Introduction

The use of the device is subject to the term and conditions provided on the log-in screen, and to the ITA (Individual Tier Agreement).

Intended Use

AION is an in vitro diagnostic medical device software intended to qualitatively process next generation sequencing (NGS) data from genetic testing. This AI-driven IVD software identifies genetic variants, predicts their classification based on the integration of multiple annotation sources and generates a variant ranking based on disease association. AION is a semi-automated clinical-decision support software to assist healthcare professionals in making informed medical decisions regarding clinically relevant genetic variants and genetic diseases.

Indications for use

AION’s indication of use relates to mendelian genetic diseases testing. Genetic testing involves the detection and assessment of specific alleles, variants and genotypes, that are associated with heritable traits and diseases or predispositions to disease for the individual or their descendants (diagnostic, screening and predictive testing).AION’s Intended patient population includes patients of all ages (prenatal, postnatal), sex, and ancestries who might undergo NGS sequencing analysis with the aim of testing for genetic diseases.

Intended users

AION is intended to be used as part of the diagnostic workflow for NGS-based genetic testing by healthcare professionals (eg. human genetics professionals, geneticists, variant scientists, and healthcare practitioners with expertise in genetics). The software is not intended to be used by lay users.

Operating Principle

The AION software is a cloud-based clinical decision support (CDS) tool designed to facilitate the interpretation of genetic variants in the context of genetic diseases. Its primary function is to predict the pathogenicity of genetic variants using artificial intelligence (AI) models, leveraging the interpretation of next-generation sequencing (NGS) data.

1. Input Data and Preprocessing:

• The software accepts VCF (Variant Call Format) files containing genetic variant information (small variants (SNVs, SNPs, INDELS) and CNVs/SVs) generated from secondary bioinformatic analysis of NGS data, including Whole Exome Sequencing (WES) and Whole Genome Sequencing (WGS). The VCF format recommendations are standardized across short-read sequencing platforms, predominantly Illumina (for further information see the IFU sections: VCF Format; Variant type, genomic regions and sequencing technologies; Limitations and Contraindications).
• Singletons (proband) and trios (proband-mother-father) are supported for small variants (for CNVs currently only probands are supported). In line with previous reports, trio analysis improves the variant classification performance over proband analysis. In addition, current analysis supports both reference genomes GRCh37/hg19 and GRCh38/hg38 which both performed with high quality. See information about AION VCF format, and variant types at Instructions for Use. 
• For optimal variant prioritizer performance (variant ranking), the input may include patient clinical data, such as phenotypic features described using Human Phenotype Ontology (HPO) terms. Inclusion of HPO information is recommended (see section of Performance Characteristics) as it improves the variant ranking performance for both small variants (in singleton cases) and CNVs, where the effect is even more significant. 

• Upon submission, the software performs quality control checks on the VCF files to ensure data integrity. See recommendations about VCF format and quality criteria at the Instructions for Use section (VCF Format), following best practices for secondary analysis, and dependent on the NGS sequencing platform. 

AION uses a comprehensive variant annotation pipeline that enriches the raw genetic data with relevant contextual information. This includes:

• Genetic Properties: Annotations such as evolutionary conservation scores (phyloP, phastCons) provide insight into the evolutionary importance of the genomic regions where variants occur.
• Molecular Properties: Predicted molecular consequences of the variants, such as whether they result in missense or frameshift mutations.
• Clinical Properties: Associations between variants and known genetic diseases, as well as related clinical phenotypes (HPO terms).

The complete list of annotations is as follows:

