Gene expression profiling-based risk prediction and profiles of immune infiltration in diffuse large B-cell lymphoma

Cross-talk between immune cells and tumor cells in non-Hodgkin lymphomas arising in common variable immunodeficiency

INTRODUCTION

CVID is the commonest and most symptomatic primary immune deficiency of adulthood. NHLs are the most prevalent malignancies in CVID. The cross-talk between tumor cells and immune cells may be an important risk factor in lymphomagenesis.

AREAS COVERED

The present review highlights immune cell, genetic and histopathological alterations in the CVID-associated NHLs.

EXPERT OPINION

CVID patients exhibit some notable immune defects that may predispose to lymphomas. T/NK cell defects including reduced T cells, naïve CD4+T cells, T regs, and Th17 cells, increased CD8+T cells with reduced T cell proliferative and cytokine responses and reduced iNKT and NK cell count and cytotoxicity. B cell defects include increased transitional and CD21low B cells, clonal IgH gene rearrangements, and increased BCMA levels. Increase in IL-9, sCD30 levels, and upregulation of BAFF-BAFFR signaling are associated with lymphomas in CVID. Increased expression of PFTK1, duplication of ORC4L, germline defects in TACI, NFKB1, and PIK3CD, and somatic mutations in NOTCH2 and MYD88 are reported in CVID-associated lymphomas. Upregulation of PD-L1-PD-1 pathway may also promote lymphomagenesis in CVID. These abnormalities need to be explored as prognostic or predictive markers of CVID-associated NHLs by large multicentric studies.

An interpretable survival model for diffuse large B-cell lymphoma patients using a biologically informed visible neural network

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) and is characterized by high heterogeneity. Assessment of its prognosis and genetic subtyping hold significant clinical implications. However, existing DLBCL prognostic models are mainly based on transcriptomic profiles, while genetic variation detection is more commonly used in clinical practice. In addition, current clustering-based subtyping methods mostly focus on genes with high mutation frequencies, providing insufficient explanations for the heterogeneity of DLBCL. Here, we proposed VNNSurv (https://bio-web1.nscc-gz.cn/app/VNNSurv), a survival model for DLBCL patients based on a biologically informed visible neural network (VNN). VNNSurv achieved an average C-index of 0.72 on the cross-validation set (HMRN cohort, n = 928), outperforming the baseline methods. The remarkable interpretability of VNNSurv facilitated the identification of the most impactful genes and the underlying pathways through which they act on patient outcomes. When only the 30 highest-impact genes were used as genetic input, the overall performance of VNNSurv improved, and a C-index of 0.70 was achieved on the external TCGA cohort (n = 48). Leveraging these high-impact genes, including 16 genes with low (<5 %) alteration frequencies, we devised a genetic-based prognostic index (GPI) for risk stratification and a subtype identification method. We stratified the patient group according to the International Prognostic Index (IPI) into three risk grades with significant prognostic differences. Furthermore, the defined subtypes exhibited greater prognostic consistency than clustering-based methods. Broadly, VNNSurv is a valuable DLBCL survival model. Its high interpretability has significant value for precision medicine, and its framework is scalable to other diseases.

Non-neoplastic cells as prognostic biomarkers in diffuse large B-cell lymphoma: A system review and meta-analysis

The microenvironment of diffuse large B-cell lymphoma (DLBCL) is composed of various components, including immune cells and immune checkpoints, some of which have been correlated with the prognosis of DLBCL, but their results remain controversial. Therefore, we conducted a systematic review and meta-analysis to investigate the association between the microenvironment and prognosis in DLBCL. We searched PubMed, Web of Science, and EMBASE for relevant articles between 2001 and 2022. Twenty-five studies involving 4495 patients with DLBCL were included in the analysis. This meta-analysis confirmed that high densities of Foxp3+Tregs and PD-1+T cells are good indicators for overall survival (OS) in DLBCL, while high densities of programmed cell death protein ligand1(PD-L1)-positive expression cells and T-cell immunoglobulin-and mucin domain-3-containing molecule 3 (TIM-3)-positive expression tumor-infiltrating cells (TILs) play a contrary role in OS. Additionally, higher numbers of T-cell intracytoplasmic antigen-1(TIA-1)-positive expression T cells imply better OS and progression-free survival (PFS), while high numbers of lymphocyte activation gene(LAG)-positive expression TILs predict bad OS and PFS. Various non-tumoral cells in the microenvironment play important roles in the prognosis of DLBCL.

