Abstract Background Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive non-Hodgkin lymphoma, and about 10% of DLBCL cases primarily occur in the gastrointestinal tract. Previous reports have revealed that primary gastrointestinal-DLBCL (pGI-DLBCL) harbors different genetic mutations from other nodal or extranodal DLBCL. However, the exonic mutation profile of pGI-DLBCL has not been fully addressed. Methods We performed whole-exome sequencing of matched tumor tissues and blood samples from 53 pGI-DLBCL patients. The exonic mutation profiles were screened, and the correlations between genetic mutations and clinicopathological characteristics were analyzed. Results A total of 6,588 protein-altering events were found and the five most frequent mutated genes in our pGI-DLBCL cohort were IGLL5 (47%), TP53 (42%), BTG2 (28%), P2RY8 (26%) and PCLO (23%). Compared to the common DLBCL, significantly less or absence of MYD88 (0%), EZH2 (0%), BCL2 (2%) or CD79B (8%) mutations were identified in pGI-DLBCL. The recurrent potential driver genes were mainly enriched in pathways related to signal transduction, infectious disease and immune regulation. In addition, HBV infection had an impact on the mutational signature in pGI-DLBCL, as positive HBsAg was significantly associated with the TP53 and LRP1B mutations, two established tumor suppressor genes in many human cancers. Moreover, IGLL5 and LRP1B mutations were significantly correlated with patient overall survival and could serve as two novel prognostic biomarkers in pGI-DLBCL. Conclusions Our study provides a comprehensive view of the exonic mutation profile of the largest pGI-DLBCL cohort to date. The results could facilitate the clinical development of novel therapeutic and prognostic biomarkers for pGI-DLBCL. Supplementary Information The online version contains supplementary material available at 10.1186/s40164-022-00325-7. Keywords: Whole-exome sequencing/WES, Diffuse large B-cell lymphoma/DLBCL, Gastrointestinal tract/GI tract, Mutation profile, IGLL5, LRP1B Introduction The incident rate of non-Hodgkin lymphomas (NHLs) in most contries has considerably increased in recent years [[49]1]. Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of NHLs, accounting for nearly one-third of all lymphoid neoplasm in China annually [[50]2, [51]3]. Though at least two DLBCL subtypes have been identified by RNA expression profiles, the germinal center B-cell-like (GCB) subtype and the activated B-cell-like (ABC) subtype, DLBCL still represents a clinical heterogenous disease due to its complex and diverse histological characteristics [[52]4, [53]5]. DLBCL patients often present with an aggressive clinical behavior, but most of them can be cured by the standard regimen based on rituximab plus cyclophosphomide, doxorubicin, vincristine and prednisone (R-CHOP) [[54]6]. The application of next-generation sequencing has helped reveal a deep degree of molecular and genetic heterogeneity in hematological diseases, and confirmed that genetic aberrations contribute to occurrence and progression of DLBCL [[55]7, [56]8]. DLBCL arises from extranodal organs in about 30% of total cases, and one third of extranodal DLBCL cases occur in the gastrointestinal tract, making it the most common primary extranodal site [[57]9, [58]10]. Patient prognosis and recurrence risk of extranodal DLBCL vary according to the primary site of origin, which may harbor different genetic mutations clarified by high through-put sequencing studies [[59]11, [60]12]. Primary gastrointestinal DLBCL (pGI-DLBCL) has a significantly decreased level of MYD88 and CD79B mutations compared to nodal DLBCL and other extranodal DLBCL in immune-privileged sites, such as central nervous system and testis [[61]13, [62]14]. Meanwhile, genetic mutations of MYC or BCL2 rearrangements could be related to the survival and prognosis of pGI-DLBCL patients [[63]15, [64]16]. The genetic mutation profiles discovered by more in-depth analysis revealed that pGI-DLBCL may have different modes of pathogenesis and progression from non-gastrointestinal DLBCL. Recently, by analyzing a small group of patients using whole-exome sequencing (WES), a study by Li et al. has shed a light on the genetic mutations in pGI-DLBCL [[65]17]. However, comprehensive research focusing on the mutational landscape of pGI-DLBCL, and the correlation between its genetic mutations and clinicopathological features are still rare. In the present study, we aimed to derive the predictive mutational profile by performing capture-based targeted WES on 53 Chinese pGI-DLBCL patients. The association between clinical characteristics and genetic alterations was also explored. In addition, we tried to identify genetic mutations possibly affecting patient survival and their underlying mechanisms. Our study provided a deeper insight into the genetic features of pGI-DLBCL, which may be helpful to clarify the lymphomagenesis process and develop putative therapeutic and prognostic biomarkers for this disease. Materials and methods Patient Cohort Fifty-three patients diagnosed with pGI-DLBCL according to the criteria defined by Lewin et al. [[66]18] were recruited in this study. All patients underwent partial gastrectomy or enterectomy plus R-CHOP based therapy in our hospital spanning from January 1, 2011 to July 21, 2021. Forty-six surgical resection specimens, seven biopsy specimens and matched patient peripheral blood mononuclear cells (PBMCs) were used for sequencing study. All specimens were reviewed by two independent