• Genomic position of the variant (chr, position, ref, alt)
• Characteristics of the variant in sequencing data (GT, GQ, DP, AD, VAF)
• Zygosity and segregation of the variant in the submitted samples(zygosity proband, zygosity mother, zygosity father, segregation)
• Reference gene, transcript and protein for annotations (hgnc_gene, hgnc_id, ENST_id, RefSeq_TranscriptID, exon_intron, HGVSc, ENSP_id, RefSeq_PeptideID, HGVSp)
• Predicted consequences of the mutation (most_sev_conseq, all_mut_types, impact)
• Population variant frequency (gnomAD_AC, gnomAD_AN, gnomAD_AF, gnomAD_hom, gnomAD_AC_genomes, gnomAD_AN_genomes, gnomAD_AF_genomes, gnomAD_hom_genomes, gnomAD_AC_exomes, gnomAD_AN_exomes, gnomAD_AF_exomes, gnomAD_hom_exomes)
• Evolutionary prediction scores (pCons46_ver, phyloP_ver)
• Splicing predictions (splice_type, dbscSNV_ada_score, dbscSNV_rf_score, NMD, NMD_boundary)
• Protein predictions (SIFT_class, SIFT_score, PolyPhen_class, PolyPhen_score, uniprot_domains)
• Gene haploinsufficiency scores (pLI, pRec, pNull, LOEUF, haploinsufficiency)

Variant classification is provided following three approaches:

• Public Database Annotations: The software incorporates classifications from public repositories like ClinVar (ClinVar classification, ClinVar_star_review, ClinVarID), which include expert-reviewed data on variant pathogenicity.
• Automated ACMG Classification: AION applies the American College of Medical Genetics and Genomics (ACMG) guidelines using a rule-based algorithm (ACMG_classification, ACMG_applied_rules ). This involves scoring each variant according to 28 criteria and assigning a classification based on these scores: “pathogenic”, “likely pathogenic”,"VUS” (Variant of Unknown Significance), likely benign" and "benign".This is currently the golden standard for variant classification.
• Proprietary AI Model (AION Predictor): AION employs a machine learning model trained to predict variant pathogenicity. This model considers various features, such as the functional impact on RNA and protein levels and constraints to variation (indicating the biological significance of the variant's location). The AION predictor (AION_classification, AION_variant_score) generates a classification and a confidence score for each variant, which are pre-computed and stored in a database. Based on this score, the algorithm provides a predicted variant classification: “pathogenic”(85% - 100%),“likely pathogenic” (60% - 85%),"VUS” (Variant of Unknown Significance, 40% - 60%), “likely benign" (15% - 40%) and "benign” (0% - 15%).

• The software outputs a ranked list of variants, prioritizing those most likely to be pathogenic based on a combination of annotation data, ACMG criteria, and AI-based predictions.
• Each variant is accompanied by a confidence score and detailed annotations, which include molecular effects, conservation data, population frequency, etc. This information aids genetic professionals in critically assessing the results.
• The list also includes potential gene-disease associations, with each variant linked to possible clinical diagnoses based on observed patient symptoms and known gene-disease relationships.

• AION offers both fully automated and semi-automated manual modes. In the semi-automated manual mode, users can apply custom filters to the annotated and classified variants, allowing for a more tailored analysis based on specific clinical questions or research interests.
• The software also supports variant storage and interpretation reporting, enabling ongoing case management and historical data analysis.

This comprehensive approach integrates bioinformatic data processing, AI-driven predictive modeling, and detailed annotation to assist healthcare professionals in making informed clinical decisions regarding genetic diseases.

Performance Characteristics

AION’s tertiary analysis approach based on AI/machine learning effectively automates the process of variant interpretation of genetic tests for the diagnosis of rare genetic disorders. Machine learning (ML) and automated tools on genetic variant classification, prioritization, and interpretation in the context of diagnosing rare diseases have significantly enhanced the accuracy, efficiency, and reliability of genetic diagnostics by integrating deep phenotyping, genotype-phenotype knowledge, and advanced computational methods (AION’s scientific validity).

AION’s clinical evidence is gathered from published experience from routine diagnostic testing supported by other sources of clinical performance data, as synthetic datasets that simulates the complexities and variability of genomic data.

AION’s performance is evaluated based on the state of the art benchmarking approach for NGS-based genetic testing. It addresses both analytical and clinical AION’s performance specifications per genetic variant type (small variants and CNVs/SV) from a retrospective real patient data submitted by diagnostic laboratories across Europe, encompassing individuals who underwent clinical exome and genome sequencing for diagnostics of rare genetic disease, being supplemented by other sources of data as synthetic cohorts from a GIAB-derived benchmark “truth set”. AION’s analytical performance is determined by the analytical sensitivity, confidence intervals and precision (repeatability/reproducibility). AION’s clinical performance is determined by the diagnostic sensitivity, confidence intervals, PPV, rank (position of the causative variant in the prioritized variant list) and total number of unique variants (total number of variants in the prioritizer list)

A summary of AION’s analytical and clinical performance results is shown in the subsection below. 

