Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes

Mono- and Biallelic Replication-Coupled Gene Editing Discriminates Dominant-Negative and Loss-of-Function Variants of DNA Mismatch Repair Genes

Replication-coupled gene editing using locked nucleic acid-modified single-stranded DNA oligonucleotides (LMOs) can genetically engineer mammalian cells with high precision at single nucleotide resolution. Based on this method, oligonucleotide-directed mutation screening (ODMS) was developed to determine whether variants of uncertain clinical significance of DNA mismatch repair (MMR) genes can cause Lynch syndrome. In ODMS, the appearance of 6-thioguanine-resistant colonies upon introduction of the variant is indicative for defective MMR and hence pathogenicity. Whereas mouse embryonic stem cells (mESCs) hemizygous for MMR genes were used previously, we now show that ODMS can also be applied in wild-type mESCs carrying two functional alleles of each MMR gene. 6-Thioguanine resistance can result from two possible events: first, the mutation is present in only one allele, which is indicative for dominant-negative activity of the variant; and second, both alleles contain the planned modification, which is indicative for a regular loss-of-function variant. Thus, ODMS in wild-type mESCs can discriminate fully disruptive and dominant-negative MMR variants. The feasibility of biallelic targeting suggests that the efficiency of LMO-mediated gene targeting at a nonselectable locus may be enriched in cells that had undergone a simultaneous selectable LMO targeting event. This turned out to be the case and provided a protocol to improve recovery of LMO-mediated gene modification events.

Basket Trials: Past, Present, and Future

Large-scale tumor molecular profiling has revealed that diverse cancer histologies are driven by common pathways with unifying biomarkers that can be exploited therapeutically. Disease-agnostic basket trials have been increasingly utilized to test biomarker-driven therapies across cancer types. These trials have led to drug approvals and improved the lives of patients while simultaneously advancing our understanding of cancer biology. This review focuses on the practicalities of implementing basket trials, with an emphasis on molecularly targeted trials. We examine the biologic subtleties of genomic biomarker and patient selection, discuss previous successes in drug development facilitated by basket trials, describe certain novel targets and drugs, and emphasize practical considerations for participant recruitment and study design. This review also highlights strategies for aiding patient access to basket trials. As basket trials become more common, steps to ensure equitable implementation of these studies will be critical for molecularly targeted drug development.

Malignant melanoma in a 12-year-old boy 17 months after completing hepatoblastoma treatment

BACKGROUND

Melanoma is rare as a secondary malignant neoplasm among childhood cancer survivors.

CASE

We report a case of a 12-year-old boy who developed malignant melanoma with systemic metastases 17 months after completing treatment for hepatoblastoma. The diagnosis was made unexpectedly based on a bone marrow examination. The patient did not respond to immune checkpoint inhibitor therapy and died 6 weeks after being diagnosed with melanoma. Whole-exome sequencing to examine 103 genes associated with cancer predisposition did not identify any germ-line variants.

CONCLUSION

This case study provides a unique example of melanoma in a childhood cancer survivor following hepatoblastoma treatment but does not identify any candidate variant to link hepatoblastoma and melanoma.

A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer

Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.

In Silico-Based Structural Evaluation to Categorize the Pathogenicity of Mutations Identified in the RAD Class of Proteins

RAD genes, known as double-strand break repair proteins, play a major role in maintaining the genomic integrity of a cell by carrying out essential DNA repair functions via double-strand break repair pathways. Mutations in the RAD class of proteins show high susceptibility to breast and ovarian cancers; however, adequate research on the mutations identified in these genes has not been extensively reported for their deleterious effects. Changes in the folding pattern of RAD proteins play an important role in protein-protein interactions and also functions. Missense mutations identified from four cancer databases, cBioPortal, COSMIC, ClinVar, and gnomAD, cause aberrant conformations, which may lead to faulty DNA repair mechanisms. It is therefore necessary to evaluate the effects of pathogenic mutations of RAD proteins and their subsequent role in breast and ovarian cancers. In this study, we have used eight computational prediction servers to analyze pathogenic mutations and understand their effects on the protein structure and function. A total of 5122 missense mutations were identified from four different cancer databases, of which 1165 were predicted to be pathogenic using at least five pathogenicity prediction servers. These mutations were characterized as high-risk mutations based on their location in the conserved domains and subsequently subjected to structural stability characterization. The mutations included in the present study were selected from clinically relevant mutants in breast cancer pedigrees. Comparative folding patterns and intra-atomic interaction results showed alterations in the structural behavior of RAD proteins, specifically RAD51C triggered by mutations G125V and L138F and RAD51D triggered by mutations S207L and E233G.

