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Supplementary Data from Pan-Cancer Analysis of Canonical and Modified miRNAs Enhances the Resolution of the Functional miRNAome in Cancer

Supplementary Figure from Pan-Cancer Analysis of Canonical and Modified miRNAs Enhances the Resolution of the Functional miRNAome in Cancer

Epitranscriptomic studies of miRNAs have added a new layer of complexity to the cancer field. Although there is fast-growing interest in adenosine-to-inosine (A-to-I) miRNA editing and alternative cleavage that shifts miRNA isoforms, simultaneous evaluation of both modifications in cancer is still missing. Here, we concurrently profiled multiple miRNA modification types, including A-to-I miRNA editing and shifted miRNA isoforms, in >13,000 adult and pediatric tumor samples across 38 distinct cancer cohorts from The Cancer Genome Atlas and The Therapeutically Applicable Research to Generate Effective Treatments data sets. The differences between canonical miRNAs and the wider miRNAome in terms of expression, clustering, dysregulation, and prognostic standpoint were investigated. The combination of canonical miRNAs and modified miRNAs boosted the quality of clustering results, outlining unique clinicopathologic features among cohorts. Certain modified miRNAs showed opposite expression from their canonical counterparts in cancer, potentially impacting their targets and function. Finally, a shifted and edited miRNA isoform was experimentally validated to directly bind and suppress a unique target. These findings outline the importance of going beyond the well-established paradigm of one mature miRNA per miRNA arm to elucidate novel mechanisms related to cancer progression.Significance:Modified miRNAs may act as cancer biomarkers and function as allies or antagonists of their canonical counterparts in gene regulation, suggesting the concurrent consideration of canonical and modified miRNAs can boost patient stratification.

Epitranscriptomic studies of miRNAs have added a new layer of complexity to the cancer field. Although there is fast-growing interest in adenosine-to-inosine (A-to-I) miRNA editing and alternative cleavage that shifts miRNA isoforms, simultaneous evaluation of both modifications in cancer is still missing. Here, we concurrently profiled multiple miRNA modification types, including A-to-I miRNA editing and shifted miRNA isoforms, in >13,000 adult and pediatric tumor samples across 38 distinct cancer cohorts from The Cancer Genome Atlas and The Therapeutically Applicable Research to Generate Effective Treatments data sets. The differences between canonical miRNAs and the wider miRNAome in terms of expression, clustering, dysregulation, and prognostic standpoint were investigated. The combination of canonical miRNAs and modified miRNAs boosted the quality of clustering results, outlining unique clinicopathologic features among cohorts. Certain modified miRNAs showed opposite expression from their canonical counterparts in cancer, potentially impacting their targets and function. Finally, a shifted and edited miRNA isoform was experimentally validated to directly bind and suppress a unique target. These findings outline the importance of going beyond the well-established paradigm of one mature miRNA per miRNA arm to elucidate novel mechanisms related to cancer progression. Significance: Modified miRNAs may act as cancer biomarkers and function as allies or antagonists of their canonical counterparts in gene regulation, suggesting the concurrent consideration of canonical and modified miRNAs can boost patient stratification.

This study concerns the analysis of the modulation of Chronic Myeloid Leukemia (CML) cell model K562 transcriptome following transfection with the tumor suppressor gene encoding for Protein Tyrosine Phosphatase Receptor Type G (PTPRG) and treatment with the tyrosine kinase inhibitor (TKI) Imatinib. Specifically, we aimed at identifying genes whose level of expression is altered by PTPRG modulation and Imatinib concentration. Statistical tests as differential expression analysis (DEA) supported by gene set enrichment analysis (GSEA) and modern methods of ontological term analysis are presented along with some results of current interest for forthcoming experimental research in the field of the transcriptomic landscape of CML. In particular, we present two methods that differ in the order of the analysis steps. After a gene selection based on fold-change value thresholding, we applied statistical tests to select differentially expressed genes. Therefore, we applied two different methods on the set of differentially expressed genes. With the first method (Method 1), we implemented GSEA, followed by the identification of transcription factors. With the second method (Method 2), we first selected the transcription factors from the set of differentially expressed genes and implemented GSEA on this set. Method 1 is a standard method commonly used in this type of analysis, while Method 2 is unconventional and is motivated by the intention to identify transcription factors more specifically involved in biological processes relevant to the CML condition. Both methods have been equipped in ontological knowledge mining and word cloud analysis, as elements of novelty in our analytical procedure. Data analysis identified RARG and CD36 as a potential PTPRG up-regulated genes, suggesting a possible induction of cell differentiation toward an erithromyeloid phenotype. The prediction was confirmed at the mRNA and protein level, further validating the approach and identifying a new molecular mechanism of tumor suppression governed by PTPRG in a CML context.

