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Introduction Peripheral T-cell lymphoma (PTCL) encompasses a heterogeneous group of clinically aggressive entities. ~30% of cases cannot be further classified and are designated as PTCL - not otherwise specified (NOS). We delineated two novel molecular subgroups among PTCL-NOS with distinct clinical and biological features: PTCL-GATA3 which is characterized by high expression of GATA3, a master regulator of T-helper-2-cell (TH2) differentiation, and its target genes (i.e., CCR4, IL4, IL13) and PTCL-TBX21 which is characterized by high expression of TBX21, a master regulator of TH1 differentiation, and its target genes (i.e., CXCR3, IFNG), with the GATA3 subtype showing significantly worse clinical outcomes (overall survival). Nonsense mutations in CCR4 are found predominately in PTCL-GATA3, often within the cytoplasmic domain and gain-of-function in nature. Methods Mutation and copy number data were generated from whole exome sequencing (TFH phenotype excluded following LLMPP pathology review; n = 153). Single nucleotide variants were determined using Mutect2 and Varsvan2 using in-house filtering. Copy number (CN) profiles were generated using CNVkit. Four CCR4 cytoplasmic domain mutants and controls (cytoplasmic domain knockout [CDKO], wild-type CCR4 overexpression (CCR4-WT), and empty vector [EV]) were transduced into healthy-donor CD4+ T cells (n = 2). Polarization studies were generated using TH1- or TH2-associated cytokine exposure. Surface plasmon resonance was used to determine the binding affinity of CCR4 proteins to CCL17 and CCL22. In silico docking studies were done through Schrödinger's BioLuminate. Antibody-dependent cell-mediated cytotoxicity (ADCC) assay was performed through co-incubation of NK92 (CD16 and CD56) and CCR4 edited primary CD4+ T-cells (stained with calcein AM) for 2 hours at a 5:1 NK:T cell ratio by 7-AAD flow cytometry. Results PTCL-GATA3 had a higher frequency of CN aberrations (e.g. TP53, PTEN CN loss) and a more aberrant genome (p < 0.001) than PTCL-TBX21, including mutations with genes associated with DNA repair and damage (TP53, ATM) and T-cell differentiation (CCR4). CCR4 mutations were virtually mutually exclusive with TP53 mutations, and these cases showed higher CCR4 mRNA and protein expression than CCR4-WT cases (p < 0.05). Primary CD4+ T-cells with ectopic expression of the CCR4 variants, especially CCR4-Q330X, did not require CD3/CD28 for proliferation, bypassing CD3/CD28 ligation for T-cell activation. In addition, proliferation rates were similar between TH2-conditional media and normal media, but proliferation and TBX21 expression in TH1-conditional media were inhibited, implying a novel role of CCR4 in T-cell receptor (TCR) activation and TH-differentiation. CCR4 variant proteins showed higher binding affinity for their natural ligands, CCL22 and CCL17, with mutants exhibiting tighter binding to both ligands (1.5- to 3-fold decrease in KD) by surface plasmon resonance and in silico modeling studies. The in silico models demonstrated mutant CCR4 bound mogamulizumab tighter than CCR4-WT, consistent with a higher ADCC-induced apoptosis rate, highest with CCR4-Q330X mutation. CCR4-Y331X exhibited a worse docking score compared to CCR4-WT and a lower ADCC-induced apoptosis rate. Conclusions Our findings reveal distinct genetic characterizations including aberrant TP53 and PI3K signaling and CCR4 mutations in PTCL-GATA3. These CCR4 mutations are shown to have a key role in PTCL-GATA3 pathogenesis by dysregulating TH-differentiation and aberrantly activating the TCR. CCR4 mutation promotes the TH2 phenotype by blocking TH1 differentiation. CCR4 mutant proteins demonstrated an increased binding affinity towards its known ligands, CCL17 and CCL22, and mogamulizumab, consistent with previous studies exhibiting mogamulizumab efficacy against CCR4 mutations. However, the most frequent CCR4 mutant, Y331X, showed worse binding compared to other CCR4 mutants or CCR4-WT, a mutation that showed Mogamulizumab-resistance in a prior CTCL study as well as in an in vitro ADCC assay. The results emphasize aberrant pathways implicated by genomic and clinical evaluation of two novel PTCL subtypes, as well as support the novel role of mutant CCR4 in enhancing T-cell lymphomagenesis and T-helper differentiation, highlighting multiple therapeutic targets.

