==== Front Cancer Biol Med Cancer Biol Med CBM Cancer Biology & Medicine 2095-3941 Compuscript Ireland 34806851 j.issn.2095-3941.2021.0898-47-05492/j.issn.2095-3941.2021.0193 Original Article Prognostic value of BCL2 and TP53 genetic alterations for diffuse large B-cell lymphoma patients treated with R-CHOP Qin Yan 1* Chen Haizhu 1* Liu Peng 1 Zhang Changgong 1 Yang Jianliang 1 Gui Lin 1 He Xiaohui 1 Zhou Liqiang 1 Zhou Shengyu 1 Jiang Shiyu 1 Jiang Hongxin 2 https://orcid.org/0000-0002-3342-4964 Shi Yuankai 1 1 Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing 100021, China 2 Department of Medical Oncology, Suzhou Municipal Hospital, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215001, China Correspondence to: Yuankai Shi, E-mail: janet94@example.net *These authors contributed equally to this work. 15 6 2022 23 11 2021 19 6 893909 25 3 2021 03 6 2021 Copyright: © 2022, Cancer Biology & Medicine 2022 Cancer Biology & Medicine https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY) 4.0, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. Objective: Limited data about the prognostic significance of BCL2 mutations and BCL2 copy number variations in diffuse large B-cell lymphoma (DLBCL) are available. This study aimed to comprehensively describe BCL2 genetic alterations in DLBCL patients, and examine correlation of BCL2, TP53 and other genetic alterations with outcomes in patients treated with R-CHOP. Methods: Probe capture-based high-resolution sequencing was performed on 191 patients diagnosed with de novo DLBCL. MYC, BCL2, and BCL6 protein expressions were detected by immunohistochemistry. Results: The presence of BCL2 alterations significantly correlated with poor progression-free survival (PFS) (5-year PFS: 13.7% vs. 40.8%; P = 0.003) and overall survival (OS) (5-year OS: 34.0% vs. 70.9%; P = 0.036). Importantly, patients who harbored BCL2 gain/amplifications (BCL2GA/AMP) also had a remarkably inferior 5-year PFS (11.1% vs. 38.3%; P < 0.001) and OS (22.1% vs. 69.6%; P = 0.009). In contrast, neither BCL2 mutations nor BCL2 translocations were significantly prognostic for survival. Multivariable analyses showed that the presence of BCL2 alterations, especially BCL2GA/AMP, TP53 mutations, and International Prognostic Index (IPI) were significantly associated with inferior PFS and OS. Novel prognostic models for OS were constructed based on 3 risk factors, including BCL2 alterations (Model 1) or BCL2GA/AMP (Model 2), TP53 mutations, and IPI, to stratify patients into 4 risk groups with different survival outcomes. Conclusions: This study showed that DLBCL patients treated with R-CHOP, BCL2 alterations, especially BCL2GA/AMP and TP53 mutations were significantly associated with inferior outcomes, which were independent of the IPI. The novel prognostic models we proposed predicted outcomes for DLBCL patients treated with R-CHOP, but further validation of the prognostic models is still warranted. BCL2 TP53 genetic alterations diffuse large B-cell lymphoma prognosis ==== Body pmcIntroduction Diffuse large B-cell lymphoma (DLBCL), the most common subtype of lymphoma in adults, can be successfully treated by standard immunochemotherapy in 50%–60% of patients1,2. To predict outcomes before treatment, the International Prognostic Index (IPI) model for aggressive non-Hodgkin lymphoma based on chemotherapy was developed in 19933. Using immunochemotherapy, the addition of rituximab to cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) has led to an improvement in survival4,5. In the rituximab treatment era, redistribution of the IPI factors into a revised IPI (R-IPI) provides a more clinically meaningful prognostic prediction6. Although IPI and R-IPI are useful tools for risk stratification of patients with DLBCL, all the risk factors are clinical features, which does not describe the biological spectrum. The molecular heterogeneity of DLBCL is considered to be related to different therapeutic outcomes of chemotherapy and immunochemotherapy7–9. The initial progress using a genetic description to predict clinical outcomes involved gene expression profiling, which distinguished 2 subtypes, including activated B-cell like (ABC) and germinal center B-cell like (GCB) in the cell-of-origin (COO) classification of DLBCL8,9. However, the COO distinction does not fully account for the heterogeneous responses and outcomes following R-CHOP. Studies using next-generation sequencing have characterized the mutational landscape and identified the genetic drivers of DLBCL7,10,11. Several gene mutations including MYD88L265P and CD79B mutations, NOTCH1 mutations, and TP53 mutations are independent risk factors related to poor prognosis in DLBCL patients7,10–15. Additionally, recent studies have emphasized the prognostic role of the tumor microenvironment (TME) in DLBCL, and several biomarkers related to the TME have thus been identified16–21. BCL2, mainly involving translocations and protein expression, has been extensively investigated as a prognostic biomarker ins DLBCL, but with controversial findings22–25. There is general consensus that patients with concurrent MYC and BCL2 or BCL6 rearrangements, referred to as double hit lymphoma (DHL), have an extremely aggressive clinical course and poor prognosis. However, DHL is relatively