Identification of six hub genes in mantle cell lymphoma patients with BTKi resistance

Introduction

Mantle cell lymphoma (MCL) is an aggressive subtype of non-Hodgkin lymphoma (NHL), accounting for 2–10% of all NHL cases (1). Despite the improvement in response to currently available therapies, patients with MCL inevitably relapse (2).

To date, Bruton tyrosine kinase inhibitors (BTKis) have been widely used in the treatment of relapse/recurrence (R/R) MCL as irreversible covalent inhibitors (3). Despite eliciting impressive responses in relapsed patients, the success of BTKi treatment has been thwarted by the development of resistance (2). Primary resistance has been observed to range from 10.2% to 35%, and acquired resistance between 17.5% and 54% among all ibrutinib-treated patients (4). The resistance is associated with a low response rate to salvage therapies, a short duration of response, and dismal overall survival (OS), which is becoming a major clinical issue in the treatment of patients with MCL (4).

In recent years, many MCL-related hub genes and clusters have been identified (5,6). Based on the WGCNA network, 12 subgenes were screened and 7 genes were associated with OS time (7). MMP9 was identified as a hub gene in MCL by protein-protein interaction (PPI) network (8). In contrast to previous analyses in MCL, our study focused on BTKi-resistant MCL mutation profile in Chinese population, which has not been reported yet, providing a real-world gene mutation analysis. In this paper, we discuss the clinical features and treatment options, and identify mutated hub genes of BTKi-resistant MCL by panel sequencing. The high-frequency mutation gene set was obtained by sequencing analysis, the prognostic related gene set was obtained by univariate Cox regression, and the hub genes were screened by intersection. Furthermore, high gene connectivity was verified, and the promising prospect of hub genes in salvage therapy was discussed. We present the following article in accordance with the REMARK reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-22-4314/rc).

Methods

Patient data

The clinical data and tumor biopsy results of 28 BTKi-resistant MCL patients at Peking University Third Hospital between 1 August 2008 and 21 March 2021 were retrospectively collected. Any MCL patients with incomplete clinical information were excluded. Treatment decisions were based solely on consensus recommendations at the time of diagnosis. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by institutional ethics board of Peking University Third Hospital (No. M2022564) and individual consent for this retrospective analysis was waived. Lymphoma tissue from patients who relapsed was then sequenced by using the GP79 panel (Table 1). Clinical characteristics were collected including gender, age, immunophenotype, lactate dehydrogenase (LDH) level, extranodal involvement, Eastern Cooperative Oncology Group performance status (ECOG PS), Ann Arbor stage, MCL International Prognostic Index (MIPI) score, treatment, and response. Tumor response was classified as either complete remission (CR), partial remission (PR), stable disease (SD), or progressive disease (PD) according to the International Workshop Criteria. The OS was defined as the time from diagnosis to death. Progression-free survival (PFS) was defined as the time from diagnosis to either disease progression or death.

Library construction, hybrid selection, and ultra-deep next-generation sequencing

The DNA was extracted from paraffin-embedded lymphoma tissues. Indexed libraries were prepared from sheared DNA using the DNA Library Preparation Kit (Beijing Mygenostics, Beijing, China). The capture of target fragments was then undertaken using the GenCap (Beijing Mygenostics). Sequencing was performed on the DNBSEQ-T7 (MGI, Shenzhen, China) followed by the setting of paired-end 150 bp.

Sequence alignment, processing, and single nucleotide variation/indel/copy number variation detection

Sequenced reads were aligned to the human genome reference hg19 by using Burrows-Wheeler Alignment (BWA: v0.7.17;). MuTect2 (v4.2.0; Broad Institute, Cambridge, MA, USA) was applied to identify somatic mutations, small insertions, and deletions. HaplotypeCaller (v4.1.0; Broad Institute) was used to call germline single-nucleotide polymorphisms. The DNA copy number analysis was conducted using CNVkit (v0.9.2; Helen Diller Family Comprehensive Cancer Center, UCSF).

Selection, analysis and verification of hub genes

Prognostic related gene set was obtained by univariate Cox regression (P<0.1), and the hub genes were screened by intersection. A co-expression network of these hub genes via GeneMANIA (https://genemania.org/) were constructed. The hub genes connectivity was verified by CytoHubba plug-in of Cytoscape with common algorithms [Betweenness, Bottleneck, Closeness, Clustering coefficient, Degree, density of maximum neighborhood component (DMNC), EcCentricity, edge percolated component (EPC), maximal clique centrality (MCC), maximum neighborhood component (MNC), Radiality, Stress].