Introduction Plasmablastic lymphoma (PBL) is a rare aggressive subtype of lymphoma. It is more common in HIV positive patients, and is often associated with Epstein-Barr virus (EBV) and MYC rearrangements. PBL is characterized by markers of plasmacytic differentiation including CD138 and MUM1/IRF4, and loss of B-cell markers such as CD20, which suggests a cell of origin similar to that of multiple myeloma (MM) or activated B-cell-like (ABC) diffuse large B-cell lymphoma (DLBCL). The standard of care for PBL is CHOP-based chemotherapy but this is frequently not curative. Other than EBV-negative status, which defines a high-risk group, no other biological features have been discovered that are associated with outcomes. Methods Here, we provide a comprehensive analysis of the genomic and transcriptomic landscape of PBL using a collection of 176 exomes and genomes (n=55 new, contributed by Lymphoma/Leukemia Molecular Profiling Project [LLMPP] sites and centrally reviewed by the LLMPP pathology panel [WHO 2016]; n=121 previously published [PMID: 33225311, 33951889, 34465776, 34714908]), 65 transcriptomes (n=44 new; n=21 published) and 37 in situ single cell transcriptomic profiles (CosMx Spatial Molecular Imager) from archival diagnostic tissue biopsies. Somatic mutations (SNVs/Indels) were identified using an ensemble of four variant callers (Strelka2, Lofreq, Mutect2, SAGE). Salmon and DESeq2 were used to identify differentially expressed genes. This multi-omics dataset was compared to other known B-cell malignancies including 92 MM, 238 Burkitt lymphomas (BL), and 208 DLBCLs. Cell segmentation of the CosMx data was performed using Baysor and downstream analyses were conducted using the Seurat workflow. For functional validation, cell viability assays (CellTiter-Glo Luminescent Assay) were conducted in the PBL cell line, PBL-1, and 4 control DLBCL cell lines. Results Within the cohort, 63% (106/169) of primary PBL cases were EBV-positive (EBV+) and 57% (82/144) harbored MYC rearrangements (MYC+). When stratifying patients by EBV and MYC rearrangement status, mutations in different genes were enriched in each group: STAT3 occurred in 55% (29/53) of EBV+/MYC+ PBLs; NOTCH1 in 19% (7/36) of EBV+/MYC-; MYC in 24% (6/25) and TP53 in 40% (10/25) of EBV-/MYC+ tumors; TET2 was mutated in 38% (9/24) and KRAS in 25% (6/24) of EBV-/MYC- PBLs. EBV-/MYC+ tumors represented a subgroup of patients with dismal outcomes. The 2-year overall survival (OS) of these patients was 22% compared to 61%, 78%, and 54% of EBV+/MYC+, EBV+/MYC-, and EBV-/MYC- PBL patients, respectively (log-rank test p-adj. < 0.05). Consistent with previous studies, we observed recurrent mutations affecting JAK-STAT (STAT3, SOCS1, SOCS3, DUSP2) in 41% of cases and RAS-RAF (NRAS, KRAS, BRAF) signaling in 38% of all PBLs. In contrast to ABC-DLBCL, very few mutations occurred in the NF-κB signalling pathway. Accordingly, PBL showed marked downregulation of genes involved in B-cell receptor and NF-κB signaling. Consistent with the lack of reliance on these pathways, functional analyses in PBL-1 showed resistance to treatment to ibrutinib as well as PI3K- and MALT1-inhibitors. Moreover, constitutive expression of the IκBα super-repressor did not affect viability of PBL-1 cells. In situ single cell transcriptomic data analyses showed a sparse tumor microenvironment (TME) in PBL, with the primary non-malignant cell types being, on average, 10% macrophages, 7% NK/T cells, and 4% stromal cells of all represented cell types. EBV+/MYC+ tumors displayed a more abundant immune cell population with higher levels of macrophages (median 12%) and NK/T cells (median 9%), whereas EBV-/MYC- tumors were composed of the lowest levels of immune cells (median 4% macrophages and 1% NK/T cells). With respect to malignant cell phenotypes, we noted a novel population of SPP1-expressing cells that were enriched in EBV+/MYC+ tumors and a population of CD44-expressing cells that were enriched in EBV+/MYC- tumors. Conclusion Here we present novel subgroups of PBL with unique biology and clinical outcomes. We show that PBL does not rely on B-cell receptor signaling, instead relying on potentially targetable vulnerabilities in JAK-STAT, NOTCH, and RAS-RAF signaling according to EBV positivity and MYC rearrangement status. Lastly, we provide insight into the TME of PBL and demonstrate a novel population of SPP1 and CD44 expressing malignant cells in EBV+ tumors.

