SWI/SNF complex gene variations are associated with a higher tumor mutational burden and a better response to immune checkpoint inhibitor treatment: a pan-cancer analysis of next-generation sequencing data corresponding to 4591 cases

DOI: https://doi.org/10.21203/rs.3.rs-1724646/v1

Abstract

Background: Genes related to the SWItch/sucrose nonfermentable (SWI/SNF) chromatin remodeling complex are frequently mutated across cancers, and SWI/SNF-mutant tumors are vulnerable to synthetic lethal inhibitors. However, the landscape of SWI/SNF mutations and their associations with tumor mutational burden (TMB), microsatellite instability (MSI) status, and response to immune checkpoint inhibitors (ICIs) have not been elucidated in large real-world Chinese patient cohorts.

Methods: The mutational rate and variation types of six SWI/SNF complex genes (ARID1A, ARID1B, ARID2, SMARCA4, SMARCB1, and PBRM1) were analyzed retrospectively by integrating next-generation sequencing data of 4591 cases covering 18 cancer types. Thereafter, characteristics of SWI/SNF mutations were depicted and the TMB and MSI status and therapeutic effects of ICIs in the SWI/SNF-mutant and SWI/SNF-non-mutant groups were compared.

Results: SWI/SNF mutations were observed in 21.8% of tumors, and endometrial (54.1%), gallbladder and biliary tract (43.4%), and gastric (33.9%) cancers exhibited remarkably higher SWI/SNF mutational rates than other malignancies. Further, ARID1A was the most frequently mutated SWI/SNF gene and ARID1A D1850fs was identified as relatively crucial. Further, the TMB value, TMB-high (H), and MSI-H proportions corresponding to SWI/SNF-mutant cancers were significantly higher than those corresponding to SWI/SNF-non-mutant cancers (25.8 vs. 5.6 mutations/Mb, 41.2% vs. 8.5%, and 16.0% vs. 0.9%, respectively; all p < 0.001). Furthermore, these indices were even higher for tumors with co-mutations of SWI/SNF genes and MLL2/3. Regarding immunotherapeutic effects, patients with SWI/SNF variations showed significantly longer progression free survival (PFS) rates than their SWI/SNF-non-mutant counterparts [HR = 0.52 (0.41–0.66), p < 0.0001], and PBRM1 mutations were associated with relatively better ICI treatment outcomes than the other SWI/SNF gene mutations [HR = 0.21 (0.12–0.37), p = 0.0007]. Additionally, patients in the SWI/SNF-mutant + TMB-L [HR = 0.65 (0.46–0.92), p = 0.0322] and SWI/SNF-mutant + TMB-H [HR = 0.42 (0.31–0.55), p <0.0001] cohorts had longer PFS rates than those in the SWI/SNF-non-mutant + TMB-L cohort.

Conclusions: SWI/SNF complex genes are frequently mutated in several cancers and are closely associated with TMB-H status, MSI-H status, and superior ICI treatment response. These findings emphasize the necessity and importance of molecular-level detection and interpretation of SWI/SNF complex mutations.

Background

Precision diagnostics are prerequisites for achieving the goal of cancer precision treatment, and the concept that cancer is a genetically driven disease is widely supported by therapeutic successes directed at particular mutations or pathways. Further, the traditional paradigm of drug development in oncology has gradually shifted to a tissue-agnostic therapeutic model, wherein patients are deemed eligible for a given treatment based on the presence of specific molecular variations rather than on the cancer type (i.e., the affected tissue) [1]. One particularly representative class of tissue-agnostic drugs is tropomycin receptor kinase (TRK) inhibitors, which have been approved for cancer treatment owing to their durable responses in a wide diversity of adult and pediatric cancer patients with NTRK fusions; moreover, various other potential tissue-agnostic drugs are being developed [2]. Notably, genes of the SWItch/sucrose nonfermentable (SWI/SNF) chromatin remodeling complex are potential candidates for tissue-agnostic drug development, as these genes are commonly mutated in 20–25% of all human cancers [3]; this prevalence is notably higher than that of NTRK fusions (0.3%) [4].

The SWI/SNF complex is an ATP-consuming multi-subunit cellular machine that modulates chromatin compaction, thereby regulating DNA-related processes, such as transcription, replication, and repair [5]. There are three subfamilies of the SWI/SNF complex in mammals, namely, the BRG1/BRM-associated factor (BAF), polybromo-associated BAF (PBAF), and noncanonical BAF (ncBAF) complexes [6]. AT-rich interactive domain 1A (ARID1A), also known as BAF250a, is a tumor suppressor that is typically mutated in Epstein-Barr virus-positive and microsatellite instability-high (MSI-H) gastric cancer [7, 8], ovarian clear cell carcinoma [9], endometrial cancer [10], and non-small cell lung cancer [11]. Further, SMARCA4 (BRG1) encodes a core catalytic component of the SWI/SNF complex and its inactivation is indicative of the presence of hypercalcemic-type small cell carcinoma of the ovary [12, 13], and loss-of-function (LOF) mutations of SMARCB1 (SNF5/INI1/BAF47), which encodes another core subunit of the SWI/SNF complex, have been identified in the majority of rhabdoid tumors [14, 15]. Furthermore, the loss of both ARID1B and SMARCB1 expression has been detected in approximately one-third of undifferentiated endometrial cancers [16]. ARID2 has also been identified as one of the most frequently altered genes in non-small cell lung cancer [17], gallbladder cancer [18], and metastatic breast cancer [19], and its deficiency can hamper DNA repair processes and enhance the sensitivity of lung cancer cells to DNA-damaging agents [20]. PBRM1 encodes polybromo 1, a specific subunit of the PBAF complex, and reportedly, in clinical practice, PBRM1 LOF variations favor the therapeutic effect of immune checkpoint inhibitors (ICIs) in renal cell carcinomas [2123]. In addition to the aforementioned associations with various cancers, accumulating evidence suggests that SWI/SNF mutations can induce certain molecular perturbations in a synthetic lethal pattern [3, 24, 25], highlighting their potential as targets for drug development.

