Olanzapine suppresses mPFC activity-norepinephrine releasing to alleviate CLOCK-enhanced cancer stemness under chronic stress.

BACKGROUND: Olanzapine (OLZ) reverses chronic stress-induced anxiety. Chronic stress promotes cancer development via abnormal neuro-endocrine activation. However, how intervention of brain-body interaction reverses chronic stress-induced tumorigenesis remains elusive. METHODS: KrasLSL-G12D/WT lung cancer model and LLC1 syngeneic tumor model were used to study the effect of OLZ on cancer stemness and anxiety-like behaviors. Cancer stemness was evaluated by qPCR, western-blotting, immunohistology staining and flow-cytometry analysis of stemness markers, and cancer stem-like function was assessed by serial dilution tumorigenesis in mice and extreme limiting dilution analysis in primary tumor cells. Anxiety-like behaviors in mice were detected by elevated plus maze and open field test. Depression-like behaviors in mice were detected by tail suspension test. Anxiety and depression states in human were assessed by Hospital Anxiety and Depression Scale (HADS). Chemo-sensitivity of lung cancer was assessed by in vivo syngeneic tumor model and in vitro CCK-8 assay in lung cancer cell lines. RESULTS: In this study, we found that OLZ reversed chronic stress-enhanced lung tumorigenesis in both KrasLSL-G12D/WT lung cancer model and LLC1 syngeneic tumor model. OLZ relieved anxiety and depression-like behaviors by suppressing neuro-activity in the mPFC and reducing norepinephrine (NE) releasing under chronic stress. NE activated ADRB2-cAMP-PKA-CREB pathway to promote CLOCK transcription, leading to cancer stem-like traits. As such, CLOCK-deficiency or OLZ reverses NE/chronic stress-induced gemcitabine (GEM) resistance in lung cancer. Of note, tumoral CLOCK expression is positively associated with stress status, serum NE level and poor prognosis in lung cancer patients. CONCLUSION: We identify a new mechanism by which OLZ ameliorates chronic stress-enhanced tumorigenesis and chemoresistance. OLZ suppresses mPFC-NE-CLOCK axis to reverse chronic stress-induced anxiety-like behaviors and lung cancer stemness. Decreased NE-releasing prevents activation of ADRB2-cAMP-PKA-CREB pathway to inhibit CLOCK transcription, thus reversing lung cancer stem-like traits and chemoresistance under chronic stress.

Oncogenic fatty acid oxidation senses circadian disruption in sleep-deficiency-enhanced tumorigenesis.

Circadian disruption predicts poor cancer prognosis, yet how circadian disruption is sensed in sleep-deficiency (SD)-enhanced tumorigenesis remains obscure. Here, we show fatty acid oxidation (FAO) as a circadian sensor relaying from clock disruption to oncogenic metabolic signal in SD-enhanced lung tumorigenesis. Both unbiased transcriptomic and metabolomic analyses reveal that FAO senses SD-induced circadian disruption, as illustrated by continuously increased palmitoyl-coenzyme A (PA-CoA) catalyzed by long-chain fatty acyl-CoA synthetase 1 (ACSL1). Mechanistically, SD-dysregulated CLOCK hypertransactivates ACSL1 to produce PA-CoA, which facilitates CLOCK-Cys194 S-palmitoylation in a ZDHHC5-dependent manner. This positive transcription-palmitoylation feedback loop prevents ubiquitin-proteasomal degradation of CLOCK, causing FAO-sensed circadian disruption to maintain SD-enhanced cancer stemness. Intriguingly, timed ß-endorphin resets rhythmic Clock and Acsl1 expression to alleviate SD-enhanced tumorigenesis. Sleep quality and serum ß-endorphin are negatively associated with both cancer development and CLOCK/ACSL1 expression in patients with cancer, suggesting dawn-supplemented ß-endorphin as a potential chronotherapeutic strategy for SD-related cancer.

ZIP1+ fibroblasts protect lung cancer against chemotherapy via connexin-43 mediated intercellular Zn2+ transfer.

