Rimsza, Lisa M.

Introduction Frontline therapy for pts with follicular lymphoma (FL) is not based on the molecular profile of the tumor. The PI3K pathway is central to FL biology, but the molecular profile of tumors most sensitive to PI3K inhibition (PI3Ki) is unknown. Copanlisib inhibits both PI3Kα and δ isoforms, and we hypothesized that most pts with treatment naïve FL will have PI3Ki-sensitive tumors and durable complete responses (CRs) may be achievable with short durations of targeted therapy. A gene expression profiling (GEP) predictor utilizes a combination of the expression of 45 selected informative genes and an additional 19 housekeeping genes to identify FL pts at high-risk for early progression within 24 months (Wright et al. ASH Annual Meeting2022). We also hypothesized that copanlisib would induce durable CRs in high-risk pts by the GEP assay. Here, we report a preliminary analysis of an ongoing “window of opportunity” study of copanlisib followed by response-adapted copanlisib and rituximab in treatment-naïve pts with FL (NCT03789240). Methods Pts with untreated grade 1-2, 3A FL, ≥stage 2 are eligible if systemic therapy is indicated: symptoms, progressive lymphadenopathy, or organ compromise. Prior radiation is permitted. Eligibility includes age ≥18 and adequate organ function unless due to lymphoma. HIV, CMV, Hep B or C, and autoimmune conditions are excluded. Pts first receive copanlisib 60mg on days 1, 8, and 15 of a 28-day “window of opportunity” to explore the activity of copanlisib. Following the window, pts receive 6 cycles of copanlisib 60mg on days 1, 8, and 15 of a 28-day cycle along with rituximab 375mg weekly x 4 then on day 1 of each cycle and response is assessed with FDG-PET and CT scans. Pts without response after C6 are taken off study and those with a partial response (PR) can continue for an additional 6 cycles and then stop therapy. Pts who achieve a CR after 6 cycles stop therapy. No maintenance is given. Supportive care includesPCP prophylaxis. The primary endpoint is the CR rate with secondary endpoints of safety, duration of CR, and PFS. Exploratory objectives include identification of a predictive signature of PI3Ki response and the response rate in high-risk pts identified by the GEP assay. Results Twenty-eight pts have enrolled. Median age was 56y (range, 24-80). Seventeen (61%) and 12 (43%) pts had high-risk FLIPI and FLIPI-2 scores ≥3, respectively; 6 (21%) pts had Grade 3A FL, and 17 (61%) pts met two or more GELF criteria. After copanlisib monotherapy in the window, 27 (96%) pts had tumor reductions by CT with a median change of -38% (-62% to +16%) (Figure 1). Among 22 pts who completed 6 cycles, 21 (96%) responded, including 9 (41%) pts with PR and 12 (55%) pts with CR. Of 9 pts with PR who received an additional 6 cycles of therapy, only 1 (11%) converted to a CR bringing the overall CR rate to 59% (13/22 pts). After a median follow-up of 25.8 months, the 2-year PFS was 43.2% (95% CI, 20.7-64.0%). The median duration of CR was 29.7 months (95% CI, 6.3 months - not estimable) with 8 (62%) CRs ongoing (Figure 2). Toxicity was evaluated in 28 pts across 220 cycles. Most common were rash (64%), mucositis (50%), and nausea/vomiting (50%), all G1-2 except 1 (4%) pt with G3 mucositis. The most common ≥G3 toxicities were neutropenia and infections in 5 (18%) pts each. ≥G3 neutropenia occurred in 10 (5%) cycles, responded to therapy interruption and/or growth factors, and did not require dose reduction. Two (8%) pts had copanlisib dose reduced, 1 (4%) each due to rash and mucositis. Therapy was prematurely discontinued in 5 pts: 3 due to COVID-19, 1 for hepatotoxicity that resolved, and 1 due to infusion reaction to rituximab. Nine of 24 (38%) pts were poor risk by the GEP assay. Complete responses were observed in 71%, 67%, and 57% of pts categorized as high-risk by FLIPI, FLIPI-2, and GEP assay, respectively. The CR rate was not affected by bulky disease (any LN >7cm; p=1.00), high-risk FLIPI (p=0.19), high-risk FLIPI-2 (p=0.67), poor-risk GEP (p=1.00), or metabolic tumor volume (p=0.33). Conclusion Nearly all pts with treatment naïve FL have PI3Ki-responsive tumors, and the combination of copanlisib with rituximab achieved a CR rate of 59% at the end of induction therapy. The median duration of CR is nearly 30 months, including ongoing remissions without maintenance. CRs were observed in pts deemed high-risk by the GEP assay and FLIPI score. The safety profile is excellent, with mostly G1-2 toxicities manageable with supportive care.