Next-generation sequencing (NGS) has been extensively applied as a cost-effective diagnostic tool in clinical practice and trials [26]. In the present study, we aimed to retrospectively integrate the NGS data corresponding to a large real-world Chinese patient cohort to comprehensively depict the landscape of SWI/SNF mutations, and explore the associations between SWI/SNF variations and tumor mutational burden (TMB), microsatellite instability (MSI) status, and therapeutic responses to ICIs across solid tumors. These findings can serve as a useful reference as well as a basis for molecular diagnostics and targeted drug development.

Methods

Study design and patient information 

NGS data and clinical information corresponding to patients who visited the Department of Molecular Diagnostics of Sun Yat-sen University Cancer Center (Guangzhou, China) for NGS analysis between September 1, 2019 and June 30, 2021 were retrospectively included. All the cancer diagnoses were also confirmed via pathological examination. Cases with genomic alterations of at least one of the six SWI/SNF complex genes (ARID1A, ARID1B, ARID2, SMARCA4, SMARCB1, and PBRM1) were classified under the SWI/SNF-mutant group. Thereafter, NGS data, including variant genes, number of variants, variation types, protein changes, TMB value, TMB status, and MSI status, as well as the clinical characteristics of the patients, including age, sex, smoking status, cancer type, TNM stage, ICI type, and the progression-free survival (PFS) during ICI treatments, were systematically collected. The use of clinical and NGS data was approved by the Ethics Committee of the Sun Yat-Sen University Cancer Center (Approval number B2020-344-01). All the patients also provided written informed consent, and the study was performed in accordance with the Declaration of Helsinki. 

NGS and data processing

The detailed experimental steps and data analysis strategies for NGS were as previously described [27–29]. For library construction, approximately 0.5 μg DNA fragments were mixed with Illumina-indexed adapters (Illumina, San Diego, CA, USA) using the KAPA Library Preparation Kit (Kapa Biosystems, Wilmington, MA, USA). A hybrid captured-based NGS assay covering approximately 1.1 megabases (Mb) of the genomic sequences of 1021 cancer-related genes (GenePlus-Beijing, China) was used for the sequencing, which was performed using a GenePlus 2000 sequencing system (Beijing, China) with 2 × 100 bp paired-end reads. Matched peripheral white blood cell DNA samples were sequenced simultaneously to filter out benign single nucleotide polymorphisms and possible germline mutations. 

The sequencing data were then analyzed by aligning the clean reads to the reference human genome (hg38) using BWA18 (version 0.7.12-r1039) [30], and small insertions and deletions (indels) and single-nucleotide variants were identified using MuTect19 (version 1.1.4) [31]. A somatic mutation was confirmed if the mutation was consistently detected in five high-quality reads (Phred score ≥ 30, mapping quality ≥ 30, and absent paired-end read bias) and had a variant allele frequency ≥ 1% [32]. Further, copy number variations (CNVs) were detected using the Copy Number Targeted Resequencing Analysis (CONTRA) software (http://contra-cnv.sourceforge.net/) [33], and mutations were then annotated to the genes using ANNOVAR20 software (http://www.openbioinformatics.org/annovar/) [34]. 

TMB and MSI evaluation

TMB was defined as the number of somatic nonsynonymous mutations/Mb of coding DNA (including small indels and single-nucleotide variants with a variant allele frequency ≥ 3%), and TMB values ≥ 9 mutations/Mb in colorectal cancer and ≥ 20 mutations/Mb in all the other cancers were classified as high TMB (TMB-H), according to the top quartile threshold corresponding to 2000 samples from the GenePlus database [35]. MSI status was analyzed using MSIsensor (version 0.5). Specifically, MSI scores were calculated as the percentage of unstable somatic microsatellite loci in predefined microsatellite regions covered by the NGS panel used; a sample was determined to have high MSI (MSI-H) if the score was > 8% [28].

Statistical analysis

The response to immunotherapy was characterized by PFS, overall response rate (ORR), and disease control rate (DCR), which were explored based on data corresponding to the subset of patients that received ICI treatment. Specifically, PFS was calculated from the start date of the ICI treatments to the date of disease progression or last follow-up. The clinical characteristics of SWI/SNF-mutant and SWI/SNF-non-mutant groups were compared by performing the chi-square test. Additionally, differences in TMB values between two groups were assessed by performing the Mann–Whitney test, while co-occurring and mutually exclusive events were detected by performing the pair-wise Fisher exact test [36]. The possible biological functions and downstream signaling pathways related to the mutated genes were explored using the Gene Ontology (GO) database [37,38]. Survival curves and estimates of the median PFS were generated using Kaplan-Meier methods and compared across different groups by performing log-rank tests. Hazard ratios (HR) and 95% confidence intervals (CI) were also reported. Statistical significance was based on two-tailed tests at p < 0.05. GraphPad Prism (version 8.4.0, GraphPad Software, San Diego, CA, USA) was used for the statistical analyses.