Tumour-stroma cell interactions impact cancer progression and therapy responses. Intercellular communication between fibroblasts and cancer cells using various soluble mediators has often been reported. In this study, we find that a zinc-transporter (ZIP1) positive tumour-associated fibroblast subset is enriched after chemotherapy and directly interconnects lung cancer cells with gap junctions. Using single-cell RNA sequencing, we identify several fibroblast subpopulations, among which Zip1+ fibroblasts are highly enriched in mouse lung tumours after doxorubicin treatment. ZIP1 expression on fibroblasts enhances gap junction formation in cancer cells by upregulating connexin-43. Acting as a Zn2+ reservoir, ZIP1+ fibroblasts absorb and transfer Zn2+ to cancer cells, leading to ABCB1-mediated chemoresistance. Clinically, ZIP1high stromal fibroblasts are also associated with chemoresistance in human lung cancers. Taken together, our results reveal a mechanism by which fibroblasts interact directly with tumour cells via gap junctions and contribute to chemoresistance in lung cancer.

Analysis of Genome-Wide Alternative Splicing Profiling and Development of Potential Drugs in Lung Adenocarcinoma.

Alternative splicing (AS) is significantly related to tumor development as well as a patient's clinical characteristics. This study was designed to systematically analyze the survival-associated AS signatures in Lung adenocarcinoma (LUAD). Among 30,735 AS events in 9,635 genes, we found that there were 1,429 AS in 1,125 genes which were conspicuously related to the overall survival of LUAD patients. Then, according to the seven types of AS events, we established AS signatures and constructed a new combined prognostic model. The Kaplan-Meier curve results showed that seven types of AS signatures and the combined prognostic model could divide patients into distinct prognoses. The ROC curve shows that all eight AS signatures had powerful predictive properties with different AUCs ranging from 0.708 to 0.849. Additionally, the elevated risk scores were positively related to higher TNM stage and metastasis. Interestingly, AS events and splicing factors (SFs) network shed light on a meaningful connection between prognostic AS genes and corresponding SFs. Moreover, we found that the combined prognostic model signature has a higher predictive ability than the mRNA signature. Furthermore, tumors at high risk might evade immune recognition by decreasing the expression of antigen presentation genes. Finally, we predicted the three most significant small molecule drugs to inhibit LUAD. Among them, NVP-AUY922 had the lowest IC50 value and might become a potential drug to prolong a patient's survival. In conclusion, our study established a potential prognostic signature for LUAD patients, revealed a splicing network between AS and SFs and possible immune escape mechanism, and provided several small-molecule drugs to inhibit tumorigenesis.

Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer.

BACKGROUND: Infiltrating immune and stromal cells are important components of the endometrial cancer (EC) microenvironment, which has a significant effect on the biological behavior of EC, suggesting that unique immune-related genes may be associated with the prognosis of EC. However, the association of immune-related genes with the prognosis of EC has not been elucidated. We attempted to identify immune-related genes with potentially prognostic value in EC using The Cancer Genome Atlas database and the relationship between immune microenvironment and EC. METHODS: We analyzed 578 EC samples from TCGA database and used weighted gene co-expression network analysis to screen out immune-related genes. We constructed a protein-protein interaction network and analyzed it using STRING and Cytoscape. Immune-related genes were analyzed through conjoint Cox regression and random forest algorithm analysis were to identify a multi-gene prediction model and stratify low-risk and high-risk groups of EC patients. Based on these data, we constructed a nomogram prediction model to improve prognosis assessment. Evaluation of Immunological, gene mutations and gene enrichment analysis were applied on these groups to quantify additional differences. RESULTS: Using conjoint Cox regression and random forest algorithm, we found that TRBC2, TRAC, LPXN, and ARHGAP30 were associated with the prognosis of EC and constructed four gene risk models for overall survival and a consistent nomogram. The time-dependent receiver operating characteristic curve analysis revealed that the area under the curve for 1-, 3-, and 5-y overall survival was 0.687, 0.699, and 0.76, respectively. These results were validated using a validation cohort. Immune-related pathways were mostly enriched in the low-risk group, which had higher levels of immune infiltration and immune status. CONCLUSION: Our study provides new insights for novel biomarkers and immunotherapy targets in EC.

