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.

Homologous recombination deficiency prediction using low-pass whole genome sequencing in breast cancer.

Homologous recombination repair deficiency (HRD) results in a defect in DNA repair and is a frequent driver of tumorigenesis. Poly(ADP-ribose) polymerase inhibitors (PARPi) or platinum-based therapies have increased theraputic effectiveness when treating HRD positive cancers. For breast cancer and ovairan cancer HRD companion diagnostic tests are commonly used. However, the currently used HRD tests are based on high-depth genome sequencing or hybridization-based capture sequencing, which are technically complex and costly. In this study, we modified an existing method named shallowHRD, which uses low-pass whole genome sequencing (WGS) for HRD detection, and estimated the performance of the modified shallowHRD pipeline. Our shallowHRD pipeline achieved an AUC of 0.997 in simulated low-pass WGS data, with a sensitivity of 0.981 and a specificity of 0.964; and achieved a higher HRD risk score in clinical BRCA-deficient breast cancer samples (p = 5.5 × 10-5, compared with BRCA-intact breast cancer samples). We also estimated the limit of detection the shallowHRD pipeline could accurately predict HRD status with a minimum sequencing depth of 0.1 ×, a tumor purity of > 20%, and an input DNA amount of 1 ng. Our study demostrates using low-pass sequencing, HRD status can be determined with high accuracy using a simple approach with greatly reduced cost.

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.

Plasma-Derived Extracellular Vesicles Circular RNAs Serve as Biomarkers for Breast Cancer Diagnosis.

Breast cancer is the second cause of cancer-associated death among women and seriously endangers women's health. Therefore, early identification of breast cancer would be beneficial to women's health. At present, circular RNA (circRNA) not only exists in the extracellular vesicles (EVs) in plasma, but also presents distinct patterns under different physiological and pathological conditions. Therefore, we assume that circRNA could be used for early diagnosis of breast cancer. Here, we developed classifiers for breast cancer diagnosis that relied on 259 samples, including 144 breast cancer patients and 115 controls. In the discovery stage, we compared the genome-wide long RNA profiles of EVs in patients with breast cancer (n=14) and benign breast (n=6). To further verify its potential in early diagnosis of breast cancer, we prospectively collected plasma samples from 259 individuals before treatment, including 144 breast cancer patients and 115 controls. Finally, we developed and verified the predictive classifies based on their circRNA expression profiles of plasma EVs by using multiple machine learning models. By comparing their circRNA profiles, we found 439 circRNAs with significantly different levels between cancer patients and controls. Considering the cost and practicability of the test, we selected 20 candidate circRNAs with elevated levels and detected their levels by quantitative real-time polymerase chain reaction. In the training cohort, we found that BCExoC, a nine-circRNA combined classifier with SVM model, achieved the largest AUC of 0.83 [95% CI 0.77-0.88]. In the validation cohort, the predictive efficacy of the classifier achieved 0.80 [0.71-0.89]. Our work reveals the application prospect of circRNAs in plasma EVs as non-invasive liquid biopsies in the diagnosis and management of breast cancer.

A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing.

Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost.

Noninvasive discrimination of benign and malignant breast lesions using genome-wide nucleosome profiles of plasma cell-free DNA.

BACKGROUND: Breast malignancy is the most frequently diagnosed malignancy in women worldwide, and the diagnosis relies on invasive examinations. However, most clinical breast changes in women are benign, and invasive diagnostic approaches cause unnecessary suffering for the patients. Thus, a novel noninvasive approach for discriminating malignant breast lesions from benign lesions is needed. METHODS: We performed cell-free DNA (cfDNA) sequencing on plasma samples from 173 malignant breast lesion patients, 158 benign breast lesion patients, and 102 healthy women. We then analyzed the cfDNA-based nucleosome profiles, which reflect the various tissues of origin and transcription factor activities. Moreover, by using machine learning classifiers along with the cfDNA sequencing data, we built classifiers for discriminating benign from malignant breast lesions. Receiver operating characteristic curve analyses were used to evaluate the performance of the classifiers. RESULTS: cfDNA-based nucleosome profiles reflected the various tissues of origin and transcription factor activities in benign and malignant breast lesions. The cfDNA-based transcription factor activities and breast malignancy-specific transcription factor-binding site accessibility profiles could accurately distinguish benign and malignant breast lesions, with area under the curve values of 0.777 and 0.824, respectively. CONCLUSIONS: Our proof-of-principle study established a methodology for noninvasively discriminating benign from malignant breast lesions.

Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer.

Gene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.

Tumour heterogeneity and intercellular networks of nasopharyngeal carcinoma at single cell resolution.

The heterogeneous nature of tumour microenvironment (TME) underlying diverse treatment responses remains unclear in nasopharyngeal carcinoma (NPC). Here, we profile 176,447 cells from 10 NPC tumour-blood pairs, using single-cell transcriptome coupled with T cell receptor sequencing. Our analyses reveal 53 cell subtypes, including tumour-infiltrating CD8+ T, regulatory T (Treg), and dendritic cells (DCs), as well as malignant cells with different Epstein-Barr virus infection status. Trajectory analyses reveal exhausted CD8+ T and immune-suppressive TNFRSF4+ Treg cells in tumours might derive from peripheral CX3CR1+CD8+ T and naïve Treg cells, respectively. Moreover, we identify immune-regulatory and tolerogenic LAMP3+ DCs. Noteworthily, we observe intensive inter-cell interactions among LAMP3+ DCs, Treg, exhausted CD8+ T, and malignant cells, suggesting potential cross-talks to foster an immune-suppressive niche for the TME. Collectively, our study uncovers the heterogeneity and interacting molecules of the TME in NPC at single-cell resolution, which provide insights into the mechanisms underlying NPC progression and the development of precise therapies for NPC.

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.

Germline Polymorphisms and Length of Survival of Nasopharyngeal Carcinoma: An Exome-Wide Association Study in Multiple Cohorts.

Germline polymorphisms are linked with differential survival outcomes in cancers but are not well studied in nasopharyngeal carcinoma (NPC). Here, a two-phase association study is conducted to discover germline polymorphisms that are associated with the prognosis of NPC. The discovery phase includes two consecutive hospital cohorts of patients with NPC from Southern China. Exome-wide genotypes at 246 173 single nucleotide polymorphisms (SNPs) are determined, followed by survival analysis for each SNP under Cox proportional hazard regression model. Candidate SNP is replicated in another two independent cohorts from Southern China and Singapore. Meta-analysis of all samples (n = 5553) confirms that the presence of rs1131636-T, located in the 3'-UTR of RPA1, confers an inferior overall survival (HR = 1.33, 95% CI = 1.20-1.47, P = 6.31 × 10-8). Bioinformatics and biological assays show that rs1131636 has regulatory effects on upstream RPA1. Functional studies further demonstrate that RPA1 promotes the growth, invasion, migration, and radioresistance of NPC cells. Additionally, miR-1253 is identified as a suppressor for RPA1 expression, likely through regulation of its binding affinity to rs1131636 locus. Collectively, these findings provide a promising biomarker aiding in stratifying patients with poor survival, as well as a potential drug target for NPC.

Genomic and transcriptomic landscapes of Epstein-Barr virus in extranodal natural killer T-cell lymphoma.

Extranodal natural killer T-cell lymphoma (nasal type; NKTCL) is an aggressive malignancy strongly associated with Epstein-Barr virus (EBV) infection. However, the role of EBV in NKTCL development is unclear, largely due to the lack of information about EBV genome and transcriptome in NKTCL. Here, using high-throughput sequencing, we obtained whole genome (n = 27) and transcriptome datasets (n = 18) of EBV derived from NKTCL tumor biopsies. We assembled 27 EBV genomes and detected an average of 1,152 single nucleotide variants and 44.8 indels (<50 bp) of EBV per sample. We also identified frequent focal EBV genome deletions and integrated EBV fragments in the host genome. Moreover, Phylogenetic analysis revealed that NKTCL-derived EBVs are closely clustered; transcriptome analysis revealed less activation of both latent and lytic genes and larger amount of T-cell epitope alterations in NKTCL, as compared with other EBV-associated cancers. Furthermore, we observed transcriptional defects of the BARTs miRNA by deletion, and the disruption of host NHEJ1 by integrated EBV fragment, implying novel pathogenic mechanisms of EBV. Taken together, we reported for the first time global mutational and transcriptional profiles of EBV in NKTCL clinical samples, revealing important somatic events of EBV and providing insights to better understanding of EBV's contribution in tumorigenesis.