This study concerns the analysis of the modulation of Chronic Myeloid Leukemia (CML) cell model K562 transcriptome following transfection with the tumor suppressor gene encoding for Protein Tyrosine Phosphatase Receptor Type G (PTPRG) and treatment with the tyrosine kinase inhibitor (TKI) Imatinib. Specifically, we aimed at identifying genes whose level of expression is altered by PTPRG modulation and Imatinib concentration. Statistical tests as differential expression analysis (DEA) supported by gene set enrichment analysis (GSEA) and modern methods of ontological term analysis are presented along with some results of current interest for forthcoming experimental research in the field of the transcriptomic landscape of CML. In particular, we present two methods that differ in the order of the analysis steps. After a gene selection based on fold-change value thresholding, we applied statistical tests to select differentially expressed genes. Therefore, we applied two different methods on the set of differentially expressed genes. With the first method (Method 1), we implemented GSEA, followed by the identification of transcription factors. With the second method (Method 2), we first selected the transcription factors from the set of differentially expressed genes and implemented GSEA on this set. Method 1 is a standard method commonly used in this type of analysis, while Method 2 is unconventional and is motivated by the intention to identify transcription factors more specifically involved in biological processes relevant to the CML condition. Both methods have been equipped in ontological knowledge mining and word cloud analysis, as elements of novelty in our analytical procedure. Data analysis identified RARG and CD36 as a potential PTPRG up-regulated genes, suggesting a possible induction of cell differentiation toward an erithromyeloid phenotype. The prediction was confirmed at the mRNA and protein level, further validating the approach and identifying a new molecular mechanism of tumor suppression governed by PTPRG in a CML context.

Objective It is debatable whether BCR-ABL1 transcript type has an impact on outcome of treatment of patients with CML, and it is not widely studied whether body weight influences response to treatment. In this study, we tried to find out if any of these factors has an impact on response to treatment and outcome. Methodology We conducted a retrospective analysis of the files of 79 patients being treated in our center for CML with known BCR-ABL1 breakpoints, and patients’ management and response assessment was done based on ELN 2013 guidelines. The analysis was performed based on two main groups, obese vs. normal BMI, and then based on BCR-ABL1 transcripts: e13a2 vs. e14a2. Cumulative incidence of MMR, CCyR, and DMR were estimated using the Kaplan–Meier survival curve method, and comparisons between groups were performed by the Log-rank/Gray test methods. Results/conclusion In the patient-cohort studied, there was no statistically significant difference in molecular response between patients with CML based on body weight or transcript type although patients in the obesity group achieved higher and faster MMR with no statistical significance.

MiRNA Epitranscriptomics has placed a new layer of complexity in the cancer field. Despite the fast-growing interest in miRNA editing and shifted miRNA isoforms, a simultaneous study of both modifications in cancer is still missing. Here, we concurrently profiled multiple miRNA modifications, including A-to-I RNA editing and shifted miRNA isoforms, in >13K adult and pediatric tumor samples across 38 distinct cancer cohorts from The Cancer Genome Atlas and The Therapeutically Applicable Research to Generate Effective Treatments datasets. We investigated the differences among canonical miRNAs and the wider miRNAome in terms of expression, clustering, dysregulation, and prognostic standpoint. The combination of canonical miRNAs/miRNA isoforms boosted the quality of clustering results, outlining unique cohorts’ clinical-pathological features. We described modified miRNAs showing opposite dysregulation with respect to their canonical counterparts in cancer, potentially impacting their targetome and function. The abundance of expressed miRNA isoforms directly impacted the activation/deactivation of critical carcinogenesis pathways. Finally, we experimentally validated unique targeting for a shifted and edited miRNA isoform. Our findings outlined once more the importance of going beyond the well-established paradigm of one-mature-miRNA per miRNA arm to elucidate novel mechanisms related to cancer progression.

Simple Summary Adenosine to inosine (A-to-I) editing is a type of RNA editing where individual adenosines are enzymatically converted into inosines. A-to-I RNA editing plays an important role in cancer biology. Several studies have demonstrated that A-to-I editing of microRNAs (miRNAs) very often affect miRNA function as oncosuppressors or oncogenes, hence showing clinical relevance. Hence, A-to-I miRNA editing has been suggested as a potential diagnostic and prognostic tool in the monitoring of cancer patients. Nevertheless, the process of identifying and characterizing miRNA editing events in tumor samples still presents several challenges. In this review, we outline molecular aspects linked to miRNA A-to-I editing and retrace methods and approaches dedicated to detection of editing sites and functional characterization of edited miRNAs in cancer. Abstract Adenosine to inosine (A-to-I) editing consists of an RNA modification where single adenosines along the RNA sequence are converted into inosines. Such a biochemical transformation is catalyzed by enzymes belonging to the family of adenosine deaminases acting on RNA (ADARs) and occurs either co- or post-transcriptionally. The employment of powerful, high-throughput detection methods has recently revealed that A-to-I editing widely occurs in non-coding RNAs, including microRNAs (miRNAs). MiRNAs are a class of small regulatory non-coding RNAs (ncRNAs) acting as translation inhibitors, known to exert relevant roles in controlling cell cycle, proliferation, and cancer development. Indeed, a growing number of recent researches have evidenced the importance of miRNA editing in cancer biology by exploiting various detection and validation methods. Herein, we briefly overview early and currently available A-to-I miRNA editing detection and validation methods and discuss the significance of A-to-I miRNA editing in human cancer.

Background: It has been reported that general adiposity in adulthood and early adulthood, and greater height may increase the risk of almost all types of lympho-haematopoietic cancers while a study done in MD Anderson found that obesity and adult weight gain are independent risk factors for CML however no study evaluated the role of obesity in the disease progression while more studies investigate the impact of translocation types. Method: We conducted a retrospective analysis of the files of 37 patients being treated in our center for CML in chronic phase (CMP-CP) with known BCR-ABL1 breakpoints, Results: patients’ management and response assessment was done based on ELN 2013 guidelines. Analysis is done based on two main groups, obese vs normal BMI, and then based on BCR-ABL1 transcripts: e13a2 vs e14a2. Although the number of cases is limited, in the patient-cohort studied an e14a2 BCR-ABL1 transcript type / normal body weight was associated with an inferior outcome when compared to e13a2 transcript / obesity groups Conclusion: our finding suggest the need to enlarge the series to better evaluate a potential role of altered metabolism and/or specific transcripts in the response to TKI in CML.

