Common genomic and transcriptomic signatures in Richter transformation highlight druggable vulnerabilities and guide drug repurposing strategies

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Richter transformation (RT) is a rare and aggressive lymphoma occurring in patients with chronic lymphocytic leukemia (CLL). Despite advancements in understanding the pathogenetic mechanisms driving disease transformation, it remains a challenging malignancy [ 1 , 2 , 3 ]. In recent years, multi-center genomic studies have shed light on the heterogeneity of RT, with no single driver alteration identified [ 4 , 5 , 6 , 7 ]. This complexity underscores the need for a comprehensive molecular profiling through multi-omic approaches, as one-fits-all therapeutic strategies are unlikely to succeed. Such profiling offers a way to capture actionable vulnerabilities, based on experimental approaches that identify and prioritize “recurrent players” across cohorts. To identify novel therapeutic opportunities through a drug repurposing strategy - aimed at accelerating translation from research to the clinics - we investigated paired genomic and transcriptomic data from a cohort of 20 RT patients. Results were then integrated with data from 3 recently published cohorts [ 4 , 5 , 8 ] to derive a RT “blueprint”, and define a hierarchy of consistent disease hallmarks. The convergent features were exploited to design an ad-hoc drug library, functionally validated through a high-throughput screening in RT models (Fig. 1A ). Fig. 1: Characterization of the genomic and transcriptomics landscape of RT led to the identification of a hierarchy of actionable signatures. Full size image A Graphical description of the crucial steps of the study workflow from nucleic acids isolation to definition of common hallmarks, based on data the genomic and transcriptomic data integration. Numbers in brackets indicated of the number of samples for each step. B Oncoprint of pathogenic and likely pathogenic single-nucleotide variants (SNVs) and small insertion/deletion (indels) identified in a cohort of 20 RT patients. Horizontal bar plot (graph on the right, Variants/patient) indicated the number of variants per patient, whereas veritical bar plot (bottom graph, Variants/gene) showed the number of pathogenic/likely pathogenic variants per gene. Genes are listed based on frequency of alteration within the study cohort. In blue are indicated genes carrying novel variants, never described before to be altered in RT. The different types of variants are indicated in a color code: missense (blue), frameshift (green), splicing (fuchsia), nonsense (darkpurple), and inframe-indel (yellow). C – D Venn diagram of detected genomic alterations (SNVs) in four separate cohorts: Turin (green), Klintman’s (blue), Nadeu’s (pink), and Parry’s (yellow) ( C ) and Venn diagram of up- and down-regulated genes of three separate cohorts (in-house cohort, Nadeu’s, Parry’s) ( D ). Numbers inside the diagram indicate the number of variants ( C ) or differentially expressed genes ( D ) detected in each cohort or in common. Colored blocks summarize the significant MSigDB Hallmark enrichment results for each Venn-diagram overlap ( P value < 0.05). For each overlap, genes shared between cohorts are indicated by the corresponding color in the legend, and the enrichment analysis was performed on the gene set defined by that specific overlap. Numbers in the Venn diagrams (and in the corresponding colored blocks) indicate the number of shared genes in each overlap. Enriched terms shown in bold denote terms observed in both the genomic- and transcriptomic- analyses. DNA and RNA were co-extracted from 20 formalin-fixed paraffin-embedded (FFPE) lymph-node biopsies from RT patients collected between 2010 and 2020. Targeted DNA sequencing was performed using TruSight Oncology 500, enabling detection of clinically relevant single-nucleotide variants (SNVs)/indels and copy-number variations (CNVs) within cancer-associated genes. Bulk RNA sequencing was feasible in 14/20 samples and allowed us to identify differential gene expression (DGE) compared to the preceding disease phase, exploiting multiple GEO/EGA CLL datasets ( n = 250). Although being a valid source of material and information for genetic analyses, FFPE samples may present quality issues due to the conservation technique [ 9 , 10 ]. Methodological fidelity was assessed in a matched comparison, using the RT-PDX model RS1316, sequencing both DNA and RNA from fresh material, and from its FFPE counterpart. Variant allele frequencies (VAF) for five known variants in RS1316 [ 11 ] were preserved between conditions (paired t-test not significant), with a Spearman correlation indicating strong concordance (ρ = 0.9; Supplementary Fig. S1A ). For transcriptomics, linear regression of log 10 TPMs between fresh and FFPE material showed strong correlation (R² = 0.74; P < 0.0001; Supplementary Fig. S1B ). In addition, a computational deconvolution analysis using MCPCOUNTER module of TIMER2.0 showed a significant enrichment in the B cell fraction - being the most predominant population - whereas other immune fractions including T, NK cells, and monocytes were almost undetectable, reducing concern that cohort signatures were driven primarily by microenvironmental contamination (Supplementary Fig. S1C ). Targeted genomic profiling highlighted extensive inter-patient heterogeneity, consistent with the view that RT does not depend on a single universal driver alteration [ 8 ]. Variant calling data allowed the detection of 3678 unique variants, 53.9% of which were in coding regions. The identified variants were predominantly synonymous (53.9%) and missense (40.2%), followed by splicing (2.1%), frameshift (1.6%), in-frame indels (1.4%), and nonsense (0.8%; Supplementary Tables S1 , S2 ). Filtering for somatic status and VAF > 0.10 yielded 102 unique coding variants (Supplementary Tables S3 , S4 ), 56 of which were classified as pathogenic/likely pathogenic (C5/C4), while the remaining were variants of unknown significance (C3; Fig. 1B ). TP53 was the most frequently altered gene, mutated in 45% of patients (9/20), in line with prior reports [ 4 , 5 , 6 ]. Other recurrent alterations, present in at least two patients of the cohort, affected ARID2 and RHOA genes (10% each; Fig. 1B ). The remaining detected variants were patient-specific and included genes previously reported as recurrently mutated in RT, such as ATM , BIRC3 , BRAF , NOTCH1 , and XPO1 (Fig. 1B ). CNVs were observed in 14 out of 20 patients, with a total of 82 events affecting 29 genes and more than half (56.4%, 38 gains and 27 losses) were found in at least two patients (Supplementary Fig. S1D and Supplementary Table S5 ). CNVs involving CDK6 , MYC , and BRAF were also detected in our cohort, highlighting their recurrent involvement in RT and further reinforcing their role in disease biology. Enrichment analysis of genes affected by genetic alterations (SNVs/indels and CNVs) highlighted Wnt/β-catenin, JAK-STAT3/MAPK, and NOTCH signaling, alongside DNA damage, cell cycle, and apoptosis, as the most affected pathways (Supplementary Fig. S1E, F ). We next characterized the RT transcriptional profile. Principal component analysis indicated that RT samples clustered together and were distinctly separated from CLL ones (Supplementary Fig. S2A ). RNA-seq data analysis identified 1964 differentially expressed genes between RT and CLL, 1156 of which up-regulated and 808 down-regulated (Supplementary Fig. S2B and Supplementary Table S6 ). Enrichment analysis highlighted up-regulation of genes related to proliferation/cell-cycle progression, DNA damage response and repair, metabolic rewiring, microtubule cytoskeleton organization, and apoptosis regulation. In contrast, the down-regulated signature revealed significant enrichment of RNA metabolism-related terms, including splicing (Supplementary Fig. S2C ). Of note, down-regulation of ribosomal RNA processing and ribonucleoprotein biogenesis was also observed (Supplementary Fig. S2C ), potentially causing cascading effects on cell survival, cell cycle progression, DNA repair, and apoptos

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