Identification of Familial Hodgkin Lymphoma Predisposing Genes Using Whole Genome Sequencing

Abstract

Hodgkin lymphoma (HL) is a lymphoproliferative malignancy of B-cell origin that accounts for 10% of all lymphomas. Despite evidence suggesting strong familial clustering of HL, there is no clear understanding of the contribution of genes predisposing to HL. In this study, whole genome sequencing (WGS) was performed on 7 affected and 9 unaffected family members from three HL-prone families and variants were prioritized using our Familial Cancer Variant Prioritization Pipeline (FCVPPv2). WGS identified a total of 98,564, 170,550, and 113,654 variants which were reduced by pedigree-based filtering to 18,158, 465, and 26,465 in families I, II, and III, respectively. In addition to variants affecting amino acid sequences, variants in promoters, enhancers, transcription factors binding sites, and microRNA seed sequences were identified from upstream, downstream, 5 ′ and 3 ′ untranslated regions. A panel of 565 cancer predisposing and other cancer-related genes and of 2,383 potential candidate HL genes were also screened in these families to aid further prioritization. Pathway analysis of segregating genes with Combined Annotation Dependent Depletion Tool (CADD) scores >20 was performed using Ingenuity Pathway Analysis software which implicated several candidate genes in pathways involved in B-cell activation and proliferation and in the network of "Cancer, Hematological disease and Immunological Disease." We used the FCVPPv2 for further in silico analyses and prioritized 45 coding and 79 non-coding variants from the three families. Further literature-based analysis allowed us to constrict this list to one rare germline variant each in families I and II and two in family III. Functional studies were conducted on the candidate from family I in a previous study, resulting in the identification and functional validation of a novel heterozygous missense variant in the tumor suppressor gene DICER1 as potential HL predisposition factor. We aim to identify the individual genes responsible for predisposition in the remaining two families and will functionally validate these in further studies.

Clinical and Prognostic Pan-Cancer Analysis of N6-Methyladenosine Regulators in Two Types of Hematological Malignancies: A Retrospective Study Based on TCGA and GTEx Databases

“…Moreover, the incidence of these two kinds of hematological malignancies is increasing year by year, which undoubtedly poses a serious threat to human health and life safety. The occurrence and development of hematological malignancies are very rapid and complex, which is a complex process of the interaction between internal genetic factors (38)(39)(40)(41)(42)(43)(44), molecular signal transduction (45,46), metabolic factors (47)(48)(49), immune factors (50)(51)(52)(53)(54)(55), and tumor microenvironment (41,(56)(57)(58)(59). Although current chemotherapy can prolong the survival time of tumor patients (60,61), the prognosis and economic burden are still difficult problems in clinical work.…”

Detection of Rare Germline Variants in the Genomes of Patients with B-Cell Neoplasms

“…Many such rare variants in cancer-related genes have been associated with particular cancer subtypes [ 15 , 16 , 17 ], but until now little attention has been focused on the genome-wide frequency, pathogenicity, and clinical implications of rare variants in lymphoid malignancies. Rare variants in ATM and CDK1 variants have been associated with CLL risk in genome-wide analysis [ 18 ], whereas evidence for the implication of infrequent events in other genes come from familial studies or single-gene analysis [ 19 , 20 , 21 ].…”

Whole Exome Sequencing Identifies APCDD1 and HDAC5 Genes as Potentially Cancer Predisposing in Familial Colorectal Cancer

“…We have developed the Familial Cancer Variant Prioritization Pipeline (FCVPPv2) for evaluation of both coding and non-coding variants and implemented it in the prioritization of novel missense variants in the tumor suppressor genes DICER1 in Hodgkin lymphoma and CPXM1 in papillary thyroid cancer and in the pathways enriched in these entities [ 14 , 15 , 16 , 17 ]. In the present study, tools such as the Combined Annotation Dependent Depletion v1.4 (CADD) tool [ 18 ], SNPnexus [ 19 ] and the Bedtools intersect function were applied as part of the non-coding analysis of our pipeline in order to identify important regulatory elements.…”