Next-generation sequencing in cancer diagnosis and treatment: clinical applications and future directions

Abstract

Next-generation sequencing (NGS) has emerged as a pivotal technology in the field of oncology, transforming the approach to cancer diagnosis and treatment. This paper provides a comprehensive overview of the integration of NGS into clinical settings, emphasizing its significant contributions to precision medicine. NGS enables detailed genomic profiling of tumors, identifying genetic alterations that drive cancer progression and facilitating personalized treatment plans targeting specific mutations, thereby improving patient outcomes. This capability facilitates the development of personalized treatment plans targeting specific mutations, leading to improved patient outcomes and the potential for better prognosis. The application of NGS extends beyond identifying actionable mutations; it is instrumental in detecting hereditary cancer syndromes, thus aiding in early diagnosis and preventive strategies. Furthermore, NGS plays a crucial role in monitoring minimal residual disease, offering a sensitive method to detect cancer recurrence at an early stage. Its use in guiding immunotherapy by identifying biomarkers that predict response to treatment is also highlighted. Ethical issues related to genetic testing, such as concerns around patient consent and data privacy, are also important considerations that need to be addressed for the broader implementation of NGS. These include the complexities of data interpretation, the need for robust bioinformatics support, cost considerations, and ethical issues related to genetic testing. Addressing these challenges is essential for the widespread adoption of NGS. Looking forward, advancements such as single-cell sequencing and liquid biopsies promise to further enhance the precision of cancer diagnostics and treatment. This review emphasizes the transformative impact of NGS in oncology and advocates for its incorporation into routine clinical practice to promote molecularly driven cancer care.

Introduction

Next-generation sequencing (NGS) represents a revolutionary leap in genomic technology, enabling the rapid sequencing of entire genomes or targeted genomic regions with unprecedented speed and accuracy [1]. Unlike traditional Sanger sequencing, which sequences DNA fragments one at a time, NGS allows for massive parallel sequencing, processing millions of fragments simultaneously [2]. This technological advancement has significantly reduced the time and cost associated with sequencing, making it more accessible for widespread clinical use.

While single-gene assays have long been a standard in clinical diagnostics, particularly for the detection of mutations in known oncogenes and tumor suppressor genes [3], these assays have significant limitations. These assays typically focus on a small set of genes and often ignore the genomic complexity of the tumor from a genetic perspective [4]. Furthermore, single-gene assays cannot detect mutations in non-coding regions that may contribute to cancer development [5]. As a result, these assays may miss opportunities for early detection and optimization of treatments.

Our understanding of genetics and the genome has been significantly advanced by the recent revolution in sequencing technology. NGS has been widely adopted for various applications, including whole-genome sequencing (WGS), whole-exome sequencing (WES), transcriptome sequencing, targeted region sequencing, epigenetic sequencing, and more [6]. This information is crucial for developing precision medicine approaches, allowing treatments to be tailored to the specific genetic profile of a patient's tumor. From a clinical perspective, this technology can be employed for a range of applications, including, but not limited to, the molecular diagnosis of genetic and infectious diseases, prenatal diagnosis, carrier detection, medical genetics and pharmacogenomics, cancer molecular diagnosis, and prognosis [7–9]. In oncology, NGS has become an essential tool for understanding the genetic complexities of cancer. The capability to sequence entire cancer genomes enables the comprehensive identification of genetic mutations, structural variations, and other genomic alterations that drive tumorigenesis [10]. This information is essential for developing precision medicine approaches, allowing treatments to be tailored to the specific genetic profile of a patient’s tumor.

This paper aims to provide a comprehensive overview of the integration of NGS into clinical settings for cancer diagnosis and treatment. It will explore the various applications of NGS in oncology, including tumor genomic profiling, detection of hereditary cancer syndromes, and monitoring of minimal residual disease. The paper will also discuss the role of NGS in guiding immunotherapy and its impact on patient outcomes. Furthermore, the challenges associated with the clinical implementation of NGS, such as data interpretation, cost, and ethical issues, will be examined. Finally, the paper will highlight future directions and innovations in NGS technology that promise to further enhance the precision of cancer diagnostics and treatment. Through this comprehensive analysis, the paper underscores the transformative potential of NGS in advancing personalized cancer care and improving patient outcomes.

Basics of next-generation sequencing

NGS is a transformative technology that enables comprehensive genomic analysis with unprecedented speed and accuracy. Its core principles—sample preparation, library construction, sequencing, and data analysis—form a robust framework for exploring the genetic basis of diseases, guiding clinical decision-making, and advancing personalized medicine. The continuous evolution of NGS technologies promises even greater precision and broader applications in the future.

Definition and principles of next-generation sequencing

The process of NGS involves several key steps: sample preparation, library construction, sequencing, and data analysis. Each step is crucial for generating accurate and comprehensive genomic data.

Sample preparation for library construction

The first step in NGS is the extraction and preparation of DNA or RNA from the sample of interest. The quality and quantity of the nucleic acids are assessed to ensure they meet the requirements for sequencing. For DNA, this typically involves extracting genomic DNA from cells or tissues [11], while RNA sequencing (RNA-seq) requires the isolation of total RNA [12], followed by reverse transcription to generate complementary DNA (cDNA). Initially, the genomic sample (DNA or cDNA) is fragmented into a library of small fragments, to which adapters (synthetic oligonucleotides with specific sequences) are subsequently attached. These libraries are then amplified and readied for sequencing [13]. Library construction includes two primary steps: (i) fragmenting the genomic sample to the correct size (around 300 bp), and (ii) attaching adapters to the DNA fragments [14]. These adapters are essential for attaching the DNA fragments to the sequencing platform and for subsequent amplification and sequencing [15]. There are three methods for cleaving nucleic acid chains: physical, enzymatic, and chemical methods [16]. The length of adapter sequences being constant determines the size of the desired library based on the platform's capabilities [17]. Adjusting the fragment length of a library can be achieved by varying the digestion reaction time with consistent reproducibility [18]. An enrichment step is necessary to isolate coding sequences, typically accomplished through PCR using specific primers or exon-specific hybridization probes [19]. Full-length cDNA is synthesized with constant 3ʹ and 5ʹ ends. Following library construction, removal of inappropriate adapters and components is performed using magnetic beads or agarose gel filtration. Finally, quantitative PCR assesses both the quantity and quality of the library. Evaluating the robustness of the cDNA library development process is crucial for its utility [14] (Fig. 1). Depending on the NGS technology, various types of libraries can be constructed, such as whole-genome, whole-exome, or targeted sequencing libraries.

