Functional relevance of circRNA aberrant expression in pediatric acute leukemia with KMT2A::AFF1 fusion.

ABSTRACT: Circular RNAs (circRNAs) are emerging molecular players in leukemogenesis and promising therapeutic targets. In KMT2A::AFF1 (MLL::AF4)-rearranged leukemia, an aggressive disease compared with other pediatric B-cell precursor (BCP) acute lymphoblastic leukemia (ALL), data about circRNAs are limited. Here, we disclose the circRNA landscape of infant patients with KMT2A::AFF1 translocated BCP-ALL showing dysregulated, mostly ectopically expressed, circRNAs in leukemia cells. Most of these circRNAs, apart from circHIPK3 and circZNF609, previously associated with oncogenic behavior in ALL, are still uncharacterized. An in vitro loss-of-function screening identified an oncogenic role of circFKBP5, circKLHL2, circNR3C1, and circPAN3 in KMT2A::AFF1 ALL, whose silencing affected cell proliferation and apoptosis. Further study in an extended cohort disclosed a significantly correlated expression of these oncogenic circRNAs and their putative involvement in common regulatory networks. Moreover, it showed that circAFF1 upregulation occurs in a subset of cases with HOXA KMT2A::AFF1 ALL. Collectively, functional analyses and patient data reveal oncogenic circRNA upregulation as a relevant mechanism that sustains the malignant cell phenotype in KMT2A::AFF1 ALL.

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision.

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.

CircFBXW7 in patients with T-cell ALL: depletion sustains MYC and NOTCH activation and leukemia cell viability.

Circular RNAs (circRNAs) are emerging as new players in leukemogenic mechanisms. In patients with T-cell Acute Lymphoblastic Leukemia (T-ALL), the recent report of a remarkable dysregulation of circRNAs incited further functional investigation. Here we focus on circFBXW7, highly expressed in T-cells, with a notably high abundance of the circular compared to linear transcript of FBXW7. Two T-ALL patient cohorts profiled with RNA-seq were analyzed in comparison with five populations of developing thymocytes as normal counterpart, quantifying circRNA and gene expression. CircFBXW7 expression was very heterogeneous in T-ALL patients allowing their stratification in two groups with low and high expression of this circRNA, not correlated with FBXW7 mutation status and T-ALL molecular subgroups. With a loss-of-function study in T-ALL in vitro, we demonstrate that circFBXW7 depletion increases leukemic cell viability and proliferation. Microarray profiling highlighted the effect of the circFBXW7 silencing on gene expression, with activation of pro-proliferative pathways, supporting a tumor suppressor role of circFBXW7 in T-ALL. Further, MYC and intracellular NOTCH1 protein levels, as well as expression of MYC target and NOTCH signaling genes were elevated after circFBXW7 depletion, suggesting an inhibitory role of circFBXW7 in these oncogenic axes. Plus, low circFBXW7 levels were associated with a particular gene expression profile in T-ALL patients, which was remarkably mirrored by the effects of circFBXW7 loss-of-function in vitro. CircFBXW7 depletion notably emerges as a new factor enhancing a proliferative phenotype and the activation of the MYC signaling pathway, key players in this aggressive malignancy.

Systematic benchmarking of statistical methods to assess differential expression of circular RNAs.

Circular RNAs (circRNAs) are covalently closed transcripts involved in critical regulatory axes, cancer pathways and disease mechanisms. CircRNA expression measured with RNA-seq has particular characteristics that might hamper the performance of standard biostatistical differential expression assessment methods (DEMs). We compared 38 DEM pipelines configured to fit circRNA expression data's statistical properties, including bulk RNA-seq, single-cell RNA-seq (scRNA-seq) and metagenomics DEMs. The DEMs performed poorly on data sets of typical size. Widely used DEMs, such as DESeq2, edgeR and Limma-Voom, gave scarce results, unreliable predictions or even contravened the expected behaviour with some parameter configurations. Limma-Voom achieved the most consistent performance throughout different benchmark data sets and, as well as SAMseq, reasonably balanced false discovery rate (FDR) and recall rate. Interestingly, a few scRNA-seq DEMs obtained results comparable with the best-performing bulk RNA-seq tools. Almost all DEMs' performance improved when increasing the number of replicates. CircRNA expression studies require careful design, choice of DEM and DEM configuration. This analysis can guide scientists in selecting the appropriate tools to investigate circRNA differential expression with RNA-seq experiments.

