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Differences in actionable genomic alterations between brain metastases and non‑brain metastases in patients with non‑small cell lung cancer
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- Published online on: July 5, 2022 https://doi.org/10.3892/ijo.2022.5390
- Article Number: 100
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Copyright: © Nian et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Brain metastases (BM) can be detected in 10-20% of patients with non-small cell lung cancer (NSCLC) at the initial diagnosis and the percentage extends up to 50% during disease progression (1-3). The development of BM leads to an evident incidence of neurocognitive and functional deficits in patients with NSCLC, which adversely affects the quality of life (4). BM is closely related to a poor prognosis, and the median overall survival (OS) of patients with NSCLC with BM is limited only up to 4-6 months (5,6). However, the molecular mechanisms underlying the high morbidity, poor prognosis, and high mortality rate of patients with NSCLC with BM remain largely unknown.
In terms of histological subtypes, BM occurs most frequently in patients with lung adenocarcinoma (54 and 58.6%), as compared to non-adenocarcinoma subtypes (large cell carcinoma, 17.7%; and squamous cell carcinoma, 9.9%) (7,8). Moreover, patients with NSCLC harboring epidermal growth factor receptor (EGFR) mutations or anaplastic lymphoma kinase (ALK) rearrangements are at a greater risk of developing BM, in the aspect of molecular subtypes (9,10). Pathogenic driver mutations have been reported to be vital for therapeutic decision-making, since targeted therapies significantly improve survival outcomes for the majority of patients (11). Specifically, a subgroup of patients with BM responds well to EGFR tyrosine kinase inhibitors (EGFR-TKIs) (12). However, actionable genomic alterations are not currently available for recurrent or progressive diseases, due to the unsafety of the invasive biopsy procedure and/or the inaccessibility of tumor sites, particularly for patients with BM. Currently, knowledge about pathogenic driver mutations is most frequently obtained from primary or metastatic tumor tissue at easily accessible sites; however, a divergent mutation landscape from these sites has been observed (13,14). Thus, safe, convenient and replaceable approaches are essential to acquiring genomic characterization for patients with NSCLC with BM.
Liquid biopsy is regarded as a promising minimally-invasive approach to obtaining tumor cells, as opposed to invasive tissue biopsies in molecular diagnostics in recent years (15). Cell-free deoxyribonucleic acid (cfDNA), which has been previously detected in blood and body fluids, is released from cells when they undergo necrosis, apoptosis and lysis (16,17). Circulating tumor DNA (ctDNA), the tumor-derived fraction of cfDNA, possesses the potential ability to represent the whole tumor burden across different metastatic sites, partly circumventing tumor spatial heterogeneity issues (18). Since the reliability of a single-tumor tissue biopsy for the obtainment of the whole mutation landscape, the limitations of personalized medicine approaches are a dilemma. Multiregional biopsy analysis has already been proposed by Gerlinger et al (19), in order to profile a more complete mutation landscape and predict the therapeutic outcome. Additionally, several studies have suggested that a dynamic sampling of somatic mutations from ctDNA analysis may represent a larger clonal hierarchy (20-23), rendering the realization of therapeutic decisions and tracking therapeutic outcomes safer and more convenient, even across different metastatic sites.
In the present study, targeted next-generation sequencing (NGS) of tumor tissues and peripheral blood samples from patients with NSCLC was conducted, using a hybridization capture-based panel consisting of 95 known cancer genes. The present study aimed to identify the characteristic of genomic alternations in patients with advanced NSCLC with or without BM. The novelty of the present study was, to the best of our knowledge, systematical comparisons of the differences in somatic mutations, somatic interactions, key signaling pathways, functional biological information and clinical actionability for the therapy of targeted agents between the BM group and the non-BM group. The findings presented herein may possibly aid towards the elucidation of the underlying molecular mechanisms underlying the initiation or progression of BM in NSCLC. Furthermore, it was also investigated whether ctDNA analysis may serve as a more credible alternative for genomic profiling in patients with advanced NSCLC with BM than in patients without BM.
