Noninvasive prediction of axillary lymph node status in breast cancer using promoter profiling of circulating cell-free DNA.

BACKGROUND: Lymph node metastasis (LNM) is one of the most important factors affecting the prognosis of breast cancer. The accurate evaluation of lymph node status is useful to predict the outcomes of patients and guide the choice of cancer treatment. However, there is still lack of a low-cost non-invasive method to assess the status of axillary lymph node (ALN). Gene expression signature has been used to assess lymph node metastasis status of breast cancer. In addition, nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of its original tissues, so it may be used to evaluate the axillary lymph node status in breast cancer. METHODS: In this study, we found that the cfDNA nucleosome footprints between the ALN-positive patients and ALN-negative patients showed different patterns by implementing whole-genome sequencing (WGS) to detect 15 ALN-positive and 15 ALN-negative patients. In order to further evaluate its potential for assessing ALN status, we developed a classifier with multiple machine learning models by using 330 WGS data of cfDNA from 162 ALN-positive and 168 ALN-negative samples to distinguish these two types of patients. RESULTS: We found that the promoter profiling between the ALN-positive patients and ALN-negative patients showed distinct patterns. In addition, we observed 1071 genes with differential promoter coverage and their functions were closely related to tumorigenesis. We found that the predictive classifier based on promoter profiling with a support vector machine model, named PPCNM, produced the largest area under the curve of 0.897 (95% confidence interval 0.86-0.93). CONCLUSIONS: These results indicate that promoter profiling can be used to distinguish ALN-positive patients from ALN-negative patients, which may be helpful to guide the choice of cancer treatment.

A genomic-augmented multivariate prognostic model for the survival of natural-killer/T-cell lymphoma patients from an international cohort.

With lowering costs of sequencing and genetic profiling techniques, genetic drivers can now be detected readily in tumors but current prognostic models for Natural-killer/T cell lymphoma (NKTCL) have yet to fully leverage on them for prognosticating patients. Here, we used next-generation sequencing to sequence 260 NKTCL tumors, and trained a genomic prognostic model (GPM) with the genomic mutations and survival data from this retrospective cohort of patients using LASSO Cox regression. The GPM is defined by the mutational status of 13 prognostic genes and is weakly correlated with the risk-features in International Prognostic Index (IPI), Prognostic Index for Natural-Killer cell lymphoma (PINK), and PINK-Epstein-Barr virus (PINK-E). Cox-proportional hazard multivariate regression also showed that the new GPM is independent and significant for both progression-free survival (PFS, HR: 3.73, 95% CI 2.07-6.73; p < .001) and overall survival (OS, HR: 5.23, 95% CI 2.57-10.65; p = .001) with known risk-features of these indices. When we assign an additional risk-score to samples, which are mutant for the GPM, the Harrell's C-indices of GPM-augmented IPI, PINK, and PINK-E improved significantly (p < .001, χ2 test) for both PFS and OS. Thus, we report on how genomic mutational information could steer toward better prognostication of NKTCL patients.

Noninvasive inferring expressed genes and in vivo monitoring of the physiology and pathology of pregnancy using cell-free DNA.