Results

Patient characteristics

A total of 4591 Chinese patients with 18 types of solid tumors were included in this study, and 21.8% of them carried variants in at least one of six SWI/SNF genes (ARID1A, ARID1B, ARID2, SMARCA4, SMARCB1, and/or PBRM1). Among them, 301 SWI/SNF-mutant and 700 SWI/SNF-non-mutant patients had received ICIs, including anti-PD-1, PD-L1, and CTLA4 or their combinations. Further, the SWI/SNF-non-mutant group had a higher proportion of patients with TNM stage Ⅰ than the SWI/SNF-mutant group, while age, sex, smoking status, and ICI type were not markedly different between the two groups (Table 1). 

The most common cancer type observed in this study was non-small cell lung cancer (32.3%), followed by colorectal cancer (29.6%) and ovarian and fallopian tube cancer (7.9%). The top five malignancies with the highest SWI/SNF mutation rates were endometrial cancer (54.1%), gallbladder and biliary tract cancer (43.4%), gastric cancer (33.9%), urothelial cancer (30.6%), and ovarian and fallopian tube cancer (23.9%). The SWI/SNF mutation rate corresponding to the “Other” subset, which comprised some relatively uncommon tumors, including skin squamous cancer, urachal cancer, gastrointestinal stromal tumor, glioma, adrenal tumors, and medullary thyroid cancer, among others, was 17.1%. 

Spectrum of SWI/SNF complex genomic variations

Among the six SWI/SNF genes, ARID1A and SMARCB1 were respectively, the most and least frequently mutated genes in the majority of the cancer types (ARID1A, 49.3%; SMARCA4, 27.7%; ARID1B, 21.6%; ARID2, 18.3%; PBRM1, 15.9%; SMARCB1, 6.1%; Table 2, Fig. 1a). Notably, SMARCA4 mutations were slightly more common than ARID1A mutations in non-small cell lung cancer, cervical cancer, and melanoma. Interestingly, up to 25.0% of the SWI/SNF-mutant tumors showed genetic aberrations at ≥ two SWI/SNF genes (Table 2).

All the genetic alterations were classified under the following seven types: frameshift indels, in-frame indels, nonsense mutations, missense mutations, splice site mutations, CNVs, and fusions (gene rearrangements). Frameshift indels constituted the most common variation type in ARIDIA, whereas missense mutations were more common in the other five genes (Fig. 1b). The proportions of LOF mutations, generally including frameshift indels, nonsense mutations, and splice site mutations, of the SWI/SNF complex genes were as follows: ARID1A, 69.8%; ARID2, 43.4%; PBRM1, 41.8%; ARID1B, 29.0%; SMARCA4, 23.8%; and SMARCB1, 18.5% (Fig. 1c). 

Although most variations were widely distributed across the full length of each gene, a number of frameshift indels (fs) and nonsense mutations (*), which led to the truncation of protein products, were relatively frequently detected. These included D1850fs, G276fs, R1989*, R1276*, and F2141fs of ARID1A (Fig. 2a), R1944* of ARID1B (Fig. 2b), I37fs, R53fs, and p.E71* of ARID2 (Fig. 2c), N258fs, I279fs, p.R146, R710*, and K909fs of PBRM1 (Fig. 2d), P109fs, G271fs, and R1077* of SMARCA4 (Fig. 2e), and R40*, T72fs, and R201* of SMARCB1 (Fig. 2f). In addition, several missense mutations such as A329G of ARID1B, R1192H/L/C and D1177N/Y of SMARCA4, and R366C/H and R377C/H of SMARCB1 were detected in a relatively greater number of cases (Fig. 2b, e, and f).

Co-occurrence and mutual exclusivity 

To uncover the potential pattern of SWI/SNF gene mutations, the co-occurrence and exclusivity of the mutations of the six SWI/SNF genes and the top 20 most frequently altered genes across all tumors were explored. Of the top 20 mutated genes excluding the six hub genes, it is well known that APC, KRAS, PIK3CA, EGFR, LRP1B, BRCA2, ATM, and ROS1 are mutated in several cancer types, such as non-small cell lung cancer, colorectal cancer, and endometrial cancer. In this study, we observed that ARID1A was the second most frequently mutated gene following TP53. Further, ARID1A variations were identified in a mutually exclusive pattern with variations in EGFR, TP53, ARID1B ARID2, and SMARCA4, while ARID2 was exclusively mutated with SMARCA4. However, PBRM1 tended to be co-mutated with ARID2 and SMARCB1 (Fig. 3).

Furthermore, it is worth noting that MLL2 (MLL4/KMT2D) and MLL3 (KMT2C), belonging to a family of mammalian histone H3 lysine 4 (H3K4) methyltransferases [39], were frequently co-mutated with SWI/SNF genes (Fig. 3). Reportedly, KMT2D collaborates with the SWI/SNF complex to promote cell type-specific enhancer activation [40], and cancer cells with KMT2C deficiency suffer from higher endogenous DNA damage and genomic instability [41]. The subset carrying both SWI/SNF and MLL2/3 mutations showed higher average TMB values (MLL2, 70.9 mutations/Mb; MLL3, 74.5 mutations/Mb), TMB-H ratios (MLL2, 80.5%; MLL3, 83.6%), and MSI-H ratios (MLL2, 48.6%; MLL3, 46.6%) than the whole SWI/SNF-mutant group (all p < 0.0001).