hematopathologists (Yan Huang and Hai-Ling Liu) according to the 2017 World Health Organization classification criteria [[67]19]. The corresponding medical records of all patients were reviewed to obtain the clinicopathological information. The study was approved by the institutional review board at the Sixth Affiliated Hospital of Sun Yat-Sen University. WES Tumor DNA was isolated from five 5-μm-thick sections of formalin-fixed paraffin-embedded tumor tissues with a minimum of 70% neoplastic cells using QIAamp FFPE DNA Tissue Kit (Qiagen, USA), and the paired normal control DNA of PBMCs was extracted with DNeasy Tissue and Blood Kit (Qiagen, USA) according to the manufacturer’s instructions. Degradation and contamination were monitored on a 1% agarose gel, and the concentration was measured by using a Qubit® DNA Assay Kit in a Qubit® 2.0 Fluorometer (Life Technologies, USA). Qualified genomic DNA from tumors and matched PBMCs from 53 pGI-DLBCL patients were fragmented by Covaris technology with resultant library fragments of 180–280 bp, and then adapters were ligated to both ends of the fragments. Extracted DNA was then amplified by ligation-mediated PCR (LM-PCR), purified, and hybridized to the Agilent SureSelect Human Exome V6 (Santa Clara, USA) for enrichment, and nonhybridized fragments were then washed out. Both uncaptured and captured LM-PCR products were subjected to real-time PCR to estimate the magnitude of enrichment. Each captured library was then loaded onto the Illumina HiSeq X platform (Hangzhou Jichenjunchuang Medical Laboratory Co., Ltd, Beijing, China). We performed high-throughput sequencing for each captured library independently. Tumor and normal DNA samples were sequenced to an average depth of > 100 × and > 40 × in targeted exonic regions, respectively. Genomic analysis After generating raw data through base calling, paired-end reads were trimmed to remove stretches of low-quality bases (< Q10) and adapters in the sequences. The clean reads were mapped to NCBI Build 37 (hg19) using BWA-0.7.12 mem with the default settings. SAMtools-1.2 was used to sort and index all the BAM files; PicardTools-1.119 was used to remove the duplicates; and GATK-3.3–0 was used for InDel realignment and base quality score recalibration. MuTect-1.1.4 and Strelka were used to call somatic SNVs and InDels in the paired normal and tumor samples. Variants identified in the 1,000 Genomes database ([68]https://www.1000genomes.org/) with a frequency > 1% (unless they were in the Catalog of Somatic Mutations in Cancer (COSMIC) database) or in the Exome Aggregation Consortium ([69]http://exac.broadinstitute.org/) with a frequency > 0.1% were discarded from the analysis. Variants with an alternate allele depth < 2 and a frequency < 5% were also excluded. In addition, SNVs and InDels were filtered to remove benign changes predicted by the following predictive software programs, including Polyphen2, MutationTaster, Mutation Assessor, FATHMM, Radial SVM, LR, SIFT, and LRT. ANNOVAR was used to annotate all the somatic mutations after filtering. Pathway enrichment analysis Gene clustering analysis of the driver mutations was performed by Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool ([70]https://david.ncifcrf.gov/) as previously described [[71]20]. Only the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis which evaluates the modules at the functional level of the selected genes was executed. Bonferroni P value < 0.05 was set as the cut-off criterion and regarded as statistically significant. Statistical analysis Statistical analysis was performed using R version 4.1.2 and GraphPad Prism version 7 (La Jolla, CA, USA). The Mann–Whitney U test and the Spearman rank correlation test were employed to analyze the relationship between the mutated genes and clinicopathological characteristics. Survival analysis was performed using Kaplan–Meier curves and compared using the log-rank test. Comparative test differences were considered significant if the 2-tailed P value was < 0.05 otherwise indicated. Results Clinicopathological characteristics of the pGI-DLBCL patient cohort The clinicopathological characteristics of the pGI-DLBCL patient cohort were summarized in Table [72]1 and Additional file [73]1: Table S1. Of note, we included 53 patients diagnosed with pGI-DLBCL in this study, which consisted of 40 males and 13 females, respectively. Tumors were primarily originated from the stomach of 11 patients, small intestine of 29 patients, or large intestine of 13 patients. Helicobacter pylori (Hp) or hepatitis B virus (HBV) infection was positive in 21 (39.6%) or 11 cases (20.8%), respectively. According to the Hans algorithm, 33 and 20 patients were classified as GCB (62.3%) and non-GCB (37.7%) DLBCL subtypes based on the immunohistochemical features. The cohort included 35 patients in clinical stage I or II, and 18 patients in clinical stage III or IV. By the end of the current study, the follow-up duration of the patients was as long as 128.4 months with 11 dead records. Table 1. Clinicopathological characteristics of 53 pGI-DLBCL patients Characteristics Patients n Percentage Age, years  ≤ 60 28 52.8% > 60 25 47.2% Gender Male 40 75.5% Female 13 24.5% Origin Large Intestine 13 24.5% Small Intestine 29 54.7% Stomach 11 20.8% Han’s Algorithm GCB 33 62.3% non-GCB 20 37.7% B Symptom Yes 14 26.4% No 39 73.6% Hp Infection Positive 21 39.6% Negative 32 60.4% LDH Level Elevated 31 58.5% Normal 22 41.5% Hypoproteinemia Yes 45 84.9% No 8 15.1% Anemia Yes 52 