Primary resistance to BCMA-directed bispecific antibodies (BCMA-BsAb) is a major clinical hurdle despite their outstanding efficacy for relapsed and refractory multiple myeloma (MM). Primary resistance has been linked clinically to patient characteristics including tumor burden. Our previous work in the immunocompetent Vk*MYC model of MM credentialed that tumor burden also correlates with resistance to a murine BCMA-BsAb, which we addressed through drug combinations with MM backbone agents. Pairing the BsAb with cyclophosphamide (Cy) was well-tolerated, reversed primary resistance, reshaped an immunosuppressive tumor microenvironment (TME) and led to durable responses with immunosurveillance in all but the most aggressive settings. Here, we aimed to improve the BCMA-BsAb and Cy combination strategy with a goal of curing models representing clinically relevant Cy-refractory aggressive settings, by adding T cell checkpoint blockade. Despite a lack of single agent activity of checkpoint inhibitors in MM (and in our models), PD1 and TIGIT are relevant targets as their expression on T cells correlates with both primary and acquired resistance to BCMA-directed cell therapies. Our team hypothesized that blockade of either PD1 or TIGIT with concurrent BCMA-BsAb and Cy would deepen anti-MM responses and provide a more durable immunotherapy regimen by sustaining activation of the T cell repertoire. Methods: We preclinically tested the ability of two weeks of concurrent dosing of BCMA-BsAb with Cy, checkpoint inhibitors targeting either PD1 or TIGIT, or the triple combination to overcome primary resistance and improve survival in a Cy-resistant transplantable Vk*MYC model of MM. We followed the response longitudinally in the blood, and in the bone marrow and spleen after complete responses were achieved at five weeks. Results: The double combination of a checkpoint inhibitor to the BCMA-BsAb was well-tolerated and abolished primary resistance, yet, failed to provide curative responses. Whereas the triple combination with either PD1 or TIGIT blockade remained safe, provided robust initial responses that overcame primary resistance and resulted in durable complete and curative responses in the entire cohort. These survival benefits represent significant improvements over control combinations tested here but also over all historical preclinical anti-MM therapy tested in our models to date. Both PD1 and TIGIT blockade with Cy/BCMA-BsAb triple combinations delivered a similar multiphasic potent anti-tumor response. This response was first characterized by highly effective T cell-mediated lysis of the tumor, as evidenced by deep reduction of M-spike after two weeks of treatment. As we previously published, the addition of Cy to BCMA-BsAb rapidly reshapes the TME by depleting Tregs and exhausted T cells and brings naive T cells to the tumor. However, we observed elevated T cell checkpoint expression at times of BsAb-relapse in aggressive models, suggesting that blockade of these pathways may help sustain T cell activation. Indeed, the ensuing T cell response in the triple combination gradually builds and encourages a pool of less terminally differentiated T cells to proliferate to allow for a more sustained response with repeat dosing. For the first time, we observe that the triple combination significantly engages the CD4 T cell compartment, which can be essential for a complete anti-tumor response. At the time of complete response, single cell analysis displays that T cells retained in the BM were less exhausted and defined by elevated CCR2 expression, a known marker of activated memory T cells, suggestive of ongoing immunosurveillance. Conclusions: Here we demonstrated through controlled in vivo experimentation how the addition of Cy reverts primary resistance to BCMA-BsAb by reshaping the TME in our Cy-insensitive preclinical model. Adding checkpoint inhibitors targeting TIGIT or PD1 further improves the durability of BsAb and Cy combination to cure all mice in these cohorts, by critically lysing the tumor, resetting the suppressive TME, and instigating a gradual, multiphasic, and sustained anti-MM T cell response. The concurrently delivered triple combination is well tolerated and provides optimal temporal control of MM especially in a preclinical model reflecting primary resistance mediated by high tumor burden and may have translational benefit to MM patients.

DDX41 MT Germline Predisposition Syndrome (GPS) is the most common adult GPS for myeloid neoplasms (MN), including MDS and AML. Although there have been reports of non-myeloid neoplasms, particularly lymphoid malignancies, in DDX41MT-GPS, these occurrences are infrequent and poorly characterized. We carried out this study in a large cohort of DDX41MT-GPS patients to address this deficiency. Following IRB approval, we retrospectively analyzed our database of 174 patients with DDX41MT-GPS to determine the rates of non-myeloid malignancies. Our previous data revealed that certain DDX41MT genotypes classified as variants of uncertain significance (VUS) exhibit a similar phenotypic spectrum to those classified as pathogenic (Badar et al Hematologica). Consequently, we included all these variants in our analysis. The median age of the cohort was 68.6 yrs (range, 13-4-93.4); 66% (n=114) male, 93% (n=162) Caucasian, with a median follow-up of 1.4 yrs (range, 0.03-5.6) from DDX41MT-GPS detection. The cohort included 34 (19.5%) asymptomatic carriers, 17 (9.8%) with clonal hematopoiesis (CH) or clonal cytopenias (CCUS), and 123 (70.7%) with