Parsons, Benjamin M.

Abstract Introduction: MYC+BCL2+ double expression (DE) is a biomarker for poor prognosis in diffuse large B-cell lymphoma (DLBCL), likely due to the cooperation between MYC and BCL2 oncoproteins in tumor cells. However, as generally MYC and BCL2 are separately assessed on two different slides by immunohistochemistry (IHC), MYC+ and BCL2+ results are from different cells. With the WHO-recommended cutoffs, ≥40% for MYC and ≥50% for BCL2 (sum < 100%), there is chance that the so-called DE comes from non-overlapping regions of the tumor (not single-cell DE). Moreover, tumor expression scores based on morphology assessment often vary among pathologists. In this study, we performed fluorescent multiplex IHC (fmIHC) using a MultiOmyxTM immunofluorescence platform (NeoGenomics Laboratories, Inc.) to determine single-cell MYC and BCL2 co-expression for prognostic analysis in DLBCL. Patients and Methods: Co-expression of MYC, BCL2, and 11 immunophenotypic markers by fmIHC was digitally quantified for 547 patients with DLBCL. Percentage of cells with MYC/BCL2 double or single expression was calculated for various cell types. Prognostic analysis was performed for de novo DLBCL cases, including 108 patients treated CHOP chemotherapy, and 356 patients treated with rituximab (R)-CHOP. Results: First, although MYC and BCL2 were expressed in multiple cell types, the vast majority (>90%) of MYC+BCL2+ DE cells were lymphoma cells. Frequencies of MYC and BCL2 co-expression were consistent with random distribution of independent MYC/BCL2 expression in positive cases. The activated B-cell-like subtype compared with the germinal center B-cell-like subtype had a significantly higher mean percentage of MYC+BCL2+ DE, but not single BCL2 or MYC expression, in PAX5+ cells. Second, in both the R-CHOP and CHOP cohorts, high percentages of DE in PAX5+ cells and in all DAPI+ cells were associated with significantly poorer survival of DLBCL patients. Very high percentage of single BCL2 expression in PAX5+ cells was also associated with poorer survival, however, only in a small number of patients in both cohorts. Only in the R-CHOP cohort, high single MYC expression in PAX5+ cells (in a significant proportion of the cohort) was associated with significantly poorer overall survival. In the R-CHOP cohort, eight cases had MYC/BCL2 double hit by FISH, including 3 cases with MYC/IGH fusions (DE) and 5 cases with MYC/non-IGH rearrangements (only one DE). The exclusion of these double hit cases did not impact the significance of DE and single MYC+ expression. Summary: True MYC+BCL2+ double protein expression at the single cell level is predominantly in lymphoma cells, and associated with significantly poor survival of DLBCL patients. Our results suggest high cutoffs for common MYC/BCL2 IHC to identify true double expressor lymphoma. Citation Format: Zijun Yidan Xu-Monette, Yong Li, Qingyan Au, Harry Nunns, Wenyu Shi, Alexandar Tzankov, Carlo Visco, Govind Bhagat, Eric Hsi, Xiaoxian X. Zhao, Karen Dybkaer, April Chiu, Wayne Tam, Youli Zu, Fredrick B. Hagemeister, Andrew Song, Michele Pellegrino, Heounjeong Go, Maurilio Ponzoni, Andrés Ferreri, Michael B. Møller, Benjamin Parsons, J. Han van Krieken, Miguel A. Piris, Jane N. Winter, Mingzhi Zhang, Bing Xu, Ken H. Young. Single-cell double expression of MYC and BCL2 proteins by fluorescent multiplex IHC is mainly in lymphoma cells with significant prognostic impact in large B-cell lymphoma while suggesting high cutoffs for routine IHC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6418.