BACKGROUND: Noninvasive monitoring of fetal development and the early detection of pregnancy-associated complications is challenging, largely because of the lack of information about the molecular spectrum during pregnancy. Recently, cell-free DNA in plasma was found to reflect the global nucleosome footprint and status of gene expression and showed potential for noninvasive health monitoring during pregnancy. OBJECTIVE: We aimed to test the relationships between plasma cell-free DNA profiles and pregnancy biology and evaluate the use of a cell-free DNA profile as a noninvasive method for physiological and pathologic status monitoring during pregnancy. STUDY DESIGN: We used genome cell-free DNA sequencing data generated from noninvasive prenatal testing in a total of 2937 pregnant women. For each physiological and pathologic condition, features of the cell-free DNA profile were identified using the discovery cohort, and support vector machine classifiers were built and evaluated using independent training and validation cohorts. RESULTS: We established nucleosome occupancy profiles at transcription start sites in different gestational trimesters, demonstrated the relationships between gene expression and cell-free DNA coverage at transcription start sites, and showed that the cell-free DNA profiles at transcription start sites represented the biological processes of pregnancy. In addition, using cell-free DNA data, nucleosome profiles of transcription factor binding sites were identified to reflect the transcription factor footprint, which may help to reveal the molecular mechanisms underlying pregnancy. Finally, by using machine-learning models on low-coverage noninvasive prenatal testing data, we evaluated the use of cell-free DNA nucleosome profiles for distinguishing gestational trimesters, fetal sex, and fetal trisomy 21 and highlighted its potential utility for predicting physiological and pathologic fetal conditions by using low-coverage noninvasive prenatal testing data. CONCLUSION: Our analyses profiled nucleosome footprints and regulatory networks during pregnancy and established a noninvasive proof-of-principle methodology for health monitoring during pregnancy.

Association of MCP-1 promoter polymorphism with susceptibility to nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC) is prevalent among populations from southern China and is influenced by both genetic and environmental risk factors. The monocyte chemoattractant protein-1 (MCP-1), a member of cysteine-cysteine chemokine family, plays critical roles in cancers. A polymorphism within the MCP-1 promoter, rs1024611, has been shown to be significantly associated with the risk of several cancers. Our purpose was to assess the role of rs1024611 in NPC susceptibility. By polymerase chain reaction-restriction fragment length polymorphism method, we genotyped rs1024611 in 593 patients with NPC (cases) and 480 cancer-free subjects (controls) among Guangxi population from southern China. We observed that the G allele of rs1024611 was significantly associated with the increased risk of NPC in an additive model and dominant model, respectively (P = 0.018 and 0.010, odds ratio = 1.25 and 1.41, respectively). No appreciable variation of the effects was found across the subgroups stratified by age, sex, nationality, smoking and drinking status, and smoking level. In addition, significantly higher messenger RNA (mRNA) expression level of MCP-1 was observed in NPC tissues than that in normal nasopharyngeal tissues, and the G allele of rs1024611 was significantly associated with elevated mRNA expression level of MCP-1 in Epstein-Barr virus-transformed lymphocytes. In conclusion, our findings suggested that rs1024611 at the MCP-1 promoter may be a risk factor for NPC. Further studies with larger sample size are necessary to confirm these findings.

Global identification and characterization of lncRNAs that control inflammation in malignant cholangiocytes.

BACKGROUND: Long noncoding RNAs (lncRNAs) are known to play important roles in different cell contexts, including cancers. However, little is known about lncRNAs in cholangiocarcinoma (CCA), a cholangiocyte malignancy with poor prognosis, and associated with chronic inflammation and damage to the biliary epithelium. This study determined whether lncRNAs were dysregulated and participated in disease diagnosis or pivotal inflammation pathways through a genome-wide lncRNA screening and functional analysis. RESULTS: We firstly identified a large number of lncRNAs abnormally expressed between 9 pairs of cancerous and adjacent tissues of CCA, and between intra-hepatic CCA and extra-hepatic CCA through a genome-wide profiling. A set of aberrant differentially expressed lncRNAs were further validated in a training set (16 pairs) and a test set (11 pairs) of CCA patient samples. Following assessment of the diagnostic value of the 7 differentially expressed lncRNAs, we confirmed the optimal combination of H19, C3P1, AC005550.3, PVT1, and LPAL2 with area under the curve of 0.8828 [95% CI: 0.7441-1.021, P < 0.001], with 93.75% sensitivity and 81.25% specificity, at the cutoff point of - 0.2884 to distinguish the CCA tissue from the normal ones, suggesting that specific lncRNAs may have potential for detecting CCA. More importantly, the genome-wide locus and lncRNA/mRNA co-expression analyses revealed a set of lncRNAs that participated in inflammation and oxidative stress response pathways by regulating genes in cis or in trans. Finally, APOC1P1, PVT1, and LPAL2 were validated to regulate the migration and some pivotal inflammation genes under the CCA pathogenesis. CONCLUSIONS: Our findings are the first to show that lncRNAs may not only be potential biomarkers of CCA progression but also respond to inflammation in CCA.