Introduction: Previously we demonstrated that HIV associated diffuse large B-cell lymphomas (DLBCL) of Germinal Center B-cell subtype (GCB) are more proliferative, with enhanced genomic stability and increased expression of DNA repair genes, particularly Fanconi anemia (FA) and homologous recombination (HR) genes compared to their HIV negative counterparts (Maguire et al, 2019). It is unknown if this signature is unique to the GCB subtype or if it is also observed in Activated B-cell (ABC) and Unclassified (UNC) DLBCL. Herein we validate our HIV associated GCB DLBCL findings in a larger independent cohort and report for the first time the aberrational profile of molecularly profiled HIV associated ABC DLBCL compared to their HIV negative counterparts respectively. Methods: An independent cohort of 353 DLBCL FFPE cases was assembled, including 125 HIV-DLBCL samples. All samples underwent hematopathological review to confirm diagnosis and determine tumor content. Samples with <60% tumor content were macro-dissected before they were RNA/DNA extracted. Cell of origin (COO) profiles were assessed using the Lymph3Cx molecular assay which can distinguish primary mediastinal B-cell lymphoma (PMBL) like from GCB, ABC and UNC DLBCL (Mottok et al 2018). Copy number (CN) analyses were performed on samples with sufficient DNA (41 HIV neg, 32 HIV pos) using the OncoScan FFPE assay kit and the resulting data analyzed using Chromosome Analysis Suite (ChAS) v4.1 and the multisample viewer module of Reproductive Health Research Analysis Software (RHAS) v1.1. Additional digital gene expression profiling was performed using the Pan-Cancer panel (NanoString, Seattle, WA) customized to include 9 BCL2 family genes for a total of 739 genes and analyzed using the NanoString nSolver Advanced Analysis module. Results: GCB, ABC, UNC and PMBL-like proportions were 50%, 30%, 14% and 6% in HIV unassociated (HIV neg) and 50%, 23%, 25% and 2% in HIV pos tumors respectively, confirming both cohorts were GCB-enriched by molecular profiling. PMBL-like cases were excluded from further analyses. OncoScan CN analyses showed that HIV pos DLBCL were significantly less aberrant than HIV neg DLBCL (median CN per sample 0.56 vs 1.01, p<0.001). HIV pos GCB tumors were significantly less aberrant than both HIV neg GCB tumors (median CN per sample 0.50 vs 1.11, p<0.001), and HIV pos ABC tumors (median CN per sample 0.50 vs 0.81, p=0.009). HIV pos ABC tumors were also less aberrant than HIV neg ABC tumors, however, the difference was not significant overall (median CN per sample 0.81 vs 1.03, p=0.162) or for gain/amplifications (median CN per sample 0.38 vs 0.42, p=0.814), but was borderline significant for loss/deletions (median CN per sample 0.31 vs 0.47, p=0.06). GEP analyses identified Cell Cycle and DNA repair as the top two upregulated pathways in HIV pos GCB tumors compared to HIV neg GCB tumors. In contrast, Cell Cycle and DNA repair were downregulated in HIV pos ABC tumors compared to HIV neg ABC tumors. However, one gene ( RBX1) was identified as significantly upregulated in both pathways in HIV pos GCB (adjusted p-value 0.0215) and HIV pos ABC (adjusted p-value 0.0046) tumors compared to their HIV neg counterparts. A second gene ( SKP1) was also significantly upregulated in HIV pos GCB (adjusted p-value 3.94E-5) and HIV pos ABC (adjusted p-value 0.0031) tumors. Together RBX1 and SKP1 encode the two principal functional subunits of the SKP1, CUL1, F-box protein (SCF) complex which is part of the ubiquitin proteasome system (UPS) and has been reported as essential for maintaining genome and chromosome stability (Thompson et al 2021). Conclusions: These results demonstrate that HIV pos DLBCL tumors are less aberrant than their HIV neg counterparts irrespective of COO subtype. However, the degree of genomic stability differs by COO, with HIV pos GCB tumors being more genomically stable than HIV pos ABC tumors. Although both have enhanced expression of UPS related SCF genes ( RBX1, SKP1), only HIV pos GCB tumors have enhanced expression of key DNA repair genes as well including Fanconi Anemia ( BRCA2/FANCD1, FANCL) and mismatch repair ( MSH2, MSH6) genes (adjusted p-values of ≤ 0.0263). Given the reported role of SCF in genomic stability maintenance, the HIV pos COO CN differences could be due to the contributions of both DNA repair and SCF mechanisms in HIV pos GCB and mainly SCF mechanisms in HIV pos ABC tumors.