Associations of SWI/SNF mutations with TMB and MSI

Previous studies have revealed the existence of a potential linkage between the SWI/SNF chromatin remodeling complex and DNA repair, TMB, and MSI [6]. Thus, in this study, these relationships were further analyzed. Our results in this regard indicated that the average TMB value corresponding to SWI/SNF-mutant tumors was markedly higher than that corresponding to SWI/SNF-non-mutant tumors, regardless of the cancer type (25.8 vs. 5.6 mutations/Mb, p < 0.001). Further, the TMB-H and MSI-H ratios corresponding to SWI/SNF-mutant tumors were also significantly higher than that corresponding to the SWI/SNF-non-mutant tumors (TMB-H ratio: 41.2% vs. 8.5%, p < 0.001; MSI-H ratio: 16.0% vs. 0.9%, p < 0.001), even though the differences were not significant for certain malignancies, such as pancreatic cancer, prostate cancer, and urothelial cancer. SWI/SNF-mutant endometrial cancer, colorectal cancer, gastric cancer, ovarian and fallopian tube cancer, and soft tissue sarcoma exhibited both higher TMB-H and MSI-H ratios than their SWI/SNF-non-mutant counterparts (Table 3). Furthermore, the patient group with mutations at ≥ two SWI/SNF genes had significantly higher TMB values (69.0 vs. 11.3 mutations/Mb, p < 0.0001), TMB-H ratios (79.6% vs. 28.4%, p < 0.0001), and MSI-H ratios (48.0% vs. 5.3%, p < 0.0001) than those with mutations in a single SWI/SNF gene (Table 3).

ICI treatment outcomes of patients with SWI/SNF mutations

Over the past few years, pre-clinical and clinical evidence has implicated the SWI/SNF complex as a potential predictor of response to ICIs [6]. For the ICI-treated patients, we observed that the presence of SWI/SNF variants was significantly associated with a longer PFS [not reached (NR) vs. 29.9 months, HR = 0.52 (0.41–0.66); p < 0.0001], regardless of the presence of LOF or non-LOF variants [NR vs. NR, HR = 0.98 (0.57–1.67); p = 0.9305, Fig. 4a]. Specifically, patients carrying mutations at ≥ two SWI/SNF genes did not show a superior PFS than those single gene mutation carriers [NR vs. NR, HR = 0.85 (0.51–1.42), p = 0.7585; Fig. 4b]. Additionally, the exploration of the predicting significance of each SWI/SNF gene mutation showed that PBRM1 mutations were associated with a relatively better outcome of ICI treatments than the other SWI/SNF gene mutations [NR vs. NR, HR = 0.21 (0.12–0.37), p = 0.0007; Fig. 4c]. Notably, the prediction value of the SWI/SNF variants increased considerably when the TMB-H status was also considered. In particular, we observed that the SWI/SNF-mutant + TMB-L cohort showed a longer PFS than the SWI/SNF-non-mutant + TMB-L cohort [NR vs. 28.0 months, HR = 0.65 (0.46–0.92), p = 0.0322], and that the SWI/SNF-mutant + TMB-H cohort showed an even longer PFS than the SWI/SNF-non-mutant + TMB-L cohort [NR vs. 28.0 months, HR = 0.42 (0.31–0.55), p < 0.0001; Fig. 4d].

Furthermore, regardless of the cancer type, patients in the SWI/SNF-mutant group showed both higher ORR values (3.32% vs. 0.43%, p = 0.0002) and DCR values (80.07% vs. 65.57%, p <0.0001) than their counterparts in the SWI/SNF-non-mutant group. For individual cancer types, SWI/SNF-mutant colorectal cancer (86.27% vs. 67.83%, p = 0.0014), gastric cancer (83.33% vs. 55.77%, p = 0.0222), and non-small cell lung cancer (85.07% vs. 71.58%, p = 0.0324) showed significantly higher DCR values in immunotherapy than their SWI/SNF-non-mutant counterparts (Table 4).

Synthetic lethality involving SWI/SNF members

In recent years, synthetic lethality has attracted considerable attention in oncology, as it may explain the sensitivity of cancer cells to certain inhibitors and provide a new angle for drug development. The previously reported synthetic lethal pairs and effective inhibitors in SWI/SNF-deficient cancers are summarized in Table 5. These synthetic lethal interactions could be classified under four main categories: (a) Two subunits within the SWI/SNF complex, e.g., the BRD2 inhibitor, JQ1 can suppress ARID1A-deficient ovarian clear cell cancer cells because BRD2 inhibition decreases ARID1B transcription [42]. (b) One SWI/SNF subunit with its competitor. Contrary to the chromatin relaxation-inducing function of the SWI/SNF complex, polycomb repressive complex 2 (PRC2), whose enzymatic catalytic subunit is the methyltransferase, EZH2, promotes chromatin compaction via histone H3 K27 trimethylation (H3K27me3). Thus, the inhibition of EZH2 using Tazemetostat or GSK126 causes synthetic lethality in ARID1A-, SMARCA4-, SMARCB1-, PBRM1-deficient cancers [43–48]. (c) Targeting the functions of the SWI/SNF complex. The SWI/SNF chromatin remodeling complex functions in DNA double-strand break repair, transcription, replication, chromosomal segregation, and several metabolic pathways. Therefore, SWI/SNF-deficient cancers are vulnerable to the inhibition of homologous recombination repair factor, PARP1 [20,49], cell cycle regulator, cyclin-dependent kinase (CDK)4/CDK6 [25,50], DNA replication checkpoint factor, ATR [51], chromosomal segregation factor, Aurora kinase A [52], and oxidative phosphorylation [53] and glutathione [54] pathways. (d) Others. PD-1/PD-L1 inhibitors had synthetic lethal effects in ARID1A- and PBRM1-deficient cancers [21,55].

