Distinctive Patterns of MicroRNA Expression Associated with Karyotype in Acute Myeloid Leukaemia

Acute myeloid leukaemia (AML) is the most common acute leukaemia in adults; however, the genetic aetiology of the disease is not yet fully understood. A quantitative expression profile analysis of 157 mature miRNAs was performed on 100 AML patients representing the spectrum of known karyotypes common in AML. The principle observation reported here is that AMLs bearing a t(15;17) translocation had a distinctive signature throughout the whole set of genes, including the up regulation of a subset of miRNAs located in the human 14q32 imprinted domain. The set included miR-127, miR-154, miR-154*, miR-299, miR-323, miR-368, and miR-370. Furthermore, specific subsets of miRNAs were identified that provided molecular signatures characteristic of the major translocation-mediated gene fusion events in AML. Analysis of variance showed the significant deregulation of 33 miRNAs across the leukaemic set with respect to bone marrow from healthy donors. Fluorescent in situ hybridisation analysis using miRNA-specific locked nucleic acid (LNA) probes on cryopreserved patient cells confirmed the results obtained by real-time PCR. This study, conducted on about a fifth of the miRNAs currently reported in the Sanger database (microrna.sanger.ac.uk), demonstrates the potential for using miRNA expression to sub-classify cancer and suggests a role in the aetiology of leukaemia

A combination of urinary biomarker panel and PancRISK score for earlier detection of pancreatic cancer: A case–control study

Funder: Pancreatic Cancer Research Fund; funder-id: http://dx.doi.org/10.13039/100011704Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with around 9% of patients surviving >5 years. Asymptomatic in its initial stages, PDAC is mostly diagnosed late, when already a locally advanced or metastatic disease, as there are no useful biomarkers for detection in its early stages, when surgery can be curative. We have previously described a promising biomarker panel (LYVE1, REG1A, and TFF1) for earlier detection of PDAC in urine. Here, we aimed to establish the accuracy of an improved panel, including REG1B instead of REG1A, and an algorithm for data interpretation, the PancRISK score, in additional retrospectively collected urine specimens. We also assessed the complementarity of this panel with CA19-9 and explored the daily variation and stability of the biomarkers and their performance in common urinary tract cancers. Methods and findings: Clinical specimens were obtained from multiple centres: Barts Pancreas Tissue Bank, University College London, University of Liverpool, Spanish National Cancer Research Center, Cambridge University Hospital, and University of Belgrade. The biomarker panel was assayed on 590 urine specimens: 183 control samples, 208 benign hepatobiliary disease samples (of which 119 were chronic pancreatitis), and 199 PDAC samples (102 stage I–II and 97 stage III–IV); 50.7% were from female individuals. PDAC samples were collected from patients before treatment. The samples were assayed using commercially available ELISAs. Statistical analyses were performed using non-parametric Kruskal–Wallis tests adjusted for multiple comparisons, and multiple logistic regression. Training and validation datasets for controls and PDAC samples were obtained after random division of the whole available dataset in a 1:1 ratio. The substitution of REG1A with REG1B enhanced the performance of the panel to detect resectable PDAC. In a comparison of controls and PDAC stage I–II samples, the areas under the receiver operating characteristic curve (AUCs) increased from 0.900 (95% CI 0.843–0.957) and 0.926 (95% CI 0.843–1.000) in the training (50% of the dataset) and validation sets, respectively, to 0.936 in both the training (95% CI 0.903–0.969) and the validation (95% CI 0.888–0.984) datasets for the new panel including REG1B. This improved panel showed both sensitivity (SN) and specificity (SP) to be >85%. Plasma CA19-9 enhanced the performance of this panel in discriminating PDAC I–II patients from controls, with AUC = 0.992 (95% CI 0.983–1.000), SN = 0.963 (95% CI 0.913–1.000), and SP = 0.967 (95% CI 0.924–1.000). We demonstrate that the biomarkers do not show significant daily variation, and that they are stable for up to 5 days at room temperature. The main limitation of our study is the low number of stage I–IIA PDAC samples (n = 27) and lack of samples from individuals with hereditary predisposition to PDAC, for which specimens collected from control individuals were used as a proxy. Conclusions: We have successfully validated our urinary biomarker panel, which was improved by substituting REG1A with REG1B. At a pre-selected cutoff of >80% SN and SP for the affiliated PancRISK score, we demonstrate a clinically applicable risk stratification tool with a binary output for risk of developing PDAC (‘elevated’ or ‘normal’). PancRISK provides a step towards precision surveillance for PDAC patients, which we will test in a prospective clinical study, UroPanc

Genome Wide Analysis of Acute Myeloid Leukemia Reveal Leukemia Specific Methylome and Subtype Specific Hypomethylation of Repeats