Patients and methods
Patients and sample collection
In total, 120 patients with NSCLC from the Department of Pathology at the Affiliated 3201 Hospital of Xi'an Jiaotong University were recruited between May, 2017 and December, 2020. The pathological diagnosis was verified by three pulmonary pathologists, based on the 4th edition of the World Health Organization Classification of Lung Tumors (24). Tumors with histological components other than NSCLC were excluded. In total, one fresh tumor tissue, 68 formalin-fixed and paraffin-embedded (FFPE) tumor specimens and 51 peripheral blood specimens were collected for next-generation sequencing (NGS) analysis. Written informed consent was acquired from all participating individuals. The study was performed according to the Code of Ethics of the World Medical Association (Declaration of Helsinki) (25), and it was approved by the Medical Ethics Committee of Affiliated 3201 Hospital of Xi'an Jiaotong University. The corresponding Institutional Review Board (IRB) number was No.008(2017).
DNA extraction and quality control
Genomic DNA (gDNA) from fresh tumor tissues, FFPE tumor specimens and plasma was extracted using the QIAamp DNA Mini kit (Qiagen GmbH), GeneRead DNA FFPE kit (Qiagen GmbH) and the HiPure Circulating DNA Midi kit C (Magen Biotechnology Co., Ltd.), respectively. The Qubit® 3.0 Fluorometer (Invitrogen; Thermo Fisher Scientific, Inc.) and NanoDrop ND-1000 (Thermo Scientific, Inc.) were used for the quantity and purity evaluation of the gDNA. The fragmentation status was evaluated using the Agilent 2100 Bioanalyzer instrument (Agilent Technologies, Inc.) with the High Sensitivity DNA Reagent (Agilent Technologies, Inc.) to produce a DNA integrity number. Additionally, the step of quality control (QC) was also performed to evaluate FFPE DNA integrity by multiplex Polymerase Chain Reaction (PCR). In brief, gDNA (30 ng) was amplified using three different primers of the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene, sized at 200-400 base pairs (Table SI). The Agilent 2100 Bioanalyzer instrument (Agilent Technologies, Inc.) was used to determine the concentration of multiplex PCR products. The fragmentation of gDNA from FFPE was estimated by the average yield ratio (AYR) value, which was calculated by dividing the yield ratio of reference DNA (Promega Corporation) by each amplicon's yield ratio.
Library preparation and hybridization capture
In total, 300 ng gDNA from each sample was mechanically fragmented using an E220 focused ultrasonicator Covaris (Covaris, LLC.). The targeted size of the DNA fragment was between 150 and 200 bp. Subsequently, 10-100 ng DNA was used for library construction with the KAPA library preparation kit (Kapa Biosystems Inc.; Roche Diagnostics), which was constructed with end-repair, A-tailing and adapter ligation without additional fragmentation, according to the manufacturer's instructions. Finally, the NGS libraries were captured using the xGen Lockdown Probe pool (Integrated DNA Technologies, Inc.), and the captured DNA fragments were amplified for 13 cycles of PCR, using 1X KAPA HiFi Hot Start Ready Mix (Kapa Biosystems Inc.; Roche Diagnostics). The thermal cycling conditions used were as follows: Initial denaturation for 45 sec at 98°C followed by 13 cycles of 98°C for 15 sec, 60°C for 30 sec, and 72°C for 30 sec. The final extension was performed at 72°C for 60 sec.
Illumina sequencing
Following QC and quantification using the Agilent 2100 Bioanalyzer (Agilent Technologies, Inc.) and Qubit® 3.0 Fluorometer (Invitrogen; Thermo Fisher Scientific, Inc.), the NGS libraries were sequenced on an Illumina NextSeq CN500 platform with a medium flux chip (NextSeq CN500 Mid Output v2 kit; Illumina Inc.).
