Kavosh: a new algorithm for finding network motifs

<p>Abstract</p> <p>Background</p> <p>Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.</p> <p>Results</p> <p>We present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of <it>E. coli </it>and <it>S. cereviciae</it>, and also on non-biological networks: a social and an electronic network.</p> <p>Conclusion</p> <p>The efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: <url>http://Lbb.ut.ac.ir/Download/LBBsoft/Kavosh/</url>.</p

Combination Therapies Targeting ALK-aberrant Neuroblastoma in Preclinical Models

PURPOSE ALK-activating mutations are identified in approximately 10% of newly diagnosed neuroblastomas and ALK amplifications in a further 1%-2% of cases. Lorlatinib, a third-generation anaplastic lymphoma kinase (ALK) inhibitor, will soon be given alongside induction chemotherapy for children with ALK-aberrant neuroblastoma. However, resistance to single-agent treatment has been reported and therapies that improve the response duration are urgently required. We studied the preclinical combination of lorlatinib with chemotherapy, or with the MDM2 inhibitor, idasanutlin, as recent data have suggested that ALK inhibitor resistance can be overcome through activation of the p53-MDM2 pathway. EXPERIMENTAL DESIGN We compared different ALK inhibitors in preclinical models prior to evaluating lorlatinib in combination with chemotherapy or idasanutlin. We developed a triple chemotherapy (CAV: cyclophosphamide, doxorubicin, and vincristine) in vivo dosing schedule and applied this to both neuroblastoma genetically engineered mouse models (GEMM) and patient-derived xenografts (PDX). RESULTS Lorlatinib in combination with chemotherapy was synergistic in immunocompetent neuroblastoma GEMM. Significant growth inhibition in response to lorlatinib was only observed in the ALK-amplified PDX model with high ALK expression. In this PDX, lorlatinib combined with idasanutlin resulted in complete tumor regression and significantly delayed tumor regrowth. CONCLUSIONS In our preclinical neuroblastoma models, high ALK expression was associated with lorlatinib response alone or in combination with either chemotherapy or idasanutlin. The synergy between MDM2 and ALK inhibition warrants further evaluation of this combination as a potential clinical approach for children with neuroblastoma

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/

Multi-omics comparison of malignant and normal uveal melanocytes reveals molecular features of uveal melanoma

Summary: Uveal melanoma (UM) is a rare cancer resulting from the transformation of melanocytes in the uveal tract. Integrative analysis has identified four molecular and clinical subsets of UM. To improve our molecular understanding of UM, we performed extensive multi-omics characterization comparing two aggressive UM patient-derived xenograft models with normal choroidal melanocytes, including DNA optical mapping, specific histone modifications, and DNA topology analysis using Hi-C. Our gene expression and cytogenetic analyses suggest that genomic instability is a hallmark of UM. We also identified a recurrent deletion in the BAP1 promoter resulting in loss of expression and associated with high risk of metastases in UM patients. Hi-C revealed chromatin topology changes associated with the upregulation of PRAME, an independent prognostic biomarker in UM, and a potential therapeutic target. Our findings illustrate how multi-omics approaches can improve our understanding of tumorigenesis and reveal two distinct mechanisms of gene expression dysregulation in UM