98.1% No 1 1.9% HBsAg Positive 11 20.8% Negative 42 79.2% ECOG PS < 2 43 81.1% ≥ 2 10 18.9% Lugano Stage I-II 35 66.0% III-IV 18 34.0% IPI 0–1 28 52.8% 2–5 25 47.2% Survival Alive 42 79.2% Dead 11 20.8% [74]Open in a new tab Exonic mutational profile of pGI-DLBCL We performed WES of patient-derived tumor tissue and matched blood DNA. Collectively, 6,588 protein-altering mutational events spanning 3,229 genes were identified from our patient cohort. Of these, 5,489 were missense variants, 171 were in frame insertions or deletions, 394 were frameshift variants, 187 were splice site mutations, 23 were start lost mutations, 13 were stop lost mutations, and 311 were stop gain mutations. The spectrum of the top 40 frequently mutated genes was presented in Fig. [75]1 and the mutational profile of the entire cohort was summarized in Additional file [76]2: Table S2. The gene with the highest mutation rate was IGLL5 (mutated in 47% pGI-DLBCL patients), which is also the top 1 mutated gene reported in HBV-related DLBCL [[77]21]. Other most frequently mutated genes (≥ 15%) included TP53, BTG2, P2RY8, PCLO, HIST1H1E, IGHM, KMT2D, CSMD3, MUC16, RYR2, CCND3, DUSP2, FAT4, IGHJ6, CARD11, HIST1H1C, LRP1B, MYC, NBPF1, SI. The genome-wide mutational signatures were also characterized according to the 96 possible mutation types [[78]22]. Three highly confident mutational signatures were extracted from our patient cohort. Of these 3 mutation signatures, signatures 1 and 3 were fitted with COSMIC signature 1 and 26, which have been linked to age and defective DNA mismatch repair in cancer, respectively. Meanwhile signature 2, which was mainly characterized by T to G mutations, was not correlated with any COSMIC signature (Fig. [79]2). Fig. 1. [80]Fig. 1 [81]Open in a new tab Top 40 mutated genes in 53 pGI-DLBCL patients. The bar graph on the top indicates the absolute number of exonic mutations in each patient. Top 40 frequently mutated genes constitute the individual rows and are arranged by their mutation rates displayed on the right. Each column represents a patient and each row represents a gene. The histogram on the right shows the number of mutations in each gene. The tracks at the bottom provide information on gender, the molecular subtype sorted by Hans algorithm, the primary tumor sites and the IPI that are color-coded as indicated in the legend. TMB: tumor mutational burden Fig. 2. [82]Fig. 2 [83]Open in a new tab Major mutational signatures were identified according to the alphabetical 96-substitution classifications from 53 pGI-DLBCL patients. The probability bars for the six types of substitutions are displayed in different colors. The mutation types are on the horizontal axes, whereas vertical axes differ between individual signatures for visualization of their patterns and indicate the percentage of mutations attributed to specific mutation types Potential driver mutations in pGI-DLBCL In order to identify potential driver mutations in pGI-DLBCL, we compared the mutation profile of our patient cohort with those pathogenic genes associated with human tumors, which have been published and indexed in the COSMIC, MDG125 [[84]23], SMG127 [[85]24], CDG291 datasets [[86]25]. A total of 417 potential driver genes were identified (Table [87]2). Among these genes, 30 commonly mutated driver genes were found in at least 5 pGI-DLBCL patients, including TP53, P2RY8, KMT2D, MUC16, CSMD3, FAT4, CCND3, HIST1H1C, CARD11, MYC, LRP1B, B2M, TET2, FOXO1, EBF1, BTG1, SETD1B, BCR, COL3A1, DDX3X, AHNAK2, PIM1, ID3, DNM2, PTPN6, FAT1, ROBO2, NFKBIA, BCL7A, SGK1. Next, we used those potential driver genes shared by at least 2 pGI-DLBCL patients to perform gene clustering analysis with the aid of DAVID algorithm. The result revealed that these recurrent driver genes were mainly enriched in pathways related to human cancers, signal transduction, cell metabolism, infection disease and immune regulation. Important signal transduction pathways were substantially affected such as thyroid hormone signaling, central carbon metabolism, HBV infection, FoxO signaling and B cell receptor signaling (Fig. [88]3 and Additional file [89]3: Table S3). These results indicated that abnormal signal transduction cascades, altered cell metabolism and virus infection may jointly contribute to the pathogenesis of pGI-DLBCL. Table 2. Potential driver mutations in pGI-DLBCL #Gene Symbol Sample COSMIC MDG125 SMG127 CDG291 Patient_Number_Count TP53 P01, P02, P03, P04, P05, P17, P18, P19, P20, P21, P22, P23, P33, P34, P35, P36, P37, P41, P42, P43, P50, P51 oncogene, TSG, fusion TSG pancan_fre:42.00% Yes 22 P2RY8 P09, P12, P13, P17, P18, P19, P20, P25, P27, P29, P30, P38, P52, P53 oncogene, fusion No No No 14 KMT2D P02, P08, P10, P21, P31, P32, P34, P40, P43, P46, P53 oncogene, TSG No No No 11 MUC16 P01, P03, P09, P10, P13, P24, P36, P45, P50, P51 oncogene No No No 10 CSMD3 P03, P06, P09, P21, P24, P28, P38, P45, P50, P53 TSG No No No 10 FAT4 P01, P06, P08, P09, P19, P20, P27, P50, P52 TSG No No No 9 CCND3 P06, P07, P11, P18, P22, P28, P40, P45, P48 oncogene, fusion No No No 9 HIST1H1C P06, P14, P18, P26, P27, P34, P38, P53 No No pancan_fre:0.60% Yes 8 CARD11 P01, P20, P40, P43, P45, P46, P48, P52 oncogene Oncogene No No 8 MYC P04, P14, P22, P26, P33, P34, P37, P50 oncogene, fusion No No No 8 LRP1B P04, P19, P20, P26, P36, P38, P41, P52 TSG No No No 8 B2M P06, P09, P11, P20, P27, P31, P38 TSG TSG No Yes 7 TET2 P07, P11, P14, P16, P27, P28, P50 TSG TSG pancan_fre:1.60% Yes 7 FOXO1 P04, P11, P14, P15, P29, P34, P50 