rare, representing only 4%–8% of DLBCL patients26,27. In contrast, the predictive value of other BCL2 genetic alterations has been less studied. In particular, limited information about the prognostic significance of BCL2 mutations and BCL2 copy number variations (CNV) are available, and no consistent results have yet been reported26,28,29. In this study, we performed capture-based targeted sequencing on 191 Chinese DLBCL patients, to comprehensively describe BCL2 genetic alterations. We also determined the correlations of clinicopathological features, BCL2, TP53, and other genetic alterations with outcomes in patients treated with R-CHOP. Materials and methods Study population A total of 205 patients diagnosed with de novo DLBCL at the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, and Suzhou Municipal Hospital, from January 2004 to January 2020, were selected for this study. Histological diagnoses were established according to the World Health Organization classification of tumors of hematopoietic and lymphoid tissues in 200830. The inclusion criteria were as follows: 1) patients with histologically confirmed DLBCL; 2) patients who had adequate tissue for DNA extraction and who provided informed consent; 3) patients initially treated with a curative intent; and 4) patients without human immunodeficiency virus infection. Individuals diagnosed with primary central nervous system DLBCL, with incomplete survival data, with a history of an indolent lymphoma, or other primary malignancies were excluded, resulting in a total of 191 patients included in this study. Only 164 patients who received R-CHOP or R-CHOP-like regimens [including R-mini CHOP, R-CDOP (rituximab, cyclophosphamide, pegylated liposomal doxorubicin, vincristine, and prednisone), R-CHO (rituximab, cyclophosphamide, doxorubicin, and vincristine), and R-CHOPE (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone etoposide)] were included for survival and prognostic analyses. The selection process is described in Supplementary Figure S1. Baseline clinical characteristics and follow-up information were collected, including age, gender, Eastern Cooperative Oncology Group (ECOG) performance status (PS), Ann Arbor stage, primary sites, number of extranodal involvement sites, IPI scores, lactate dehydrogenase (LDH) levels, response to treatment, and survival data. Ann Arbor stage was categorized into 2 stage groups involving the limited stage (I–II) and advanced stage (III–IV). IPI was grouped into 2 risk groups involving the low/low-intermediate group (IPI score: 0–2) and the high-intermediate/high group (IPI score: 3–5). This study was performed in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College (No. NCC2018JJJ-004). Written informed consent was obtained from all patients. Capture-based targeted DNA sequencing Archived formalin-fixed paraffin-embedded (FFPE) tissue samples were obtained from enrolled patients. Matched peripheral blood was also collected as the germline control. Library construction was performed based on genomic DNA extracted from FFPE using the QIAamp DNA FFPE Tissue Kit (Qiagen, Hilden, Germany), and from peripheral blood using the QIAamp DNA Blood Mini Kit (Qiagen). The concentration of DNA was assessed using a Qubit fluorometer and Qubit dsDNA HS (High Sensitivity) Assay Kit (Invitrogen, Carlsbad, CA, USA). The Agilent 2100 Bio Analyzer and the DNA HS Kit (Agilent Technologies, Santa Clara, CA, USA) were used to measure the distributions of plasma DNA. DNA was fragmented into 200–250 bp sizes using a Covaris S2 Ultrasonicator (Covaris, Woburn, MA, USA). Hybridization with capture probe baits, hybrid selection with magnetic beads, and the polymerase chain reaction amplification were subsequently conducted. Two capture probes covering genes that were commonly altered in human lymphoma and hematological malignancies were selected, with 1 covering 112 genes and another consisting of 413 genes. A total of 101 genes overlapped between the 2 panels14. Sample sequencing was performed on a Next Seq500 Sequencer (Illumina, Hayward, CA, USA) with pair-end reads at Burning Rock Biotech (Guangzhou, China) or Geneplus-Beijing (Beijing, China). The detailed sequencing procedure was performed as described previously14,15,31. Sequencing data analysis After removal of terminal adapter sequences and low quality data, sequencing data were mapped to the reference human genome (hg19) and assigned with a Burrows-Wheeler assigner 0.7.10 (Broad Institute, Cambridge, MA, USA)32. GATK 3.2 and MuTect (both from Broad Institute), and VarScan (Genome Institute, Washington University, St. Louis, MO, USA) were used to perform local alignment optimization, variant calling, and annotation33,34. The VarScan filter pipeline was used to filter-out loci with a depth < 100. Single nucleotide variants were determined using MuTect (version 1.1.4) and NChot35,36. The average sequencing depth for all targeted regions was 1,402×. Selected exons of several genes of interest overlapping in the 2 panels, including BCL2, MYC, BCL6, TP53, NOTCH1, MYD88, and CD79B, were analyzed. In accordance with the Exome Aggregation Consortium, 1,000 Genomes Project, ESP6500SI-V2, and dbSNP databases, single nucleotide polymorphisms, which were defined as variants with a frequency > 0.1%, were excluded from