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

Spatially-resolved transcriptomics reveal macrophage heterogeneity and prognostic significance in diffuse large B-cell lymphoma

Macrophages are abundant immune cells in the microenvironment of diffuse large B-cell lymphoma (DLBCL). Macrophage estimation by immunohistochemistry shows varying prognostic significance across studies in DLBCL, and does not provide a comprehensive analysis of macrophage subtypes. Here, using digital spatial profiling with whole transcriptome analysis of CD68+ cells, we characterize macrophages in distinct spatial niches of reactive lymphoid tissues (RLTs) and DLBCL. We reveal transcriptomic differences between macrophages within RLTs (light zone /dark zone, germinal center/ interfollicular), and between disease states (RLTs/ DLBCL), which we then use to generate six spatially-derived macrophage signatures (MacroSigs). We proceed to interrogate these MacroSigs in macrophage and DLBCL single-cell RNA-sequencing datasets, and in gene-expression data from multiple DLBCL cohorts. We show that specific MacroSigs are associated with cell-of-origin subtypes and overall survival in DLBCL. This study provides a spatially-resolved whole-transcriptome atlas of macrophages in reactive and malignant lymphoid tissues, showing biological and clinical significance.

STAT6 mutations enriched at diffuse large B-cell lymphoma relapse reshape the tumor microenvironment

Diffuse large B-cell lymphoma (DLBCL) relapses in approximately 40% of patients following frontline therapy. We reported that STAT6D419 mutations are enriched in relapsed/refractory DLBCL (rrDLBCL) samples, suggesting that JAK/STAT signaling plays a role in therapeutic resistance. We hypothesized that STAT6D419 mutations can improve DLBCL cell survival by reprogramming the microenvironment to sustain STAT6 activation. Thus, we investigated the role of STAT6D419 mutations on DLBCL cell growth and its microenvironment. We found that phospho-STAT6D419N was retained in the nucleus longer than phospho-STAT6WT following IL-4 stimulation, and STAT6D419N recognized a more restricted DNA-consensus sequence than STAT6WT. Upon IL-4 induction, STAT6D419N expression led to a higher magnitude of gene expression changes, but in a more selective list of gene targets compared with STATWT. The most significantly expressed genes induced by STAT6D419N were those implicated in survival, proliferation, migration, and chemotaxis, in particular CCL17. This chemokine, also known as TARC, attracts helper T-cells to the tumor microenvironment, especially in Hodgkin's lymphoma. To this end, in DLBCL, phospho-STAT6+ rrDLBCL cells had a greater proportion of infiltrating CD4+ T-cells than phospho-STAT6- tumors. Our findings suggest that STAT6D419 mutations in DLBCL lead to cell autonomous changes, enhanced signaling, and altered composition of the tumor microenvironment.

SurvIAE: Survival prediction with Interpretable Autoencoders from Diffuse Large B-Cells Lymphoma gene expression data

BACKGROUND

In Diffuse Large B-Cell Lymphoma (DLBCL), several methodologies are emerging to derive novel biomarkers to be incorporated in the risk assessment. We realized a pipeline that relies on autoencoders (AE) and Explainable Artificial Intelligence (XAI) to stratify prognosis and derive a gene-based signature.

METHODS

AE was exploited to learn an unsupervised representation of the gene expression (GE) from three publicly available datasets, each with its own technology. Multi-layer perceptron (MLP) was used to classify prognosis from latent representation. GE data were preprocessed as normalized, scaled, and standardized. Four different AE architectures (Large, Medium, Small and Extra Small) were compared to find the most suitable for GE data. The joint AE-MLP classified patients on six different outcomes: overall survival at 12, 36, 60 months and progression-free survival (PFS) at 12, 36, 60 months. XAI techniques were used to derive a gene-based signature aimed at refining the Revised International Prognostic Index (R-IPI) risk, which was validated in a fourth independent publicly available dataset. We named our tool SurvIAE: Survival prediction with Interpretable AE.

RESULTS

From the latent space of AEs, we observed that scaled and standardized data reduced the batch effect. SurvIAE models outperformed R-IPI with Matthews Correlation Coefficient up to 0.42 vs. 0.18 for the validation-set (PFS36) and to 0.30 vs. 0.19 for the test-set (PFS60). We selected the SurvIAE-Small-PFS36 as the best model and, from its gene signature, we stratified patients in three risk groups: R-IPI Poor patients with High levels of GAB1, R-IPI Poor patients with Low levels of GAB1 or R-IPI Good/Very Good patients with Low levels of GPR132, and R-IPI Good/Very Good patients with High levels of GPR132.

CONCLUSIONS

SurvIAE showed the potential to derive a gene signature with translational purpose in DLBCL. The pipeline was made publicly available and can be reused for other pathologies.

Characterizing influence of rCHOP treatment on diffuse large B-cell lymphoma microenvironment through in vitro microfluidic spheroid model

For over two decades, Rituximab and CHOP combination treatment (rCHOP) has remained the standard treatment approach for diffuse large B-cell lymphoma (DLBCL). Despite numerous clinical trials exploring treatment alternatives, few options have shown any promise at further improving patient survival and recovery rates. A wave of new therapeutic approaches have recently been in development with the rise of immunotherapy for cancer, however, the cost of clinical trials is prohibitive of testing all promising approaches. Improved methods of early drug screening are essential for expediting the development of the therapeutic approaches most likely to help patients. Microfluidic devices provide a powerful tool for drug testing with enhanced biological relevance, along with multi-parameter data outputs. Here, we describe a hydrogel spheroid-based microfluidic model for screening lymphoma treatments. We utilized primary patient DLBCL cells in combination with NK cells and rCHOP treatment to determine the biological relevance of this approach. We observed cellular viability in response to treatment, rheological properties, and cell surface marker expression levels correlated well with expected in vivo characteristics. In addition, we explored secretory and transcriptomic changes in response to treatment. Our results showed complex changes in phenotype and transcriptomic response to treatment stimuli, including numerous metabolic and immunogenic changes. These findings support this model as an optimal platform for the comparative screening of novel treatments.

Dissecting cellular states of infiltrating microenvironment cells in melanoma by integrating single-cell and bulk transcriptome analysis

BACKGROUND

Cellular states of different immune cells can affect the activity of the whole immune microenvironment.

METHODS

Here, leveraging reference profiles of microenvironment cell states that were constructed based on single-cell RNA-seq data of melanoma, we dissected the composition of microenvironment cell states across 463 skin cutaneous melanoma (SKCM) bulk samples through CIBERSORT-based deconvolution of gene expression profiles and revealed high heterogeneity of their distribution. Correspondence analysis on the estimated cellular fractions of melanoma bulk samples was performed to identify immune phenotypes. Based on the publicly available clinical survival and therapy data, we analyzed the relationship between immune phenotypes and clinical outcomes of melanoma.

RESULTS

By analysis of the relationships among those cell states, we further identified three distinct tumor microenvironment immune phenotypes: "immune hot/active", "immune cold-suppressive" and "immune cold-exhausted". They were characterized by markedly different patterns of cell states: most notably the CD8 T Cytotoxic state, CD8 T Mixed state, B non-regulatory state and cancer-associated fibroblasts (CAFs), depicting distinct types of antitumor immune response (or immune activity). These phenotypes had prognostic significance for progression-free survival and implications in response to immune therapy in an independent cohort of anti-PD1 treated melanoma patients.

CONCLUSIONS

The proposed strategy of leveraging single-cell data to dissect the composition of microenvironment cell states in individual bulk tumors can also extend to other cancer types, and our results highlight the importance of microenvironment cell states for the understanding of tumor immunity.

Identifying CD1c as a potential biomarker by the comprehensive exploration of tumor mutational burden and immune infiltration in diffuse large B cell lymphoma

Background

Tumor mutational burden (TMB) is a valuable prognostic biomarker. This study explored the predictive value of TMB and the potential association between TMB and immune infiltration in diffuse large B-cell lymphoma (DLBCL).

Methods

We downloaded the gene expression profile, somatic mutation, and clinical data of DLBCL patients from The Cancer Genome Atlas (TCGA) database. We classified the samples into high-and low-TMB groups to identify differentially expressed genes (DEGs). Functional enrichment analyses were performed to determine the biological functions of the DEGs. We utilized the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm to estimate the abundance of 22 immune cells, and the significant difference was determined by the Wilcoxon rank-sum test between the high- and low-TMB group. Hub gene had been screened as the prognostic TMB-related immune biomarker by the combination of the Immunology Database and Analysis Portal (ImmPort) database and the univariate Cox analysis from the Gene Expression Omnibus (GEO) database including six DLBCL datasets. Various database applications such as Tumor Immune Estimation Resource (TIMER), CellMiner, konckTF, and Genotype-Tissue Expression (GTEx) verified the functions of the target gene. Wet assay confirmed the target gene expression at RNA and protein levels in DLBCL tissue and cell samples.

Results

Single nucleotide polymorphism (SNP) occurred more frequently than insertion and deletion, and C > T was the most common single nucleotide variant (SNV) in DLBCL. Survival analysis showed that the high-TMB group conferred poor survival outcomes. A total of 62 DEGs were obtained, and 13 TMB-related immune genes were identified. Univariate Cox analysis results illustrated that CD1c mutation was associated with lower TMB and manifested a satisfactory clinical prognosis by analysis of large samples from the GEO database. In addition, infiltration levels of immune cells in the high-TMB group were lower. Using the TIMER database, we systematically analyzed that the expression of CD1c was positively correlated with B cells, neutrophils, and dendritic cells and negatively correlated with CD8+ T cells, CD4+ T cells, and macrophages. Drug sensitivity showed a significant positive correlation between CD1c expression level and clinical drug sensitivity from the CellMiner database. CREB1, AHR, and TOX were used to comprehensively explore the regulation of CD1c-related transcription factors and signaling pathways by the KnockTF database. We searched the GETx database to compare the mRNA expression levels of CD1c between DLBCL and normal tissues, and the results suggested a significant difference between them. Moreover, wet experiments were conducted to verify the high expression of CD1c in DLBCL at the RNA and protein levels.