Fig. 1.

Figure showing the key steps in NGS library construction, from DNA/RNA extraction to fragmentation, adapter ligation, amplification, and quality control, ensuring samples are ready for sequencing

Sequence reaction

The first step in the sequencing reaction is to convert the library to single-stranded DNA and to separate single-stranded molecules for sequencing. It should be noted that the signal emitted from the sequence of a single molecule will not be identified, so the single-stranded molecule must be amplified to generate a suitable signal for sequence identification [20]. Several different technologies are used for NGS, each with its own method for reading nucleotide sequences. The most commonly used technology is Illumina sequencing, which involves the following steps [21]: (i) The library fragments are immobilized on a solid surface (flow cell) and amplified to form clusters of identical sequences. This amplification process, known as bridge PCR, creates millions of clusters, each derived from a single DNA fragment. (ii) Nucleotides labeled with fluorescent dyes are incorporated into the growing DNA strands during each cycle of synthesis. The sequencing instrument detects the fluorescence emitted by each incorporated nucleotide, allowing the sequence of each cluster to be determined in real-time. Other NGS platforms, such as Ion Torrent and Pacific Biosciences, use different sequencing chemistries and detection methods, such as semiconductor-based detection and single-molecule real-time (SMRT) sequencing, respectively [22, 23].

Data analysis

The final stage of NGS involves analyzing the vast amount of data generated during sequencing. This technology has revolutionized genome analysis by enabling large-scale, rapid, and cost-effective processing. Given the sheer volume of data produced, accurately interpreting it presents a significant challenge [24]. Various software packages are available for data analysis, with the primary outputs typically being raw sequences and sequence-quality metrics. These metrics assess the accuracy of the sequences based on platform-specific criteria due to variations in NGS chemicals [25].

The initial step in data interpretation involves sequence assembly to piece together the sequences. Subsequently, for diagnostic purposes, the assembled genome is compared to a reference genome to identify variations. Bioinformatics tools automatically map these sequences and generate interpretable files detailing mutation information, variant locations, and read counts per location. However, interpreting these findings hinges on the reliability of the sequence data [14, 20].

Achieving comprehensive genome and transcript coverage at significant depths is crucial for detecting all mutations. Currently, different analytical approaches emphasize varying aspects of the data.

Comparison with traditional sequencing methods

Sanger sequencing, also known as chain termination or dideoxy sequencing, involves the selective incorporation of chain-terminating dideoxynucleotides (ddNTPs) during DNA synthesis. The process generates a series of DNA fragments of varying lengths, each terminating at a ddNTP. These fragments are then separated by capillary electrophoresis, and the sequence is read by detecting the fluorescent labels attached to the ddNTPs [26, 27]. More than three decades ago, Sanger et al. [28] pioneered a DNA sequencing method based on electrophoresis. However, its limited throughput and relatively high costs prevented the sequencing of large numbers of genes and samples. To overcome these challenges, new sequencing technologies were developed. NGS technologies sequence thousands of DNA molecules simultaneously, offering rapid throughput and high speed. They can generate both quantitative and qualitative sequence data comparable to that of the Human Genome Project in just 2 weeks. NGS has revolutionized genomic research and clinical diagnostics, offering numerous advantages over traditional sequencing methods, such as Sanger sequencing (Table 1). These improvements have revolutionized genomics by enabling comprehensive genome, transcriptome, and epigenome analyses, which are essential for personalized medicine, especially in oncology. The continued advancements in NGS technologies promise to further expand its applications and efficiency, solidifying its role as a cornerstone of modern genomic research and clinical diagnostics. Addressing these advancements will provide a clearer context for understanding NGS’s transformative role in both research and clinical settings.

Table 1.

Contrasting NGS and Sanger sequencing based on cost, speed, throughput, and application

Feature	Next-generation sequencing	Sanger sequencing
Cost-effectiveness	Higher for large-scale projects	Lower for small-scale projects
Speed	rapid sequencing	time-consuming
Application	Whole-genome sequencing, targeted sequencing	Ideal for sequencing single genes
Throughput	multiple sequences simultaneously	single sequence at a time
Data output	Large amount of data	Limited data output
Clinical utility	Detects mutations, structural variants	Identifies specific mutations

Currently, several NGS platforms are commercially available, including Illumina HiSeq and MiSeq, Roche 454 GS and Junior, Ion Torrent Personal Genome Machine, and Life Technologies SOLiD. Understanding the various NGS platforms and their roles in oncology helps demonstrate just how adaptable this technology has become. For example, Illumina HiSeq and MiSeq platforms are known for their sequencing by synthesis (SBS) method, which allows for high-throughput analysis. This makes them invaluable for tasks like WGS or targeted gene analysis, particularly when identifying mutations or structural changes in tumors. On the other hand, Ion Torrent PGM uses semiconductor sequencing, which offers faster results at a lower cost—perfect for clinical settings where actionable mutations need to be identified quickly for cancer treatment decisions. Though less frequently used today, platforms like Roche 454 and Life Technologies SOLiD played key roles in early cancer research due to their high accuracy in deep sequencing [29–32]. Each platform brings unique strengths, whether it's for large-scale genomic studies or the real-time detection of mutations in cancer patients, ultimately enhancing personalized cancer treatments based on a tumor's genetic profile.

Types of NGS technologies

NGS encompasses various technologies tailored to different research and clinical applications. These technologies can be broadly categorized based on the scope of the genomic regions they target. The primary types of NGS include WGS, and WES. NGS allows genome sequencing to be explored across various levels, tailored to specific needs and targets. It enables comprehensive genome-wide analyses, such as examining DNA methylation patterns or interactions between DNA and proteins. Additionally, NGS facilitates the analysis of all RNA types, including mRNA, microRNA, and ribosomal RNA, a technique known as RNA-Seq [33, 34]. However, achieving all clinical objectives with NGS remains challenging, and some goals may permanently remain within the realm of research.