Detecting differentially expressed circular RNAs from multiple quantification methods using a generalized linear mixed model.

Finding differentially expressed circular RNAs (circRNAs) is instrumental to understanding the molecular basis of phenotypic variation between conditions linked to circRNA-involving mechanisms. To date, several methods have been developed to identify circRNAs, and combining multiple tools is becoming an established approach to improve the detection rate and robustness of results in circRNA studies. However, when using a consensus strategy, it is unclear how circRNA expression estimates should be considered and integrated into downstream analysis, such as differential expression assessment. This work presents a novel solution to test circRNA differential expression using quantifications of multiple algorithms simultaneously. Our approach analyzes multiple tools' circRNA abundance count data within a single framework by leveraging generalized linear mixed models (GLMM), which account for the sample correlation structure within and between the quantification tools. We compared the GLMM approach with three widely used differential expression models, showing its higher sensitivity in detecting and efficiently ranking significant differentially expressed circRNAs. Our strategy is the first to consider combined estimates of multiple circRNA quantification methods, and we propose it as a powerful model to improve circRNA differential expression analysis.

CRAFT: a bioinformatics software for custom prediction of circular RNA functions.

Circular RNAs (circRNAs), transcripts generated by backsplicing, are particularly stable and pleiotropic molecules, whose dysregulation drives human diseases and cancer by modulating gene expression and signaling pathways. CircRNAs can regulate cellular processes by different mechanisms, including interaction with microRNAs (miRNAs) and RNA-binding proteins (RBP), and encoding specific peptides. The prediction of circRNA functions is instrumental to interpret their impact in diseases, and to prioritize circRNAs for functional investigation. Currently, circRNA functional predictions are provided by web databases that do not allow custom analyses, while self-standing circRNA prediction tools are mostly limited to predict only one type of function, mainly focusing on the miRNA sponge activity of circRNAs. To solve these issues, we developed CRAFT (CircRNA Function prediction Tool), a freely available computational pipeline that predicts circRNA sequence and molecular interactions with miRNAs and RBP, along with their coding potential. Analysis of a set of circRNAs with known functions has been used to appraise CRAFT predictions and to optimize its setting. CRAFT provides a comprehensive graphical visualization of the results, links to several knowledge databases, and extensive functional enrichment analysis. Moreover, it originally combines the predictions for different circRNAs. CRAFT is a useful tool to help the user explore the potential regulatory networks involving the circRNAs of interest and generate hypotheses about the cooperation of circRNAs into the modulation of biological processes.

Sensitive, reliable and robust circRNA detection from RNA-seq with CirComPara2.

Circular RNAs (circRNAs) are a large class of covalently closed RNA molecules originating by a process called back-splicing. CircRNAs are emerging as functional RNAs involved in the regulation of biological processes as well as in disease and cancer mechanisms. Current computational methods for circRNA identification from RNA-seq experiments are characterized by low discovery rates and performance dependent on the analysed data set. We developed CirComPara2 (https://github.com/egaffo/CirComPara2), a new automated computational pipeline for circRNA discovery and quantification, which consistently achieves high recall rates without losing precision by combining multiple circRNA detection methods. In our benchmark analysis, CirComPara2 outperformed state-of-the-art circRNA discovery tools and proved to be a reliable and robust method for comprehensive transcriptome characterization.

CircIMPACT: An R Package to Explore Circular RNA Impact on Gene Expression and Pathways.