Conclusions

Higher TMB correlated with poor survival outcomes and inhibited the immune infiltrates in DLBCL. Our results suggest that CD1c is a TMB-related prognostic biomarker.

Complete Absence of FAPI Uptake in a Patient With Aggressive Diffuse Large B-Cell Lymphoma Involving Multiple Nodal and Extranodal Sites

ABSTRACT

A 73-year-old man with histopathologically confirmed diffuse large B-cell lymphoma underwent both 18 F-FDG and 18 F-FAPI PET/CT. Although 18 F-FDG PET showed abnormally increased tracer uptake in multiple nodal sites and organs, indicating the aggressiveness of the disease status, 18 F-FAPI PET showed no obvious FAPI uptake in any of the FDG-avid lesions. Our case suggests that low expression of FAP in diffuse large B-cell lymphoma, as indicated by FAPI PET, might help determine a subgroup of patients with poorer outcome.

Fibroblast Activation Protein and Glycolysis in Lymphoma Diagnosis: Comparison of 68Ga-FAPI PET/CT and 18F-FDG PET/CT

Our objective was to compare the diagnostic performance of 68Ga-labeled fibroblast activation protein (FAP) inhibitor (FAPI) and 18F-labeled FDG PET/CT in diagnosing lymphomas and to characterize the influence of FAP and glycolytic markers on tracer uptake by involved lesions. Methods: Participants with different lymphoma subtypes who were prospectively recruited from May 2020 to December 2021 underwent 68Ga-FAPI and 18F-FDG PET/CT. Immunohistochemistry was performed to evaluate FAP, hexokinase 2, and glucose transporter 1 (GLUT1) expression, and the paired-samples t test and Wilcoxon signed-rank test were used to compare parameters. The correlation between the immunochemistry results and tracer uptake was determined by the Spearman rank correlation coefficient. Results: In total, 186 participants (median age, 52 y [interquartile range, 41-64 y]; 95 women) were included. Dual-tracer imaging produced 3 types of imaging profiles. 18F-FDG PET possessed a higher staging accuracy (98.4%) than 68Ga-FAPI PET (86.0%). In 5,980 lymphoma lesions, 18F-FDG PET/CT detected more nodal (4,624 vs. 2,196) and extranodal (1,304 vs. 845) lesions than 68Ga-FAPI PET/CT. Additionally, 52 68Ga-FAPI-positive/18F-FDG-negative lesions and 2,939 68Ga-FAPI-negative/18F-FDG-positive lesions were observed. In many lymphoma subtypes, semiquantitative evaluation revealed no significant differences in SUVmax or target-to-liver ratios between 68Ga-FAPI and 18F-FDG PET/CT (P > 0.05). Interestingly, GLUT1 and hexokinase 2 were overexpressed both in lymphoma cells and in the tumor microenvironment, whereas FAP was expressed only in stromal cells. FAP and GLUT1 expression correlated positively with 68Ga-FAPI SUVmax (r = 0.622, P = 0.001) and 18F-FDG SUVmax (r = 0.835, P < 0.001), respectively. Conclusion: 68Ga-FAPI PET/CT was inferior to 18F-FDG PET/CT in diagnosing lymphomas with low FAP expression. However, the former may supplement the latter and help reveal the molecular profile of lymphomas.

Prognostic Impact of the Immunoscore Based on Whole-Slide Image Analysis of CD3+ Tumor-Infiltrating Lymphocytes in Diffuse Large B-Cell Lymphoma

An Immunoscore based on tumor-infiltrating T-cell density was validated as a prognostic factor in patients with solid tumors. However, the potential utility of the Immunoscore in predicting the prognosis of patients with diffuse large B-cell lymphoma (DLBCL) is unclear. Here, the prognostic value of an Immunoscore based on tumor-infiltrating CD3+ T-cell density was evaluated in 104 patients with DLBCL who underwent R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone) therapy. Digitally scanned whole-slide images were analyzed using Aperio ImageScope software. CD3+ cell densities in the whole tumor area were quantitated using 3 different methods, including number of CD3+ cells/area (mm2), ratio of CD3+ cells to total cells, and ratio of CD3+ cells to CD20+ cells. There was a high concordance among the 3 methods. Patients with low CD3+ cell density had an elevated serum lactate dehydrogenase level and a high Ki-67 proliferation index (all, P < .05). Patients with low CD3+ cell density, according to all 3 methods, had worse overall survival (OS) and worse progression-free survival (P < .05, all). They also had poor OS, independent of MYC/BCL2 double expression (DE) status, Eastern Cooperative Oncology Group performance status, or Ann Arbor stage (all, P < .05). These results were validated using 2 publicly available data sets. In both validation cohorts, patients with low CD3E mRNA expression had an elevated serum lactate dehydrogenase level, extranodal site involvement, and DE status (P < .05). They also had worse progression-free survival (P = .067 and P = .002, respectively) and OS (both P < .05). A low CD3E mRNA level was predictive of poor OS, independent of DE status. An Immunoscore based on whole-slide image analysis of CD3+ T-cell infiltration was sufficient to predict survival in patients with DLBCL. Low CD3+ cell density was a poor prognostic factor, independent of other prognostic parameters and DE status.

RNA Extraction Method Impacts Quality Metrics and Sequencing Results in Formalin-Fixed, Paraffin-Embedded Tissue Samples

Archived formalin-fixed, paraffin-embedded (FFPE) tissue samples are being increasingly used in molecular cancer research. Compared with fresh-frozen tissue, the nucleic acid analysis of FFPE tissue is technically more challenging. This study aimed to compare the impact of 3 different RNA extraction methods on yield, quality, and sequencing-based gene expression results in FFPE samples. RNA extraction was performed in 16 FFPE tumor specimens from patients with diffuse large B-cell lymphoma and in reference FFPE material from microsatellite-stable and microsatellite-instable cell lines (3 replicates each) using 2 silica-based procedures (A, miRNeasy FFPE; C, iCatcher FFPE Tissue RNA) and 1 isotachophoresis-based procedure (B, Ionic FFPE to Pure RNA). The RNA yield; RNA integrity, as reflected by the distribution value 200; and RNA purity, as reflected by the 260/280 and the 260/230 nm absorbance ratios, were determined. The RNA was sequenced on the NovaSeq 6000 instrument using the TruSeq RNA Exome and SMARTer Stranded Total RNA-Seq Pico v3 library preparations kits. Our results highlight the impact of RNA extraction methodology on both preanalytical and sequencing-based gene expression results. Overall, methods B and C outperformed method A because these showed significantly higher fractions of uniquely mapped reads, an increased number of detectable genes, a lower fraction of duplicated reads, and better representation of the B-cell receptor repertoire. Differences among the extraction methods were generally more explicit for the total RNA sequencing method than for the exome-capture sequencing method. Importantly, the predicative value of quality metrics varies among extraction kits, and caution should be applied when comparing and interpreting results obtained using different methods.

NR1H3 (LXRα) is associated with pro-inflammatory macrophages, predicts survival and suggests potential therapeutic rationales in diffuse large b-cell lymphoma

The role of macrophages (Mo) and their prognostic impact in diffuse large B-cell lymphomas (DLBCL) remain controversial. By regulating the lipid metabolism, Liver-X-Receptors (LXRs) control Mo polarization/inflammatory response, and their pharmacological modulation is under clinical investigation to treat human cancers, including lymphomas. Herein, we surveyed the role of LXRs in DLBCL for prognostic purposes. Comparing bulk tumors with purified malignant and normal B-cells, we found an intriguing association of NR1H3, encoding for the LXR-α isoform, with the tumor microenvironment (TME). CIBERSORTx-based purification on large DLBCL datasets revealed a high expression of the receptor transcript in M1-like pro-inflammatory Mo. By determining an expression cut-off of NR1H3, we used digital measurement to validate its prognostic capacity on two large independent on-trial and real-world cohorts. Independently of classical prognosticators, NR1H3^high patients displayed longer survival compared with NR1H3^low cases and a high-resolution Mo GEP dissection suggested a remarkable transcriptional divergence between subgroups. Overall, our findings indicate NR1H3 as a Mo-related biomarker identifying patients at higher risk and prompt future preclinical studies investigating its mouldability for therapeutic purposes.

Synaptophysin-like 2 expression correlates with lymph node metastasis and poor prognosis in colorectal cancer patients

BACKGROUND

Colorectal cancer (CRC) is one of the most common and fatal cancers worldwide. Synaptophysin-like 2 (SYPL2) is a neuroendocrine-related protein highly expressed in skeletal muscle and the tongue. The involvement of SYPL2 in CRC, including its level of expression and function, has not been evaluated.

AIM

To evaluate the correlations of SYPL2 expression with lymph node metastasis (LNM) and prognosis in patients with CRC.

