Analytical Validation of a Telomerase Reverse Transcriptase (TERT) Promoter Mutation Assay.

CONTEXT: TERT promoter mutated thyroid cancers are associated with a decreased rate of disease free and disease specific survival. High quality analytical validation of a diagnostic test promotes confidence in the results which inform clinical decision making. OBJECTIVE: To demonstrate the analytical validation of the Afirma TERT promoter mutation assay. METHODS: TERT promoter C228T and C250T variant detection in genomic DNA (gDNA) was analyzed by assessing variable DNA input and the limit of detection (LOD) of variant allele frequency (VAF). The negative and positive percent agreement (NPA and PPA) of the Afirma TERT test was examined against a reference primer pair as was the analytical specificity from potential interfering substances (RNA and blood gDNA). Further, the intra-run, inter-run and inter-laboratory reproducibility of the assay were tested. RESULTS: The Afirma TERT test is tolerant to variation in DNA input amount (7-13 ng) and can detect expected positive TERT promoter variants down to 5% VAF LOD at 7ng DNA input with > 95% sensitivity. Both NPA and PPA were 100% against the reference primer pair. The test remains accurate in presence of 20% RNA or 80% blood gDNA for an average patient sample that typically has 30% VAF. The test also demonstrated a 100% confirmation rate when compared with an external NGS-based reference assay executed in a non-Veracyte laboratory. CONCLUSION: The analytical robustness and reproducibility of the Afirma TERT test support its routine clinical use among thyroid nodules with indeterminate cytology that are Afirma GSC suspicious or among Bethesda V/VI nodules.

A Nasal Swab Classifier to Evaluate the Probability of Lung Cancer in Patients With Pulmonary Nodules.

BACKGROUND: Accurate assessment of the probability of lung cancer (pCA) is critical in patients with pulmonary nodules (PNs) to help guide decision-making. We sought to validate a clinical-genomic classifier developed using whole-transcriptome sequencing of nasal epithelial cells from patients with a PN ≤ 30 mm who smoke or have previously smoked. RESEARCH QUESTION: Can the pCA in individuals with a PN and a history of smoking be predicted by a classifier that uses clinical factors and genomic data from nasal epithelial cells obtained by cytologic brushing? STUDY DESIGN AND METHODS: Machine learning was used to train a classifier using genomic and clinical features on 1,120 patients with PNs labeled as benign or malignant established by a final diagnosis or a minimum of 12 months of radiographic surveillance. The classifier was designed to yield low-, intermediate-, and high-risk categories. The classifier was validated in an independent set of 312 patients, including 63 patients with a prior history of cancer (other than lung cancer), comparing the classifier prediction with the known clinical outcome. RESULTS: In the primary validation set, sensitivity and specificity for low-risk classification were 96% and 42%, whereas sensitivity and specificity for high-risk classification was 58% and 90%, respectively. Sensitivity was similar across stages of non-small cell lung cancer, independent of subtype. Performance compared favorably with clinical-only risk models. Analysis of 63 patients with prior cancer showed similar performance as did subanalyses of patients with light vs heavy smoking burden and those eligible for lung cancer screening vs those who were not. INTERPRETATION: The nasal classifier provides an accurate assessment of pCA in individuals with a PN ≤ 30 mm who smoke or have previously smoked. Classifier-guided decision-making could lead to fewer diagnostic procedures in patients without cancer and more timely treatment in patients with lung cancer.

Preoperative Identification of Medullary Thyroid Carcinoma (MTC): Clinical Validation of the Afirma MTC RNA-Sequencing Classifier.

Background: Cytopathological evaluation of thyroid fine-needle aspiration biopsy (FNAB) specimens can fail to raise preoperative suspicion of medullary thyroid carcinoma (MTC). The Afirma RNA-sequencing MTC classifier identifies MTC among FNA samples that are cytologically indeterminate, suspicious, or malignant (Bethesda categories III-VI). In this study we report the development and clinical performance of this MTC classifier. Methods: Algorithm training was performed with a set of 483 FNAB specimens (21 MTC and 462 non-MTC). A support vector machine classifier was developed using 108 differentially expressed genes, which includes the 5 genes in the prior Afirma microarray-based MTC cassette. Results: The final MTC classifier was blindly tested on 211 preoperative FNAB specimens with subsequent surgical pathology, including 21 MTC and 190 non-MTC specimens from benign and malignant thyroid nodules independent from those used in training. The classifier had 100% sensitivity (21/21 MTC FNAB specimens correctly called positive; 95% confidence interval [CI] = 83.9-100%) and 100% specificity (190/190 non-MTC FNAs correctly called negative; CI = 98.1-100%). All positive samples had pathological confirmation of MTC, while all negative samples were negative for MTC on surgical pathology. Conclusions: The RNA-sequencing MTC classifier accurately identified MTC from preoperative thyroid nodule FNAB specimens in an independent validation cohort. This identification may facilitate an MTC-specific preoperative evaluation and resulting treatment.