Whole genome sequencing

In this approach, WGS encompasses both nuclear and mitochondrial DNA, allowing simultaneous examination of thousands of active genes and silent sequences. It stands out as one of the most effective methods for detecting genetic mutations, covering over 98% of coding sequences [35]. Over the past decade, there has been a steady increase in the number of publicly available whole-genome sequences of tumors. These analyses have provided profound insights into cancer biology, especially through the study of structural variants (SVs) in tumor genomes [36]. They span from the characterization of cancer cell lines [37] and detailed N-of-One case reports [38] to deep sequencing of individual tumors to reveal clonal heterogeneity [39]. For instance, Nik-Zainal et al. [40] sequenced the entire genomes of 560 breast cancers, with 260 additionally undergoing transcriptome sequencing. Their work demonstrated how such approaches fill gaps in our understanding of the genome beyond exons, expanding our knowledge of biological mechanisms underlying tumorigenesis with potential clinical implications [40].

Analysis of a sample taken after disease progression provided deep insights into how the tumor evolved to resist the treatment regimen, which a targeted approach alone could not have revealed. The success of this study paved the way for the Personalized OncoGenomics clinical trial pilot, now in its fifth year, aiming to utilize whole-genome analysis for treatment decisions based on genomic information. Sequencing the initial 100 patients in this trial necessitated the development of pipelines and comprehensive interpretation tools [41], establishing a more unbiased approach to selecting cancer types. One advantage of this method lies in its ability to facilitate sequencing of rare tumor types that might otherwise receive less extensive profiling [42]. Furthermore, the development and implementation of larger-scale gene panels are becoming routine for analyzing a significant number of patient samples [43].

Whole exome sequencing

WES has become a clinical diagnostic tool for rare diseases, examining over 20,000 known human genes and detecting minor insertions and deletions. The success rate of clinical diagnostic tests for Mendelian diseases using WES is approximately 30% [44, 45]. WES covers nearly the entire protein-coding region of the human genome, making it a remarkably powerful tool for medical genetic research [46]. Indeed, WES has already pinpointed causative mutations for numerous rare familial syndromes [47]. Moreover, WES has unveiled previously unknown roles of certain cancer-related genes. For instance, PALB, initially identified as a breast cancer gene, was found through whole exome studies to also play a role in familial aggregation of pancreatic cancer [48]. As expected, the catalog of newly identified cancer-predisposing genes continues to grow rapidly. However, the impact of WES studies in fully resolving the issue of missing heritability in cancer patients has been somewhat less than originally anticipated [49].

The technical limitations of WES are well recognized. Some protein-coding regions of the genome are not effectively covered by current WES tools [50, 51]. Additionally, while false-positive findings in WES can be assessed using Sanger sequencing, systematic evaluation of false-negative rates is costly and rarely conducted in routine practice. Most assessments of WES reliability rely on studies with prior knowledge of the involvement of specific gene groups in disease predisposition. However, this approach is inherently biased because it focuses primarily on known genes, limiting its unbiased nature [52].

Conclusions regarding the sensitivity of WES often hinge on the successful detection of disease-causing mutations in single control DNA samples. Truly understanding the incidence of false negatives would require blind WES testing across a sizable panel of DNA samples with known mutation statuses in multiple genes. Moreover, detecting small intragenic deletions and insertions, which constitute a significant proportion of germ-line mutations predisposing to cancer, can be more challenging than identifying missense variants [47]. Given the rarity of these variants, inadequate sensitivity of WES could significantly impact study outcomes.

Each type of NGS technology offers unique advantages and is suited to different applications in research and clinical settings. WGS provides the most comprehensive genetic insights but at a higher cost and data complexity. WES strikes a balance between comprehensiveness and efficiency by focusing on coding regions. The choice of NGS technology depends on the specific objectives, budget, and required depth of genetic information for each project.

Applications of NGS in cancer diagnosis

NGS has significantly advanced cancer diagnosis by enabling detailed genomic profiling of tumors, detection of hereditary cancer syndromes, and monitoring of minimal residual disease (MRD). Through comprehensive analysis of genetic alterations such as single nucleotide variants (SNVs), insertions, deletions (indels), and copy number variations (CNVs), NGS provides crucial insights into the molecular landscape of cancers, facilitating personalized treatment approaches and targeted therapies. NGS identifies prognostic and predictive biomarkers, guiding therapeutic decisions and improving patient outcomes (Table 2). Moreover, NGS enhances MRD monitoring, particularly in hematologic malignancies, by offering higher sensitivity and specificity than traditional methods, allowing early detection of residual cancer cells and informing timely intervention. As NGS technology continues to evolve, its integration into clinical practice promises to further enhance the precision and efficacy of cancer diagnosis and treatment.

Table 2.

The applications of NGS in cancer diagnosis

Application	Number of Sample	Description	Benefits	Challenges	Examples	Refs.
Genetic mutation detection	N	Identification of mutations in cancer-related genes	Enables precise diagnosis and targeted therapy	High complexity and need for validation	Detection of BRCA1/BRCA2 mutations in breast cancer	[53]
Tumor profiling	N	Comprehensive analysis of genetic alterations in tumors	Guides personalized treatment plans	Requires high-quality samples and data interpretation	Profiling of lung cancer for EGFR, ALK, and ROS1 mutations	[54]
Minimal residual disease	128	Detection of small numbers of cancer cells remaining after treatment	Allows early intervention and monitoring of relapse	Sensitivity and specificity issues	Monitoring of residual leukemia cells post-treatment	[55]
Liquid biopsy	345	Analysis of cancer-derived material in blood	Non-invasive, allows real-time monitoring	Limited by the amount of ctDNA	Detection of mutations in ctDNA for tracking treatment response	[56]
Fusion gene detection	N	Identification of gene fusions that drive cancer development	Helps in diagnosing specific cancer subtypes and choosing therapies	Complex bioinformatics analysis required	Detection of BCR-ABL fusion in chronic myeloid leukemia (CML)	[57]

Ref References, ctDNA circulating tumor DNA, CML chronic myeloid leukemia, N denotes an unspecified number of samples

Genomic profiling of tumors

Genomic profiling of tumors using NGS has revolutionized cancer diagnosis by enabling comprehensive analysis of the genetic alterations that drive cancer development and progression. This approach provides detailed insights into the molecular landscape of tumors, facilitating personalized medicine and targeted therapies. NGS can detect various types of mutations, these mutations can provide crucial information about oncogenes (genes that promote cancer) and tumor suppressor genes (genes that prevent cancer) [58].