oncogene, TSG, fusion No No No 7 EBF1 P01, P04, P17, P18, P26, P32, P53 TSG, fusion No No No 7 BTG1 P06, P25, P27, P38, P39, P40, P42 TSG, fusion No No No 7 SETD1B P08, P18, P31, P33, P46, P47, P52 TSG No No No 7 BCR P15, P18, P26, P35, P48, P53 fusion No No No 6 COL3A1 P05, P10, P23, P24, P28, P38 fusion No No No 6 DDX3X P09, P10, P20, P29, P32, P50 TSG No No Yes 6 AHNAK2 P04, P06, P20, P24, P26, P31 No No No Yes 6 PIM1 P21, P26, P35, P37, P46, P52 oncogene, fusion No No No 6 ID3 P14, P15, P22, P26, P29, P51 TSG No No No 6 DNM2 P01, P13, P20, P28, P38, P40 TSG No No No 6 PTPN6 P06, P11, P12, P25, P38 TSG No No No 5 FAT1 P03, P07, P09, P13, P36 TSG No No No 5 ROBO2 P03, P06, P19, P24, P33 TSG No No No 5 NFKBIA P12, P18, P43, P50, P53 No No No No 5 BCL7A P12, P26, P34, P40, P53 fusion No No No 5 SGK1 P04, P06, P18, P25, P28 oncogene No No Yes 5 ZEB2 P06, P13, P31, P48 No No No Yes 4 MEF2B P08, P34, P47, P52 No No No No 4 PRDM1 P36, P37, P44, P45 TSG TSG No No 4 CD79B P02, P03, P08, P46 oncogene No No No 4 NFKBIE P17, P19, P38, P48 TSG No No No 4 SOCS1 P26, P28, P38, P43 TSG TSG No No 4 FAT3 P05, P20, P21, P40 No No No 4 CHD4 P07, P24, P35, P40 oncogene No No Yes 4 NCOR2 P02, P20, P36, P42 TSG No No Yes 4 ZFP36L2 P08, P20, P26, P39 No No No Yes 4 DST P04, P05, P45, P47 No No No Yes 4 KIAA1549 P20, P37, P40, P43 fusion No No No 4 AHNAK P17, P45, P47, P51 No No No Yes 4 GNAQ P06, P38, P46, P51 oncogene Oncogene No No 4 TBL1XR1 P06, P18, P26, P51 oncogene, TSG, fusion No pancan_fre:0.80% Yes 4 HLA-B P13, P19, P24, P27 No No No Yes 4 BRAF P01, P04, P06, P53 oncogene, fusion Oncogene pancan_fre:1.50% Yes 4 ACTB P06, P17, P20, P35 No No No Yes 4 PLEC P06, P11, P28, P40 No No No Yes 4 SYNE1 P04, P06, P33, P34 No No No Yes 4 DCC P03, P24, P36, P52 No No No 4 ROS1 P01, P20, P24, P45 oncogene, fusion No No No 4 ARID1A P04, P11, P18, P22 TSG, fusion TSG pancan_fre:5.40% Yes 4 TNFRSF14 P06, P11, P14, P25 TSG No No No 4 STAT3 P04, P18, P19, P48 oncogene No No Yes 4 PIK3CD P13, P16, P20 No No No No 3 FAM135B P06, P20, P38 No No No 3 TRIO P04, P36, P40 No No No Yes 3 TRIM24 P03, P20, P50 oncogene, TSG, fusion No No No 3 UBR5 P04, P20, P43 TSG No No No 3 FAM47C P04, P17, P34 No No No 3 LRRK2 P09, P42, P52 No No pancan_fre:2.80% Yes 3 GRIN2A P01, P04, P20 TSG No No No 3 FBN2 P01, P09, P20 No No No Yes 3 NEB P01, P36, P51 No No No Yes 3 IRS2 P02, P50, P53 No No No Yes 3 PRKCD P06, P11, P24 No No No Yes 3 ACTG1 P06, P14, P26 No No No Yes 3 KALRN P20, P31, P43 No No No Yes 3 BIRC6 P06, P09, P20 oncogene, fusion No No No 3 CLTC P16, P20, P50 TSG, fusion No No Yes 3 APC P06, P18, P36 TSG TSG pancan_fre:7.30% Yes 3 PTEN P01, P09, P35 TSG TSG pancan_fre:9.70% Yes 3 CXCR4 P01, P26, P50 oncogene No No No 3 JMJD1C P03, P08, P12 No No No Yes 3 FAS P06, P09, P18 TSG No No No 3 BCL6 P05, P43, P52 oncogene, fusion No No No 3 PCBP1 P09, P44, P46 No pancan_fre:0.30% Yes 3 BCL11B P07, P11, P12 oncogene, TSG, fusion No No No 3 PTPRB P01, P36, P50 TSG No No No 3 CIITA P11, P25, P40 TSG, fusion No No No 3 HGF P09, P36, P48 No No pancan_fre:1.70% Yes 3 IRF4 P08, P38, P42 oncogene, TSG, fusion No No No 3 NIN P17, P27, P36 fusion No No Yes 3 RARA P10, P33, P48 oncogene, fusion No No No 3 TRRAP P20, P36, P50 oncogene No No No 3 MAP2K1 P12, P28, P50 oncogene Oncogene No No 3 KMT2C P05, P11, P15 TSG No No No 3 PABPC1 P25, P26, P32 oncogene, TSG No No Yes 3 PIK3CB P32, P53 oncogene No No Yes 2 CBLB P26, P52 TSG No No No 2 MDN1 P09, P53 No No No Yes 2 RAB11FIP5 P07, P20 No No No Yes 2 FIP1L1 P01, P15 fusion No No No 2 CFH P09, P20 No No No Yes 2 KDM6B P26, P53 No No No Yes 2 MYCN P25, P27 oncogene No No No 2 CAMTA1 P37, P51 TSG, fusion No No No 2 TCF7 P41, P44 No No No Yes 2 PDGFRA P20, P40 oncogene, fusion Oncogene pancan_fre:1.90% Yes 2 TET1 P09, P20 oncogene, TSG, fusion No No No 2 ARHGAP32 P01, P04 No No No Yes 2 SFRP4 P09, P12 TSG No No No 2 PRRC2A P20, P50 No No No Yes 2 NTRK2 P04, P25 No No No No 2 HSP90AB1 P11, P20 fusion No No Yes 2 KRAS P25, P28 oncogene Oncogene pancan_fre:6.70% Yes 2 PCM1 P06, P24 fusion No No Yes 2 SMARCA4 P15, P28 TSG TSG No Yes 2 CHD8 P38, P50 No No No Yes 2 NCOR1 P03, P32 TSG TSG pancan_fre:2.20% Yes 2 ZFP36L1 P26, P46 No No No Yes 2 MKI67 P17, P45 No No No Yes 2 RGPD3 P45, P48 No No No 2 FBXO11 P07, P51 TSG No No Yes 2 LRIG3 P01, P20 TSG, fusion No No No 2 NFATC2 P08, P43 oncogene, fusion No No No 2 KIT P10, P23 oncogene Oncogene pancan_fre:1.40% Yes 2 CREBBP P09, P20 oncogene, TSG, fusion TSG No No 2 TCL1A P07, P25 oncogene, fusion No No No 2 MSH3 P12, P42 No No No No 2 SF3B1 P01, P11 oncogene Oncogene pancan_fre:1.30% Yes 2 PRKCB P04, P13 No No No 2 ZNF91 P24, P40 No No No Yes 2 BCLAF1 P09, P53 No No Yes 2 MAP3K4 P11, P13 No No No Yes 2 FGFR4 P45, P50 oncogene No No No 2 FGFR2 P45, P52 oncogene, fusion Oncogene pancan_fre:1.50% Yes 2 PRPF8 P01, P09 No No No Yes 2 SPEN P11, P38 TSG No No Yes 2 SPEG P45, P53 No No No Yes 2 PDE4DIP P03, P38 fusion No No No 2 AFF3 P01, P17 oncogene, fusion No No No 2 SALL4 P40, P50 oncogene No No No 2 ANKRD11 P04, P35 No No No Yes 2 TFDP1 P26, P42 No No No Yes 2 INPP4B P36, P50 No No No No 2 MICAL1 P09, P40 No No No Yes 2 SIN3A P15, P34 No No pancan_fre:1.10% Yes 2 HLA-A P12, P18 fusion No No Yes 2 TFEB P04, P28 oncogene, fusion No No No 2 KIAA1109 P20, P40 No No No Yes 2 TNFAIP3 P11, P36 TSG TSG No No 2 TP63 P09, P11 oncogene, TSG No No No 2 PTPRD P40, P45 TSG No No No 2 CLTCL1 P20, P48 TSG, fusion No No Yes 2 ZMYM3 P09, P20 TSG No No No 2 MGA P01, P41 No No No Yes 2 NSD1 P48, P51 fusion No pancan_fre:2.40% Yes 2 CSF1R