A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants

The rapid integration of genomic technologies in clinical diagnostics has resulted in the detection of a multitude of missense variants whose clinical significance is often unknown. As a result, a plethora of computational tools have been developed to facilitate variant interpretation. However, choosing an appropriate software from such a broad range of tools can be challenging; therefore, systematic benchmarking with high-quality, independent datasets is critical. Using three independent benchmarking datasets compiled from the ClinVar database, we evaluated the performance of ten widely used prediction algorithms with missense variants from 21 clinically relevant genes, including BRCA1 and BRCA2. A fourth dataset consisting of 1053 missense variants was also used to investigate the impact of type 1 circularity on their performance. The performance of the prediction algorithms varied widely across datasets. Based on Matthews Correlation Coefficient and Area Under the Curve, SNPs&GO and PMut consistently displayed an overall above-average performance across the datasets. Most of the tools demonstrated greater sensitivity and negative predictive values at the expense of lower specificity and positive predictive values. We also demonstrated that type 1 circularity significantly impacts the performance of these tools and, if not accounted for, may confound the selection of the best performing algorithms.

Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays

Single-nucleotide polymorphisms (SNPs) are the most common genetic variations for various complex human diseases, including cancers. Genome-wide association studies (GWAS) have identified numerous SNPs that increase cancer risks, such as breast cancer, colorectal cancer, and leukemia. These SNPs were cataloged for scientific use. However, GWAS are often conducted on certain populations in which the Orang Asli and Malays were not included. Therefore, we have developed a bioinformatic pipeline to mine the whole-genome sequence databases of the Orang Asli and Malays to determine the presence of pathogenic SNPs that might increase the risks of cancers among them. Five different in silico tools, SIFT, PROVEAN, Poly-Phen-2, Condel, and PANTHER, were used to predict and assess the functional impacts of the SNPs. Out of the 80 cancer-related nsSNPs from the GWAS dataset, 52 nsSNPs were found among the Orang Asli and Malays. They were further analyzed using the bioinformatic pipeline to identify the pathogenic variants. Three nsSNPs; rs1126809 (TYR), rs10936600 (LRRC34), and rs757978 (FARP2), were found as the most damaging cancer pathogenic variants. These mutations alter the protein interface and change the allosteric sites of the respective proteins. As TYR, LRRC34, and FARP2 genes play important roles in numerous cellular processes such as cell proliferation, differentiation, growth, and cell survival; therefore, any impairment on the protein function could be involved in the development of cancer. rs1126809, rs10936600, and rs757978 are the important pathogenic variants that increase the risks of cancers among the Orang Asli and Malays. The roles and impacts of these variants in cancers will require further investigations using in vitro cancer models.

Internal Standardization of the Interpretation and Reporting of Sequence Variants in Hematologic Neoplasms

INTRODUCTION

Accurate classification of somatic genetic alterations detected by next-generation sequencing (NGS) assays is of paramount importance to ensure the provision of high-quality clinical data. Clinical significance of variants can be assessed and tiered based on guidelines from the Association for Molecular Pathology (AMP), the American Society of Clinical Oncology, and the College of American Pathology for the interpretation of somatic sequence variants identified in cancer.

METHODS

We sought to develop a formal structured approach for the classification of somatic variants in hematologic neoplasms, to account for both a variant's clinical significance and its ability to drive tumorigenesis, by adapting elements from these existing guidelines. However, we additionally utilized key criteria from the American College of Medical Genetics/AMP standards for variant reporting to focus evaluation into specific categories of evidence and to gauge the effect of a given variant on tumorigenesis.