Introduction: In recent years, increasing evidence suggests an important role of tumor-infiltrating B lymphocytes (TIL-Bs) in cancer immunity. In various types of solid tumors, high density of TIL-Bs and presence of mature tertiary lymphoid structures consistently correlate with better prognosis and response to immune checkpoint blockade therapies. Diffuse large B-cell lymphoma (DLBCL) is a type of aggressive lymphoma arising from mature B cells. TIL-Bs have been identified in DLBCL based on single-cell gene-expression data in several previous studies, but their prognostic significance in DLBCL is unknown. In this study, we determined the clinical significance of TIL-B abundance and investigated the potential underlying mechanisms. Patients and Methods: This abstract includes a discovery study in which we distinguished TIL-Bs from malignant B-cells by immunoglobulin gene (IG) clonotype analysis and a validation study using flow cytometry. In the discovery study, a molecularly well-characterized DLBCL cohort assembled by the International DLBCL R-CHOP Consortium Program was analyzed. DNA samples from DLBCL patients were sequenced by high-throughput ultra-deep sequencing assays using multiplexed forward and reverse primers. Based on the IG heavy chain (IGH) sequencing results, malignant B-cell and TIL-B VDJ clonotypes were identified for 138 patients, and the frequencies and numbers of TIL-B clonotypes in individual sequence repertoires were calculated. In 79 DLBCL cases, a DJ-only sequence was predominant and no productive VDJ sequences met the criteria for diagnostic sequences. Total frequencies of productive VDJ sequences in the sequence repertoires were then used to access high and low TIL-B frequencies. TIL-B frequencies were also evaluated by IG light chain clonotypes for these 217 patients and additional 52 patients. The TIL-B frequencies were correlated with patient survival, gene-expression profiling (GEP) data GSE31312, and frequencies of DLBCL-infiltrating immune cells quantified by fluorescent multiplex immunohistochemistry (mIHC). To validate the prognostic significance of TIL-B frequencies, we assembled a validation cohort of 94 DLBCL patients treated at Duke University Medical Center. TIL-Bs in tissues were quantified by flow cytometry immunophenotyping. Ratios of TIL-Bs to abnormal B cells were assessed for prognostic effects. Results: First, we found thathigh frequencies of TIL-Bs and clonotypic diversity were associated with significantly favorable prognostic effects in 138 DLBCL patients with their malignant B-cell IGH-VDJ clonotypes identified (Figure A). The prognostic effects of TIL-Bs outweighed those of the germinal center B-cell-like/activated B-cell-like classification (Figure B). Second, patients with high frequencies of TIL-Bs compared with those without had significant upregulation of immune response genes by GEP (Figure C), higher CD4 T cell infiltration quantified by fluorescent mIHC (Figure D), and enrichment of EcoTyper-defined specific cell states, including significantly increased frequencies of B cell state S2 (pre-memory), CD4 T cell state S3 (naïve), follicular helper T cell state S3, and endothelial cell state S3, whereas lower frequencies of B cell state S5 (plasmablast) and follicular helper T cell state S1. Third, high TIL-B abundance in 79 ‘DJ-only’ clonotype cases and additional 52 cases assessed by light chain clonotype analysis showed similar favorable prognostic effects and associations with upregulated immune gene expression and CD4 T cell infiltration. Fourth, the identified GEP signatures, prominent with genes involved in T-dependent B cell activation and T cell activation and differentiation, showed significant prognostic effects in two GEO datasets. Fifth, TIL-B abundance showed significant prognostic effects in DLBCL cases infiltrated with T-cells in both the discovery study (Figure E) and validation study (Figure F, flow cytometry cohort). Conclusions: TIL-B frequencies and clonotypic diversity have remarkable prognostic and biological effects in DLBCL, which has significant implications for developing novel therapeutic strategies. Our study identified a new prognostic biomarker in DLBCL and an important player in anti-lymphoma immune responses, providing a new point of view to analyze the tumor biology in aggressive B-cell lymphoma and precisely identify prognostic determinants.