Bioinformatics analysis
Clean data were obtained following filtering low-quality reads, which includes reads with adapter sequences and reads with length <36 bp. All filtered reads were aligned to the human genome (University of California Santa Cruz ID: hg19) using the Burrows-Wheeler-Alignment Tool (BWA v.0.7.12; Wellcome Trust Sanger Institute) (26). Subsequently, the Picard and Genome Analysis Toolkit (GATK v.3.2; Broad Institute) method was used for duplicate removal, local realignment, and base quality score recalibration, and it was also adopted for generating quality statistics, including mapped reads, mean mapping quality, and mean coverage. Finally, the VarDict (v.1.6.0; GitHub, Inc.) was adopted for the identification of single nucleotide variation (SNV) and Insertion/Deletion (InDel) (27).
The ANNOVAR software tool (v. 20210202; https://annovar.openbioinformatics.org/en/latest/) was used for annotating somatic variants (28). The candidates of somatic variants were identified by the following filter conditions: i) Removal of the variants coverage depth (VDP) <10; ii) removal of the variant sites with mutant allele frequency (MAF) >0.001 in the 1,000 Genomes databases (1,000 Genomes Project Consortium; https://www.internationalgenome.org/) and Exome Aggregation Consortium (ExAC) (https://ncbiinsights.ncbi.nlm.nih.gov/tag/exac/); iii) retainment of variant sites with MAF≥0.001 and <0.1 in the 1,000 Genomes databases with COSMIC evidence (http://cancer.sanger.ac.uk/cosmic); iv) retainment of variations in the exon or splicing region (10 bp upstream and downstream of splicing sites); v) remove synonymous mutations; vi) remove unknown variant classification; and vii) removal of the functional benign variant sites, predicted by PolyPhen-2 (Polymorphism Phenotyping v2; http://genetics.bwh.harvard.edu/pph2/) (29) or MutationTaster (MutationTaster2020; https://www.muta-tiontaster.org/) (30). Additionally, the association between the identified somatic mutations and their clinical significance was established by OncoKB Precision Oncology Database (http://oncokb.org/). Kyoto Encyclopedia Of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis were used to explore the biological consequences of mutant genes by using the cluster Profiler package (http://bioconductor.org/packages/release/bioc/html/clusterProfiler.Html) in R software (R 4.0.3, R Core Team; https://www.RProject.org) (31).
Statistical analysis
The maftools package in R software (R 4.0.3, R Core Team;https://www.R-Project.org) was used to create somatic mutation landscapes, co-Barplots, co-Oncoplots, lollipop plots, and spectrums of co-occurring and mutually exclusive genomic alterations. Fisher's exact test was used to evaluate the statistical differences in categorical variables between the BM group and the non-BM group using R software (R 4.0.3, R Core Team; https://www.RProject.org). Statistical analyses were two-sided, and P<0.05 was considered to indicate a statistically significant difference.
Results
Patient characteristics
In the present retrospective study, the numbers of patients with different stages of NSCLC were as follows: I, II, III and IV were as follows: i) Stage I, 6 patients (aged 61-78 years; median, 76 years); ii) stage II, 3 patients (aged 57-71 years; median, 58 years); iii) stage III, 21 patients (aged 43-77 years; median, 61 years); and iv) stage IV, 90 patients (aged 30-84 years; median, 63 years). Patients with stage IV NSCLC (n=90) were divided into the BM group (n=26, aged 43-84 years) and non-BM group (n=64, aged 30-84 years) by clinically detectable metastatic lesions. There were no significant differences in the demographic and clinical characteristics between the BM and the non-BM group (Table I).
Landscape of somatic mutations
To delineate the somatic mutation landscape, the somatic mutations from the tumor tissue DNA of 69 patients were first analyzed by applying an NGS panel of 95 known cancer genes (Table SII). The present study mainly focused on protein-altering variants, based on the annotation of somatic SNVs and InDels. A total of 157 somatic variants of 32 mutated genes were detected in 62 out of 69 (89.86%) tumor tissues (Fig. 1A and Table SIII). To explore the feasibility of genomic profiling using peripheral blood samples, the same NGS panel was used to detect 51 plasma-derived ctDNA. In total, 77 somatic variants of 30 mutated genes were identified in 38 out of 51 (74.51%) peripheral blood samples, which was lower than that from tissue DNA (Fig. 1B and Table SIII). Furthermore, the percentages of tissue DNA-specific mutated genes and ctDNA-specific mutated genes were 28.57% (12/42) and 23.81% (10/42), respectively, whereas the consistency rate of mutated genes was 47.62% (20/42) in NSCLC (Fig. 1C and Table SIII).