Discussion

Throughout development, chromatin architecture undergoes dynamic changes that are critical for enhancer activation and gene expression. The mammalian SWI/SNF chromatin remodeling complex plays a crucial role in cellular and tissue development, and SWI/SNF subunits have been implicated as suppressors in a variety of human cancers [56, 57]. In the present study, NGS data corresponding to 4591 solid tumors covering 18 types of malignancies were retrospectively integrated to depict the spectrum of SWI/SNF variations. The SWI/SNF genes, ARID1A, ARID1B, ARID2, SMARCA4, SMARCB1, and PBRM1 were mutated in up to 21.8% of all the cancers, and SWI/SNF mutation carriers had significantly higher TMB values as well as higher TMB-H and MSI-H proportions than their SWI/SNF-non-mutant counterparts. Further, ARID1A was the most frequently altered SWI/SNF gene and ARID1A D1850fs was identified as a relatively hot spot. Clinically, SWI/SNF mutations, especially in colorectal cancer, gastric cancer, and non-small cell lung cancer, were found to be closely associated with a better response to ICI treatments. Furthermore, given that most SWI/SNF mutations were dispersed across the full length of each gene, NGS showed potential as the most suitable strategy for detecting SWI/SNF alterations.

ARID1A/B (BAF250a/b) contains two primary domains: an N-terminal AT-rich interacting domain (ARID, 1017–1104 amino acids) and a C-terminal domain DUF3518, also annotated as BAF250_C (1975–2231 amino acids). Specifically, ARID, which is a conserved helix-turn-helix motif-containing domain, plays a role in recruiting SWI/SNF to the target gene promoters, while the function of the BAF250_C domain, which contains motifs, such as NES and LXXLL-motif that putatively mediate protein–protein interactions, is still unknown [58]. A search of the TCGA database revealed that the R1989* nonsense mutation in the DUF3518 domain is a hotspot mutation of ARID1A across cancers [59]. In this study, we observed that R1989* was captured less frequently than D1850Tfs*33 and D1850Gfs*4 (Fig. 3), possibly because the study included a very high proportion of colorectal cancer cases, and reportedly, D1850fs is an ARID1A hot spot in colorectal cancer [60]. Additionally, the DUF3518 domain of ARID1A was found to be functionally necessary to antagonize EZH2, and both the R1989* variant and the deletion of the DUF3518 domain cannot rescue EZH2-mediated IFN-γ signaling gene repression in ARID1A-knockout ovarian cancer cells [61]. D1850Tfs*33 and D1850Gfs*4, which are frameshift truncating mutations, brought about the loss of more amino acids than R1989*. Therefore, we concluded that D1850Tfs*33 and D1850Gfs*4 might exert their functions via the deletion of the DUF3518/BAF250_C domain. The previously reported V1067G mutation, which destabilizes the ARID domain, was not detected in any of the cases included in this study [62].

Somatic mutations in SMARCA4 and/or BRG1 (Brahma-related gene 1) loss represent a subset of non-small cell lung carcinomas with distinct morphological features, harboring less EGFR but more KRAS, STK11, and KEAP1 mutations [63, 64]. A study on lung cancer demonstrated that the most frequently co-mutated genes with SMARCA4 were TP53 (56%), KEAP1 (41%), STK11 (39%), KRAS (36%), and EGFR (14%) [63]. Among the 44 lung cancer cases with SMARCA4 LOF mutations in our study, the mutation rates corresponding to the above hot genes were almost consistent with the previously reported rates, i.e., 77.3, 34.1, 22.7, 20.5, and 15.9% for TP53, KEAP1, STK11, KRAS, and EGFR, respectively. In this subset, 9 of 10 patients treated with ICIs attained a stable disease state, with only one patient showing disease progression (median PFS = 17.5 months). Thus, SMARCA4 variant detection via NGS was useful not only in defining the particular pathological diagnosis but also in providing important clues for the treatment choice for SMARCA4-deficient lung cancer.