Allele frequency deviation (AFD) as a new prognostic model to predict overall survival in lung adenocarcinoma (LUAD).

BACKGROUND: Lung adenocarcinoma (LUAD) remains one of the world's most known aggressive malignancies with a high mortality rate. Molecular biological analysis and bioinformatics are of great importance as they have recently occupied a large area in the studies related to the identification of various biomarkers to predict survival for LUAD patients. In our study, we attempted to identify a new prognostic model by developing a new algorithm to calculate the allele frequency deviation (AFD), which in turn may assist in the early diagnosis and prediction of clinical outcomes in LUAD. METHOD: First, a new algorithm was developed to calculate AFD using the whole-exome sequencing (WES) dataset. Then, AFD was measured for 102 patients, and the predictive power of AFD was assessed using Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and area under the curve (AUC). Finally, multivariable cox regression analyses were conducted to evaluate the independence of AFD as an independent prognostic tool. RESULT: The Kaplan-Meier analysis showed that AFD effectively segregated patients with LUAD into high-AFD-value and low-AFD-value risk groups (hazard ratio HR = 1.125, 95% confidence interval CI 1.001-1.26, p = 0.04) in the training group. Moreover, the overall survival (OS) of patients who belong to the high-AFD-value group was significantly shorter than that of patients who belong to the low-AFD-value group with 42.8% higher risk and 10% lower risk of death for both groups respectively (HR for death = 1.10; 95% CI 1.01-1.2, p = 0.03) in the training group. Similar results were obtained in the validation group (HR = 4.62, 95% CI 1.22-17.4, p = 0.02) with 41.6%, and 5.5% risk of death for patients who belong to the high and low-AFD-value groups respectively. Univariate and multivariable cox regression analyses demonstrated that AFD is an independent prognostic model for patients with LUAD. The AUC for 5-year survival were 0.712 and 0.86 in the training and validation groups, respectively. CONCLUSION: AFD was identified as a new independent prognostic model that could provide a prognostic tool for physicians and contribute to treatment decisions.

A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD).

BACKGROUND: Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients. METHODS: Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature. RESULTS: A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041-39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779-3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis. CONCLUSION: Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

Systematic Profiling of Alternative Splicing Events in Ovarian Cancer.

Alternative splicing (AS) is significantly related to the development of tumor and the clinical outcome of patients. In this study, our aim was to systematically analyze the survival-related AS signal in ovarian serous cystadenocarcinoma (OV) and estimate its prognostic validity in 48,049 AS events out of 21,854 genes. We studied 1,429 AS events out of 1,125 genes, which were significantly related to the overall survival (OS) in patients with OV. We established alternative splicing features on the basis of seven AS events and constructed a new comprehensive prognostic model. Kaplan-Meier curve analysis showed that seven AS characteristics and comprehensive prognostic models could strongly stratify patients with ovarian cancer and make them distinctive prognosis. ROC analysis from 0.781 to 0.888 showed that these models were highly efficient in distinguishing patient survival. We also verified the prognostic characteristics of these models in a testing cohort. In addition, uni-variate and multivariate Cox analysis showed that these models were superior independent risk factors for OS in patients with OV. Interestingly, AS events and splicing factor (SFs) networks revealed an important link between these prognostic alternative splicing genes and splicing factors. We also found that the comprehensive prognosis model signature had higher prediction ability than the mRNA signature. In summary, our study provided a possible prognostic prediction model for patients with OV and revealed the splicing network between AS and SFs, which could be used as a potential predictor and therapeutic target for patients with OV.

RIdeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms.