Differences in somatic mutations between the BM and non-BM group
Patients with NSCLC with BM have been shown to be significantly associated with an increased mortality rate (4). However, the underlying molecular mechanisms of the initiation and progression of BM have not yet been fully elucidated in NSCLC. In the present study, to explore this issue, the genomic characterizations of patients both from the BM and the non-BM group were first depicted using an NGS panel. In total, 47 somatic variants of 17 mutated genes were identified in 23 out of 26 (88.46%) patients with BM (Figs. 2 and S1A, and Table SIV), and 127 somatic variants of 30 mutated genes were observed in 55 of 64 (85.94%) non-BM patients (Figs. 2 and S1C, and Table SIV). To further discover the molecular differences between the BM and the non-BM group, a Venn diagram was plotted (Fig. S1B). Among the total number of 35 mutant genes, ALK, cytidine deaminase (CDA), SMAD family member 4 (SMAD4), superoxide dismutase 2 (SOD2), and Von Hippel-Lindau tumor suppressor (VHL) were uniquely present in the BM group, whereas 18 mutant genes [ATM serine/threonine kinase (ATM), cyclin-dependent kinase inhibitor 2A (CDKN2A), Erb-B2 receptor tyrosine kinase 4 (ERBB4), HRAS, PIK3CA, etc.] only emerged in the non-BM group. In total, 12 mutant genes [APC regulator of WNT signaling pathway (APC), DNA-directed RNA polymerase I subunit RPA34 (CD3EAP), catenin beta-1 (CTNNB1), cytochrome P450 2D6 (CYP2D6), dihydropyrimidine dehydrogenase (DPYD), EGFR, Kirsten rat sarcoma (KRAS), NRAS, TP53, telomerase reverse transcriptase (TERT), thiopurine S-methyltransferase (TPMT) and RB transcriptional co-repressor 1 (RB1)] were concurrently present in both groups.
Moreover, the frequencies and sites of the above concurrently mutated genes were also investigated. Of note, apart from CD3EAP, DPYD and TPMT, the frequencies and/or sites of the concurrently mutated genes were different (Fig. 3 and Table SV). In relation to the KRAS gene, marked differences were observed in the mutation sites and frequencies (p.L19F vs. p.G12D, p.G12V, p.Q61H, and p.A146T, 3.85 vs. 12.5%) of these two groups (Fig. 3A and Table SV). Although about half of the patients harbored TP53 mutations in the two groups, the types and sites of amino acid alterations were different (Fig. 3C and Table SV). The frameshift insertion of TP53 was exclusively identified in the BM group, and in-frame deletion and nonsense mutation were only detected in the non-BM group.
Differences in somatic interactions between the BM and non-BM group
In NSCLC, patients presenting with EGFR mutations have a much higher incidence of BM compared to those without EGFR mutation. EGFR mutations and KRAS mutations are usually mutually exclusive, and KRAS mutations could confer resistance to EGFR-TKIs when they co-exist (32). In the present study, the somatic interactions in the BM group were different from the interactions of the non-BM group. EGFR and KRAS were a mutually exclusive set of genes in the non-BM group (P=0.0183) (Fig. 4B and Table SV), whereas limited mutually exclusive interactions were observed in the BM group (P=0.999) (Fig. 4A and Table SVI). Additionally, obvious differences were also detected in the co-occurring set of genes between the two groups. As demonstrated in Fig. 4, VHL and ALK (P=0.0435), SOD2 and KRAS (P=0.0435), TERT and KRAS (P=0.0435), TERT and SOD2 (P=0.0435) were co-occurring pair of genes in the BM group (Fig. 4A and Table SV), while FGFR3 and CD3EAP (P=0.0141), isocitrate dehydrogenase [NADP(+)] 1 (IDH1) and BRAF (P=0.0182), neurotrophic receptor tyrosine kinase 1 (NTRK1) and KRAS (P=0.0189), PIK3CA and KRAS (P=0.0340), CDKN2A and BRAF (P=0.0364), and interactions between other six co-occurring pair of genes were detected in the BM group (Fig. 4B and Table SVI).
