Development of a Reproducible Prognostic Gene Signature to Predict the Clinical Outcome in Patients with Diffuse Large B-Cell Lymphoma

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.

Targeting positive cofactor 4 induces autophagic cell death in MYC-expressing diffuse large B-cell lymphoma

MYC-expressing diffuse large B-cell lymphoma (DLBCL) is one of the refractory lymphomas. Currently, the pathogenesis of MYC-expressing DLBCL is still unclear, and there is a lack of effective therapy. We characterized positive cofactor 4 (PC4) as an upstream regulator of c-Myc, and PC4 is overexpressed in DLBCL and is closely related to clinical staging, prognosis, and c-Myc expression. Furthermore, our in vivo and in vitro studies revealed that PC4 knockdown can induce autophagic cell death and enhance the therapeutic effect of doxorubicin in MYC-expressing DLBCL. Inhibition of c-Myc-mediated aerobic glycolysis and activation of the AMPK/mTOR signaling pathway are responsible for the autophagic cell death induced by PC4 knockdown in MYC-expressing DLBCL. Using dual-luciferase reporter assay and electrophoretic mobility shift assay assays, we also found that PC4 exerts its oncogenic functions by directly binding to c-Myc promoters. To sum up, our study provides novel insights into the functions and mechanisms of PC4 in MYC-expressing DLBCL and suggests that PC4 may be a promising therapeutic target for MYC-expressing DLBCL.

Molecular phenotyping of malignant canine mammary tumours: Detection of high-risk group and its relationship with clinicomolecular characteristics

Canine mammary gland tumours (CMTs) constitute the most common cancer in female dogs and comprise approximately 50% of all canine cancers. With the advent of high-throughput technologies such as microarray and next-generation sequencing, the molecular phenotyping (classification) of various cancers has been extensively developed. The present study used a canine RNA-sequencing dataset, namely GSE119810, to classify 113 malignant CMTs and 64 matched normal samples via an unsupervised hierarchical algorithm with a view to evaluating the association between the resulting subtypes (clusters) (n = 4) and clinical and molecular characteristics. Finally, a molecular classifier was developed, and it detected 1 high-risk molecular subtype in the training dataset (GSE119810) and 2 independent validation datasets (GSE20718 and GSE22516). Our results revealed four molecular subtypes (C2-C5) in malignant CMTs. Furthermore, the normal samples constituted a distinct group in the clustering analysis. Marked significant associations were observed between the molecular subtypes (especially C5) and clinical/molecular features, including positive lymphatic invasion, high tumour grades, histopathology diagnoses, short survival and high TP53 mutation rates (ps <.05). The high-risk subtype (C5) was further characterized through the development of a cell cycle-based gene signature, which comprised 37 proliferation-related genes according to the support vector machine algorithm. This signature identified the high-risk group in both training and validation datasets (ps <.001). In the validation analysis, our potential classifier robustly predicted patients with positive lymphatic invasion, metastases and short survival.

Joint Secondary Transcriptomic Analysis of Non-Hodgkin's B-Cell Lymphomas Predicts Reliance on Pathways Associated with the Extracellular Matrix and Robust Diagnostic Biomarkers

Approximately 450,000 cases of Non-Hodgkin's lymphoma are annually diagnosed worldwide, resulting in ~240,000 deaths. An augmented understanding of the common mechanisms of pathology among larger numbers of B-cell Non-Hodgkin's Lymphoma (BCNHL) patients is sorely needed. We consequently performed a large joint secondary transcriptomic analysis of the available BCNHL RNA-sequencing projects from GEO, consisting of 322 relevant samples across ten distinct public studies, to find common underlying mechanisms and biomarkers across multiple BCNHL subtypes and patient subpopulations; limitations may include lack of diversity in certain ethnicities and age groups and limited clinical subtype diversity due to sample availability. We found ~10,400 significant differentially expressed genes (FDR-adjusted p-value < 0.05) and 33 significantly modulated pathways (Bonferroni-adjusted p-value < 0.05) when comparing BCNHL samples to non-diseased B-cell samples. Our findings included a significant class of proteoglycans not previously associated with lymphomas as well as significant modulation of genes that code for extracellular matrix-associated proteins. Our drug repurposing analysis predicted new candidates for repurposed drugs including ocriplasmin and collagenase. We also used a machine learning approach to identify robust BCNHL biomarkers that include YES1, FERMT2, and FAM98B, which have not previously been associated with BCNHL in the literature, but together provide ~99.9% combined specificity and sensitivity for differentiating lymphoma cells from healthy B-cells based on measurement of transcript expression levels in B-cells. This analysis supports past findings and validates existing knowledge while providing novel insights into the inner workings and mechanisms of transformed B-cell lymphomas that could give rise to improved diagnostics and/or therapeutics.