further analysis. The remaining variants were annotated with ANNOVAR as well as SnpEff v.3.6 software. CNV analysis was performed based upon the depth of coverage data of capture intervals. Coverage data were corrected against sequencing bias resulting from GC content and probe design. Based on the average coverage of all capture regions, the coverage of different samples was normalized to comparable scales. Copy number was computed based on the ratio between the depth of coverage in tumor samples and the average coverage of an adequate number (n > 50) of samples without CNVs as referenced per capture interval. CNV was defined when the coverage data of the gene region was quantitatively and significantly different from the reference control. CNV detection with a threshold value ≤ 1.5 was defined as loss, and a threshold ≥ 2.64 was referred to as gain or amplification. The copy number gains or amplifications of genes, including the BCL2, MYC, and BCL6 genes, were grouped into a single group, and designated as BCL2GA/AMP, MYCGA/AMP or BCL6GA/AMP. Analysis of DNA translocation was performed using Tophat 2 (Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA and the Genome Sciences Department, University of Washington, Seattle, WA, USA) and Factera 1.4.337. Immunohistochemistry (IHC) The COO classification was determined by IHC using anti-CD10, MUM1, and BCL6 antibodies (Fuzhou 100 Maixin Biotech, Fuzhou, China), according to the Hans algorithm38. Patients were grouped into GCB or non-GCB subgroups. IHC staining was also performed for MYC, BCL2, and BCL6. Double-expressor lymphoma (DEL) was defined as MYC expression in ≥ 40% of tumor cells and BCL2 expression in ≥ 60% of tumor cells, as previously described39. Statistical analysis Comparisons between categorical variables were performed using χ2-tests or Fisher's exact tests. Refractory disease was defined as patients who achieved less than a partial response in the first-line setting or those who relapsed within the first 12 months since the initiation of front-line treatment. Progression-free survival (PFS) was calculated from the date of initial diagnosis until the first disease progression/relapse or death from any cause. Overall survival (OS) was calculated from the date of initial diagnosis until death from any cause. Survival curves were estimated using the Kaplan-Meier method and compared with the log-rank test. Multivariable Cox proportional hazard regression models were used to estimate hazard ratios for an evolving event and to identify independent prognostic factors. Comparisons between the novel prognostic model and IPI or R-IPI were performed using the C-index. The area under curve (AUC) of the time-dependent receiver operating characteristic was used to evaluate the predictive performance of models. P values less than 0.05 were considered as statistically significant, and all P values were 2-tailed. All statistical analyses were conducted using SPSS statistical software for Windows, version 26.0 (SPSS, Chicago, IL, USA) and R software, version 3.6.2 (https://www.R-project.org). Results The incidence of BCL2 genetic alterations and BCL2 protein expressions In total, 40 single nucleotide variants (SNVs) were identified in BCL2 [referred to as BCL2 mutation (BCL2MUT)] among 8.9% (17/191) of the patients, and more than half [65% (26/40)] the SNVs were missense mutations. Only 1 hotspot mutation (> 2 SNVs) with BCL2 (G47) was identified (Figure 1A). BCL2GA/AMP occurred in 9.4% (18/191) of the patients. The median threshold for BCL2GA/AMP detection was 3.29 (range: 2.7–5.56). Additionally, only 4.2% (8/191) of the cases harbored a BCL2 translocation (BCL2TR). As a result, BCL2 alterations, comprised of the above 3 genetic alterations, were observed in 18.3% (35/191) of the patients. Only 1 patient (0.5%) had concurrent BCL2MUT and BCL2GA/AMP. Out of 8 patients with BCL2TR, 7 patients had concurrent BCL2MUT, of which 3 cases had hypermutation. However, no case harbored concurrent BCL2TR and BCL2GA/AMP. Among 171 patients with available BCL2 protein expression data, 83 (48.5%) patients were BCL2 positive, with the cutoff value of ≥ 60%. Twenty-eight out of 117 (23.9%) patients had DEL, and only 3 of 191 (1.6%) patients had DHL. Figure 1 The distribution of BCL2 and other genes in 191 DLBCL patients. DLBCL, diffuse large B-cell lymphoma; IPI, International Prognostic Index; GCB, germinal center-B cell like; non-GCB, non-germinal center-B cell like. The association between patient characteristics and BCL2 genetic alterations Compared to those without BCL2 alterations, patients with BCL2 alterations were more likely to have advanced Ann Arbor stage (77.1% vs. 54.2%, P = 0.014). BCL2TR was significantly more commonly seen in the GCB subtype (P = 0.026). Among those with BCL2 protein expressions, 78.1% (25/32) of the patients with BCL2 alterations were BCL2 protein positive, whereas 41.7% (58/139) of those without BCL2 alterations were positive (P < 0.001). Both the presence of BCL2MUT and BCL2GA/AMP were significantly associated with higher positive percentages of BCL2 protein expressions (P = 0.011 and P = 0.015, respectively). However, no significant association between BCL2TR and BCL2 protein expression was observed (P = 0.267). There were significantly positive correlations of