Double knockout of the two miR-15/16 loci in mouse resulted in the development of acute myeloid leukemia (AML). This result suggested that, at least, a fraction of human AMLs could be due to a similar mechanism. We analyzed the role of the two miR-15/16 clusters in 93 myelodysplastic syndrome (MDS) patients divided in three subgroups: patients with MDS, patients with MDS before transforming into AML (MDS-T), and patients with AML evolving from MDS (MDS-AML). Then, we tested 139 AML cases and 14 different AML cell lines by assessing microRNA (miRNA) expression, target protein expression, genetic loss, and silencing. MDS-T and MDS-AML patients show a reduction of the expression of miR-15a/-15b/-16 compared to MDS patients. Each miRNA can significantly predict MDS and MDS-T groups. Then, 79% of primary AMLs show a reduced expression of miR-15a and/or miR-15b. The expression of miR-15a/-15b/-16 significantly stratified AML patients in two prognostic classes. Furthermore, 40% of AML cell lines showed a combined loss of the expression of miR-15a/-15b and overexpression of their direct/indirect targets. As potential mechanisms involved in the silencing of the two miR-15/16 loci, we identified a genetic loss of miR-15a and miR-15b and silencing of these two loci by methylation. We identified a potential driver oncogenic role in the loss of expression of both miR-15/16 clusters in the progression of MDS into AML and in AML pathogenesis. The stratification of AML patients, based on miR-15/16 expression, can lead to targeted and combination therapies for the treatment of this incurable disease.

Protein tyrosine phosphatase receptor type &gamma, (PTPRG) is a tumor suppressor gene, down-regulated in Chronic Myeloid Leukemia (CML) cells by the hypermethylation of its promoter region. &beta, catenin (CTNNB1) is a critical regulator of Leukemic Stem Cells (LSC) maintenance and CML proliferation. This study aims to demonstrate the antagonistic regulation between &beta, catenin and PTPRG in CML cells. The specific inhibition of PTPRG increases the activation state of BCR-ABL1 and modulates the expression of the BCR-ABL1- downstream gene &beta, Catenin. PTPRG was found to be capable of dephosphorylating &beta, catenin, eventually causing its cytosolic destabilization and degradation in cells expressing PTPRG. Furthermore, we demonstrated that the increased expression of &beta, catenin in PTPRG-negative CML cell lines correlates with DNA (cytosine-5)-methyl transferase 1 (DNMT1) over-expression, which is responsible for PTPRG promoter hypermethylation, while its inhibition or down-regulation correlates with PTPRG re-expression. We finally confirmed the role of PTPRG in regulating BCR-ABL1 and &beta, catenin phosphorylation in primary human CML samples. We describe here, for the first time, the existence of a regulative loop occurring between PTPRG and &beta, catenin, whose reciprocal imbalance affects the proliferation kinetics of CML cells.

