More accurate prognostic prediction in diffuse large B-cell lymphoma: beyond cell-of-origin
Authors: J. Koya, K. Kataoka
Journal: Annals of Oncology (2018)

Diffuse large B-cell lymphoma (DLBCL) represents a morphologically, genetically, and biologically heterogeneous entity with varied prognoses and clinical presentations. Although accurate diagnosis is generally straightforward, the allocation of therapy remains suboptimal for approximately 40% of patients not cured by standard treatment strategy. Therefore, the identification of DLBCL patients who benefit from more intensive chemo-immunotherapy and/or molecular-targeted agents is of paramount value. The international prognostic index (IPI) score, based on patient and clinical characteristics, has been widely used for patient stratification in the past 20 years [1.International Non-Hodgkin's Lymphoma Prognostic Factors Project A predictive model for aggressive non-Hodgkin's lymphoma.N Engl J Med. 1993; 329: 987-994Crossref PubMed Scopus (5013) Google Scholar]. To better classify DLBCL patients, the predictive power of molecular biomarkers for prognosis has been extensively investigated. In particular, gene expression profiling determining cell-of-origin (COO) has been shown to successfully divide DLBCL cases into three categories: germinal center B-cell-like (GCB), activated B-cell-like (ABC), and unclassified subtypes. GCB subtype shows a better clinical outcome than ABC and unclassified subtype, independently of the IPI score [2.Rosenwald A. Wright G. Chan W.C. et al.The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma.N Engl J Med. 2002; 346: 1937-1947Crossref PubMed Scopus (3197) Google Scholar]. Instead of comprehensive gene expression profiling analysis, immunohistochemical methods according to more simplified criteria, such as Hans method [3.Hans C.P. Weisenburger D.D. Greiner T.C. et al.Confirmation of the molecular classification of diffuse large B-cell lymphoma by immunohistochemistry using a tissue microarray.Blood. 2004; 103: 275-282Crossref PubMed Scopus (3194) Google Scholar], based on CD10, BCL6, and MUM1 expression were developed as an alternative for predicting COO. Although the coincidence rate has been improved by devising the combination of markers to be analyzed [4.Visco C. Li Y. Xu-Monette Z.Y. et al.Comprehensive gene expression profiling and immunohistochemical studies support application of immunophenotypic algorithm for molecular subtype classification in diffuse large B-cell lymphoma: a report from the International DLBCL Rituximab-CHOP Consortium Program Study.Leukemia. 2012; 26: 2103-2113Crossref PubMed Scopus (278) Google Scholar], a recent systematic review analysis demonstrated that COO classification determined by immunohistochemistry had no additional value for survival prediction [5.Schmidt-Hansen M. Berendse S. Marafioti T. McNamara C. Does cell-of-origin or MYC, BCL2 or BCL6 translocation status provide prognostic information beyond the International Prognostic Index score in patients with diffuse large B-cell lymphoma treated with rituximab and chemotherapy? A systematic review.Leuk Lymphoma. 2017; 58: 2403-2418Crossref PubMed Scopus (13) Google Scholar]. More recently, Lymph2Cx, analyzing 20 gene expression by a parsimonious digital gene expression analysis (manufactured by NanoString Technologies, Seattle, WA), have been developed for COO determination. This method is applicable to formalin fixed, paraffin-embedded (FFPE) samples and shows an excellent (more than 95%) concordance with conventional genome-wide gene expression profiling [6.Scott D.W. Wright G.W. Williams P.M. et al.Determining cell-of-origin subtypes of diffuse large B-cell lymphoma using gene expression in formalin-fixed paraffin-embedded tissue.Blood. 2014; 123: 1214-1217Crossref PubMed Scopus (430) Google Scholar]. Tumor microenvironment (TME), consisting of non-malignant cells and stromal elements, such as lymphocytes, dendritic cells, macrophages, and fibroblasts, has been recognized as an important contributing factor for tumor development, progression, and therapeutic resistance [7.Scott D.W. Gascoyne RD. The tumour microenvironment in B cell lymphomas.Nat Rev Cancer. 2014; 14: 517-534Crossref PubMed Scopus (330) Google Scholar]. For instance, M2 tumor-associated macrophages affect degradation of the extracellular matrix and angiogenesis via overexpression of legumain, leading to DLBCL progression [8.Shen L. Li H. Shi Y. et al.M2 tumour-associated macrophages contribute to tumour progression via legumain remodelling the extracellular matrix in diffuse large B cell lymphoma.Sci Rep. 2016; 6: 30347Crossref PubMed Scopus (72) Google Scholar]. Chemokine-mediated recruitment of mature neutrophils accelerate DLBCL development by secreting a proliferation-inducing TNF ligand [9.Manfroi B. McKee T. Mayol J.F. et al.CXCL-8/IL8 produced by diffuse large B-cell lymphomas recruits neutrophils expressing a proliferation-inducing ligand APRIL.Cancer Res. 2017; 77: 1097-1107Crossref PubMed Scopus (54) Google Scholar]. Moreover, DLBCL-derived endothelial cells inhibit activation of CD4+ T cells and Th1 polarization though up-regulation of interleukin-6/STAT3 pathway, contributing to immune evasion of tumor cells [10.Huang X. Bai X. Cao Y. et al.Lymphoma endothelium preferentially expresses Tim-3 and facilitates the progression of lymphoma by mediating immune evasion.J Exp Med. 2010; 207: 505-520Crossref PubMed Scopus (119) Google Scholar] (Figure 1). In addition to these experimental observations, the relevance of TME has also been substantiated in translational studies estimating the prognosis of DLBCL patients. Particularly, Lenz et al. identified stroma-associated gene expression signatures, stromal-1 and stromal-2, which were correlated with prognosis in DLBCL receiving chemo- and chemo-immunotherapy [11.Lenz G. Wright G. Dave S.S. et al.Stromal gene signatures in large-B-cell lymphomas.N Engl J Med. 2008; 359: 2313-2323Crossref PubMed Scopus (1342) Google Scholar]. The stromal-1 signature reflects the existence of extra-cellular matrix and monocytic lineage infiltration, whose up-regulation is associated with better prognosis. By contrast, the stromal-2 signature includes genes encoding endothelial markers and key regulators of angiogenesis. Indeed, high expression of the stromal-2 signature is accompanied by increased tumor blood vessel density and adverse survival. A number of researches have attempted to incorporate microenvironmental factors into prognostic prediction. Increased expression of TNFRSF9, an activated T- and natural killer cell marker was demonstrated to be predictive of good prognosis in combination with LMO2, which is one of the indicators of COO [12.Alizadeh A.A. Gentles A.J. Alencar A.J. et al.Prediction of survival in diffuse large B-cell lymphoma based on the expression of 2 genes reflecting tumor and microenvironment.Blood. 2011; 118: 1350-1358Crossref PubMed Scopus (158) Google Scholar]. In addition, increased CD68+ M2 macrophages [13.Nam S.J. Go H. Paik J.H. et al.An increase of M2 macrophages predicts poor prognosis in patients with diffuse large B-cell lymphoma treated with rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone.Leuk Lymphoma. 2014; 55: 2466-2476Crossref PubMed Scopus (81) Google Scholar] and mature neutrophils [14.Han X. Ruan J. Zhang W. et al.Prognostic implication of leucocyte subpopulations in diffuse large B-cell lymphoma.Oncotarget. 2017; 8: 47790-47800Crossref PubMed Scopus (11) Google Scholar], as well as high microvessel density [15.Cardesa-Salzmann T.M. Colomo L. Gutierrez G. et al.High microvessel density determines a poor outcome in patients with diffuse large B-cell lymphoma treated with rituximab plus chemotherapy.Haematologica. 2011; 96: 996-1001Crossref PubMed Scopus (85) Google Scholar], were associated with shorter survival, whereas CD4+ T-cell infiltration yielded better clinical outcome [16.Keane C. Gill D. Vari F. et al.CD4(+) tumor infiltrating lymphocytes are prognostic and independent of R-IPI in patients with DLBCL receiving R-CHOP chemo-immunotherapy.Am J Hematol. 2013; 88: 273-276Crossref PubMed Scopus (61) Google Scholar]. However, due to the lack of systematic analysis, the overall composition of immune and stromal cells in the TME and their relative contribution to DLBCL pathogenesis remain elusive. In this issue of Annals of Oncology, Ciavarella et al. revealed that a 45 TME-associated gene panel using the NanoString technology, which was established by a computational deconvolution method called CIBERSORT, estimating immune cell populations from gene expression data, could stratify patient prognosis, independently of COO status [17.Ciavarella S. Vegliante M.C. Fabbri M. et al.Dissection of DLBCL microenvironment provides a gene expression-based predictor of survival applicable to formalin-fixed paraffin-embedded tissue.Ann Oncol. 2018; Abstract Full Text Full Text PDF PubMed Scopus (62) Google Scholar]. In particular, higher expression of myofibroblast-, dendritic cell-, and CD4+ T-cell-associated genes was indicative of better survival. Among them, the major contributor to the TME signature was myofibroblast-related genes, pointing to the potential significance of extracellular matrix determinants in the DLBCL pathophysiology. The present study is the first to clearly show that integration of TME evaluation with COO assignment can improve prognostic stratification in DLBCL in a robust and cost-effective manner. In addition, the comprehensive analysis on the cellular composition of TME provides new biological insights into the role of immune and stromal components and their cross talk in DLBCL. Most recently, several groups have analyzed newly diagnosed DLBCL biopsies in a large cohort by integrative methods, such as whole-exome and transcriptome sequencing, to refine the COO classification with the information of genomic alterations, including mutations, copy number alterations, and structural variations [18.Schmitz R. Wright G.W. Huang D.W. et al.Genetics and pathogenesis of diffuse large B-cell lymphoma.N Engl J Med. 2018; 378: 1396-1407Crossref PubMed Scopus (970) Google Scholar, 19.Chapuy B. Stewart C. Dunford A.J. et al.Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes.Nat Med. 2018; 24: 679-690Crossref PubMed Scopus (836) Google Scholar, 20.Reddy A. Zhang J. Davis N.S. et al.Genetic and functional drivers of diffuse large B cell lymphoma.Cell. 2017; 171: 481-494Abstract Full Text Full Text PDF PubMed Scopus (589) Google Scholar]. These analyses not only enabled further subdivision of the prognostic classification but also provided more extensive pathophysiology and potential actionable targets in each subtype. Although there are some differences with respect to prognosis and association with COO status among these analyses, the reproducibility of the clustering of certain recurrent somatic alterations in a subset of patients is reassuring, allowing us to tailor the therapeutic strategy. Even though the relationship between these genetic alterations and TME remains unclear, a comprehensive clinicogenomic classifier that concurrently considers the IPI, COO, and genomic alterations as well as TME is likely to provide the best possible risk stratification for DLBCL patients. Therefore, in addition to single-cell multi-omics analyses to uncover a more detailed picture of the TME, further large-scale studies integrating clinical, genomic, and transcriptomic (COO and TME) features are warranted for advancing our biological and clinical understanding of DLBCL pathogenesis and refining patient classification in the precision medicine era. None declared.