Prognostic efficacy of the RTN1 gene in patients with diffuse large B-cell lymphoma

Gene expression profiling has been vastly used to extract the genes that can predict the clinical outcome in patients with diverse cancers, including diffuse large B-cell lymphoma (DLBCL). With the aid of bioinformatics and computational analysis on gene expression data, various prognostic gene signatures for DLBCL have been recently developed. The major drawback of the previous signatures is their inability to correctly predict survival in external data sets. In other words, they are not reproducible in other datasets. Hence, in this study, we sought to determine the gene(s) that can reproducibly and robustly predict survival in patients with DLBCL. Gene expression data were extracted from 7 datasets containing 1636 patients (GSE10846 [n = 420], GSE31312 [n = 470], GSE11318 [n = 203], GSE32918 [n = 172], GSE4475 [n = 123], GSE69051 [n = 157], and GSE34171 [n = 91]). Genes significantly associated with overall survival were detected using the univariate Cox proportional hazards analysis with a P value < 0.001 and a false discovery rate (FDR) < 5%. Thereafter, significant genes common between all the datasets were extracted. Additionally, chromosomal aberrations in the corresponding region of the final common gene(s) were evaluated as copy number alterations using the single nucleotide polymorphism (SNP) data of 570 patients with DLBCL (GSE58718 [n = 242], GSE57277 [n = 148], and GSE34171 [n = 180]). Our results indicated that reticulon family gene 1 (RTN1) was the only gene that met our rigorous pipeline criteria and associated with a favorable clinical outcome in all the datasets (P < 0.001, FDR < 5%). In the multivariate Cox proportional hazards analysis, this gene remained independent of the routine international prognostic index components (i.e., age, stage, lactate dehydrogenase level, Eastern Cooperative Oncology Group [ECOG] performance status, and number of extranodal sites) (P < 0.0001). Furthermore, no significant chromosomal aberration was found in the RTN1 genomic region (14q23.1: Start 59,595,976/End 59,870,966).

GPNMB promotes the progression of diffuse large B cell lymphoma via YAP1-mediated activation of the Wnt/β-catenin signaling pathway

Glycoprotein non-metastatic melanoma protein B (GPNMB) has been confirmed to be related to the pathogenesis of tumors. However, the potential impact of GPNMB on the progression of diffuse large B-cell lymphoma (DLBCL) is unclear. In this study, the expression levels of GPNMB and Yes-associated protein (YAP) were analyzed using qRT-PCT and Western blot assay. Cell counting kit-8, EdU, and flow cytometry assays were used to detect the proliferation and apoptosis of DLBCL cells. A nude mice xenograft model was established for in vivo research. Results showed that GPNMB and YAP1 were upregulated in DLBCL cell lines. Knockdown of GPNMB inhibited cell proliferation and promoted apoptosis in DLBCL cells. Additionally, the expression levels of YAP1 and the downstream effector of Hippo pathway (c-myc) were markedly decreased when GPNMB was knocked down. Moreover, knockdown of GPNMB inhibited the nuclear translocation of β-catenin protein, which could be abolished by YAP1 overexpression. Simultaneously, the anti-proliferative and pro-apoptotic effects of GPNMB knockdown could be reversed by YAP1 overexpression or LiCl (the activator of Wnt/β-catenin pathway). Furthermore, the mice xenograft model confirmed that inhibition of GPNMB restrained the tumorigenesis of DLBCL in vivo. In conclusion, GPNMB could partly activate the Wnt/β-catenin signaling pathway by targeting YAP1, so as to participate in tumorigenesis of DLBCL.