RESULTS

The combined approach was applied to the annotation of 87 variants identified by a targeted NGS panel for myeloid neoplasms. In the application of our variant evaluation, we classified 2/87 variants as benign, 6/87 as likely benign, 56/87 as variants of unknown significance (VUS), 13/87 variants as likely pathogenic, and 10/87 variants as pathogenic.

CONCLUSION

Well-established oncogenic alterations were accurately classified as pathogenic. Although there is no defined benchmark for the remaining variants, drawing from two existing guidelines enabled the creation of a modified curation process for variant interpretation that emphasizes systematic review of relevant evidence.

In silico analysis of BRCA1 and BRCA2 missense variants and the relevance in molecular genetic testing

Over the years since the genetic testing of BRCA1 and BRCA2 has been conducted for research and later introduced into clinical practice, a high number of missense variants have been reported in the literature and deposited in public databases. Polymorphism Phenotyping v2 (PolyPhen-2) and Sorting Intolerant from Tolerant (SIFT) are two widely applied bioinformatics tools used to assess the functional impacts of missense variants. A total of 2605 BRCA1 and 4763 BRCA2 variants from the ClinVar database were analysed with PolyPhen2 and SIFT. When SIFT was evaluated alongside PolyPhen-2 HumDiv and HumVar, it had shown top performance in terms of negative predictive value (NPV) (100%) and sensitivity (100%) for ClinVar classified benign and pathogenic BRCA1 variants. Both SIFT and PolyPhen-2 HumDiv achieved 100% NPV and 100% sensitivity in prediction of pathogenicity of the BRCA2 variants. Agreement was achieved in prediction outcomes from the three tested approaches in 55.04% and 68.97% of the variants of unknown significance (VUS) for BRCA1 and BRCA2, respectively. The performances of PolyPhen-2 and SIFT in predicting functional impacts varied across the two genes. Due to lack of high concordance in prediction outcomes among the two tested algorithms, their usefulness in classifying the pathogenicity of VUS identified through molecular testing of BRCA1 and BRCA2 is hence limited in the clinical setting.

The Science and Art of Clinical Genetic Variant Classification and Its Impact on Test Accuracy

Clinical genetic variant classification science is a growing subspecialty of clinical genetics and genomics. The field's continued improvement is essential for the success of precision medicine in both germline (hereditary) and somatic (oncology) contexts. This review focuses on variant classification for DNA next-generation sequencing tests. We first summarize current limitations in variant discovery and definition, and then describe the current five- and four-tier classification systems outlined in dominant standards and guideline publications for germline and somatic tests, respectively. We then discuss measures of variant classification discordance and the field's bias for positive results, as well as considerations for panel size and population screening in the context of estimates of positive predictive value thatincorporate estimated variant classification imperfections. Finally, we share opinions on the current state of variant classification from some of the authors of the most widely used standards and guideline publications and from other domain experts.

Computational analysis of Cyclin D1 gene SNPs and association with breast cancer

CCND1 encodes for Cyclin D1 protein and single-nucleotide polymorphisms (SNPs) can modulate its activity. In the present study, the impact of CCND1 SNPs on structure and/or function of Cyclin D1 protein using in silico tools was investigated. Our analysis revealed only one splice site SNP (c.1988+5G<A) can effect CCND1 function. Subsequently, 78 out of 169 missense variants were predicted as pathogenic by Polyphen2, SIFT, PROVEAN, SNPs&GO, and PANTHER, and 4/78 missense SNPs were further evaluated because these four SNPs were found to be reside in highly conserved region of Cyclin D1. However, they did not show any major impact on tertiary structure and domain of Cyclin D1 but overall R15S and A190S has displayed a significant diseased phenotype and an altered molecular mechanism predicted by MutPred, FATHMM, SNPeffect, SNAP2, and PredictSNP. Consistently, A190S, R179L, and R15S may also cause a decrease in stability of Cyclin D1 anticipated by I-Mutant, HOPE and SNP effect. Furthermore, the Kaplan–Meier plotter has explained that high expression of CCND1 is associated with less survival rate of breast cancer patients. Altogether our study suggests that c.1988+5G<A, R15S, R179L, and A190S SNPs could directly or indirectly destabilize Cyclin D1.