Background: DNA mismatch repair (MMR) deficiency (dMMR), which leads to genomic and microsatellite instability, is a biomarker that predicts response to immunotherapies and has variable prognostic effects for chemotherapies in solid tumors. The dMMR can be detected using gene sequencing or immunohistochemistry for four essential MMR proteins: MSH6, MSH2, MLH1, and PMS2. Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive lymphoma. Previous studies have suggested the role of MMR gene variants in DLBCL lymphomagenesis, yet the prognostic role of dMMR in DLBCL has not been well studied. Methods: We performed targeted next-generation sequencing and immunohistochemistry for MSH6, MSH2, MLH1, and PMS2 in a large cohort of DLBCL patients treated with standard chemoimmunotherapy. Gene expression profiling (GEP) was performed using the Affymetrix GeneChip Human Genome HG-U133 Plus 2.0 microarray (data in GSE31312). Fluorescent multiplex immunohistochemistry (mIHC) was performed using MultiOmyx multiplexing immunofluorescence staining protocols and antibodies against 13 immune markers. We investigated the frequencies of dMRR and MMR protein expression and correlated the mutation status and expression levels with patient survival and DLBCL biology, including the number of mutated genes, GEP data, previously analyzed DLBCL biomarkers, and immune cell immunophenotypes. Results: MMR gene mutations and loss of MMR protein expression were infrequent in DLBCL (Figure A) and did not show a significant prognostic impact. MMR proteins were commonly expressed in DLBCL samples and the germinal centers of reactive tonsil controls (Figure B), with higher mean and median percentages of tumor cells expressing MSH6 and MLH1 proteins than in the solid tumor samples used for comparison. High expression of MMR proteins was associated with Ki-67, MYC, and p53 overexpression as determined by immunohistochemistry. GEP analysis identified significantly upregulated genes involved in the mitotic cell cycle and DNA metabolism and prominent downregulated immune gene signatures in DLBCL with high MMR protein expression. Fluorescent mIHC confirmed the associations with decreased T cell abundance in MSH6/MSH2/MLH1/PMS2 highly expressing DLBCL (Figure C) independent of p53 and MYC expression status, whereas MSH6/ MLH1 mutations were associated with increased T cell frequencies. High versus low expression of MSH6, MLH1, and PMS2 showed significant unfavorable prognostic effects in DLBCL when the optimal cutoffs were used for high expression. However, when the median percentages were used as cutoffs, the prognostic effects were not significant; nonetheless, MSH6 and PMS2 high expression showed significant adverse prognostic effects in the MYC¯ DLBCL subset. Interestingly, when we used Ki-67, MYC + or BCL2 + percentages in DLBCL assessed by immunohistochemistry to subtract MMR protein percentages and then correlated the differences to survival, we found significant favorable prognostic impact by higher percentage expression differences between MMR proteins and BCL2, or by intermediately higher differences between MMR proteins and MYC. For MSH2/MLH1/PMS2, the higher percentages compared with MYC/BCL2 showed no significant correlations with T cell infiltration levels, whereas the higher differences between MSH6 and MYC/BCL2 were associated with significantly lower T cell frequencies (Figure D). Conclusions: This study revealed that high expression of MMR proteins is a common feature of DLBCL associated with lower tumor-infiltrating T cells, MYC and p53 overexpression, and context-dependent prognostic effects. In contrast, dMMR and loss of MMR proteins are infrequent and have no significant prognostic effects in DLBCL treated with standard chemoimmunotherapy. These results may have implications for understanding DLBCL biology and the low efficacy of PD-1 blockade immunotherapy in DLBCL.