Identification and Validation of a Prognostic Gene Signature for Diffuse Large B-Cell Lymphoma Based on Tumor Microenvironment-Related Genes

Diffuse large B-cell lymphoma (DLBCL) is an extremely heterogeneous tumor entity, which makes prognostic prediction challenging. The tumor microenvironment (TME) has a crucial role in fostering and restraining tumor development. Consequently, we performed a systematic investigation of the TME and genetic factors associated with DLBCL to identify prognostic biomarkers for DLBCL. Data for a total of 1,084 DLBCL patients from the Gene Expression Omnibus database were included in this study, and patients were divided into a training group, an internal validation group, and two external validation groups. We calculated the abundance of immune–stromal components of DLBCL and found that they were related to tumor prognosis and progression. Then, differentially expressed genes were obtained based on immune and stromal scores, and prognostic TME‐related genes were further identified using a protein–protein interaction network and univariate Cox regression analysis. These genes were analyzed by the least absolute shrinkage and selection operator Cox regression model to establish a seven-gene signature, comprising TIMP2, QKI, LCP2, LAMP2, ITGAM, CSF3R, and AAK1. The signature was shown to have critical prognostic value in the training and validation sets and was also confirmed to be an independent prognostic factor. Subgroup analysis also indicated the robust prognostic ability of the signature. A nomogram integrating the seven-gene signature and components of the International Prognostic Index was shown to have value for prognostic prediction. Gene set enrichment analysis between risk groups demonstrated that immune-related pathways were enriched in the low-risk group. In conclusion, a novel and reliable TME relevant gene signature was proposed and shown to be capable of predicting the survival of DLBCL patients at high risk of poor survival.

A prognostic gene signature for predicting survival outcome in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is an heterogenous cancer that can have profound differences in survival outcomes. Molecular profiling has allowed for the identification of DLBCL subclasses, and together with clinical prognostic factors, such as the international prognostic index, have improved clinical care and survival. Despite these advances, a gene signature that is associated with overall survival (OS) and is reproducible across different DLBCL studies could better classify risk and predict OS. Here, we have identified genes that are associated with OS in DLBCL using data from the Lymphoma/Leukemia Molecular Profiling Project and developed a prognostic gene signature consisting of 33 genes that - when transformed into a risk score - can stratify individuals into high or low risk groups that have significantly different OS. The prognostic gene signature was associated with OS in multiple clinical studies, and when used in conjunction with DLBCL molecular subtype and IPI score, significantly predicted OS. Thus, we identified a potential prognostic gene signature that can discriminate high-risk from low-risk DLBCL patients.

Development of an RNA sequencing-based prognostic gene signature in multiple myeloma

Several prognostic gene signatures have been developed to predict the clinical outcome in patients with multiple myeloma (MM). The most salient disadvantage of the previous signatures is their non-reproducibility in external datasets. Given the disadvantages and the superiority of RNA sequencing over microarrays in transcriptome profiling to produce more reliable outputs, we sought to develop a reproducible RNA sequencing-based prognostic gene signature for MM. Genes significantly associated with survival were detected in The Cancer Genome Atlas (TCGA) MM RNA sequencing dataset (MMRF-CoMMpass) (n = 412) through a strict pipeline containing four rigid filters. The reproducibility of the selected genes was checked in an independent dataset (GSE24080), containing 559 newly diagnosed patients with MM. The RNA sequencing-based prognostic signature was reconstructed based on the final genes in the training dataset (MMRF-CoMMpass) and externally validated in five independent datasets (i.e. GSE2658, GSE13624, GSE9782, GSE6477 and GSE57317), containing 1461 MM cases. The RNA sequencing-based signature was reconstructed using finally five reproducible genes: CCT2, CKS1B, PRKDC, NONO and UBE2A. This signature was able to robustly discriminate between low- and high-risk patients in both training and validation datasets (Ps ≤ 0·001). Our signature was also independent of and more powerful than the routine MM prognostic factors (i.e. β2-microglobulin, albumin, age and sex) (Ps ≤ 0·01). Treatment regimens had no effect on RNA sequencing-based signature insofar as this signature succeeded in predicting the clinical outcome in various treatment groups (Ps ≤ 0·001).