Busso, Miles Del

Abstract Introduction: A subset of patients with chronic lymphocytic leukemia (CLL) experiences disease progression from CLL to Richter transformation (RT), characterized by the occurrence of a high-grade lymphoma. The outcomes of patients with RT are typically dismal and identifying better therapeutic strategies remains critically important for these patients. To discern the biologic underpinnings driving this transformation, we studied immune changes and genetic evolution in samples from CLL patients who developed RT. Methods: We identified 18 patients with CLL who developed RT; and performed single cell (sc) RNA-sequencing(seq) on sorted B and T cells from peripheral blood mononuclear cells (PBMCs) at the time of RT (n=18) and at the earlier time of CLL diagnosis (n=11). The median time between the CLL sample and the one closest to RT was 4.3 years (range, 0.2-7.3); and patients received a median of 1 CLL directed treatment (range, 0 – 7). A total of 749,906 cells and 36,601 genes were captured during scRNAseq and 720,580 cells and 21,892 genes were retained for downstream analysis, respectively. Analysis was performed using the immunopipe (https://github.com/pwwang/immunopipe) that wraps around Seurat (v5.1.0) and identified enriched pathways using Enrichr service. The sc-T cell receptor (TCR)-repertoire(R)-seq data was analyzed and visualized using scplotter (). The whole exome sequencing (WES) analysis with tumor B-cells from same patients of CLL & RT stages were done using the Agilent sureselect Human all exon v5 + UTR capture kit. Reads from paired end sequencing were processed with the sarek pipeline. Results: Analysis of 720,580 cells from 11 CLL and 18 RT samples showed 16 distinct cell types- 3 B and 13 T cell subtypes. We observed an increasing trend of CD8 TEM [mean 0.548 vs. 0.384, p = 0.12 (Wilcoxon test)] but a decreasing trend of CD4 TCM (mean 0.236 vs. 0.337, p = 0.08) and B-memory (mean 0.0085 vs. 0.0195, p = 0.51) subsets in RT compared to their CLL stage. When comparing transcriptional changes, we found that among the top 18 highly variable genes, the expression of FP236383.3 (lncRNA) and TCL1A was significantly higher (p<0.0001) in the B cell subsets of patients with RT vs. CLL stage. Additionally, in T cell subsets—primarily dnT, γδT, and CD8/CD4 naïve cells—the expression of FP236383.3, NKG7, GNLY, and CCL5 was significantly higher (p<0.0001), while the expression of LDHB, GZMK, and IL32 was significantly lower (p<0.0001) in RT vs. CLL stage samples. Comparison of specific gene set enrichment analysis showed significantly increased expression of GZMB in gdT (p<0.001) and decreased expression of GZMA, GZMK, TIGIT and TCF7 in dnT and CD8 Naïve cells (p<0.001) for RT vs. CLL stage. The top 10 differentially expressed pathways in RT included oxidative phosphorylation, allograft rejection, IFN-α/-γ responses, TNF-α signaling via NF-κβ, complement, Myc targets V1, mTORC1, hypoxia, and p53 signaling across various B and T cell subsets compared to the CLL stage. Initial analysis of TCR-R data showed that patients with RT stage have lower number of unique clones (p<0.5) and reduced clonal diversity (p<0.5), and a higher number of large and hyperexpanded clones compared to their CLL stage. Additionally, WES data indicated that the most frequently mutated gene is TP53 in tumor B cells of both CLL and RT stages. However, a higher percentage of RT-stage samples carry mutations in MGA, ATM, and IRF2BP2 genes compared to their CLL-stage. WES data also showed a trend toward differing frequencies of copy number amplification at 19q12 (p=0.10) and 4q13.1 (p=0.52) between RT and CLL stages. Conclusions: The main findings of our study are as follows:The transcriptional landscape showed significant changes in multiple genes and pathway enrichment related to cell growth, differentiation and immune response in RT vs. CLL stage.A decreased fraction of CD4 TCM cells and reduced TCR clonal diversity were observed in samples at the RT stage and may indicate a poor prognosis for the patients at this stage. WES data of tumor B cells also indicated possible genetic alterations in the RT vs. CLL stage. Data from in-depth analyses on understanding of the dynamics of T cell response and additional experiments on these patients are expected to provide valuable insights into tumor genetic heterogeneity, transcriptional and immune regulation in blood-based immune subsets.