Introduction The hallmark of CML is BCR-ABL1 (breakpoint cluster region gene-Abelson murine leukemia viral oncogene homolog 1) on Philadelphia chromosome, which is the result of a reciprocal translocation between the long arms of chromosomes 9 and 22 (t[9;22][q34;q11]) [1]. Chromosome 22 breakpoints influence the BCR portions preserved in the BCL-ABL1 fusion mRNA and protein and are mainly localized to one of three BCRs, namely major-BCR (M-BCR), minor BCR (m-BCR) and micro-BCR (µ-BCR). In comparison, breaks in chromosome 9 arise most frequently by alternative splicing of the two first ABL1 exons, and can also be generated in a large genetic region, upstream of exon Ib at the 5' end, or downstream of exon Ia at the 3' end. In the majority of CML cases, the breakpoint lies within the M-BCR and gives rise to e13a2 or e14a2 fusion mRNAs (previously denoted as b2a2 and b3a2) and a p210BCR-ABL fusion protein [2]. [3] Methodology We conducted a retrospective analysis of the files of 79 patients being treated in our center for CML with known BCR-ABL1 breakpoints; there were few more patients with known transcript type but excluded because either travelled immediately on diagnosis or had a failure due to confirmed compliance issues. Patients' management and response assessment was done based on ELN 2013 guidelines. The analysis is done based on two main groups, obese versus normal BMI, and then based on BCR-ABL1 transcripts: e13a2 versus e14a2. Ethical approval was obtained from Medical Research Center for Hamad Medical Corporation (MRC-01-18-337). Results Patients included 62 males (78.5%) and 17 females (21.5%) with the mean age at diagnosis 38.8±11.8 years (median, 38; range 21 to 69 years). The characteristics (demographics, anthropometric, hematological and clinico-pathological) of the patients and their association with transcript types and obesity are summarized in Table 1. Patient outcomes, cytogenetic and molecular responses The median follow-up was 30 months (range 6 to 196 months) and 38 months (range 3 to 192 months) in normal weight and obesity groups, respectively. The median follow-up was 28 months (range 3 to 196 months) and 39 months (range 10 to 192 months) in e14a2 and e13a2 patients, respectively. A total of 22 patients distributed among different groups ended up leaving the country (censored) after a variable duration of follow-up (6 - 196 months), 18 of them CML-CP, and 4 CML-AP. 3 patients died in our cohort, all of them had e14a2 transcript, one of them was in the normal weight/BMI group, two were in the obesity group. In e14a2 group, more patients were on imatinib at the time of analysis (15 (39.5%) vs 7 (17.1%) in e13a2 group, p = 0.026). The percentage of patients of had to switch TKI was similar in both groups (47.4% vs 53.7%, p = 0.576). However, less patients in e14a2 group had to switch TKI because of failure/progression (10 (55.6%) vs 17 (77.3%), p = 0.145); however, this didn't translate into a significant difference of achieving MMR at 1 year, where in e14a2 group, 10 patients achieved MMR at 1 year (31.3%), same as in e13a2 group (10 patients = 29.3%) p 0.331 (all shown in table 1). When comparing long-term outcomes, there was also no significant difference between groups based on transcript type with regards to MMR (44.7% vs 46.3% in e14a2 vs e13a2 respectively) or DMR (26.3% vs 22% respectively) as shown in figure. In the obesity group, there were 2 patients using ponatinib due to T315I mutation, compared to none in normal weight group. However, there were no significant differences in TKI used, switch of TKI, or reason for switch. Same applies for achieving MMR at 1 year, as 11 patients in the obesity group achieved MMR (28.2%) compared to 9 patients in normal weight group (33.3%), p = 0.778 (as shown in table 1). Regarding the long-term outcomes, more patients in the obesity group achieved MMR (53.2%) compared to normal weight group (34.3%), and this response was faster, but not statistically significant. This difference was less clear with regards to DMR (25.5% in the obesity group compared to 21.9% in normal weight group) as shown in figure. Conclusion In the patient-cohort studied there were no significant differences in molecular response based on transcript type or body weight/BMI. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Loss of miR-15/16 is the most common genetic lesion in chronic lymphocytic leukemia (CLL), promoting overexpression of BCL2, which factors in leukemia pathogenesis. Indeed, an inhibitor of Bcl2, venetoclcax, is highly active in the treatment of patients with CLL. However, single-agent venetoclcax fails to eradicate minimal residual disease in most patients. Accordingly, we were interested in other genes that may be regulated by miR-15/16, which may target other drivers in CLL. We found that miR-15/16 targets ROR1, which encodes an onco-embryonic surface protein expressed on the CLL cells of over 90% of patients, but not on virtually all normal postpartum tissues. CLL with high-level expression of ROR1 also have high-level expression of Bcl2, but low-to-negligible miR-15/16. Moreover, CLL cases with high-level ROR1 have deletion(s) at the chromosomal location of the genes encoding miR-15/16 (13q14) more frequently than cases with low-to-negligible ROR1, implying that deletion of miR-15/16 may promote overexpression of ROR1, in addition to BCL2. ROR1 is a receptor for Wnt5a, which can promote leukemia-cell proliferation and survival, and can be targeted by cirmtuzumab, a humanized anti-ROR1 mAb. We find that this mAb can enhance the in vitro cytotoxic activity of venetoclcax for CLL cells with high-level expression of ROR1, indicating that combining these agents, which target ROR1 and Bcl2, may have additive, if not synergistic, activity in patients with this disease.

Background: Several studies showed that the aberrant DNA methylations are involved in leukemia and cancer pathogenesis. PTPRG is a natural inhibitory mechanism that is found to be down-regulated in Chronic Myeloid Leukemia (CML) disease. The mechanism behind its down-regulation has not been fully elucidated. Aim: The study investigates the role of methylation as a possible mechanism of PTPRG under-expression in CML patients. Method: Peripheral blood samples from CML patients treated with tyrosine kinase inhibitors (TKIs) and healthy controls were collected. DNA was extracted and treated with bisulfite treatment, followed by PCR and sequencing of 25 of CpG in Promoter and 26 of CpG in the Intronic regions of PTPRG. Bisulfite-sequencing technique as a high-resolution method was employed. Results : CML groups (New Diagnosed and Failed treatment) have significantly higher methylation levels in the Promoter and Intron regions compared to the healthy group. There were also significant differences in methylation levels of CpG sites in the Promoter and Intronic regions amongst the groups. Conclusion: Aberrant methylation of PTPRG is documented and potentially is one of the possible mechanisms of PTPRG down-regulation in CML.

Chronic lymphocytic leukemia (CLL) is the most common human leukemia, and dysregulation of tRNA-derived short noncoding RNA (tsRNA) (tRF-1) expression is an accompanying event in the development of this disease. tsRNAs are fragments originating from the 3′ end of tRNA precursors and do not contain mature tRNA sequences. In contrast to tsRNAs, mature tRFs (tRF-3s, tRF-5s, and internal tRFs) are produced from mature tRNA sequences and are redundant fragments. We investigated tsRNA expression in CLL and determined tsRNA signatures in indolent CLL and aggressive CLL vs. normal B cells. We noticed that both ts-43 and ts-44 are derived from distinct genes of pre-tRNA His , and are down-regulated in CLL 3- to 5-fold vs. normal B cells. Thus, we investigated expression levels of tRF-5 fragments from tRNA His in CLL samples and healthy controls, and determined that such fragments are down-regulated by 5-fold in CLLs vs. normal controls. Given these results, we investigated the expression of all mature tRFs in CLLs vs. normal controls. We found a drastic dysregulation of the expression of mature tRFs in CLL. In aggressive CLL, for the top 15 up-regulated fragments, linear fold change varied from 2,053- to 622-fold. For the top 15 down-regulated fragments in CLL, linear fold change varied from 314- to 52-fold. In addition, 964 mature tRFs were up-regulated at least 2-fold in CLL, while 701 fragments were down-regulated at least 2-fold. Similar results were obtained for indolent CLL. Our results suggest that mature tRFs may have oncogenic and/or tumor suppressor function in CLL.