BCL2 alterations or BCL2GA/AMP with DEL (P = 0.034 and P = 0.039, respectively). However, no correlation was found between BCL2MUT and DEL (P = 0.723). In addition, 97.1% of the patients in the BCL2 alteration group received front-line R-CHOP or R-CHOP-like regimens, whereas 85.9% of those without an BCL2 alteration received the abovementioned immunochemotherapy (P = 0.033). Detailed patient characteristics are shown in Table 1. Table 1 Patient characteristics according to BCL2 genetic alterations Characteristics Total (%) BCL2 alteration P value BCL2 mutation P value BCL2 GA/AMP P value Yes, n (%) No, n (%) Yes, n (%) No, n (%) Yes, n (%) No, n (%) Age, years 0.338 0.617 0.434 Median (range) 55 (11–82) 60 (27–82) 54 (11–81) 55 (27–80) 55 (11–82) 60 (38–82) 54 (11–81) ≥ 60 79 (41.4) 17 (48.6) 62 (39.7) 8 (47.1) 71 (40.8) 9 (50.0) 70 (40.5) < 60 112 (58.6) 18 (51.4) 94 (60.3) 9 (52.9) 103 (59.2) 9 (50.0) 103 (59.5) Gender 0.362 0.463 0.996 Male 106 (55.5) 17 (48.6) 89 (57.1) 8 (47.1) 98 (56.3) 10 (55.6) 96 (55.5) Female 85 (44.5) 18 (51.4) 67 (42.9) 9 (52.9) 76 (43.7) 8 (44.4) 77 (44.5) ECOG PS 0.535 0.377 0.999 0–1 173 (90.6) 33 (94.3) 140 (89.7) 17 (100.0) 156 (89.7) 17 (94.4) 156 (90.2) ≥ 2 18 (9.4) 2 (5.7) 16 (10.3) 0 (0.0) 18 (10.3) 1 (5.6) 17 (9.8) Ann Arbor stage 0.014 0.118 0.219 I–II 79 (41.4) 8 (22.9) 71 (45.5) 13 (76.5) 75 (43.1) 5 (27.8) 74 (42.8) III–IV 112 (58.6) 27 (77.1) 85 (54.2) 4 (23.5) 99 (56.9) 13 (72.2) 99 (57.2) Primary site 0.857 0.978 0.833 Nodal 123 (64.4) 23 (65.7) 100 (64.1) 11 (64.7) 112 (64.4) 12 (66.7) 111 (64.2) Extranodal 68 (35.6) 12 (34.3) 56 (35.9) 6 (35.3) 62 (35.6) 6 (33.3) 62 (35.8) Extranodal involvement 0.856 0.250 0.734 < 2 sites 134 (70.2) 25 (71.4) 109 (69.9) 14 (82.4) 120 (69.0) 12 (66.7) 122 (70.5) ≥ 2 sites 57 (29.8) 10 (28.6) 47 (30.1) 3 (17.6) 54 (31.0) 6 (33.3) 51 (29.5) COO subtype† 0.571 0.123 0.451 GCB 66 (34.6) 14 (40.0) 52 (33.3) 9 (52.9) 57 (32.8) 5 (27.8) 61 (35.3) Non-GCB 118 (61.8) 21 (60.0) 97 (62.2) 8 (47.1) 110 (63.2) 13 (72.2) 105 (60.7) Unknown 7 (3.7) 0 (0.0) 7 (4.5) 0 (0.0) 7 (4.0) 0 (0.0) 7 (4.0) IPI score 0.748 0.240 0.836 0–2 121 (63.4) 23 (65.7) 98 (62.8) 13 (76.5) 108 (62.1) 11 (61.1) 110 (63.6) 3–5 70 (36.6) 12 (34.3) 58 (37.2) 4 (23.5) 66 (37.9) 7 (38.9) 63 (36.4) LDH† 0.837 0.695 0.695 Elevated 97 (50.8) 17 (48.6) 80 (51.3) 8 (47.1) 89 (51.1) 8 (44.4) 89 (51.4) Normal 91 (47.6) 17 (48.6) 74 (47.4) 9 (52.9) 82 (47.1) 9 (50.0) 82 (47.4) Unknown 3 (1.6) 1 (2.9) 2 (1.3) 0 (0.0) 3 (1.7) 1 (5.6) 2 (1.2) BCL2 IHC† < 0.001 0.011 0.015 ≥ 60% 83 (43.5) 25 (71.4) 58 (37.2) 12 (70.6) 71 (40.8) 13 (72.2) 70 (40.5) < 60% 88 (46.1) 7 (8.0) 81 (51.9) 3 (17.6) 85 (48.9) 4 (22.2) 84 (48.6) Unknown 20 (10.5) 3 (8.6) 17 (10.9) 2 (11.8) 18 (10.3) 1 (5.6) 19 (11.0) MYC/BCL2 IHC† 0.034 0.723 0.039 DEL 28 (14.7) 10 (28.6) 18 (11.5) 3 (17.6) 25 (14.4) 7 (38.9) 21 (12.1) Non-DEL 89 (46.6) 15 (42.9) 74 (47.4) 8 (47.1) 81 (46.6) 7 (38.9) 82 (47.4) Unknown 74 (38.7) 10 (28.6) 64 (41.0) 6 (35.3) 68 (39.1) 4 (22.2) 70 (40.5) First-line chemotherapy 0.033 0.475 0.081 R-CHOP/R-CHOP-like 164 (85.9) 34 (97.1) 130 (83.3) 16 (94.1) 148 (85.1) 18 (100.0) 146 (84.4) CHOP/CHOP-like 27 (14.1) 1 (2.9) 26 (16.7) 1 (5.9) 26 (14.9) 0 (0.0) 27 (15.6) Bold indicates significance. ECOG, Eastern Cooperative Oncology Group; PS, performance status; COO, cell of origin; GCB, germinal center-B cell like; non-GCB, non-germinal center-B cell like; IPI, International Prognostic Index; LDH, lactate dehydrogenase; IHC, immunohistochemistry; DEL, double-expressor lymphoma; GA, gain; AMP, amplification. †P value is calculated after excluding the patients for whom the information of the corresponding variable was unknown. The associations between other genes and COO subtypes or IPI scores Overall, the frequency of TP53 mutations, MYD88 mutations, CD79B mutations, BCL6 alterations, MYC alterations, and NOTCH1 alterations were 30.9% (59/191), 27.7% (53/191), 22.0% (42/191), 15.7% (30/191), 15.2% (29/191), and 9.4% (18/151), respectively (Figure 1B). CD79B mutations were seen more commonly in the non-GCB subtype (P = 0.039). NOTCH1 alterations and MYD88 mutations also tended to be more frequently found in non-GCB subtypes, but these did not reach statistical significance (P = 0.058 and P = 0.057, respectively). However, MYC alterations showed a trend, occurring more frequently in the GCB subtype (P = 0.082). Additionally, there was no significant difference between the IPI risk groups and these genes. The correlations of these genes with COO subtypes or IPI are shown in Figure 1B and detailed in Supplementary Table S1. The associations between BCL2 genetic alterations and other genes The associations of BCL2 alterations, BCL2MUT, and BCL2GA/AMP with other genes were analyzed. The presence of BCL2 alterations and BCL2MUT tended to be positively associated with MYD88 mutations, but the difference did not reach statistical significance (P = 0.072 and P = 0.089, respectively). No significant correlation was found between BCL2 genetic alterations and all other genes (Supplementary Table S2). Survival analysis of DLBCL patients treated with R-CHOP Impact of BCL2 genetic variations on survival outcomes Overall, 164 patients treated with R-CHOP or R-CHOP-like regimens were included for survival and prognostic analyses. With a median