MN. Eighteen (10%) patients had a lymphoid neoplasm (78% male, 100% Caucasian), with a median age at lymphoid neoplasm diagnosis of 67.7 yrs (range, 12.2-85.9). The most common lymphoid neoplasm was chronic lymphocytic leukemia (CLL) seen in 2.3% (n=), followed by follicular lymphoma (FL) and acute lymphoblastic leukemia (ALL) present in 1.7% each (n=3; 1 T-ALL and 2 B-ALL: 1 Ph+ and1 Ph-like). Additionally, there was 1 patient each with monoclonal B cell lymphocytosis (MBL), Waldenstroms macroglobulinemia , peripheral T cell lymphoma , large granular lymphocytic leukemia, and marginal zone lymphoma. Germline genotypes were classified as pathogenic in 50% (n=9, D140Gfs*2 x2, K331del, M1I x3, M316Dfs*31, P258L, Y340N) and VUS in 50% (n=9, E426K, E69D, G313D, K184R, P206A, P70S, Q246E, R164W, c.572-3T>C. Among these 18, 9 (50%) patients had co-existing myeloid disorders (CCUS x3, MDS x5, AML x1), and 5 (27.8%) patients were later diagnosed with therapy-related myeloid neoplasms (TMN) at a median of 5.9 yrs (range,1.6-38.7) after lymphoid neoplasm diagnosis and treatment. Seven (4%) patients had plasma cell dyscrasias (PCD; 3 with monoclonal gammopathy of undetermined significance(MGUS) and 4 with multiple myeloma (MM)); 57% (n=4) were male, with a median age of 61.3 yrs (range, 41.4-83.8) at PCD diagnosis. Subtypes of MGUS included IgG Kappa (n=2) and IgG Lambda (n=1), subtypes of MM included IgG Kappa (n=2), IgA Kappa (n=1), and Kappa light chain only (n=1). DDX41MT-GPS genotypes were pathogenic in 28.6% (n=2, G67Vfs*13, G313S) and VUS in 71.4% (n=5, A555T, R164W, Q246E x2, M307T). All patients with MM (n=4) received an autologous stem cell transplant, with 3 (75%) being subsequently diagnosed with a t-MN; median 3.6yrs after PCD diagnosis (range, 3.2-12.3). Seventeen percent (n=31) had visceral malignancies; 2.8% (n=5) having >1malignancy, 61% (n=19) male, 93.5% (n=29) Caucasian and 6.5% (n=2) Hispanic. Prostate cancer was most common (n=8, 4.6%), followed by breast (n=7, 4.0%), colon (n=5, 2.9%), squamous cell carcinoma (n=4, 2.3%, 3 of the head/neck and 1 of the leg), bladder (n=4, 2.3%), testicular (n=3, 1.7%), gynecologic (n=3, 1.7%), lung (n=2, 1.2%), thyroid (n=1, 0.6%) and pancreatic neuroendocrine tumor (PNET, n=1, 0.6%), respectively. The median age at first solid tumor diagnosis was 62.4 yrs (range, 10.9-81.9). DDX41MT-GPS was pathogenic in 64.5% (n=20, D140Gfs*2 x3, E113Kfs*14, G67Vfs*13, I396T, L283Cfs*21, M1I x4, M316Dfs*31 x2, p.M1?, P258L, Q368*, Q41* x2, R159*, R311*) and VUS in 35.5% (n=11, D30Y, H203Y, I215L, I337V, M155I, p.?, S289L, T13I, V318G, A555T). Of the 31 patients with visceral malignancies , 51.6% (n=16) were diagnosed with t-MN at a median of 4.9 yrs (range, 1.1-34.3) after malignancy diagnosis. While DDX41MT-GPS are clearly associated with an increased risk of myeloid neoplasms, we demonstrate potential associations with other neoplasms, especially lymphoproliferative disorders (10%). We for the first-time report associations with PCD (4%) and visceral malignancies (17%) and also highlight a worrisome trend for t-MN in DDX41MT-GPS treated with genotoxic agents. Given limitations of a retrospective analysis, this study underscores the need for a comprehensive natural history study in patients with DDX41MT-GPS .

Background: Anti-BCMA CAR T cells are a new standard of care for the treatment of RRMM patients. Hence, it is paramount to identify determinants of response and resistance for individualized approaches. There is emerging data about the importance of depth of response in prolonged progression-free survival (PFS) and of genetic antigen escape in treatment resistance. By contrast, there is limited understanding of how patient' outcomes are shaped by immune fitness prior aphaeresis, and virtually no data about CAR T and endogenous profiles post infusion. Aim: Characterize immune dynamics and investigate its prognostic value in RRMM patients treated with CAR T cells. Methods: Immune profiling was performed using multidimensional and computational flow cytometry in 285 bone marrow aspirates from 120 RRMM patients enrolled in the KarMMa phase 2 trial. In total, 170 immune clusters were identified and systematically quantified in a pileup analysis of samples collected before and at months 1, 3, 6 and 12 after CAR T infusion. Immune scores were developed using cox regression with 10-fold cross-validation. Results: At baseline (n = 63), there were significant associations between the bone marrow immune composition and the number of prior lines of treatment, prior autologous transplant and staging. Namely, there were higher proportions of ICOS, PD1, TIGIT and TIM3 positive cells within the CD4+ and CD8+ T cell compartments from heavily pretreated patients and those with R-ISS 3. A baseline immune score defined by the percentages of GrzmB+HLADR+ cytotoxic NK cells, TIGIT+ Tregs, CD4+CD127+ICOS+ and CD4+CD25+CD127+TIGIT+, CD8+TIGIT+, CD8+GrzmB+HLADR+ and CD8+CD27+CD95+CD45RAloCD197lo T cells was associated with PFS. Patients with low vs high risk immune score at baseline had median PFS of 25 vs 6 months, respectively (p < .001). Interestingly, there were no significant associations between the baseline risk score and treatment or staging, suggesting potential complementarity of immune profiling with other prognostic factors. At month 1 after infusion (n = 55), the presence of >1% vs <1% CAR T cells was associated with longer PFS (medians of 20 vs 8 months, p = .002). In addition to the magnitude of CAR T persistence, it was interesting to observe increased