BACKGROUND: Owing to the rapid advances in DNA sequencing technologies, whole genome from more and more species are becoming available at increasing pace. For whole-genome analysis, idiograms provide a very popular, intuitive and effective way to map and visualize the genome-wide information, such as GC content, gene and repeat density, DNA methylation distribution, genomic synteny, etc. However, most available software programs and web servers are available only for a few model species, such as human, mouse and fly, or have limited application scenarios. As more and more non-model species are sequenced with chromosome-level assembly being available, tools that can generate idiograms for a broad range of species and be capable of visualizing more data types are needed to help better understanding fundamental genome characteristics. RESULTS: The R package RIdeogram allows users to build high-quality idiograms of any species of interest. It can map continuous and discrete genome-wide data on the idiograms and visualize them in a heat map and track labels, respectively. CONCLUSION: The visualization of genome-wide data mapping and comparison allow users to quickly establish a clear impression of the chromosomal distribution pattern, thus making RIdeogram a useful tool for any researchers working with omics.

Longitudinal whole-genome sequencing reveals the evolution of MPAL.

PURPOSE: Mixed phenotype acute leukemia (MPAL) is a rare subtype of acute leukemia and its progressive genomic basis over time remains unclear. We aimed to investigate the longitudinal genomic evolution of MPAL from diagnosis to relapse. METHODS: We performed whole genome sequencing (WGS) on bone marrow (BM) samples obtained at the four stages of this disease in a male patient with Philadelphia chromosome positive (Ph+) MPAL, including primary, complete cytogenetic remission (CCR), complete molecular remission (CMR), and relapse stage during the 3 year follow-up period. RESULTS: 156 single-nucleotide variants (SNVs) and indels were detected, which exhibited distinctive evolutionary behaviors. Seventeen mutations disappeared quickly upon DCTER treatment and never came back. Seven mutations, although disappeared initially, reoccurred with the withdrawal of TKI treatment. Notably, ten mutations emerged in spite of the active DCTER chemotherapy. Moreover, copy number loss played critical roles in monitoring MPAL progression, displaying 7, 0, 0, and 383 losses at the stages of primary, CCR, CMR, and relapse respectively. CONCLUSION: This longitudinal genomic investigation of the Ph+ MPAL patient established one MPAL evolution model in which the primary tumor acquired additional variations leading to tumor relapse. Moreover, the event of copy number loss remained a valuable hallmark in the progression of MPAL.

Targeted deep sequencing from multiple sources demonstrates increased NOTCH1 alterations in lung cancer patient plasma.

INTRODUCTION: Targeted therapies are based on specific gene alterations. Various specimen types have been used to determine gene alterations, however, no systemic comparisons have yet been made. Herein, we assessed alterations in selected cancer-associated genes across varying sample sites in lung cancer patients. MATERIALS AND METHODS: Targeted deep sequencing for 48 tumor-related genes was applied to 153 samples from 55 lung cancer patients obtained from six sources: Formalin-fixed paraffin-embedded (FFPE) tumor tissues, pleural effusion supernatant (PES) and pleural effusion cell sediments (PEC), white blood cells (WBCs), oral epithelial cells (OECs), and plasma. RESULTS: Mutations were detected in 96% (53/55) of the patients and in 83% (40/48) of the selected genes. Each sample type exhibited a characteristic mutational pattern. As anticipated, TP53 was the most affected sequence (54.5% patients), however this was followed by NOTCH1 (36%, across all sample types). EGFR was altered in patient samples at a frequency of 32.7% and KRAS 10.9%. This high EGFR/ low KRAS frequency is in accordance with other TCGA cohorts of Asian origin but differs from the Caucasian population where KRAS is the more dominant mutation. Additionally, 66% (31/47) of PEC samples had copy number variants (CNVs) in at least one gene. Unlike the concurrent loss and gain in most genes, herein NOTCH1 loss was identified in 21% patients, with no gain observed. Based on the relative prevalence of mutations and CNVs, we divided lung cancer patients into SNV-dominated, CNV-dominated, and codominated groups. CONCLUSIONS: Our results confirm previous reports that EGFR mutations are more prevalent than KRAS in Chinese lung cancer patients. NOTCH1 gene alterations are more common than previously reported and reveals a role of NOTCH1 modifications in tumor metastasis. Furthermore, genetic material from malignant pleural effusion cell sediments may be a noninvasive manner to identify CNV and participate in treatment decisions.

Using plasma cell-free DNA to monitor the chemoradiotherapy course of cervical cancer.