Key signaling pathways and biological functions analysis of somatic mutations in the BM and the non-BM group
In order to acquire a more incisive understanding of the biological consequences in these two groups, KEGG and GO enrichment analyses were performed. The top 10 KEGG pathways enriched by mutated genes were depicted according to gene count and P-value, and most signaling pathways were cancer-related (Fig. 5A and B, and Table II). Since patients with BM respond well to EGFR-TKIs, the P-value of EGFR-TKI resistance was markedly higher in the BM group, than in the non-BM group (4.50E-04 vs. 7.84E-18) (Tables II and SVII). In GO enrichment analysis, functional categories were most involved in RNA polymerase II transcription regulator complex in the BM group (Fig. 5D and Table III) and promyelocytic leukemia nuclear body in the non-BM group (Fig. 5E and Table III). Furthermore, five mutated genes uniquely present in the BM group were prevailingly distributed in transcription regulator complex, RNA polymerase complex, bicellular tight junction, tight junction, apical junction complex, and so forth (Tables III and SVII). Collectively, a high consistency of altered signaling pathways between these two groups according to KEGG analysis was observed (Fig. 5C), whereas the percentage was decreased in GO analysis-related altered functional terms [Fig. 5F; KEGG analysis, 77.27% (85/110) vs. GO analysis, 30.00% (15/50)].
Clinical actionability for the therapy of targeted agents
In order to evaluate the clinical utility of anticipative molecular profiling, all mutations were divided into different levels, according to the evidence of clinical actionability in OncoKB (Fig. 6A). As standard therapeutic biomarkers, a cluster of gene mutations was approved by the FDA. In the present cohort, 47 out of 120 (39.17%) patients possessed at least one actionable alteration. Among the patients with stage IV disease, level_1 accounted for 34.44% (31/90), including missense mutations of BRAF, EGFR, IDH1, PDGFRA and PIK3CA, a nonsense mutation of ATM and an in-frame insertion of EGFR; level_2 accounted for 4.44% (4/90), including missense mutations of NRAS and PIK3CA and an in-frame deletion of PIK3CA; level_3 accounted for 1.11% (1/90), including an in-frame insertion of EGFR; level_4 accounted for 1.11% (1/90), including missense mutations of CDKN2A (Fig. 6B, E and H, and Table SVIII). Additionally, it was also observed that non-BM patients had a slightly higher percentage of actionable alterations than patients with BM, namely 45.31% (29/64) vs. 30.77% (8/26) (Fig. 6D, F, G, I, J, and Table SVIII).
ctDNA analysis has a higher consistency of genomic profiling in the non-BM group as compared with that in the BM group
To compare the feasibility of genomic profiling of advanced patients with or without BM using plasma-derived ctDNA, somatic mutations from 14 tumor tissues and 12 peripheral blood samples were analyzed in the BM group by the above NGS panel. A total of 32 somatic variants of 12 mutated genes were identified in 13 out of 14 (92.86%) tumor tissue DNA samples (Fig. 7A and Table SIX), and 15 somatic variants of 10 mutant genes were detected in 10 of 12 (83.33%) plasma-derived ctDNA (Fig. 7B and Table SIX). Meanwhile, eighty-three somatic variants of 22 mutated genes in 36 of 39 (92.31%) tumor tissue DNA were also detected (Fig. 7C and Table SX), as well as 44 somatic variants of 21 mutated genes in 19 out of 25 (76.00%) plasma-derived ctDNA (Fig. 7D and Table SX) in the non-BM group. In summary, 43.33% (13/30) of the mutated genes were detected by both tumor tissue DNA analysis and ctDNA analysis in the non-BM group (Fig. 7E), whereas the percentage was only 29.41% (5/17) in the BM group (Fig. 7E).