P20, P42 oncogene Oncogene No No 2 MEGF6 P11, P45 No No No Yes 2 HIST1H3B P01, P26 oncogene Oncogene No No 2 ADCY1 P03, P20 No No No Yes 2 RET P17, P27 oncogene, fusion Oncogene No No 2 EPHA7 P26, P36 No No No 2 EPHA3 P20, P51 No pancan_fre:2.10% Yes 2 RBM15 P04, P09 fusion No No No 2 ZNF521 P08, P09 oncogene, fusion No No No 2 CNTNAP2 P09, P35 TSG No No No 2 RASA1 P28, P51 No No No Yes 2 PTPRC P26, P31 TSG No No No 2 CAD P20, P37 No No No Yes 2 EPS15 P32, P50 TSG, fusion No No No 2 EXT2 P05, P20 TSG No No No 2 RAG1 P24, P38 No No No Yes 2 CDH10 P03, P12 TSG No No No 2 ZFHX3 P01, P20 TSG No No Yes 2 MTOR P07, P51 oncogene No pancan_fre:3.00% Yes 2 EP300 P06, P09 TSG, fusion TSG pancan_fre:2.50% Yes 2 CNBD1 P06, P12 No No No 2 ABCB1 P24, P42 No No No Yes 2 CTNNA2 P09, P25 oncogene No No No 2 NOTCH1 P33, P37 oncogene, TSG, fusion TSG pancan_fre:3.10% Yes 2 IKBKB P09, P27 oncogene No No No 2 MYO5A P01, P38 fusion No No No 2 STRN P20, P50 fusion No No No 2 NRG1 P20, P53 TSG, fusion No No No 2 MALT1 P28, P48 oncogene, fusion No No No 2 PHF6 P08, P20 TSG TSG pancan_fre:0.80% Yes 2 NAV3 P04, P45 No No pancan_fre:4.60% Yes 2 MYCBP2 P04, P43 No No No Yes 2 NBEA P48, P53 No No Yes 2 HSP90AA1 P04, P26 fusion No No No 2 CHD7 P31, P37 No No No Yes 2 PIK3CG P52 No No pancan_fre:1.70% Yes 1 HIST1H4I P14 fusion No No No 1 HSPA8 P04 No No No Yes 1 NUP98 P20 oncogene, fusion No No Yes 1 XPA P46 TSG No No No 1 CEP89 P04 fusion No No No 1 XPO1 P28 oncogene No No No 1 CSDE1 P51 No No No Yes 1 TTK P09 No No No Yes 1 COL1A1 P26 fusion No No No 1 ZEB1 P52 oncogene No No No 1 ITGAV P13 No No No 1 ZNF703 P14 No No No Yes 1 ERBB2IP P14 No No No Yes 1 ARHGEF12 P20 TSG, fusion No No No 1 MUC1 P29 fusion No No No 1 EWSR1 P20 oncogene, fusion No No Yes 1 AHCTF1 P26 No No No Yes 1 RPL22 P09 TSG, fusion No pancan_fre:1.00% Yes 1 SIX2 P22 oncogene No No No 1 PRX P20 No No pancan_fre:0.90% Yes 1 ARID2 P06 TSG TSG No Yes 1 SET P20 oncogene, fusion No No No 1 ELK4 P36 oncogene, fusion No No No 1 TRIM7 P46 No No No Yes 1 FBXW7 P05 TSG TSG pancan_fre:3.00% Yes 1 TGFBR2 P11 TSG No pancan_fre:1.10% Yes 1 SH3PXD2A P20 No No No Yes 1 SVIL P20 No No No Yes 1 PHLDA1 P21 No No No Yes 1 NBPF10 P28 No No No Yes 1 PBX1 P50 oncogene, fusion No No No 1 ARHGAP35 P20 No No pancan_fre:2.50% Yes 1 PTCH1 P33 TSG TSG No No 1 CUL1 P23 No No No Yes 1 CDX2 P20 TSG, fusion No No No 1 PTPN13 P12 TSG No No Yes 1 IRS4 P09 oncogene, TSG No No No 1 DMD P06 No No No Yes 1 PPM1D P09 oncogene No No No 1 SRSF2 P14 oncogene Oncogene No No 1 RALGAPA1 P17 No No No Yes 1 EIF1AX P04 No No No 1 MED12 P11 TSG Oncogene No Yes 1 NTRK3 P45 oncogene, fusion No No No 1 MED13 P20 No No No Yes 1 ARHGAP26 P21 TSG, fusion No No No 1 SRGAP3 P01 fusion No No No 1 ACSL6 P01 fusion No No No 1 FLI1 P01 oncogene, fusion No No No 1 CHD2 P28 TSG No No No 1 POLG P20 TSG No No No 1 DDX5 P23 oncogene, fusion No No Yes 1 MN1 P52 oncogene, fusion No No Yes 1 PRDM16 P24 oncogene, fusion No No No 1 POT1 P53 TSG No No No 1 ARHGAP5 P20 oncogene No No No 1 SOS1 P51 No No No Yes 1 KIF20B P20 No No No Yes 1 TSHZ2 P47 No No pancan_fre:1.80% No 1 EIF3E P45 TSG, fusion No No No 1 BCL2L12 P39 oncogene No No No 1 KAT6A P41 oncogene, fusion No No No 1 CDH11 P27 TSG, fusion No No No 1 BAP1 P53 TSG TSG pancan_fre:2.00% Yes 1 UBE4A P20 No No No Yes 1 JAK2 P09 oncogene, fusion Oncogene No Yes 1 N4BP2 P26 TSG No No No 1 GRM3 P13 oncogene No No No 1 ZNF384 P06 fusion No No No 1 AKAP9 P01 fusion No No Yes 1 EEF1A1 P08 No No No Yes 1 PBRM1 P20 TSG TSG pancan_fre:5.40% Yes 1 ERC1 P48 fusion No No No 1 ERG P36 oncogene, fusion No No No 1 MYOD1 P36 oncogene No No No 1 CDK12 P25 TSG No pancan_fre:1.50% Yes 1 A1CF P45 oncogene No No No 1 WT1 P23 oncogene, TSG, fusion TSG pancan_fre:1.00% Yes 1 BARD1 P31 TSG No No Yes 1 BAZ1A P31 TSG No No No 1 FN1 P01 No No No Yes 1 FUBP1 P51 oncogene TSG No No 1 PRRX1 P51 fusion No No No 1 ATR P25 TSG No pancan_fre:2.40% Yes 1 BRIP1 P53 TSG No No No 1 FLT1 P01 No No No No 1 FANCF P40 TSG No No No 1 PTK6 P12 oncogene, TSG No No No 1 MSH6 P20 TSG TSG No No 1 SPECC1 P45 fusion No No No 1 PRKCI P01 No No No No 1 MATK P48 No No No Yes 1 ACKR3 P50 oncogene, fusion No No No 1 ERBB3 P32 oncogene No No No 1 IDH2 P42 oncogene Oncogene pancan_fre:0.80% Yes 1 FGFR3 P13 oncogene, fusion Oncogene pancan_fre:1.00% Yes 1 FGFR1 P51 oncogene, fusion No No No 1 AFF4 P31 oncogene, fusion No No No 1 MAP1 B P08 No No No Yes 1 EPB41L3 P04 No No No Yes 1 TPR P43 fusion No No Yes 1 GNAS P19 oncogene Oncogene No Yes 1 RBMX P53 No No No Yes 1 AFF1 P06 fusion No No No 1 CDKN2C P26 TSG No pancan_fre:0.20% Yes 1 WHSC1L1 P04 oncogene, fusion No No Yes 1 GOT2 P47 No No No Yes 1 LYN P11 No No No Yes 1 MGMT P06 TSG No No No 1 PMS1 P20 No No No 1 PMS2 P20 TSG No No No 1 LHFP P14 fusion No No No 1 AMER1 P52 TSG No No No 1 NACA P09 fusion No No No 1 FGF4 P13 No No No No 1 FGF3 P35 No No No No 1 HOXD11 P40 oncogene, fusion No No No 1 SMCHD1 P03 No No No Yes 1 JAZF1 P19 fusion No No No 1 BCOR P40 TSG, fusion TSG No Yes 1 ADAM10 P03 No No No Yes 1 G3BP1 P09 No No No Yes 1 BCL10 P05 TSG, fusion No No No 1 CDKN1B P40 TSG No pancan_fre:0.70% Yes 1 SETBP1 P12 oncogene, fusion Oncogene pancan_fre:2.20% No 1 AKT1 P14 oncogene Oncogene pancan_fre:0.90% Yes 1 PSIP1 P50 oncogene, fusion No No No 1 CCDC6 P36 TSG, fusion No No No 1 ARHGEF10 P25 TSG No No No 1 REL P19 oncogene No No No 1 COL2A1 P17 fusion No No No 1 TSC1 P12 TSG TSG No No 1 SMC3 P26 No No pancan_fre:1.20% Yes 1 ARID5B P37 No No pancan_fre:1.60% Yes 1 IGF1R P15 No No No No 1 HNF1A P20 TSG TSG No No 1 E2F3 P26 No No No No 