Optimization of in silico tools for predicting genetic variants: individualizing for genes with molecular sub-regional stratification

Genes are unique in functional role and differ in their sensitivities to genetic defects, but with difficulties in pathogenicity prediction. This study attempted to improve the performance of existing in silico algorithms and find a common solution based on individualization strategy. We initiated the individualization with the epilepsy-related SCN1A variants by sub-regional stratification. SCN1A missense variants related to epilepsy were retrieved from mutation databases, and benign missense variants were collected from ExAC database. Predictions were performed by using 10 traditional tools with stepwise optimizations. Model predictive ability was evaluated using the five-fold cross-validations on variants of SCN1A, SCN2A, and KCNQ2. Additional validation was performed in SCN1A variants of damage-confirmed/familial epilepsy. The performance of commonly used predictors was less satisfactory for SCN1A with accuracy less than 80% and varied dramatically by functional domains of Nav1.1. Multistep individualized optimizations, including cutoff resetting, domain-based stratification, and combination of predicting algorithms, significantly increased predictive performance. Similar improvements were obtained for variants in SCN2A and KCNQ2. The predictive performance of the recently developed ensemble tools, such as Mendelian clinically applicable pathogenicity, combined annotation-dependent depletion and Eigen, was also improved dramatically by application of the strategy with molecular sub-regional stratification. The prediction scores of SCN1A variants showed linear correlations with the degree of functional defects and the severity of clinical phenotypes. This study highlights the need of individualized optimization with molecular sub-regional stratification for each gene in practice.

Comparison of Predictive In Silico Tools on Missense Variants in GJB2, GJB6, and GJB3 Genes Associated with Autosomal Recessive Deafness 1A (DFNB1A)

In silico predictive software allows assessing the effect of amino acid substitutions on the structure or function of a protein without conducting functional studies. The accuracy of in silico pathogenicity prediction tools has not been previously assessed for variants associated with autosomal recessive deafness 1A (DFNB1A). Here, we identify in silico tools with the most accurate clinical significance predictions for missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes associated with DFNB1A. To evaluate accuracy of selected in silico tools (SIFT, FATHMM, MutationAssessor, PolyPhen-2, CONDEL, MutationTaster, MutPred, Align GVGD, and PROVEAN), we tested nine missense variants with previously confirmed clinical significance in a large cohort of deaf patients and control groups from the Sakha Republic (Eastern Siberia, Russia): Сх26: p.Val27Ile, p.Met34Thr, p.Val37Ile, p.Leu90Pro, p.Glu114Gly, p.Thr123Asn, and p.Val153Ile; Cx30: p.Glu101Lys; Cx31: p.Ala194Thr. We compared the performance of the in silico tools (accuracy, sensitivity, and specificity) by using the missense variants in GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) genes associated with DFNB1A. The correlation coefficient (r) and coefficient of the area under the Receiver Operating Characteristic (ROC) curve as alternative quality indicators of the tested programs were used. The resulting ROC curves demonstrated that the largest coefficient of the area under the curve was provided by three programs: SIFT (AUC = 0.833, p = 0.046), PROVEAN (AUC = 0.833, p = 0.046), and MutationAssessor (AUC = 0.833, p = 0.002). The most accurate predictions were given by two tested programs: SIFT and PROVEAN (Ac = 89%, Se = 67%, Sp = 100%, r = 0.75, AUC = 0.833). The results of this study may be applicable for analysis of novel missense variants of the GJB2 (Cx26), GJB6 (Cx30), and GJB3 (Cx31) connexin genes.