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.

LncRNAs H19 and HULC, activated by oxidative stress, promote cell migration and invasion in cholangiocarcinoma through a ceRNA manner.

BACKGROUND: Long non-coding 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, associated with chronic inflammation and damage to the biliary epithelium. The aim of the study is to identify if any lncRNA might associate with inflammation or oxidative stress in CCA and regulate the disease progression. METHODS: In this study, RNA-seqs datasets were used to identify aberrantly expressed lncRNAs. Small interfering RNA and overexpressed plasmids were used to modulate the expression of lncRNAs, and luciferase target assay RNA immunoprecipitation (RIP) was performed to explore the mechanism of miRNA-lncRNA sponging. RESULTS: We firstly analyzed five available RNA-seqs datasets to investigate aberrantly expressed lncRNAs which might associate with inflammation or oxidative stress. We identified that two lncRNAs, H19 and HULC, were differentially expressed among all the samples under the treatment of hypoxic or inflammatory factors, and they were shown to be stimulated by short-term oxidative stress responses to H2O2 and glucose oxidase in CCA cell lines. Further studies revealed that these two lncRNAs promoted cholangiocyte migration and invasion via the inflammation pathway. H19 and HULC functioned as competing endogenous RNAs (ceRNAs) by sponging let-7a/let-7b and miR-372/miR-373, respectively, which activate pivotal inflammation cytokine IL-6 and chemokine receptor CXCR4. CONCLUSIONS: Our study revealed that H19 and HULC, up-regulated by oxidative stress, regulate CCA cell migration and invasion by targeting IL-6 and CXCR4 via ceRNA patterns of sponging let-7a/let-7b and miR-372/miR-373, respectively. The results suggest that these lncRNAs might be the chief culprits of CCA pathogenesis and progression. The study provides new insight into the mechanism linking lncRNA function with CCA and may serve as novel targets for the development of new countermeasures of CCA.

MIR125B1 represses the degradation of the PML-RARA oncoprotein by an autophagy-lysosomal pathway in acute promyelocytic leukemia.

Acute promyelocytic leukemia (APL) is characterized by the t(15;17)-associated PML-RARA fusion gene. We have previously found that MIR125B1 is highly expressed in patients with APL and may be associated with disease pathogenesis; however, the mechanism by which MIR125B1 exerts its oncogenic potential has not been fully elucidated. Here, we demonstrated that MIR125B1 abundance correlates with the PML-RARA status. MIR125B1 overexpression enhanced PML-RARA expression and inhibited the ATRA-induced degradation of the PML-RARA oncoprotein. RNA-seq analysis revealed a direct link between the PML-RARA degradation pathway and MIR125B1-arrested differentiation. We further demonstrated that the MIR125B1-mediated blockade of PML-RARA proteolysis was regulated via an autophagy-lysosomal pathway, contributing to the inhibition of APL differentiation. Furthermore, we identified DRAM2 (DNA-damage regulated autophagy modulator 2), a critical regulator of autophagy, as a novel target that was at least partly responsible for the function of MIR125B1 involved in autophagy. Importantly, the knockdown phenotypes for DRAM2 are similar to the effects of overexpressing MIR125B1 as impairment of PML-RARA degradation, inhibition of autophagy, and myeloid cell differentiation arrest. These effects of MIR125B1 and its target DRAM2 were further confirmed in an APL mouse model. Thus, MIR125B1 dysregulation may interfere with the effectiveness of ATRA-mediated differentiation through an autophagy-dependent pathway, representing a novel potential APL therapeutic target.

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.