Discussion
Exploring the genomic alterations is crucial for clinical management in NSCLC patients with BM. Although dynamic mutation landscapes have been reported, systematic comparisons of genomic characteristics between the BM and the non-BM groups remain limited. In the present study, 174 somatic mutations of 35 mutated genes were identified in 90 patients with stage IV NSCLC using an NGS panel of 95 known cancer genes. Significant differences between the BM and the non-BM group were detected in somatic mutations, somatic interactions, key signaling pathways, functional biological information and clinical actionability for the therapy of targeted agents. Finally, it was also observed that ctDNA analysis presented with a higher consistency for genomic profiling of the non-BM than that of the BM group, indicating that ctDNA analysis may serve as a more credible alternative for genomic profiling in advanced NSCLC patients without BM.
In the present study, 17 mutated genes and 30 mutated genes were identified in the BM and the non-BM group, respectively. Among these genes, five genes, including ALK, CDA, SMAD4, SOD2 and VHL were uniquely present in patients with BM. ALK is a tyrosine kinase and its constitutively activated mutation renders ALK a formidable cancer driver gene (33,34). BM occurs frequently in tumors harboring ALK rearrangements (10), and its clinical significance has been considered to be critical. Several ALK inhibitors have been reported to demonstrate conspicuous activity in brain metastatic patients with crizotinib-resistant ALK-positive NSCLC, including second-generation (brigatinib and alectinib) (35-37), and third-generation therapeutics (lorlatinib) (38,39). Additionally, ALK and VHL (P=0.0435) are exclusively co-occurring genes in the BM group, which was reported in Chinese patients with NSCLC for the first time, to the best of our knowledge. More notably, as the first generation of the blood-brain barrier (BBB)-penetrating TKIs, AZD3759 can activate a p53-SMAD4 positive feedback loop and lead to apoptosis in hepatoma cells (40), offering a promising future approach for the treatment of brain metastatic NSCLC patients by AZD3759 (41). Collectively, the data of the present study may contribute to an improved comprehension of the underlying molecular mechanisms of patients with NSCLC with BM and may provide prospective therapeutic targets for this specific subgroup.
Additionally, five genes exclusively identified in the BM group were distributed in the Hippo signaling pathway, pyrimidine metabolism (PyM), and pantothenate and CoA biosynthesis, according to KEGG enrichment analyses. As a key mediator in the Hippo signaling pathway, Yes-associated protein (YAP) has been founded to facilitate drug resistance and tumorigenesis in NSCLC (42-45). Furthermore, YAP has been reported to play a crucial role for the promotion of BM in lung adenocarcinoma patients, and the inhibition of YAP by shRNA may significantly suppress migration and invasion abilities of metastatic NSCLC cell lines H2030-BrM3 in a murine model (46). Combined with the current results, these findings may provide prospective therapeutic approaches by modulating the members or mediators of the Hippo signaling pathway in brain metastatic patients with NSCLC. As a complex enzymatic network, the main function of PyM is to integrate de novo nucleotide synthesis, nucleoside salvage, and catalytic degradation of pyrimidines. In cancer cells, the de novo nucleotide synthesis pathway continuously provides deoxyribonucleoside triphosphates (dNTPs) to sustain uncontrolled proliferation, being different from normal cell de novo nucleotide synthesis (47,48). Until recently, PyM has been mainly implicated in the differentiation of leukemic cells; however, little is known about its roles in the differentiation of solid tumors (49). To the best of our knowledge, the present finding is the first report on PyM as an exclusive signaling pathway in Chinese patients with NSCLC with BM. However, further studies are required in order to discern whether PyM signaling pathway plays a critical role in the initiation and/or progression of BM in NSCLC.
