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. 2021 Oct 22:12:732006.
doi: 10.3389/fimmu.2021.732006. eCollection 2021.

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

Shidai Mu  1 , Deyao Shi  2 , Lisha Ai  1 , Fengjuan Fan  1 , Fei Peng  1 , Chunyan Sun  1 , Yu Hu  1
Affiliations

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

Shidai Mu et al. Front Immunol. .

Abstract

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.

Keywords: diffuse large B-cell lymphoma; immune prognostic model; immunotherapy; nomogram; tumor microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
A flowchart for the process of the present study.
Figure 2
Figure 2
Analysis of immune-related genes in DLBCL patients of different IPI risk groups. (A) Principal component analysis of RNA-Seq count data from three included projects. (B) Survival analysis of overall survival between IPI Risk groups. (C) Gene set variance analysis (GSVA) of enriched gene sets between IPI Risk groups. (D) Volcano plot of differentially expressed immune-related genes (DEGs) between the high and low IPI risk groups.
Figure 3
Figure 3
Weighted gene co-expression network analysis (WGCNA) for the identification of modules related to IPI risk group. (A) Process of clustering dendrogram of included genes, assigning module colors, and merging modules. (B) Analysis and visualization of Module-trait relationship to identify IPI risk group related modules. (C) Correlation between gene module membership and gene significance for IPI risk group in brown, pink, and dark-red modules.
Figure 4
Figure 4
Construction of an IPI-based immune prognostic model. (A) A plot for displaying the cross-validation error according to the log of lambda in the Lasso penalized Cox regression. (B) A forest plot for hazard ratios of the eight genes composing the IPI-based immune prognostic model (IPI-IPM). (C) Time-dependent ROC curves for the IPI-IPM risk scores on overall survival. (D) Kaplan–Meier Survival analysis of overall survival for patients of high and low IPI-IPM risk groups. (E) Time-dependent ROC curves for the IPI-IPM risk scores on progression-free survival. (F) Kaplan–Meier survival analysis of progression-free survival for patients of high and low IPI-IPM risk groups. (G) Heatmap of the gene expression in high and low IPI-IPM risk groups. (H) The multivariate analysis of IPI-IPM risk score and clinicopathologic parameters including age, Ann Arbor clinical stage, LDH ratio, and ECOG performance status and the number of extranodal sites. P value, * < 0.05, ** < 0.01, *** < 0.001.
Figure 5
Figure 5
Construction of an IPI-based immune nomogram and validation of the IPI-IPM by using independent cohorts. (A) Nomogram for the prediction of the survival probability of 1-, 3-, and 5-year overall survival. (B) The DCA analysis of all parameters in the nomogram. (C) Calibration plots of nomogram-predicted probability of 1-, 3-, 5-year, and median survival. (D) Comparison of overall survival predictive ability between the IPI risk group and IPI-IPM via time-dependent ROC curve analysis. (E) Comparison of progression-free survival predictive ability between the IPI risk group and IPI-IPM via time-dependent ROC curve analysis. (F–K) Time-dependent ROC curve analysis of the IPI-IPM risk scores in validation cohorts on overall survival [(F) GSE10846, (H) GSE87371, (J) GSE117556] and Kaplan–Meier survival analysis of overall survival for patients of IPI-IPM high and low-risk groups in validation cohorts [(G) GSE10846, (I) GSE87371, (K) GSE117556].
Figure 6
Figure 6
Gene expression analysis of IPI-IPM and identification of IPI-IPM-associated immune genes. (A) Volcano plot of immune-related DEGs between the high and low IPI-IPM risk groups. (B) Pre-ranked GSEA of enriched gene sets between the high and low IPI-IPM risk groups. (C) The t-SNE algorithm was applied to show the gene expression diversity between DLBCL patients in the high and low IPI-IPM risk groups. (D) Pearson correlation analysis of the eight genes composing the IPI-IPM. (E) Heatmap of immune-related genes of which expression correlate with the IPI-IPM risk score.
Figure 7
Figure 7
Molecular characteristics of IPI-IPM-associated immune genes. (A, B) Over-representative analysis: a chord map of the enriched GO biological processes and a Sankey plot of the enriched Reactome pathway terms. (C) Protein–protein interaction network (PPI) analysis based on the STRING database. (D) Transcription factor network analysis based on the TFTRUST database.
Figure 8
Figure 8
Somatic mutational analysis of the high and low risk IPI-IPM groups. (A) Mutated genes of high and low IPI-IPM risk groups. Top 10 mutated genes (rows) are ordered by mutation rate. The color-coding legends indicate the mutation types and survival status of patients. (B) Lollipop plots for amino acid changes of KMT2D and MUC16. (C) Oncodrive plots of the high and low risk IPI-IPM groups.
Figure 9
Figure 9
Tumor immune microenvironment characteristics of IPI-IPM subgroups. (A, B) Analysis of immune cell infiltration by using the CIBERSORTx algorithm: relative proportion of each type of cell infiltration in DLBCL patients and bar plots for visualization of significantly differentially TME-infiltrating cells between high and low IPI-IPM risk groups. (C) Analysis of immune cell infiltration by using the MCPcounter and xCell algorithm. P value, * < 0.05, ** < 0.01, *** < 0.001, **** < 0.0001.
Figure 10
Figure 10
Molecular and TME subtypes for DLBCL of IPI-IPM subgroups. (A) Distribution of IPI groups, gene expression subtypes, and genetic subtypes between high and low IPI-IPM risk groups. (B) Correlation analysis of the IPI-IPM risk score and Bcl-2, Bcl-6, and c-Myc. (C) Consensus clustering to detect lymphoma microenvironment (LME) clusters. (D, E) Distribution of LME patterns between high and low IPI-IPM risk groups. (F) GSVA enrichment score of LME functional signature between high and low IPI-IPM risk groups.
Figure 11
Figure 11
Potential therapeutic value based on IPI-IPM. (A) Connectivity map (CMap) results of top IPI-IPM associated immune genes. (B) The expression of inhibitory immune checkpoints between high and low IPI-IPM risk groups. (C) Kaplan–Meier survival analysis of overall survival for patients of IPI-IPM high and low risk groups in the IMvigor210 Cohort.
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