percentages of CD8+ CAR T cells in patients with <5 prior lines of therapy, of CD4+HLADR+ and CD8+TEMRA CAR T cells in patients with R-ISS 1/2, as well as of PD1+TIM3+ CD4+ and CD8+ CAR T cells in patients with R-ISS 3. Moreover, the phenotype of CAR T cells was associated with PFS. Patients with low vs high risk immune score (ie, based on the percentages of CD4+CD57+, CD8+CD27-EM, CD8+PD1+TIM3+ and CD4+CD8+ CAR T cells) had median PFS not reached vs 11 months, respectively (p < .0001). Similarly to the above, there were no significant associations between the CAR T immune risk score and treatment or staging, suggesting potential complementarity of CAR T quantification and phenotyping post-infusion with other patient' characteristics. Next, we investigated endogenous T cell dynamics in 51 RRMM patients with paired samples prior and after CAR T infusion. There were significant differences in the frequencies of 30 immune clusters, including CD27+ naïve and EM CD4+ and CD8+ T cells, ICOS+ and TIGIT+ Tregs, CD4+ and CD8+ T cells, as well as of CD25+CD127+ CD4+ and CD8+ T cells. Furthermore, an immune risk score defined at the latest available time point (n = 95) was associated with PFS (p < .0001). Patients with lower levels of GrzmB+ cytotoxic NK cells and of immunoregulatory HLADR- NK cells, as well as of CD4+CD27+CD95+CM, CD4+HLADR+, CD8+PD1+ and CD8+CD27-CD95+EM T cells showed a high risk score and a median PFS of 6 vs 20 months in patients with low risk immune score. Conclusions: This is one of the largest dynamic studies of immune profiling in a clinical trial investigating anti-BCMA CAR T cells in RRMM. Our results uncovered that patient' immune status is shaped by prior treatment and staging, and that the distribution of different NK, Treg, CD4+ and CD8+ T cells prior aphaeresis is associated with PFS. Furthermore, the phenotype of CAR T cells at month 1 after infusion may be as prognostic as its percentage. We also showed how treatment with CAR T cells modulates the bone marrow immune composition and how it may contribute to immune escape vs surveillance. Altogether, this study supports immune profiling for improved understanding of response and resistance to CAR T cells in RRMM.

Background: Monoclonal B-cell lymphocytosis (MBL) is a common premalignant condition that can be classified into chronic lymphocytic leukemia (CLL)-type (CD5, CD23, and CD20 co-expression), and non-CLL-type (CD5-negative) based on flow cytometry immunophenotypes. CLL-type MBL is relatively well characterized with increased risk of hematologic (especially lymphoid) malignancies, serious infections, and decreased response to vaccinations. However, the prevalence, genetic predispositions, and clinical consequences of non-CLL-type MBL remain to be fully elucidated. Herein, we report findings in non-CLL-type MBL in a large screening cohort. Methods: We screened 10,330 individuals (40 years or older with no prior history of hematologic cancers) for MBL with highly sensitive flow cytometry, in the Mayo Clinic Biobank, with patients recruited from primary care-based clinics. Individuals were genotyped by Regeneron Genetics. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). Among those individuals residing in the 27 counties surrounding Rochester, Minnesota, we identified incident hematologic malignancies and melanomas using ICD codes and the Rochester Epidemiology Project, a population based medical records linkage system. All diagnoses were manually reviewed for confirmation. Cox regression was used to calculate hazard ratios (HR) and 95% CIs, adjusted for age and sex. Results: We identified 1,736 individuals with MBL and 8,341 individuals without MBL. Among those with MBL, 246 (14.2%) had non-CLL-type MBL as the major clone. The median age of non-CLL-type MBL was 77 years (range, 47-95). The prevalence of non-CLL-type MBL significantly increased with age, with 0.5% prevalence among those between 40 to 59 years of age, 2.3% prevalence among those between 60 and 79 years, and 7.8% prevalence in those 80 years of age or older. The prevalence of non-CLL-type MBL was significantly higher (P < .001) among males (3.1%) than females (2.0%). Using the genome-wide complex trait analysis tool, the heritability of non-CLL-type MBL was estimated to be ~26%, suggesting inherited genetics plays a role in etiology. To identify common inherited variants, we first evaluated the known CLL susceptibility variants, many of which also associated with CLL-type MBL. Among the 42 variants, none were statistically associated (all P > .05) with non-CLL-type MBL. We next conducted a genome-wide association study to identify novel variants, and the most significant variant resided in 10q26.3 (minor allele frequency [MAF]control = 0.7%, MAFMBL = 3.5%, OR = 6.4, 95% CI 3.6-11.3, P = 5.7 * 10-8). Among the 6,478 individuals (192 with non-CLL-type MBL and 6,286 without MBL) followed for incident hematologic malignancies, 36 developed lymphoid and 23 developed myeloid malignancies after a median of 4.2 years of follow up. Similar to CLL-type MBL, non-CLL-type MBL was associated with a 5.2-fold risk for lymphoid malignancies (95% CI 2.0-13.0, P < .001). Elevated but not significant risks were observed for myeloid malignancies (HR = 2.3, 95% CI 0.5-10.3, P = 0.27). The lymphoid malignancies developed by individuals with non-CLL-type MBL included diffuse large B-cell lymphoma, multiple myeloma, marginal zone lymphoma, and other low-grade lymphomas. We recently reported that individuals with CLL-type MBL are at higher risk for