Zinc finger protein ZNF638 regulates triglyceride metabolism via ANGPTL8 in an estrogen dependent manner.

BACKGROUND AND AIM: Triglyceride (TG) levels are closely related to obesity, fatty liver and cardiovascular diseases, while the regulatory factors and mechanism for triglyceride homeostasis are still largely unknown. Zinc Finger Protein 638 (ZNF638) is a newly discovered member of zinc finger protein family for adipocyte function in vitro. The aim of the present work was to investigate the role of ZNF638 in regulating triglyceride metabolism in mice. METHODS: We generated ZNF638 adipose tissue specific knockout mice (ZNF638 FKO) by cross-breeding ZNF638 flox to Adiponectin-Cre mice and achieved adipose tissue ZNF638 overexpression via adenoviral mediated ZNF638 delivery in inguinal adipose tissue (iWAT) to examined the role and mechanisms of ZNF638 in fat biology and whole-body TG homeostasis. RESULTS: Although ZNF638 FKO mice showed similar body weights, body composition, glucose metabolism and serum parameters compared to wild-type mice under chow diet, serum TG levels in ZNF638 FKO mice were increased dramatically after refeeding compared to wild-type mice, accompanied with decreased endothelial lipoprotein lipase (LPL) activity and increased lipid absorption of the small intestine. Conversely, ZNF638 overexpression in iWAT reduced serum TG levels while enhanced LPL activity after refeeding in female C57BL/6J mice and obese ob/ob mice. Specifically, only female mice exhibited altered TG metabolism upon ZNF638 expression changes in fat. Mechanistically, RNA-sequencing analysis revealed that the TG regulator angiopoietin-like protein 8 (Angptl8) was highly expressed in iWAT of female ZNF638 FKO mice. Neutralizing circulating ANGPTL8 in female ZNF638 FKO mice abolished refeeding-induced TG elevation. Furthermore, we demonstrated that ZNF638 functions as a transcriptional repressor by recruiting HDAC1 for histone deacetylation and broad lipid metabolic gene suppression, including Angptl8 transcription inhibition. Moreover, we showed that the sexual dimorphism is possibly due to estrogen dependent regulation on ZNF638-ANGPTL8 axis. CONCLUSION: We revealed a role of ZNF638 in the regulation of triglyceride metabolism by affecting Angptl8 transcriptional level in adipose tissue with sexual dimorphism.

Genomic profiling of NGS-based ctDNA from Chinese non-small cell lung cancer patients.

PURPOSE: Cell-free circulating tumor DNA (ctDNA) in plasma enables rapid and repeat testing of actionable mutations. Next-generation sequencing (NGS) is an attractive platform for multiplex sequencing capabilities compared to traditional methods such as PCR. The purpose of this study is to evaluate the value of the NGS-based ctDNA assay and to identify the genomic alteration profile of ctDNA in real-world Chinese non-small cell lung (NSCLC) patients. METHODS: In total, 294 Chinese patients with pathological diagnosis of Phase III-IV NSCLC were enrolled. 3-4 mL peripheral blood was collected and NGS-based analysis was carried out using a 20-gene panel. The analytical sensitivity and specificity of ctDNA NGS-based assay was validated using droplet digital PCR (ddPCR). RESULTS: We have tested 570 sites from 286 samples using ddPCR, which included 108 positive sites and 462 negative sites from NGS results, and the concordance rate was 99.8% (418/419) for single-nucleotide variants (SNVs) and 96.7% (146/151) for insertions and deletions (InDels). The most frequent genes were TP53 (32%), EGFR (31.97%), KRAS (6.46%), PIK3CA (4.76%), and MET (4.08%). Exon 19 deletion (19del) was the most common alteration in EGFR and G12C was the most common alteration in KRAS. Furthermore, the detection rate of TP53 was higher in the male and patients with squamous cell carcinoma. We also found the prevalence of TP53 in L858R was higher than in 19del (61.29% vs. 40%; p = 0.1115). CONCLUSION: The results indicate that the results of NGS-based ctDNA assay are highly consistent with ddPCR. In Chinese NSCLC patients, TP53 mutation was more frequently associated with male and squamous cell carcinoma. The prevalence of concomitant mutations in L858R may be different from that in 19del.