METHODS

The levels of expression of SYPL2 in CRC and normal colorectal tissues were analyzed in multiple public and online databases. The associations between clinical variables and SYPL2 expression were evaluated statistically, and the associations between SYPL2 expression and prognosis in patients with CRC were analyzed using the Kaplan-Meier method and univariate/multivariate Cox regression analyses. SYPL2 expression was assessed in 20 paired CRC tissue and adjacent normal colorectal tissue samples obtained from Fuyang People’s Hospital, and the associations between SYPL2 expression and the clinical characteristics of these patients were investigated. Correlations between the levels of expression of SYPL2 and key targeted genes were determined by Pearson’s correlation analysis. The distribution of immune cells in these samples was calculated using the CIBERSORT algorithm. Gene set enrichment analysis (GSEA) was performed to evaluate the biofunction and pathways of SYPL2 in CRC.

RESULTS

SYPL2 expression was significantly lower in CRC tissue samples than in normal colorectal tissue samples (P < 0.05). High SYPL2 levels in CRC tissues correlated significantly with LNM (P < 0.05) and a poorer patient prognosis, including significantly shorter overall survival (OS) [hazard ratio (HR) = 1.9, P < 0.05] and disease-free survival (HR = 1.6, P < 0.05). High SYPL2 expression was an independent risk factor for OS in both univariate (HR = 2.078, P = 0.014) and multivariate (HR = 1.754, P = 0.018) Cox regression analyses. In addition, SYPL2 expression correlated significantly with the expression of KDR (P < 0.0001, r = 0.47) and the BRAF^V600E mutation (P < 0.05). Higher SYPL2 expression was associated with the enrichment of CD8 T-cells and M0 macrophages in the tumor microenvironment. GSEA revealed that SYPL2 was associated with the regulation of epithelial cell migration, vasculature development, pathways in cancer, and several vital tumor-related pathways.

CONCLUSION

SYPL2 expression was lower in CRC tissue than in normal colorectal tissue. Higher SYPL2 expression in CRC was significantly associated with LNM and poorer survival.

An ion-channel-gene-based prediction model for head and neck squamous cell carcinoma: Prognostic assessment and treatment guidance

PURPOSE

Head and neck squamous cell carcinoma (HNSCC) is a very diverse malignancy with a poor prognosis. The purpose of this study was to develop a new signature based on 12 ion channel genes to predict the outcome and immune status of HNSCC patients.

METHODS

Clinicopathological information and gene sequencing data of HNSCC patients were generated from the Cancer Genome Atlas and Gene Expression Omnibus databases. A set of 323 ion channel genes was obtained from the HUGO Gene Nomenclature Committee database and literature review. Using univariate Cox regression analysis, the ion channel genes related to HNSCC prognosis were identified. A prognostic signature and nomogram were then created using machine learning methods. Kaplan-Meier analysis was used to explore the relevance of the risk scores and overall survival (OS). We also investigated the association between risk scores, tumor immune infiltration, and gene mutational status. Finally, we detected the expression levels of the signature genes by quantitative real-time polymerase chain reaction, western blotting, and immunohistochemistry.

RESULTS

We separated the patients into high- and low-risk groups according to the risk scores computed based on these 12 ion channel genes, and the OS of the low-risk group was significantly longer (p<0.001). The area under the curve for predicting 3-year survival was 0.729. Univariate and multivariate analyses showed that the 12-ion-channel-gene risk model was an independent prognostic factor. We also developed a nomogram model based on risk scores and clinicopathological variables to forecast outcomes. Furthermore, immune cell infiltration, gene mutation status, immunotherapy response, and chemotherapeutic treatment sensitivity were all linked to risk scores. Moreover, high expression levels of ANO1, AQP9, and BEST2 were detected in HNSCC tissues, whereas AQP5, SCNN1G, and SCN4A expression was low in HNSCC tissues, as determined by experiments.

CONCLUSION

The 12-ion-channel-gene prognostic signatures have been demonstrated to be highly efficient in predicting the prognosis, immune microenvironment, gene mutation status, immunotherapy response, and chemotherapeutic sensitivity of HNSCC patients.

Gene set-based identification of two immune subtypes of diffuse large B cell lymphoma for guiding immune checkpoint blocking therapy

Background: Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Tumour microenvironment is closely related to tumour prognosis and immune checkpoint blocking therapy (ICBT). This study aimed to investigate the immunological and prognostic characteristics of the tumour microenvironment (TME), as well as the regulatory mechanisms.

Methods: Gene expression profiles and clinical data of patients with DLBCL were obtained from GEO database. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to explore microenvironment characteristics and regulatory mechanism of the immune subtypes, which were identified by consistent clustering. The differences in enriched pathways were showed by GSEA. Hub genes associated with CD8⁺ T cells, which were identified by WCGNA, were exhibited biological functions through GO and KEGG. Immune-related gene scores (IRGSs) based on hub genes were used to evaluate the prediction of immune subtypes and ICBT, and retrospective analysis was used for validation. Finally, prognostic genes were screened to construct risk models.

Results: Consensus clustering divided patients with DLBCL into two subtypes with significant heterogeneities in prognosis and immune microenvironment. Low immune infiltration was associated with poor prognosis. Subtype C1 with high immune infiltration was enriched in multiple immune pathways. We observed that two common mutated genes (B2M and EZH2) in DLBCL were closely related to MHC-I and microenvironment. Our further analysis manifested that MYD88^L265P may be the main cause of TLR signalling pathway activation in subtype C1. Hub genes (SH2D1A, CD8A, GBP2, ITK, CD3D, RORA, IL1R2, CD28, CD247, CD3G, PRKCQ, CXCR6, and CD3E) in relation with CD8⁺ T cells were used to establish IRGS, which was proved an accurate predictor of immune subtypes, and patients in high-IRGS group were more likely to benefit from ICBT. Retrospective analysis showed that absolute lymphocyte count (ALC) was higher in the group that responded to the PD-1 inhibitor. Finally, the risk model was constructed based on two genes (CD3G and CD3D), and the low-risk group showed better prognosis.

Conclusion: DLBCL immune classifications with highly heterogeneity are a powerful predictor of prognosis and ICBT. The IRGS is proved to be a reliable tool to distinguish immune subtypes as a substitute for gene expression profile.

Lymphoma Microenvironment in DLBCL and PTCL-NOS: the key to uncovering heterogeneity and the potential for stratification

Diffuse large B-cell lymphoma (DLBCL) and peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) are the most common subtypes of mature B cell neoplasm and T/NK cell lymphoma, respectively. They share a commonality in that they are, by definition, highly heterogeneous populations. Recent studies are revealing more about the heterogeneity of these diseases, and at the same time, there is an active debate on how to stratify these heterogeneous diseases and make them useful in clinical practice. The various immune cells and non-cellular components surrounding lymphoma cells, i.e., the lymphoma microenvironment, have been the subject of intense research since the late 2000s, and much knowledge has been accumulated over the past decade. As a result, it has become clear that the lymphoma microenvironment, despite its paucity in tissues, significantly impacts the lymphoma pathogenesis and clinical behavior, such as its prognosis and response to therapy. In this article, we review the role of the lymphoma microenvironment in DLBCL and PTCL-NOS, with particular attention given to its impact on the prognosis and stratification.

Prognostic value of lesion dissemination in doxorubicin, bleomycin, vinblastine, and dacarbazine-treated, interimPET-negative classical Hodgkin Lymphoma patients: A radio-genomic study

We evaluated the prognostic role of the largest distance between two lesions (Dmax), defined by positron emission tomography (PET) in a retrospective cohort of newly diagnosed classical Hodgkin Lymphoma (cHL) patients. We also explored the molecular bases underlying Dmax through a gene expression analysis of diagnostic biopsies. We included patients diagnosed with cHL from 2007 to 2020, initially treated with ABVD, with available baseline PET for review, and with at least two FDG avid lesions. Patients with available RNA from diagnostic biopsy were eligible for gene expression analysis. Dmax was deduced from the three-dimensional coordinates of the baseline metabolic tumor volume (MTV) and its effect on progression free survival (PFS) was evaluated. Gene expression profiles were correlated with Dmax and analyzed using CIBERSORTx algorithm to perform deconvolution. The study was conducted on 155 eligible cHL patients. Using its median value of 20 cm, Dmax was the only variable independently associated with PFS (HR = 2.70, 95% CI 1.1-6.63, pValue = 0.03) in multivariate analysis of PFS for all patients and for those with early complete metabolic response (iPET-). Among patients with iPET-low Dmax was associated with a 4-year PFS of 90% (95% CI 82.0-98.9) significantly better compared to high Dmax (4-year PFS 72.4%, 95% CI 61.9-84.6). From the analysis of gene expression profiles differences in Dmax were mostly associated with variations in the expression of microenvironmental components. In conclusion our results support tumor dissemination measured through Dmax as novel prognostic factor for cHL patients treated with ABVD.

An integrated prognostic model for diffuse large B‐cell lymphoma treated with immunochemotherapy

Diffuse large B‐cell lymphoma (DLBCL), the most frequent non‐Hodgkin's lymphoma subtype, is characterized by strong biological, morphological, and clinical heterogeneity, but patients are treated with immunochemotherapy in a relatively homogeneous way. Here, we have used a customized NanoString platform to analyze a series of 197 homogeneously treated DLBCL cases. The platform includes the most relevant genes or signatures known to be useful for predicting response to R‐CHOP (Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone) in DLBCL cases. We generated a risk score that combines the International Prognostic Index with cell of origin and double expression of MYC/BCL2, and stratified the series into three groups, yielding hazard ratios from 0.15 to 5.49 for overall survival, and from 0.17 to 5.04 for progression‐free survival. Group differences were highly significant (p < 0.0001), and the scoring system was applicable to younger patients (<60 years of age) and patients with advanced or localized stages of the disease. Results were validated in an independent dataset from 166 DLBCL patients treated in two distinct clinical trials. This risk score combines clinical and biological data in a model that can be used to integrate biological variables into the prognostic models for DLBCL cases.

