Cost-effective low-coverage whole-genome sequencing assay for the risk stratification of gastric cancer.

BACKGROUND: Gastric cancer (GC), a multifactorial disease, is caused by pathogens, such as Helicobacter pylori (H. pylori) and Epstein-Barr virus (EBV), and genetic components. AIM: To investigate microbiomes and host genome instability by cost-effective, low-coverage whole-genome sequencing, as biomarkers for GC subtyping. METHODS: Samples from 40 GC patients were collected from Taizhou Hospital, Zhejiang Province, affiliated with Wenzhou Medical University. DNA from the samples was subjected to low-coverage whole-genome sequencing with a median genome coverage of 1.86 × (range: 1.03 × to 3.17 ×) by Illumina × 10, followed by copy number analyses using a customized bioinformatics workflow ultrasensitive chromosomal aneuploidy detector. RESULTS: Of the 40 GC samples, 20 (50%) were found to be enriched with microbiomes. EBV DNA was detected in 5 GC patients (12.5%). H. pylori DNA was found in 15 (37.5%) patients. The other 20 (50%) patients were found to have relatively higher genomic instability. Copy number amplifications of the oncogenes, ERBB2 and KRAS, were found in 9 (22.5%) and 7 (17.5%) of the GC samples, respectively. EBV enrichment was found to be associated with tumors in the gastric cardia and fundus. H. pylori enrichment was found to be associated with tumors in the pylorus and antrum. Tumors with elevated genomic instability showed no localization and could be observed in any location. Additionally, H. pylori-enriched GC was found to be associated with the Borrmann type II/III and gastritis history. EBV-enriched GC was not associated with gastritis. No statistically significant correlation was observed between genomic instability and gastritis. Furthermore, these three different molecular subtypes showed distinct survival outcomes (P = 0.019). EBV-positive tumors had the best prognosis, whereas patients with high genomic instability (CIN+) showed the worst survival. Patients with H. pylori infection showed intermediate prognosis compared with the other two subtypes. CONCLUSION: Thus, using low-coverage whole-genome sequencing, GC can be classified into three categories based on disease etiology; this classification may prove useful for GC diagnosis and precision medicine.

Non-invasive detection of biliary tract cancer by low-coverage whole genome sequencing from plasma cell-free DNA: A prospective cohort study.

BACKGROUND: The diagnosis of biliary tract cancer (BTC) is challenging in clinical practice. We performed a prospective study to evaluate the value of plasma copy number variation (CNV) assays in diagnosing BTC. METHODS: 47 treatment-naïve patients with suspicious biliary lesions were recruited. Plasma samples were collected at admission. Cell-free DNA was analyzed by low coverage whole genome sequencing, followed by CNV analyses via a customized bioinformatics workflow, namely the ultrasensitive chromosomal aneuploidy detector. RESULTS: 29 patients were pathologically diagnosed as BTC, including 8 gallbladder cancers (GBCs) and 21 cholangiocarcinomas (CCs). Cancer patients had more CNV signals as compared with benign patients (26/29 vs. 2/18, P < 0.001). The most frequent copy number gains were chr3q (7/29) and chr8q (6/29). The most frequent copy number losses were chr7p (6/29), chr17p (6/29), and chr19p (6/29). The sensitivity and specificity of plasma CNV assays in diagnosing BTC were 89.7% and 88.9%, respectively. For CA 19-9 (cutoff: 37 U/ml), the sensitivity was 58.6% and the specificity was 72.2%. The diagnostic accuracy of CNV assays significantly outperformed CA 19-9 (AUC 0.91 vs. 0.62, P = 0.004). Compared with CA 19-9 alone, the adding of CNV profiles to CA 19-9 increased the sensitivity in diagnosing GBC (75.0% vs. 25.0%) and CC (100% vs. 52.4%). Higher CNV burden was also associated with decreased overall survival (Hazard ratio = 4.32, 95% CI 2.06-9.08, P = 0.033). DISCUSSION: Our results suggest that BTC harbors rich plasma CNV signals, and their assays might be useful for diagnosing BTC.

Classification of transcription factors using protein primary structure.

The transcription factor (TF) is a protein that binds DNA at specific site to help regulate the transcription from DNA to RNA. The mechanism of transcriptional regulatory can be much better understood if the category of transcription factors is known. We introduce a system which can automatically categorize transcription factors using their primary structures. A feature analysis strategy called "mRMR" (Minimum Redundancy, Maximum Relevance) is used to analyze the contribution of the TF properties towards the TF classification. mRMR is coupled with forward feature selection to choose an optimized feature subset for the classification. TF properties are composed of the amino acid composition and the physiochemical characters of the proteins. These properties will generate over a hundred features/parameters. We put all the features/parameters into a classifier, called NNA (nearest neighbor algorithm), for the classification. The classification accuracy is 93.81%, evaluated by a Jackknife test. Feature analysis using mRMR algorithm shows that secondary structure, amino acid composition and hydrophobicity are the most relevant features for classification. A free online classifier is available at http://app3.biosino.org/132dvc/tf/.