follow-up of 35 months (range: 1–118 months), 48 deaths occurred. For all 164 patients, the 5-year PFS and 5-year OS were 34.0% and 62.0%, respectively. Compared with patients with the absence of BCL2 alterations, the 5-year PFS (13.7% vs. 40.8%, P = 0.003) and OS (34.0% vs. 70.9%, P = 0.036) were significantly decreased in cases that harbored BCL2 alterations (Figure 2A–B). Importantly, patients who harbored BCL2GA/AMP also had a remarkably poorer PFS (5-year PFS, 11.1% vs. 38.3%, P < 0.001) and OS (5-year OS, 22.1% vs. 69.6%, P = 0.009) compared with those without BCL2GA/AMP (Figure 2C–D). In contrast, neither BCL2MUT nor BCL2TR were significantly prognostic for the PFS and OS. Figure 2 Survival stratified by BCL2 genetic alterations in 164 DLBCL patients receiving R-CHOP/R-CHOP-like regimens. (A) PFS stratified by BCL2 alteration; (B) OS stratified by BCL2 alteration; (C) PFS stratified by BCL2GA/AMP; (D) OS stratified by BCL2GA/AMP. DLBCL, diffuse large B-cell lymphoma; PFS, progression-free survival; OS, overall survival; GA, gain; AMP, amplification. Univariable analysis for PFS and OS In addition to BCL2 alterations and BCL2GA/AMP, univariate analyses also showed that age > 60 years (PFS, P = 0.033; OS, P = 0.009), ECOG PS of ≥ 2 (PFS, P = 0.009; OS, P < 0.001), advanced stage (PFS, P < 0.001; OS, P < 0.001), number of extranodal involvement sites ≥ 2 (PFS, P = 0.013; OS, P < 0.001), elevated LDH (PFS, P = 0.001; OS, P < 0.001), IPI score of 3–5 (PFS, P < 0.001; OS, P < 0.001) (Supplementary Figure S2), and TP53 mutation (PFS, P = 0.014; OS, P = 0.047) (Supplementary Figure S2) were all significantly associated with poor survival. The presence of MYC translocations (P = 0.062) and CD79B mutations (P = 0.066) tended to have negative prognostic effects on the OS, but without statistical significance. The results of univariate analysis for the PFS and OS are summarized in Table 2. Table 2 Univariable analysis for PFS and OS in patients receiving R-CHOP/R-CHOP-like regimens Risk factors PFS OS 5-year PFS rate (%) P value 5-year OS rate (%) P value Age, years (≥ 60 vs. < 60) 17.1 vs. 46.8 0.033 48.2 vs. 76.9 0.009 Gender (male vs. female) 35.4 vs. 32.7 0.687 63.9 vs. 60.0 0.450 ECOG PS (0–1 vs. ≥ 2) 35.2 vs. 29.4 0.009 65.5 vs. 31.9 < 0.001 Ann Arbor stage (I–II vs. III–IV) 56.2 vs. 20.6 < 0.001 82.5 vs. 49.2 < 0.001 COO subtype (GCB vs. non-GCB) 30.2 vs. 34.9 0.738 65.4 vs. 61.2 0.533 Primary site (nodal vs. extranodal) 31.7 vs. 45.0 0.771 63.0 vs. 62.2 0.539 Extranodal involvement (< 2 vs. ≥ 2 sites) 38.6 vs. 22.8 0.013 72.7 vs. 38.7 < 0.001 LDH (elevated vs. normal) 25.6 vs. 46.0 0.001 47.6 vs. 80.8 < 0.001 IPI score (0–2 vs. 3–5) 45.3 vs. 18.6 < 0.001 76.6 vs. 39.5 < 0.001 BCL2 IHC (≥ 60% vs. < 60%) 28.0 vs. 31.1 0.143 64.5 vs. 60.0 0.856 DEL (yes vs. no) 38.6 vs. 39.5 0.361 52.1 vs. 66.6 0.595 TP53 mutation (yes vs. no) 29.8 vs. 35.5 0.014 53.4 vs. 66.3 0.047 BCL2 alteration (yes vs. no) 13.7 vs. 40.8 0.003 38.5 vs. 70.9 0.036 BCL2 mutation (yes vs. no) 35.1 vs. 31.3 0.67 67.0 vs. 61.4 0.671 BCL2GA/AMP (yes vs. no) 0 vs. 38.3 < 0.001 22.1 vs. 69.6 0.009 BCL2 translocation (yes vs. no) 0 vs. 36.7 0.116 68.6 vs. 61.8 0.778 MYC alteration (yes vs. no) 33.5 vs. 34.3 0.501 42.8 vs. 66.6 0.137 MYC mutation (yes vs. no) 39.7 vs. 33.1 0.845 65.2 vs. 61.4 0.804 MYC translocation (yes vs. no) 18.5 vs. 35.1 0.097 21.0 vs. 66.6 0.062 NOTCH1 alteration (yes vs. no) 47.1 vs. 32.4 0.813 67.2 vs. 60.9 0.398 MYD88 mutation (yes vs. no) 13.7 vs. 39.9 0.094 53.3 vs. 66.7 0.271 MYD88 L265P mutation (yes vs. no) 34.4 vs. 34.8 0.812 73.7 vs. 61.2 0.807 MYD88 other mutation (yes vs. no) 13.9 vs. 38.3 0.126 48.6 vs. 67.0 0.339 CD79B mutation (yes vs. no) 39.2 vs. 32.0 0.978 53.4 vs. 64.6 0.066 BCL6 alteration (yes vs. no) 41.6 vs. 34.4 0.195 67.9 vs. 61.6 0.397 BCL6 translocation (yes vs. no) 66.7 vs. 30.6 0.094 85.6 vs. 60.0 0.219 BCL6 mutation (yes vs. no) 34.2 vs. 52.5 0.116 87.5 vs. 59.7 0.178 Bold indicates significance. PFS, progression-free survival; OS, overall survival; ECOG, Eastern Cooperative Oncology Group; PS, performance status; COO, cell of origin; IPI, International Prognostic Index; LDH, lactate dehydrogenase; IHC, immunohistochemistry; DEL, double-expressor lymphoma; GA, gain; AMP, amplification. The prognostic effects of BCL2 alterations and BCL2GA/AMP within IPI and TP53 mutations The prognostic power of BCL2 alterations seemed to be similar between 2 IPI risk subgroups, whereas a significant association of BCL2GA/AMP with survival outcomes was found in patients with an IPI score of 1–2 (Supplementary Figure S3), probably due to the small number of patients with BCL2GA/AMP in the IPI score 3–5 subgroup. The impact of BCL2 alterations and BCL2GA/AMP on PFS and OS in patients either with or without TP53 mutations were similar to that in the entire patient cohort (Figure 3). Figure 3 Survival stratified by BCL2 genetic alterations in patients with or without TP53 mutations. (A) PFS stratified by BCL2 alteration in patients without TP53 mutations. (B) OS stratified by BCL2 alterations in patients without TP53 mutations. (C) PFS stratified by BCL2 alterations in patients with TP53 mutations. (D) OS stratified by BCL2 alterations in patients with TP53 mutations. (E) PFS stratified by BCL2GA/AMP in patients without TP53 mutations. (F) OS