Methylated DNA immunoprecipitation followed by high-throughput sequencing (MeDIP-seq) has the potential to identify changes in DNA methylation important in cancer development. In order to understand the role of epigenetic modulation in the development of acute myeloid leukemia (AML) we have applied MeDIP-seq to the DNA of 12 AML patients and 4 normal bone marrows. This analysis revealed leukemia-associated differentially methylated regions that included gene promoters, gene bodies, CpG islands and CpG island shores. Two genes (SPHKAP and DPP6) with significantly methylated promoters were of interest and further analysis of their expression showed them to be repressed in AML. We also demonstrated considerable cytogenetic subtype specificity in the methylomes affecting different genomic features. Significantly distinct patterns of hypomethylation of certain interspersed repeat elements were associated with cytogenetic subtypes. The methylation patterns of members of the SINE family tightly clustered all leukemic patients with an enrichment of Alu repeats with a high CpG density (P<0.0001). We were able to demonstrate significant inverse correlation between intragenic interspersed repeat sequence methylation and gene expression with SINEs showing the strongest inverse correlation (R2 = 0.7). We conclude that the alterations in DNA methylation that accompany the development of AML affect not only the promoters, but also the non-promoter genomic features, with significant demethylation of certain interspersed repeat DNA elements being associated with AML cytogenetic subtypes. MeDIP-seq data were validated using bisulfite pyrosequencing and the Infinium array

Demographic, clinical, and pathological features of early onset pancreatic cancer patients.

BACKGROUND: Early onset pancreatic cancer (EOPC), i.e. pancreatic ductal adenocarcinoma (PDAC) occurring in patients below 50 years of age, is rare and there is limited information regarding risk factors, molecular basis and outcome. This study aimed to determine the demographic and clinicopathological features and survival figures for EOPC. METHODS: A retrospective analysis of patients treated at the Royal London Hospital for PDAC between September 2004 and September 2015 was performed. Data on demographics, risk factors, presentation, pathological features, treatment and survival outcome were compared in EOPC and older PDAC patients. RESULTS: Of 369 PDAC cases identified, 35 (9.5%) were EOPC. Compared to older patients, EOPC patients were more frequently male (71% vs 54%, p = 0.043) and less commonly of British origin (37% vs 70%, p = 0.002). There was no significant difference regarding the prevalence of any of the risk factors known to be associated with older PDAC patients. Fewer EOPC patients presented with resectable disease (23% vs 44%, p = 0.015) and more received adjuvant chemo/radiotherapy (60% vs 46%, p = 0.008). The overall median survival and stage specific survival did not differ significantly between the two groups, although a longer survival for localized disease was seen in EOPC patients (25 months (12.9-37, 95%CI) vs 13 months (10.5-15.5 95%CI) for older PDAC patients). CONCLUSIONS: The EOPC patients had different demographics and were more likely than their older PDAC counterparts to be male. Typically they presented with more advanced disease, received more aggressive treatment, and had on overall similar survival outcome

A study of molecular mechanisms in leukaemia

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

<i>SPHKAP</i>, <i>DPP6</i> and <i>ID4</i> gene expression in AML.

<p>(A.1, B.1, C.1) Relative expression of <i>SPHKAP</i>, <i>DPP6</i>, <i>ID4</i> (respectively) in AML and normal tissues. The genes were down regulated in AML patients and in cancer cell lines, while the genes were up regulated in normal tissues. (A.2, B.2, C.2) Relative expression of <i>SPHKAP</i>, <i>DPP6</i> and <i>ID4</i> (respectively) in OCI-AML2 and CTS cell lines before and after treatment by DAC. Gene expression was restored in most of cell lines treated by DAC.</p

Hierarchical clustering of AML versus NBM in the interspersed repeats.

<p>In each figure, each column represents AML patient/NBM and each row represents a single DMR. First row represents cluster analysis of all AMLs versus all NBMs and the second row represents cluster analysis of AML subtypes in SINEs (A, D), LINEs (B, E) and LTRs (C, F). Distinctive hypomethylated SINEs, LINEs and LTRs clearly distinguished each AML subtype (second row).</p

Correlation between DNA methylation and gene expression.

<p>(A–F) For a single AML patient we categorized the gene methylation into 4 groups (>0.4, 0.4–0.6, 0.6–0.8, >0.8 Batman scores). We correlated the average of each methylation group to corresponding average of gene expression. (G) Box plots of DNA methylation levels of over- and under-expressed genes in each triplicate of t(8;21), t(15;17), NK and trisomy 8 AML subtypes. N refers to the number of genes in each set. Mann Whitney test of the two sets of genes demonstrated a significant methylation difference between the medians in t(8;21), NK and trisomy 8 AML subtypes.</p