Abstract Background: Waldenström Macroglobulinemia (WM) is an indolent B-cell lymphoplasmacytic lymphoma characterized by heterogeneous clinical behavior and variable progression patterns. While MYD88L265Pand CXCR4 mutations are frequently encountered, the broader genomic landscape underpinning disease evolution from smoldering WM (SWM) to active treatment naïve WM (TNWM), post-treatment remission WM (PTWM) and relapsed/refractory WM (RRWM) remains incompletely understood. To address this gap, we studied the mutational profile across the WM disease spectrum. Methods We performed whole exome sequencing (WES) on sorted (CD19 and/or CD138) bone marrow aspirates of patients across the spectrum of WM. Paired germline sequencing was performed using peripheral blood mononuclear cells (PBMCs). Unique molecular identifier-tagged reads were deduplicated using fgbio. WES data were processed through the Sarek pipeline. Any valid mutation from either caller, present on the index or in COSMIC was considered. Copy number variations (CNVs) were inferred via CNVkit and summarized at the gene and arm level (≥50% directional change). Mean tumor and germline coverages were 174X and 74X, respectively. Fisher's exact test was used for enrichment and hypergeometric test for overlap analyses. Results A total of 78 high-quality WM samples [15 SWM, 44 TNWM, 13 RRWM, 6 PTWM in remission (all with paired TNWM samples)] were sequenced. The mutational burden varied across disease states (median SWM=1.48 m/Mb, TNWM= 1.12 m/Mb, RRWM = 1.59 m/Mb; p=0.04). Density plots revealed bimodal VAF distributions, consistent with clonal and subclonal architectures in WM. Overall, 77% harbored a MYD88L265P mutation by WES. Among MYD88^WT^ patients, AS-PCR identified MYD88L265P in 4 (25%) not detected by WES, raising the overall mutation rate to 83%, plus one with a non-L265P variant. Low VAF (<10%) MYD88L265P mutations were seen in 6% of matched PBMCs. MYD88 copy number gain was seen in 2 (17%) MYD88WT TNWM patients. CXCR4 mutations were detected in 38% by WES (18% each frameshift and nonsense). Two (4%) CXCR4WT samples were positive by PCR, giving an overall rate of 40%. CXCR4 mutations were more frequent in RRWM (54%) vs TNWM (41%) and SWM (27%), though not statistically significant. Del(6q) and Del(17p) were noted in 16% (n=12) and 7% (n=5) of patient samples. All del(17p) events were noted in TNWM samples and in one paired post-treatment sample. Overall rate of del(17p) and TP53 mutation was 14% (n=11). RRWM was enriched in ARID1A alterations, a gene implicated in chromatin remodeling and plasma cell identity formation [38% (n=5) in RRWM vs. 11% (n=6) in TNWM and SWM], compared to TNWM and SWM p=0.03]. Alterations in NF-kB signaling gene MAP3K14 were more frequent in RRWM (31%, n=4) compared to the SWM/TNWM cohort [(7%), n=4; p=0.036). We noted an enrichment in the TP53 mutation rate in RRMW [n=3, 23%, OR 7.7] compared to TNWM and SWM cohorts [n=2, 4%), p=0.04]. Mutations in FUBP1 (p=0.03), an upstream regulator of MYC, and CARD11 (p=.043), a B-cell signaling scaffold protein were also more frequent in RRWM. Notably, none of the SWM patients harbored an ARID1A alteration (p=0.057), suggesting acquisition during progression. CNV analysis revealed that MAP3K14 amplifications were frequent in RRWM (n=3, 23%) compared to SWM/TNWM [n=2 (4%), p=0.043]. RRWM samples showed increased CD79B mutations (15%) and significant overlap with the C5/ABC diffuse large B-cell lymphoma (DLBCL) genomic cluster (Chapuy et al., Nat. Med. 2018; p=0.007). This overlap remained significant after excluding MYD88L265P (p=0.019), driven by CARD11, CD79B, and IGLL5, suggesting that a subset of RRWM may acquire an ABC DLBCL-like mutational profile. These findings warrant further investigation using transcriptional profiling. Conclusions This study provides a genomic map of WM evolution from precursor to relapse. Increasing somatic mutations and CNVs support stepwise progression. Enrichment of genes involved in chromatin remodeling and plasma cell identity formation (ARID1A), NF-kB or BCR signaling (CARD11, MAP3K14), and DNA damage repair (TP53) were noted at relapse, all of which have been shown to drive aggressive disease biology. Emergence of an ABC-like genotype in a proportion of RRWM indicates convergent evolution with aggressive lymphomas. Transcriptional studies are warranted to explore this further.