AION’s clinical performance based on published experience from routine diagnostic testing

A retrospective clinical cohort of 252 cases was assembled from real patient data submitted by diagnostic laboratories across Europe, encompassing individuals who underwent clinical exome and genome sequencing for diagnostics of rare genetic disease. These positive cases were carefully selected to evaluate AION because they reflect the diversity encountered in real-world diagnostic settings, including both technical complexities from varied laboratory workflows and clinical challenges presented by edge cases. Diagnostic variants were defined as variants considered clinically relevant and reported back to the clinician based on genetic, phenotypic, and segregation information from the patient. 

Utilizing data from routine diagnostic testing offers significant value by providing insights from larger, heterogeneous and more complex populations. This cohort captures variations in wet lab protocols, sequencing approaches, secondary analysis workflows and the quality of clinical information available for analysis. The cohort included 138 cases analyzed using the GRCh37 reference genome, including 122 cases with small variants and 16 cases with copy number variations (CNVs). The remaining 114 cases utilized the GRCh38 reference genome and included 90 cases with small variants and 24 cases with CNVs. Overall, the cohort included 159 singletons, comprising 119 singletons with small variants and 40 singletons with CNVs, and 93 trios, which exclusively featured small variants. 

To evaluate the influence of phenotypic data on variant interpretation, all cases were initially submitted with Human Phenotype Ontology (HPO) terms and subsequently re-analyzed without them. This diverse distribution of reference genomes, variant types and case structures ensures that the evaluation of AION’s performance accurately reflects the varied genomic configurations and clinical scenarios encountered in everyday diagnostic settings. This diversity is essential to identify less common, potentially serious device limitations and adverse events. By incorporating this comprehensive and varied data, our aim is to demonstrate AION’s capability to perform reliably across a wide range of clinical environments, ensuring robust and accurate variant interpretation in routine diagnostic workflows.

Table 3: Analytical and clinical performance results from the retrospective clinical cohort for SNVs (n=212) and CNVs/SVs (n=40). Results presented include a subset of the analytical performance results (analytical sensitivity, including confidence intervals), as well as clinical performance results (diagnostic sensitivity, including confidence intervals, positive predictive value (PPV), average variant rank and average number of unique variants). CI: Confidence Interval, PPV: Positive Predictive Value.

*Performance results for CNVs in GRCh38 correspond to a previous version of AION (v3.8.0.0 with rd-aion-wrapper v7.0.0). While we previously had access to this dataset, it is no longer available to benchmark v3.10.0.0. Efforts are currently underway to update our cohort and we anticipate providing updated results in the near future.

**Without HPOs, the average rank for CNVs exceeds the predefined acceptance criteria of <10, reaching 12.6 ± 1 compared to 8.7 ± 2.1 when HPOs are included. While the ranking performance without HPOs is affected, the system retains its diagnostic sensitivity, ensuring the identification of clinically significant variants and maintaining alignment with its intended clinical use. The optional nature of the HPO feature provides flexibility for users who may not have phenotypic data available. This limitation in CNV ranking performance in the absence of HPO data is explicitly included as a limitation of AION for CNV analysis and can be found in the Limitations of Use and Contraindications section.

Performance in Small Variants

The small variant analysis cohorts provide real-world evidence of AION's use cases, reflecting its application in diverse clinical scenarios. For GRCh37, analytical sensitivity was 85.3%, while GRCh38 achieved a sensitivity of 77.8%. Diagnostic sensitivity was 70.5% for GRCh37 and 63.3% for GRCh38. These datasets include borderline and complex cases, offering valuable insights for performance optimization. Although the diagnostic sensitivity is slightly lower than that observed in synthetic datasets (see Summary of Performance Data from Other Sources available for comparative purposes), AION consistently prioritized disease-causing variants with an average rank of 2.1, demonstrating its reliability in handling diverse and challenging cases.

