Xu-Monette, Zijun Y

Diffuse large B-cell lymphoma (DLBCL), the most common type of lymphoma, arises from various pathogenic mechanisms including gene translocations and fusions. Detection of gene rearrangements using fluorescence in situ hybridization (FISH) is a standard practice for DLBCL diagnosis and guides treatment decisions. High-throughput chromosome conformation capture (Hi-C) DNA sequencing is a next-generation sequencing-based technology to identify genome-wide rearrangements using a single assay. In this study, Hi-C sequencing was performed using FFPE tissues from 159 patients with DLBCL, and identified 746 cancer genes at or proximal to the rearrangement breakpoints with a total of 1903 occurrences. Focusing on clinically important rearrangements, Hi-C detected 102 rearrangements of MYC, BCL2, and/or BCL6 in 92 patients and revealed the fusion partners, including 25 rearrangements missed by FISH. Causes of FISH negative results included FISH-cryptic breakpoints, complex or faint FISH signals, low percentage of FISH positivity, and issues related to tissue fixation. Moreover, in 20 patients (12.6%), Hi-C sequencing detected 22 rearrangements characteristic of other lymphoma types, including CCND1 rearrangements that could lead to reclassification as mantle cell lymphoma. Survival analysis for genome-wide rearrangements using machine learning models identified MYC, PD-L1, CCND1, BCL2, NTRK1/PRCC, RRAS, and FANCE rearrangements with significant prognostic effects in the DLBCL cohort. In conclusion, Hi-C sequencing detects gene rearrangements crucial for diagnosis in an unbiased and molecular manner and showed high sensitivity and specificity in our study. These advantages of Hi-C sequencing offer help to improve the workflow of clinical pathology laboratories, diagnostic precision, and treatment of large B-cell lymphoma.

Abstract Pyroptosis is a form of programmed cell death characterized by the cleavage of the gasdermin (GSDM) family proteins that form pores in the plasma membrane, cell rupture, and the release of proinflammatory cytokines. In this study, we performed immunohistochemistry for cleaved gasdermin D (GSDMD), gasdermin E (GSDME) N-terminus, and gasdermin C (GSDMC) in 2 different cohorts of diffuse large B-cell lymphoma (DLBCL) and analyzed the impact of GSDM expression on prognosis and immunity. The results showed frequent cleaved GSDMD (N-terminal) expression. Only cytoplasmic GSDMD N-terminal expression correlated with significantly better patient survival in the 2 cohorts. In contrast, GSDME was mainly expressed in the vascular endothelium, and correlated with significantly adverse prognostic effect. Correlating with the multiplex fluorescent immunohistochemistry results, we found that cytoplasmic GSDMD N-terminal expression was associated with increased CD38+ (activated) M1 macrophages in both cohorts, cognate interactions between live DLBCL cells and activated M1 macrophages (and T cells), and lower PD-1/PD-L1 expression in the analyzed cases. In contrast, T-cell pyroptosis, lymphoma cell-resistance to cell death, and phagocytosis by M2 macrophages were observed in tissues with predominantly nuclear GSDMD N-terminal expression. Bulk gene expression profiling and deconvolution analysis revealed associations of cytoplasmic GSDMD N-terminal expression with downregulation of “Don’t eat me”–signaling genes, upregulation of many RNA genes, decreased frequency of “inflammatory” lymphoma microenvironment subtype, increased frequencies of prognostically favorable cell states and ecotypes, and decreased frequency of T-cell exhaustion state. In summary, this study showed distinct cellular and subcellular patterns of 3 GSDM proteins and their associated immune response phenotypes and prognostic effects, with implications for novel therapeutic strategies for B-cell lymphoma.

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 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.

Key PointsPhenotypic and genotypic profiling of MDM2 in DLBCL. MDM2 as a negative regulator of p53 tumor suppressor function.