A Cluster of Evolutionarily Recent KRAB Zinc Finger Proteins Protects Cancer Cells from Replicative Stress-Induced Inflammation

Heterochromatin loss and genetic instability enhance cancer progression by favoring clonal diversity, yet uncontrolled replicative stress leads to mitotic catastrophe and inflammatory responses that promote immune rejection. KRAB domain-containing zinc finger proteins (KZFP) contribute to heterochromatin maintenance at transposable elements (TE). Here, we identified an association of upregulation of a cluster of primate-specific KZFPs with poor prognosis, increased copy-number alterations, and changes in the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL). Depleting two of these KZFPs targeting evolutionarily recent TEs, ZNF587 and ZNF417, impaired the proliferation of cells derived from DLBCL and several other tumor types. ZNF587 and ZNF417 depletion led to heterochromatin redistribution, replicative stress, and cGAS-STING-mediated induction of an interferon/inflammatory response, which enhanced susceptibility to macrophage-mediated phagocytosis and increased surface expression of HLA-I, together with presentation of a neoimmunopeptidome. Thus, cancer cells can exploit KZFPs to dampen TE-originating surveillance mechanisms, which likely facilitates clonal expansion, diversification, and immune evasion.

SIGNIFICANCE

Upregulation of a cluster of primate-specific KRAB zinc finger proteins in cancer cells prevents replicative stress and inflammation by regulating heterochromatin maintenance, which could facilitate the development of improved biomarkers and treatments.

Prognostic mutation signature would serve as a potential prognostic predictor in patients with diffuse large B-cell lymphoma

The present study aimed to elucidate the prognostic mutation signature (PMS) associated with long-term survival in a diffuse large B-cell lymphoma (DLBCL) cohort. All data including derivation and validation cohorts were retrospectively retrieved from The Cancer Genome Atlas (TCGA) database and whole-exome sequencing (WES) data. The Lasso Cox regression analysis was used to construct the PMS based on WES data, and the PMS was determined using the area under the receiver operating curve (AUC). The predictive performance of eligible PMS was analyzed by time-dependent receiver operating curve (ROC) analyses. After the initial evaluation, a PMS composed of 94 PFS-related genes was constructed. Notably, this constructed PMS accurately predicted the 12-, 36-, and 60-month PFS, with AUC values of 0.982, 0.983, and 0.987, respectively. A higher level of PMS was closely linked to a significantly worse PFS, regardless of the molecular subtype. Further evaluation by forest plot revealed incorporation of international prognostic index or tumor mutational burden into PMS increased the prediction capability for PFS. The drug-gene interaction and pathway exploration revealed the PFS-related genes were associated with DNA damage, TP53, apoptosis, and immune cell functions. In conclusion, this study utilizing a high throughput genetic approach demonstrated that the PMS could serve as a prognostic predictor in DLBCL patients. Furthermore, the identification of the key signaling pathways for disease progression also provides information for further investigation to gain more insight into novel drug-resistant mechanisms.

Genetic and transcriptomic analyses of diffuse large B-cell lymphoma patients with poor outcomes within two years of diagnosis

Despite the improvements in clinical outcomes for DLBCL, a significant proportion of patients still face challenges with refractory/relapsed (R/R) disease after receiving first-line R-CHOP treatment. To further elucidate the underlying mechanism of R/R disease and to develop methods for identifying patients at risk of early disease progression, we integrated clinical, genetic and transcriptomic data derived from 2805 R-CHOP-treated patients from seven independent cohorts. Among these, 887 patients exhibited R/R disease within two years (poor outcome), and 1918 patients remained in remission at two years (good outcome). Our analysis identified four preferentially mutated genes (TP53, MYD88, SPEN, MYC) in the untreated (diagnostic) tumor samples from patients with poor outcomes. Furthermore, transcriptomic analysis revealed a distinct gene expression pattern linked to poor outcomes, affecting pathways involved in cell adhesion/migration, T-cell activation/regulation, PI3K, and NF-κB signaling. Moreover, we developed and validated a 24-gene expression score as an independent prognostic predictor for treatment outcomes. This score also demonstrated efficacy in further stratifying high-risk patients when integrated with existing genetic or cell-of-origin subtypes, including the unclassified cases in these models. Finally, based on these findings, we developed an online analysis tool ( https://lymphprog.serve.scilifelab.se/app/lymphprog ) that can be used for prognostic prediction for DLBCL patients.

Prediction significance of autophagy-related genes in survival probability and drug resistance in diffuse large B-cell lymphoma

BACKGROUND/AIMS

Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma, has significant prognostic heterogeneity. This study aimed to generate a prognostic prediction model based on autophagy-related genes for DLBCL patients.