MicroRNAs (miRNAs) are small noncoding RNAs of 22-25 nucleotides that control gene expression at the posttranscriptional level through the degradation of mRNAs or translational repression. In the last 15 years, the study of these small molecules helped elucidate their role in the regulation of many cellular processes and the onset and development of several diseases. Therefore, many computational tools based on algorithms for target prediction have been developed to identify potential miRNA-target interactions. The improvement of experimental approaches to more easily and quickly confirm in silico predictions has become essential for the study of these small RNAs and their molecular functions. In this chapter, we summarized the principal steps of one of the most used techniques for the validation of microRNA targets, the Luciferase assay, thus explaining the underlying principles and the procedures to apply it best.

MicroRNAs are a class of small noncoding RNA involved in the mechanism of RNA silencing and regulation of gene expression at a posttranscriptional level. Recently, the discovery of their targets led to the understanding of the molecular role of these small molecules and their involvement in the pathogenesis of numerous diseases, including cancer. Not long ago, the improvement of several informatics tools for microRNA target prediction has supported the experimental research through the selection of potential mRNA as microRNA target candidates. Since the regulation mediated by microRNA affects gene expression at a posttranscriptional level, the analysis of the proteins encoded by the gene targets is essential in understanding the involvement of these small molecules in biological processes and their role in several diseases. In this chapter, we describe the experimental procedure of Western blotting applied to the validation of microRNA targets. Western blotting is one of the most common and largest know technique for protein analysis. This method, coupled with the luciferase assay, represents the standard procedure for the experimental confirmation of microRNA targeting.

MicroRNAs (miRNAs) are small noncoding RNAs that are 22-25 nucleotides in length and control gene expression posttranscriptionally by degrading mRNAs or by translational repression. Many computational tools based on algorithms for target prediction have already been developed to find potential miRNA-target interactions. Since it is essential to confirm in silico predictions, experimental approaches have been improved to validate computationally predicted targets. One of the most widely used techniques is the luciferase assay which allows for the confirmation of specific binding between microRNA and the mRNA target using a reporter plasmid containing the 3' UTR of the target. Through the mutagenesis of this region it is possible to provide indirect evidence of the specific microRNA-mRNA interaction demonstrated using this assay. In this chapter we review the main experimental steps of the 3' UTR mutagenesis and the best way to apply this method to support and complete the luciferase assay procedure.

Introduction Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm characterized by the dysregulated production and uncontrolled proliferation of mature and maturing granulocytes with fairly normal differentiation. The hallmark of CML is BCR-ABL1 (breakpoint cluster region gene-Abelson murine leukemia viral oncogene homolog 1) on Philadelphia chromosome, which is the result of a reciprocal translocation between the long arms of chromosomes 9 and 22 (t[9;22][q34;q11]). With rare exceptions, breaks in chromosome 22 localize to one of three BCRs and determine the portions of BCR retained in the BCR-ABL1 fusion mRNA and protein. In contrast, the chromosome 9 breaks can occur over a large genetic region, 5′ of ABL1 exon Ib, 3′ of ABL1 exon Ia, or most commonly between the two alternative first ABL1 exons. In an overwhelming majority of CML patients, the break occurs in the major BCR (M-BCR), generating e13a2 or e14a2 fusion mRNAs and a p210BCR-ABL fusion protein. p230BCR-ABL, the largest of the fusion proteins, corresponds to a break in the micro BCR (μ-BCR), an e19a2 fusion mRNA, and is associated with neutrophilic predominance and possibly less aggressive disease. Molecular monitoring of BCR-ABL1 transcript levels following treatment with tyrosine kinase inhibitors (TKIs) is central to the effective clinical management of patients with CML. BCR-ABL1 transcripts measured at standardized time points is used to define responses at key milestones in treatment allowing early signs of poor adherence or resistance to treatment to be detected and allow for early, effective clinical intervention. Objective The aim of this study is to evaluate response to treatment with standard dose TKI in obese and non-obese CML patients together with BCR-ABL1 transcript type Methodology A retrospective analysis of clinicopathological variables and response to treatment was performed for 37 chronic phase CMLs to compare, obese vs normal weight, and BCR-ABL1 transcript type determined at diagnosis. Patients' management and response assessment was done based on ELN 2013 guidelines. Response to treatment was assessed using RT-qPCR analysis of blood calculated on the International Scale (IS). Various statistical methods used, all Statistical analyses were done using statistical packages SPSS 22.0 (SPSS Inc. Chicago, IL) and Epi-info (Centers for Disease Control and Prevention, Atlanta, GA) software. Results The study cohort included 26 males (70.3%) and 11 females (29.7%) with mean age at diagnosis 36.8 years. 59.5% (n=22) expressed an e14a2 transcript, and 40.5% (n=15) an e13a2 transcript, most patients were started on imatinib, then switched either due to toxicity or failure. Median follow-up was 18 months for both transcript types. WBC, platelet counts, spleen size and Sokal scores at diagnosis, both median and Inter-quartile range (IQR) were observed to be higher in e14a2 compared to e13a2 transcript group, and, lower in obese patients compared patients with normal weight. At one year, patients with e13a2 transcript had higher percentage of CCyR (or better) 60% (95% CI 36.6, 80.3%) compared to e14a2 group 46.7% (95% CI 24.8, 69.9%), however this difference was statistically insignificant (odds ratio =1.71, 95% CI 0.40, 7.29; P=0.464) Overall, there was higher and faster achievement of CCyR and MMR in patients with e13a2 transcript compared to e14a2 transcript, and in the obese vs normal-weight patients. Patients in e13a2 group and obesity group had a lower rate of treatment failure and fewer indications to switch TKI. Of note MMR was observed to be significantly higher in patients of African origin (n=10) compared to patients with Asian ethnicity (50% vs 16%; P=0.038), which could be reflect differences in disease biology. Conclusion In the patient cohort studied an e14a2 BCR-ABL1 transcript type / normal body weight was associated with an inferior outcome when compared to e13a2 transcript / obesity groups Disclosures No relevant conflicts of interest to declare.