stratified by BCL2GA/AMP in patients without TP53 mutations. (G) PFS stratified by BCL2GA/AMP in patients with TP53 mutations. (H) OS stratified by BCL2GA/AMP in patients with TP53 mutations. PFS, progression-free survival; OS, overall survival; GA, gain; AMP, amplification. Notably, 9 patients who harbored concurrent BCL2 alterations and TP53 mutations had a very poor prognosis, with the median PFS of only 4 months and OS of 13 months. Eight out of these 9 patients were primary refractory to first-line R-CHOP regimens, whereas only 1 case (Case 47) remained disease progression free at the last follow-up (Supplementary Table S3). Case 47 was a 38-year-old male, diagnosed with stage IE primary testicular DLBCL. He underwent radical orchiectomy, and then received R-CHOP chemotherapy, followed by prophylactic irradiation to the contralateral testis. This patient had a PFS of 28 months as of August 2020. Independent prognostic factors for DLBCL patients treated with R-CHOP Considering that the IPI involved age, ECOG PS, number of extranodal involvement site, LDH and Ann Arbor stage, these 5 prognostic indicators, though statistically significant in univariate analyses, were not incorporated into further multivariable analyses. In a multivariable analysis for PFS that incorporated BCL2 alterations, TP53 mutations, and the IPI, BCL2 alterations [hazard ratio (HR): 2.519; 95% confidence interval (CI): 1.586–4.001; P < 0.001], TP53 mutation (HR: 2.055; 95% CI: 1.334–3.167; P = 0.001), and IPI (HR: 2.479; 95% CI: 1.641–3.746; P < 0.001) were independent factors predicting PFS (Table 3). Similarly, in a multivariable analysis incorporating BCL2GA/AMP, TP53 mutations, and IPI, BCL2GA/AMP (HR: 3.074; 95% CI: 1.801–5.246; P < 0.001) remained an independent prognostic factor for PFS, in addition to TP53 mutations and the IPI. Regarding OS, multivariable analyses, including BCL2 alterations, TP53 mutations, and IPI, showed that BCL2 alterations (HR: 2.610; 95% CI: 1.391–4.896; P = 0.003), TP53 mutations (HR: 2.295; 95% CI: 1.263–4.170; P = 0.006) and IPI (HR: 4.068; 95% CI: 2.236–7.401; P < 0.001) were significant predictors of OS. The presence of BCL2GA/AMP also showed a high degree of correlation with OS (HR: 2.586; 95% CI: 1.279–5.232; P = 0.008), independent of TP53 mutations and the IPI. Table 3 Multivariable analysis for PFS and OS in patients receiving R-CHOP/R-CHOP-like regimens Model and variables PFS OS HR (95% CI) P value HR (95% CI) P value Score Model 1 BCL2 alteration (yes vs. no) 2.519 (1.586–4.001) < 0.001 2.610 (1.391–4.896) 0.003 1 vs. 0 TP53 mutation (yes vs. no) 2.055 (1.334–3.167) 0.001 2.295 (1.263–4.170) 0.006 1 vs. 0 IPI score (3–5 vs. 1–2) 2.479 (1.641–3.746) < 0.001 4.068 (2.236–7.401) < 0.001 2 vs. 0 Model 2 BCL2GA/AMP (yes vs. no) 3.074 (1.801–5.246) < 0.001 2.586 (1.279–5.232) 0.008 1 vs. 0 TP53 mutation (yes vs. no) 1.901 (1.239–2.918) 0.003 2.138 (1.182–3.870) 0.012 1 vs. 0 IPI score (3–5 vs. 1–2) 2.266 (1.503–3.415) < 0.001 3.750 (2.067–6.800) < 0.001 2 vs. 0 Bold indicates significance. PFS, progression-free survival; OS, overall survival; IPI, International Prognostic Index; HR, hazard ratio; CI, confidence interval; GA, gain; AMP, amplification. A novel prognostic model for OS in DLBCL patients treated with R-CHOP Based on the prognostic factors derived from the multivariable analyses and corresponding HRs, novel prognostic risk models for OS were proposed. Accordingly, the adopted weights of each adverse prognostic factor were as follows: 1 point for each risk factor BCL2 alteration (Model 1) or BCL2GA/AMP (Model 2), or TP53 mutation; and 2 points for the IPI score ≥ 3 (Table 3). Patients were further stratified into 4 risk groups based on their scores (low risk, 0 point; low-intermediate risk, 1 point; high-intermediate risk, 2 points; high risk, 3–4 points). In Model 1, 52 (31.7%), 45 (27.4%), 41 (25.0%), and 26 (15.9%) patients were classified into the low risk, low-intermediate risk, high-intermediate risk, and high risk groups, respectively. There were significant differences in survival outcomes among these 4 risk groups, with a 5-year OS of 89.4%, 67.3%, 58.7%, and 15.8% (P < 0.001), respectively, for the 4 risk groups (Figure 4A). According to Model 2, the distribution of 164 patients were as follows: the low risk, 63 (38.4%) patients; the low-intermediate risk, 33 (20.1%) patients; the high-intermediate risk, 45 (27.4%), and the high risk group, 23 (14.0%) patients. The 5-year OS rates were 86.6%, 64.1%, 55.3%, and 16.4% for the 4 risk groups (P < 0.001), respectively (Figure 4B). Figure 4 Survival outcomes stratified by prognostic models. (A) Overall survival (OS) stratified by Model 1. (B) OS stratified by Model 2. The new prognostic models also showed better levels of accuracy for predicting OS than both the IPI and R-IPI, with a C-index of 0.715 for Model 1 and 0.722 for Model 2, when compared with that of 0.664 for the IPI and 0.693 for the R-IPI. Additionally, the AUC for predicting the 5-year OS of Model 1 (0.781) and Model 2 (0.790) were higher than that of IPI (0.697) and R-IPI (0.725) (Supplementary Figure S4). Discussion In this study involving patients with de novo DLBCL uniformly treated with R-CHOP/R-CHOP-like regimens, the methodology of probe capture-based high-resolution sequencing was used to