Circular RNAs (circRNAs) are transcripts generated by back-splicing. CircRNAs might regulate cellular processes by different mechanisms, including interaction with miRNAs and RNA-binding proteins. CircRNAs are pleiotropic molecules whose dysregulation has been linked to human diseases and can drive cancer by impacting gene expression and signaling pathways. The detection of circRNAs aberrantly expressed in disease conditions calls for the investigation of their functions. Here, we propose CircIMPACT, a bioinformatics tool for the integrative analysis of circRNA and gene expression data to facilitate the identification and visualization of the genes whose expression varies according to circRNA expression changes. This tool can highlight regulatory axes potentially governed by circRNAs, which can be prioritized for further experimental study. The usefulness of CircIMPACT is exemplified by a case study analysis of bladder cancer RNA-seq data. The link between circHIPK3 and heparanase (HPSE) expression, due to the circHIPK3-miR558-HPSE regulatory axis previously determined by experimental studies on cell lines, was successfully detected. CircIMPACT is freely available at GitHub.

Bioinformatic Analysis of Circular RNA Expression.

Circular RNAs (circRNAs) are stable RNA molecules generated by backsplicing that play regulatory functions through interaction with other RNA and proteins, as well as by encoding peptides. Dysregulation of circRNA expression can drive cancer development and progression with different mechanisms. CircRNAs are currently regarded as extremely attractive molecules in cancer research for the identification of new and possibly targetable disease regulatory networks and for the development of biomarkers for cancer diagnosis, prognosis definition, and monitoring. Using specific experimental and computational protocols, circRNAs can be identified through RNA-seq by spotting the reads spanning backsplice junctions, which are specific to circular molecules. In this chapter, we report a state-of-the-art computational protocol for a genome-wide analysis of circRNAs from RNA-seq data, which considers circRNA detection, quantification, and differential expression testing. Finally, we indicate how to determine circular transcript sequences and the resources for an in silico functional characterization of circRNAs.

Usability and Accuracy of a Smartwatch for the Assessment of Physical Activity in the Elderly Population: Observational Study.

BACKGROUND: Regular physical activity (PA) contributes to the primary and secondary prevention of several chronic diseases and reduces the risk of premature death. Physical inactivity is a modifiable risk factor for cardiovascular disease and a variety of chronic disorders such as diabetes, obesity, hypertension, bone and joint diseases (eg, osteoporosis and osteoarthritis), depression, and colon and breast cancer. Population aging and the related increase in chronic diseases have a major impact on the health care systems of most Western countries and will produce an even more significant effect in the future. Monitoring PA is a valuable method of determining whether people are performing enough PA so as to prevent chronic diseases or are showing early symptoms of those diseases. OBJECTIVE: The aim of this study was to estimate the accuracy of wearable devices in quantifying the PA of elderly people in a real-life setting. METHODS: Participants aged 70 to 90 years with the ability to walk safely without any walking aid for at least 300 meters, who had no walking disabilities or episodes of falling while walking in the last 12 months, were asked to walk 150 meters at their preferred pace wearing a vívoactive HR device (Garmin Ltd) and actual steps were monitored and tallied by a researcher using a hand-tally counter to assess the performance of the device at a natural speed. A Bland-Altman plot was used to analyze the difference between manually counted steps and wearable device-measured steps. The intraclass correlation coefficient (ICC) was computed (with a 95% confidence interval) between step measurements. The generalized linear mixed-model (GLMM) ICCs were estimated, providing a random effect term (random intercept) for the individual measurements (gold standard and device). Both adjusted and conditional ICCs were computed for the GLMM models considering separately the effect of age, sex, BMI, and obesity. Analyses were performed using R software (R Foundation for Statistical Computing) with the rms package. RESULTS: A total of 23 females and 26 males were enrolled in the study. The median age of the participants was 75 years. The Bland-Altman plot revealed that, excluding one observation, all differences across measurements were in the confidence bounds, demonstrating the substantial agreement between the step count measurements. The results were confirmed by an ICC equal to .98 (.96-.99), demonstrating excellent agreement between the two sets of measurements. CONCLUSIONS: The level of accuracy of wearable devices in quantifying the PA of elderly people in a real-life setting that was found in this study supports the idea of considering wrist-wearable nonmedical devices (widely available in nonspecialized stores) as reliable tools. Both health care professionals and informal caregivers could monitor the level of PA of their patients.

Systematic Review and Meta-Analysis of Surgical Treatment for Isolated Local Recurrence of Pancreatic Cancer.