The Tumor Suppressor PALB2: Inside Out

Partner and Localizer of BRCA2 (PALB2) has emerged as an important and versatile player in genome integrity maintenance. Biallelic mutations in PALB2 cause Fanconi anemia (FA) subtype FA-N, whereas monoallelic mutations predispose to breast, and pancreatic familial cancers. Herein, we review recent developments in our understanding of the mechanisms of regulation of the tumor suppressor PALB2 and its functional domains. Regulation of PALB2 functions in DNA damage response and repair occurs on multiple levels, including homodimerization, phosphorylation, and ubiquitylation. With a molecular emphasis, we present PALB2-associated cancer mutations and their detailed analysis by functional assays.

Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics

Background

The use of next-generation sequencing approaches in clinical diagnostics has led to a tremendous increase in data and a vast number of variants of uncertain significance that require interpretation. Therefore, prediction of the effects of missense mutations using in silico tools has become a frequently used approach. Aim of this study was to assess the reliability of in silico prediction as a basis for clinical decision making in the context of hereditary breast and/or ovarian cancer.

Methods

We tested the performance of four prediction tools (Align-GVGD, SIFT, PolyPhen-2, MutationTaster2) using a set of 236 BRCA1/2 missense variants that had previously been classified by expert committees. However, a major pitfall in the creation of a reliable evaluation set for our purpose is the generally accepted classification of BRCA1/2 missense variants using the multifactorial likelihood model, which is partially based on Align-GVGD results. To overcome this drawback we identified 161 variants whose classification is independent of any previous in silico prediction. In addition to the performance as stand-alone tools we examined the sensitivity, specificity, accuracy and Matthews correlation coefficient (MCC) of combined approaches.

Results

PolyPhen-2 achieved the lowest sensitivity (0.67), specificity (0.67), accuracy (0.67) and MCC (0.39). Align-GVGD achieved the highest values of specificity (0.92), accuracy (0.92) and MCC (0.73), but was outperformed regarding its sensitivity (0.90) by SIFT (1.00) and MutationTaster2 (1.00). All tools suffered from poor specificities, resulting in an unacceptable proportion of false positive results in a clinical setting. This shortcoming could not be bypassed by combination of these tools. In the best case scenario, 138 families would be affected by the misclassification of neutral variants within the cohort of patients of the German Consortium for Hereditary Breast and Ovarian Cancer.

Conclusion

We show that due to low specificities state-of-the-art in silico prediction tools are not suitable to predict pathogenicity of variants of uncertain significance in BRCA1/2. Thus, clinical consequences should never be based solely on in silico forecasts. However, our data suggests that SIFT and MutationTaster2 could be suitable to predict benignity, as both tools did not result in false negative predictions in our analysis.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0353-y) contains supplementary material, which is available to authorized users.

MERRF Classification: Implications for Diagnosis and Clinical Trials

BACKGROUND

Given the etiologic heterogeneity of disease classification using clinical phenomenology, we employed contemporary criteria to classify variants associated with myoclonic epilepsy with ragged-red fibers (MERRF) syndrome and to assess the strength of evidence of gene-disease associations. Standardized approaches are used to clarify the definition of MERRF, which is essential for patient diagnosis, patient classification, and clinical trial design.

METHODS

Systematic literature and database search with application of standardized assessment of gene-disease relationships using modified Smith criteria and of variants reported to be associated with MERRF using modified Yarham criteria.

RESULTS

Review of available evidence supports a gene-disease association for two MT-tRNAs and for POLG. Using modified Smith criteria, definitive evidence of a MERRF gene-disease association is identified for MT-TK. Strong gene-disease evidence is present for MT-TL1 and POLG. Functional assays that directly associate variants with oxidative phosphorylation impairment were critical to mtDNA variant classification. In silico analysis was of limited utility to the assessment of individual MT-tRNA variants. With the use of contemporary classification criteria, several mtDNA variants previously reported as pathogenic or possibly pathogenic are reclassified as neutral variants.