analyze the relationships of genetic alterations with clinical outcomes. Our findings showed that the presence of BCL2 alterations, especially BCL2GA/AMP and TP53 mutations were significantly associated with inferior outcomes, and were independent of the IPI. Therefore, we proposed novel prognostic models that incorporated both clinical and genetic characteristics. The models were based on 3 risk factors, including BCL2 alterations (Model 1) or BCL2GA/AMP (Model 2), combined with TP53 mutations and IPI, identifying 4 risk groups with different survival outcomes. With the emergence of targeted therapy, such as BCL2 inhibitors or immune checkpoint inhibitors, such prognostic models with biomarkers could aid in further defining the patients suitable for alternative treatment approaches and/or enrollment in clinical trials. The prognostic significance of BCL2 protein expression and gene aberrations has been reported in a great number of studies, but with conflicting findings22–25,40–44. The percentage of high BCL2 expression in DLBCL, with the cutoff value of 70%, ranged between 40%–50%22,23,40,41,45. BCL2 high expression has been found to be a strong predictor of poor prognosis in some studies23,40, but not all studies41,45. Thereafter, it is believed that double expression of MYC and BCL2 protein contributes to inferior outcomes22,46, nevertheless, there is still controversy40. Interestingly, a study showed that DEL was significantly associated with inferior survival within the GCB subgroup, but not within the ABC subgroup46. In this study, neither BCL2 high expression alone, nor co-expression with MYC predicted poor survival, consistent with previous reports41,45. The finding that BCL2 protein expression may be less relevant to outcome should be further confirmed. Also, the prognostic significance of the BCL2 translocation t (14;18) in DLBCL has not yet been fully investigated. Some studies showed no prognostic implication of BCL2TR23,24,47, whereas others demonstrated adverse prognostic significance in GCB subtype independent of the IPI26,43. The worse outcome associated with BCL2TR might be related to the second hit of MYC translocation27. In this study, the incidence of BCL2TR was only 4.2% (7/191), which was lower than that reported in previous studies23,26, probably owing to different methodologies used across studies. Our results also showed that BCL2TR was not associated with survival, and the analysis for DHL was not conducted due to the relative rarity of cases. Further studies with larger numbers of patients are needed to confirm these observations. Unlike extensive studies on BCL2 expression and BCL2TR, there are few reports on BCL2GA/AMP and BCL2MUT. Recently, 2 studies with large cohorts reported that BCL2GA/AMP was independently associated with poor outcomes in DLBCL patients26,48, and the prognostic power was particularly observed in ABC subtype26. Schuetz et al.28 reported that the BCL2MUT was not independently associated with survival. In the present study, we focused on the prognostic effects of BCL2GA/AMP and BCL2MUT, which were not concurrently examined in the overwhelming majority of previous studies. Our analyses showed that BCL2 alterations significantly correlated with inferior survival, but it was noteworthy that only BCL2GA/AMP predicted a poor prognosis, rather than BCL2MUT or BCL2TR, which was in accordance with previous findings26,28,48. In addition, consistent with recent studies49,50, we also found that positive BCL2 expression was significantly associated with BCL2GA/AMP, indicating the BCL2 expression was partly driven by CNV. We speculate that the inconsistent results obtained by different studies regarding the effect of BCL2 on prognoses for DLBCL, may be partly attributable to the complexity of BCL2 genetic alterations. Another reason may be that the testing methods most studies used were not able to include all genetic alterations. By using probe capture-based high resolution sequencing, we simultaneously obtained comprehensive information about BCL2 genetic alterations, including mutations, gain/amplifications, and translocations. In this study, BCL2TR was frequently accompanied by BCL2MUT (7/8 cases), sometimes hypermutation (3/7 cases), whereas BCL2GA/AMP cases rarely were characterized with concurrent BCL2MUT (1/18 cases). This phenomenon confirmed the previous finding that BCL2TR played a pivotal role in the acquisition of BCL2MUT, and BCL2MUT likely occurred as a result of aberrant somatic hypermutations28. Some BCL2 mutations, especially hypermutations, may partly influence BCL2 protein functions51. Because BCL2TR is frequently accompanied by BCL2MUT, and the effect of BCL2TR on the function of BCL2 protein is unpredictable, these characteristics could partly explain the inconsistent prognostic results of BCL2TR. In contrast, BCL2GA/AMP is rarely accompanied by BCL2MUT, resulting in high expression of BCL2 protein whose function has not been affected, thus enhancing the anti-apoptotic ability of tumor cells. Taken together, the biological basis of the association between BCL2 genetic alterations and prognoses remains unclear, so further investigations are needed to elucidate the responsible mechanisms. The mutation percentage of TP53 in DLBCL is approximately 20%–25%13,14,52. In this study, the