A robust microsatellite instability detection model for unpaired colorectal cancer tissue samples.

BACKGROUND: Microsatellite instability (MSI) is a key biomarker for cancer immunotherapy and prognosis. Integration of MSI testing into a next-generation-sequencing (NGS) panel could save tissue sample, reduce turn-around time and cost, and provide MSI status and comprehensive genomic profiling in single test. We aimed to develop an MSI calling model to detect MSI status along with the NGS panel-based profiling test using tumor-only samples. METHODS: From January 2019 to December 2020, a total of 174 colorectal cancer (CRC) patients were enrolled, including 31 MSI-high (MSI-H) and 143 microsatellite stability (MSS) cases. Among them, 56 paired tumor and normal samples (10 MSI-H and 46 MSS) were used for modeling, and another 118 tumor-only samples were used for validation. MSI polymerase chain reaction (MSI-PCR) was performed as the gold standard. A baseline was built for the selected microsatellite loci using the NGS data of 56 normal blood samples. An MSI detection model was constructed by analyzing the NGS data of tissue samples. The performance of the model was compared with the results of MSI-PCR. RESULTS: We first intersected the target genomic regions of the NGS panels used in this study to select common microsatellite loci. A total of 42 loci including 23 mononucleotide repeat sites and 19 longer repeat sites were candidates for modeling. As mononucleotide repeat sites are more sensitive and specific for detecting MSI status than sites with longer length motif and the mononucleotide repeat sites performed even better than the total sites, a model containing 23 mononucleotide repeat sites was constructed and named Colorectal Cancer Microsatellite Instability test (CRC-MSI). The model achieved 100% sensitivity and 100% specificity when compared with MSI-PCR in both training and validation sets. Furthermore, the CRC-MSI model was robust with the tumor content as low as 6%. In addition, 8 out of 10 MSI-H samples showed alternations in the four mismatch repair genes ( MLH1 , MSH2 , MSH6 , and PMS2 ). CONCLUSION: MSI status can be accurately determined along the targeted NGS panels using only tumor samples. The performance of mononucleotide repeat sites surpasses loci with longer repeat motif in MSI calling.

Establishment and validation of a multigene model to predict the risk of relapse in hormone receptor-positive early-stage Chinese breast cancer patients.

BACKGROUND: Breast cancer patients who are positive for hormone receptor typically exhibit a favorable prognosis. It is controversial whether chemotherapy is necessary for them after surgery. Our study aimed to establish a multigene model to predict the relapse of hormone receptor-positive early-stage Chinese breast cancer after surgery and direct individualized application of chemotherapy in breast cancer patients after surgery. METHODS: In this study, differentially expressed genes (DEGs) were identified between relapse and nonrelapse breast cancer groups based on RNA sequencing. Gene set enrichment analysis (GSEA) was performed to identify potential relapse-relevant pathways. CIBERSORT and Microenvironment Cell Populations-counter algorithms were used to analyze immune infiltration. The least absolute shrinkage and selection operator (LASSO) regression, log-rank tests, and multiple Cox regression were performed to identify prognostic signatures. A predictive model was developed and validated based on Kaplan-Meier analysis, receiver operating characteristic curve (ROC). RESULTS: A total of 234 out of 487 patients were enrolled in this study, and 1588 DEGs were identified between the relapse and nonrelapse groups. GSEA results showed that immune-related pathways were enriched in the nonrelapse group, whereas cell cycle- and metabolism-relevant pathways were enriched in the relapse group. A predictive model was developed using three genes ( CKMT1B , SMR3B , and OR11M1P ) generated from the LASSO regression. The model stratified breast cancer patients into high- and low-risk subgroups with significantly different prognostic statuses, and our model was independent of other clinical factors. Time-dependent ROC showed high predictive performance of the model. CONCLUSIONS: A multigene model was established from RNA-sequencing data to direct risk classification and predict relapse of hormone receptor-positive breast cancer in Chinese patients. Utilization of the model could provide individualized evaluation of chemotherapy after surgery for breast cancer patients.