Clinical validation and utility of Percepta GSC for the evaluation of lung cancer.

The Percepta Genomic Sequencing Classifier (GSC) was developed to up-classify as well as down-classify the risk of malignancy for lung lesions when bronchoscopy is non-diagnostic. We evaluated the performance of Percepta GSC in risk re-classification of indeterminate lung lesions. This multicenter study included individuals who currently or formerly smoked undergoing bronchoscopy for suspected lung cancer from the AEGIS I/ II cohorts and the Percepta Registry. The classifier was measured in normal-appearing bronchial epithelium from bronchial brushings. The sensitivity, specificity, and predictive values were calculated using predefined thresholds. The ability of the classifier to decrease unnecessary invasive procedures was estimated. A set of 412 patients were included in the validation (prevalence of malignancy was 39.6%). Overall, 29% of intermediate-risk lung lesions were down-classified to low-risk with a 91.0% negative predictive value (NPV) and 12.2% of intermediate-risk lesions were up-classified to high-risk with a 65.4% positive predictive value (PPV). In addition, 54.5% of low-risk lesions were down-classified to very low risk with >99% NPV and 27.3% of high-risk lesions were up-classified to very high risk with a 91.5% PPV. If the classifier results were used in nodule management, 50% of patients with benign lesions and 29% of patients with malignant lesions undergoing additional invasive procedures could have avoided these procedures. The Percepta GSC is highly accurate as both a rule-out and rule-in test. This high accuracy of risk re-classification may lead to improved management of lung lesions.

Intra-operative neurophysiological mapping to identify distorted functional anatomy of the 4th ventricle in a 5-month-old infant.

BACKGROUND: Neurophysiological brainstem mapping techniques facilitate the intra-operative localisation of cranial nerve nuclei amidst distorted anatomy. Neurophysiological recording in young infants can be limited due to immature myelination and synaptogenesis, as well as an increased sensitivity to anaesthetic agents. CASE REPORT: A 5-month-old boy was diagnosed with a cystic brainstem lesion located dorsally within the pons and upper medulla. An open surgical biopsy was undertaken via a posterior fossa craniotomy, revealing a grossly distorted fourth ventricular floor. Intra-operative neurophysiological mapping produced oculomotor, facial, glossopharyngeal and vagal muscle responses allowing a deviated functional midline to be identified. Direct stimulation was used to identify an area in the floor of the fourth ventricle eliciting no cranial nerve responses and allow safe entry into the tumour cavity and biopsy. Transcranial motor evoked responses (TcMEPs), short-latency somatosensory evoked potentials (SSEPs) and brainstem auditory evoked potentials (BAEPs) were all successfully recorded throughout the procedure, despite the use of halogenated gaseous anaesthesia. CONCLUSIONS: We describe the use of brainstem mapping techniques for identification of a distorted midline on the floor of the 4th ventricle in an infant, with reproducible recordings of intra-operative TcMEPs, SSEPs and BAEPs.

Maximizing Small Biopsy Patient Samples: Unified RNA-Seq Platform Assessment of over 120,000 Patient Biopsies.

Despite its wide-ranging benefits, whole-transcriptome or RNA exome profiling is challenging to implement in a clinical diagnostic setting. The Unified Assay is a comprehensive workflow wherein exome-enriched RNA-sequencing (RNA-Seq) assays are performed on clinical samples and analyzed by a series of advanced machine learning-based classifiers. Gene expression signatures and rare and/or novel genomic events, including fusions, mitochondrial variants, and loss of heterozygosity were assessed using RNA-Seq data generated from 120,313 clinical samples across three clinical indications (thyroid cancer, lung cancer, and interstitial lung disease). Since its implementation, the data derived from the Unified Assay have allowed significantly more patients to avoid unnecessary diagnostic surgery and have played an important role in guiding follow-up decisions regarding treatment. Collectively, data from the Unified Assay show the utility of RNA-Seq and RNA expression signatures in the clinical laboratory, and their importance to the future of precision medicine.

