase the risk of developing cancers other than CRC. Our analysis provides evidence for ATM and PALB2 as CRC risk genes, underscoring the importance of the homologous recombination pathway in CRC. In addition, we identified frequent complete homologous recombination deficiency in CRC tumors, representing a unique opportunity to explore targeted therapeutic interventions such as PARPi.

genetics↗

bioRxiv2017

Resting heart rate and psychopathy: Findings from the Add Health Survey

Despite the prior linkages of low resting heart rate to antisocial behavior broadly defined, less work has been done examining possible associations between heart rate to psychopathic traits. The small body of research on the topic that has been conducted so far seems to suggest an inverse relationship between the two constructs. A smaller number of studies have found the opposite result, however, and some of the previous studies have been limited by small sample sizes and unrepresentative samples. The current study attempts to help clarify the relationship between resting heart rate and psychopathic traits in a large, nationally representative sample (analytical N ranged from 14,173-14,220) using an alternative measure of psychopathic traits that is less focused on antisocial processes, and rooted in personality traits. No significant relationship between heart rate and psychopathic traits, or heart rate and a measure of cold heartedness, was found after controlling for age, sex, and race. Implications of the findings, study limitations, and directions for future research are discussed.

physiology↗