Studies have shown that LOF variants of the SWI/SNF complex can influence response to ICIs by increasing CD8 + T-cell infiltration, enhancing the cytotoxicity of T cells [65], or creating an immune-responsive milieu [21]. In the current study, the PFS of patients with SWI/SNF LOF mutations was not significantly longer than that of the SWI/SNF non-LOF mutation carriers, suggesting that at least part of the SWI/SNF non-LOF mutations, the most of which are missense mutations, that occur at pivotal sites might be functional. However, further studies are required to provide additional evidence for more accurate bioinformatics interpretation. The patients carrying mutations of two or more SWI/SNF genes did not show better responses to ICI therapy than those with single gene mutations, indicating that the increase in the number of SWI/SNF complex mutated genes may not directly cause an accumulative effect. The immunotherapeutic effect-predicting biomarker section of several commercially available NGS panels includes positively-related gene variations such as: TMB-H [66], MSI-H [67], inactivating mutations of mismatch repair-related genes (MLH1, MSH2, MSH6, PMS2) [68], homologous recombination repair-related genes (ATM, ATR, BRCA1/2, CHEK1, FANCA, PALB2, etc.) [69], and POLE and POLD1 mutations [70]; as well as negatively-related gene variations, including inactivating mutations of PTEN [71], B2M [72], JAK1/2 [73], DNMT3A [74], STK11 [75], copy number gain of MDM2/4 [74], and CCND1 [76]. Given that SWI/SNF-mutant + TMB-L and SWI/SNF-mutant + TMB-H patients both had longer PFS than those SWI/SNF-non-mutant + TMB-L patients, the SWI/SNF variations could be added to the list of positively-predicting biomarkers for immunotherapeutic effects.

The high mutation rate of the SWI/SNF complex across all cancers highlights its potential as a target for tissue-agnostic drugs. Synthetic lethality occurs when a combination of deficiencies in two genes leads to cell death, whereas deficiency in only one gene results in a viable phenotype [44]. Notably, PARP inhibitors targeting BRCA1/2-mutant tumors represent a notable example of such synthetic lethality [77]. A series of inhibitors, ranging from chemical probes to FDA-approved drugs, that target the synthetic lethal partners of SWI/SNF members have exhibited clear therapeutic effects in several cancers [2021, 25, 4254, 78100]. Furthermore, an overview of the possible biological functions and downstream signaling pathways s using the GO database suggested that SWI/SNF genes and covariant genes were enriched in the PI3K signaling pathway (Fig. S1). Reportedly, ARID1A-deficient gastric cancer cells are vulnerable to AKT inhibitor, GSK690693, and the addition of GSK690693 possibly potentiates the suppressive function of conventional chemotherapy [100]. Accordingly, the therapeutic effect of AKT inhibitors in cancers with SWI/SNF deficiencies is promising and should be explored further.

By integrating NGS data from a large real-world patient cohort, this study offers a detailed overview of the genomic alterations of SWI/SNF complex genes in various cancer types, and reveals the significant associations between SWI/SNF variants and TMB, MSI, and response to ICI treatment; this could be of great significance in molecular screening and translational research. Admittedly, the molecular functions and relevant signaling mechanisms involving the SWI/SNF variations were not investigated experimentally, thus warranting further exploration.

Conclusions

SWI/SNF complex genes are frequently mutated in a wide range of cancers and are closely associated with TMB-H, MSI-H, and superior responses to ICIs. Therefore, the detection and interpretation of SWI/SNF complex genomic alterations using NGS could provide new predictors of immunotherapeutic effects as well as useful data for translational research.

Abbreviations

DCR, disease control rate; ICI, immune checkpoint inhibitor; LOF, loss-of-function; MSI-H, high microsatellite instability; Mb, megabase; NGS, next-generation sequencing; ORR, overall response rate; PFS, progression-free survival; SWI/SNF, SWItch/sucrose nonfermentable; TMB-H, high tumor mutational burden.

Declarations

Ethics approval and consent to participate

The use of clinical and NGS data was approved by the Ethics Committee of the Sun Yat-Sen University Cancer Center (Approval number B2020-344-01). All patients provided signed informed consent, and the study was performed in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Availability of data and materials

The datasets supporting the conclusions of this article are available in the Research Data Deposit repository (No. RDDA2021338857, http://www.researchdata.org.cn/), and are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was funded by the National Natural Science Foundation of China (grant number 82002561), Guangdong Basic and Applied Basic Research Foundation (grant numbers 2020A1515010098 and 2020A1515010314), Natural Science Foundation of Guangdong Province (grant number 2017A030310192), and Fundamental Research Funds for the Central Universities (grant number 17ykpy84).

Authors’ contributions

WF and HCY designed the study; LY and ZWJ collected the clinical information; LY and YXH analyzed the data; XYX and MJJ performed the experiments; and LY and WF wrote the paper. All authors read and approved the final version of the manuscript.

Acknowledgements

Not applicable.

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Tables

Table 1 The clinical information of the study population grouped by whether carrying SWI/SNF variations.


Total

Treated by ICIs

Characteristics

SWI/SNF-mutant

SWI/SNF-non-mutant

p value

SWI/SNF-mutant

SWI/SNF-non-mutant

p value

No. of patients

1001

3590


301

700


Age at the diagnosis, median (range, years)

56 (14–90)

56 (1–89)


57 (14–87)

55 (9–85)


≥ 55

572

1961

0.1611

157

361

0.8904

< 55

429

1629


144

339


Sex







Male

517

1748

0.1002

187

394

0.0938

Female

484

1842


114

306


TNM stage







140

629

< 0.0001

12

30

0.1377

202

502


64

109


269

951


106

245


364

1396


119

316


Unknow

26

112


0

0


Smoking history







Current/Former

178

668

0.5542

63

168

0.3263

Never

797

2810


238

532


Unknow

26

112


0

0


ICI types

 

 

 

 

 

 

Anti PD-1

 

 

 

284

658

0.2511

Anti PD-L1

 

 

 

11

33

 

Anti PD-1 + CTLA4

 

 

 

6

6

 

Anti PD-1 + PD-L1

 

 

 

0

3

 

CTLA4: cytotoxic T lymphocyte-associated protein 4; ICIs: immune checkpoint inhibitors; PD-1: programmed death-1;PD-L1: programmed death-ligand 1; SWI/SNF: SWItch/sucrose nonfermentable.

