Chronic lymphocytic leukemia (CLL) stereotyped subsets #6 and #8 include cases expressing unmutated B cell receptor immunoglobulin (BcR IG) (U‐CLL). Subset #6 (IGHV1‐69/IGKV3‐20) is less aggressive compared to subset #8 (IGHV4‐39/IGKV1(D)‐39) which has the highest risk for Richter's transformation among all CLL. The underlying reasons for this divergent clinical behavior are not fully elucidated. To gain insight into this issue, here we focused on epigenomic signatures and their links with gene expression, particularly investigating genome‐wide DNA methylation profiles in subsets #6 and #8 as well as other U‐CLL cases not expressing stereotyped BcR IG. We found that subset #8 showed a distinctive DNA methylation profile compared to all other U‐CLL cases, including subset #6. Integrated analysis of DNA methylation and gene expression revealed significant correlation for several genes, particularly highlighting a relevant role for the TP63 gene which was hypomethylated and overexpressed in subset #8. This observation was validated by quantitative PCR, which also revealed TP63 mRNA overexpression in additional nonsubset U‐CLL cases. BcR stimulation had distinct effects on p63 protein expression, particularly leading to induction in subset #8, accompanied by increased CLL cell survival. This pro‐survival effect was also supported by siRNA‐mediated downregulation of p63 expression resulting in increased apoptosis. In conclusion, we report that DNA methylation profiles may vary even among CLL patients with similar somatic hypermutation status, supporting a compartmentalized approach to dissecting CLL biology. Furthermore, we highlight p63 as a novel prosurvival factor in CLL, thus identifying another piece of the complex puzzle of clinical aggressiveness.

Recent evidence suggests that the prognostic impact of gene mutations in patients with chronic lymphocytic leukemia (CLL) may differ depending on the immunoglobulin heavy variable (IGHV) gene somatic hypermutation (SHM) status. In this study, we assessed the impact of nine recurrently mutated genes (BIRC3, EGR2, MYD88, NFKBIE, NOTCH1, POT1, SF3B1, TP53, and XPO1) in pre-treatment samples from 4580 patients with CLL, using time-to-first-treatment (TTFT) as the primary end-point in relation to IGHV gene SHM status. Mutations were detected in 1588 (34.7%) patients at frequencies ranging from 2.3–9.8% with mutations in NOTCH1 being the most frequent. In both univariate and multivariate analyses, mutations in all genes except MYD88 were associated with a significantly shorter TTFT. In multivariate analysis of Binet stage A patients, performed separately for IGHV-mutated (M-CLL) and unmutated CLL (U-CLL), a different spectrum of gene alterations independently predicted short TTFT within the two subgroups. While SF3B1 and XPO1 mutations were independent prognostic variables in both U-CLL and M-CLL, TP53, BIRC3 and EGR2 aberrations were significant predictors only in U-CLL, and NOTCH1 and NFKBIE only in M-CLL. Our findings underscore the need for a compartmentalized approach to identify high-risk patients, particularly among M-CLL patients, with potential implications for stratified management.

The COVID-19 pandemic represents an unprecedented global crisis necessitating novel approaches for, amongst others, early detection of emerging variants relating to the evolution and spread of the virus. Recently, the detection of SARS-CoV-2 RNA in wastewater has emerged as a useful tool to monitor the prevalence of the virus in the community. Here, we propose a novel methodology, called lineagespot, for the monitoring of mutations and the detection of SARS-CoV-2 lineages in wastewater samples using next-generation sequencing (NGS). Our proposed method was tested and evaluated using NGS data produced by the sequencing of 14 wastewater samples from the municipality of Thessaloniki, Greece, covering a 6-month period. The results showed the presence of SARS-CoV-2 variants in wastewater data. lineagespot was able to record the evolution and rapid domination of the Alpha variant (B.1.1.7) in the community, and allowed the correlation between the mutations evident through our approach and the mutations observed in patients from the same area and time periods. lineagespot is an open-source tool, implemented in R, and is freely available on GitHub and registered on bio.tools.

No abstract