Introduction: Non-Follicular Low-Grade B-Cell Lymphomas (LGBCL) are a subset of lymphomas that share overlapping clinical and histologic features. While most cases of LGBCL have an indolent course, some experience aggressive disease with no definitive biomarkers to identify aggressive cases at diagnosis. Significant advances have been made in the molecular classification of other lymphomas, yet molecular profiling and identification of mechanisms driving aggressive disease in LGBCL remain an unmet need. We previously used a multi-omic approach to characterize the molecular and immune landscapes of LGBCL tumors and identified a transcriptomic signature associated with cell proliferation and inferior overall survival (OS) in two independent cohorts (HR 7.82; 95% CI 2.4-25.4; p < 0.001 & HR 10.07; 95% CI 2.00-50.61; p = 0.005) and cases that transformed to DLBCL (Hopper et al., 2023). Here, we identify the oncoprotein DEK as a novel transcriptional regulator of this signature and use a functional genomic approach to further interrogate the role of DEK in B-cell lymphoma. Methods: Tumors from 64 newly-diagnosed/untreated LGBCL cases enrolled in the University of Iowa/Mayo Clinic Molecular Epidemiology Resource were included in this study (SMZL = 48, NMZL = 6, LPL = 5, B-NOS = 3, EMZL = 2). Tumor RNA-seq and paired tumor-normal WES were used to assess DEK expression and identify copy number alterations (CNAs), respectively. DEK deficient (KO) cell line models were generated using CRIPSR/Cas9. DEK expression was assessed by western blot (WB) and immunofluorescence (IF) using standard techniques. Proliferation was assessed by MTT assay. Results: To identify transcriptional regulators of our previously identified signature, we used the tool MAGIC to mine publicly available ChIP-seq data and identified DEK, an oncoprotein associated with tumorigenesis and poor prognosis in several cancers, as a key regulator of genes in our signature (p = 0.008). RNA-seq indicated that high levels of DEK expression associated with inferior OS in LGBCL (HR 2.99; 95% CI 1.21-7.36; p = 0.02). Additionally, we identified DEK as a target of copy number gains/amps in LGBCL (7% of cases), which corresponded to significantly higher DEK expression (p = 0.04). The association of DEK with aggressive lymphoma was confirmed when we assessed its expression in public cell line RNA-seq data and observed the highest expression in lines derived from aggressive lymphomas, which we further validated at the protein level in a panel of B-cell lymphoma cell lines by WB and IF confocal microscopy. Additionally, we detected nuclear localization of DEK, consistent with its role as a transcriptional regulator. As a model of DEK overexpression for functional characterization, we used the SUDHL6 lymphoma cell line, which has an amplification of DEK at 6p22.3 and expresses high levels of DEK protein. Using CRISPR technology, we generated DEK deficient SUDHL6 and tested the impact of loss of DEK expression. DEK KO cells proliferated significantly slower than WT SUDHL6 cells in vitro (p < 0.0001)(Panel A), indicating DEK plays a role in regulating proliferative capacity in lymphoma. To identify mechanisms responsible for reduced proliferation in DEK KOs, we assessed baseline protein expression levels of apoptosis proteins in WT versus KO cells. We found near complete abrogation of anti-apoptotic protein Bcl-2 and reduction of Bcl-xL expression in DEK KO cells (Panel B). These results indicate DEK may contribute to tumor proliferative capacity by regulating susceptibility to apoptosis. Conclusion: DEK has been implicated in regulation of gene expression, but its precise role has remained elusive, with little known about its role in lymphoma. Using an integrative molecular and functional genomics approach, we identified DEK as a novel oncoprotein in LGBCL that is upregulated in cases with inferior clinical outcome and is regulated by copy number gains/amps. DEK plays a role in regulating tumor proliferative capacity by altering susceptibility to apoptosis through regulation of Bcl-2 and Bcl-xL. Collectively, this data suggests high-risk LGBCL tumors may enrich for cells with DEK overexpression or CNAs as a mechanism driving oncogene addiction, highlighting DEK, and its downstream targets, as potential therapeutic targets in a subset of high-risk LGBCL.

