Mohit Aggarwal

A Unifying Microenvironment Model in Follicular Lymphoma: Outcome Is Predicted by Programmed Death-1–Positive, Regulatory, Cytotoxic, and Helper T Cells and Macrophages

Purpose: The microenvironment influences outcome in follicular lymphoma. Our hypothesis was that several immune cell subsets are important for disease outcome and their individual prognostic importance should be demonstrable in the same analysis and in competition with clinical factors. Experimental Design: Seventy follicular lymphoma patients with extreme clinical outcome (“poor” and “good” cases) were selected in a population-based cohort of 197. None of the 37 good-outcome patients died from lymphoma, whereas all the 33 poor-outcome patients succumbed in ≤5 years. Furthermore, the good-outcome patients were followed for a long time and needed no or little treatment. A tissue microarray was constructed from diagnostic, pretreatment biopsies. Cellular subsets were quantified after immunostaining, using computerized image analysis, separating cells inside and outside the follicles (follicular and interfollicular compartments). Flow cytometry data from the same samples were also used. Results: Independently of the Follicular Lymphoma International Prognostic Index, CD4+ cells were associated with poor outcome and programmed death-1–positive and CD8+ cells were associated with good outcome. The prognostic values of CD4+ and programmed death-1–positive cells were accentuated when they were follicular and that of CD8+ cells were accentuated when they were interfollicular. Follicular FOXP3+ cells were associated with good outcome and interfollicular CD68+ cells were associated with poor outcome. Additionally, high CD4/CD8 and CD4 follicular/interfollicular ratios correlated with poor outcome. Conclusion: There are many important immune cell subsets in the microenvironment of follicular lymphoma. Each of these is independently associated with outcome. This is the first study showing the effect of the balance of the entire microenvironment, not only of individual subsets. Clin Cancer Res; 16(2); 637–50.

TCL1A expression delineates biological and clinical variability in B-cell lymphoma.

The assembly of a collection of gene-expression signatures of the major types of B-cell non-Hodgkins lymphoma has identified increased T-cell leukemia/lymphoma 1A (TCL1) expression in multiple lymphoma types and cases, and has enabled the investigation of the functional and clinical importance of TCL1 expression. Specifically, Burkitts lymphoma cases show a homogeneously strong expression of TCL1, whereas diffuse large B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, chronic lymphocytic leukemia, nodal marginal zone lymphoma, and splenic marginal zone lymphoma display a striking variability in the intensity of TCL1 staining. This was validated in two independent series. A Gene-Set Enrichment Analysis of the genes correlated with TCL1A expression found that variation in the level of expression of TCL1A was significantly associated with some of the most important gene signatures recognizing B-cell lymphoma pathogenesis and heterogeneity, such as germinal center, B-cell receptor, NF-κB (and its target genes), death, MAP kinases, TNFR1, TOLL, and IL1R. Additionally, TCL1 expression was correlated with shorter time to treatment in chronic lymphocytic leukemia cases and shorter lymphoma-specific survival in mantle cell lymphoma series, thus indicating the clinical and biological significance of TCL1 expression, and suggesting TCL1A as a potential therapeutic target.

Somatic hypermutation signature in B-cell low-grade lymphomas

Immunoglobulin gene somatic hypermutation (SHM) is a biologically relevant and clinically useful prognostic factor in different types of low-grade B-cell lymphomas. This study of gene expression profiling identified a large number of genes that may be surrogate markers of the SHM process. Background Immunoglobulin gene somatic hypermutation is a biologically relevant and clinically useful prognostic factor in different types of low-grade B-cell lymphomas, including chronic lymphocytic leukemia, mantle cell lymphoma and splenic marginal zone lymphoma. Design and Methods With the aim of identifying surrogate markers of somatic hypermutation, a combined investigation of IgVH mutational status and expression profiles of 93 samples from patients with small B-cell lymphoma was performed. Results The analysis identified an somatic hypermutation signature of genes involved in the regulation of gene transcription, DNA repair and replication, and chromosome maintenance. Eight of these genes were subjected to protein analysis using tissue microarrays, for a set of 118 cases. We found a clear link between RAD51C and CDK7 protein expression and somatic hypermutation status, in that positive expression of either marker was significantly associated with a mutated status (p<0.003). We also found that positive expression of TFDP1 and POLA was significantly associated with ongoing somatic hypermutation (p<0.001). To assess the potential clinical applicability of these somatic hypermutation markers, we studied a series of cases of mantle cell lymphoma included in a tissue microarray. The expression of RCC1 and CDK7, separately and together, was found to be significantly associated with longer overall survival. Conclusions An somatic hypermutation signature has been identified for different types of small B-cell lymphoma. This has a potential mechanistic and diagnostic value.

Functional signatures identified in B-cell non-Hodgkin lymphoma profiles

Gene-expression profiling in B-cell lymphomas has provided crucial data on specific lymphoma types, which can contribute to the identification of essential lymphoma survival genes and pathways. In this study, the gene-expression profiling data of all major B-cell lymphoma types were analyzed by unsupervised clustering. The transcriptome classification so obtained, was explored using gene set enrichment analysis generating a heatmap for B-cell lymphoma that identifies common lymphoma survival mechanisms and potential therapeutic targets, recognizing sets of coregulated genes and functional pathways expressed in different lymphoma types. Some of the most relevant signatures (stroma, cell cycle, B-cell receptor (BCR)) are shared by multiple lymphoma types or subclasses. A specific attention was paid to the analysis of BCR and coregulated pathways, defining molecular heterogeneity within multiple B-cell lymphoma types.