Exploration of whole genome and transcriptome sequencing data lacks evidence for oncogenic viral elements to drive the pathogenesis of T-cell prolymphocytic leukemia

Figures & data

Figure 1. Viral reads detected in T-PLL samples and non-tumor samples in the WGS data. (A) The heatmap depicts viral reads per million extracted reads (PMER) per sample (in columns) for three viruses: EBV, roseolovirus and cytomegalovirus (in rows) based on the WGS data of T-PLL cases (n = 16) and SUP-T11 (the case 124 and case 124a which is the case analysis twice is marked with an asterisk) and non-tumor samples. The samples published in Patil et al. [16] and in Schrader et al. [19] are depicted in the top bar. The heatmap shows PMER for viruses detected per sample with low to high read values. (B) The dot plot shows the viral load in PMER on x-axis for all samples (T-PLL and non-tumor). (C) Bar plot shows the viral reads detected three T-PLL samples and their respective non-tumor controls. The y-axis depicts the PMER for viruses detected per sample.

Patil P, Cieslak A, Bernhart SH, et al. Reconstruction of rearranged T-cell receptor loci by whole genome and transcriptome sequencing gives insights into the initial steps of T-cell prolymphocytic leukemia. Genes Chromosomes Cancer. 2020;59(4):261–267.

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Schrader A, Crispatzu G, Oberbeck S, et al. Actionable perturbations of damage responses by TCL1/ATM and epigenetic lesions form the basis of T-PLL. Nat Commun. 2018;9(1):697.

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