Ibrutinib, rituximab, and lenalidomide in unfit or frail patients aged 75 years or older with de novo diffuse large B-cell lymphoma: a phase 2, single-arm study

BACKGROUND

The optimal treatment for older adults with diffuse large B-cell lymphoma (DLBCL) needs to be further explored due to patient comorbidities, standard immunochemotherapy intolerance, and unfavourable genetic features. We did a phase 2 trial of ibrutinib, rituximab, and lenalidomide (iR2) to evaluate the efficacy and safety in older adult patients with de novo DLBCL.

METHODS

In this phase 2, single-arm study, unfit or frail patients with de novo DLBCL aged 75 years or older were enrolled at Shanghai Ruijin Hospital, Shanghai, China. During the induction phase from cycle 1 to 6, 560 mg ibrutinib was given orally daily throughout each 21-day treatment cycle, 375 mg/m² rituximab was given intravenously on day 1, and 25 mg lenalidomide was given orally daily from day 1 to 10 in each cycle. Patients who had a complete response after induction were given another 6 cycles of lenalidomide maintenance (25 mg orally daily from day 1 to 10 every 21 days from cycle 7 to 12). The primary endpoint was complete response rate after 6 cycles or at the end of the induction treatment. This trial is registered with ClinicalTrials.gov, NCT03949062.

FINDINGS

Between May 15, 2019, and May 8, 2020, a total of 30 patients were enrolled. The end of induction complete response rate was 56·7% (95% CI 37·4-74·5), and overall response rate was 66·7% (95% CI 47·2-82·7). With a median follow-up of 27·6 months (IQR 23·9-29·6), the 2-year progression-free survival rate was 53·3% (95% CI 34·3-69·1) and the 2-year overall survival rate was 66·7% (95% CI 46·9-80·5). The main grade 3-4 haematological adverse events were neutropenia (seven patients [23%]), thrombocytopenia (three patients [10%]), and anaemia (two patients [7%]). The most common grade 3-4 non-haematological adverse event was pulmonary infection (seven patients [23%]). Atrial fibrillation was observed in three (10%) patients, including one grade 2 and two grade 3.

INTERPRETATION

A chemotherapy-free iR2 regimen is clinically effective and safe and warrants further investigation in phase 3 trials as first-line treatment in older adult patients with DLBCL.

FUNDING

National Natural Science Foundation of China, Shanghai Municipal Education Commission Gaofeng Clinical Medicine Grant Support, Clinical Research Plan of Shanghai Hospital Development Center, and Multicenter Clinical Research Project by Shanghai Jiao Tong University School of Medicine.

Immunophenotypic Landscape and Prognosis-Related mRNA Signature in Diffuse Large B Cell Lymphoma

Diffuse large B cell lymphoma (DLBCL) exhibits a tightly complexity immune landscape. In this study, we intended to identify different immune phenotype and to examine the immune related mRNA signature for clinical characteristic, therapeutic responsiveness as well as risk stratification and survival prediction in DLBCL. We identified two immune infiltration subtypes of DLBCL patients based on 28 immune cell types. GSEA analysis uncovered the concordant classification of two robust significant subtypes of DLBCL. Considering the convenient application of the immune infiltration subtypes for prognostic prediction, we developed a risk score based on the differentially expressed genes between the Immunity-H and Immunity-L groups. By a least absolute shrinkage and selection operator (LASSO)-Cox regression model, a sixteen-gene risk signature, comprising ANTXR1, CD3D, TIMP1, FPR3, NID2, CTLA4, LPAR6, GPR183, LYZ, PTGDS, ITK, FBN1, FRMD6, PLAU, MICAL2, C1S, was established. The comprehensive results showed that the high-risk group was correlated with lower immune infiltration, more aggressive phenotypes, lower overall survival and more sensitive to lenalidomide. In contrast, a low-risk group score was associated with higher immune infiltration, less aggressive phenotypes, better overall survival and more likely to benefit from PD-1/PD-L1 inhibitors. Finally, a nomogram comprised of the risk score and IPI score was verified to more accurately predict the overall survival of DLBCL than traditional clinical prediction models. Altogether, our data demonstrate the heterogeneity of immune patterns within DLBCL and deepen our molecular understanding of this tumor entity.

Decreased CD11c-positive dendritic cells in the tumor microenvironment predict double-hit/triple-hit genotype and survival in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma and is a potentially curable disease. However, it is heterogenous, and the prognosis is poor if the tumor cells harbor fusions involving MYC and BCL2 or MYC and BCL6 (double-hit [DH] lymphoma), or fusions involving all three genes (triple-hit [TH] lymphoma). Fluorescence in situ hybridization is currently the gold standard for confirming the presence of DH/TH genotypes. However, the test is laborious and not readily available in some laboratories. Germinal center B (GCB) signatures and dual expression of MYC and BCL2 are commonly used as initial screening markers (traditional model) in clinical practice. Our study proposes immunohistochemical markers for more conveniently and accessibly screening DH/TH genotypes in DLBCL. We retrospectively reviewed the clinical and pathological parameters of patients with DLBCL. We assessed the proliferative index, apoptotic index, and tumor microenvironment (TME), with regard to T cells and CD11c(+) dendritic cells, in formalin-fixed paraffin-embedded tissue. We then generated a decision tree as a screening algorithm to predict DH/TH genotypes and employed decision curve analysis to demonstrate the superiority of this new model in prediction. We also assessed the prognostic significance of related parameters. Our study revealed that GCB subtypes, a Ki67 proliferative index higher than 70%, and BCL2 expression were significantly associated with DH/TH genotypes. Decreased CD11c(+) dendritic cells in the TME indicated additional risk. Our proposed screening algorithm outperformed a traditional model in screening for the DH/TH genotypes. In addition, decreased CD11c(+) dendritic cells in the DLBCL TME were an independent unfavorable prognosticator. In conclusion, we provide a convenient, well-performing model that predicts DH/TH genotypes in DLBCL. The prognostic significance of CD11c(+) dendritic cells in the TME might influence the classification and development of immunotherapy for DLBCL in the future.

Single-cell spatial analysis of tumor immune architecture in diffuse large B-cell lymphoma

Multiplexed immune cell profiling of the tumor microenvironment (TME) in cancer has improved our understanding of cancer immunology, but complex spatial analyses of tumor-immune interactions in lymphoma are lacking. Here we used imaging mass cytometry (IMC) on 33 cases of diffuse large B cell lymphoma (DLBCL) to characterize tumor and immune cell architecture and correlate it to clinicopathological features such as cell of origin, gene mutations, and responsiveness to chemotherapy. To understand the poor response of DLBCL to immune checkpoint inhibitors (ICI), we compared our results to IMC data from Hodgkin lymphoma (HL), a cancer highly responsive to ICI, and observed differences in the expression of PD-L1, PD-1, and TIM-3. We created a spatial classification of tumor cells and identified tumor-centric sub-regions of immune activation, immune suppression, and immune exclusion within the topology of DLBCL. Finally, the spatial analysis allowed us to identify markers such as CXCR3, which are associated with penetration of immune cells into immune desert regions, with important implications for engineered cellular therapies. This is the first study to integrate tumor mutational profiling, cell of origin classification, and multiplexed immuno-phenotyping of the TME into a spatial analysis of DLBCL at the single cell level. We demonstrate that, far from being histo-pathologically monotonous, DLBCL has a complex tumor architecture, and that changes in tumor topology can be correlated with clinically relevant features. This analysis identifies candidate biomarkers and therapeutic targets such as TIM-3, CCR4, and CXCR3 that are relevant for combination treatment strategies in immuno-oncology and cellular therapies.

New Prognostic Gene Signature and Immune Escape Mechanisms of Bladder Cancer

Background: The immune microenvironment profoundly affects tumor prognosis and therapy. The present study aimed to reveal potential immune escape mechanisms and construct a novel prognostic signature via systematic bioinformatic analysis of the bladder cancer (BLCA) immune microenvironment.

Patients and Methods: The transcriptomic data and clinicopathological information for patients with BLCA were obtained from The Cancer Genome Atlas (TCGA). Consensus clustering analysis based on the CIBERSORT and ESTIMATE algorithms was performed with patients with BLCA, which divided them into two clusters. Subsequently, the differentially expressed genes (DEGs) in the two were subjected to univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses to identify prognostic genes, which were used to construct a prognostic model. The predictive performance of the model was verified by receiver operating characteristic (ROC) and Kaplan–Meier (K-M) analyses. In addition, we analyzed the differentially altered immune cells, mutation burden, neoantigen load, and subclonal genome fraction between the two clusters to reveal the immune escape mechanism.

Results: Based on the ESTIMATE and clustering analyses, patients with BLCA were classified into two heterogeneous clusters: ImmuneScoreH and ImmuneScoreL. Univariate Cox and LASSO regression analyses identified CD96 (HR = 0.83) and IBSP (HR = 1.09), which were used to construct a prognostic gene signature with significant predictive accuracy. Regarding potential immune escape mechanisms, ImmuneScoreH and ImmuneScoreL were characterized by inactivation of innate immune cell chemotaxis. In ImmuneScoreL, a low tumor antigen load might contribute to immune escape. ImmuneScoreH featured high expression of immune checkpoint molecules.