METHODS

Utilizing bioinformatics techniques, we analyzed the clinical information and transcriptome data of DLBCL patients from the Gene Expression Omnibus (GEO) database. Through unsupervised clustering, we identified new autophagy-related molecular subtypes and pinpointed differentially expressed genes (DEGs) between these subtypes. Based on these DEGs, a prognostic model was constructed using Cox and Lasso regression. The effectiveness, accuracy, and clinical utility of this prognostic model were assessed using numerous independent validation cohorts, survival analyses, receiver operating characteristic (ROC) curves, multivariate Cox regression analysis, nomograms, and calibration curves. Moreover, functional analysis, immune cell infiltration, and drug sensitivity analysis were performed.

RESULTS

DLBCL patients with different clinical characterizations (age, molecular subtypes, ECOG scores, and stages) showed different expression features of autophagy-related genes. The prediction model was constructed based on the eight autophagy-related genes (ADD3, IGFBP3, TPM1, LYZ, AFDN, DNAJC10, GLIS3, and CCDC102A). The prognostic nomogram for overall survival of DLBCL patients incorporated risk level, stage, ECOG scores, and molecular subtypes, showing excellent agreement between observed and predicted outcomes. Differences were noted in the proportions of immune cells (native B cells, Treg cells, CD8+ T cell, CD4+ memory activated T cells, gamma delta T cells, macrophages M1, and resting mast cells) between high-risk and low-risk groups. LYZ and ADD3 exhibited correlations with drug resistance to most chemotherapeutic drugs.

CONCLUSIONS

This study established a novel prognostic assessment model based on the expression profile of autophagy-related genes and clinical characteristics of DLBCL patients, explored immune infiltration and predicted drug resistance, which may guide precise and individualized immunochemotherapy regimens.

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.

Baseline tumor gene expression signatures correlate with chemoimmunotherapy treatment responsiveness in canine B cell lymphoma

Pet dogs develop spontaneous diffuse large B cell lymphoma (DLBCL), and veterinary clinical trials have been employed to treat canine DLBCL and to inform clinical trials for their human companions. A challenge that remains is selection of treatment to improve outcomes. The dogs in this study were part of a larger clinical trial evaluating the use of combinations of doxorubicin chemotherapy, anti-CD20 monoclonal antibody, and one of three small molecule inhibitors: KPT-9274, TAK-981, or RV1001. We hypothesized that significant differential expression of genes (DEGs) in the tumors at baseline could help predict which dogs would respond better to each treatment based on the molecular pathways targeted by each drug. To this end, we evaluated gene expression in lymph node aspirates from 18 trial dogs using the NanoString nCounter Canine Immuno-oncology (IO) Panel. We defined good responders as those who relapsed after 90 days, and poor responders as those who relapsed prior to 90 days. We analyzed all dogs at baseline and compared poor responders to good responders, and found increased CCND3 correlated with poor prognosis and increased CD36 correlated with good prognosis, as is observed in humans. There was minimal DEG overlap between treatment arms, prompting separate analyses for each treatment cohort. Increased CREBBP and CDKN1A for KPT-9274, increased TLR3 for TAK-981, and increased PI3Kδ, AKT3, and PTEN, and decreased NRAS for RV1001 were associated with better prognoses. Trends for selected candidate biomarker genes were confirmed via qPCR. Our findings emphasize the heterogeneity in DLBCL, similarities and differences between canine and human DLBCL, and ultimately identify biomarkers that may help guide the choice of chemoimmunotherapy treatment in dogs.

Machine learning models-based on integration of next-generation sequencing testing and tumor cell sizes improve subtype classification of mature B-cell neoplasms

Background

Next-generation sequencing (NGS) panels for mature B-cell neoplasms (MBNs) are widely applied clinically but have yet to be routinely used in a manner that is suitable for subtype differential diagnosis. This study retrospectively investigated newly diagnosed cases of MBNs from our laboratory to investigate mutation landscapes in Chinese patients with MBNs and to combine mutational information and machine learning (ML) into clinical applications for MBNs, especially for subtype classification.

Methods

Samples from the Catalogue Of Somatic Mutations In Cancer (COSMIC) database were collected for ML model construction and cases from our laboratory were used for ML model validation. Five repeats of 10-fold cross-validation Random Forest algorithm was used for ML model construction. Mutation detection was performed by NGS and tumor cell size was confirmed by cell morphology and/or flow cytometry in our laboratory.