PURPOSE: To perform a systematic review and meta-analysis on the outcome of surgical treatment for isolated local recurrence of pancreatic cancer. METHODS: A systematic review and meta-analysis based on Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines was conducted in PubMed, Scopus, and Web of Science. RESULTS: Six studies concerning 431 patients with recurrent pancreatic cancer met the inclusion criteria and were included in the analysis: 176 underwent redo surgery, and 255 received non-surgical treatments. Overall survival and post-recurrence survival were significantly longer in the re-resected group (ratio of means (ROM) 1.99; 95% confidence interval (CI), 1.54-2.56, I2 = 75.89%, p = 0.006, and ROM = 2.05; 95% CI, 1.48-2.83, I2 = 76.39%, p = 0.002, respectively) with a median overall survival benefit of 28.7 months (mean difference (MD) 28.7; 95% CI, 10.3-47.0, I2 = 89.27%, p < 0.001) and median survival benefit of 15.2 months after re-resection (MD 15.2; 95% CI, 8.6-21.8, I2 = 58.22%, p = 0.048). CONCLUSION: Resection of isolated pancreatic cancer recurrences is safe and feasible and may offer a survival benefit. Selection of patients and assessment of time and site of recurrence are mandatory.

Large-scale circular RNA deregulation in T-ALL: unlocking unique ectopic expression of molecular subtypes.

Circular RNAs (circRNAs) are stable RNA molecules that can drive cancer through interactions with microRNAs and proteins and by the expression of circRNA encoded peptides. The aim of the study was to define the circRNA landscape and potential impact in T-cell acute lymphoblastic leukemia (T-ALL). Analysis by CirComPara of RNA-sequencing data from 25 T-ALL patients, immature, HOXA overexpressing, TLX1, TLX3, TAL1, or LMO2 rearranged, and from thymocyte populations of human healthy donors disclosed 68 554 circRNAs. Study of the top 3447 highly expressed circRNAs identified 944 circRNAs with significant differential expression between malignant T cells and normal counterparts, with most circRNAs displaying increased expression in T-ALL. Next, we defined subtype-specific circRNA signatures in molecular genetic subgroups of human T-ALL. In particular, circZNF609, circPSEN1, circKPNA5, and circCEP70 were upregulated in immature, circTASP1, circZBTB44, and circBACH1 in TLX3, circHACD1, and circSTAM in HOXA, circCAMSAP1 in TLX1, and circCASC15 in TAL-LMO. Backsplice sequences of 14 circRNAs ectopically expressed in T-ALL were confirmed, and overexpression of circRNAs in T-ALL with specific oncogenic lesions was substantiated by quantification in a panel of 13 human cell lines. An oncogenic role of circZNF609 in T-ALL was indicated by decreased cell viability upon silencing in vitro. Furthermore, functional predictions identified circRNA-microRNA gene axes informing modes of circRNA impact in molecular subtypes of human T-ALL.

CircRNAs Dysregulated in Juvenile Myelomonocytic Leukemia: CircMCTP1 Stands Out.

Juvenile myelomonocytic leukemia (JMML), a rare myelodysplastic/myeloproliferative neoplasm of early childhood, is characterized by clonal growth of RAS signaling addicted stem cells. JMML subtypes are defined by specific RAS pathway mutations and display distinct gene, microRNA (miRNA) and long non-coding RNA expression profiles. Here we zoom in on circular RNAs (circRNAs), molecules that, when abnormally expressed, may participate in malignant deviation of cellular processes. CirComPara software was used to annotate and quantify circRNAs in RNA-seq data of a "discovery cohort" comprising 19 JMML patients and 3 healthy donors (HD). In an independent set of 12 JMML patients and 6 HD, expression of 27 circRNAs was analyzed by qRT-PCR. CircRNA-miRNA-gene networks were reconstructed using circRNA function prediction and gene expression data. We identified 119 circRNAs dysregulated in JMML and 59 genes showing an imbalance of the circular and linear products. Our data indicated also circRNA expression differences among molecular subgroups of JMML. Validation of a set of deregulated circRNAs in an independent cohort of JMML patients confirmed the down-regulation of circOXNAD1 and circATM, and a marked up-regulation of circLYN, circAFF2, and circMCTP1. A new finding in JMML links up-regulated circMCTP1 with known tumor suppressor miRNAs. This and other predicted interactions with miRNAs connect dysregulated circRNAs to regulatory networks. In conclusion, this study provides insight into the circRNAome of JMML and paves the path to elucidate new molecular disease mechanisms putting forward circMCTP1 up-regulation as a robust example.