Background Diffuse large B cell lymphoma (DLBCL) is the most common aggressive lymphoid malignancy in adults. Though standard immunochemotherapy regimens can result in clinical remission and cure in a majority of patients, approximately 30% of patients are primary treatment resistant or eventually relapse (rrDLBCL), and their prognosis is very poor. Recent molecular classification by large scale genomic and transcriptomic profiling of newly diagnosed DLBCL tumors has resulted in a deeper understanding of disease drivers, however, the molecular heterogeneity of rrDLBCL is yet to be fully explored. Therefore, in this study, we used an unsupervised approach to better define transcriptional and genetic programs in rrDLBCL samples hypothesizing that molecular stratification within this high-risk group may lead to a better understanding of this disease, which remains an unmet need, and a more personalized therapeutic approach. Methods Data used in this study included RNA (n=143) and whole exome (n=126) sequencing data from available FFPE tumor samples at the time of a relapse (any line of treatment, r1-r10 relapse timepoints included in analysis, one per patient), consented to the Molecular Epidemiology Resource (n=61), banked in the Mayo Lymphoma Biobank (n=50), or consented to the CC-122-ST-001 clinical trial (n=32, NCT01421524). Unsupervised clustering was performed on protein coding gene expression values using the non-negative matrix factorization (NMF) approach. NMF was carried out by performing 200 runs on cluster sizes from 2 to 6. Cell of origin (COO) was determined using the method described by Reddy et al. The tumor microenvironment was analyzed using CIBERSORTx and Lymphoma Microenvironment Classification (LME). Genetic classification was done using LymphGen. Results In order to identify unique transcriptional programs in rrDLBCL, we implemented NMF and consensus clustering to define an ideal quantity of stable patient clusters. We identified 4 rrDLBCL patient clusters (rrC1-rrC4) as the most stable solution, based on consensus clustering selection criteria of iterative NMF runs. While relapse timepoint was not associated with individual clusters, we did observe significant differences in tumor characteristics between the clusters. COO classification found that rrC1 and rrC4 were enriched for GCB-DLBCL while rrC3 was enriched for ABC-DLBCL classification (P < .001). To explore the biologic programs in each cluster, we used pathFindR, which leverages protein-protein interaction networks to identify disease related pathways, rrC1 and rrC4 were defined by pathways involved in cell cycle and mismatch repair; oxidative phosphorylation in rrC2; and MAPK and NF-kB in rrC3. Next, LME classification was done to define patterns in the tumor microenvironment and we found a significant difference between groups (P < .001), with rrC2 and rrC4 tumors enriched for a depleted TME, while rrC1 and rrC3 had a more immune rich or inflammatory TME. This was supported by CIBERSORTx analysis identifying significant differences (P > .001) in abundance of CD8 T cells, memory CD4 T cells, and M1 macrophages between clusters, with rrC4 having the lowest abundance. We then explored genetic patterns in each cluster. Gene level mutation enrichment analysis showed BCL7A mutation exclusively in C4 (6/6, P = .03). The clusters were not associated with LymphGen classification. Lastly, our recently published high risk signature classification that is associated with early clinical failure and is characterized by metabolic dysregulation, a depleted TME, T53 alterations, and poor outcome, was significantly different between the groups (P = .03) with enrichment for high-risk cases in rrC4. In summary, we show for the first time that rrDLBCL patients can be classified into four gene expression clusters that are associated with distinct pathway, TME, and genetic programs. These clusters should now be tested to learn if they can help select patients for newer therapies for rrDLBCL such as CAR-T and bispecific antibodies.