The liquid biopsy is being integrated into cancer diagnostics and surveillance. However, critical questions still remain, such as how to precisely evaluate cancer mutation burden and interpret the corresponding clinical implications. Herein, we evaluated the role of peripheral blood cell-free DNA (cfDNA) in characterizing the dynamic mutation alterations of 48 cancer driver genes from cervical cancer patients. We performed targeted deep sequencing on 93 plasma cfDNA from 57 cervical cancer patients and from this developed an algorithm, allele fraction deviation (AFD), to monitor in an unbiased manner the dynamic changes of genomic aberrations. Differing treatments, including chemotherapy (n = 22), radiotherapy (n = 14) and surgery (n = 15), led to a significant decrease in AFD values (Wilcoxon, p = 0.029). The decrease of cfDNA AFD values was accompanied by shrinkage in the size of the tumor in most patients. However, in a subgroup of patients where cfDNA AFD values did not reflect a reduction in tumor size, there was a detection of progressive disease (metastasis). Furthermore, a low AFD value at diagnosis followed a later increase of AFD value also successfully predicted relapse. These results show that plasma cfDNA, together with targeted deep sequencing, may help predict treatment response and disease development in cervical cancer.

The prognostic landscape of interactive biological processes presents treatment responses in cancer.

BACKGROUND: Differential gene expression patterns are commonly used as biomarkers to predict treatment responses among heterogeneous tumors. However, the link between response biomarkers and treatment-targeting biological processes remain poorly understood. Here, we develop a prognosis-guided approach to establish the determinants of treatment response. METHODS: The prognoses of biological processes were evaluated by integrating the transcriptomes and clinical outcomes of ~26,000 cases across 39 malignancies. Gene-prognosis scores of 39 malignancies (GEO datasets) were used for examining the prognoses, and TCGA datasets were selected for validation. The Oncomine and GEO datasets were used to establish and validate transcriptional signatures for treatment responses. FINDINGS: The prognostic landscape of biological processes was established across 39 malignancies. Notably, the prognoses of biological processes varied among cancer types, and transcriptional features underlying these prognostic patterns distinguished response to treatment targeting specific biological process. Applying this metric, we found that low tumor proliferation rates predicted favorable prognosis, whereas elevated cellular stress response signatures signified resistance to anti-proliferation treatment. Moreover, while high immune activities were associated with favorable prognosis, enhanced lipid metabolism signatures distinguished immunotherapy resistant patients. INTERPRETATION: These findings between prognosis and treatment response provide further insights into patient stratification for precision treatments, providing opportunities for further experimental and clinical validations. FUND: National Natural Science Foundation, Innovative Research Team in University of Ministry of Education of China, National Key Research and Development Program, Natural Science Foundation of Guangdong, Science and Technology Planning Project of Guangzhou, MRC, CRUK, Breast Cancer Now, Imperial ECMC, NIHR Imperial BRC and NIH.

Transcriptomic but not genomic variability confers phenotype of breast cancer stem cells.

BACKGROUND: Breast cancer stem cells (BCSCs) are considered responsible for cancer relapse and drug resistance. Understanding the identity of BCSCs may open new avenues in breast cancer therapy. Although several discoveries have been made on BCSC characterization, the factors critical to the origination of BCSCs are largely unclear. This study aimed to determine whether genomic mutations contribute to the acquisition of cancer stem-like phenotype and to investigate the genetic and transcriptional features of BCSCs. METHODS: We detected potential BCSC phenotype-associated mutation hotspot regions by using whole-genome sequencing on parental cancer cells and derived serial-generation spheres in increasing order of BCSC frequency, and then performed target deep DNA sequencing at bulk-cell and single-cell levels. To identify the transcriptional program associated with BCSCs, bulk-cell and single-cell RNA sequencing was performed. RESULTS: By using whole-genome sequencing of bulk cells, potential BCSC phenotype-associated mutation hotspot regions were detected. Validation by target deep DNA sequencing, at both bulk-cell and single-cell levels, revealed no genetic changes specifically associated with BCSC phenotype. Moreover, single-cell RNA sequencing showed profound transcriptomic variability in cancer cells at the single-cell level that predicted BCSC features. Notably, this transcriptomic variability was enriched during the transcription of 74 genes, revealed as BCSC markers. Breast cancer patients with a high risk of relapse exhibited higher expression levels of these BCSC markers than those with a low risk of relapse, thereby highlighting the clinical significance of predicting breast cancer prognosis with these BCSC markers. CONCLUSIONS: Transcriptomic variability, not genetic mutations, distinguishes BCSCs from non-BCSCs. The identified 74 BCSC markers have the potential of becoming novel targets for breast cancer therapy.