Adaptive parameter of standard deviation enhances the power of noninvasive prenatal screens.

PURPOSE: Traditional Z-test methods during noninvasive prenatal screens (NIPS) use the fixed parameter of standard deviation (SD), which ignores the influence of actual sequencing read counts of a sample on the results. The aim of this study is to eliminate the influence of the sequencing depth of individual samples on the results and enhance the power of NIPS. METHODS: In this study, we propose an improved NIPS method, which calculates the SD in the Z-score process adaptively according to the actual read count of the test sample. Our approach obtained the SD linear fitting function along with the read count with a large number of reference samples, in which SD and read count fit well. The effectiveness of our enhanced NIPS method was evaluated on three common trisomy syndromes and five recurrent CNV syndromes with 3219 and 6592 samples based on whole genome sequencing of maternal peripheral blood. RESULTS: A total of 3,219 pregnant samples have been used for validating the proposed method on detecting fetal trisomy syndromes (T13, T18, and T21), in which eight false negative (FN) samples have been corrected as true positive (TP) and eight false positive (FP) samples have been fixed as true negative (TN) with our proposed adaptive-SD method. Another 6592 samples were used to compare the two methods on detecting five recurrent fetal copy number variation (CNV) syndromes, in which the FP samples have decreased from 99 to 39. CONCLUSIONS: Our adaptive-SD NIPS method shows more power on detecting both trisomy syndromes and five recurrent CNVs in the pregnant samples with diverse read counts. Besides, our proposed method contributes to lower FP and FN samples than the traditional Z-test method in NIPS. Our results show that our enhanced NIPS methods are effective in detecting both abnormal fetal trisomy syndromes and recurrent CNV syndromes in pregnant women.

Concordance of Genomic Alterations between Circulating Tumor DNA and Matched Tumor Tissue in Chinese Patients with Breast Cancer.

PURPOSE: Circulating tumor DNA (ctDNA) served as a noninvasive method with less side effects using peripheral blood. Given the studies on concordance rate between liquid and solid biopsies in Chinese breast cancer (BC) patients were limited, we sought to examine the concordance rate of different kinds of genomic alterations between paired tissue biopsies and ctDNA samples in Chinese BC cohorts. MATERIALS AND METHODS: In this study, we analyzed the genomic alteration profiles of 81 solid BC samples and 41 liquid BC samples. The concordance across 136 genes was evaluated. RESULTS: The median mutation counts per sample in 41 ctDNA samples was higher than the median in 81 tissue samples (p=0.0254; Wilcoxon rank sum test). For mutation at the protein-coding level, 39.0% (16/41) samples had at least one concordant mutation in two biopsies. 20.0% tissue-derived mutations could be detected via ctDNA-based sequencing, whereas 11.7% ctDNA-derived mutations could be found in paired tissues. At gene amplification level, the overall concordant rate was 68.3% (28/41). The concordant rate at gene level for each patient ranged from 83.8% (114/136) to 99.3% (135/136). And, the mean level of variant allele frequency (VAF) for concordant mutations in ctDNA was statistically higher than that for the discordant ones (p < 0.001; Wilcoxon rank sum test). Across five representative genes, the overall sensitivity and specificity were 49.0% and 85.9%, respectively. CONCLUSION: Our results indicated that ctDNA could provide complementary information on genetic characterizations in detecting single nucleotide variants (SNVs) and insertions and deletions (InDels).