Genetic alterations detected through NGS can serve as prognostic biomarkers, offering insights into disease progression and patient outcomes. NGS also identifies predictive biomarkers that indicate potential responses to specific therapies, thereby guiding treatment decisions. In personalized medicine, challenges with tissue biopsy include accessibility to tumor tissue, invasive procedures, turnaround times, and limited representation of tumor heterogeneity. Recently, liquid biopsy has emerged as a noninvasive alternative capable of addressing these challenges. Liquid biopsy involves minimally invasive testing of circulating cell-free DNA (cfDNA) from the peripheral blood of cancer patients, utilizing comprehensive genomic profiling (CGP) based on digital polymerase chain reaction and NGS technologies [59].

The association of genes with specific diseases has provided the basis for finding diagnostic biomarkers [60]. Over the past decade, numerous NGS panel tests for liquid biopsy have been developed, marking advancements in sensitivity, specificity, accuracy, feasibility, cost-effectiveness, and real-world application. As a result, liquid biopsy is increasingly becoming mainstream in precision medicine, enabling real-time profiling of cancer heterogeneity in patients at a more affordable cost and in less time [60–62].

FoundationOne Liquid (F1L) assay, developed by Foundation Medicine, is a target-specific NGS-based device for liquid biopsy. It utilizes cfDNA extracted from plasma obtained from anticoagulated peripheral whole blood. The assay employs high-throughput hybridization-based capture technology to analyze a panel of 70 targeted genes, detecting substitutions, insertions and indels, copy number alterations (CNAs), selected gene rearrangements in 7 genes, and high microsatellite instability (MSI-H) status [63]. In September 2020, F1LCDx was released as an updated version, expanding the genomic analysis to over 300 cancer-related genes (with a targeted panel of 324 genes) and multiple genomic signatures [64].

Between October and November 2020, the Food and Drug Administration (FDA) approved F1LCDx as a companion diagnostic device for several biomarkers detected in cfDNA, including ALK rearrangement, EGFR exon 19 deletion, and EGFR exon 21 L858R substitution in lung cancer; BRCA1, BRCA2, and ATM alterations in prostate cancer; and BRCA1, BRCA2, and PIK3CA mutations in breast cancer [65]. Many clinical laboratories offer NGS-based cancer gene panels that target a set of genes frequently mutated in specific types of cancer. These panels provide a cost-effective way to obtain relevant genetic information for diagnosis and treatment.

Genomic profiling of tumors using NGS is a powerful tool in modern oncology, offering detailed insights into the genetic basis of cancer. By identifying actionable mutations, understanding tumor heterogeneity, and discovering relevant biomarkers, NGS enables personalized treatment approaches that improve patient outcomes. As NGS technology continues to evolve, its integration into clinical practice will likely expand, further enhancing the precision and efficacy of cancer diagnosis and treatment.

Detection of hereditary cancer syndromes

Hereditary cancer syndromes involve inherited genetic mutations that elevate an individual’s cancer risk. Specifically, certain mutations can disrupt the normal growth regulation of cells, leading them to become cancerous. Key genes associated with hereditary cancer syndromes include BRCA1/2 for hereditary breast and ovarian cancer syndrome (HBOC) and TP53 for Li-Fraumeni syndrome. Approximately 7% of breast cancers and 13% of ovarian cancers are primarily attributable to germline mutations in BRCA1/2 genes [68].

The cumulative risks for breast and ovarian cancers in BRCA1/2 mutation carriers are reported as 72% (95% CI 65 to 79) and 44% (95% CI 36 to 53) respectively for BRCA1 carriers, and 69% (95% CI 61 to 77) and 17% (95% CI 11 to 25) respectively for BRCA2 carriers [66]. TP53 mutations are associated with a cumulative cancer risk as high as 90% by age 60 [67]. Besides BRCA1/2 and TP53, mutations in numerous other genes are linked to over 50 hereditary cancer syndromes. Genetic testing for hereditary cancer syndromes can identify individuals and families at elevated cancer risk. Once identified, these individuals and families can undergo risk assessment and receive personalized management strategies such as intensive cancer surveillance, risk-reducing surgeries, and genetic counseling [68]. Understanding these syndromes is crucial as they contribute significantly to specific cancer types, enabling early detection, targeted surveillance, and preventive interventions for at-risk individuals and their families.

Li et al. [69] identified non-BRCA1/2 mutations, including ATM, CDH1, CHEK2, PALB2, PTEN, STK11, and TP53, in 11.5% of 660 cases of familial breast cancer within a Western population. Ricker et al. [70] demonstrated that multiple-gene panel testing increased the detection of deleterious mutations from 8.6 to 15.6% compared to traditional single-gene testing methods. They also found no significant differences in mutation rates across different racial or ethnic groups [70].

The initiation and progression of gastric cancer are driven by the combined effects of genetic and epigenetic alterations [71]. The CDH1 mutation is linked to invasive lobular carcinoma and diffuse gastric cancer [72]. According to NCCN guidelines, women with the CDH1 mutation should undergo regular breast examinations, including annual mammograms and breast MRI scans. Additionally, they are advised to consider prophylactic total gastrectomy or undergo regular esophagogastroduodenoscopy with multiple random biopsies [73].

The germline mutation of TP53 is associated with Li-Fraumeni syndrome, a condition linked to multiple cancers such as breast cancer, soft tissue sarcoma, acute leukemia, brain tumors, adrenal carcinoma, and colon cancer. Patients with Li-Fraumeni syndrome often face a poor prognosis. However, with the identification of TP53 mutations as the cause of this syndrome, it has become feasible to detect carriers of inherited TP53 mutations. Currently, individuals with TP53 mutations are advised to undergo targeted surveillance tailored to their medical and family history [74]. Villani et al. [75] found that individuals with TP53 mutations who underwent intensive surveillance, including colonoscopy, whole-body MRI, breast MRI, brain MRI, skin examination, and physical examination, showed improved overall survival compared to those without surveillance (p = 0.013). This study underscores the effectiveness of personalized surveillance programs in enhancing survival rates and benefiting individuals with deleterious mutations.

To identify germline mutations associated with hereditary cancer syndromes, a multigene panel based on next-generation sequencing (NGS) of 64 cancer susceptibility genes was designed. The panel was applied to 496 breast cancer patients who exhibited clinical features of hereditary breast and ovarian cancer (HBOC) syndrome and underwent surgery between 2002 and 2017. Germline mutations were more prevalent in patients with additional primary cancers (15.6%), a strong family history of breast cancer (22.5%), bilateral breast cancer (28.1%), or early cancer diagnosis before the age of 40 (34.5%). The most common mutations were in the BRCA1/2 genes (63.2%), but 40% of mutations were observed in non-BRCA1/2 genes. In addition, novel mutations in the BRCA2 and MLH1 genes were identified, highlighting the importance of comprehensive genetic testing in diverse populations [68]. As NGS technology advances, the integration of these panels into clinical practice will provide a more effective, accurate, and comprehensive approach to the management of hereditary cancer syndromes.