1 ARHGEF6 P51 No No No Yes 1 CDH1 P48 TSG TSG pancan_fre:2.50% Yes 1 KIFC3 P01 No No No Yes 1 ARHGEF10L P21 TSG No No No 1 NEK8 P17 No No No Yes 1 FAM129B P20 No No No Yes 1 IL7R P36 oncogene No No No 1 MYH9 P10 TSG, fusion No No Yes 1 CYLD P20 TSG TSG No Yes 1 CASC5 P09 TSG, fusion No No No 1 NUTM1 P48 oncogene, fusion No No No 1 SOX17 P11 No No pancan_fre:0.30% Yes 1 BRCA1 P11 TSG TSG pancan_fre:1.90% Yes 1 BRCA2 P20 TSG TSG pancan_fre:2.70% Yes 1 WNK2 P53 TSG No No No 1 P4HB P26 No No No Yes 1 ARNT P53 oncogene, TSG, fusion No No No 1 BCL3 P07 oncogene, fusion No No No 1 RNF213 P20 fusion No No Yes 1 DOCK2 P32 No No No Yes 1 09-Sep P31 fusion No No No 1 05-Sep P12 fusion No No No 1 DCAF12L2 P23 No No No 1 NEDD4L P20 No No No Yes 1 RAP1GDS1 P38 oncogene, fusion No No No 1 RPP38 P20 No No No Yes 1 CTNND2 P43 oncogene No No No 1 ATRX P19 TSG TSG pancan_fre:2.80% Yes 1 RAD51B P44 TSG, fusion No No No 1 TP53BP1 P20 No No No Yes 1 PICALM P20 fusion No No No 1 BCL2 P26 oncogene, fusion Oncogene No No 1 ASXL2 P40 TSG No No No 1 SMC1A P35 TSG No pancan_fre:1.50% Yes 1 TLR4 P43 No No pancan_fre:1.90% Yes 1 KDM6A P50 oncogene, TSG TSG pancan_fre:2.00% Yes 1 MET P06 oncogene Oncogene No No 1 DNM3 P36 No No No Yes 1 BCL11A P20 oncogene, fusion No No No 1 GATA3 P20 oncogene, TSG TSG pancan_fre:3.20% Yes 1 RPN1 P45 fusion No No No 1 EPPK1 P11 No No pancan_fre:1.40% Yes 1 AXL P20 No No No No 1 CBL P26 oncogene, TSG, fusion Oncogene No No 1 PRDM2 P46 TSG No No Yes 1 GIGYF2 P03 No No No Yes 1 NR4A2 P12 No No No Yes 1 MITF P38 oncogene No No No 1 RPTOR P08 No No No No 1 CNOT3 P46 TSG No No Yes 1 BRD3 P20 oncogene, fusion No No No 1 SPTAN1 P43 No No No Yes 1 PPFIBP1 P20 fusion No No No 1 MKL1 P50 oncogene, TSG, fusion No No No 1 FANCD2 P50 TSG No No No 1 ZBTB16 P06 TSG, fusion No No No 1 DOCK4 P47 No No No Yes 1 SND1 P50 oncogene, fusion No No No 1 ERCC3 P45 TSG No No No 1 USP6 P07 oncogene, fusion No No No 1 HIP1 P52 oncogene, fusion No No No 1 INTS1 P32 No No No Yes 1 TGOLN2 P38 No No No Yes 1 IDH1 P14 oncogene Oncogene pancan_fre:1.50% Yes 1 PTPRK P39 TSG, fusion No No No 1 GMPS P40 fusion No No No 1 ATIC P03 fusion No No No 1 FOXA2 P20 No No pancan_fre:0.50% Yes 1 CDKN2A P22 TSG TSG pancan_fre:3.60% Yes 1 SKI P45 oncogene No No No 1 CCR7 P11 oncogene No No No 1 FOSL2 P06 No No No Yes 1 PWWP2A P51 fusion No No No 1 DDR2 P09 oncogene No No No 1 CD274 P07 TSG, fusion No No No 1 CDH17 P32 oncogene No No No 1 FANCA P26 TSG No No Yes 1 ARID1B P38 TSG TSG No No 1 NIPBL P09 No No No Yes 1 KMT2A P19 oncogene, fusion No No No 1 ANKRD6 P01 No No No Yes 1 CTNND1 P03 No No Yes 1 MACF1 P11 No No No Yes 1 PABPC4 P27 No No No Yes 1 PREX2 P26 oncogene No No No 1 ZNRF3 P04 TSG No No No 1 ETV1 P20 oncogene, fusion No No No 1 ETV5 P09 oncogene, fusion No No No 1 TAF1 P06 No No pancan_fre:2.30% Yes 1 HOXA11 P14 oncogene, TSG, fusion No No No 1 ABL2 P01 oncogene, fusion No No No 1 POLD1 P20 TSG No No No 1 HMGA2 P13 oncogene, fusion No No No 1 MSN P04 fusion No No Yes 1 ZRSR2 P22 TSG No No No 1 [90]Open in a new tab Fig. 3. [91]Fig. 3 [92]Open in a new tab Heatmap of potential oncogenic pathways affected by exonic mutations in 53 pGI-DLBCL patients. A Thyroid hormone signaling pathway. B Central carbon metabolism in cancer. C Hepatitis B. D FoxO signaling pathway. E B cell receptor signaling pathway. The mutation rate of each gene is displayed on the right of each row. The histogram on the right shows the number of mutations in each gene Associations between clinicopathological characteristics and exonic mutations in pGI-DLCBL pateints We analyzed the correlations between the status of top 30 mutated genes and the clinicopathological characteristics, such as age, gender, Hp or HBV infection, LDH level, Eastern Cooperative Oncology Group (ECOG) score, B symptoms, International Prognostic Index (IPI), tumor stage, etc. The result was displayed in Fig. [93]4, and the correlations with statistical significance were summarized in Additional file [94]4: Table S4. Interestingly, younger patients tended to have FAT4 and FOXO1 mutations, and patients with non-GCB tumors were correlated with CARD11 mutations. Hp infection showed no association with any parameter, however, HBV infection seemed to be related to certain mutations in pGI-DLBCL, as positive HBsAg was significantly associated with the mutations of TP53 and LRP1B, two important tumor suppressor genes (TSGs) reported in many human cancers (Fig. [95]5A, B). Moreover, HBsAg positive pGI-DLBCL patients have a significant shorter overall survival (OS), when compared to those without HBV infection (Fig. [96]5C). These results indicated that genetic mutations in pGI-DLBCL patients were associated with certain clinicopathological parameters, and HBV infection could possibly cause worse prognosis due to mutation in TSGs. Fig. 4. [97]Fig. 4 [98]Open in a new tab The Spearman correlation matrix between major clinicopathological parameters and the status of top 30 mutated genes across 53 pGI-DLBCL patients. The correlations were obtained by deriving Spearman's correlation coefficients. Red represents a positive correlation and blue represents a negative correlation. The cross mark in each box denotes that the correlation did not reach statistical significance Fig. 5. [99]Fig. 5 [100]Open in a new tab HBV infection was associated with certain mutations and patient OS in pGI-DLBCL. A, B The bar graph indicates the Spearman’s correlation between HBsAg and TP53 (A) or LRP1B (B) mutation. The stacked percentage for each group is shown and the number