While HPO data do not directly affect AION’s analytical performance metrics, their inclusion significantly improves diagnostic sensitivity, confidence intervals, and variant ranking. Running AION without HPOs results in a more than threefold reduction in ranking performance, with the causative variant dropping to as low as the seventh position in the prioritized list. This underscores the importance of HPOs in enhancing AION's diagnostic capabilities.

These findings align with observations from synthetic cohorts, although the effects of HPO inclusion are more pronounced in real-world cases than in GIAB-derived synthetic datasets. This indicates that HPOs are especially critical for improving AION's diagnostic performance in clinical settings. Consequently, we strongly recommend including HPOs in the analysis, as advised in the Instructions for Use, to maximize diagnostic accuracy.

The inclusion of HPOs had a significant impact on clinical performance metrics, notably diagnostic sensitivity, confidence intervals, and variant ranking. Without HPOs, AION's ranking performance decreased significantly, with the causative variant dropping to 7.5 ± 0.7 on the prioritized list compared to 2.1 ± 0.3 with HPOs. This pattern was more prominent in the clinical cohort than in synthetic datasets, further emphasizing the relevance of clinical data in enhancing AION's diagnostic sensitivity. Despite the reduced performance in clinical cohorts compared to synthetic datasets, the observed trends are consistent, underscoring AION's adaptability to real-world variability and its potential to improve diagnostic outcomes in complex clinical environments.

Performance metrics differ between reference genomes, with GRCh37/hg19 outperforming GRCh38/hg38 in analytical and diagnostic sensitivity and confidence intervals. These differences may stem from the smaller GRCh38/hg38 cohort size, reduced annotation maturity, and variability in real-world cases. Clinical cohorts show lower sensitivity and wider confidence intervals compared to synthetic datasets, which rely on highly curated GIAB data with spiked-in variants and much larger sample sizes (>1,000 vs. 90–122). Despite these disparities, consistent trends across datasets underscore the need for optimized annotations and larger real-world datasets to further enhance AION’s diagnostic accuracy and clinical utility across reference genomes. Overall, these findings reaffirm the robustness of AION's clinical performance across reference genomes and emphasize the importance of selecting appropriate annotations to maintain diagnostic accuracy and consistency in clinical workflows.

Performance in CNVs/SVs

Copy Number Variants (CNVs) present unique challenges compared to small variants, primarily due to their reliance on upstream processes (e.g., sequencing and secondary analysis) to distinguish true disease-causing variants from false positives caused by sequencing artifacts. These challenges are further compounded by the relative rarity of CNVs in clinical practice, as they account for only about 10% of disease-causing variants in monogenic disorders. This scarcity limits the availability of robust benchmarking datasets for evaluating CNV detection and interpretation.

The analytical sensitivity for CNVs is 92.3% (95% CI: 64–99.8%), comparable to that of small variants. However, the wider confidence intervals reflect the inherent variability and uncertainty in accurately interpreting larger structural changes. In the GRCh37 cohort for CNVs (n=16), AION demonstrated a diagnostic sensitivity of 76.3% (95% CI: 54.55%–98.08%) and a precision (PPV) of 94.6%, highlighting its ability to accurately prioritize causative variants. The average rank of causative variants was 8.7 ± 2.1, underscoring its reliable prioritization capabilities. In the GRCh38 cohort (n=24), analyzed using an earlier version of AION (v3.8.0.0), the platform achieved a PPV of 95% and an average rank of 1.6 for causative variants, providing a benchmark for its performance in different reference genomes.

Establishing a comprehensive benchmarking dataset for CNVs remains particularly challenging. The rarity of these variants, coupled with variability in detection due to differing sequencing technologies, creates significant hurdles. Precisely identifying breakpoints is another complication, making validation efforts even more difficult. Despite these obstacles, ongoing improvements to CNV workflows and efforts to establish more reliable benchmarking standards are critical to enhancing AION’s performance in this area and bolstering its overall clinical utility. 

Clinical performance for CNVs is strongly influenced by the inclusion of HPO data. Although the absence of HPO terms does not significantly affect diagnostic sensitivity, it has a notable impact on variant ranking, underscoring the importance of phenotypic data in CNV/SV analyses. While the optional nature of the HPO feature provides flexibility for users without phenotypic data, including HPO terms ensures more accurate and clinically relevant variant ranking. For optimal CNV/SV analysis, we strongly recommend incorporating HPO data (see the Limitations of Use and Contraindications section). Despite ranking impacts, AION maintains its ability to identify clinically significant variants, ensuring its diagnostic sensitivity aligns with its intended role as a clinical decision support tool.

