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Immunoprofiling and survival in DLBCL
Tumor microenvironment (TME) surrounding malig- nant B cells consists of immune cells, such as T lympho- cytes, macrophages and natural killer (NK) cells, as well as stromal cells, blood vessels and extracellular matrix (ECM).9,10 The composition of immune cells in the TME varies between tumors and is associated with outcome in many cancers,11 including DLBCL.12-14 Chronic inflamma- tion in cancer may affect tumor-infiltrating T cells by inducing exhaustion, a state of dysfunction, where the dif- ferentiation, proliferation and effector function of T cells are suppressed.15 This is caused by sustained expression of inhibitory receptors, such as programmed cell death pro- tein 1 (PD1), lymphocyte-activation gene 3 (LAG3), and T-cell immunoglobulin and mucin-domain containing 3 (TIM3) on the surface of T cells.15 With the suppressed immune response, TME can protect tumor cells from immune surveillance. Alternatively, cancer cells may avoid detection through the loss of human leukocyte antigen (HLA) class I and II expression.9,10,16-20 However, much is still unknown concerning the impact of TME on the pathogenesis and outcome of DLBCL. In the present study, we sought to further characterize the immune microenvironment in primary DLBCL and find TME-asso- ciated prognostic biomarkers.
Methods
Patients
The study population consisted of three separate cohorts (Table 1). The gene expression cohort included 81 samples from the patients with primary high-risk DLBCL. The patients were treated in the Nordic LBC-05 and LBC-04 trials with bi-weekly R-CHOEP (rituximab, cyclophosphamide, doxorubicin, etoposide and pred- nisone) immunochemotherapy and systemic central nervous sys- tem (CNS) prophylaxis (high-dose [HD] methotrexate and HD- cytarabine).21,22
Immunohistochemistry (IHC) was performed on a total of 188 samples divided into two cohorts. The Nordic Lymphoma Group (NLG) Trial cohort consisted of 51 patients treated in the Nordic NLG-LBC-05 trial. Of these patients, 42 were overlapping with the gene expression cohort. The 137 patients from the Helsinki (HEL)-DLBCL study cohort had been diagnosed with primary DLBCL and treated with R-CHOP or R-CHOEP. Tissue microar- rays (TMA) were constructed from primary diagnostic formalin- fixed paraffin-embedded (FFPE) tumor tissue. Patients with pri- mary mediastinal B-cell lymphoma were excluded from all cohorts.
The study was approved by the ethics committee in Helsinki, Finland, by the Finnish National Authority for Medicolegal Affairs, and by the Institutional Review Boards of the institutes involved in the study. NLG-LBC-04 and NLG-LBC-05 protocols were registered at clinicaltrials.gov identifiers NCT01502982 and NCT01325194, respectively. All patients signed informed consent before entering the study.
Gene expression profiling
Digital multiplexed gene expression profiling (GEP) was per- formed using a Nanostring nCounter Human PanCancer Immunoprofiling Panel (XT-CSO-HIP1-12, NanoString Technologies, Seattle, WA, USA) on primary diagnostic FFPE tumor tissue, as previously described.23 The data were analyzed with nSolver 3.0 software (NanoString Technologies) and normal- ized with the geNorm algorithm.24 Further details are available in the Online Supplementary Methods.
Immunohistochemistry
For multiplex immunohistochemistry (mIHC), T-cell pheno- types were characterized with 4-plex antibody panels using mark- ers for CD4+ T-cell regulation (CD3, CD4, TIM3, LAG3), CD8+ T- cell regulation (CD8, PD1, TIM3, LAG3), cytotoxic T cells (CD8, GrB, Ki67, OX40), and regulatory T cells (Treg) and Th1 T-cells (CD3, CD4, FOXP3, TBET). Automated digital quantification was performed using CellProfiler software.25 Samples with poor stain- ing quality or poor TMA cores were excluded in the subsequent analyses. Molecular subtypes were classified according to Hans’ algorithm.26 The expression of HLA-DR, HLA-ABC and β2 microglobulin (B2M) was evaluated by IHC, as previously described.23 More details on mIHC and IHC are provided in the Online Supplementary Methods.
In silico immunophenotyping
CIBERSORTx27 was used on publicly available datasets6-8,28 to
infer the proportions and gene expression profiles (GEP) of infil- trating immune cells. Further details are provided in the Online Supplementary Methods.
Statistical analysis
Statistical analyses were performed with IBM SPSS v.24.0 (IBM, Armonk, NY, USA) and R v.3.5.1. The prognostic impact was esti- mated by Cox univariate and multivariate regression analysis (95% confidence interval). Hierarchical clustering was performed by J-Express Pro 201229 using Euclidean distance or Cosine correla- tion with average linkage for gene or protein expression, respec- tively. Kaplan-Meier method with log-rank test was used to esti- mate the difference in survival between the patient groups. Overall survival (OS) and progression-free survival (PFS) were defined as the time from diagnosis until death for OS and progression or death from any other cause for PFS. Mann-Whitney U test and Kruskal-Wallis H test were used to compare two or more groups, respectively.
Results
Gene expression analysis reveals distinct tumor microen- vironment-associated signatures
Patient demographics are described in Table 1. In the gene expression cohort, median age was 55 years (range 22- 64) and the majority of patients were males. Disease char- acteristics were typical of high-risk DLBCL with advanced clinical stage, elevated lactate dehydrogenase (LDH), more than one extranodal (EN) sites, and B symptoms. At a medi- an follow-up of 61 months, 14 patients had relapsed and 11 had died. In this cohort, neither the International Prognostic Index (IPI) score nor the COO were associated with the outcome.
GEP of DLBCL samples revealed a high degree of hetero- geneity. In a correlation matrix analysis of genes with the highest variance, immune cell-related genes created a large cluster, dominating the transcriptome landscape (Figure 1). This signature, denoted as a TME immune cell signature, contained genes encoding markers for T cells (e.g., CD3D/E/G, CD8A/B, CD2, CD28), macrophages (e.g., CD68, CD163), cytolytic factors and NK cells (e.g., GZMB, PRF, IFNG, KLRG1), as well as checkpoint molecules (PDCD1 [PD1], CD274 [PD-L1], PDCD1LG2 [PD-L2], HAVCR2 [TIM3], LAG3). In addition to the TME immune cell signature, a B-cell signature (e.g., CD19, MS4A1, CD79A, and CD79B) and two distinct extracellular matrix (ECM) signatures (signature A, e.g., ITGA2B, ITGB3, ARG,
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