in the bar denotes patient number count for each group. C OS for pGI-DLBCL patients stratified by HBsAg status Mutations correlated with patient survival in pGI-DLBCL In order to find potential genetic mutations with predictive value for patient OS, we performed survival analysis with the top 30 mutated genes in our pGI-DLBCL patient cohort. Most of the mutated genes were not significantly associated with patient OS. However, we did observe that patients with IGLL5 mutations presented with a better OS, and LRP1B mutations led to a shorter OS (Fig. [101]6A). A large proportion of the mutations in IGLL5 were missense variants located at its N-terminus uncharacterized domains, and the LRP1B mutations were all missense variants evenly distributed across the entire protein structure (Fig. [102]6B and Additional file [103]5: Table S5). How these mutations affect individual gene function and the patient survival needs further exploration. Fig. 6. [104]Fig. 6 [105]Open in a new tab IGLL5 and LRP1B mutations were correlated with OS in pGI-DLBCL. A OS for pGI-DLBCL patients stratified by IGLL5 (upper panel) or LRP1B (lower panel) mutation. B Lollipop plots with the distribution of somatic mutations on the linear protein and domains of IGLL5 (upper panel) or LRP1B (lower panel) in pGI-DLBCL. Each lollipop denotes a unique mutation location, and its height represents the number of observed mutations. Colored bars indicate the individual protein domains. The type of the mutation is indicated in the legend Discussion In the current study, we performed WES of the largest cohort of pGI-DLBCL to date and identified putative cancer driver mutations and their enriched signaling pathways. We also revealed that HBV infection had an impact on the exonic mutation profile pGI-DLBCL, and mutations of IGLL5 and LRP1B genes could predict patient survival, which to our knowledge, was previously unreported by others. In accordance with the previous reports [[106]17], our analysis of the pGI-DLBCL exome confirmed the high prevalence of mutations in the cell cycle and apoptosis regulatory pathway, with potential tumor driver mutations in TP53 (22/53), CCND3 (9/53) and MYC (8/53) in over 60% patients. TP53 mutations displayed a significantly increased frequency and MYD88 (0/53), NFKBIE (4/53) or CD79B (4/53) mutations were less or not found in our pGI-DLBCL cohort, suggesting that the pathogenesis of pGI-DLBCL were different from the nodal or other extranodal DLBCL, which relies on an activated NF-κB signaling pathway due to the common mutations in the above mentioned MYD88, NFKBIE, or CD79B genes [[107]26]. Furthermore, mutation frequencies of MUC16 (10/53), CSMD3 (10/53), RYR2 (10/53), FAT4 (9/53), TET2 (7/53), EBF1 (7/53) and SETD1B (7/53), which functions at the transcriptional regulation, epigenetic modification or either cellular attachment, were also increased compared to those in common DLBCL according to COSMIC database. Third, we also identified a relatively large proportion of gene mutations, like P2RY8 (14/53), LRP1B (8/53), B2M (7/53), BCR (6/53), that seldom mentioned by other DLBCL sequencing studies but may probably become the oncogenic events by modulating the B cell migratory behavior and signaling activation [[108]27, [109]28]. Therefore, we hypothesized that the mutation signature of pGI-DLBCL was different from other DLBCL subtypes, and the potential oncogenic driver mutations should be validated by further research. Another important finding of our study was that HBV infection may affect the mutation spectrum of pGI-DLBCL. We showed that the oncogenic driver mutations were significantly enriched in the HBV regulatory pathway, and patients with positive HBsAg status had a relatively shorter OS and were more likely to carry TP53 and LRP1B mutations, both of which are supposed to function as TSGs during lymphomagenesis process. Previous studies have shown that HBV infection could cause an enhanced rate of mutagenesis and a distinct set of mutation targets in common DLBCL genome [[110]21]. It is worth mentioning that the three genes, namely IGLL5, TP53 and BTG2, are among the top 5 most mutated genes among their and our WES data. Interestingly, LRP1B have been described as a common target gene for HBV integration in liver cancer [[111]29]. In addition, meta-analysis also revealed that patients infected with HBV had a higher risk of developing DLBCL, and those HBsAg-positive DLBCL patients tended to be diagnosed at a younger age with a more advanced clinical stage and worse outcome [[112]30, [113]31]. Our study presents the first genomic analysis reinforcing the relationship between HBV infection and the mutation signature of pGI-DLBCL. However, further investigations are needed to verify the interactive mechanism between HBV integration and pGI-DLBCL genome, and how the HBV-related mutations affect the pathogenesis and development processes of pGI-DLBCL disease. Highlighting the clinical significance of our finding, we identified that two recurrent mutations, IGLL5 and LRP1B, could serve as prognostic biomarkers for pGI-DLBCL patients. Although the function of IGLL5 has not been clarified, pervious reports have shown that it was commonly mutated in DLBCL [[114]32, [115]33] and is homologous to IGLL1, a gene which is critical for B-cell development [[116]34]. In chronic lymphocytic leukaemia (CLL), IGLL5 mutations were associated with a trend towards decreased overall gene expression, and patients bearing IGLL5 mutations were suggestive for the low-risk of CLL [[117]35], which to some extent, was consistent to our result showing that IGLL5 mutated pGI-DLBCL patients had a better OS. On the other hand, LRP1B is giant membrane molecule that is among the most altered genes in human malignancies [[118]36]. Functional studies have confirmed that LRP1B expression in cancer cells could reduce in vitro cell proliferation and migration abilities, and also suppress in vivo tumorigenicity in mouse models [[119]37–[120]40]. Genetic alteration events, such as deletions, point mutations or frameshift mutations commonly led to the inactivation of this TSG [[121]41–[122]43]. Therefore, it is speculated that LRP1B mutations found in our pGI-DLBCL cohort was associated with the impairment of its gene function, which could cause inferior result on disease progression. Despite we first propose that mutations of IGLL5 and LRP1B were significantly related to the survival of pGI-DLBCL patients, there is still a lack of detailed information on how the mutations affect their expression and/or functional role. Some research suggested that Tumor mutation burden estimated by cancer gene panels (CGPs) could be a potential predictor for prognostic stratification of Chinese DLBCL patients [[123]44]. However, IGLL5 and LRP1B discovered in our study as potential biomarkers for the therapeutics or prognosis of pGI-DLBCL remain to be fully elucidated. In summary, we performed a comprehensive analysis of the exonic mutation profile of the largest pGI-DLBCL cohort to date, which was characterized by an increased mutation frequency in TP53 and MYC, and a decrease rate or absence of MYD88 or CD79B alteration. We also revealed that HBV infection was related to the mutational signature and patient prognosis of pGI-DLBCL. IGLL5 and LRP1B could serve as predictive biomarkers for patient survival. Our study provides a deeper understanding of the genomic information of pGI-DLBCL and could facilitate the clinical development of novel therapeutic and prognostic biomarkers for pGI-DLBCL. Supplementary Information [124]40164_2022_325_MOESM1_ESM.xlsx^ (17KB, xlsx) Additional file 1: Table S1. Clinicopathological information of 53 pGI-DLBCL patients. [125]40164_2022_325_MOESM2_ESM.xlsx^ (854KB, xlsx) Additional file 2: Table S2. Exonic mutation profile of 53 pGI-DLBCL patients. [126]40164_2022_325_MOESM3_ESM.docx^ (19.2KB, docx) Additional file 3: Table S3. KEGG enrichment results of recurrent driver genes in pGI-DLBCL. [127]40164_2022_325_MOESM4_ESM.docx^ (20KB, docx) Additional file 4: Table S4. Summary of the statistically significant correlations in the matrix. [128]40164_2022_325_MOESM5_ESM.xlsx^ (15.1KB, xlsx) Additional file 5: Table S5. Summary of IGLL5 and LRP1B mutations in pGI-DLBCL. Abbreviations ABC Activated B-cell-like CLL Chronic lymphocytic leukaemia COSMIC Catalog of somatic mutations in cancer DAVID Database for annotation, visualization and integrated discovery DLBCL Diffuse large B-cell lymphoma; ECOG Eastern cooperative oncology group GCB Germinal center B-cell-like HBV Hepatitis B virus Hp Helicobacter pylori InDel Insertion or deletion IPI International prognostic index KEGG Kyoto encyclopedia of genes and genomes LM-PCR Ligation-mediated PCR NHL Non-Hodgkin lymphoma OS Overall survival PBMCs Peripheral blood mononuclear cells pGI-DLBCL Primary gastrointestinal diffuse large B-cell lymphoma R-CHOP Rituximab plus cyclophosphomide, doxorubicin, vincristine and prednisone SNV Single nucleotide variant TSG Tumor suppressor gene WES Whole-exome sequencing Author contributions LSS, LYF and XJ conceived and designed the study. LSS, ZXH, LTZ, CTY, CDM, XLX, GXQ, CK and HWJ collected samples and patient information. LSS, ZXH and LHL performed the experiment. LHL and HY reviewed and confirmed the specimens. LSS, HY, LYF and XJ analyzed the data. LSS and LYF wrote the manuscript. LYF and XJ supervised the project and provided funding. All authors contributed to the article and approved the submitted version. All authors read and approved the final manuscript. Funding This study was supported by National Natural Science Foundation of China (grant number 81902397), Major Talent Training Project of the Third Affiliated Hospital of Sun Yat-sen University (granted to Yi-Fan Lian), General Planned Project of Guangzhou Science and Technology (grant number 202201010950 and 202102080283), Fundamental Research Funds for the Central Universities (grant number 22qntd3401), Key Project of Rural Science and Technology Commissioner of Guangdong Province (grant number KPT20190263), Research Fund of Wu Jieping Medical Foundation (grant number 320.6750.2020-01-32) and Bethune-Tuoyi Young and Middle-aged Doctors' Research Ability Development Project (grant number BQE-TY-SSP(C-3)-S-03). Availability of data and materials The data that support the findings of this study are available from the corresponding authors upon reasonable request. Declarations Ethics approval and consent to participate The study was approved by the Institute Research Ethics Committee at the Sixth Affiliated Hospital of Sun Yat-sen University. Written informed consent was obtained from each patient. Competing interests The authors declare that they have no competing interests. Footnotes Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Shan-Shan Li, Xiao-Hui Zhai and Hai-Ling Liu have contributed equally to this work Contributor Information Yan Huang, Email: huangy27@mail.sysu.edu.cn. Yi-Fan Lian, Email: lianyf6@mail.sysu.edu.cn. Jian Xiao, Email: xiaoj26@mail.sysu.edu.cn. References