developing melanoma (HR = 1.9, 95% CI 1.3-2.8). Interestingly, non-CLL-type MBL had no evidence of an association with melanoma risk (HR = 0.4, 95% CI 0.1-1.8). We did not observe a significant difference in overall survival between individuals with non-CLL-type MBL vs those without MBL. Conclusions: In the largest screening cohort to date, we evaluated the etiology and clinical outcomes of non-CLL-type MBL, an understudied yet common condition that occurs in ~4 million adults in the United States. Non-CLL-type MBL is more common in men and older individuals, and evidence supports that inherited genetics plays an etiological role with 10q26.3 locus identified as a novel susceptibility locus. Individuals with non-CLL-type MBL had significantly increased risk of lymphoid malignancies, similar to CLL-type MBL, but, distinct from CLL-type MBL, no evidence of an increased risk of melanoma. Our study provides insights into the prevalence, genetic predispositions and clinical consequences of non-CLL-type MBL and how it differs from the classic CLL-type MBL.

Background: Polatuzumab vedotin (Pola) is an antibody drug conjugate targeting CD79b that incorporates the microtubule disrupting monomethyl auristatin E payload. Pola was approved in 2023 for the upfront treatment of diffuse large B cell lymphoma (DLBCL) in combination with rituximab, cyclophosphamide, doxorubicin, and prednisone (polaRCHP) based on the POLARIX trial. However, the activity of pola appears to vary based on DLBCL cell of origin (COO) subtype, with greater efficacy in activated B cell (ABC) DLBCL compared to germinal center B cell (GCB) DLBCL (Russler-Germain Blood 2023). When outcomes in POLARIX are stratified by COO, polaRCHP demonstrated progression free survival (PFS) and overall survival (OS) benefits over RCHOP in patients with ABC but not GCB subtype DLBCL (PFS HR 0.34 ABC vs 1.03 GCB, OS HR 0.27 ABC vs 1.64 GCB; Morschhauser ASH 2023 and FDA data). Most prospective studies of pola used gene expression to assess COO, which is not readily clinically available. This study aims to examine if COO subtype by Hans algorithm (based on CD10, BCL6, and MUM1 expression by immunohistochemistry) is predictive of pola responses and outcomes in patients with large B cell lymphoma (LBCL). Methods: We conducted a retrospective, multicenter observational study of adults with LBCL who received pola (monotherapy or in combination) from 2015-2024 with Hans algorithm COO data and response assessment available. Patients were stratified by receipt of pola in the frontline vs relapsed/refractory (R/R) setting. The primary endpoint was overall response rate (ORR) to pola-based treatment in GCB vs non-GCB subtypes. Secondary endpoints included complete response rate (CRR), PFS, and OS. For time to event endpoints, patients responding to pola who received CAR T or stem cell transplantation prior to progression were censored at time of cellular therapy. Demographics were summarized by descriptive statistics. Comparative and survival analyses used Chi squared/Fischer exact tests and Kaplan Meier analysis. Data cutoff was July 1, 2024. Results: 718 patients were included. Median follow-up was 15 months (interquartile range [IQR] 8-32). Median age at LBCL diagnosis was 66 years (IQR 56-74); 62% were men; 69 (10%) had double hit lymphoma (DHL); 129 (18%) had underlying low grade lymphoma. Pola was used in the frontline setting in 293 (41%) patients, of whom 139 (47%) had GCB vs 154 (53%) non-GCB COO. Of the 426 patients receiving pola for R/R LBCL (including one patient receiving pola retreatment), 224 (53%) had GCB vs 202 (47%) non-GCB COO. Among patients receiving pola-based regimens in the R/R setting (GCB vs non-GCB: pola monotherapy = 7% vs 4%, pola + R = 26% vs 26%, pola + BR = 55% vs 61%, pola + other = 12% vs 9%; p = 0.44), the ORR in GCB vs non-GCB LBCL was 36% vs 61% (p < 0.001), while CRR in GCB vs non-GCB was 17% vs 36% (p < 0.001). COO remained a significant component of logistic multivariate regression models of ORR (p < 0.001) and CRR (p < 0.001) when incorporating IPI at original LBCL diagnosis, histology, DHL status, and refractory status to frontline therapy. Despite this, no significant PFS or OS differences by COO were seen, potentially due to approximately 1/4 patients being censored due to receiving subsequent cellular therapy (e.g. CAR T) prior to progression. In patients receiving frontline pola-based regimens (of which 275 [94%] included an anthracycline), ORR and CRR were similar in GCB vs non-GCB (ORR 89% vs 88% p > 0.5; CRR 76% vs 77% p > 0.5). While PFS and OS were similar between COO groups in the frontline cohort, follow-up and cohort size were limited. Conclusion: Based on this real-world evidence, COO classification by Hans algorithm is a strong predictor of the activity of pola-containing therapy in R/R LBCL, with significantly higher ORR and CRR in patients with non-GCB compared to GCB COO LBCL. This aligns with the cumulative existing evidence. ORR and CRR to pola-containing therapy in the frontline setting did not differ significantly by COO subtype, but longer follow-up of larger cohorts is needed to clarify the predictive role of COO by Hans algorithm in this setting, especially considering the PFS advantage with polaRCHP in POLARIX was not associated with significant differences in ORR or CRR. Overall, clinicians should consider factoring COO subtype into treatment decisions regarding pola-containing regimens, and studies of pola-based therapies should stratify patients and report outcomes by COO subtype.

Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P<.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P<.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P<.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P<.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.

Background: The majority of patients respond to steroid treatment for graft-vs-host disease (GVHD), but the typically high doses and prolonged courses can cause substantial morbidity. GVHD can be classified at onset into two groups with significant differences in treatment response and non-relapse mortality (NRM) by clinical criteria (Minnesota risk) but the more favorable standard risk group still experiences high rates of treatment failure and NRM [MacMillan, BBMT 2015]. We previously validated a serum biomarker-based (ST2+REG3α) risk stratification system, MAGIC algorithm probabilities (MAPs), that can stratify patients both at initiation and during treatment into risk groups for treatment response and NRM [Hartwell, JCI Insight 2017, Srinagesh, Blood Adv 2019]. GVHD that is Minnesota standard risk and has a low MAP (i.e., Ann Arbor 1) at start of treatment represents a low risk group with >80% response rate to steroids and ~10% NRM [Etra, Blood 2023]. We hypothesized that serial monitoring of GVHD symptom severity and MAPs twice (days (d) 7 and 14) in patients with clinical and biomarker defined low risk GVHD could further stratify patients into clinically meaningful groups. Methods: We retrospectively determined MAPs at d7 and 14 in 450 patients with low risk GVHD who participated in a prospective, multi-center, observational trial and were treated with systemic steroids between 2015-2023. MAPs <0.291 at days 7 and 14 were defined as low risk based on a previously validated threshold [Major-Monfried Blood 2018]. Clinical responses were scored as complete (CR), partial (PR), or non-response (NR) by standard criteria. Initiation of second line therapy was considered NR. Patients were categorized into three groups (A-C) based on clinical responses and MAPs at days d7 and 14. A: CR/PR by d14 and low MAP at both d7+14; B: stable or worse GVHD (NR) by d14 with low MAP at both d7 and 14; C: any response with a high MAP at d7 or 14. The endpoints were clinical response at day 28 (overall response rate, ORR=CR or PR), regardless of earlier response status, and 6-month NRM. Results: The median age of the study population was 54 years (range: 0-79); 19% of patients received PT-Cy for GVHD prophylaxis. Target organ involvement was skin ± upper GI (66%), lower GI (23%), isolated UGI (11%), and skin+liver (1%), and GVHD severity was grade I (35%), II (61%), and III (4%) at start of treatment. Patients in group A (n=310, 69%), clinical responders with low MAPs through day 14, had excellent d28 ORR (93%) and very low NRM (4%). GVHD in this group was considered ultra-low risk (ULR) and subset analyses revealed highly consistent d28 ORR and NRM, regardless of target organ severity, age (<18, 18-59, >60), HCT-CI score, conditioning intensity, and GVHD prophylaxis. Group B patients comprised clinical non-responders with low MAPs through day 14 (n=112, 25%); half of them responded by d28 and their NRM was low (8%), albeit double that of group A (p=0.079). Group C was small (n=28, 6%) and included both clinical responders (n=20, 4%) and clinical non-responders (n=8, 2%) who had at least one high MAP through day 14. Their d28 ORR was 54% but NRM increased four-fold (32%) and was significantly worse than patients who maintained low MAPs regardless of clinical response (p<0.001). Conclusion: Serial monitoring by clinical response and biomarkers during the first two weeks of treatment identifies a small number of high risk patients whose GVHD is initially low risk (group C). This group experiences high NRM regardless of clinical response to steroid treatment and may benefit from treatment escalation. Patients with low risk GVHD who do not have an early clinical response to steroids but maintain low MAPs, (group B) have reasonably good long-term outcomes with standard treatment. Finally, serial monitoring identifies a very large subset of patients with ULR GVHD (group A) who respond exceptionally well to standard treatment and experience very low rates of 6-month NRM regardless of pre-transplant characteristics and GVHD prophylaxis. Such patients may benefit from treatment de-escalation strategies such as shorter courses of lower dose steroids, a strategy we are currently testing in a clinical trial (NCT05090384).