A robust method for the rapid detection of microsatellite instability in colorectal cancer.

Although several computational tools using next-generation sequencing (NGS) data have been proposed to detect microsatellite instability (MSI) status, they still have limitations and need improvement. We developed a NovoPM-MSI method to detect MSI status based on NGS data. This method evaluated target mononucleotide microsatellite loci that were sequenced during targeted gene enrichment analysis and reported sample instability score as the fraction of unstable loci within the target set after assessing locus instability by comparing length distribution in paired tumor-normal samples. We validated this method against the conventional MSI-PCR method in 113 paired colorectal cancer (CRC) specimens and compared the performance of NovoPM-MSI to that of mSINGS and MANTIS in accuracy and runtime efficiency. By using the MSI status from MSI-PCR as the gold standard, the three computational methods showed the same sensitivity of 88.9% but different specificities (NovoPM-MSI 97.1%, MANTIS 86.5% and mSINGS 99.0%). Only NovoPM-MSI could greatly improve both the sensitivity and specificity by setting an ambiguous interval. MANTIS had the shortest average runtime (16.3 sec), followed by NovoPM-MSI (18.3 sec) and mSINGS (109.0 sec). In short, the NovoPM-MSI method provides a fast and reliable MSI detection method with accuracy comparable to MSI-PCR in paired CRC samples.

A novel NGS-based microsatellite instability (MSI) status classifier with 9 loci for colorectal cancer patients.

BACKGROUND: With the recent emergence of immune checkpoint inhibitors, microsatellite instability (MSI) status has become an important biomarker for immune checkpoint blockade therapy. There are growing technical demands for the integration of different genomic alterations profiling including MSI analysis in a single assay for full use of the limited tissues. METHODS: Tumor and paired control samples from 64 patients with primary colorectal cancer were enrolled in this study, including 14 MSI-high (MSI-H) cases and 50 microsatellite stable (MSS) cases determined by MSI-PCR. All the samples were sequenced by a customized NGS panel covering 2.2 MB. A training dataset of 28 samples was used for selection of microsatellite loci and a novel NGS-based MSI status classifier, USCI-msi, was developed. NGS-based MSI status, single nucleotide variant (SNV) and tumor mutation burden (TMB) were detected for all patients. Most of the patients were also independently detected by immunohistochemistry (IHC) staining. RESULTS: A 9-loci model for detecting microsatellite instability was able to correctly predict MSI status with 100% sensitivity and specificity compared with MSI-PCR, and 84.3% overall concordance with IHC staining. Mutations in cancer driver genes (APC, TP53, and KRAS) were dispersed in MSI-H and MSS cases, while BRAF p.V600E and frameshifts in TCF7L2 gene occurred only in MSI-H cases. Mismatch repair (MMR)-related genes are highly mutated in MSI-H samples. CONCLUSION: We established a new NGS-based MSI classifier, USCI-msi, with as few as 9 microsatellite loci for detecting MSI status in CRC cases. This approach possesses 100% sensitivity and specificity, and performed robustly in samples with low tumor purity.

Comprehensive profiling of BRCA1 and BRCA2 variants in breast and ovarian cancer in Chinese patients.

The identification and interpretation of germline BRCA1/2 variants become increasingly important in breast and ovarian cancer (OC) treatment. However, there is no comprehensive analysis of the germline BRCA1/2 variants in a Chinese population. Here we performed a systematic review and meta-analysis on such variants from 94 publications. A total of 2,128 BRCA1/2 variant records were extracted, including 601 from BRCA1 and 632 from BRCA2. In addition, 414, 734, 449, and 307 variants were also recorded in the BIC, ClinVar, ENIGMA, and UMD databases, respectively, and 579 variants were newly reported. Subsequent analysis showed that the overall germline BRCA1/2 pathogenic variant frequency was 5.7% and 21.8% in Chinese breast and OC, respectively. Populations with high-risk factors exhibited a higher pathogenic variant percentage. Furthermore, the variant profile in Chinese is distinct from that in other ethnic groups with no distinct founder pathogenic variants. We also tested our in-house American College of Medical Genetics-guided pathogenicity interpretation procedure for Chinese BRCA1/2 variants. Our results achieved a consistency of 91.2-97.6% (5-grade classification) or 98.4-100% (2-grade classification) with public databases. In conclusion, this study represents the first comprehensive meta-analysis of Chinese BRCA1/2 variants and validates our in-house pathogenicity interpretation procedure, thereby providing guidance for further PARP inhibitor development and companion diagnostics in the Chinese population.