Regarding the impact of the reference genome, the lack of access to an updated hg38 cohort necessitated using data from an earlier pipeline version for comparison. Results from both reference genomes and pipeline versions are analytically comparable within the same performance range. Differences, particularly in confidence intervals, likely stem from the limited sample size in current CNV customer cohorts. Notably, PPV data remain consistent, although significant differences in variant ranking are observed.

To address current limitations in datasets for CNV analysis, we are actively expanding our cohorts to include a greater number of samples, which will further enhance benchmarking reliability and performance evaluation.

Performance Conclusions

AION’s benchmarking results highlight its effectiveness in addressing diagnostic challenges, handling complex cases, and supporting clinical laboratories in managing their workflows. Its continuous validation process ensures alignment with current standards and regulatory requirements, contributing to its reliability as a tool for genetic diagnostics. These findings underscore AION’s potential to improve diagnostic workflows and deliver better outcomes for patients with rare genetic diseases.

Our results demonstrate the robust analytical and clinical performance of AION, highlighting its reliability, adaptability and impact in advancing genetic diagnostics. Analytical evaluations validate AION’s technical precision and consistency, while clinical assessments underscore its effectiveness in real-world diagnostic settings. The inclusion of phenotypic information through HPO terms further strengthens performance, particularly in CNV cases.

The comparison between GRCh37 and GRCh38 reference genomes illustrates AION’s adaptability across different genomic assemblies, with advanced annotation resources in GRCh38 contributing to improved prioritization outcomes. While current limitations in CNV datasets present challenges, AION demonstrates strong diagnostic capabilities, achieving high sensitivity and precision across diverse scenarios. Ongoing efforts to expand clinical datasets, particularly for GRCh38, will further enhance its validation and address existing gaps. 

The results of these evaluations position AION as a state-of-the-art solution in genomic medicine. Its analytical and clinical performance showcases AION’s ability to deliver accurate, efficient and actionable insights for rare disease diagnostics. By bridging analytical precision with real-world applicability, AION sets a new standard for the integration of artificial intelligence in clinical genomics, reaffirming its value as a transformative tool for modern medicine.

Of note, limitations to these performance data include but are not limited to: 

• Performance data for NGS tertiary analysis is limited by the lack of standardized benchmarking sets with validation cohorts that might not reflect real-world diversity (genetic backgrounds, etc.). Inconsistent data quality from different sequencing platforms and GDPR restrictions on data access further complicate the benchmarking process.
• Limitations regarding the availability of validation data sets for certain variant types (eg. CNVs/SV, n=16) shall be taken into consideration when evaluating these data as well as other limitations related to the state of the art benchmarking validation approach for NGS analysis (see limitations section for more details).
• AION’s clinical performance validation methodology is subjected to continuous improvement and to reference data set collection refinement by addition of novel gold set standards and/or real-world-data (CNV/SV variants limitation).

Results gathered in the tables above are representative of AION’s analytical and clinical performance and obtained from AION version v.3.10.0.0. Data updated on December 20th, 2024.

For more information see the white paper “Enhancing Rare Disease Diagnostics: Updated Performance Evaluation and Advancements of the AION AI-Driven Variant Interpretation Platform” available at the Nostos Genomic’s website Nostos Genomics - Latest News (nostos-genomics.com), under the section of Resources/Whitepapers

Summary of Performance data from other sources

Concerns over data privacy and security significantly limit access to large and diverse patient genetic and clinical datasets essential for robust validation. While real patient samples provide the most accurate assessments, these limitations prompted us to simulate genetic data for 1089 individuals with rare genetic diseases. We achieved this by introducing disease-causative variants into high-quality control reference genomes and linking this genetic data to its associated clinical phenotype. The variants and clinical data used in this process were meticulously identified and curated from reputable peer-reviewed publications and through collaborations with scientific research groups. This approach enabled the creation of an extensive and heterogeneous reference dataset, allowing for a comprehensive evaluation of AION’s variant interpretation capabilities at scale while effectively circumventing the privacy and security constraints associated with accessing actual patient genetic data. 