SUMMARY Ewing sarcoma (EwS) is a group of bone and soft tissue cancers in children and young adults. Since EwS cells have pronounced sensitivity to radiation and chemotherapy-induced DNA damage, the role of the oncoprotein, EWS-FLI1, in DNA repair is likely. Here, we demonstrate that EWS-FLI1 causes a defect in microhomology-mediated end-joining (MMEJ) repair. EWSR1 is a splicing factor that promotes the faithful splicing of the POLQ pre-mRNA, required for the expression of POLΘ, a critical protein in the MMEJ pathway. Expression of EWS-FLI1, or loss of EWSR1, causes exon 25 skipping of the POLQ transcript, decreased POLΘ expression, impaired MMEJ, and cellular sensitivity to inhibitors of the Fanconi Anemia (FA), NHEJ, or HR pathways, through the mechanism of synthetic lethality. Knockdown of EWS-FLI1 expression restores POL0 mitotic foci and increases MMEJ activity. Inhibitors of the FA, NHEJ, or HR therefore may provide a targeted therapy for patients with EwS. Highlights - Ewing sarcoma tumors have a deficiency in POLθ expression and a corresponding loss of MMEJ activity - EWSR1 is a splicing factor that interacts with other splicing factors such as FUBP1 and KHSRP/FUBP2 to accurately splice the POLQ mRNA. - The EWS-FLI1 fusion oncoprotein, or loss of EWSR1, causes a splicing defect, leading to exon25 skipping of the POLQ pre-mRNA and loss of POLθ expression - The MMEJ deficiency of EwS cells results in cellular sensitivity to inhibitors of Fanconi Anemia, Homologous Recombination or Non-Homologous End-Joining - Exon 25 skipping of POLQ mRNA is a predictive biomarker for HR inhibitors in human cancers

Despite notable advancements in treating chronic lymphocytic leukemia (CLL) with targeted therapies, a significant clinical challenge arises as a subset of CLL patients progress and develop Richter's transformation (RT), an aggressive form of lymphoma. While immune cell dysfunction and T cell defects are well-known characteristics of CLL, the alterations in immune cell subsets during CLL progression to RT remain largely unexplored. While CLL patients typically fail to respond to anti-PD1 pembrolizumab, a positive response has been observed in 20-40% of RT patients. Previously, our study involving three RT patients (1 responder and 2 progressors) revealed enriched regulatory T cells (Tregs) in the responders compared to progressors after pembrolizumab treatment (Iyer et al, ASH2020). Hence, we propose that dynamic changes in T cell subsets at baseline may be associated with response to pembrolizumab treatment. To this end, we performed single-cell RNA sequencing on sorted T cells (CD3 +) from PBMCs derived from 20 patients (responders (RES), n=5; stable disease (SD), n=5; progressive disease/progressors (PD), n=10). A total of 408,627 cells were captured, and an average of 1,393 genes were detected per cell per patient. Using a graph-based clustering approach (Seurat V4.3), we identified 19 clusters based on marker genes and transcriptional signatures and identified enriched pathways using Enrichr analysis. Our results showed that RES had an increased Th17 subset at baseline compared to PD (15% vs. 5%, p=0.01) and reduced Th2 T cells compared to SD (p=0.004) and PD (p=0.05). The Th17/Th2 ratio was higher in RES at baseline (Th17/Th2 R: 1.24, PD: 0.21, SD: 0.25, p0.05) and post-pembrolizumab (Th17/Th2 RES: 1.31, PD: 0.34, SD: 0.30, p0.05). Furthermore, RES had an increased fraction of proliferating CD4 T cells at baseline (4% RES vs. 0.4% SD and 0.4% PD, p=0.02) and post-treatment (1.9% RES vs. 0.8% PD and 0.34% SD, p=0.02). Although we observed an increasing trend in the fraction of Tregs in the RES at baseline [(7.6% RES vs.6.1% PD and 6.5% SD, p=0.4; ns) and post-treatment (10.3% RES vs. 8.3% PD and 5.8% SD, p= 0.2; ns] compared to PD and SD, the difference was not significant. Similarly, among CD8+ T cells, we observed an increasing trend in CD8+ effector T cell fraction in the responders at baseline (29.8% RES vs. 21% PD and 15.6% SD, p=0.9, ns) and post-treatment (30.1% RES vs. 21.2% PD and 19.9% SD, p=0.44, ns). However, the difference was not statistically significant. These findings highlight the dynamic changes in T cell subsets associated with pembrolizumab response. In terms of transcriptional changes in Tregs following pembrolizumab treatment among RES, PD, and SD, we observed dynamic changes on a few Treg marker genes, including TIGIT and TOX. TIGIT and TOX expression drastically decreased after treatment in RES (p < 0.0001), while expression did not change in PD and SD. There were no significant changes in the expression of FOXP3 or CD27 in RES, PD, and SD before and after the treatment. In CD8 effector T cells, GZMK, GZMA (granzymes), and PRF1 (perforin) are marker genes. We observed higher expression of GZMK and GZMA in RES (p <0.0001) at the baseline compared to SD and PD. In the proliferating CD4 T cells, we observed increased expression of IDH2 (Isocitrate dehydrogenase 2-key enzyme of TCA cycle) in RES at the baseline, as compared to PD and SD, indicating increased TCA cycle activity in the proliferating T cells. Pathway analysis of T cell clusters using the Enrichr database showed TNFA-IFNG-MYC targets are enriched in CD8 + cells, IL2-STAT5 signaling is upregulated in Th17, TREGs, and E2F cell cycle associated pathways enriched in CD4 + proliferation cluster. In conclusion, our study unveils significant baseline differences in CD4 subsets between RES and PD/SD subjects and dynamic changes in T cell subsets and transcriptional signatures. These findings offer potential predictors of pembrolizumab response in RT, such as increased Th17/Th2 ratio, enriched proliferating CD4 + T cells, and elevated TIGIT, TOX gene expression in Tregs, GZMK, and GZMA in CD8 effector cells. This deeper understanding of the immunological landscape in RT patients sheds light on the interplay between T cell subsets and treatment response in RT, paving the way for personalized therapeutic strategies. Further validation and exploration can lead to improved outcomes and tailored interventions for RT.