MicroRNAs (miRs), a class of regulatory single-stranded small non-coding RNAs, are frequently deregulated in cancer and have been proposed as diagnostic and prognostic biomarkers in this fatal disease. Recent improvements in high-throughput sequencing (HTS) technologies have allowed the discovery of post-transcriptional modification phenomena involving miRs (e.g., isomiRs, A-to-I editing sites) able to alter the miR canonical sequence and thus their function. However, albeit earlier studies have proposed the exploitation of modified miRs as potential cancer biomarkers, their comprehensive and concurrent profiling, in cancer, still lacks. In light of this, we have applied the miRge2 pipeline (PubmedID:30153801) to more than 12K samples across 38 different cancer types from TCGA and TARGET, aiming at profiling the expression of all detectable canonical and modified miR isoforms in cancer. We identified over 29K unique miR isoforms (21 novel ones) expressed in at least one tumor type, whereby about 85% contains at least one modification (e.g., SNPs, RNA editing). According to our results, taking into account both canonical and modified miR isoforms throughout the analysis leads to an increase of about 20-fold in the identification of potential diagnostic and prognostic miRs with respect to conventional methods, which exclusively consider canonical miRs (miRBase v22). Then, leveraging on an independent cohort of 38 colorectal cancers and 21 adjacent healthy tissues (EGAS00001001127), we found that ~600 significantly deregulated miR isoforms were in common with the ones derived from the TCGA COAD dataset. Focusing on two miR isoforms, the canonical miR-151a-3p and its isoform (miR-151a-3p_-2|-2) with two left-shifted residues in both 5' and 3' end, which we found to be up-regulated in both cohorts, we performed a target prediction coupled with a miR/protein anti-correlation analysis. Interestingly, we found that the two miRs have a distinct set of targets. Finally, employing an Ingenuity(R) Pathway Analysis on their predicted targets, we observed that miR-151a-3p_-2|-2, in contrast to the canonical counterpart, is significantly involved in oncogenic pathways, such as PAK and CXCR4 signaling. This suggests that the final tumor phenotype could be substantially affected by modified miR isoforms. Here, we present one of the most in-depth and extensive miRNome profiling in cancer performed to date, revealing one magnitude of novel potential diagnostic and prognostic cancer biomarkers and enlightening the contribution of miR isoforms to the wider miRNome in cancer pathogenesis. Citation Format: Rosario Distefano, Luisa Tomasello, Gioacchino P. Marceca, Marina Bagnoli, Alessandro Lagana', Mario Acunzo, Chi Song, Pierluigi Gasparini, Giovanni Nigita, Carlo M. Croce. Concurrent profiling of canonical and modified miRNAomes from TCGA and TARGET cohorts leads to enhanced resolution in cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2543.

Noncoding RNAs (ncRNAs) have received much attention due to their central role in gene expression and translational regulation as well as due to their involvement in several biological processes and disease development. Small noncoding RNAs (sncRNAs), such as microRNAs and piwiRNAs, have been thoroughly investigated and functionally characterized. Long noncoding RNAs (lncRNAs), known to play an important role in chromatin-interacting transcription regulation, posttranscriptional regulation, cell-to-cell signaling, and protein regulation, are also being investigated to further elucidate their functional roles.Next-generation sequencing (NGS) technologies have greatly aided in characterizing the ncRNAome. Moreover, the coupling of NGS technology together with bioinformatics tools has been essential to the genome-wide detection of RNA modifications in ncRNAs. RNA editing, a common human co-transcriptional and posttranscriptional modification, is a dynamic biological phenomenon able to alter the sequence and the structure of primary transcripts (both coding and noncoding RNAs) during the maturation process, consequently influencing the biogenesis, as well as the function, of ncRNAs. In particular, the dysregulation of the RNA editing machineries have been associated with the onset of human diseases.In this chapter we discuss the potential functions of ncRNA editing and describe the knowledge base and bioinformatics resources available to investigate such phenomenon.