PPI network construction and module analysis

Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org) (version 11.5) were used to construct a PPI network with complex regulatory relationships. Cytoscape (http://www.cytoscape.org) (version 3.8.2) was used to visualize this PPI network. Molecular complex detection technology (MCODE), a plug-in of Cytoscape, was used to analyze key functional modules. Set the selection criteria as: K-core =2, degree cutoff =2, max depth =100, and node score cutoff =0.2.

Statistical analysis

All statistical analyses were conducted with SPSS 22.0 (IBM Corp., Armonk, NY, USA) and R (version 4.1.2; https://www.r-project.org/) as appropriate. All the data were complete. The log-rank test was used to compare differences between Kaplan-Meier curves. The coefficients of the univariate Cox regression model were analyzed with the “survminer” package. Differences in qualitative variables were compared with the chi-square test. A two-tailed P value <0.05 was considered statistically significant.

Results

Baseline characteristics

A total of 28 MCL patients with BTKi resistance were enrolled at Peking University Third Hospital in China. In our cohort, the median age at diagnosis was 61 (range, 36 to 77) years; 17 (60.7%) of patients were pathologically classified as classical MCL; 10 patients (35.7%) had experienced B symptoms; and β2 microglobulin (B2M) elevation presented in 18 (64.3%) patients. Compared with the largest published Chinese MCL data from Chinese Hematology Centers (CHC), the baseline characteristics are summarized in Table 2. Age, gender, and Ann Arbor stage were not statistically different from the CHC data (P=0.415; P=0.495; P=0.559). The ECOG PS (P=0.039), MIPI (P=0.033), LDH (P=0.001), bone marrow (BM) positive (P=0.019), and Ki67 (P=0.038) were significantly different in our BTKi cohort.

Prognosis and treatment plan

After a median 22-month follow-up (range, 4 to 151 months), 11 patients died and 17 relapsed. The 1-year OS was 81.1% and 3-year OS was 63.9% (Figure 1A). The 1-year PFS was 57.14%, and 3-year PFS was 7.14%, with a 14-month median PFS (m-PFS) (Figure 1B). The 1-year PFS2 and OS2 rates were 26.0% and 81.2%, respectively, with 6-month m-PFS2 and 17-month median OS (m-OS)2 (Figure 1C,1D). After first-line treatment, 12 patients achieved CR, 10 achieved PR, 3 achieved SD, and 3 progressed (Figure 1E). Some 60.71% received ibrutinib (n=17), 17.86% received zebotinib (n=5), 14.28% received orelabrutinib (n=4), and the remainder received acalabrutinib (n=2). After a short unsatisfactory treatment response (CR/PR =17, SD/PD =11), BTK resistance occurred quickly (m-PFS2: 6 months), with 11 cases deceased by the end of follow-up.

Figure 1 Kaplan-Meier survival curves and Sankey diagram shows the patient’s treatment process. (A) Kaplan-Meier survival curves of OS. (B) Kaplan-Meier survival curves of PFS. (C) Kaplan-Meier survival curves of OS2. (D) Kaplan-Meier survival curves of PFS2. (E) Sankey diagram shows the initial treatment effect of the patient, the type of BTK drug used, and survival at the end of follow-up. OS, overall survival; PFS, progression-free survival; CR, complete remission; PR, partial remission; SD, stable disease; PD, progressive disease; BTKi, Bruton tyrosine kinase inhibitor.

Several salvage treatments have been used in patients with BTK resistance, including B cell lymphoma-2 (BCL-2)⁺ (n=9), bendamustine and rituximab (BR) (n=10), BTKi⁺ (n=15), chimeric antigen receptor T-cell (CAR-T; n=6), chemotherapy (CT) (n=6), ibrutinib-rituximab (IR) (n=5), and lenalidomide (n=4) (Figure 2A). The BTK combined with other drugs is the most commonly used treatment. The response to BTK⁺ reached CR in 2 patients, SD in 7 patients, and PD in 6 patients. A total of 6 patients were treated with CAR-T and responded well with 4 CR, 1 PR, and 1 PD. Some 66% of CR benefited from CAR-T treatment, 50% of PR benefited from BR treatment, 27% and 23% of SD occurred from BTKi and BR treatment, and 31% of PD came from BTKi treatment (Figure 2B). According to the treatment effect, Patients with BTK, NOTCH1, and PTPRD mutations respond poorly to salvage therapy (Figure 2C).