Monitoring minimal residual disease

Monitoring MRD using NGS has become an essential component in the management of cancer, particularly hematologic malignancies. MRD refers to the small number of cancer cells that may remain in a patient’s body after treatment and could lead to relapse [76]. NGS offers highly sensitive and precise methods for detecting these residual cells, aiding in early intervention and personalized treatment adjustments.

MRD monitoring is particularly crucial in blood cancers such as acute lymphoblastic leukemia (ALL), chronic lymphocytic leukemia (CLL), and multiple myeloma. NGS can detect residual leukemic cells that harbor characteristic genetic alterations [77]. There is compelling evidence linking MRD negativity to improved long-term survival outcomes [78]. Tailored treatment modalities and novel therapies can be administered based on MRD risk stratification to increase the likelihood of cure [79, 80]. Multiparameter flow cytometry (MFC) or polymerase chain reaction (PCR) are commonly used to detect MRD, crucial for assessing disease risks and guiding therapeutic strategies in children with ALL [81, 82]. MFC-based MRD measurements rely on the surface immunophenotype of leukemia cells, while PCR-based MRD measurements detect leukemia-specific fusion genes or associated target genes. However, antigenic changes in leukemic clones post-therapy and tumor heterogeneity can lead to false-negative results, limiting MFC’s application [83]. Recent studies have shown that NGS offers higher sensitivity and accuracy than MFC or quantitative PCR (qPCR) in monitoring MRD in hematological malignancies [84–87]. Using a sensitive NGS approach enhances MRD detection, particularly post-hematopoietic stem cell transplantation or chimeric antigen receptor-modified T-cell therapy [88, 89], and enables the identification of emerging clones at each monitoring time point [90].

Monitoring MRD in solid tumors presents challenges but is achievable through the analysis of circulating tumor DNA (ctDNA) in blood samples, enabling non-invasive detection of residual disease [91]. cfDNA consists of double-stranded DNA approximately 150–200 base pairs in length, circulating predominantly in the blood due to apoptosis, necrosis, and phagocytosis [92]. cfDNA originates from hematopoietic cells like erythrocytes, leukocytes, and endothelial cells in healthy individuals, with contributions from normal tissues affected by ischemia, trauma, infection, or inflammation. ctDNA constitutes a small fraction of cfDNA released by malignant tumors into the bloodstream or other bodily fluids [93]. ctDNA typically exhibits greater fragmentation compared to non-mutant cfDNA, with peak enrichment between 90 and 150 base pairs, contrasting with 250–320 base pairs for non-mutant cfDNA [94–96]. Levels of ctDNA correlate with clinical and pathological characteristics of cancer, including stage, tumor burden, localization, vascularization, and response to therapy [97–99]. Moreover, ctDNA levels vary based on tumor type, shedding rate, and other biological factors [98, 100]. ctDNA testing holds promise in solid tumors by confirming MRD presence during the postoperative period and monitoring biomarkers that indicate the efficacy of adjuvant chemotherapy and potential drug resistance [101].

Non-Small Cell Lung Cancer (NSCLC) is one of the most common types of lung cancer. ctDNA plays a crucial prognostic role in patients with localized NSCLC, informing clinical decisions on consolidation therapy post-surgery. The TracerX consortium pioneered ctDNA detection during surveillance of early-stage NSCLC patients, anticipating recurrence before it could be detected by imaging [102]. Due to its limited limit of detection (LOD), researchers leveraged prior knowledge of tumor-tissue mutations to enhance the sensitivity of the CAncer Personalized Profiling by deep Sequencing (CAPP-Seq) test. TracerX findings revealed that nearly all patients experiencing postoperative relapse had detectable ctDNA either before or at the time of recurrence diagnosis, indicating early ctDNA detection correlates with aggressive tumor biology and rapid tumor progression [103]. In a similar study, Chaudhuri et al. aimed to identify post-treatment MRD in NSCLC patients earlier than current standard of care (SOC), providing a treatment window when tumor burden and heterogeneity are minimal. They concluded that both node-positive and node-negative stage I–III NSCLC patients could benefit from personalized adjuvant therapy. Specifically, assessment of actionable mutations and mutational load in ctDNA could enable early administration of tyrosine kinase inhibitors (TKIs) or immune checkpoint inhibitors (ICIs) for patients lacking available tissue material [104].

Surgery for stages I and II CRC is typically curative, while adjuvant chemotherapy is recommended for higher stages to reduce recurrence risk [105]. However, predicting recurrence in MRD-positive patients remains challenging due to the lack of effective biomarkers. In stages II and III, detection of ctDNA post-adjuvant treatment completion is linked to poorer relapse-free survival (RFS) [106–108]. Persistent ctDNA detection after treatment suggests the presence of micrometastatic disease, which often leads to clinical recurrence. Despite several active drugs showing no clinical benefit in adjuvant trials for advanced CRC [109–111], current trials are exploring additional therapies based on ctDNA assessment to mitigate relapse risk. For instance, the TRACC study (NCT04050345), focusing on stage II-III CRC, reported a median survival follow-up of approximately 15 months. Among MRD-positive patients, 6 out of 14 (43%) experienced relapse, compared to 8 out of 93 (9%) MRD-negative patients (HR: 10; 95% CI 3.3–30; p < 0.001). Post-operative ctDNA status was identified as the most significant prognostic factor for RFS (HR: 28.8, 95% CI 3.5–234.1; p < 0.001), highlighting its role in identifying CRC patients at high risk of recurrence [112]. In another study, plasma-only ctDNA analysis using a tumor-uninformed assay (Reveal test, Guardant Health) integrated genomic and epigenomic cancer signatures to enhance sensitivity by 25%–36% compared to genomic alterations alone. Out of 84 patients analyzed post-therapy completion, 14 with detectable ctDNA experienced recurrences, while 49 without ctDNA had 12 relapses. This approach showed promising sensitivity for recurrence detection during surveillance and favorable specificity post-definitive therapy in colorectal cancer patients, comparable to tumor-informed methods [113].