Abstract Purpose: Tumor-infiltrating B lymphocytes (TIL-B) have demonstrated prognostic and predictive significance in solid cancers. In this study, we aimed to distinguish TIL-Bs from malignant B-cells in diffuse large B-cell lymphoma (DLBCL) and determine the clinical and biological significance. Experimental Design: A total of 269 patients with de novo DLBCL from the International DLBCL R-CHOP Consortium Program were studied. Ultra-deep sequencing of the immunoglobulin genes was performed to determine B-cell clonotypes. The frequencies and numbers of TIL-B clonotypes in individual repertoires were correlated with patient survival, gene expression profiling (GEP) data, and frequencies of DLBCL-infiltrating immune cells quantified by fluorescent multiplex IHC at single-cell resolution. Results: TIL-B abundance, evaluated by frequencies of normal B-cell clonotypes in the immunoglobulin repertoires, remarkably showed positive associations with significantly better survival of patients in our sequenced cohorts. DLBCLs with high versus low TIL-B abundance displayed distinct GEP signatures, increased pre-memory B-cell state and naïve CD4 T-cell state fractions, and higher CD4+ T-cell infiltration. TIL-B frequency, as a new biomarker in DLBCL, outperformed the germinal center (GC) B-cell–like/activated B-cell–like classification and TIL-T frequency. The identified TIL-B–high GEP signature, including genes upregulated during T-dependent B-cell activation and those highly expressed in normal GC B cells and T cells, showed significant favorable prognostic effects in several external validation cohorts. Conclusions: TIL-B frequency is a significant prognostic factor in DLBCL and plays a crucial role in antitumor immune responses. This study provides novel insights into the prognostic determinants in DLBCL and TIL-B functions with important therapeutic implications.

Abstract Purpose: Diffuse large B-cell lymphoma (DLBCL) is molecularly and clinically heterogeneous, and can be subtyped according to genetic alterations, cell-of-origin, or microenvironmental signatures using high-throughput genomic data at the DNA or RNA level. Although high-throughput proteomic profiling has not been available for DLBCL subtyping, MYC/BCL2 protein double expression (DE) is an established prognostic biomarker in DLBCL. The purpose of this study is to reveal the relative prognostic roles of DLBCL genetic, phenotypic, and microenvironmental biomarkers. Experimental Design: We performed targeted next-generation sequencing; IHC for MYC, BCL2, and FN1; and fluorescent multiplex IHC for microenvironmental markers in a large cohort of DLBCL. We performed correlative and prognostic analyses within and across DLBCL genetic subtypes and MYC/BCL2 double expressors. Results: We found that MYC/BCL2 double-high-expression (DhE) had significant adverse prognostic impact within the EZB genetic subtype and LymphGen-unclassified DLBCL cases but not within MCD and ST2 genetic subtypes. Conversely, KMT2D mutations significantly stratified DhE but not non-DhE DLBCL. T-cell infiltration showed favorable prognostic effects within BN2, MCD, and DhE but unfavorable effects within ST2 and LymphGen-unclassified cases. FN1 and PD-1–high expression had significant adverse prognostic effects within multiple DLBCL genetic/phenotypic subgroups. The prognostic effects of DhE and immune biomarkers within DLBCL genetic subtypes were independent although DhE and high Ki-67 were significantly associated with lower T-cell infiltration in LymphGen-unclassified cases. Conclusions: Together, these results demonstrated independent and additive prognostic effects of phenotypic MYC/BCL2 and microenvironment biomarkers and genetic subtyping in DLBCL prognostication, important for improving DLBCL classification and identifying prognostic determinants and therapeutic targets.