Measuring Caloric Intake at the Population Level (NOTION): Protocol for an Experimental Study.

BACKGROUND: The monitoring of caloric intake is an important challenge for the maintenance of individual and public health. The instruments used so far for dietary monitoring (eg, food frequency questionnaires, food diaries, and telephone interviews) are inexpensive and easy to implement but show important inaccuracies. Alternative methods based on wearable devices and wrist accelerometers have been proposed, yet they have limited accuracy in predicting caloric intake because analytics are usually not well suited to manage the massive sets of data generated from these types of devices. OBJECTIVE: This study aims to develop an algorithm using recent advances in machine learning methodology, which provides a precise and stable estimate of caloric intake. METHODS: The study will capture four individual eating activities outside the home over 2 months. Twenty healthy Italian adults will be recruited from the University of Padova in Padova, Italy, with email, flyers, and website announcements. The eligibility requirements include age 18 to 66 years and no eating disorder history. Each participant will be randomized to one of two menus to be eaten on weekdays in a predefined cafeteria in Padova (northeastern Italy). Flows of raw data will be accessed and downloaded from the wearable devices given to study participants and associated with anthropometric and demographic characteristics of the user (with their written permission). These massive data flows will provide a detailed picture of real-life conditions and will be analyzed through an up-to-date machine learning approach with the aim to accurately predict the caloric contribution of individual eating activities. Gold standard evaluation of the energy content of eaten foods will be obtained using calorimetric assessments made at the Laboratory of Dietetics and Nutraceutical Research of the University of Padova. RESULTS: The study will last 14 months from July 2017 with a final report by November 2018. Data collection will occur from October to December 2017. From this study, we expect to obtain a series of relevant data that, opportunely filtered, could allow the construction of a prototype algorithm able to estimate caloric intake through the recognition of food type and the number of bites. The algorithm should work in real time, be embedded in a wearable device, and able to match bite-related movements and the corresponding caloric intake with high accuracy. CONCLUSIONS: Building an automatic calculation method for caloric intake, independent on the black-box processing of the wearable devices marketed so far, has great potential both for clinical nutrition (eg, for assessing cardiovascular compliance or for the prevention of coronary heart disease through proper dietary control) and public health nutrition as a low-cost monitoring tool for eating habits of different segments of the population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12116.

Metastatic tumors to the pancreas: a systematic review and meta-analysis.

INTRODUCTION: Metastases to the pancreas from other primary tumors are increasingly recognized in clinical practice, but the real role of surgery remains unclear. This study was designated to evaluate by a meta-analytic approach the results of surgical treatment for the most common malignancies metastasizing to the pancreas. EVIDENCE ACQUISITION: MEDLINE, PubMED, Scopus and Web of Sciences were searched from January 2000 to December 2015. Studies reporting postoperative complications, postoperative mortality, disease-free and overall survival of patients undergoing resection for secondary tumours of the pancreas, were included. EVIDENCE SYNTHESIS: Fourteen publication with 281 patients met the inclusion criteria and were subjected to the analysis. Operative morbidity and mortality were 34% and 1.3% respectively. Pancreatic resection for renal cell cancer showed better survival compared to other non-renal cell cancer (ratio of mean 1.83; 95% CI: 1.42-2.36, I2=74.52%, P<0.001). Disease-free interval was longer for metastatic renal cell carcinoma patients (mean difference 6.36, 95% CI: 3.803-8.912 years, I2=76:54%, P<0.001). A meta-regression was used to correlate the two endpoints and showed that a longer DFI is associated to a longer survival. CONCLUSIONS: Pancreatic resection for metastasis should be reserved to patients in good health conditions, with isolated disease from renal cell cancer. For other types of tumor, surgery should be performed only in individual basis. There is a need of studies evaluating the role of chemotherapy in the neoadjuvant setting or the best sequential use of multimodality treatment (targeted therapy, radiotherapy, surgery, etc.).