Spatial Vulnerabilities of the Escherichia coli Genome to Spontaneous Mutations Revealed with Improved Duplex Sequencing.

Investigation of spontaneous mutations by next-generation sequencing technology has attracted extensive attention lately due to the fundamental roles of spontaneous mutations in evolution and pathological processes. However, these studies only focused on the mutations accumulated through many generations during long-term (possibly be years of) culturing, but not the freshly generated mutations that occur at very low frequencies. In this study, we established a molecularly barcoded deep sequencing strategy to detect low abundant spontaneous mutations in genomes of bacteria cell cultures. Genome-wide spontaneous mutations in 15 Escherichia coli cell culture samples were defined with a high confidence (P < 0.01). We also developed a hotspot-calling approach based on the run-length encoding algorithm to find the genomic regions that are vulnerable to the spontaneous mutations. The hotspots for the mutations appeared to be highly conserved across the bacteria samples. Further biological annotation of these regions indicated that most of the spontaneous mutations were located at the repeat domains or nonfunctional domains of the genomes, suggesting the existence of mechanisms that could somehow prevent the occurrence of mutations in crucial genic areas. This study provides a more faithful picture of mutation occurrence and spectra in a single expansion process without long-term culturing.

Conflicts of CpG density and DNA methylation are proximally and distally involved in gene regulation in human and mouse tissues.

The relationship between CpG content and DNA methylation has attracted considerable interest in recent years. Direct or indirect methods have been developed to investigate their regulatory functions based on various hypotheses, large cohort studies, and meta-analyses. However, all of these analyses were performed at units of CpG blocks and, thus, the influence of finer genome structure has been neglected. Herein, we present a novel algorithm of base-pair resolution to systematically investigate the relationship between CpG contents and DNA methylation. By introducing the concept of 'complementary index' we examined the methylomes of 34 adult and 7 embryonic tissues and successfully fitted the relationship of DNA methylation and CpG density into a nonlinear mathematical model. A further algorithm was developed to locate the regions where CpG density does not match expectations from the model, termed 'conflict of gap' (COG) regions. Interestingly, COGs are highly concordant in human and mouse and their distributions display a tissue-specific pattern. Based on COG methylation patterns we correctly classified tissues according to their function or origin. We demonstrate that COGs based on our method can reveal more and deeper information than traditional differential methylation region (DMR) approaches. We also found that when COGs are located near to transcription start site (TSS), these regions can determine which promoters will be utilized for initiating gene transcription. Furthermore, COGs located far from the TSS perform as enhancers in terms of histone modification, sequence conservation, transcription factor binding, and DNase I-hypersensitivity.

Rad6-Bre1-mediated H2B ubiquitination regulates telomere replication by promoting telomere-end resection.