A MeSH-based text mining method for identifying novel prebiotics.

Prebiotics contribute to the well-being of their host by altering the composition of the gut microbiota. Discovering new prebiotics is a challenging and arduous task due to strict inclusion criteria; thus, highly limited numbers of prebiotic candidates have been identified. Notably, the large numbers of published studies may contain substantial information attached to various features of known prebiotics that can be used to predict new candidates. In this paper, we propose a medical subject headings (MeSH)-based text mining method for identifying new prebiotics with structured texts obtained from PubMed. We defined an optimal feature set for prebiotics prediction using a systematic feature-ranking algorithm with which a variety of carbohydrates can be accurately classified into different clusters in accordance with their chemical and biological attributes. The optimal feature set was used to separate positive prebiotics from other carbohydrates, and a cross-validation procedure was employed to assess the prediction accuracy of the model. Our method achieved a specificity of 0.876 and a sensitivity of 0.838. Finally, we identified a high-confidence list of candidates of prebiotics that are strongly supported by the literature. Our study demonstrates that text mining from high-volume biomedical literature is a promising approach in searching for new prebiotics.

Defining the multivalent functions of CTCF from chromatin state and three-dimensional chromatin interactions.

CCCTC-binding factor (CTCF) is a multi-functional protein that is assigned various, even contradictory roles in the genome. High-throughput sequencing-based technologies such as ChIP-seq and Hi-C provided us the opportunity to assess the multivalent functions of CTCF in the human genome. The location of CTCF-binding sites with respect to genomic features provides insights into the possible roles of this protein. Here we present the first genome-wide survey and characterization of three important functions of CTCF: enhancer insulator, chromatin barrier and enhancer linker. We developed a novel computational framework to discover the multivalent functions of CTCF based on chromatin state and three-dimensional chromatin architecture. We applied our method to five human cell lines and identified ∼46 000 non-redundant CTCF sites related to the three functions. Disparate effects of these functions on gene expression were found and distinct genomic features of these CTCF sites were characterized in GM12878 cells. Finally, we investigated the cell-type specificities of CTCF sites related to these functions across five cell types. Our study provides new insights into the multivalent functions of CTCF in the human genome.

DELTA: A Distal Enhancer Locating Tool Based on AdaBoost Algorithm and Shape Features of Chromatin Modifications.

Accurate identification of DNA regulatory elements becomes an urgent need in the post-genomic era. Recent genome-wide chromatin states mapping efforts revealed that DNA elements are associated with characteristic chromatin modification signatures, based on which several approaches have been developed to predict transcriptional enhancers. However, their practical application is limited by incomplete extraction of chromatin features and model inconsistency for predicting enhancers across different cell types. To address these issues, we define a set of non-redundant shape features of histone modifications, which shows high consistency across cell types and can greatly reduce the dimensionality of feature vectors. Integrating shape features with a machine-learning algorithm AdaBoost, we developed an enhancer predicting method, DELTA (Distal Enhancer Locating Tool based on AdaBoost). We show that DELTA significantly outperforms current enhancer prediction methods in prediction accuracy on different datasets and can predict enhancers in one cell type using models trained in other cell types without loss of accuracy. Overall, our study presents a novel framework for accurately identifying enhancers from epigenetic data across multiple cell types.