Conclusion: CD96 and IBSP were considered prognostic factors for BLCA. Innate immune inactivation and a low tumor antigen load may be associated with immune escape mechanisms in both clusters. Our research complements the exploration of the immune microenvironment in BLCA.

Immunosuppressive Tumor Microenvironment and Immunotherapy of Epstein–Barr Virus-Associated Malignancies

The Epstein–Barr virus (EBV) can cause different types of cancer in human beings when the virus infects different cell types with various latent patterns. EBV shapes a distinct and immunosuppressive tumor microenvironment (TME) to its benefit by influencing and interacting with different components in the TME. Different EBV-associated malignancies adopt similar but slightly specific immunosuppressive mechanisms by encoding different EBV products to escape both innate and adaptive immune responses. Strategies reversing the immunosuppressive TME of EBV-associated malignancies have been under evaluation in clinical practice. As the interactions among EBV, tumor cells, and TME are intricate, in this review, we mainly discuss the epidemiology of EBV, the life cycle of EBV, the cellular and molecular composition of TME, and a landscape of different EBV-associated malignancies and immunotherapy by targeting the TME.

Molecular Diagnostic Review of Diffuse Large B-Cell Lymphoma and Its Tumor Microenvironment

Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma. It is a clinically and morphologically heterogeneous entity that has continued to resist complete subtyping. Molecular subtyping efforts emerged in earnest with the advent of gene expression profiling (GEP). This molecular subtyping approach has continued to evolve simultaneously with others including immunohistochemistry and more modern genomic approaches. Recently, the veritable explosion of genomic data availability and evolving computational methodologies have provided additional avenues, by which further understanding and subclassification of DBLCLs is possible. The goal of this review is to provide a historical overview of the major classification timepoints in the molecular subtyping of DLBCL, from gene expression profiling to present day understanding.

A single-cell atlas of diffuse large B cell lymphoma

Diffuse large B cell lymphoma (DLBCL) is one of the most common yet aggressive types of B cell lymphoma and remains incurable in 40% of patients. Herein, we profile the transcriptomes of 94,324 cells from 17 DLBCLs and 3 control samples using single-cell RNA sequencing. Altogether, 73 gene expression programs are identified in malignant cells, demonstrating high intra- and intertumor heterogeneity. Furthermore, 2,754 pairs of suggestive cell-cell interactions are predicted, indicating a complex and highly dynamic tumor microenvironment. Especially for B cell lymphomas, a strong costimulatory CD70-CD27 interaction is predicted between malignant and T cells. Furthermore, coinhibitory signals mediated by TIM3 or TIGIT seem to be the main driving force for T cell exhaustion. Finally, we find that chronic hepatitis B virus infection may have a significant impact on tumor cell survival and immune evasion in DLBCL. Our results provide insights into B cell lymphomagenesis and may facilitate the design of targeted immunotherapy strategies.

Biological and Clinical Implications of Gene-Expression Profiling in Diffuse Large B-Cell Lymphoma: A Proposal for a Targeted BLYM-777 Consortium Panel as Part of a Multilayered Analytical Approach

Gene-expression profiling (GEP) is used to study the molecular biology of lymphomas. Here, advancing insights from GEP studies in diffuse large B-cell lymphoma (DLBCL) lymphomagenesis are discussed. GEP studies elucidated subtypes based on cell-of-origin principles and profoundly changed the biological understanding of DLBCL with clinical relevance. Studies integrating GEP and next-generation DNA sequencing defined different molecular subtypes of DLBCL entities originating at specific anatomical localizations. With the emergence of high-throughput technologies, the tumor microenvironment (TME) has been recognized as a critical component in DLBCL pathogenesis. TME studies have characterized so-called "lymphoma microenvironments" and "ecotypes". Despite gained insights, unexplained chemo-refractoriness in DLBCL remains. To further elucidate the complex biology of DLBCL, we propose a novel targeted GEP consortium panel, called BLYM-777. This knowledge-based biology-driven panel includes probes for 777 genes, covering many aspects regarding B-cell lymphomagenesis (f.e., MYC signature, TME, immune surveillance and resistance to CAR T-cell therapy). Regarding lymphomagenesis, upcoming DLBCL studies need to incorporate genomic and transcriptomic approaches with proteomic methods and correlate these multi-omics data with patient characteristics of well-defined and homogeneous cohorts. This multilayered methodology potentially enhances diagnostic classification of DLBCL subtypes, prognostication, and the development of novel targeted therapeutic strategies.

The Hodgkin Lymphoma Immune Microenvironment: Turning Bad News into Good

The classic Hodgkin lymphoma (cHL) tumor microenvironment (TME) is by far the most abundant component of tumors and is responsible for most of their biological and clinical characteristics. Recent advances in our knowledge of these networks in cellular interactions allow us to understand that the neoplastic Hodgkin and Reed Sternberg (HRS) cells, although they are in the minority, are the main architects of this dysregulated immune milieu. Here, we review the major changes that have happened in recent years: from TME as a helpless bystander, reflecting an ineffective immune response, to a dynamic tumor-promoting and immunosuppressive element. The HRS cells promote survival through interconnected intrinsic and extrinsic alterations, boosting pro-tumoral signaling pathways through genetic aberrations and autocrine growth signals, in parallel with abnormal cytokine secretion for the recruitment and selection of the best cell partners for this immunosuppressive TME. In turn, cHL is already proving to be the perfect model with which to address an immune checkpoint blockade. Preliminary data demonstrate the utility of druggable key signaling pathways in this ensemble, such as JAK-STAT, NF-κB, and others. In addition, myriad biomarkers predicting a response await validation by new in situ multiplex analytical methods, single-cell gene expression, and other techniques. Together, these components will define the functional phenotypes with which we will elucidate the molecular pathogenesis of the disease and improve the survival of patients who are refractory to conventional therapies.

Reproducibility of Gene Expression Signatures in Diffuse Large B-Cell Lymphoma

Multiple gene expression profiles have been identified in diffuse large B-cell lymphoma (DLBCL). Besides the cell of origin (COO) classifier, no signatures have been reproduced in independent studies or evaluated for capturing distinct aspects of DLBCL biology. We reproduced 4 signatures in 175 samples of the HOVON-84 trial on a panel of 117 genes using the NanoString platform. The four gene signatures capture the COO, MYC activity, B-cell receptor signaling, oxidative phosphorylation, and immune response. Performance of our classification algorithms were confirmed in the original datasets. We were able to validate three of the four GEP signatures. The COO algorithm resulted in 94 (54%) germinal center B-cell (GCB) type, 58 (33%) activated B-cell (ABC) type, and 23 (13%) unclassified cases. The MYC-classifier revealed 77 cases with a high MYC-activity score (44%) and this MYC-high signature was observed more frequently in ABC as compared to GCB DLBCL (68% vs. 32%, p < 0.00001). The host response (HR) signature of the consensus clustering was present in 55 (31%) patients, while the B-cell receptor signaling, and oxidative phosphorylation clusters could not be reproduced. The overlap of COO, consensus cluster and MYC activity score differentiated six gene expression clusters: GCB/MYC-high (12%), GCB/HR (16%), GCB/non-HR (27%), COO-Unclassified (13%), ABC/MYC-high (25%), and ABC/MYC-low (7%). In conclusion, the three validated signatures identify distinct subgroups based on different aspects of DLBCL biology, emphasizing that each classifier captures distinct molecular profiles.

Impact of Qi-Invigorating Traditional Chinese Medicines on Diffuse Large B Cell Lymphoma Based on Network Pharmacology and Experimental Validation

Background: It has been verified that deficiency of Qi, a fundamental substance supporting daily activities according to the Traditional Chinese Medicine theory, is an important symptom of cancer. Qi-invigorating herbs can inhibit cancer development through promoting apoptosis and improving cancer microenvironment. In this study, we explored the potential mechanisms of Qi-invigorating herbs in diffuse large B cell lymphoma (DLBCL) through network pharmacology and in vitro experiment. Methods: Active ingredients of Qi-invigorating herbs were predicted from the Traditional Chinese Medicine Systems Pharmacology Database. Potential targets were obtained via the SwissTargetPrediction and STITCH databases. Target genes of DLBCL were obtained through the PubMed, the gene-disease associations and the Malacards databases. Overlapping genes between DLBCL and each Qi-invigorating herb were collected. Hub genes were subsequently screened via Cytoscape. The Gene Ontology and pathway enrichment analyses were performed using the DAVID database. Molecular docking was performed among active ingredients and hub genes. Hub genes linked with survival and tumor microenvironment were analyzed through the GEPIA 2.0 and TIMER 2.0 databases, respectively. Additionally, in vitro experiment was performed to verify the roles of common hub genes. Results: Through data mining, 14, 4, 22, 22, 35, 2, 36 genes were filtered as targets of Ginseng Radix et Rhizoma, Panacis Quinquefolii Radix, Codonopsis Radix, Pseudostellariae Radix, Astragali Radix, Dioscoreae Rhizoma, Glycyrrhizae Radix et Rhizoma for DLBCL treatment, respectively. Then besides Panacis Quinquefolii Radix and Dioscoreae Rhizoma, 1,14, 10, 14,13 hub genes were selected, respectively. Molecular docking studies indicated that active ingredients could stably bind to the pockets of hub proteins. CASP3, CDK1, AKT1 and MAPK3 were predicted as common hub genes. However, through experimental verification, only CASP3 was considered as the common target of Qi-invigorating herbs on DLBCL apoptosis. Furthermore, the TIMER2.0 database showed that Qi-invigorating herbs might act on DLBCL microenvironment through their target genes. Tumor-associated neutrophils may be main target cells of DLBCL treated by Qi-invigorating herbs. Conclusion: Our results support the effects of Qi-invigorating herbs on DLBCL. Hub genes and immune infiltrating cells provided the molecular basis for each Qi-invigorating herb acting on DLBCL.