Rad6 and Bre1, ubiquitin-conjugating E2 and E3 enzymes respectively, are responsible for histone H2B lysine 123 mono-ubiquitination (H2Bub1) in Saccharomyces cerevisiae. Previous studies have shown that Rad6 and Bre1 regulate telomere length and recombination. However, the underlying molecular mechanism remains largely unknown. Here we report that H2BK123 mutation results in telomere shortening, while inactivation of Ubp8 and/or Ubp10, deubiquitinases of H2Bub1, leads to telomere lengthening in Rad6-Bre1-dependent manner. In telomerase-deficient cells, inactivation of Rad6-Bre1 pathway retards telomere shortening rate and the onset of senescence, while deletion of UBP8 and/or UBP10 accelerates senescence. Thus, Rad6-Bre1 pathway regulates both telomere length and recombination through its role in H2Bub1. Additionally, inactivation of both Rad6-Bre1-H2Bub1 and Mre11-Rad50-Xrs2 (MRX) pathways causes synthetic growth defects and telomere shortening in telomerase-proficient cells, and significantly accelerates senescence and eliminates type II telomere recombination in telomerase-deficient cells. Furthermore, RAD6 or BRE1 deletion, or H2BK123R mutation decreases the accumulation of ssDNA at telomere ends. These results support the model that Rad6-Bre1-H2Bub1 cooperates with MRX to promote telomere-end resection and thus positively regulates both telomerase- and recombination-dependent telomere replication. This study provides a mechanistic link between histone H2B ubiquitination and telomere replication.

Clonal evolution of acute myeloid leukemia highlighted by latest genome sequencing studies.

Decades of years might be required for an initiated cell to become a fully-pledged, metastasized tumor. DNA mutations are accumulated during this process including background mutations that emerge scholastically, as well as driver mutations that selectively occur in a handful of cancer genes and confer the cell a growth advantage over its neighbors. A clone of tumor cells could be superseded by another clone that acquires new mutations and grows more aggressively. Tumor evolutional patterns have been studied for years using conventional approaches that focus on the investigation of a single or a couple of genes. Latest deep sequencing technology enables a global view of tumor evolution by deciphering almost all genome aberrations in a tumor. Tumor clones and the fate of each clone during tumor evolution can be depicted with the help of the concept of variant allele frequency. Here, we summarize the new insights of cancer evolutional progression in acute myeloid leukemia. Cancer evolution is currently thought to start from a clone that has accumulated the requisite somatically-acquired genetic aberrations through a series of increasingly disordered clinical and pathological phases, eventually leading to malignant transformation [1-3]. The observations in invasive colorectal cancer that usually emerges from an antecedent benign adenomatous polyp and in cervical cancer that proceeds through intraepithelial neoplasia support the idea of stepwise or linear cancerous progression [3-5]. Genetically, such progression is achieved by successive waves of clonal expansion during which cells acquire novel genomic alterations including single nucleotide variants (SNVs), small insertions and deletions (indels), and/or copy number variations (CNVs) [6]. The latest improvement in sequencing technology has allowed the deciphering of the whole exome or genome in different types of tumor and normal tissue pairs, providing detailed catalogue about genome aberrations during tumor initiation and progression, which have been reviewed in several papers [7-10]. Here, we focus on demonstrating the cancer clonal evolution pattern revealed by recent deep sequencing studies of samples from acute myeloid leukemia (AML) patients.

Systematic characterization of lncRNAs' cell-to-cell expression heterogeneity in glioblastoma cells.

Glioblastoma (GBM) is the most common malignant adult brain tumor generally associated with high level of cellular heterogeneity and a dismal prognosis. Long noncoding RNAs (lncRNAs) are emerging as novel mediators of tumorigenesis. Recently developed single-cell RNA-seq provides an unprecedented way for analysis of the cell-to-cell variability in lncRNA expression profiles. Here we comprehensively examined the expression patterns of 2,003 lncRNAs in 380 cells from five primary GBMs and two glioblastoma stem-like cell (GSC) lines. Employing the self-organizing maps, we displayed the landscape of the lncRNA expression dynamics for individual cells. Further analyses revealed heterogeneous nature of lncRNA in abundance and splicing patterns. Moreover, lncRNA expression variation is also ubiquitously present in the established GSC lines composed of seemingly identical cells. Through comparative analysis of GSC and corresponding differentiated cell cultures, we defined a stemness signature by the set of 31 differentially expressed lncRNAs, which can disclose stemness gradients in five tumors. Additionally, based on known classifier lncRNAs for molecular subtypes, each tumor was found to comprise individual cells representing four subtypes. Our systematic characterization of lncRNA expression heterogeneity lays the foundation for future efforts to further understand the function of lncRNA, develop valuable biomarkers, and enhance knowledge of GBM biology.