incidence was as high as 31%, possibly due to the preference for relapsed and refractory subsets in patient selection. Cumulative studies have shown that TP53 mutations were significantly associated with a lower rate of complete remission and shorter PFS and OS in patients with DLBCL treated with either CHOP52 or R-CHOP regimens13,14. In the present study, TP53 mutations were also identified as an independent factor predicting poor outcomes, in agreement with previous reports. TP53 mutations could potentially provide predictive information to guide precise treatment for patients with DLBCL. Both BCL2 and TP53 have been shown to play a central role in the inhibition of apoptosis and tumor suppression. In this study, among 9 patients with concurrent BCL2 alterations and TP53 mutations, 8 were primary refractory to first-line R-CHOP regimens. Despite the small number of patients, the incorporation of BCL2 alterations and TP53 mutations could define a subset of cases with an extremely poor prognosis. This phenomenon might further reflect the crucial role of the inactivated anti-apoptotic pathway in rendering B-cells resistant to standard immunochemotherapy. From this perspective, therapeutic approaches making the anti-apoptotic pathway activated, such as inhibition of BCL2, might confer reversal of drug-resistance and further improve survival outcomes of patients with DLBCL treated with R-CHOP. Venetoclax, a highly selective BCL2 inhibitor, plus R-CHOP in a first-line setting, have demonstrated promising antitumor activities in DLBCL patients53,54. However, more studies are required to provide definitive evidence. The novel prognostic models were constructed using clinical and genetic characteristics involving BCL2 alterations (Model 1), BCL2GA/AMP (Model 2), TP53 mutations, and the IPI. To the best of our knowledge, no study using these 3 combined indicators for individual risk prediction has been reported. The prognostic models incorporating both genetic and clinical information are important for risk stratification, and also have significant therapeutic implications, which may aid physicians in making clinical decisions. For those high risk patients defined by the novel models, the 5-year OS was only approximately 16%, thus novel therapeutic strategies, including new targeted therapy, immune checkpoint inhibitors, or additional therapies are needed for better efficacy. Despite these challenges, further external validation of the novel prognostic models is still warranted. Several limitations to this study need to be acknowledged. This study was limited by its retrospective nature, which could inevitably have caused bias during patient selection and subsequent study processes. Another limitation was the lack of validation of the novel prognostic models in an independent cohort. In addition, the sample size was relatively small in several patient subgroups, which limited the power of our analysis. For instance, with only 8 cases harboring BCL2TR, the finding that BCL2TR was not significantly correlated with BCL2 expression and survival should be further confirmed. In spite of these limitations, this study provided important insight into individual risk assessments, and provided the basis for future investigations. In conclusion, this study comprehensively described genetic alterations of BCL2. In patients treated with R-CHOP or R-CHOP-like regimens, the presence of BCL2 alterations, especially BCL2GA/AMP, and TP53 mutations were significantly associated with poor outcomes, independent of the IPI. We proposed a novel prognostic model based on 3 risk factors, including BCL2 alterations (Model 1), BCL2GA/AMP (Model 2), TP53 mutations, and the IPI, which identified 4 risk groups with different survival outcomes. Once the new prognostic models have been validated in an independent cohort, the models will help to further define DLBCL patients with poor prognoses, who were treated with R-CHOP, and will identify patients suitable for alternative treatment approaches and/or enrollment in clinical trials. Supporting Information Click here for additional data file. Acknowledgements The authors thank all participating patients, their families, and all investigators who contributed to this study. The authors also thank Jing Lin (Burning Rock Biotech, Guangzhou, China), Xinhua Du (Geneplus-Beijing, Beijing, China), and their teams for performing the DNA sequencing and sequencing data analyses. Special thanks to Professor Wenqiang Wei (the National Central Cancer Registry, the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College) for his editorial assistance. Grant support This work was financially supported in part by the Beijing Natural Science Foundation (Grant No. H201820659), the China National Major Project for New Drug Innovation (Grant No. 2017ZX09304015), and the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS) (Grant No. 2016-I2M-1-001). Conflict of interest statement No potential conflicts of interest are disclosed. Author contributions Conceived and designed the study: YKS and YQ. Collected the data: YQ and HZC. Contributed data or analysis tools: YKS, YQ, HZC, PL, CGZ, JLY, LG, XHH, LQZ, SYZ, SYJ, and HXJ. Performed the analysis: YQ and HZC. Wrote the paper: YKS, YQ, and HZC. 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