More importantly, it was also demonstrated that ctDNA analysis is more feasible as an alternative for somatic mutation landscapes of non-BM patients than that of BM patients by the higher consistency between ctDNA analysis and tumor DNA analysis (43.33 vs. 29.41%). A possible explanation for the discrepancies between ctDNA analysis and tumor DNA analysis may be the inhibition of tumor cell release into the bloodstream by the BBB in patients with BM (50,51). Thus, the basic detection rate of genomic alterations derived from peripheral blood ctDNA in the BM group has been reported to be lower than that in the non-BM group (52,53). In a similar study, Aldea et al (53) demonstrated that ctDNA was positive in 52% of isolated central nervous system progression (iCNS) vs. 84% in extra-CNS only (noCNS), which was accompanied by a lower detection rate of pathogenic driver mutations (37 vs. 77%) and resistance alterations (6 vs. 45%). However, it cannot be overlooked that liquid biopsy is a potent method with which to improve the identification of actionable biomarkers when tumor tissue is unavailable (52,54,55). In the present study, plasma-derived ctDNA analysis improved the detection rate of EGFR actionable mutations by a 15.39% (4/26) increase in the BM group, and four patients had the opportunity to receive targeted therapies (erlotinib/erlotinib + ramucirumab/afatinib/gefitinib/osimertinib/dacomitinib) and/or participate in clinical trials. Consequently, the data of the present study are consistent with those of previous studies in which the identification of actionable mutations is growing in advanced NSCLC patients with the aid of plasma-derived ctDNA (56,57).
In the present study, one of the main limitations is that NGS data were obtained from tumor tissue DNA or plasma-derived ctDNA without the simultaneous analysis of matched normal tissue to delete the germline mutations. Thus, for a single sample the analysis was not complete; however, it may be considered adequate for the acquisition of actionable genomic alterations for the application of guided clinical treatment based on the suitable filter conditions (please see the 'Materials and methods' section, 'Bioinformatics analysis') (30,58-65). However, it cannot be disregarded that either multiregional biopsies or more than one type of biopsies may be costlier than the analysis of a single tumor sample without the inclusion of matched normal tissue. Another main limitation is the absence of a genomic profile, derived from brain tumor tissue DNA due to difficulties in the acquisition of brain tissue samples from NSCLC patients with BM. Additionally, although all patients were recruited for a prospective study, the collection of NGS data was retrospective. Lastly, further multiple-institution research with larger sample sizes is required to validate the present conclusions.
In conclusion, the somatic mutation landscapes of NSCLC with and without BM were compared, and significant differences in somatic mutations, somatic interactions, key signaling pathways, functional biological information, and clinical actionability for the therapy of targeted agents were observed between the BM group and the non-BM group. Moreover, plasma-derived ctDNA analysis may be a more reliable alternative for genomic profiling of advanced patients without BM, based on the higher consistency between ctDNA analysis and tumor DNA analysis in NSCLC.
Supplementary Data
Availability of data and materials
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. The next generation sequencing data are available at the NCBI BioProject database (Reference no. PRJNA759391; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA759391).
Authors' contributions
RN, JZ, JL and WL conceptualized and designed the present study. RN, HZ, JM, PL, SW and JZ were involved in the acquisition of samples. YW and SW performed the high-throughput sequencing experiments. YW, HJ, WH and LJ performed the bioinformatics analysis. HJ, WH, YX and LJ were involved in the statistical analysis. RN, HJ, LJ, YW, JZ and WL were responsible for administrative/technical/material support and study supervision. HJ and JL wrote the manuscript. RN, HJ, WH, HZ, JM, PL, LJ, YX, SW, and JL critically revised the article. All authors have read and approved the final manuscript. LJ and WL confirm the authenticity of all the raw data.
Ethics approval and consent to participate
The part of this study involving human participants was reviewed and approved by the Medical Ethics Committee of Affiliated 3201 Hospital of Xi'an Jiaotong University [No.008(2017)]. Written informed consent was obtained from all participants involved in the present study, according to national legislation and institutional requirements.
Patient consent for publication
The publication of data was approved by all patients.
Competing interests
The authors declare that they have no competing interests.
Acknowledgments
Not applicable.
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