Table 2 Mutation rate of SWI/SNF complex genes in different cancer types.

Cancer type

n

n/Total (%)

SWI/SNF mutation rate (%)

ARID1A

(%)

ARID1B

(%)

ARID2

(%)

PBRM1

(%)

SMARCA4

(%)

SMARCB1

(%)

≥ 2 genes

(%)

Breast cancer

86

1.9

16.3

9.3

4.7

3.5

0

1.2

1.2

2.3

Cervical cancer

118

2.6

12.7

1.7

2.5

2.5

2.5

4.2

1.7

0

colorectal cancer

1358

29.6

23.0

12.1

6.8

5.0

5.4

6.8

1.8

5.0

Endometrial cancer

122

2.7

54.1

48.4

18.0

10.7

10.7

13.1

4.9

7.4

Esophagus cancer

37

0.8

21.6

8.1

5.4

5.4

0

0

2.7

5.4

Gallbladder and Biliary tract cancer

53

1.2

43.4

26.4

13.2

11.3

3.8

9.4

1.9

5.7

Gastric cancer

168

3.7

33.9

25.0

4.8

2.4

2.4

7.7

1.8

4.8

Kidney cancer

34

0.7

20.6

14.7

0

2.9

2.9

2.9

0

2.9

Liver cancer

84

1.8

17.9

8.3

2.4

2.4

7.1

3.6

1.2

2.4

Melanoma

119

2.6

12.6

2.5

3.4

3.4

3.4

3.4

0

0

Non-small cell lung cancer

1485

32.3

18.9

5.8

3.4

3.4

2.2

6.2

0.8

0.9

Other

242

5.3

18.6

7.0

1.2

4.1

4.5

6.2

1.7

1.7

Ovarian and Fallopian tube cancer

364

7.9

23.9

14.8

2.5

1.4

2.2

5.5

0.5

0.3

Pancreatic cancer

93

2.0

17.2

9.7

0

3.2

1.1

2.2

1.1

0

Prostatic cancer

19

0.4

15.8

10.5

5.3

5.3

0

5.3

5.3

0

Small cell lung cancer

20

0.4

15.0

0

0

10.0

0

5.0

0

0

Soft tissue sarcoma

127

2.8

11.0

3.9

1.6

3.9

0.8

1.6

0.8

0.8

Urothelial cancer

62

1.4

30.6

21.0

8.1

1.6

0

6.5

0

3.2

Total

4591

100.0

21.8

10.7

4.7

4.0

3.5

6.0

1.3

2.5

n: number; SWI/SNF: SWItch/sucrose nonfermentable.

Table 3 The associations of SWI/SNF mutations with TMB and MSI status in different malignancies.

Cancer type

Average TMB value (mutations / Mb)

TMB-H proportion

MSI-H proportion

SWI/SNF-non-mutant

SWI/SNF-mutant

p

SWI/SNF-non-mutant

SWI/SNF-mutant

p

SWI/SNF-non-mutant

SWI/SNF-mutant

p

Breast cancer

4.9

9.3

0.001

12.5%

42.9%

0.006

0.0%

0.0%

NA

Cervical cancer

4.7

9.7

0.001

10.7%

33.3%

0.017

1.9%

0.0%

0.586

Colorectal cancer

7.2

44.1

< 0.0001

1.6%

39.6%

< 0.0001

1.2%

33.2%

< 0.0001

Endometrial cancer

6.2

61.6

< 0.0001

10.7%

74.2%

< 0.0001

8.9%

48.5%

< 0.0001

Esophagus cancer

6.5

13.9

< 0.0001

24.1%

75.0%

0.008

0.0%

0.0%

NA

Gallbladder and Biliary tract cancer

6.1

17.6

0.019

16.7%

43.5%

0.032

3.3%

17.4%

0.083

Gastric cancer

4.2

15.3

0.002

6.3%

26.3%

< 0.0001

0.0%

12.3%

< 0.0001

Kidney cancer

3.5

9.1

0.004

3.7%

28.6%

0.039

0.0%

0.0%

NA

Liver cancer

4.6

12.4

0.003

7.2%

20.0%

0.127

0.0%

6.7%

0.031

Melanoma

3.3

6.8

< 0.0001

1.9%

20.0%

0.001

0.0%

0.0%

NA

Non-small cell lung cancer

5.6

12.7

< 0.0001

15.5%

48.0%

< 0.0001

0.2%

1.1%

0.051

Other

4.0

16.6

< 0.0001

8.1%

42.2%

< 0.0001

1.0%

2.2%

0.509

Ovarian and Fallopian tube cancer

3.7

9.1

< 0.0001

2.2%

14.9%

< 0.0001

1.1%

4.6%

0.037

Pancreatic cancer

4.0

6.7

0.006

2.6%

12.5%

0.076

1.3%

0.0%

0.647

Prostatic cancer

6.9

46.1

0.010

12.5%

33.3%

0.364

6.3%

33.3%

0.161

Small cell lung cancer

9.2

22.1

0.016

41.2%

100.0%

0.060

0.0%

0.0%

NA

Soft tissue sarcoma

2.5

9.4

< 0.0001

2.7%

42.9%

< 0.0001

2.7%

14.3%

0.035

Urothelial cancer

7.3

15.1

0.005

27.9%

52.6%

0.061

0.0%

5.3%

0.129

Total

5.6

25.8

< 0.0001

8.5%

41.2%

< 0.0001

0.9%

16.0%

< 0.0001

Mb: megabase; MSI-H: high microsatellite instability; SWI/SNF: SWItch/sucrose nonfermentable; TMB-H: high tumor mutation burden.