A comprehensive range of genetic scenarios was simulated to mirror the diverse conditions found in real-world clinical laboratories, ensuring AION’s reliability across various genomic configurations and data availability. This included both trios and singletons to evaluate performance in familial and individual cases, the use of GRCh37 and GRCh38 reference genomes for compatibility across different assemblies and simulations with and without clinical data encoded as Human Phenotype Ontology (HPO) terms to assess effectiveness under varying levels of phenotypic information.

Table 4: Analytical and clinical performance results from the synthetic validation cohort for Single Nucleotide Variants (SNVs) (n=1089). Cases were analyzed as trios versus singletons to evaluate the impact of including parental genetic data. The analyses for the GRCh37 cohort were repeated with and without HPO terms to assess the effect of incorporating phenotype data. Additionally, the same cases were analyzed using reference genomes GRCh37 and GRCh38 to compare the impact of different genome assemblies on clinical performance. Results presented include a subset of the analytical performance results (analytical sensitivity, including confidence intervals), as well as clinical performance results (diagnostic sensitivity, including confidence intervals, positive predictive value (PPV), average variant rank and average number of unique variants). CI: Confidence Interval, PPV: Positive Predictive Value.

The trio analysis demonstrates higher analytical sensitivity and improved confidence intervals for sensitivity, indicating reduced bias in the trio-based approach. Regarding clinical performance, results for small variants reveal enhanced diagnostic sensitivity with the trio approach, reflected in less biased sensitivity confidence intervals. There are no significant differences in Positive Predictive Value (PPV) or variant ranking. Importantly, incorporating parental genetic background (trios) significantly impacts the total number of identified variants and associated performance metrics in variant classification and prioritization. Clinical performance data align with the analytical performance trends, particularly in sensitivity and confidence intervals. However, variant rankings remain consistent, suggesting that while variant discovery and performance metrics improve with trios, the prioritization process itself remains stable.

Clinical information input (e.g., HPO terms) does not significantly affect AION’s analytical specifications. For clinical performance, results from synthetic cohorts show that HPO data have minimal impact on diagnostic sensitivity in the trio approach, as parental information provides sufficient clinical context. Conversely, in the singleton approach, where only the proband file is analyzed, diagnostic sensitivity significantly decreases in the absence of HPO terms. Furthermore, in singletons without HPO data, variant ranking drops, with the causative variant averaging the third position in the prioritized list, compared to the first position when HPO terms are included. Therefore, optimal variant prioritization (ranking) requires patient clinical data (HPO terms) as input.

AION’s analytical performance is not significantly affected by the choice of reference genome, regardless of the genetic analysis approach (singleton vs. trios). The observed trend of enhanced analytical performance in trios compared to singletons (e.g., 94% vs. 90% analytical sensitivity) remains consistent across reference genomes. Clinical performance evaluation indicates some differences between reference genomes; however, these differences are within acceptable performance criteria and align with expected outcomes. These findings confirm the robustness of AION’s clinical performance across reference genomes, emphasizing the importance of selecting appropriate annotations to ensure diagnostic accuracy and consistency in clinical workflows.

Intended Environment

The typical use environment is an office or genetic testing laboratory.

AION runs on cloud computing.

Browser specifications to run AION are as follows. Minimum hardware requirements to run app UI, the minimal requirements would be:

• A device with 2GB of RAM or more

• A recent version of a web browser (such as Google Chrome, Firefox, Safari, or Microsoft Edge)

• A CPU with 2 cores or more

• A resolution of 1024x768 or higher

Limitations of use and contraindications

AION’s indications of use relate to mendelian genetic diseases, consequently interpretation of variants that may not follow traditional mendelian patterns (such as somatic variants implicated in cancer or pharmacogenetics, PGx) is currently excluded from the indications of use (“limitations of use or contraindications”). Thus: 

• AION is not intended for the analysis of somatic variants implicated in cancer.
• AION is not intended for gene-drug association analysis or drug dosing recommendations based on pharmacogenetic variant analysis (PGx). It is also not intended for biomarker analysis in combination with therapeutic drugs (companion diagnostics, CDx).
• AION is not intended for the interpretation of genetic variation associated with trisomy 21, diagnosing phenylketonuria and determining HLA tissue types DR, A and B.