Figure 2 Therapy for MCL patients after BTKi resistance. (A) Efficacy of different treatment options. (B) The proportion of each treatment plan in different treatment efficacy groups. (C) Comparison of gene mutations between CR patients and non-CR patients. BCL-2, B cell lymphoma-2; BR, bendamustine and rituximab; BTKi, Bruton tyrosine kinase inhibitor; CAR-T, chimeric antigen receptor T-cell; CT, chemotherapy; IR, ibrutinib-rituximab; PD, progressive disease; SD, stable disease; PR, partial remission; CR, complete remission; MCL, mantle cell lymphoma.

The landscape of MCL-GP79

To visualize the distribution of mutations in the 79 genes for MCL patients, we depicted the landscape of MCL-BTKi-GP79 (Figure 3). A total of 38 gene mutations were detected in our cohort. Of the 28 MCL cases, all patients detected ≥1 coding mutation within the 79-panel (mean number of mutated genes per case, 3.66; range, 2 to 11). The most common mutated genes were TP53 (50%), ATM (43%), KMT2D (21%), NOTCH2 (18%), NSD2 (18%), NOTHC1 (14%), BTK (14%), BIRC3, CXCR4, and TRAF2 (11% each). Mutation frequencies greater than 5% are defined as high-frequency mutant genes.

Figure 3 Oncoplot of mutations in MCL patients with BTKi resistance. The most recurrently mutated genes were TP53 (50%), ATM (43%), KMT2D (21%), NOTCH2 (18%), NSD2 (18%), NOTHC1 (14%), BTK (14%), BIRC3, CXCR4, and TRAF2 (11% each). BTKi, Bruton tyrosine kinase inhibitor; CR, complete remission; PD, progressive disease; PR, partial remission; SD, stable disease; MCL, mantle cell lymphoma.

Identification of hub genes

We further explored genes with potential prognostic effects (P<0.1) by a univariate Cox regression model (Figure 4A). Based on Cox regression, CARD11, CD79B, and MYC were associated with poor OS. The genes ATM, B2M, CARD11, CREBBP, MTOR, NOTCH2, PIK3CA, PTEN, PTPRD, and TP53 were associated with poor PFS. Additionally, the following 8 genes were associated with poor OS2: ARID1A, ARID1B, CD79B, CXCR, KRAS, STAG1, TET2, and TTN. Of these, CARD11 is related to both PFS and OS, and CD79B is related to both OS2 and OS. The results of the Cox analysis of each gene are shown in Table S1.

Figure 4 Screening of prognostic related genes, identification of hub genes, and establishment of biological network. (A) A univariate Cox regression model showing potential prognostic effects (P<0.1). (B) Hub genes are identified by the intersection of the high-frequency mutation gene set and prognostic-related gene set. (C) Construction of a biological network by GeneMANIA.

Among the potential prognostic-related genes, the mutation frequencies for ATM, CARD11, KRAS, NOTCH2, PTPRD, and TP53 were greater than 5%, considered as the hub genes by the intersection of the high-frequency mutation gene set and prognostic-related gene set (Figure 4B). Based on the GeneMANIA (https://genemania.org/) database, we analyzed the co-expression network and related functions of these genes (Figure 4C). A total of 20 related genes were integrated, with 7 pathways identified, including cell cycle regulation, signaling in ERBB, Notch, Ras, and TOR.

PPI network construction and analysis

Through the 12 algorithms of plug-in CytoHubba, we confirmed that hub genes rank highly in the PPI network (Table S2). The PPI network of the mutation gene was constructed using Cytoscape, which contained 36 nodes and 229 interaction pairs. According to degree algorithms, we visualized the gene connectivity (Figure 5A,5B). Through MCODE plug-in of Cytoscape, three closely connected gene modules were obtained (Figure 5C-5E). Among the six hub genes, ATM, KRAS, TP53 and NOTCH2 were enriched in model 1 and scored the highest (14.533) among the three clusters, implying a closely connection among the four hub genes (Figure 5C).