Table 4 The overall response rate and disease control rate in the SWI/SNF-mutant and SWI/SNF-non-mutant groups.

Cancer type

SWI/SNF-mutant

SWI/SNF-non-mutant

for ORR

p for DCR

n

ORR (%)

DCR (%)

n

ORR (%)

DCR (%)

Total

301

3.32 

80.07 

700

0.43 

65.57 

0.0002

< 0.0001

Breast cancer

4

0

25.00

15

0

40.00

0.9999

0.9999

Cervical cancer

6

0

50.00

25

4.00

64.00

0.9999

0.6526

colorectal cancer

102

4.90

86.27

115

0.87

67.83

0.1016

0.0014

Endometrial cancer

17

0

64.71

16

0

75.00

0.9999

0.7080

Esophagus cancer

6

0

50.00

17

0

47.06

0.9999

0.9999

Gallbladder and Biliary tract cancer

6

0

50.00

11

0

90.91

0.9999

0.0987

Gastric cancer

24

0

83.33

52

0

55.77

0.9999

0.0222

kidney cancer

5

0

100.00

7

0

71.43

0.9999

0.4697

Liver cancer

7

0

100.00

27

0

74.07

0.9999

0.2997

Melanoma

8

12.50

62.50

74

0

50.00

0.0976

0.7130

Non-small cell lung cancer

67

2.99

85.07

190

0.53

71.58

0.1674

0.0324

Other

16

12.50

81.25

62

0

69.35

0.0400

0.5345

Ovarian and Fallopian tube cancer

6

0

66.67

12

0

50.00

0.9999

0.6380

Pancreatic cancer

4

0

100.00

13

0

69.23

0.9999

0.5193

Prostatic cancer

1

0

100.00

5

0

60.00

0.9999

0.9999

Small cell lung cancer

1

0

100.00

12

0

66.67

0.9999

0.9999

Soft tissue sarcoma

4

0

50.00

19

0

52.63

0.9999

0.9999

Urothelial cancer

17

0

76.47

28

0

82.14

0.9999

0.7109

DCR: disease control rate; n: number; ORR: overall response rate; SWI/SNF: SWItch/sucrose nonfermentable.

 Table 5 Synthetic lethal interactive pairs and chemical inhibitors involving SWI/SNF members.

SWI/SNF member

synthetic lethal partner

Inhibitors

Disease

Reference

ARID1A

ATR

VE-821, VX-970 (M6620), AZD6783

Breast cancer, ovarian cancer, colorectal cancer

51


AURKA

TCS-7010

Colorectal cancer

52

 

BIRC5/Survivin

YM-155

Gastric cancer

87


BRD2

JQ1/ iBET762

Ovarian cancer

42

 

CARM1, p53

TP064+Nutlin-3

Gastric cancer

88


CCNE1

NA

Ovarian cancer

83


EZH2

GSK126, GSK343, EPZ005687

Ovarian cancer, gastric cancer

43, 44

 

GSH

buthionine sulfoximine, APR-246

Ovarian cancer, gastric cancer

54

 

HDAC6

ACY1215 (rocilinostat), SAHA

Ovarian cancer

89, 90

 

mTOR

RAD001

Gastric cancer

91

 

PARP1

olaparib, rucaparib, and veliparib

Breast cancer, colorectal cancer

49

 

PD-1/PD-L1

Nivolumab

Endometrial cancer

92

 

PI3K/AKT

GSK690693, BKM120

Gastric cancer

93, 100

 

YES1/SRC

Dasatinib (BMS-354825)

Ovarian cancer

81

SMARCA4

AURKA

VX-680

Lung cancer

94


CDK4/CDK6

Palbociclib, Abemaciclib, Ribociclib

Lung cancer, ovarian cancer

25, 50

 

EZH2

GSK126, Tazemetostat, CPI-169

Ovarian cancer

45, 78–80 

 

KDM6A

GSK-J4

Lung cancer, ovarian cancer

85

 

OXPHOS

IACS-010759

Lung cancer

53

 

PTEN

PFI-3

Prostate cancer

95

PBRM1

EZH2

L501-1669

Renal cell cancer

48

 

PD-1/PD-L1

Nivolumab

Renal cell cancer

21

ARID2

PARP1

veliparib

Lung cancer

20

SMARCB1

AURKA

Alisertib (MLN8273)

Rhabdoid tumors

96

 

CCND1

Flavopiridol

Rhabdoid tumors

97

 

EZH2

Tazemetostat

Pooly differentiated chordoma

46, 47

 

GLI1

arsenic trioxide

Rhabdoid tumors

98

 

MDM2/MDM4

Idasanutlin, ATSP-7041

Rhabdoid tumors

86

 

UBE2C

Bortezomib/MLN2238

Renal medullary carcinoma

99

OXPHOS: oxidative phosphorylation; PD-1: programmed death-1; PD-L1: programmed death-ligand 1; SWI/SNF: SWItch/sucrose nonfermentable.