A distinct set of long non-coding RNAs in childhood MLL-rearranged acute lymphoblastic leukemia: biology and epigenetic target.

Long non-coding RNAs (lncRNAs) have been recently found to be pervasively transcribed in human genome and link to diverse human diseases. However, the expression patterns and regulatory roles of lncRNAs in hematopoietic malignancies have not been reported. Here, we carried out a genome-wide lncRNA expression study in MLL-rearranged acute lymphoblastic leukemia (MLL-r ALL) and established lncRNA/messenger RNA coexpression networks to gain insight into the biological roles of these dysregulated lncRNAs. We detected a number of lncRNAs that were differentially expressed in MLL-r ALL samples compared with MLL-r wild-type and identified unique lncRNA expression patterns between MLL-r subtypes with different translocations as well as between infant MLL-r ALL with other MLL-r ALL patients, suggesting that they might be served as novel biomarkers for the disease. Importantly, several lncRNAs that correspond with membrane protein genes, including a lysosome-associated membrane protein, were identified. No such link between the membrane proteins and MLL-r leukemia has been reported previously. Impressively, the functional analysis showed that several lncRNAs corresponded to the expression of MLL-fusion protein target genes, including HOXA9, MEIS1, etc., while some other associated with histone-related functions or membrane proteins. Further experiments characterize the effect of some lncRNAs on MLL-r leukemia apoptosis and proliferation as the function of the coexpressed HOXA gene cluster. Finally, a set of lncRNAs epigenetically regulated by H3K79 methylation were also discovered. These findings may provide novel insights into the mechanisms of lncRNAs involved in the initiation of MLL-r leukemia. This is the first study linking lncRNAs to leukemogenesis.

Epstein-Barr virus deubiquitinating enzyme BPLF1 is involved in EBV carcinogenesis by affecting cellular genomic stability.

Increased mutational burden and EBV load have been revealed from normal tissues to Epstein-Barr virus (EBV)-associated gastric carcinomas (EBVaGCs). BPLF1, encoded by EBV, is a lytic cycle protein with deubiquitinating activity has been found to participate in disrupting repair of DNA damage. We first confirmed that BPLF1 gene in gastric cancer (GC) significantly increased the DNA double strand breaks (DSBs). Ubiquitination mass spectrometry identified histones as BPLF1 interactors and potential substrates, and co-immunoprecipitation and in vitro experiments verified that BPLF1 regulates H2Bub by targeting Rad6. Over-expressing Rad6 restored H2Bub but partially reduced γ-H2AX, suggesting that other downstream DNA repair processes were affected. mRNA expression of BRCA2 were significantly down-regulated by next-generation sequencing after over-expression of BPLF1, and over-expression of p65 facilitated the repair of DSBs. We demonstrated BPLF1 may lead to the accumulation of DSBs by two pathways, reducing H2B ubiquitination (H2Bub) and blocking homologous recombination which may provide new ideas for the treatment of gastric cancer.

A deep-learning-based genomic status estimating framework for homologous recombination deficiency detection from low-pass whole genome sequencing.