Noncoding RNAs (ncRNAs) have received much attention due to their central role in gene expression and translational regulation as well as due to their involvement in several biological processes and disease development. Small noncoding RNAs (sncRNAs), such as microRNAs and piwiRNAs, have been thoroughly investigated and functionally characterized. Long noncoding RNAs (lncRNAs), known to play an important role in chromatin-interacting transcription regulation, posttranscriptional regulation, cell-to-cell signaling, and protein regulation, are also being investigated to further elucidate their functional roles.Next-generation sequencing (NGS) technologies have greatly aided in characterizing the ncRNAome. Moreover, the coupling of NGS technology together with bioinformatics tools has been essential to the genome-wide detection of RNA modifications in ncRNAs. RNA editing, a common human co-transcriptional and posttranscriptional modification, is a dynamic biological phenomenon able to alter the sequence and the structure of primary transcripts (both coding and noncoding RNAs) during the maturation process, consequently influencing the biogenesis, as well as the function, of ncRNAs. In particular, the dysregulation of the RNA editing machineries have been associated with the onset of human diseases.In this chapter we discuss the potential functions of ncRNA editing and describe the knowledge base and bioinformatics resources available to investigate such phenomenon.

MicroRNAs are a class of small noncoding RNA involved in the mechanism of RNA silencing and regulation of gene expression at a posttranscriptional level. Recently, the discovery of their targets led to the understanding of the molecular role of these small molecules and their involvement in the pathogenesis of numerous diseases, including cancer. Not long ago, the improvement of several informatics tools for microRNA target prediction has supported the experimental research through the selection of potential mRNA as microRNA target candidates. Since the regulation mediated by microRNA affects gene expression at a posttranscriptional level, the analysis of the proteins encoded by the gene targets is essential in understanding the involvement of these small molecules in biological processes and their role in several diseases. In this chapter, we describe the experimental procedure of Western blotting applied to the validation of microRNA targets. Western blotting is one of the most common and largest know technique for protein analysis. This method, coupled with the luciferase assay, represents the standard procedure for the experimental confirmation of microRNA targeting.

MicroRNAs (miRNAs) are small noncoding RNAs that are 22-25 nucleotides in length and control gene expression posttranscriptionally by degrading mRNAs or by translational repression. Many computational tools based on algorithms for target prediction have already been developed to find potential miRNA-target interactions. Since it is essential to confirm in silico predictions, experimental approaches have been improved to validate computationally predicted targets. One of the most widely used techniques is the luciferase assay which allows for the confirmation of specific binding between microRNA and the mRNA target using a reporter plasmid containing the 3' UTR of the target. Through the mutagenesis of this region it is possible to provide indirect evidence of the specific microRNA-mRNA interaction demonstrated using this assay. In this chapter we review the main experimental steps of the 3' UTR mutagenesis and the best way to apply this method to support and complete the luciferase assay procedure.

MicroRNAs (miRNAs) are small noncoding RNAs of 22-25 nucleotides that control gene expression at the posttranscriptional level through the degradation of mRNAs or translational repression. In the last 15 years, the study of these small molecules helped elucidate their role in the regulation of many cellular processes and the onset and development of several diseases. Therefore, many computational tools based on algorithms for target prediction have been developed to identify potential miRNA-target interactions. The improvement of experimental approaches to more easily and quickly confirm in silico predictions has become essential for the study of these small RNAs and their molecular functions. In this chapter, we summarized the principal steps of one of the most used techniques for the validation of microRNA targets, the Luciferase assay, thus explaining the underlying principles and the procedures to apply it best.

Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is characterized by the accumulation of CD19+/CD5+ lymphocytes and can have variable outcomes. Richter syndrome (RS) is a lethal complication in CLL patients that results in aggressive B-cell lymphomas, and there are no tests to predict its occurrence. Because alterations in microRNA expression can predict the development and progression of several cancers, we investigated whether dysregulation of specific microRNAs can predict RS in CLL patients. Thus, we compared microRNA expression levels in samples from 49 CLL patients who later developed RS with samples from 59 CLL patients who did not. We found that high expression of miR-125a-5p or low expression of miR-34a-5p can predict ∼50% of RS with a false positive rate of ∼9%. We found that CLL patients predicted to develop RS show either an increase of miR-125a-5p expression (∼20-fold) or a decrease of miR-34a-5p expression (∼21-fold) compared with CLL patients that are not predicted to develop RS. Thus, miR-125a-5p and miR-34a-5p can be valuable predictor markers of RS and have the potential to provide physicians with information that can indicate the best therapeutic strategy for CLL patients.

Small, noncoding RNAs are short untranslated RNA molecules, some of which have been associated with cancer development. Recently we showed that a class of small RNAs generated during the maturation process of tRNAs (tRNA-derived small RNAs, hereafter “tsRNAs”) is dysregulated in cancer. Specifically, we uncovered tsRNA signatures in chronic lymphocytic leukemia and lung cancer and demonstrated that the ts-4521/3676 cluster (now called “ts-101” and “ts-53,” respectively), ts-46, and ts-47 are down-regulated in these malignancies. Furthermore, we showed that tsRNAs are similar to Piwi-interacting RNAs (piRNAs) and demonstrated that ts-101 and ts-53 can associate with PiwiL2, a protein involved in the silencing of transposons. In this study, we extended our investigation on tsRNA signatures to samples collected from patients with colon, breast, or ovarian cancer and cell lines harboring specific oncogenic mutations and representing different stages of cancer progression. We detected tsRNA signatures in all patient samples and determined that tsRNA expression is altered upon oncogene activation and during cancer staging. In addition, we generated a knocked-out cell model for ts-101 and ts-46 in HEK-293 cells and found significant differences in gene-expression patterns, with activation of genes involved in cell survival and down-regulation of genes involved in apoptosis and chromatin structure. Finally, we overexpressed ts-46 and ts-47 in two lung cancer cell lines and performed a clonogenic assay to examine their role in cell proliferation. We observed a strong inhibition of colony formation in cells overexpressing these tsRNAs compared with untreated cells, confirming that tsRNAs affect cell growth and survival.

