Tumor mutation burden estimated by a 69-gene-panel is associated with overall survival in patients with diffuse large B-cell lymphoma

Abstract

Background: Tumor mutation burden (TMB) as estimated by cancer gene panels (CGPs) has been confirmed to be associated with prognosis and is effective in predicting clinical benefit from immune checkpoint blockade (ICB) in solid tumors. However, whether the TMB calculated by CGPs is associated with overall survival (OS) for patients with diffuse large B-cell lymphoma (DLBCL) is worth exploring.

Methods: The prognostic value of panel-TMB, calculated by a panel of 69 genes (GP69), for 87 DLBCL patients in our clinical center (GDPH dataset) was explored. The results were further validated using 37 DLBCL patients from the Cancer Genome Atlas (TCGA) database (TCGA dataset).

Results: Spearman correlation analysis suggested that panel-TMB is positively correlated with the TMB calculated by whole-exome sequencing (wTMB) in the TCGA dataset (R = 0.76, P < 0.0001). Both GDPH and TCGA results demonstrated that higher panel-TMB is significantly associated with a poor OS for DLBCL patients (P < 0.05) where a panel of 13 genes was associated with poor OS, and another panel of 26 genes was correlated with a favorable OS for DLBCL patients. Further subgroup analysis indicated that higher panel-TMB had shorter OS in DLBCL patients with younger than 60 years, elevated LDH, greater than one extranodal involvement, stage III/IV, an IPI score of 3-5, or HBsAg, anti-HBc, or HBV-DNA negativity (P < 0.05). Interestingly, the nomogram model constructed by panel-TMB, stage, and IPI could individually and visually predict the 1-, 2- and 3-year OS rates of DLBCL patients.

Conclusions: We established GP69 for the evaluation of OS for Chinese DLBCL patients. panel-TMB might be a potential predictor for prognostic stratification of DLBCL patients.

Keywords: Biomarker; Diffuse large B-cell lymphoma; Gene panel; Prognosis; TMB.

Fig. 1

Workflow of study. A total of 87 DLBCL patients from our clinical center were designated as a GDPH cohort, and their whole blood and tumor biopsies were obtained to isolate genomic deoxyribonucleic acid (DNA). Construction of DNA sequence library for exon sequencing and data mining. Furthermore, the UCSC-XENA platform (https://xenabrowser.net/datapages/) was used to download the whole-exome sequencing data of 37 DLBCL patients in the Cancer Genome Atlas (TCGA) database for data analysis. The mutation frequency and type of a panel of 69 genes (GP69) and the relationship between panel-tumor mutation burden (panel-TMB) calculated by GP69 and TMB estimated by whole-exome sequencing (wTMB) and prognosis were investigated. GDPH, Guangdong Provincial People's Hospital

Fig. 2

The relationship between panel-TMB and the tumor mutation burden estimated by whole-exome sequencing (wTMB) in the TCGA dataset. a The distribution of NsMs was obtained by whole-exome sequencing (upper panel) and a 69-gene panel (lower panel) for 37 DLBCL patients. b panel-TMB and wTMB demonstrated a significant positive correlation in 37 DLBCL patients. R, Spearman correlation coefficient

Fig. 3

The mutational landscape of 69 genes in patients with diffuse large B cell lymphoma (DLBCL). a, b Mutation landscape for the 69 genes in the GDPH (a) and TCGA (b) datasets. The histogram above each plot showing the number of non-synonymous mutations (NsMs) in each patient, and the histogram on the right displays the number of patients with a mutation in each gene. c The overlap of genes whose mutation frequency is greater than 10% in the clinical and TCGA datasets is shown. The histogram shows the number of overlapping genes (left panel). The circos plot displays the overlapping genes (right panel). d The positions of the 69-panel genes on the chromosomes are shown. The outermost layer is the name of the chromosome, the second layer is the specific location of the gene, and the innermost layer is the name of the 69 genes. GP69, a panel composed of 69 genes

Fig. 4

Overall survival (OS) analysis of tumor mutation burden as estimated by a 69-gene-panel (panel-TMB) in DLBCL patients. panel-TMB was associated with poor OS in the GDPH (a) and TCGA (c) datasets. Kaplan–Meier curves between low and high panel-TMB groups (left panel). The restricted mean survival time (RMST) was determined by "survRM2" package in R (version 3.6.1, https://www.r-project.org/) (right panel). The interaction of panel-TMB and treatment options in the GDPH (b) and TCGA (d) datasets

Fig. 5

The effects of panel-TMB on OS in DLBCL patients of different ages, LDH levels, extranodal involvement, Ann Arbor stage, IPI, HBsAg, anti-HBc, and HBV-DNA in the GDPH dataset. LDH, lactate dehydrogenase; IPI, international prognostic index; HBsAg, hepatitis B surface antigen; anti-HBc, antibody to hepatitis B core antigen; HBV-DNA, hepatitis B virus DNA

Fig. 6

Construction of nomogram model in the GDPH dataset. a A combination of panel-TMB, Ann Arbor stage, and IPI visualized and personalized the OS rate of DLBCL patients. After the nomogram assigned a point for panel-TMB, stage, and IPI of each patient, the total points could be obtained to predict patients' OS rates. b The time-dependent receiver operating characteristic (ROC) (upper panel) and the calibration (bottom panel) curves were used to evaluate the performance of the nomogram model. AUC, the area under a curve