Results

Totally 849 newly diagnosed MBN cases from our laboratory were retrospectively identified and included in mutational landscape analyses. Patterns of gene mutations in a variety of MBN subtypes were found, important to investigate tumorigenesis in MBNs. A long list of novel mutations was revealed, valuable to both functional studies and clinical applications. By combining gene mutation information revealed by NGS and ML, we established ML models that provide valuable information for MBN subtype classification. In total, 8895 cases of 8 subtypes of MBNs in the COSMIC database were collected and utilized for ML model construction, and the models were validated on the 849 MBN cases from our laboratory. A series of ML models was constructed in this study, and the most efficient model, with an accuracy of 0.87, was based on integration of NGS testing and tumor cell sizes.

Conclusions

The ML models were of great significance in the differential diagnosis of all cases and different MBN subtypes. Additionally, using NGS results to assist in subtype classification of MBNs by method of ML has positive clinical potential.

Longitudinal expression profiling identifies a poor risk subset of patients with ABC-type diffuse large B-cell lymphoma

Despite the effectiveness of immuno-chemotherapy, 40% of patients with diffuse large B-cell lymphoma (DLBCL) experience relapse or refractory disease. Longitudinal studies have previously focused on the mutational landscape of relapse but fell short of providing a consistent relapse-specific genetic signature. In our study, we have focused attention on the changes in GEP accompanying DLBCL relapse using archival paired diagnostic/relapse specimens from 38 de novo patients with DLBCL. COO remained stable from diagnosis to relapse in 80% of patients, with only a single patient showing COO switching from activated B-cell-like (ABC) to germinal center B-cell-like (GCB). Analysis of the transcriptomic changes that occur following relapse suggest ABC and GCB relapses are mediated via different mechanisms. We developed a 30-gene discriminator for ABC-DLBCLs derived from relapse-associated genes that defined clinically distinct high- and low-risk subgroups in ABC-DLBCLs at diagnosis in datasets comprising both population-based and clinical trial cohorts. This signature also identified a population of <60-year-old patients with superior PFS and OS treated with ibrutinib-R-CHOP as part of the PHOENIX trial. Altogether this new signature adds to the existing toolkit of putative genetic predictors now available in DLBCL that can be readily assessed as part of prospective clinical trials.

Peripheral immune cell profiling of double-hit lymphoma by mass cytometry

BACKGROUND

Double-hit or Triple-hit lymphoma (DHL/THL) is a subset of high-grade B cell lymphoma harboring rearrangements of MYC and BCL2 and/or BCL6, and usually associate with aggressive profile, while current therapies tend to provide poor clinical outcomes and eventually relapsed. Further explorations of DHL at cellular and molecular levels are in demand to offer guidance for clinical activity.

METHODS

We collected the peripheral blood of DHL patients and diffused large B cell lymphoma (DLBCL) patients from single institute and converted them into PBMC samples. Mass cytometry was then performed to characterize these samples by 42 antibody markers with samples of healthy people as control. We divided the immune cell subtypes based on the expression profile of surface antigens, and the proportion of each cell subtype was also analyzed. By comparing the data of the DLBCL group and the healthy group, we figured out the distinguished immune cell subtypes of DHL patients according to their abundance and marker expression level. We further analyzed the heterogeneity of DHL samples by pairwise comparison based on clinical characteristics.

RESULTS

We found double-positive T cells (DPT) cells were in a significantly high percentage in DHL patients, whereas the ratio of double-negative T cells (DNT) was largely reduced in patients. Besides, CD38 was uniquely expressed at a high level on some naïve B cells of DHL patients, which could be a marker for the diagnosis of DHL (distinguishing from DLBCL), or even be a drug target for the treatment of DHL. In addition, we illustrated the heterogeneity of DHL patients in terms of immune cell landscape, and highlighted TP53 as a major factor that contributes to the heterogeneity of the T cells profile.

CONCLUSION

Our study demonstrated the distinct peripheral immune cell profile of DHL patients by contrast to DLBCL patients and healthy people, as well as the heterogeneity within the DHL group, which could provide valuable guidance for the diagnosis and treatment of DHL.