Abstract Introduction: Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology. However, these biological subgroups overlap clinically. While R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard of care for treating patients with DLBCL, predicting which patients will not benefit from such therapy is important so that alternative therapy or clinical trials can be considered. Most of the studies stratifying patients select biomarkers first, then explore how these biomarkers can stratify patients based on outcome. We explored the potential of using machine learning to first group patients with DLBCL based on survival, then isolating the biomarkers necessary for predicting these survival subgroups. Methods: RNA was extracted from tissue paraffin blocks from 379 R-CHOP treated patients with de novo DLBCL, and from 247 patients with extranodal DLBCL. A targeted hybrid capture RNA panel of 1408 genes was used for next generation sequencing (NGS). Sequencing was performed using an Illumina NextSeq 550 System platform. Ten million reads per sample in a single run were required, and the read length was 2 × 150 bp. An expression profile was generated from the sequencing coverage profile of each individual sample using Cufflinks. A machine learning system was developed to classify patients into four groups based on their overall survival. This machine learning approach based on Naïve Bayesian algorithm was also used to discover the relevant subset of genes with which to classify patients into each of the four survival groups. To eliminate the underflow problem commonly associated with the standard Naïve Bayesian classifiers, we applied Geometric Mean Naïve Bayesian (GMNB) as the classifier to predict the survival group for each patient. Results: Using machine learning, patients were first divided into two groups: short survival (S) and long survival (L). To refine this model, we used the same approach and divided the patients in each group into two subgroups, generating four groups: long survival in the long group (LL), short survival in the long group (LS), long survival in the short group (SL), and short survival in the short group (SS). The hazard ratio for this model was 0.174 (confidence interval: 0.120-0.251), and P-value <0.0001. After defining these four groups, a machine learning algorithm was used to discover the biomarkers from the expression data of the 1408 genes from NGS data. To reduce the effects of noise and avoid overfitting, we employed a 12-step cross validation to obtain a robust measure. For an individual gene, a generalized Naïve Bayesian classifier was constructed on the training of one of the 12 subsets and tested on the other 11 testing subsets. This allowed us to limit the prediction process to 60 genes for each separation step. Using the selected biomarkers, we classified the patients in the original set (379 patients) into LL, LS, SL, and SS groups and then evaluated the survival pattern of these groups. As shown in Fig. 1A, the selected biomarkers predicted survival as expected in the overall survival groups prior to biomarker selection. For additional validation of the system, we used the selected biomarkers to classify a completely new set of 247 samples of patients with extranodal DLBCL. As shown in Fig. 1B, these selected biomarkers successfully predicted the overall survival in this group of patients with an HR of 0.530 (confidence interval: 0.234-1.197, P=0.005). This classification correlated with cell of origin classification, TP53 mutation status, MYC expression, and IRF4 expression. However, in a multivariate analysis, only TP53 mutation was independent in predicting prognosis (P=0.005) and age (below or over 60) (P=0.01) along with the survival grouping (P<0.000001). Conclusions: Using a novel machine learning approach with the expression levels of 180 genes, we developed a model that can reliably stratify patients with DLBCL treated with R-CHOP into four survival subgroups. This model can be used to identify patients who may not respond well to R-CHOP to be considered for alternative therapy and clinical trials. Figure 1 Figure 1. Disclosures Hsi: AbbVie Inc, Eli Lilly: Research Funding. Ferreri: Ospedale San Raffaele srl: Patents & Royalties; BMS: Research Funding; Pfizer: Research Funding; Beigene: Research Funding; Hutchison Medipharma: Research Funding; Amgen: Research Funding; Genmab: Research Funding; ADC Therapeutics: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; PletixaPharm: Membership on an entity's Board of Directors or advisory committees; x Incyte: Membership on an entity's Board of Directors or advisory committees; Adienne: Membership on an entity's Board of Directors or advisory committees. Piris: Millenium/Takeda, EUSA, Jansen, NanoString, Kyowa Kirin, Gilead and Celgene.: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Winter: BMS: Other: Husband: Data and Safety Monitoring Board; Actinium Pharma: Consultancy; Janssen: Other: Husband: Consultancy; Agios: Other: Husband: Consultancy; Gilead: Other: Husband: Consultancy; Epizyme: Other: Husband: Data and Safety Monitoring Board; Ariad/Takeda: Other: Husband: Data and Safety Monitoring Board; Merck: Consultancy, Honoraria, Research Funding; Novartis: Other: Husband: Consultancy, Data and Safety Monitoring Board; Karyopharm (Curio Science): Honoraria.