AION mitigates the risk of use for the diagnosis of the aforementioned conditions out-of-device's intended use through different approaches, including the design of the user interface for case submission, as well as quality controls within the genetic analysis pipeline. 

• Somatic variants implicated in cancer. The analysis of somatic variants in a tumor would typically require the submission of a tumor sample and a germline sample for comparison. The submission of this type of analysis is not supported by the device, which only allows for the uploading of VCF files including two formats: 1) singleton (only proband) or 2) trio (proband, mother and father). Furthermore, variants with a variant allele frequency deviating from the expectation for germline variants (i.e. somatic variants with a VAF < 20%) are flagged in our quality control module of the analysis and are excluded from the prioritized results.

Additional control mitigating actions to prevent the risk of AION reporting variants out of its intended use, include a meticulous manual curation of AION’s datasets (gene-disease dictionaries), in order to ensure that only clinically relevant Mendelian diseases are retained and variants included on AION’s intended use reported. This process effectively excludes the majority of somatic cancer-related diseases (exclusion criteria): 

• Late-onset somatic mutations
• Diseases requiring additional disease processes (e.g. clonal expansion of mutated hematopoietic stem cells) to manifest.
• Disease heavily influenced by environmental factors.
• Complex multigenic diseases where no single gene mutation can definitively be linked to the disease phenotype.

• Pharmacogenetic variant analysis and companion Dx. AION user interface does not support uploading of information related to patient’s drug prescription and treatments, which are needed for pharmacogenetics and companion diagnostics analysis. In addition, the platform is not intended to provide or analyze pharmacogenetic information:

• AION does not include specific data related to pharmacogenes or gene-drug interactions. 
• AION does not rely on any dedicated PGx database (such as PharmGKB, CPIC, etc.), but on data from OMIM (Online Mendelian Inheritance in Man), which is not comprehensive for PGx purposes as it does not explicitly tag pharmacogenetic relationships. Nevertheless, AION datasets have been manually curated to exclude diseases potentially associated with susceptibility factors and connected to pharmacogenetic data within OMIM. 
• AION currently does not contain genes with clear ties to pharmacogenetics in the context of drug metabolism. For example, genes like CYP2D6 and CYP2C9—key players in pharmacogenetic analysis—have been removed to ensure compliance with AION's exclusion policy.

• Genetic variation associated with trisomy 21.The diagnosis of trisomy 21 is typically carried out through other methods such as karyotypes where an aneuploidy can be directly visualized or by microarray to observe the increase of signal indicating aneuploidy. The output files from these methods and the type of genetic variation detected are not compatible with AION’s platform, which only allows for uploading VCF files containing SNVs, indels, CNVs and SVs in one of the following two formats: 1) singleton (only proband) or 2) trio (proband, mother and father). Consequently, it will be rejected during analysis, indicating to the user that an error has occurred due to file incompatibility.

Of note, although AION’s analysis results could potentially include variants associated with somatic mutations, variants with companion diagnostic indications, or other variants listed as limitations/contraindications the mechanisms reported herein efficiently mitigate the reporting of these variants and ensure that only variants included on AION’s intended use are reported. 

Nevertheless, AION is contraindicated for somatic variants, T21 and PGx, and it is responsibility of the healthcare professional as intended user of AION to acknowledge this potential foreseeable device’s misused. 

Additional limitations of use of AION are related to:

• Device’s analytical performance: AION supports gene panel data prioritisation as long as there is data within the supported regions. The supported regions for prioritisation are:

• Coding regions
• ±50 bp around coding regions
• Known variants from ClinVar outside of these regions are also included in the analysis

• Device’s performance testing limitations (benchmarking): AION’s benchmarking-based performance testing includes limitations regarding to the control reference datasets, such as: 
  • The control dataset shares a uniform genetic background, so the device’s performance across different ethnicities has not yet been evaluated.
  • The control dataset does not include variants located on chromosome Y (chrY) or mitochondrial DNA (chrMT).
  • The control dataset is limited to unaffected singletons and parent-child scenarios..
  • Control genomes from GIAB are of exceptionally high quality and lack the artifacts that may be present in actual customer cases, resulting in a potential risk of overfitting throughout device’s performance testing.