Meta-Analysis of MS-Based Proteomics Studies Indicates Interferon Regulatory Factor 4 and Nucleobindin1 as Potential Prognostic and Drug Resistance Biomarkers in Diffuse Large B Cell Lymphoma

The prognosis of diffuse large B cell lymphoma (DLBCL) is inaccurately predicted using clinical features and immunohistochemistry (IHC) algorithms. Nomination of a panel of molecules as the target for therapy and predicting prognosis in DLBCL is challenging because of the divergences in the results of molecular studies. Mass spectrometry (MS)-based proteomics in the clinic represents an analytical tool with the potential to improve DLBCL diagnosis and prognosis. Previous proteomics studies using MS-based proteomics identified a wide range of proteins. To achieve a consensus, we reviewed MS-based proteomics studies and extracted the most consistently significantly dysregulated proteins. These proteins were then further explored by analyzing data from other omics fields. Among all significantly regulated proteins, interferon regulatory factor 4 (IRF4) was identified as a potential target by proteomics, genomics, and IHC. Moreover, annexinA5 (ANXA5) and nucleobindin1 (NUCB1) were two of the most up-regulated proteins identified in MS studies. Functional enrichment analysis identified the light zone reactions of the germinal center (LZ-GC) together with cytoskeleton locomotion functions as enriched based on consistent, significantly dysregulated proteins. In this study, we suggest IRF4 and NUCB1 proteins as potential biomarkers that deserve further investigation in the field of DLBCL sub-classification and prognosis.

Combined Focused Next-Generation Sequencing Assays to Guide Precision Oncology in Solid Tumors: A Retrospective Analysis from an Institutional Molecular Tumor Board

BACKGROUND

Increasing knowledge of cancer biology and an expanding spectrum of molecularly targeted therapies provide the basis for precision oncology. Despite extensive gene diagnostics, previous reports indicate that less than 10% of patients benefit from this concept.

METHODS

We retrospectively analyzed all patients referred to our center's Molecular Tumor Board (MTB) from 2018 to 2021. Molecular testing by next-generation sequencing (NGS) included a 67-gene panel for the detection of short-sequence variants and copy-number alterations, a 53- or 137-gene fusion panel and an ultra-low-coverage whole-genome sequencing for the detection of additional copy-number alterations outside the panel's target regions. Immunohistochemistry for microsatellite instability and PD-L1 expression complemented NGS.

RESULTS

A total of 109 patients were referred to the MTB. In all, 78 patients received therapeutic proposals (70 based on NGS) and 33 were treated accordingly. Evaluable patients treated with MTB-recommended therapy (n = 30) had significantly longer progression-free survival than patients treated with other therapies (n = 17) (4.3 vs. 1.9 months, p = 0.0094). Seven patients treated with off-label regimens experienced major clinical benefits.

CONCLUSION

The combined focused sequencing assays detected targetable alterations in the majority of patients. Patient benefits appeared to lie in the same range as with large-scale sequencing approaches.

Genomic crossroads between non-Hodgkin's lymphoma and common variable immunodeficiency

Common variable immunodeficiency (CVID) represents the largest group of primary immunodeficiencies that may manifest with infections, inflammation, autoimmunity, and cancer, mainly B-cell non-Hodgkin's lymphoma (NHL). Indeed, NHL may result from chronic or recurrent infections and has, therefore, been recognized as a clinical phenotype of CVID, although rare. The more one delves into the mechanisms involved in CVID and cancer, the stronger the idea that both pathologies can be a reflection of the same primer events observed from different angles. The potential effects of germline variants on specific somatic modifications in malignancies suggest that it might be possible to anticipate critical events during tumor development. In the same way, a somatic alteration in NHL could be conditioning a similar response at the transcriptional level in the shared signaling pathways with genetic germline alterations in CVID. We aimed to explore the genomic substrate shared between these entities to better characterize the CVID phenotype immunodeficiency in NHL. By means of an in-silico approach, we interrogated the large, publicly available datasets contained in cBioPortal for the presence of genes associated with genetic pathogenic variants in a panel of 50 genes recurrently altered in CVID and previously described as causative or disease-modifying. We found that 323 (25%) of the 1,309 NHL samples available for analysis harbored variants of the CVID spectrum, with the most recurrent alteration presented in NHL occurring in PIK3CD (6%) and STAT3 (4%). Pathway analysis of common gene alterations showed enrichment in inflammatory, immune surveillance, and defective DNA repair mechanisms similar to those affected in CVID, with PIK3R1 appearing as a central node in the protein interaction network. The co-occurrence of gene alterations was a frequent phenomenon. This study represents an attempt to identify common genomic grounds between CVID and NHL. Further prospective studies are required to better know the role of genetic variants associated with CVID and their reflection on the somatic pathogenic variants responsible for cancer, as well as to characterize the CVID-like phenotype in NHL, with the potential to influence early CVID detection and therapeutic management.