Abstract Background Primary bone diffuse large B cell lymphoma (DLBCL) is a variant of extranodal non-Hodgkin lymphoma (NHL) that is relatively rare, accounting for 3-15% of extranodal NHL and less than 1% of all NHL. Patients often present with pain and swelling or pathologic fracture of the affected area of the skeleton, and B symptoms are often less prevalent. Many cases are limited stage at presentation. Patients are usually treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or similar induction therapy. Based on older studies showing improved outcomes associated with the addition of radiation (many completed prior to the rituximab era), patients often still receive combined-modality therapy today. However, it remains unknown whether consolidative radiation therapy (RT) confers additional benefit following rituximab-based chemoimmunotherapy (CIT) induction. We sought to address this question in a multicenter retrospective study. Methods We conducted a retrospective analysis of outcomes in a modern cohort of patients who underwent treatment for primary bone DLBCL using chemotherapy regimens in the rituximab era either with or without consolidative RT. Data was collected from 13 academic medical centers in the U.S., with patients treated between 2005 and 2019. Patients were identified using each institution's hematologic malignancy registries. Analysis was limited to patients with primary bone DLBCL, with stage I-II disease (primary site +/- locoregional lymph nodes) that could be encompassed in a radiation field. Patients who received CIT alone or CIT followed by RT were included. Treatment selection was at the treating physician's discretion. Electronic medical records were reviewed for: demographics, chemotherapy regimens, radiation treatments, and PET scan data. Local IRB approval was obtained at sites. All data was distributed in a de-identified fashion. The primary outcomes were overall survival (OS), and relapse-free survival (RFS). Secondary outcomes included: response after induction CIT induction and the need for additional therapies. Chi-square test analysis was used to compare categorical variables and the Wilcoxon rank-sum test was used for continuous and ordinal measures. Survival curves were estimated using the Kaplan-Meier method and compared between groups via the log-rank test. Multi-variable analysis (MVA) for OS and RFS was performed using RT vs no RT, PET status post CIT (negative vs positive) and rituximab in induction (Y/N) as variables. Results A total of 127 patients were included: 91 who received CIT and radiation (RT group), and 36 who received CIT alone (no RT group). The median age of patients at diagnosis in the RT group and no RT group was 58.5 and 57.0, respectively. For CIT, the majority of patients received R-CHOP (84%). Despite focusing on 2005-2019, a small percentage of patients (5 - 14%) in each group (RT and no RT) did not receive rituximab with their induction therapy. The OS at 10 years was 74.5% in the RT group and 86.5% in the no RT group (p = 0.29). The RFS at 10 years was 70.9% in the RT group and 78.2% in the no RT group (p = 0.85). Among the 91 patients who received RT, 67 (77.0%) received a dose of 36 Gy or higher. There was no difference in OS (p = 0.63) or RFS (p = 0.90) in patients who received > 36 Gy vs < 36 Gy of radiation. On MVA for OS, RT was not associated with improved OS, although rituximab as part of induction was (p = 0.048). On MVA for RFS, neither RT nor rituximab were associated with improved RFS. For rituximab the trend was in the direction of rituximab benefit (p = 0.11). Conclusion Our results demonstrate that, in the rituximab era, patients with limited stage primary bone DLBCL have excellent outcomes overall. In this study, neither RFS or OS appeared to be improved with the addition of consolidative RT. Rituximab, however, was associated with improved