Of note, to avoid these limitations additional performance datasets from real world data is used for AION’s performance evaluation.

Additional limitations to the state-of-art benchmarking approach for NGS tertiary analysis performance evaluation are indicated at the end of the Performance characteristics section. 

• Effect of HPO terms on variant prioritization: The inclusion of HPO terms is crucial for optimizing variant prioritization in genetic analysis. This is particularly relevant for both small variants (singleton cases) and CNVs/SVs. For small variants, the addition of HPO terms enhances the performance of variant ranking, improving the algorithm’s ability to identify clinically relevant findings. Although the absence of HPO terms does not compromise the diagnostic sensitivity of the software, it impacts its ability to effectively prioritize variants, potentially delaying or complicating clinical interpretation.
  In the case of CNVs, the importance of HPO data is even greater due to the inherent complexity of analyzing these types of variants. Without phenotypic information, the software’s capability to prioritize relevant CNVs is diminished, increasing the risk of overlooking significant findings or misinterpreting results. To mitigate this, it is strongly recommended to include HPO terms for CNV/SV analysis. The absence of phenotypic data is a limitation and a potential risk that may impact the analysis’s clinical utility. Including HPO terms is essential for achieving accurate and clinically acceptable analysis results. 

• Input VCF files quality. The quality of the VCF files, influenced by the secondary analysis performed by users, is crucial for optimal results, please see AION requirements for more information on VCF input data and recommendations for secondary analysis best practices.
• Sequencing technologies. AION’s quality control module includes tools to detect potential artifacts associated with specific sequencing technologies. We have also implemented a control list of artifacts (including information from over 200 VCF files and from scientific literature, Maffucci et al, 2018), which filters out variants commonly associated with sequencing errors across various platforms.In addition in the IVDB (Internal Variant Database) feature the software compute frequencies internal to each lab, and they could add specific filters, according to its potential sequencing  artifacts.
  • Short-read platforms

• AION has been designed and thoroughly validated with Next Generation Sequencing data from short-read platforms, mainly from Illumina technology.
• Data from IonTorrent platforms and its evolutions are supported, but have produced many artifacts. Before using it, we recommend to AION's users that the VCF data has been filtered for common artifacts from their setup.

• Long-read platforms. 

Data from long read technologies has not been thoroughly tested. If you need to run samples with this type of data, we recommend you get in touch so we can quickly troubleshoot any potential issues and make sure the results have high quality.

• AION is a AI/ML-based tertiary analysis software. ML-based automated variant interpretation faces challenges such as incomplete analysis due to specialized algorithms, poor input data quality, and performance variability across diverse populations due to biased variant databases. Furthermore, though the algorithm is designed to classify genetic variants with high accuracy, it is not infallible. Incorrect variant classification predictions by ML-based algorithms might lead to the exclusion of clinically relevant variants from analysis and results (false negatives or false positives).  

As a clinical decision support software, we recommend the healthcare professional  to use the algorithm's results as a supportive tool rather than the sole basis for clinical decisions, and recommend that they conduct additional validation or consult with clinical experts when interpreting results to ensure accurate diagnosis and patient care.

Additional precautions or warnings are disclosed at AION’s Safety information section within this Instructions for Use.

Training for users

Users can request or schedule training sessions during the demo and onboarding trial period. These sessions are designed to cover the initial setup of AION, the key functionalities of AION and learn how to use them effectively, and common troubleshooting and support strategies. 

To schedule a session, please contact us at:

• Website: nostos-genomics.com
• Email: support@nostos-genomics.com

However, these Instructions for Use provide all the essential information needed for the safe and effective operation of AION. These instructions also include visual support with clickable flows to enhance user guidance.

Additionally, in the “Example Use Cases” section, we provide practical examples illustrating how AION can assist in identifying causative variants in patients. These cases include both clinical information as well as VCF files for users' training.

For more information or additional assistance, please feel free to contact our support team.

Safety Information

Please see AION-IFU section Safety Information for product safety information in English and applicable EU members' languages.