Intratumoral T-cell receptor repertoire is predictive of interim PET scan results in patients with diffuse large B-cell lymphoma treated with rituximab/cyclophosphamide/doxorubicin/prednisolone/vincristine (R-CHOP) chemoimmunotherapy

Objectives

A diverse intratumoral T‐cell receptor (TCR) repertoire is associated with improved survival in diffuse large B‐cell lymphoma (DLBCL) treated with rituximab/cyclophosphamide/doxorubicin/prednisolone/vincristine (R‐CHOP) chemoimmunotherapy. We explored the impact of intratumoral TCR repertoire on interim PET (iPET) done after four cycles of R‐CHOP, the relationships between intratumoral and circulating repertoire, and the phenotypes of expanded clonotypes.

Methods

We sequenced the third complementarity‐determining region of TCRβ in tumor samples, blood at pre‐therapy and after four cycles of R‐CHOP in 35 patients enrolled in ALLGNHL21 trial in high‐risk DLBCL. We correlated the TCR diversity metrics with iPET status, gene expression profiles and HLA‐class I genotypes. We then sequenced the FACS‐sorted peripheral blood T cells in six patients, and pentamer‐sorted EBV‐specific CD8⁺ T cells in one patient from this cohort.

Results

Compared with iPET⁻ patients, the intratumoral TCR repertoire in iPET⁺ patients was characterised by higher cumulative frequency of abundant clonotypes and higher productive clonality. There was a variable overlap between circulating and intratumoral repertoires, with the dominant intratumoral clonotypes more likely to be detected in the blood. The majority of shared clonotypes were CD8⁺ PD‐1^HI T cells, and CD8⁺ T cells had the largest clonal expansions in tumor and blood. In a patient with EBV⁺ DLBCL, EBV‐specific intratumoral clonotypes were trackable in the blood.

Conclusion

This study demonstrates that clonally expanded intratumoral TCR repertoires are associated with iPET⁺ and that the blood can be used to track tumor‐associated antigen‐specific clonotypes. These findings assist the rationale design and therapeutic monitoring of immunotherapeutic strategies in DLBCL.

International Prognostic Index-Based Immune Prognostic Model for Diffuse Large B-Cell Lymphoma

Background

The International Prognostic Index (IPI) is widely used to discriminate the prognosis of patients with diffuse large B-cell lymphoma (DLBCL). However, there is a significant need to identify novel valuable biomarkers in the context of targeted therapy, such as immune checkpoint blockade (ICB).

Methods

Gene expression data and clinical DLBCL information were obtained from The Cancer Genome Atlas and Gene Expression Omnibus datasets. A total of 371 immune-related genes in DLBCL patients associated with different IPI risk groups were identified by weighted gene co-expression network analysis, and eight genes were selected to construct an IPI-based immune prognostic model (IPI-IPM). Subsequently, we analyzed the somatic mutation and transcription profiles of the IPI-IPM subgroups as well as the potential clinical response to immune checkpoint blockade (ICB) in IPI-IPM subgroups.

Results

The IPI-IPM was constructed based on the expression of CMBL, TLCD3B, SYNDIG1, ESM1, EPHA3, HUNK, PTX3, and IL12A, where high-risk patients had worse overall survival than low-risk patients, consistent with the results in the independent validation cohorts. The comprehensive results showed that high IPI-IPM risk scores were correlated with immune-related signaling pathways, high KMT2D and CD79B mutation rates, and upregulation of inhibitory immune checkpoints, including PD-L1, BTLA, and SIGLEC7, indicating a greater potential response to ICB therapy.

Conclusion

The IPI-IPM has independent prognostic significance for DLBCL patients, which provides an immunological perspective to elucidate the mechanisms of tumor progression and sheds light on the development of immunotherapy for DLBCL.

The microenvironment of DLBCL is characterized by noncanonical macrophages recruited by tumor-derived CCL5

Tissue invasion by tumor cells induces a host inflammatory response that variably impacts tumorigenesis. This has been well documented for tumor-associated macrophages (TAMs) that could play a pro/M2- or an anti/M1-tumoral function. TAMs frequently infiltrate diffuse large B-cell lymphoma (DLBCL), an aggressive neoplasm arising from germinal center-experienced B cells. However, the pathway leading to the presence of TAMs in DLBCL remains unknown, and their impact is unclear. Here, we show that some DLBCL tumor cells expressed the chemokine CCL5, enabling the differential recruitment of blood monocytes through their expression of CCR1 and CCR5. CCL5 expression by DLBCL was not related to molecular subtypes, and healthy tonsillar B cells did not produce this chemokine, implying a posttransformation event. A single-cell analysis revealed that most DLBCL TAMs had a noncanonical gene signature with the concomitant expression of M1 and M2 genes. The presence of noncanonical TAMs may explain the lack of impact of macrophages on DLBCL development reported in some survival studies.

A Germinal Center-Associated Microenvironmental Signature Reflects Malignant Phenotype and Outcome of DLBCL

The DLBCL microenvironment signature scoring system was established using nCounter-based profiling of GC-related microenvironmental genes.

DMS scores stratified DLBCL patients with different prognosis independently of existing prognostic models.

Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell malignancy, with varying prognosis after the gold standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Several prognostic models have been established by focusing primarily on characteristics of lymphoma cells themselves, including cell-of-origin (COO), genomic alterations, and gene/protein expressions. However, the prognostic impact of the lymphoma microenvironment and its association with characteristics of lymphoma cells are not fully understood. Using the nCounter-based gene expression profiling of untreated DLBCL tissues, we assess the clinical impact of lymphoma microenvironment on the clinical outcomes and pathophysiological, molecular signatures in DLBCL. The presence of normal germinal center (GC)-microenvironmental cells, including follicular T cells, macrophage/dendritic cells, and stromal cells in lymphoma tissue indicates a positive therapeutic response. Our prognostic model, based on quantitation of transcripts from distinct GC-microenvironmental cell markers, clearly identified patients with graded prognosis independently of existing prognostic models. We observed increased incidences of genomic alterations and aberrant gene expression associated with poor prognosis in DLBCL tissues lacking GC-microenvironmental cells relative to those containing these cells. These data suggest that the loss of GC-associated microenvironmental signature dictates clinical outcomes of DLBCL patients reflecting the accumulation of “unfavorable” molecular signatures.

Revised International Prognostic Index and genetic alterations are associated with early failure to R-CHOP in patients with diffuse large B-cell lymphoma

Relapsed or refractory diffuse large B-cell lymphoma (DLBCL) cases have a poor outcome. Here we analysed clinico-biological features in 373 DLBCL patients homogeneously treated with rituximab, cyclophosphamide, doxorubicin, vincristine and prednisolone (R-CHOP), in order to identify variables associated with early failure to treatment (EF), defined as primary refractoriness or relapse within 12 months from diagnosis. In addition to clinical features, mutational status of 106 genes was studied by targeted next-generation sequencing in 111 cases, copy number alterations in 87, and gene expression profile (GEP) in 39. Ninety-seven cases (26%) were identified as EF and showed significantly shorter overall survival (OS). Patients with B symptoms, advanced stage, high levels of serum lactate dehydrogenase (LDH) or β2-microglobulin, low lymphocyte/monocyte ratio and higher Revised International Prognostic Index (R-IPI) scores, as well as those with BCL2 rearrangements more frequently showed EF, with R-IPI being the most important in logistic regression. Mutations in NOTCH2, gains in 5p15·33 (TERT), 12q13 (CDK2), 12q14·1 (CDK4) and 12q15 (MDM2) showed predictive importance for EF independently from R-IPI. GEP studies showed that EF cases were significantly enriched in sets related to cell cycle regulation and inflammatory response, while cases in response showed over-representation of gene sets related to extra-cellular matrix and tumour microenvironment.

A novel prognostic signature based on immune-related genes of diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) presents a great clinical challenge and has a poor prognosis, with immune-related genes playing a crucial role. We aimed to develop an immune-related prognostic signature for improving prognosis prediction in DLBCL. Samples from the GSE31312 dataset were randomly allocated to discovery and internal validation cohorts. Univariate Cox, random forest, LASSO regression and multivariate Cox analyses were utilized to develop a prognostic signature, which was verified in the internal validation cohort, entire validation cohort and external validation cohort (GSE10846). The tumor microenvironment was investigated using the CIBERSORT and ESTIMATE tools. Gene set enrichment analysis (GSEA) was further applied to analyze the entire GSE31312 cohort. We identified four immune-related genes (CD48, IL1RL, PSDM3, RXFP3) significantly associated with overall survival. Based on discovery and validation cohort analyses, this four-gene signature could classify patients into high- and low-risk groups, with significantly different prognoses. Activated memory CD4 T cells and activated dendritic cells were significantly decreased in the high-risk group, and these patients had lower immune scores. GSEA revealed enrichment of signaling pathways, such as T cell receptor, antigen receptor-mediated, antigen processing and presentation of peptide antigen via MHC class I, in the low-risk group. In conclusion, a robust signature based on four immune-related genes was successfully constructed for predicting prognosis in DLBCL patients.

The pretreatment platelet count predicts survival outcomes of diffuse large B-cell lymphoma: An analysis of 1007 patients in the rituximab era

BACKGROUND

The prognostic value of platelet count in diffuse large B-cell lymphoma (DLBCL) has not been extensively investigated. We aimed to examine the association of pretreatment platelet count with disease features, and further examine the prognostic significance of platelet count in DLBCL treated with the R-CHOP regimen.