CONCLUSIONS

MERRF is primarily an MT-TK disease, with pathogenic variants in this gene accounting for ~90% of MERRF patients. Although MERRF is phenotypically and genotypically heterogeneous, myoclonic epilepsy is the clinical feature that distinguishes MERRF from other categories of mitochondrial disorders. Given its low frequency in mitochondrial disorders, myoclonic epilepsy is not explained simply by an impairment of cellular energetics. Although MERRF phenocopies can occur in other genes, additional data are needed to establish a MERRF disease-gene association. This approach to MERRF emphasizes standardized classification rather than clinical phenomenology, thus improving patient diagnosis and clinical trial design.

Zebrafish models of orofacial clefts

Zebrafish is a model organism that affords experimental advantages toward investigating the normal function of genes associated with congenital birth defects. Here we summarize zebrafish studies of genes implicated in orofacial cleft (OFC). The most common use of zebrafish in this context has been to explore the normal function an OFC-associated gene product in craniofacial morphogenesis by inhibiting expression of its zebrafish ortholog. The most frequently deployed method has been to inject embryos with antisense morpholino oligonucleotides targeting the desired transcript. However, improvements in targeted mutagenesis strategies have led to widespread adoption of CRISPR/Cas9 technology. A second application of zebrafish has been for functional assays of gene variants found in OFC patients; such in vivo assays are valuable because the success of in silico methods for testing allele severity has been mixed. Finally, zebrafish have been used to test the tissue specificity of enhancers that harbor single nucleotide polymorphisms associated with risk for OFC. We review examples of each of these approaches in the context of genes that are implicated in syndromic and non-syndromic OFC. Developmental Dynamics 246:897-914, 2017. © 2017 Wiley Periodicals, Inc.

DNA repair-related functional assays for the classification of BRCA1 and BRCA2 variants: a critical review and needs assessment

Mutation of BRCA1 and BRCA2 is the most common cause of inherited breast and ovarian cancer. Genetic screens to detect carriers of variants can aid in cancer prevention by identifying individuals with a greater cancer risk and can potentially be used to predict the responsiveness of tumours to therapy. Frequently, classification cannot be performed based on traditional approaches such as segregation analyses, including for many missense variants, which are therefore referred to as variants of uncertain significance (VUS). Functional assays provide an important alternative for classification of BRCA1 and BRCA2 VUS. As reviewed here, both of these tumour suppressors promote the maintenance of genome stability via homologous recombination. Thus, related assays may be particularly relevant to cancer risk. Progress in implementing functional assays to assess missense variants of BRCA1 and BRCA2 is considered here, along with current limitations and the path to more impactful assay systems. While functional assays have been developed to independently evaluate BRCA1 and BRCA2 VUS, high-throughput assays with sufficient sensitivity to characterise the large number of identified variants are lacking. Additionally, because of relatively low conservation of certain domains of BRCA1, and of BRCA2, between humans and rodents, heterologous expression in rodent cells may have limited reliability or capacity to assess variants present throughout either protein. Moving forward, it will be important to perform assays in human cell lines with relevance to particular tumour types, and to strengthen risk predictions based on multifactorial statistical analyses that also include available data on cosegregation and tumour pathology.

Antimicrobial activity predictors benchmarking analysis using shuffled and designed synthetic peptides

The antimicrobial activity prediction tools aim to help the novel antimicrobial peptides (AMP) sequences discovery, utilizing machine learning methods. Such approaches have gained increasing importance in the generation of novel synthetic peptides by means of rational design techniques. This study focused on predictive ability of such approaches to determine the antimicrobial sequence activities, which were previously characterized at the protein level by in vitro studies. Using four web servers and one standalone software, we evaluated 78 sequences generated by the so-called linguistic model, being 40 designed and 38 shuffled sequences, with ∼60 and ∼25% of identity to AMPs, respectively. The ab initio molecular modelling of such sequences indicated that the structure does not affect the predictions, as both sets present similar structures. Overall, the systems failed on predicting shuffled versions of designed peptides, as they are identical in AMPs composition, which implies in accuracies below 30%. The prediction accuracy is negatively affected by the low specificity of all systems here evaluated, as they, on the other hand, reached 100% of sensitivity. Our results suggest that complementary approaches with high specificity, not necessarily high accuracy, should be developed to be used together with the current systems, overcoming their limitations.