Introduction Lymphoid malignancy (LM) is a heterogenous neoplasm with respect to biology, aggressiveness, and treatment with over 100 subtypes, including Hodgkin lymphoma (HL), non-Hodgkin lymphoma (NHL), and multiple myeloma (MM). Genome-wide association studies have identified common variants associated with risk of specific LM subtypes, but less is known about the contribution of rare inherited pathogenic variants (PV) in the genetic architecture of LM risk. Rare PV have been shown to contribute to the genetic architecture of solid tumor cancer risk, many of which have been found in genes that are important to the DNA-damage repair (DDR) pathway (e.g., BRCA1, BRCA1, ATM). Here, we investigated the prevalence of PV in cancer predisposition genes regularly included on multi-gene panel testing based on NCCN guidelines and correlated with risk of LM overall and by common subtypes. Methods In this association study, cases were newly diagnosed lymphomas (excluding chronic lymphocytic leukemia) from Mayo Clinic who were enrolled between 2002 and 2015 in the Lymphoma SPORE Molecular Epidemiology Resource (MER) and MM from Mayo Clinic enrolled primarily between 1998 and 2022 in a clinical registry. Controls were from the Mayo Clinic Biobank who were enrolled between 2009 and 2016, from which we excluded individuals with a history of hematologic malignancy. All participants provided a blood sample for whole exome sequencing, performed by Regeneron on an Illumina NovaSeq panel (mean coverage of 48X). Variants were called using GATK v4 and variant annotation was performed using BioR. All loss-of-function variants (i.e., nonsense, frameshift, and consensus splice sites) and pathogenic missense in 19 cancer predisposition genes with a minor allele frequency < 0.5% were included in the analyses. Logistic regression was used to estimate odds ratio (OR) and 95% confidence intervals (CI) for the association between mutation status by gene (burden test) and risk of LM overall, and by subtype that had more than 5 mutation carriers. All analyses were adjusted for age (at diagnosis for cases and at enrollment for controls) and sex. Results A total of 6990 cases and 42432 controls were included. Median age was 63 years for both cases and controls (case range 18-94 years, control range 18-99 years). Males accounted for 58.5% (n=4090) of the cases and 41.7% (n=17762) of the controls. MM accounted for 31% (n=2138) of the cases, followed by diffuse large B-cell lymphoma (DLBCL, n=1146) and follicular lymphoma (FL, n=1132) each accounting for 16% of cases. CHEK2 (1.0%), ATM (0.4%), BRCA2 (0.4%), and BRCA1 (0.3%) had the most PV carriers in controls; these frequencies are in the range of published control populations. A total of 1816 (3.7%) individuals had a PV across the 19 genes, with 4.7% of cases and 3.5% of controls carrying any PV (OR=1.34, 95% CI: 1.18-1.51). ATM (OR=1.86, 95% CI: 1.36-2.49), CHEK2 (OR=1.74, 95% CI: 1.42-2.13) and TP53 (OR=9.07, 95% CI: 4.51-18.87) were all significantly associated with increased risk of LM overall (Figure 1). When investigating subtypes (Table 1), CHEK2 (OR=1.93, 95% CI: 1.23-2.89) and TP53(OR=10.97, 95% CI: 3.06-31.43) were significantly associated with DLBCL. ATM (OR=2.22, 95% CI: 1.13-3.90) was significantly associated with FL. ATM (OR=1.97, 95% CI: 1.16-3.12), CHEK2 (OR=1.75, 95% CI: 1.24-2.41) and TP53 (OR=7.67, 95% CI: 2.43-20.72) were significantly associated with MM and ATM (OR=8.62, 95% CI: 4.47-15.12), NBN (OR=6.94, 95% CI: 2.41-15.76) and TP53 (OR=38.30, 95% CI: 10.27-116.69) were significantly associated with mantle cell lymphoma (MCL). When subset to LM cases overall, there was no evidence of enrichment for young onset (age <60 years), male sex, or family history of hematologic malignancy between PV carriers and non-PV carriers. However, FL cases with an ATM PV tended to be less than 60 years (OR=3.21, P=0.09) compared to non- ATM PV carriers. Similarly, MM cases with a TP53 PV tended to be less than 60 years of age (OR=7.39, P=0.07) compared non-carriers. Discussion In this large case-control study, PV in established cancer predisposition genes were associated with increased risk of LM overall, demonstrating that rare inherited variants may play an important etiologic role. This study also suggests that PV in cancer predisposition genes may be subtype specific. These results will help inform LM risk for individuals who undergo genetic testing of cancer predisposition genes.