METHODS

Patients with DLBCL diagnosed between Jan 1 st, 2005 and Dec 31 st, 2018 at Cancer Hospital, Chinese Academy of Medical Sciences were retrospectively analyzed. Propensity score matching (PSM) was used to balance confounding factors.

RESULTS

A total of 1007 eligible patients who received frontline R-CHOP or R-CHOP-like regimens were included in this study. The optimal cutoff value of platelet count was 157 × 10⁹/L, as determined by the Maximally Selected Rank Statistics method. Patients with the platelet count ≤157 × 10⁹/L had significantly inferior overall survival (OS) (5-year OS, 44.4 % vs. 74.9 %, P < 0.001) and progression-free survival (PFS) (5-year PFS, 35.5 % vs. 65.9 %, P < 0.001) than those with the platelet count >157×10⁹/L. Multivariate analyses showed that pretreatment platelet count ≤ 157 × 10⁹/L was an adversely independent prognostic factor for OS (hazard ratio [HR] 1.960, 95 % confidence interval [CI] 1.418-2.709, P<0.001) and PFS (HR 1.443, 95 %CI 1.080-1.927, P = 0.013). The PSM analysis and subgroup analyses further confirmed the significantly negative impact of low platelet count on OS and PFS.

CONCLUSION

The pretreatment platelet count may be a simple, cost-effective and useful prognostic factor in DLBCL patients treated with frontline R-CHOP regimens. Further investigation is warranted to elucidate the biologic mechanism underlying the prognostic significance of platelet count in DLBCL.

A gene expression-based immune content predictor for survival and postoperative radiotherapy response in head and neck cancer

The immune infiltration in the tumor microenvironment has been demonstrated to be relevant to radiotherapy response. Here, we sought to understand the immune infiltration in head and neck cancer (HNC) and evaluate its significance in predicting prognosis and radiotherapy response. Using RNA sequencing data of 522 retrospective head and neck squamous cell carcinomas (HNSCCs), we constructed an immune content score based on genes related to 6 prognostic infiltrating cell types. Unsupervised hallmark pathway clustering demonstrated an immune-related tumor cluster containing the immune content score. Patients with high immune content scores exhibited favorable overall survival and disease-free survival (DFS). Moreover, the immune content score was an independent prognostic factor for DFS in HNSCC. Interestingly, the immune content score was strongly associated with radiation response pathways. These results were also extended to nasopharyngeal carcinoma. Furthermore, patients in the low immune content score group significantly gained overall survival benefits from postoperative radiotherapy (PORT), whereas patients in the high immune content score group did not. Therefore, this study identifies the immune content score as a prognostic tool, which might have a potential association with PORT response, thereby facilitating outcome prediction and treatment decision in HNC.

A novel 3D culture model recapitulates primary FL B cell features and promotes their survival

3D alginate spheroid model supports self-organization of lymphoma B cells and stromal cells mimicking lymphoma cell niche.

This high-throughput 3D model is suitable for testing new therapeutic agents in B-NHL.

Non-Hodgkin B-cell lymphomas (B-NHL) mainly develop within lymph nodes as aggregates of tumor cells densely packed with their surrounding microenvironment, creating a tumor niche specific to each lymphoma subtypes. In vitro preclinical models mimicking biomechanical forces, cellular microenvironment, and 3D organization of B-cell lymphomas remain scarce, while all these parameters are key determinants of lymphomagenesis and drug resistance. Using a microfluidic method based on cell encapsulation inside permeable, elastic, and hollow alginate microspheres, we developed a new tunable 3D model incorporating lymphoma B cells, extracellular matrix (ECM), and/or tonsil stromal cells (TSC). Under 3D confinement, lymphoma B cells were able to form cohesive spheroids resulting from overexpression of ECM components. Moreover, lymphoma B cells and TSC dynamically formed self-organized 3D spheroids favoring tumor cell growth. 3D culture induced resistance to the classical chemotherapeutic agent doxorubicin, but not to the BCL2 inhibitor ABT-199, identifying this approach as a relevant in vitro model to assess the activity of therapeutic agents in B-NHL. RNA-sequence analysis highlighted the synergy of 3D, ECM, and TSC in upregulating similar pathways in malignant B cells in vitro than those overexpressed in primary lymphoma B cells in situ. Finally, our 3D model including ECM and TSC allowed long-term in vitro survival of primary follicular lymphoma B cells. In conclusion, we propose a new high-throughput 3D model mimicking lymphoma tumor niche and making it possible to study the dynamic relationship between lymphoma B cells and their microenvironment and to screen new anti-cancer drugs.

The PD-1/PD-L1 Checkpoint in Normal Germinal Centers and Diffuse Large B-Cell Lymphomas

Besides a recognized role of PD-1/PD-L1 checkpoint in anti-tumour immune evasion, there is accumulating evidence that PD-1/PD-L1 interactions between B and T cells also play an important role in normal germinal center (GC) reactions. Even when smaller in number, T follicular helper cells (TFH) and regulatory T (TFR) or B (Breg) cells are involved in positive selection of GC B cells and may result critical in the lymphoma microenvironment. Here, we discuss a role of PD-1/PD-L1 during tumour evolution in diffuse large B cell lymphoma (DLBCL), a paradigm of GC-derived lymphomagenesis. We depict a progression model, in two phases, where malignant B cells take advantage of positive selection signals derived from correct antigen-presentation and PD-1/PD-L1 inter-cellular crosstalks to survive and initiate tumour expansion. Later, a constant pressure for the accumulation of genetic/epigenetic alterations facilitates that DLBCL cells exhibit higher PD-L1 levels and capacity to secrete IL-10, resembling Breg-like features. As a result, a complex immunosuppressive microenvironment is established where DLBCL cells sustain proliferation and survival by impairing regulatory control of TFR cells and limiting IL-21-mediated anti-tumour functions of TFH cells and maximize the use of PD-1/PD-L1 signaling to escape from CD8+ cytotoxic activity. Integration of these molecular and cellular addictions into a framework may contribute to the better understanding of the lymphoma microenvironment and contribute to the rationale for novel PD-1/PD-L1-based combinational immunotherapies in DLBCL.

Evaluation of a five-gene signature associated with stromal infiltration for diffuse large B-cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a common non-Hodgkin lymphoma. The development of immunotherapy greatly improves the patient prognosis but there are some exceptions. Thus, screening for better biomarkers for prognostic evaluation could contribute to the treatment of DLBCL patients.

AIM

To screen the novel mediators involved in the development of DLBCL.

METHODS

The GSE60 dataset was applied to identify the differentially expressed genes (DEGs) in DLBCL, and the principal components analysis plot was used to determine the quality of the included samples. The protein-protein interactions were analyzed by the STRING tool. The key hub genes were entered into to the GEPIA database to determine their expressions in DLBCL. Furthermore, these hub gene alterations were analyzed in cBioportal. The UALCAN portal was employed to analyze the expression of the hub genes in different stages of DLBCL. The Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data Score was conducted to evaluate the correlation between the gene expression and tumor purity. The gene-gene correlation analysis was conducted in the GEPIA. The stromal score analysis was conducted in TIMER to confirm the correlation between the gene expression and infiltrated stromal cells. The correlation between the indicated genes and infiltration level of cancer-associated fibroblasts (CAFs) was also completed in TIMER with two methods, MCP-Counter and Tumor immune dysfunction and exclusion. The correlation between fibronectin (FN1) protein level and secreted protein acidic and cysteine-rich (SPARC) messenger ribonucleic acid expression was confirmed in the cBioportal.

RESULTS

The top 20 DEGs in DLBCL were identified, and the principal components analysis plot confirmed the quality of the significant DEGs. The pairwise correlation coefficient analysis among all samples showed that these DEGs have a certain co-expression pattern. The DEGs were subjected to STRING to identify the hub genes, alpha-2-macroglobulin (A2M), cathepsin B (CTSB), FN1, matrix metallopeptidase 9 (MMP9), and SPARC. The five hub genes were confirmed to be overexpressed in DLBCL. The cBioportal portal detected these five hub genes that had gene alteration, including messenger ribonucleic acid high amplification and missense mutation, and the gene alteration percentages of A2M, FN1, CTSB, MMP9, and SPARC were 5%, 8%, 5%, 2.7%, and 5%, respectively. Furthermore, the five hub genes had a potential positive correlation with tumor stage. The correlation analysis between the five genes and tumor purity confirmed that the five genes were overexpressed in DLBCL and had a positive correlation with the development of DLBCL. More interestingly, the five genes had a significant correlation with the stromal infiltration scores. The correlation analysis between the fives genes and CAFs also showed a significant value, among which the top two genes, FN1 and SPARC, had a remarkable co-expression pattern.

CONCLUSION

The top DEGs were identified, and the five hub genes were overexpressed in DLBCL. Furthermore, the gene alterations were confirmed and the positive correlation with tumor purity revealed the overexpression of the five genes and close association with the development of DLBCL. More interestingly, the five genes were positively correlated with stromal infiltration, especially in CAFs. The top two genes, FN1 and SPARC, showed a co-expression pattern, which indicates their potential as novel therapeutic targets for DLBCL.