The randomized clinical trials POPLAR (NCT01903993) and OAK (NCT02008227) demonstrated significant survival benefits with ICI (atezolizumab) therapy in previously treated NSCLC patients. A retrospective analysis of these trials revealed that high blood-based TMB (TMB > 16 mut/Mb) correlated with better clinical outcomes following second-line ICI treatment compared to chemotherapy (median OS 13.5 months versus 6.8 months, respectively) [114]. However, the predictive value of blood TMB as a biomarker requires validation in prospective clinical trials.

NGS-based monitoring of MRD represents a significant advancement in cancer diagnosis and management. Its high sensitivity and specificity allow for early detection of residual cancer cells, enabling personalized treatment adjustments and improving patient outcomes. As NGS technology continues to evolve and become more accessible, its role in MRD monitoring is expected to expand, further enhancing the precision and effectiveness of cancer care.

NGS in cancer treatment

Cancer, as a complex disease driven by DNA mutations, benefits greatly from advancements in sequencing technologies. The advent of new sequencing technologies, particularly NGS, has revolutionized cancer diagnosis, management, and treatment. By sequencing human genomes and thousands of cancer genomes, NGS provides a comprehensive map of both normal genetic variations and mutations present in various types of cancer. This deepens our understanding of the molecular mechanisms underlying cancer development and supports the development of targeted therapies based on molecular profiles [7]. The profound impact of sequencing technology in recent years has expanded its applications across disease management, treatment strategies, genetic counseling, and risk assessment, highlighting its potential to transform clinical practices in oncology (Table 3). Malignant peripheral nerve sheath tumors (MPNST) are rare sarcomas with a poor prognosis, and conventional treatments have shown limited efficacy. A case report describes an 82-year-old male with a long history of MPNST and multiple recurrences, whose tumor genomic profile revealed significant alterations, including CD274/PD-L1 amplification, CDKN2A deletion, and TP53 mutation. These findings underscore the importance of genomic profiling in identifying therapeutic targets and highlight the necessity of integrating personalized immunotherapy into treatment plans [115]. In non-small cell lung cancer (NSCLC), genomic profiling has also proven critical for uncovering resistance mechanisms. For instance, a case of a 68-year-old male with EGFR-mutant NSCLC initially treated with dacomitinib revealed an acquired CCDC6-RET fusion as a resistance mechanism. Next-generation sequencing (NGS) played a pivotal role in identifying this alteration, and combined treatment with dacomitinib and selpercatinib resulted in significant clinical improvement. This case highlights the importance of NGS in identifying RET fusions, suggesting that the combination of dacomitinib and selpercatinib could overcome resistance in NSCLC patients with RET rearrangements. Additionally, when RET inhibitors are unavailable, pemetrexed-based chemotherapy remains a viable alternative, providing a therapeutic option for patients with acquired resistance [116]. In pediatric well-differentiated thyroid cancer (WDTC), NGS has also demonstrated its value in understanding genetic alterations (GAs) and their clinical implications. A retrospective study of 46 pediatric WDTC patients identified genetic alterations in 69.6% of cases, with BRAFV600E being the most common mutation (37.9%). The study further revealed that RET-PTC fusions were detected in 27.6% of the cases, with PAX8-PPARγ fusions and other fusion-oncogenes like STRN-ALK also present. Notably, non-RET fusions were associated with higher rates of vascular and lymphatic invasion, with a significant correlation to cervical metastasis in patients with papillary thyroid cancer (PTC). The findings emphasize that while BRAFV600E is the most common mutation in older patients, fusions, including RET-PTC and others, are more prevalent and may require additional therapeutic strategies. This suggests that molecular testing through NGS plays a crucial role in guiding treatment strategies and predicting disease progression in pediatric WDTC [117]. Taken together, these cases from diverse malignancies illustrate how genomic profiling can guide personalized treatment strategies, whether through targeting specific mutations like KRAS, EGFR, or RET fusions or through identifying alternative therapeutic pathways. From immunotherapy in MPNST to combinatorial treatment approaches in NSCLC and tailored systemic therapy in pediatric WDTC, genomic data offers a deeper understanding of disease mechanisms and treatment resistance, improving clinical outcomes across various cancers.

Table 3.

The applications of NGS in cancer treatment

Application	Description	Benefits	Challenges	Examples	Refs.
Precision Oncology and Personalized Treatment Plans	Tailoring medical treatments to each patient based on their unique genetic profile identified through NGS	Enhances treatment efficacy by targeting specific genetic mutations. Reduces adverse drug reactions	High costs, limited availability of approved targeted therapies, and complexity in data interpretation	EGFR TK inhibitors for EGFR-mutated NSCLC, ALK inhibitors for ALK-positive NSCLC	[53, 54]
Identification of Predictive Biomarkers	Using NGS to identify biomarkers that predict how patients will respond to specific treatments	Allows for more accurate selection of patient cohorts for clinical trials, improving treatment outcomes	Requires validation of biomarkers and integration into clinical practice	Testing for BRAF, EGFR, ALK, and ROS1 mutations in various cancers	[55, 56]
Liquid Biopsy	Non-invasive analysis of cancer-derived material in blood using NGS	Real-time monitoring of treatment response and disease progression	Limited by the amount of ctDNA and sensitivity issues	Detection of ctDNA for tracking mutations and treatment response	[57]
Monitoring of Treatment Response and Resistance	Using NGS to track genetic changes over time to monitor how cancers respond to treatment and develop resistance	Enables early intervention and adjustment of treatment plans to overcome resistance	Requires frequent testing and high sensitivity to detect minor genetic changes	Tracking EGFR T790M mutation for resistance to EGFR inhibitors in lung cancer	[53, 56]
Integration into Cancer Staging	Proposals to integrate NGS into traditional cancer staging systems to enhance diagnostic accuracy	Provides a more detailed understanding of tumor biology, improving staging and treatment planning	High costs, need for standardized protocols, and integration into existing clinical workflows	TNM-B system integrating genetic information for more precise cancer staging	[54, 118]

Ref References, ctDNA circulating tumor DNA

Precision oncology and personalized treatment plans

NGS has significantly advanced our understanding of cancer and propelled the development of Personalized Medicine (PM) in oncology. PM involves tailoring medical treatments to each patient based on their unique genetic profile, encompassing factors such as environment, behaviors, medication history, and comprehensive genome mutations identified through NGS [119]. By identifying predictive biomarkers from cancer genomes, PM allows oncologists to select patient cohorts more accurately for clinical trials, enhancing treatment efficacy. Approximately 10% of FDA-approved drug labels now include pharmacogenomics information, underscoring the integration of genetic data into clinical practice [120]. Unlike traditional methods like Sanger sequencing or PCR, NGS enables the screening of a wide array of genes in a single test, even with limited biopsy tissue, thereby expanding diagnostic capabilities [121]. As NGS costs decline and new biomarkers are validated, its adoption becomes increasingly cost-effective compared to testing a limited number of alterations.