Background: Chronic Myelocytic Leukemia (CML) is a clonal myeloproliferative disorder characterized by constitutive phosphorylation of Protein Tyrosine kinases (PTKS) that continuously activates multiple proliferative and antiapoptotic signaling pathways. Protein Tyrosine Phosphatases (PTPs) on the other hand is potential natural inhibitory mechanism for regulating the tyrosine kinase activities in which phosphorylation is reciprocally controlled and maintained in equilibrium state by PTKs and PTPs. As a member of PTPs family, Protein Tyrosine Phosphatase Receptor Gamma (PTPRG) was found to act as a tumor suppressor gene. This negative regulatory mechanism of PTPRG was observed to be down-regulated and disabled in CML and one of the possible mechanisms that alter the negative regulatory effect of PTPs is mutations. Several mutations have been identified in PTPs in many different leukemias such as Acute Myeloid Leukemia (AML), Juvenile MyeloMonocytic Leukemia (JMML), Myelodysplasic Syndrome (MDS), B-cell Acute Lymphoblastic Leukemia (B-ALL) and these mutations are associated with hyper-cellular proliferation, disease progression and poor outcome. However, relatively little is known about PTPRG mutations and no studies on CML are available in the literature while mutations inBCR-ABL1tyrosine kinase have been extensively characterized. Thus, understanding the role of PTPRG in antagonizing the PTK phosphorylation of BCR-ABL1 will be important to determine its role in CML development and progression. Aim: 1) To identify potential genetic alterations causing inactivation of PTPRG and 2) correlate the PTPRG findings with patients' response to the Tyrosine kinase Inhibitors. Methods: 16 CML patients, 9 from Qatar and 7 from Italy respectively, were studied for PTPRG mutations by exome sequencing. Custom primers were designed for Human PTPRG gene (5 Kb of exonic region of interest) using Ion AmpliSeq Designer. Target regions were enriched and amplified for the 16 DNA samples using Ion AmpliSeq Library kit 2.0. The amplicons were partially digested with FuPa reagent and phosphorylated prior to ligation of Ion Xpress Barcode Adapters followed by cleanup using HighPrep reagent. The adapter ligated molecules were enriched with adapter specific primers using a limited cycle PCR followed by a cleanup using HighPrep reagent. The final libraries were quantified on Qubit Flurometer using Qubit dsDNA HS Assay Kit and Agilent Bioanalyzer using Agilent High Sensitivity DNA Kit. All samples were pooled according to the concentrations on the Bioanalyzer and loaded on Ion 318TM Chip kit V2 to be sequenced on Ion Personal Genome Machine (PGM) system. European Leukemia Net (ELN) 2013 criteria were employed to assess the response/resistance of patients to treatment. Responses are defined at the hematological, cytogenetic and molecular levels. Patients response was classified into optimal and failure Results: Four mutations/variants were identified in PTPRG genes, three were missense Y92H, G574S, S561Y and 1 was frameshift Y285fs in the 16 CML patients. PTPRG Y92H was identified in 5 (1 Homozygous and 4 heterozygous alleles) patients and the 5 patient failed the Imatinib Mesylate (IM) treatment. On the other hand, The PTPRG G574S was identified in 6 (2 homozygous and 4 heterozygous alleles) patients. Out of the 6 patients, 4 were classified as failure to the treatment and 2 responded optimally. In addition, the PTPRG S561Y and Y285fs were identified on 1 and 3 patients respectively and these patients responded optimally to IM treatment. Discussion and Conclusions: This is the first prospective pilot study to investigate PTPRG gene mutations as underlying mechanism to explain treatment failure. Our preliminary data showed that the identified variant PTPRG Y92H might be associated with IM failure although it has been reported as Single Nucleotide Polymorphisms (SNPs) (rs62620047) and this could be attributed that some polymorphisms might behave like a mutation. On the other hand, PTPRG G574S variant (rs2292245) showed various clinical outcomes regardless to its allele zygosity as 67% (4/6) of patients failed the TKIs treatment. From the results of our pilot study we recommend carrying out PTPRG sequencing in a significantly larger cohort of patients to further explore and pinpoint the crucial mutations that can be correlated with CML resistance/response to treatment. Disclosures No relevant conflicts of interest to declare.

Background PTPRG is a widely expressed protein tyrosine phosphatase present in various isoforms. Peptides from its extracellular domain have been detected in plasma by proteomic techniques. We aim at characterizing the plasmatic PTPRG (sPTPRG) form and to identify its source. Methodology/Principal Findings The expression of sPTPRG was evaluated in human plasma and murine plasma and tissues by immunoprecipitation and Western blotting. The polypeptides identified have an apparent Mr of about 120 kDa (major band) and 90 kDa (minor band) respectively. Full length PTPRG was identified in the 100.000×g pelleted plasma fraction, suggesting that it was present associated to cell-derived vesicles (exosomes). The release of sPTPRG by HepG2 human hepatocellular carcinoma cell line was induced by ethanol and sensitive to metalloproteinase and not to Furin inhibitors. Finally, increased levels of the plasmatic ∼120 kDa isoform were associated with the occurrence of liver damage. Conclusions These results demonstrate that sPTPRG represent a novel candidate protein biomarker in plasma whose increased expression is associated to hepatocyte damage. This observation could open a new avenue of investigation in this challenging field.