Clinical Impact of the Histopathological Index and Neuroimaging Features Status in Primary Central Nervous System Diffuse Large B-Cell Lymphoma: A Single-Center Retrospective Analysis of 51 Cases

Primary central nervous system diffuse large B-cell lymphoma (PCNS-DLBCL) is an uncommon non-Hodgkin lymphoma subtype, and its clinical and pathological characteristics remain unclear. PCNS-DLBCL patient data were retrospectively evaluated to determine clinical and pathological characteristics and prognostic factors. Furthermore, prognoses were calculated by Kaplan–Meier and Cox regression models based on clinical observations. In total, 51 immunocompetent patients were enrolled. The median age was 55 (range, 16–82) years, and the male-to-female ratio was 3:2. Headache (n = 19; 37%) and the frontal lobe (n = 16; 31%) were the most common presenting symptom and location, respectively. The median follow-up was 33 (range, 3–86) months, and the median overall survival (OS) and progression-free survival (PFS) were 18 months [95% confidence interval (CI), 21.2–34.2] and 15 months (95% CI, 16.9–28.7), respectively. Ki-67, cluster of differentiation-3, and deep brain involvement were independent prognostic markers. Moreover, multifocal lesions and deep brain involvement were unfavorable independent prognostic markers for PFS. This study indicates that targeted drug development for adverse prognostic factors is possible and provides guidance for clinical treatment decision-making.

Identification of CXCR4 Upregulation in Diffuse Large B-Cell Lymphoma Associated with Prognostic Significance and Clinicopathological Characteristics

Background

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous malignant lymphoma with distinct characteristics. Patients with treatment failure after the standard immunochemotherapy have worse prognosis, which implies the necessity to uncover novel targets. The C-X-C chemokine receptor 4 (CXCR4) overexpression has been identified in several hematopoietic malignancies. However, the expression signatures and prognostic significance of CXCR4 in DLBCL associated with clinicopathological features remain unclear.

Methods

Gene expression profiles of DLBCL were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then, a meta-analysis with an integrated bioinformatic analysis was performed to assess the relationship between CXCR4 expression and clinicopathological features of DLBCL. Finally, experimental verification including immunohistochemical (IHC) staining and real-time quantitative PCR (qPCR) was carried out using patient samples. In vitro cell line viability tests were conducted using CXCR4 inhibitor WZ811.

Results

DLBCL patients with activated B-cell-like (ABC) subtype have higher expression level of CXCR4 with worse survival. Differential expressed genes in the CXCR4-upregulation group were enriched in canonical pathways associated with oncogenesis. DLBCL with CXCR4 upregulation had lower degree of CD8⁺ T cell infiltration. TIMER analysis demonstrated that the CXCR4 expression was positively correlated with the expression of CD5, MYC, NOTCH1, PDCD1, CD274, mTOR, FOXO1, and hnRNPA2B1 in DLBCL. IHC study in patient samples showed the positive correlation between CXCR4 and nongerminal center B-cell (non-GCB) subtype and mTOR expression. Meanwhile, quantitative polymerase chain reaction results revealed that high CXCR4 mRNA level was correlated to double-hit DLBCL. Finally, cell viability test showed that WZ811 exerted antiproliferation effect in DLBCL cell lines in a dose-dependent manner.

Conclusion

CXCR4 was upregulated in ABC-DLBCL associated with worse prognosis. Our analysis predicted CXCR4 as a potential target for DLBCL treatment, which may serve as an inhibitor both on BCR signaling and nuclear export warranting further investigation in clinical trials.

A Novel Defined Super-Enhancer Associated Gene Signature to Predict Prognosis in Patients With Diffuse Large B-Cell Lymphoma

Background: Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous disease that can have profound differences in survival outcomes. A variety of powerful prognostic factors and models have been constructed; however, the development of more accurate prognosis prediction and targeted treatment for DLBCL still faces challenges. An explosion of research on super-enhancer (SE)–associated genes provide the possibility to use in prognostication for cancer patients. Here, we aimed to establish a novel effective prognostic model using SE-associated genes from DLBCL.

Methods: A total of 1,105 DLBCL patients from the Gene Expression Omnibus database were included in this study and were divided into a training set and a validation set. A total of 11 SE-associated genes (BCL2, SPAG16, PXK, BTG1, LRRC37A2, EXT1, TGFBR2, ANKRD12, MYCBP2, PAX5, and MYC) were initially screened and identified by the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, univariate and multivariate Cox regression analysis. Finally, a risk score model based on these 11 genes was constructed.

Results: Kaplan–Meier (K–M) curves showed that the low-risk group appeared to have better clinical survival outcomes. The excellent performance of the model was determined via time-dependent receiver operating characteristic (ROC) curves. A nomogram based on the polygenic risk score was further established to promote reliable prognostic prediction. This study proposed that the SE-associated-gene risk signature can effectively predict the response to chemotherapy in DLBCL patients.