Background: Immune dysfunction is a hallmark of chronic lymphocytic leukemia (CLL), and several studies suggest that hypogammaglobulinemia at the time of CLL diagnosis may predict shorter time to first therapy (TTFT; Parikh et al, 2015). However, it is unclear if hypogammaglobulinemia can predict TTFT independent of the CLL-International Prognostic Index (CLL-IPI) and the tumor mutational load (TML). We sought to determine if serum immunoglobulin (Ig) levels at diagnosis can independently predict outcomes in patients with CLL and its precursor, high-count monoclonal B-cell lymphocytosis (MBL). Methods: We used the Mayo Clinic CLL Database to identify newly diagnosed CLL/MBL consented within 2 years of diagnosis. Next generation sequencing (NGS) was performed using a 59 gene panel (SureSelect) to detect mutated CLL genes. Serum Ig levels were quantitated by radial immunodiffusion (Immunoplates). Serum Ig levels below the lower limit of normal were defined as hypogammaglobulinemia. We used Chi-squared or Fisher's exact tests to compare discrete variables and the Kruskal Wallis test for continuous variables. TTFT was calculated from date of diagnosis to date of first treatment or date last known to be untreated. We used Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for TTFT and overall survival (OS) associations. Results: We identified 895 newly diagnosed patients (588 CLL and 307 MBL) who had undergone NGS for recurrent somatic mutations and had serum Ig levels available at the time of diagnosis. The median absolute B-cell count was 6.7 x109/L; 613 (69%) were male, 375 (42%) had unmutated IGHV genes, and 81 (9%) had TP53 disruption. Based on CLL-IPI, 412 (46%) patients had low risk, 285 (32%) had intermediate risk, and 198 (22%) had high/very high risk. The most commonly mutated genes were NOTCH1 (14%), SF3B1 (11%), TP53 (10%), NFKBIE (8%), ATM (6%), and BIRC3 (6%). Based on the number of genes with high impact or hotspot mutations, we classified 40%, 29%, and 31% patients with TML scores of 0, 1, or 2+, respectively. At CLL/MBL diagnosis, the median serum IgG, IgA, and IgM was 891 mg/dL (111-2620), 121 mg/dL (1-1150), and 44 mg/dL (5-1320), respectively; 284 (32%) patients had low serum IgG, 128 (17%) had low serum IgA, and 322 (42%) had low serum IgM. Low serum IgA was associated with mutations in NOTCH1, SF3B1, ATM, BIRC3 and NFKBIE, low IgM was associated with SF3B1 and BIRC3 mutations (all comparisons, P < .05), and low IgG was associated with BIRC3 mutation (P = .05). The median follow-up for the cohort was 8.4 years. A total of 359 patients progressed requiring therapy (299 CLL and 60 MBL), and 265 patients died (177 CLL and 88 MBL). The TTFT was significantly shorter for patients with low serum IgG compared to normal serum IgG at diagnosis (median 4.4 vs 10.8 years) and similar findings were observed in individuals with low serum IgA (1.7 vs 10.8 years) and those with low serum IgM (4.6 vs 11.1 years). On univariable analysis, the following were associated with a shorter TTFT: low serum IgG (HR: 1.8, 95% CI 1.4-2.2), low serum IgA (HR: 3.5, 95% CI 2.7-4.4), low serum IgM (HR: 1.9, 95% CI 1.5-2.3), TML score of 1 (HR: 1.7, 95% CI 1.3-2.2), TML of 2+ (HR: 4.4, 95% CI 3.4-5.7), CLL-IPI intermediate risk (HR: 3.7, 95% CI 2.8-4.8), and CLL-IPI high/very high risk (HR: 7.6, 95% CI 5.8-10.1) (all comparisons, P < .0001). In multivariable analyses after adjusting for CLL-IPI and TML, low serum levels of IgG (HR: 1.8, 95% CI: 1.5-2.3), IgA (HR: 2.4, 95% CI: 1.8-3.0), and IgM (HR: 1.8, 95% CI: 1.5-2.3) were independently associated with a shorter TTFT (all comparisons, P < .0001). No significant differences in OS were detected between patients with low and normal serum IgG, IgM, and IgA on univariate analysis. Similar results for TTFT and OS were observed after adding clonal B-cell count to the Cox model. Conclusions: Our study shows that low serum Ig levels at the time of initial diagnosis is predictive of TTFT independently of the CLL-IPI and TML, which can further risk stratify individuals with newly diagnosed CLL/MBL. Additionally, low serum IgA, IgM, and IgG levels at diagnosis are significantly associated with specific somatic mutations in individuals with newly diagnosed CLL/MBL.