Dissection of DLBCL microenvironment provides a gene expression-based predictor of survival applicable to formalin-fixed paraffin-embedded tissue

Abstract

BackgroundGene expression profiling (GEP) studies recognized a prognostic role for tumor microenvironment (TME) in diffuse large B-cell lymphoma (DLBCL), but the routinely adoption of prognostic stromal signatures remains limited.Patients and methodsHere, we applied the computational method CIBERSORT to generate a 1028-gene matrix incorporating signatures of 17 immune and stromal cytotypes. Then, we carried out a deconvolution on publicly available GEP data of 482 untreated DLBCLs to reveal associations between clinical outcomes and proportions of putative tumor-infiltrating cell types. Forty-five genes related to peculiar prognostic cytotypes were selected and their expression digitally quantified by NanoString technology on a validation set of 175 formalin-fixed, paraffin-embedded DLBCLs from two randomized trials. Data from an unsupervised clustering analysis were used to build a model of clustering assignment, whose prognostic value was also assessed on an independent cohort of 40 cases. All tissue samples consisted of pretreatment biopsies of advanced-stage DLBCLs treated by comparable R-CHOP/R-CHOP-like regimens.Results In silico analysis demonstrated that higher proportion of myofibroblasts (MFs), dendritic cells, and CD4+ T cells correlated with better outcomes and the expression of genes in our panel is associated with a risk of overall and progression-free survival. In a multivariate Cox model, the microenvironment genes retained high prognostic performance independently of the cell-of-origin (COO), and integration of the two prognosticators (COO + TME) improved survival prediction in both validation set and independent cohort. Moreover, the major contribution of MF-related genes to the panel and Gene Set Enrichment Analysis suggested a strong influence of extracellular matrix determinants in DLBCL biology.ConclusionsOur study identified new prognostic categories of DLBCL, providing an easy-to-apply gene panel that powerfully predicts patients’ survival. Moreover, owing to its relationship with specific stromal and immune components, the panel may acquire a predictive relevance in clinical trials exploring new drugs with known impact on TME.

Improved Personalized Survival Prediction of Patients With Diffuse Large B-cell Lymphoma Using Gene Expression Profiling

“…For example, Ciavarella et al (2018) presented a new prognostic classi cation of DLBCL based on computational deconvolution of gene expression from whole-tissue biopsies, and detected transcriptomic prints corresponding to myo broblasts, dendritic cells and CD4+ lymphocytes that were associated with improved survival [25]. Similarly, Ennishi et al (2019) used gene expression data to demonstrate the existence of a clinical and biological subgroup of GCB-DLCBLs that resemble double-hit lymphomas [26], whereas Sha et al (2018) identi ed a gene expression signatures that characterizes a group of molecular high grade DLBCLs [27]. Our results add to the growing evidence that improved transcriptome-based risk strati cation beyond classical biomarkers is possible.…”

“…Nevertheless, their study was centered in prognostic groups rather than individualized predictions. In the same line, the accuracy of gene expression classi ers [25][26][27] for making personalized predictions was not tested. Recently, machine learning techniques were used by Biccler et al (2018) for individualized survival prediction in DLBCL.…”

The Tumor Microenvironment of DLBCL in the Computational Era

“…For instance, genetically unstable DLBCL cells display reduced surface expression of MHC and CD58 molecules, thus lowering T cell and NK infiltration and cytotoxicity (51). Conversely, DLBCL-released lymphotoxins and TNF-alpha were (9) reported to promote the proliferative attitude of podoplanin-, PD-L1/L2-positive fibroblasts, while lowering their ability to contract collagen fibers and attract cytotoxic T cells (52). Overall, it is conceivable that the local extent of constitutional and reactive processes of both stromal and inflammatory nature shapes the final cellular composition of the affected lymph nodes, forming specialized contextures with topographical and functional identity (Figure 1).…”

“…These niches may vary within the same tumor, across different tumor sites in the same patients, and between different patients, resulting in a relevant biological and outcome diversity. The application of innovative computational tools (9,26) added texture to this picture in DLBCL, yet remaining elusive about the precise mechanisms and timing of TME-centered dynamics. The recognition of a single biological trait unifying the complexity of tumor/TME interactions is very challenging, owing to their potential variation at different disease stages and type of treatment.…”

“…Such observation remained poorly applied on clinical ground owing to an incomplete comprehension of the specific cellular/molecular TME determinants and the precise mechanisms of their prognostic impact. In a recent translational effort, our group exploited a computational approach to reinterpret large transcriptional data and provide a pure TME-based prognosticator that improves the COO risk stratification (9). Latest results from sequencing studies and clinical trials on new drugs (i.e., lenalidomide and ibrutinib) underscored the relevance of studying DLBCL heterogeneity, taking into proper account the impact of TME in diagnostics, prognostics, and therapeutic prediction.…”