Conclusion: A novel and reliable SE-associated-gene signature that can effectively classify DLBCL patients into high-risk and low-risk groups in terms of overall survival was developed, which may assist clinicians in the treatment of DLBCL.

Epigenetic, Metabolic, and Immune Crosstalk in Germinal-Center-Derived B-Cell Lymphomas: Unveiling New Vulnerabilities for Rational Combination Therapies

B-cell non-Hodgkin lymphomas (B-NHLs) are highly heterogenous by genetic, phenotypic, and clinical appearance. Next-generation sequencing technologies and multi-dimensional data analyses have further refined the way these diseases can be more precisely classified by specific genomic, epigenomic, and transcriptomic characteristics. The molecular and genetic heterogeneity of B-NHLs may contribute to the poor outcome of some of these diseases, suggesting that more personalized precision-medicine approaches are needed for improved therapeutic efficacy. The germinal center (GC) B-cell like diffuse large B-cell lymphomas (GCB-DLBCLs) and follicular lymphomas (FLs) share specific epigenetic programs. These diseases often remain difficult to treat and surprisingly do not respond advanced immunotherapies, despite arising in secondary lymphoid organs at sites of antigen recognition. Epigenetic dysregulation is a hallmark of GCB-DLBCLs and FLs, with gain-of-function (GOF) mutations in the histone methyltransferase EZH2, loss-of-function (LOF) mutations in histone acetyl transferases CREBBP and EP300, and the histone methyltransferase KMT2D representing the most prevalent genetic lesions driving these diseases. These mutations have the common effect to disrupt the interactions between lymphoma cells and the immune microenvironment, via decreased antigen presentation and responsiveness to IFN-γ and CD40 signaling pathways. This indicates that immune evasion is a key step in GC B-cell lymphomagenesis. EZH2 inhibitors are now approved for the treatment of FL and selective HDAC3 inhibitors counteracting the effects of CREBBP LOF mutations are under development. These treatments can help restore the immune control of GCB lymphomas, and may represent optimal candidate agents for more effective combination with immunotherapies. Here, we review recent progress in understanding the impact of mutant chromatin modifiers on immune evasion in GCB lymphomas. We provide new insights on how the epigenetic program of these diseases may be regulated at the level of metabolism, discussing the role of metabolic intermediates as cofactors of epigenetic enzymes. In addition, lymphoma metabolic adaptation can negatively influence the immune microenvironment, further contributing to the development of immune cold tumors, poorly infiltrated by effector immune cells. Based on these findings, we discuss relevant candidate epigenetic/metabolic/immune targets for rational combination therapies to investigate as more effective precision-medicine approaches for GCB lymphomas.

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.

A CIBERSORTx-based immune cell scoring system could independently predict the prognosis of patients with myelodysplastic syndromes

Aside from cell intrinsic factors such as genetic alterations, immune dysregulation in the bone marrow (BM) microenvironment plays a role in the development and progression of myelodysplastic syndromes (MDS). However, the prognostic implications of various immune cells in patients with MDS remain unclear. We adopted CIBERSORTx to estimate the relative fractions of 22 subtypes of immune cells in the BM of 316 patients with MDS and correlated the results with clinical outcomes. A lower fraction of unpolarized M0 macrophages and higher fractions of M2 macrophages and eosinophils were significantly associated with inferior survival. An immune cell scoring system (ICSS) was constructed based on the proportion of these 3 immune cells in the BM. The ICSS high-risk patients had higher BM blast counts, higher frequencies of poor-risk cytogenetics, and more NPM1, TP53, and WT1 mutations than intermediate- and low-risk patients. The ICSS could stratify patients with MDS into 3 risk groups with distinct leukemia-free survival and overall survival among the total cohort and in the subgroups of patients with lower and higher disease risk based on the revised International Prognostic Scoring System (IPSS-R). The prognostic significance of ICSS was also validated in another independent cohort. Multivariable analysis revealed that ICSS independently predicted prognosis, regardless of age, IPSS-R, and mutation status. Bioinformatic analysis demonstrated a significant correlation between high-risk ICSS and nuclear factor κB signaling, oxidative stress, and leukemic stem cell signature pathways. Further studies investigating the mechanistic insight into the crosstalk between stem cells and immune cells are warranted.

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 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.

SPARC-positive macrophages are the superior prognostic factor in the microenvironment of diffuse large B-cell lymphoma and independent of MYC rearrangement and double-/triple-hit status

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with respect to outcome. Features of the tumor microenvironment (TME) are associated with prognosis when assessed by gene expression profiling. However, it is uncertain whether assessment of the microenvironment can add prognostic information to the most relevant and clinically well-established molecular subgroups when analyzed by immunohistochemistry (IHC).