Identification of driver mutations informs treatment strategies with targeted therapies tailored to specific biological targets such as BRAF, EGFR, ALK, and ROS1 mutations [122]. For example, EGFR mutations and ALK rearrangements in lung cancer are routinely tested using PCR and immunohistochemistry, but NGS offers a more comprehensive approach by simultaneously analyzing multiple genes [122]. The MAP consensus recommends NGS in clinical trials, advocating its use in NSCLC to analyze at least 20 genes including EGFR, BRAF, ALK, and ROS1, among others [123]. This approach has proven successful in developing new targeted therapies that improve outcomes for patients with specific genetic alterations, such as EGFR TK inhibitors for EGFR-mutated NSCLC and ALK inhibitors for ALK-positive NSCLC [124]. Small NGS assays typically target a limited set of genes or hotspots known to be associated with specific diseases, providing focused and cost-effective diagnostic information. In contrast, comprehensive NGS assays cover broader genomic regions, such as whole exomes or genomes, enabling the identification of novel or rare variants but often requiring more resources for analysis and interpretation.

NGS not only identifies common mutations but also rare ones (< 1%), which can offer insights into drug sensitivity. Current tests mostly use tumor tissue obtained invasively via fresh biopsies, posing risks and discomfort to patients, limiting multiple biopsies. Alternative sources like CTCs and ctDNA from liquid biopsies offer noninvasive options. NGS enables detailed genetic analysis from these sources, capturing intratumoral heterogeneity, identifying prognostic factors, predictive markers, and resistance mechanisms [125]. Proposals to integrate NGS into cancer staging, possibly adopting a TNM-B system, aim to enhance diagnostic accuracy and stage progression monitoring [126]. While PM is standard in some cancers, NGS faces challenges in everyday use due to high costs and limited availability of approved targeted therapies.

Impact on patient outcomes and prognosis

NGS, characterized by its speed, accuracy, and affordability, has been pivotal in advancing precision medicine. This approach tailors treatment based on the specific molecular alterations that drive a person’s disease [127]. In oncology, NGS is extensively used to sequence tumor genomes, enabling oncologists to match patients with therapies targeted at these genetic abnormalities. However, studies need to stratify their findings to clearly demonstrate the evidence supporting improved patient care through targeted therapies. Moreover, further research is necessary to understand barriers preventing more patients with actionable mutations from receiving appropriate targeted treatments. This includes assessing the timing of sequencing within patient care, physician interpretation of results, and the accessibility and cost-effectiveness of treatments in clinical trials or other settings [128].

Projects like the TAPUR Study and the NCI MATCH Study exemplify efforts to assess the impact of sequencing-driven cancer care [128, 129]. These initiatives have gained significance, particularly since CMS’s final NCD eliminated the coverage with evidence development category. This decision by CMS eliminated the requirement for tracking patient outcomes to some extent. Therefore, it is crucial for initiatives like TAPUR, NCI MATCH, and partnerships such as GA4GH [130] to continue tracking comprehensive data, including overall survival, progression-free survival, response rates, and other metrics. This data will be instrumental in evaluating the effectiveness of sequencing-matched therapies on patient outcomes, shaping future treatment approaches, and influencing insurance coverage standards.

Critics of CMS’s decision to cover F1CDx argue for more evidence from trials such as TAPUR and NCI MATCH to justify the broader use of NGS tests beyond targeting specific actionable genes [131]. They contend that while F1CDx includes additional genes, the incremental benefit may not justify the increased costs and potential risks associated with more off-label sequencing-matched therapies [127–133]. Recent findings from the NCI MATCH Study illustrate this point with modest objective response rates of 0% [134], 8.1% [135], and 5% [136] for patients treated with taselisib for PIK3CA mutations, ado-trastuzumab emtansine for HER2 protein overexpression, and AZD4547 for FGFR pathway mutations, respectively. These results highlight potential efficacy in specific subgroups but underscore challenges in achieving substantial benefits across broader populations.

Future directions

While tumor DNA sequencing provides valuable insights, it's crucial to recognize that DNA itself is inert. Integrating data from other modalities enhances our understanding of cancer functionality. RNA sequencing, for instance, reveals the relative expression of mutated genes, while mass spectrometry approaches illuminate the proteomics of cancer more clearly [137]. TCGA has amassed diverse data across multiple tumor types, offering insights at various levels, yet integrating these data comprehensively remains a significant challenge [138]. The term “-omics” refers to a suite of biological disciplines that analyze large-scale datasets to provide comprehensive insights into various aspects of cellular function and organization. Genomics focuses on the complete set of genes within an organism, examining DNA sequences, mutations, and genetic variations that contribute to disease. Transcriptomics, on the other hand, investigates the RNA transcripts produced by genes, providing valuable information about gene expression levels and regulatory mechanisms under specific conditions. Proteomics delves into the study of proteins, encompassing their functions, structures, and interactions, which are essential for understanding cellular processes and disease pathways. Additionally, metabolomics involves the analysis of small molecules, or metabolites, that reflect the biochemical activity within cells, offering insights into metabolic changes associated with different diseases. By integrating these various -omics data, researchers can achieve a more holistic view of biological systems, particularly in cancer research, ultimately leading to improved diagnostics and therapeutics [139, 140]. Recent reviews have examined methods for predicting phenotypes from integrated-omics data [141].