Background: Tumor mutational burden (TMB), defined as the number of nonsynonymous mutations per megabase (Mb) in the coding regions of a tumor genome, has been associated with improved response to immune checkpoint blockade treatments and more broadly with overall survival (OS) in many cancers. Limited prior studies have found that TMB is higher in diffuse large B-cell lymphoma (DLBCL) compared to many other cancers. However, only two studies have evaluated TMB and prognosis in newly diagnosed DLBCL patients. The prognostic role of TMB in diffuse large B-cell lymphoma (DLBCL) remains unclear as prior studies have been limited to panel sequencing of cancer genes and/or lacked matched germline-tumor samples for filtering. Methods: We characterized TMB from whole exome sequencing (WES) in newly diagnosed DLBCL patients with matched diagnostic tumor-normal samples prior to treatment with standard immunochemotherapy (IC). Tumor DNA was extracted from formalin-fixed, paraffin embedded tissue sections and germline DNA from peripheral blood. We evaluated the association between TMB and event-free (EFS) and OS using Cox regression adjusted for the international prognostic index (IPI) and other factors including cell of origin (COO), MYC double hit (DH) with BLC2 or BCL6 by FISH, and TP53 mutation status. EFS and OS were defined as time from diagnosis to earliest of treatment failure (EFS only), death, or last follow-up; EFS at 24 months (EFS24) was also modeled as a marker of early aggressive DLBCL. An optimal high/low TMB cutpoint was determined based on EFS using maximally selected rank statistics; sensitivity analyses used an 20/80 percentile to define the threshold. We also evaluated the association of driver tumor mutational load (driverTML) with EFS and OS, defined as the total number of mutations in 292 DLBCL driver genes. Results: In the analysis cohort of 394 DLBCL patients, the median age at diagnosis was 65 years (interquartile range: 56-73), 43% were female, 62.2% had IPI 0-2, and 57.4% germinal center B-cell COO. MYC was DH in 7.5% and TP53 was mutated in 16.2% of patients. Through a median follow-up of 7.5 years, there were 141 events and 122 deaths. EFS24 was achieved in 75.1% of the cohort; 5-year EFS was 69% (95% CI 64-74%) and 5-year OS was 75% (95% CI 71-80%). Median TMB in our cohort of DLBCL patients was 3.6 mutations/Mb, which was the fifth highest compared with 32 cancers in The Cancer Genome Atlas. As a continuous variable, increasing TMB (mutation/Mb) was associated with superior EFS (HR=0.89, 95% CI 0.81-0.98) and OS (HR=0.89, 95% CI 0.81-0.99) after adjustment for IPI. Spline analyses confirmed linear associations of TMB with EFS and OS. The optimal modeled cutpoint based on EFS was 4.5 mutations/Mb, with 31% classified as high TMB (>4.5). No significant ( P >.05) differences were observed between high and low TMB patients with clinical (i.e., age, sex, IPI category) or molecular (cell of origin, MYC DH, TP53 mutated) factors. In Cox regression, high-TMB was significantly protective for EFS and for OS compared to low-TMB after adjustment for each clinical factor ( Figure 1), with simultaneous adjustment for all these factors associated with superior EFS (HR=0.52, 95% CI 0.33-0.84) and OS (HR=0.59, 95% CI 0.36-1.00). TMB (high/low) combined with IPI (0-2, 3-5) was significantly ( P =.001) associated with EFS in Kaplan-Meier analysis ( Figure 2). Among patients with IPI 3-5, high TMB was significantly associated with superior EFS (HR=0.47, 95% CI 0.27-0.81) and OS (HR=0.57, 95% CI 0.33-0.99). Among patients with IPI 0-2, we also observed longer EFS (HR=0.62, 95% CI 0.34-1.11) and OS (HR=0.62, 95% CI 0.32-1.20) in high TMB patients, although the associations were not statistically significant ( P >.05). In sensitivity analyses, a 20/80 percentile TMB threshold found similar results for EFS (HR=0.64, 95% CI 0.41-1.03) and OS (HR=0.69, 95% CI 0.42-1.12) as in our main analyses. TMB and driverTML were significantly correlated ( R=0.65, P <10 -16) and we found similar effect sizes for EFS (HR=0.45, 95% CI 0.22-0.91) and OS (HR=0.55, 95% 0.27-1.13) as in our main analyses. Conclusions: In a large and well-defined cohort of IC treated DLBCL patients, we found DLBCL patients with higher TMB had superior EFS and OS after adjustment for other clinical and molecular prognostic factors, and the prognostic association for TMB was robust to threshold and gene set.