OS on MVA. Outcomes do not appear to differ based on the dose of RT (> 36 Gy vs < 36 Gy). Our data suggests that RT may have less benefit for primary bone DLBCL in the rituximab era, although it is possible that a subset of patients may benefit from consolidative radiation therapy. A larger data set would likely be needed to evaluate this. We plan to present data regarding PET responses at the meeting. Figure 1 Figure 1. Disclosures Lunning: Acrotech: Consultancy; AstraZeneca: Consultancy; Myeloid Therapeutics: Consultancy; Janssen: Consultancy; Morphosys: Consultancy; Novartis: Consultancy; Karyopharm: Consultancy; Kyowa Kirin: Consultancy; Verastem: Consultancy; Kite, a Gilead Company: Consultancy; Celgene, a Bristol Myers Squibb Co.: Consultancy; Spectrum: Consultancy; Daiichi-Sankyo: Consultancy; Beigene: Consultancy; Legend: Consultancy; ADC Therapeutics: Consultancy; TG Therapeutics: Consultancy; AbbVie: Consultancy. Cashen: Secura Bio, ADC Therapeutics: Consultancy. Bartlett: Autolus: Research Funding; Bristol Myers Squibb: Research Funding; Celgene: Research Funding; Forty Seven: Research Funding; Genentech: Research Funding; Janssen: Research Funding; Kite, a Gilead Company: Research Funding; Merck: Research Funding; Millennium: Research Funding; Pharmacyclics: Research Funding; Seagen: Consultancy, Research Funding; Roche/Genentech: Consultancy; ADC Therapeutics: Consultancy, Research Funding. Caimi: XaTek: Patents & Royalties: Royalties from patents (wife); Amgen Therapeutics.: Consultancy; ADC Theraputics: Consultancy, Research Funding; Genentech: Research Funding; TG Therapeutics: Honoraria; Seattle Genetics: Consultancy; Verastem: Consultancy; Kite Pharmaceuticals: Consultancy. Rodgers: MJH Lifesciences: Consultancy. Barr: Morphosys: Consultancy; Gilead: Consultancy; Genentech: Consultancy; TG Therapeutics: Consultancy; Janssen: Consultancy; Beigene: Consultancy; Bristol Meyers Squibb: Consultancy; Seattle Genetics: Consultancy; AstraZeneca: Consultancy; Abbvie/Pharmacyclics: Consultancy. Epperla: Verastem: Speakers Bureau; Beigene: Speakers Bureau; Karyopharm: Other: Ad Board; Genzyme: Honoraria. Hill: AstraZenica: Consultancy, Honoraria; Celgene (BMS): Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Other: Travel Support, Research Funding; Pfizer: Consultancy, Honoraria; Gentenech: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Epizyme: Consultancy, Honoraria; Incyte/Morphysis: Consultancy, Honoraria, Research Funding; Beigene: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding. Karmali: BMS/Celgene/Juno: Consultancy, Research Funding; Epizyme: Consultancy; Karyopharm: Consultancy; Janssen/Pharmacyclics: Consultancy; Morphosys: Consultancy, Speakers Bureau; AstraZeneca: Speakers Bureau; BeiGene: Consultancy, Speakers Bureau; EUSA: Consultancy; Takeda: Research Funding; Genentech: Consultancy; Roche: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding, Speakers Bureau. Portell: Acerta/AstraZeneca: Research Funding; Xencor: Research Funding; Abbvie: Research Funding; Aptitude Health: Honoraria; Morphosys: Honoraria; Targeted Oncology: Honoraria; SeaGen: Research Funding; BeiGene: Honoraria, Research Funding; Merck: Honoraria, Research Funding; Pharmacyclics: Honoraria; Kite: Honoraria, Research Funding; TG Therapeutics: Honoraria, Research Funding; Genentech: Research Funding; VelosBio: Research Funding. Fenske: Pharmacyclics: Consultancy; AstraZeneca: Speakers Bureau; Servier Pharmaceuticals: Consultancy; MorphoSys: Consultancy; Adaptive Biotechnologies: Consultancy; KaryoPharm: Consultancy; CSL Therapeutics: Consultancy; TG Therapeutics: Consultancy, Speakers Bureau; Bristol-Myers Squibb: Speakers Bureau; Sanofi: Speakers Bureau; Seattle Genetics: Speakers Bureau; Biogen: Consultancy; ADC Therapeutics: Consultancy; Kite (Gilead): Speakers Bureau; Beigene: Consultancy; AbbVie: Consultancy.

