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. 2023 Feb 12;12(4):596.
doi: 10.3390/cells12040596.

In Silico Identification and Functional Characterization of Genetic Variations across DLBCL Cell Lines

Prashanthi Dharanipragada  1 , Nita Parekh  1
Affiliations

In Silico Identification and Functional Characterization of Genetic Variations across DLBCL Cell Lines

Prashanthi Dharanipragada et al. Cells. .

Abstract

Diffuse large B-cell lymphoma (DLBCL) is the most common form of non-Hodgkin lymphoma and frequently develops through the accumulation of several genetic variations. With the advancement in high-throughput techniques, in addition to mutations and copy number variations, structural variations have gained importance for their role in genome instability leading to tumorigenesis. In this study, in order to understand the genetics of DLBCL pathogenesis, we carried out a whole-genome mutation profile analysis of eleven human cell lines from germinal-center B-cell-like (GCB-7) and activated B-cell-like (ABC-4) subtypes of DLBCL. Analysis of genetic variations including small sequence variants and large structural variations across the cell lines revealed distinct variation profiles indicating the heterogeneous nature of DLBCL and the need for novel patient stratification methods to design potential intervention strategies. Validation and prognostic significance of the variants was assessed using annotations provided for DLBCL samples in cBioPortal for Cancer Genomics. Combining genetic variations revealed new subgroups between the subtypes and associated enriched pathways, viz., PI3K-AKT signaling, cell cycle, TGF-beta signaling, and WNT signaling. Mutation landscape analysis also revealed drug-variant associations and possible effectiveness of known and novel DLBCL treatments. From the whole-genome-based mutation analysis, our findings suggest putative molecular genetics of DLBCL lymphomagenesis and potential genomics-driven precision treatments.

Keywords: cell lines; copy number variations; diffuse large B-cell lymphoma (DLBCL); precision medicine; sequence variations; structural variations.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Distribution of five structural variations across DLBCL cell lines, shown as a component bar graph, while the number of SSVs is depicted by ‘×’.
Figure 2
Figure 2
Fraction of three classes of mobile element insertions (MEIs), viz., ALU, LINEs, and SVA. Total number of MEIs per cell line is indicated on the right panel.
Figure 3
Figure 3
Waterfall plot depicting the mutational landscape of 41 oncogenes and tumor suppressor genes (rows) with SSVs in 11 DLBCL cell lines (columns).
Figure 4
Figure 4
Integration of key SSVs (black dot), CNVs (gain: red, loss: blue), and SVs (translocation: green, inversion: cyan) revealed altered immuno-oncogenic pathways across 11 DLBCL cell lines. Complex genetic rearrangements with ≥2 SVs spanning the gene were also observed (gold yellow).
Figure 5
Figure 5
Circos diagram showing key oncogenes and tumor suppressor genes overlapping SSVs, CNVs, and SVs in the OCI-LY1 cell line. Translocations/fusions are shown by connecting lines, inner-to-outer circles: MEIs, INVs, SSVs (dots), and CNVs (red and blue bars). Chromosome numbers 1–22 are indicated next to the outermost circle.
Figure 6
Figure 6
Key pathways disrupted in different sets of DLBCL cell lines according to their shared genetic variations.
See this image and copyright information in PMC

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