Peripheral T-cell lymphoma (PTCL) encompasses a diverse group of post-thymic lymphomas, with ~40% of cases not further classifiable and designated as PTCL-not otherwise specified (PTCL-NOS). Two molecular prognostic subtypes of PTCL-NOS, PTCL-TBX21 and PTCL-GATA3, were identified through gene expression profiling (GEP). A subset of PTCL-NOS with T-follicular helper (TFH) differentiation was subsequently categorized as nodal PTCL with TFH phenotype (PTCL-TFH), also known in current classifications as nodal PTCL-TFH, NOS, or TFH lymphoma, NOS. However, the boundary between PTCL-NOS and PTCL-TFH remains poorly defined. To refine these subtypes, 101 PTCL-NOS with centralized pathology review and extensive immunophenotyping by immunohistochemistry (IHC) using 22 antibodies, digital GEP (nCounter, NanoString Inc) (n= 70), and RNA sequencing (n= 73) were included in this study. IHC was used to identify PTCL-TFH cases (those with strong expression of more than two TFH markers); the remaining patients were classified as PTCL-GATA3 and PTCL-TBX21 using pre-defined GEP signatures. Cases were reclassified into PTCL-NOS (n= 63) and PTCL-TFH (n= 38), with PTCL-NOS subclassified into PTCL-GATA3 (n= 22; 34%) and PTCL-TBX21 (n= 41; 66%) and showing significant differences in OS (p<0.001). PTCL-GATA3 was characterized by medium to large transformed cells (average= 90%, 70-100%) and had minimal tumor microenvironment (TME) (TME-poor: 100%). In contrast, PTCL-TBX21 was more heterogeneous, associated with pleomorphic cells in a polymorphous background (TME-rich: 78%), including Lennert lymphoma-like cases (22%). mRNA and CIBERSORT analysis substantiated the findings and identified a subset enriched in cytotoxic features in the PTCL-TBX21 subtype. IHC indicated that PTCL-GATA3 cases were CD4+/CD8- (83%) or CD4-/CD8- (17%) and lacked expression of CD8 or cytotoxic markers compared to PTCL-TBX21 (p<0.01). PTCL-GATA3 showed significantly higher expression of LEF1 (average= 80%), Ki67 (80%), and MYC (25%) than PTCL-TBX21 (25%, 30%, <5%, respectively, p<0.01). mRNA and protein expression of these biomarkers showed a significant positive correlation (r=0.5, p<0.001), and expectedly higher mRNA expression of LEF1 and MYC was observed in the PTCL-GATA3 versus PTCL-TBX21 (p<0.05). Strong expression of CD30 (>50% of cells) was only seen in PTCL-GATA3 cases. EBER positivity, found only in rare background cells, was not significantly different (18% of PTCL-GATA3 vs. 12% of PTCL-TBX21). Within PTCL-TBX21, we identified cytotoxic and non-cytotoxic subsets with divergent morphological, phenotypic, and clinical findings. The cytotoxic PTCL-TBX21 exhibited an activated cytotoxic phenotype (23/25, 96%), denoted by TIA1 and granzyme-B and/or perforin expression. Extranodal involvement and single-cell apoptosis were observed in 17% and 45% of the cytotoxic cases, respectively, but absent in non-cytotoxic PTCL-TBX21 cases. A trend towards higher Ki67 expression (average= 40% vs. 20%, p= 0.08) was seen in the cytotoxic subgroup. In contrast, the non-cytotoxic PTCL-TBX21 was associated with a CD4+/CD8- phenotype and higher ICOS expression (average= 30%) and CCR4 (60%) compared to the cytotoxic PTCL-TBX21 (p= 0.001). PTCL-TFH cases showed a CD4+/CD8- phenotype (90%) and rarely a CD4-/CD8- phenotype (10%). While a subset of PTCL-TFH cases had AITL-like features such as numerous clear cells and/or prominent vasculature (31% of cases), which were not seen in the other subgroups, the remaining cases exhibited morphology