Moreover, immunotherapies are increasingly prominent in cancer treatment, particularly in melanoma [142]. In their meta-analysis, DU et al. [3] explored the practicality of NGS in informing targeted therapies and immunotherapy for advanced hepatocellular carcinoma (HCC). Their findings reveal that high-frequency mutations, particularly in TP53 and CTNNB1, are associated with an increased tumor mutational burden (TMB), and patients with TP53 neoantigens generally exhibit improved overall survival (OS). In contrast, mutations in TERT, CTNNB1, BRD4, or MLL, as well as co-mutations involving TP53 and TERT or BRD4, correlate with significantly worse survival outcomes. The analysis underscores that TERT mutations are linked to poor prognosis, while PD-L1 expression, TMB, and TP53 mutations serve as predictors of response to immune checkpoint blockade in patients undergoing targeted therapies and immunotherapy for advanced HCC [143]. NGS holds potential in predicting responses to immunotherapy. Neoantigens, generated by somatic mutations, correlate with mutation rates and clinical responses [144]. T-cells recognize these neoantigens, mediating immune responses [145]. Exome sequencing, combined with mass spectrometry, identifies which neoantigens are effectively presented by the major histocompatibility complex (MHC) [146]. MHC molecules are crucial for immune recognition as they bind to peptide fragments derived from various proteins, including those that are mutated or abnormal in cancer cells. Once these peptide-MHC complexes are formed, they are presented on the surface of antigen-presenting cells, enabling T-cells to identify and initiate an immune response against the cancer cells that express these neoantigens. This mechanism is essential for the success of immunotherapy, as targeting neoantigens can elicit a stronger and more precise immune attack on tumors.

Bioinformatics plays a crucial role in analyzing NGS data for immunotherapy by identifying key biomarkers like tumor mutational burden (TMB) and neoantigens, and mapping the tumor microenvironment [147, 148]. By integrating genomic, transcriptomic, and proteomic data, bioinformatics aids in designing personalized immunotherapy strategies tailored to individual tumor characteristics, while also enabling the longitudinal monitoring of treatment response and resistance mechanisms [149]. Thus, bioinformatics plays a pivotal role in maximizing the potential of immunotherapy for cancer treatment.

One of the main challenges is that not all mutations detected are actionable or even well understood, making treatment decisions more complex [150]. While some genetic alterations clearly point to specific therapies, many others fall into a gray area where their impact on treatment is uncertain. Additionally, the ethical concerns around incidental findings—mutations that aren’t related to the patient’s current condition but may signal future health risks—need more attention. Deciding whether or not to disclose these findings can be difficult for both clinicians and patients. To manage these complexities, there needs to be more standardized guidelines for interpreting NGS data, and collaboration between oncologists, geneticists, bioinformaticians, and ethicists is crucial. This multidisciplinary approach can help ensure that NGS is integrated into cancer care in a way that is both scientifically sound and ethically responsible.

Liquid biopsies and single-cell sequencing are revolutionizing oncology by addressing the shortcomings of traditional tissue biopsies. Unlike conventional methods that require invasive procedures to collect tissue samples, liquid biopsies analyze ctDNA or tumor cells found in blood or other bodily fluids. This non-invasive approach not only makes it easier to monitor tumors but also provides real-time insights into tumor evolution, allowing clinicians to track genetic changes and adjust treatment strategies more swiftly. Single-cell sequencing complements this by enabling researchers to examine the genetic material of individual cells within a tumor. This level of detail reveals the heterogeneity of tumors, showing how different cell populations can respond differently to therapies. Understanding this complexity is crucial for tailoring treatments to improve patient outcomes.

Moreover, the integration of NGS data with artificial intelligence (AI) and machine learning (ML) represents an exciting trend in oncology. These technologies can analyze large datasets to uncover patterns and relationships that might not be evident through traditional methods. By combining NGS with AI, researchers and clinicians can enhance predictive modeling, leading to more accurate forecasts of treatment responses and potential resistance mechanisms.

Conclusion

NGS is at the forefront of revolutionary technologies in oncology, transforming cancer diagnosis and treatment through its capacity to deliver comprehensive genomic insights. The in-depth genomic profiling enabled by NGS allows for the identification of specific genetic alterations that drive cancer progression, thus facilitating highly personalized and effective treatment plans. Its applications also encompass the detection of hereditary cancer syndromes and the monitoring of minimal residual disease, significantly aiding in early diagnosis, preventive strategies, and the timely identification of cancer recurrence.

However, the broad implementation of NGS in clinical settings faces several challenges, including complex data interpretation, the need for advanced bioinformatics infrastructure, cost constraints, and ethical considerations related to genetic testing. To harness the full potential of NGS, these challenges must be addressed through collaborative efforts among clinicians, researchers, bioinformaticians, and policymakers. Future innovations, such as single-cell sequencing and liquid biopsies, promise to further enhance cancer diagnostics and treatment, making precision oncology more accessible and effective. NGS has undeniably transformed cancer care, providing a pathway toward more personalized, informed, and effective treatment strategies. Its integration into routine clinical practice is crucial for advancing the era of molecularly informed cancer care, ultimately leading to improved patient outcomes and a deeper understanding of cancer biology.

Abbreviations

NGS

Next-generation sequencing

WGS

Whole-genome sequencing

WES

Whole-exome sequencing

RNA-seq

RNA sequencing

cDNA

Complementary DNA

SMRT

Single-molecule real-time

ddNTPs

Dideoxynucleotides

SBS

Sequencing by synthesis

SVs

Structural variants

MRD

Minimal residual disease

SNVs

Single nucleotide variants

indels

Deletions

CNVs

Copy number variations

cfDNA

Cell-free DNA

CGP

Comprehensive genomic profiling

F1L

FoundationOne Liquid

CNAs

Copy number alterations

MSI-H

High microsatellite instability

FDA

Food and Drug Administration

HBOC

Hereditary breast and ovarian cancer syndrome

ALL

Acute lymphoblastic leukemia

CLL

Chronic lymphocytic leukemia

MFC

Multiparameter flow cytometry

PCR

Polymerase chain reaction

qPCR

Quantitative PCR

ctDNA

Circulating tumor DNA

NSCLC

Non-Small Cell Lung Cancer

LOD

Limit of detection

SOC

Standard of care

TKIs

Tyrosine kinase inhibitors

ICIs

Immune checkpoint inhibitors

RFS

Relapse-free survival

PM

Personalized Medicine

HCC

Hepatocellular carcinoma

TMB

Tumor mutational burden

OS

Overall survival

MHC

Major histocompatibility complex

Author contributions

N.G., M.V., and J.B. developed the concept and designed the study. R.H., M.C., N.F., and A.F. did a systematic search and prepared the first draft. N.G., R.H., A.F., and J.B. prepared the first draft. All authors participated in revising the manuscript before submission.

Funding

Funding not received.

Availability of data and materials

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.