Abstract Diffuse large B-cell lymphoma (DLBCL) is the major type of aggressive B-cell lymphoma. High-grade B-cell lymphoma (HGBCL) with MYC/BCL2 double-hit (DH) represents a distinct entity with dismal prognosis after standard immunochemotherapy in the current WHO lymphoma classification. However, whether TP53 mutation synergizes with MYC abnormalities (MYC rearrangement and/or Myc protein overexpression) contributing to HGBCL-like biology and prognosis is not well investigated. In this study, patients with DLBCL with MYC/TP53 abnormalities demonstrated poor clinical outcome, high-grade morphology, and distinct gene expression signatures. To identify more effective therapies for this distinctive DLBCL subset, novel MYC/TP53/BCL-2–targeted agents were investigated in DLBCL cells with MYC/TP53 dual alterations or HGBCL-MYC/BCL2-DH. A BET inhibitor INCB057643 effectively inhibited cell viability and induced apoptosis in DLBCL/HGBCL cells regardless of MYC/BCL2/TP53 status. Combining INCB057643 with a MDM2-p53 inhibitor DS3032b significantly enhanced the cytotoxic effects in HGBCL-DH without TP53 mutation, while combining with the BCL-2 inhibitor venetoclax displayed potent therapeutic synergy in DLBCL/HGBCL cells with and without concurrent TP53 mutation. Reverse-phase protein arrays revealed the synergistic molecular actions by INCB057643, DS3032b and venetoclax to induce cell-cycle arrest and apoptosis and to inhibit AKT/MEK/ERK/mTOR pathways, as well as potential drug resistance mechanisms mediated by upregulation of Mcl-1 and RAS/RAF/MEK/ERK pathways. In summary, these findings support subclassification of DLBCL/HGBCL with dual MYC/TP53 alterations, which demonstrates distinct pathobiologic features and dismal survival with standard therapy, therefore requiring additional targeted therapies. Implications: The clinical and pharmacologic studies suggest recognizing DLBCL with concomitant TP53 mutation and MYC abnormalities as a distinctive entity necessary for precision oncology practice. Visual Overview: http://mcr.aacrjournals.org/content/molcanres/19/2/249/F1.large.jpg.

Abstract Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routine practice by the World Health Organization classification for biological and therapeutic insights. Genetic subtypes uncovered recently are based on distinct genetic alterations in DLBCL, which are different from the COO subtypes defined by gene expression signatures of normal B cells retained in DLBCL. We hypothesize that classifiers incorporating both genome-wide gene-expression and pathogenetic variables can improve the therapeutic significance of DLBCL classification. To develop such refined classifiers, we performed targeted RNA sequencing (RNA-Seq) with a commercially available next-generation sequencing (NGS) platform in a large cohort of 418 DLBCLs. Genetic and transcriptional data obtained by RNA-Seq in a single run were explored by state-of-the-art artificial intelligence (AI) to develop a NGS-COO classifier for COO assignment and NGS survival models for clinical outcome prediction. The NGS-COO model built through applying AI in the training set was robust, showing high concordance with COO classification by either Affymetrix GeneChip microarray or the NanoString Lymph2Cx assay in 2 validation sets. Although the NGS-COO model was not trained for clinical outcome, the activated B-cell–like compared with the germinal-center B-cell–like subtype had significantly poorer survival. The NGS survival models stratified 30% high-risk patients in the validation set with poor survival as in the training set. These results demonstrate that targeted RNA-Seq coupled with AI deep learning techniques provides reproducible, efficient, and affordable assays for clinical application. The clinical grade assays and NGS models integrating both genetic and transcriptional factors developed in this study may eventually support precision medicine in DLBCL.