Figure 5 PPI network and key modules. (A) A PPI network diagram constructed with 36 nodes and 229 interaction pairs, showing the correlation between hub gene and other mutated genes in panel. (B) A PPI network exhibits high connectivity of hub genes. (C) 16 genes key module with 16 genes scored 14.533 containing 4 hub genes (ATM, KRAS, TP53 and NOTCH2). (D) Three genes key module with BTK, MTOR and CXCR4 scored 3. (E) Five genes key module scored 3. PPI, protein-protein interaction.

Discussion

About 5–10% of NHLs are MCL, a rare incurable B-cell lymphoma (9,10). Despite an enhanced understanding of biology and the development of effective therapeutic strategies resulting in improved survival (11), MCL patients continue to experience a poor prognosis overall and inevitable relapse (2). Currently, BTKi are widely used in the treatment of R/R MCL, with an impressive response, and uniform development of resistance (2). Once ibrutinib-treated patients relapse, the 1-year survival rate is only 22% (12,13). The resistance to BTKi is a key issue in MCL patients. A total of 28 MCL patients with BTKi resistance were included in our cohort, and their clinical characteristics, prognosis, and treatment data were collected. Gene mutations of patients were obtained by GP79 panel sequencing, with six hub genes identified.

In our cohort, the median age of onset was 61 years and the male-to-female ratio was 2.4:1 which is consistent with overall MCL patients (14). Compared with the largest published Chinese MCL data from CHC (15), our cohort had higher ECOG (P=0.039) and MIPI (P=0.033) scores, a higher percentage of elevated LDH (P=0.001), BM positive (P=0.019), and Ki67 (P=0.038), which showed statistically significant differences. Some 93% of patients were stage III–IV in our cohort, higher than the 80.7% in the CHC cohort. These data suggest that patients with BTKi resistance have less favorable initial clinical features in terms of physical situation, disease score, involvement, proliferation index, and hematologic indicators.

Based on our treatment and prognosis data, the response rate of BTKi was 60.71%, which is consistent with previous articles (55–72%) (16-18). As in previous studies, patients with BTK resistance had a very short OS. Once ibrutinib-treated patients relapse, their 1-year survival rate is only 22% (12,13). After an unsatisfactory treatment response (CR/PR =17, SD/PD =11), BTKi resistance occurred quickly (m-PFS2: 6 months), with a total of patients 11 dying, and the m-OS2 was 47 months. The median duration of response to BTKi in our cohort was 6 months, which is exactly the same as that reported by Cheah et al. [2015] (12). Our cohort suggested that BTKi-resistant patients have poor treatment response, rapid resistance occurrence, and death within a short time.

The efficacy of salvage therapy after resistance was unsatisfactory, with the longest OS of 19.2 months and the shortest OS of 3.7 months (4). After the onset of resistance, 15 patients were treated with other BTKi drugs, yielding 2 cases of CR, 7 SD, and 6 PD. Compared with other salvage therapy, BTKi⁺ had the highest SD/PD ratio among all kinds of rescue treatments. Two resistant patients treated with BTKi showed CR, without BTK mutation, implying the great role of BTK mutation in BTK resistance and providing clinical evidence for the use of other BTK drugs (e.g., LOXO-305) (19). The therapeutic effect of CAR-T has been verified. A total of 6 patients were treated with CAR-T (4 CR, 1 PR, and 1 PD) and 66% of CR benefited from CAR-T treatment, suggesting that CAR-T exerts a positive treatment response (20).

Tumor immune microenvironment and genes mutations are closely associated with the evolution of BTKi resistance in MCL (4). In our cohort, the most common mutated genes were TP53 (50%), ATM (43%), KMT2D (21%), NOTCH2 (18%), NSD2 (18%), NOTHC1 (14%), BTK (14%), BIRC3, CXCR4, and TRAF2 (11% each) (Figure 3).