that was indistinguishable from other PTLC-NOS cases including sheets of transformed cells or pleomorphic cells in a polymorphous background. Morphologically, PTCL-TFH cases, like PTCL-TBX21 were associated with a rich TME (TME-rich: 75%). CIBERSORT analysis showed enrichment of plasma cells (p<0.01) in PTCL-TFH, compared to PTCL-TBX21, and validated by morphology (30% of cases vs. 5%). Expression of one TFH marker was frequent in some PTCL-NOS cases (PTCL-GATA3= 41% of cases, PTCL-TBX21-non cytotoxic= 47%, PTCL-TBX21-cytotoxic= 5%). Conclusion: Our comprehensive evaluation underscores the importance of integrating morphology, immunophenotyping, and GEP in achieving an accurate diagnosis, potentially leading to more tailored treatment strategies for PTCL. Correlation with pending whole-exome sequencing studies will be provided at the time of presentation.

SummaryMantle cell lymphoma (MCL) is clinically and biologically heterogeneous. While various prognostic features have been proposed, none currently impact therapy selection, particularly in older patients, for whom treatment is primarily dictated by age and comorbidities. Herein, we undertook a comprehensive comparison of clinicopathological features in a cohort of patients 60 years and older, uniformly treated with bendamustine and rituximab, with a median survival of >8 years. The strongest prognostic indicators in this cohort were a high‐risk call by a simplified MCL international prognostic index (s‐MIPI) (HR: 3.32, 95% CI: 1.65–6.68 compared to low risk), a high‐risk call by MCL35 (HR: 10.34, 95% CI: 2.37–45.20 compared to low risk) and blastoid cytology (HR: 4.21, 95% CR: 1.92–9.22 compared to classic). Patients called high risk by both the s‐MIPI and MCL35 had the most dismal prognosis (HR: 11.58, 95% CI: 4.10–32.72), while those with high risk by either had a moderate but clinically relevant prognosis (HR: 2.95, 95% CI: 1.49–5.82). A robust assay to assess proliferation, such as MCL35, along with stringent guidelines for cytological evaluation of MCL, in combination with MIPI, may be a strong path to risk‐stratify older MCL patients in future clinical trials.

Abstract Although TP53 is commonly mutated in transformed follicular lymphoma, mutations are reported in <5% of pretreatment follicular lymphoma (FL) specimens. We assayed archival follicular B-cell non-Hodgkin lymphoma specimens from a completed clinical trial, Southwest Oncology Group S0016, a phase 3 randomized intergroup trial of CHOP (cyclophosphamide, hydroxydaunorubicin, oncovin, and prednisone) chemotherapy plus R-CHOP (rituximab-CHOP) compared with CHOP chemotherapy plus 131-iodine tositumomab (radioimmunotherapy [RIT]-CHOP). Subclonal TP53 mutations (median allele frequency 0.02) were found in 25% of diagnostic FL specimens and in 27% of a separate validation cohort. In the R-CHOP arm, pathogenic TP53 mutations were not associated with progression-free survival (PFS) (10-year PFS 43% vs 44%). In contrast, among patients with no detectable pathogenic TP53 mutation, RIT-CHOP was associated with a longer PFS than with R-CHOP (10-year PFS 67% vs 44%; hazard ratio = 0.49; P = .008). No relationship was detected between PFS and the extent of activation-induced cytidine deaminase (AICDA)–mediated heterogeneity. In summary, subclonal TP53 mutations are common in FL and are a distinct phenomenon from AICDA-mediated genetic heterogeneity. The absence of a detectable subclonal mutation in TP53 defined a population that particularly benefited from RIT.