Genome-wide sequencing allows for prediction of clinical treatment responses and outcomes by estimating genomic status. Here, we developed Genomic Status scan (GSscan), a long short-term memory (LSTM)-based deep-learning framework, which utilizes low-pass whole genome sequencing (WGS) data to capture genomic instability-related features. In this study, GSscan directly surveys homologous recombination deficiency (HRD) status independent of other existing biomarkers. In breast cancer, GSscan achieved an AUC of 0.980 in simulated low-pass WGS data, and obtained a higher HRD risk score in clinical BRCA-deficient breast cancer samples (p = 1.3 × 10-4, compared with BRCA-intact samples). In ovarian cancer, GSscan obtained higher HRD risk scores in BRCA-deficient samples in both simulated data and clinical samples (p = 2.3 × 10-5 and p = 0.039, respectively, compared with BRCA-intact samples). Moreover, HRD-positive patients predicted by GSscan showed longer progression-free intervals in TCGA datasets (p = 0.0011) treated with platinum-based adjuvant chemotherapy, outperforming existing low-pass WGS-based methods. Furthermore, GSscan can accurately predict HRD status using only 1 ng of input DNA and a minimum sequencing coverage of 0.02 × , providing a reliable, accessible, and cost-effective approach. In summary, GSscan effectively and accurately detected HRD status, and provide a broadly applicable framework for disease diagnosis and selecting appropriate disease treatment.

Development of fecal microbial diagnostic marker sets of colorectal cancer using natural language processing method.

BACKGROUND: Cancer screening and early detection greatly increase the chances of successful treatment. However, most cancer types lack effective early screening biomarkers. In recent years, natural language processing (NLP)-based text-mining methods have proven effective in searching the scientific literature and identifying promising associations between potential biomarkers and disease, but unfortunately few are widely used. METHODS: In this study, we used an NLP-enabled text-mining system, MarkerGenie, to identify potential stool bacterial markers for early detection and screening of colorectal cancer. After filtering markers based on text-mining results, we validated bacterial markers using multiplex digital droplet polymerase chain reaction (ddPCR). Classifiers were built based on ddPCR results, and sensitivity, specificity, and area under the curve (AUC) were used to evaluate the performance. RESULTS: A total of 7 of the 14 bacterial markers showed significantly increased abundance in the stools of colorectal cancer patients. A five-bacteria classifier for colorectal cancer diagnosis was built, and achieved an AUC of 0.852, with a sensitivity of 0.692 and specificity of 0.935. When combined with the fecal immunochemical test (FIT), our classifier achieved an AUC of 0.959 and increased the sensitivity of FIT (0.929 vs. 0.872) at a specificity of 0.900. CONCLUSIONS: Our study provides a valuable case example of the use of NLP-based marker mining for biomarker identification.

A set of miRNAs that involve in the pathways of drug resistance and leukemic stem-cell differentiation is associated with the risk of relapse and glucocorticoid response in childhood ALL.

Relapse is a major challenge in the successful treatment of childhood acute lymphoblastic leukemia (ALL). Despite intensive research efforts, the mechanisms of ALL relapse are still not fully understood. An understanding of the molecular mechanisms underlying treatment outcome, therapy response and the biology of relapse is required. In this study, we carried out a genome-wide microRNA (miRNA) microarray analysis to determine the miRNA expression profiles and relapse-associated miRNA patterns in a panel of matched diagnosis-relapse or diagnosis-complete remission (CR) childhood ALL samples. A set of miRNAs differentially expressed either in relapsed patients or at diagnosis compared with CR was further validated by quantitative real-time polymerase chain reaction in an independent sample set. Analysis of the predicted functions of target genes based on gene ontology 'biological process' categories revealed that the abnormally expressed miRNAs are associated with oncogenesis, classical multidrug resistance pathways and leukemic stem cell self-renewal and differentiation pathways. Several targets of the miRNAs associated with ALL relapse were experimentally validated, including FOXO3, BMI1 and E2F1. We further investigated the association of these dysregulated miRNAs with clinical outcome and confirmed significant associations for miR-708, miR-223 and miR-27a with individual relapse-free survival. Notably, miR-708 was also found to be associated with the in vivo glucocorticoid therapy response and with disease risk stratification. These miRNAs and their targets might be used to optimize anti-leukemic therapy, and serve as novel targets for development of new countermeasures of leukemia. This fundamental study may also contribute to establish the mechanisms of relapse in other cancers.