CXCR4 has been shown to be associated with MCL drug resistance by immune microenvironment. In MCL, CXCR4 mediates homing and invasion of SOX11-positive subtype, ROS-induced bortezomib resistance and CT resistance by ERK1/2, AKT and NF-κB signal (21-23). Our mutation rate of CXCR4 (14%) is higher than previous general MCL reports, suggests the role of microenvironment in BTKi resistance (24-26). By attenuated the covalent binding affinity of ibrutinib, BTK was the first identified mutation revealing the molecular mechanisms underlying acquired ibrutinib resistance (27). Occurred in 14% of patients, BTK mutations explaining part of resistance in our cohort. Besides, our data (Figure 3) validated mutations mediated BTKi resistance in various pathways, including the classical/alternative NF-κB pathway (TRAF2 11%, BIRC3 11%, CARD11 7% and TRAF3 4%), SWI-SNF (ARID1 4% and SMARCA4 4%) and cell cycle initiation/progression (CCND1 7%) (28,29).

To further explore the association between drug resistance, mutations and prognosis, a cox regression modelling (Figure 4A) was established, and six genes (ATM, CARD11, KRAS, NOTCH2, PTPRD, and TP53) were considered as hub genes (Figure 4B) and verified by connectivity (Figure 5B).

Hub genes screened in this manuscript has been proved that correlated with patients’ clinical characteristics and prognosis, and some clinical and preclinical drugs, which provide inspiration for rescue therapy of BTKI-resistant MCL. ATM mutation is one of the most common genes mutation in previous studies and our finding (Figure 2C) (24-26). Closely related in DNA damage response (DDR) pathway, the ATM inhibitors suppressed DNA double-strand breaks repair and potentiated antitumor activity in cancer cell lines, considered as a promising target. Oral administration of ATM inhibitor (M3541 and M4076) to immunodeficient mice strongly enhanced the antitumor activity, leading to complete tumor regressions in breast cancer (30). Mutations of TP53 in MCL cells abrogated the DDR pathway more severely than those of ATM, contributing to a significantly worse prognosis (31). The TP53 mutations showed a negative effect on PFS in our data (P=0.0052, Figure 4A). Another hub gene screened in our cohort was CARD11, a critical gene in primary BTKi resistance for BCR-induced NF-κB activation, which conferred resistance to ibrutinib (4). The NOTCH2 gene is closely related to MCL resistance and increased in frequency from 33% to 50% in venetoclax-resistant MCL patients (32). In our cohort, the NOTCH2 mutation reached 18%, which was higher than the previous Chinese MCL sequencing of 6.3%, suggesting an increased frequency of NOTCH2 mutation in the BTKi resistant cohort (25). In our study, 80% of NOTCH2 mutation patients showed an ECOG PS >1, and only 47.8% of wild-type NOTCH2 patients showed ECOG PS >1, which was consistent with the poor performance status of NOTCH2 (33). The PTPRD is one of the most common recurrent lesions in nodular marginal zone lymphoma (NMZL), accounting for about 20% (34). Mutation of PTPRD in NMZL leads to loss of phosphatase activity of PTPRD with deregulation of cell cycle and increased proliferation index, which needs to be further verified in MCL (35). KRAS is the most frequent mutations in RAS/mitogen-activated protein kinase (MAPK) pathway (36). Though KRAS has a high mutation frequency in other solid tumors, it is a blind spot in MCL research (37). Until now, selective inhibitors of KRAS^G12C oncoproteins in phase I/II clinical trials have caused great excitement in non-small-cell lung carcinoma, cervix and gastrointestinal tumors (37,38) Although the frequency of KRAS mutations in our cohort was only 7%, T-cell transfer therapy of KRAS and oral administration of the KRAS inhibitor sotorasib provide the possibility of salvage treatment after MCL BTKi resistance (39,40).

In summary, based on 28 MCL patients’ mutation data and clinical data, prognosis and responses to salvage therapy regimen were analyzed. Patients with BTKi had less favorable clinical features at baseline. The CAR-T therapy yielded the best response among salvage treatments. Although a retrospective study with a limited number of patients, this is the first gene analysis paper for MCL in the field of BTKi resistance in a real-world sequencing setting. We screened 6 hub genes by a gene panel of 69 genes (GP79) providing a potential predictor for MCL patients with BTKi resistance.

Acknowledgments

Funding: This work was supported by the Wu Jieping Medical Foundation (No. WJPMF320.6750 to HJ), and Peking University Third Hospital Foundation (No. BYSYDL2021006).

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-22-4314/rc

Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-4314/dss

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-4314/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by institutional ethics board of Peking University Third Hospital (No. M2022564) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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