Abstract Our understanding of familial predisposition to lymphoma (collectively defined as non-Hodgkin lymphoma [NHL], Hodgkin lymphoma [HL], and chronic lymphocytic leukemia [CLL]) outside of rare hereditary syndromes has progressed rapidly during the last decade. First-degree relatives of NHL, HL, and CLL patients have an ∼1.7-fold, 3.1-fold, and 8.5-fold elevated risk of developing NHL, HL, and CLL, respectively. These familial risks are elevated for multiple lymphoma subtypes and do not appear to be confounded by nongenetic risk factors, suggesting at least some shared genetic etiology across the lymphoma subtypes. However, a family history of a specific subtype is most strongly associated with risk for that subtype, supporting subtype-specific genetic factors. Although candidate gene studies have had limited success in identifying susceptibility loci, genome-wide association studies (GWAS) have successfully identified 67 single nucleotide polymorphisms from 41 loci, predominately associated with specific subtypes. In general, these GWAS-discovered loci are common (minor allele frequency >5%), have small effect sizes (odds ratios, 0.60-2.0), and are of largely unknown function. The relatively low incidence of lymphoma, modest familial risk, and the lack of a screening test and associated intervention, all argue against active clinical surveillance for lymphoma in affected families at this time.
Allergic Diseases and Risk of Hematopoietic Malignancies in a Cohort of Postmenopausal Women: A Report from the Iowa Women's Health Study
Key Points ALK-negative ALCLs have chromosomal rearrangements of DUSP22 or TP63 in 30% and 8% of cases, respectively. DUSP22-rearranged cases have favorable outcomes similar to ALK-positive ALCLs, whereas other genetic subtypes have inferior outcomes.
PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data
Abstract Motivation: Exome sequencing (exome-seq) data, which are typically used for calling exonic mutations, have also been utilized in detecting DNA copy number variations (CNVs). Despite the existence of several CNV detection tools, there is still a great need for a sensitive and an accurate CNV-calling algorithm with built-in QC steps, and does not require a paired reference for each sample. Results: We developed a novel method named PatternCNV, which (i) accounts for the read coverage variations between exons while leveraging the consistencies of this variability across different samples; (ii) reduces alignment BAM files to WIG format and therefore greatly accelerates computation; (iii) incorporates multiple QC measures designed to identify outlier samples and batch effects; and (iv) provides a variety of visualization options including chromosome, gene and exon-level views of CNVs, along with a tabular summarization of the exon-level CNVs. Compared with other CNV-calling algorithms using data from a lymphoma exome-seq study, PatternCNV has higher sensitivity and specificity. Availability and implementation: The software for PatternCNV is implemented using Perl and R, and can be used in Mac or Linux environments. Software and user manual are available at http://bioinformaticstools.mayo.edu/research/patterncnv/ , and R package at https://github.com/topsoil/patternCNV/ . Contact: Asmann.Yan@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Pooling Prospective Studies to Investigate the Etiology of Second Cancers