An Overview on Diffuse Large B-Cell Lymphoma Models: Towards a Functional Genomics Approach

Simple Summary

Lymphoma research is a paradigm of integrating basic and applied research within the fields of molecular marker-based diagnosis and therapy. In recent years, major advances in next-generation sequencing have substantially improved the understanding of the genomics underlying diffuse large B-cell lymphoma (DLBCL), the most frequent type of B-cell lymphoma. This review addresses the various approaches that have helped unveil the biology and intricate alterations in this pathology, from cell lines to more sophisticated last-generation experimental models, such as organoids. We also provide an overview of the most recent findings in the field, their potential relevance for designing targeted therapies and the corresponding applicability to personalized medicine.

Abstract

Lymphoma research is a paradigm of the integration of basic and clinical research within the fields of diagnosis and therapy. Clinical, phenotypic, and genetic data are currently used to predict which patients could benefit from standard treatment. However, alternative therapies for patients at higher risk from refractoriness or relapse are usually empirically proposed, based on trial and error, without considering the genetic complexity of aggressive B-cell lymphomas. This is primarily due to the intricate mosaic of genetic and epigenetic alterations in lymphomas, which are an obstacle to the prediction of which drug will work for any given patient. Matching a patient’s genes to drug sensitivity by directly testing live tissues comprises the “precision medicine” concept. However, in the case of lymphomas, this concept should be expanded beyond genomics, eventually providing better treatment options for patients in need of alternative therapeutic approaches. We provide an overview of the most recent findings in diffuse large B-cell lymphomas genomics, from the classic functional models used to study tumor biology and the response to experimental treatments using cell lines and mouse models, to the most recent approaches with spheroid/organoid models. We also discuss their potential relevance and applicability to daily clinical practice.

Detecting fibroblast activation proteins in lymphoma using 68Ga-FAPI PET/CT

Purpose: Cancer-associated fibroblasts (CAFs) that overexpress fibroblast activation protein (FAP) are enriched in many epithelial carcinomas and in hematological neoplasms. Positron emission tomography/computed tomography (PET/CT) with radiolabeled FAP inhibitor (FAPI) is a new diagnostic tool for visualizing the tumor stroma. This prospective study aimed to profile FAPs in different subtypes of lymphomas and explore the potential utility of ⁶⁸Ga-FAPI PET/CT in lymphomas. Methods: In this prospective study, we recruited 73 lymphoma patients who underwent ⁶⁸Ga-FAPI PET/CT and recorded and measured semiquantitative parameters and ratios of their scan results. FAPI expression was assessed by immunochemistry in samples obtained from 22 of the lymphoma patients. Results: We evaluated 11 patients with Hodgkin lymphoma (HL) and 62 with non-Hodgkin lymphoma (NHL). Significantly elevated FAP uptake was observed in HL lesions, correlating with the intensity of FAP immunostaining (score, 3+). A positive association was found between the corresponding clinical classification of NHL and the ⁶⁸Ga-FAPI uptake activity of the lesion. Aggressive NHL lesions, with moderate-to-strong FAP immunostaining (score, 2+ to 3+), exhibited intense to moderate ⁶⁸Ga-FAPI uptake. Indolent NHL lesions showed weak FAP staining and mild to moderate ⁶⁸Ga-FAPI uptake. No statistically significant correlation emerged between the sum of the product of the diameters and the corresponding maximum standardized uptake value (P = 0.424). The tumor-to-liver ratios were 6.26 ± 4.17 in indolent NHL and > 9 in other subtypes. Conclusion: ⁶⁸Ga-FAPI imaging can be used to detect FAP expression in lymphoma lesions and may be an alternate method for characterizing lymphoma profiles.

Prognostic molecular biomarkers in diffuse large B-cell lymphoma in the rituximab era and their therapeutic implications

Diffuse large B-cell lymphoma (DLBCL) represents a group of tumors characterized by substantial heterogeneity in terms of their pathological and biological features, a causal factor of their varied clinical outcome. This variation has persisted despite the implementation of rituximab in treatment regimens over the last 20 years. In this context, prognostic biomarkers are of great importance in order to identify high-risk patients that might benefit from treatment intensification or the introduction of novel therapeutic agents. Herein, we review current knowledge on specific immunohistochemical or genetic biomarkers that might be useful in clinical practice. Gene-expression profiling is a tool of special consideration in this effort, as it has enriched our understanding of DLBCL biology and has allowed for the classification of DLBCL by cell-of-origin as well as by more elaborate molecular signatures based on distinct gene-expression profiles. These subgroups might outperform individual biomarkers in terms of prognostication; however, their use in clinical practice is still limited. Moreover, the underappreciated role of the tumor microenvironment in DLBCL prognosis is discussed in terms of prognostic gene-expression signatures, as well as in terms of individual biomarkers of prognostic significance. Finally, the efficacy of novel therapeutic agents for the treatment of DLBCL patients are discussed and an evidence-based therapeutic approach by specific genetic subgroup is suggested.

Role of Microenvironment in Non-Hodgkin Lymphoma: Understanding the Composition and Biology

Lymphoma microenvironment is a dynamic and well-orchestrated network of various immune and stromal cells that is indispensable for tumor cell survival, growth, migration, immune escape, and drug resistance. Recent progress has enhanced our knowledge of the pivotal role of microenvironment in lymphomagenesis. Understanding the characteristics, functions, and contributions of various components of the tumor niche, along with its bidirectional interactions with tumor cells, is paramount. It offers the potential to identify new therapeutic targets with the ability to restore antitumor immune surveillance and eliminate the protumoral factors contributed by the tumor niche.

Predictive and Prognostic Molecular Factors in Diffuse Large B-Cell Lymphomas

Diffuse large B-cell lymphoma (DLBCL) is the commonest form of lymphoid malignancy, with a prevalence of about 40% worldwide. Its classification encompasses a common form, also termed as “not otherwise specified” (NOS), and a series of variants, which are rare and at least in part related to viral agents. Over the last two decades, DLBCL-NOS, which accounts for more than 80% of the neoplasms included in the DLBCL chapter, has been the object of an increasing number of molecular studies which have led to the identification of prognostic/predictive factors that are increasingly entering daily practice. In this review, the main achievements obtained by gene expression profiling (with respect to both neoplastic cells and the microenvironment) and next-generation sequencing will be discussed and compared. Only the amalgamation of molecular attributes will lead to the achievement of the long-term goal of using tailored therapies and possibly chemotherapy-free protocols capable of curing most (if not all) patients with minimal or no toxic effects.

A Combination of Multilayer Perceptron, Radial Basis Function Artificial Neural Networks and Machine Learning Image Segmentation for the Dimension Reduction and the Prognosis Assessment of Diffuse Large B-Cell Lymphoma

The prognosis of diffuse large B-cell lymphoma (DLBCL) is heterogeneous. Therefore, we aimed to highlight predictive biomarkers. First, artificial intelligence was applied into a discovery series of gene expression of 414 patients (GSE10846). A dimension reduction algorithm aimed to correlate with the overall survival and other clinicopathological variables; and included a combination of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) artificial neural networks, gene-set enrichment analysis (GSEA), Cox regression and other machine learning and predictive analytics modeling [C5.0 algorithm, logistic regression, Bayesian Network, discriminant analysis, random trees, tree-AS, Chi-squared Automatic Interaction Detection CHAID tree, Quest, classification and regression (C&R) tree and neural net)]. From an initial 54,613 gene-probes, a set of 488 genes and a final set of 16 genes were defined. Secondly, two identified markers of the immune checkpoint, PD-L1 (CD274) and IKAROS (IKZF4), were validated in an independent series from Tokai University, and the immunohistochemical expression was quantified, using a machine-learning-based Weka segmentation. High PD-L1 associated with poor overall and progression-free survival, non-GCB phenotype, Epstein–Barr virus infection (EBER+), high RGS1 expression and several clinicopathological variables, such as high IPI and absence of clinical response. Conversely, high expression of IKAROS was associated with a good overall and progression-free survival, GCB phenotype and a positive clinical response to treatment. Finally, the set of 16 genes (PAF1, USP28, SORT1, MAP7D3, FITM2, CENPO, PRCC, ALDH6A1, CSNK2A1, TOR1AIP1, NUP98, UBE2H, UBXN7, SLC44A2, NR2C2AP and LETM1), in combination with PD-L1, IKAROS, BCL2, MYC, CD163 and TNFAIP8, predicted the survival outcome of DLBCL with an overall accuracy of 82.1%. In conclusion, building predictive models of DLBCL is a feasible analytical strategy.

Clinical and Biological Subtypes of B-cell Lymphoma Revealed by Microenvironmental Signatures

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease. Transcriptomic and genetic characterization of DLBCL has increased the understanding of its intrinsic pathogenesis and provided potential therapeutic targets. However, the role of the microenvironment in DLBCL biology remains less understood. Here, we performed a transcriptomic analysis of the microenvironment of 4,655 DLBCLs from multiple independent cohorts and described four major lymphoma microenvironment categories that associate with distinct biological aberrations and clinical behavior. We also found evidence of genetic and epigenetic mechanisms deployed by cancer cells to evade microenvironmental constraints of lymphoma growth, supporting the rationale for implementing DNA hypomethylating agents in selected patients with DLBCL. In addition, our work uncovered new therapeutic vulnerabilities in the biochemical composition of the extracellular matrix that were exploited to decrease DLBCL proliferation in preclinical models. This novel classification provides a road map for the biological characterization and therapeutic exploitation of the DLBCL microenvironment. SIGNIFICANCE: In a translational relevant transcriptomic-based classification, we characterized the microenvironment as a critical component of the B-cell lymphoma biology and associated it with the DLBCL clinical behavior establishing a novel opportunity for targeting therapies.This article is highlighted in the In This Issue feature, p. 1307.