PATIENTS AND METHODS

We carried out a histopathologic analysis of biomarkers related to TME in a very large cohort (n = 455) of DLBCL treated in prospective trials and correlated with clinicopathologic and molecular data, including chromosomal rearrangements and gene expression profiles for cell-of-origin and TME.

RESULTS

The content of PD1+, FoxP3+ and CD8+, as well as vessel density, was not associated with outcome. However, we found a low content of CD68+ macrophages to be associated with inferior progression-free survival (PFS) and overall survival (OS; P = 0.023 and 0.040, respectively) at both univariable and multivariable analyses, adjusted for the factors of the International Prognostic Index (IPI), MYC break and BCL2/MYC and BCL6/MYC double-hit status. The subgroup of PDL1+ macrophages was not associated with survival. Instead, secreted protein acidic and cysteine rich (SPARC)-positive macrophages were identified as the subtype of macrophages most associated with survival. SPARC-positive macrophages and stromal cells directly correlated with favorable PFS and OS (both, P[log rank] <0.001, P[trend] < 0.001). The association of SPARC with prognosis was independent of the factors of the IPI, MYC double-/triple-hit status, Bcl2/c-myc double expression, cell-of-origin subtype and a recently published gene expression signature [lymphoma-associated macrophage interaction signature (LAMIS)].

CONCLUSIONS

SPARC expression in the TME detected by a single IHC staining with fair-to-good interobserver reproducibility is a powerful prognostic parameter. Thus SPARC expression is a strong candidate for risk assessment in DLBCL in daily practice.

Development and Validation of a Nomogram and a Comprehensive Prognostic Analysis of an LncRNA-Associated Competitive Endogenous RNA Network Based on Immune-Related Genes for Locally Advanced Rectal Cancer With Neoadjuvant Therapy

Colorectal cancer (CRC) is a common digestive tract tumor worldwide. In recent years, neoadjuvant chemoradiotherapy (CRT) has been the most comprehensive treatment for locally advanced rectal cancer (LARC). In this study, we explored immune infiltration in rectal cancer (RC) and identified immune-related differentially expressed genes (IRDEGs). Then, we identified response markers in datasets in GEO databases by principal component analysis (PCA). We also utilized three GEO datasets to identify the up- and downregulated response-related genes simultaneously and then identified genes shared between the PCA markers and three GEO datasets. Based on the hub IRDEGs, we identified target mRNAs and constructed a ceRNA network. Based on the ceRNA network, we explored prognostic biomarkers to develop a prognostic model for RC through Cox regression. We utilized the specimen to validate the expression of the two biomarkers. We also utilized LASSO regression to screen hub IRDEGs and built a nomogram to predict the response of LARC patients to CRT. All of the results show that the nomogram and prognostic model offer good prognostic value and that the ceRNA network can effectively highlight the regulatory relationship. hsa-mir-107 and WDFY3-AS2 may be prognostic biomarkers for RC.

Plasma Metabolomics Reveals Metabolic Profiling For Diabetic Retinopathy and Disease Progression

BACKGROUNDS

Diabetic retinopathy (DR), the main retinal vascular complication of DM, is the leading cause of visual impairment and blindness among working-age people worldwide. The aim of this study was to investigate the difference of plasma metabolic profiles in patients with DR to better understand the mechanism of this disease and disease progression.

METHODS

We used ultrahigh-performance liquid Q-Exactive mass spectrometry and multivariate statistical analyses to conduct a comprehensive analysis of plasma metabolites in a population with DR and proliferative DR (PDR). A risk score based on the level of the selected metabolite was established and evaluated using the least absolute shrinkage and selection operator regularization logistic regression (LASSO-LR) based machine learning model.

RESULTS

22 differentially expressed metabolites which belonged to different metabolic pathway were identified and confirmed to be associated with the occurrence of DR. A risk score based on the level of the selected metabolite pseudouridine was established and evaluated to strongly associated with the occurrence of DR. Four circulating plasma metabolites (pseudouridine, glutamate, leucylleucine and N-acetyltryptophan) were identified to be differentially expressed between patients with PDR and other patients, and a risk score formula based on these plasma metabolites was developed and assessed to be significantly related to PDR.

CONCLUSIONS

Our work highlights the possible use of the risk score assessment based on the plasma metabolites not only reveal in the early diagnosis of DR and PDR but also assist in enhancing current therapeutic strategies in the clinic.