A review on diatom biosilicification and their adaptive ability to uptake other metals into their frustules for potential application in bone repair.

Diatoms are unicellular eukaryotic algae that have a distinctive siliceous cell wall (frustule) with unique architectures. The nanotopography of the frustule is perfectly replicated between generations, offering a source of highly intricate and identical silica microparticles. In recent years, the ability to alter their cell wall chemistry both in terms of functionalisation with organic moieties or by incorporation of the metal ions in their frustules has increased interest in their utility for catalysis technologies, and semiconductor and biomedical applications. Herein we review the fundamental biological mechanisms in which diatoms produce their frustule and their ability to substitute different metal ions in their frustule fabrication process. The review focuses on the potential of diatom frustules as a naturally derived biomaterial in bone tissue engineering applications and how their cell walls, comprising biogenic silica, could either partially or fully incorporate other bone therapeutic metal ions, e.g., titanium or calcium, into their frustule. The use of diatom frustules in bone repair also potentially offers a 'greener', more environmentally friendly, biomaterial as they can naturally synthesise oxides of silicon and other metals into their frustules under ambient conditions at a relatively neutral pH. This process would negate the use of harsh organic chemicals and high-temperature processing conditions, often used in the fabrication of silica based biomaterials, e.g., bioactive glass.

Taxonomic annotation of 16S rRNA sequences of pig intestinal samples using MG-RAST and QIIME2 generated different microbiota compositions.

Environmental microbiome studies rely on fast and accurate bioinformatics tools to characterize the taxonomic composition of samples based on the 16S rRNA gene. MetaGenome Rapid Annotation using Subsystem Technology (MG-RAST) and Quantitative Insights Into Microbial Ecology 2 (QIIME2) are two of the most popular tools available to perform this task. Their underlying algorithms differ in many aspects, and therefore the comparison of the pipelines provides insights into their best use and interpretation of the outcomes. Both of these bioinformatics tools are based on several specialized algorithms pipelined together, but whereas MG-RAST is a user-friendly webserver that clusters rRNA sequences based on their similarity to create Operational Taxonomic Units (OTU), QIIME2 employs DADA2 in the construction of Amplicon Sequence Variants (ASV) by applying an error model that considers the abundance of each sequence and its similarity to other sequences. Taxonomic compositions obtained from the analyses of amplicon sequences of DNA from swine intestinal gut and faecal microbiota samples using MG-RAST and QIIME2 were compared at domain-, phylum-, family- and genus-levels in terms of richness, relative abundance and diversity. We found significant differences between the microbiota profiles obtained from each pipeline. At domain level, bacteria were relatively more abundant using QIIME2 than MG-RAST; at phylum level, seven taxa were identified exclusively by QIIME2; at family level, samples processed in QIIME2 showed higher evenness and richness (assessed by Shannon and Simpson indices). The genus-level compositions obtained from each pipeline were used in partial least squares-discriminant analyses (PLS-DA) to discriminate between sample collection sites (caecum, colon and faeces). The results showed that different genera were found to be significant for the models, based on the Variable Importance in Projection, e.g. when using sequencing data processed by MG-RAST, the three most important genera were Acetitomaculum, Ruminococcus and Methanosphaera, whereas when data was processed using QIIME2, these were Candidatus Methanomethylophilus, Sphaerochaeta and Anaerorhabdus. Furthermore, the application of differential filtering procedures before the PLS-DA revealed higher accuracy when using non-restricted datasets obtained from MG-RAST, whereas datasets obtained from QIIME2 resulted in more accurate discrimination of sample collection sites after removing genera with low relative abundances (<1%) from the datasets. Our results highlight the differences in taxonomic compositions of samples obtained from the two separate pipelines, while underlining the impact on downstream analyses, such as biomarkers identification.

Analytical validation of the Percepta genomic sequencing classifier; an RNA next generation sequencing assay for the assessment of Lung Cancer risk of suspicious pulmonary nodules.

BACKGROUND: Bronchoscopy is a common procedure used for evaluation of suspicious lung nodules, but the low diagnostic sensitivity of bronchoscopy often results in inconclusive results and delays in treatment. Percepta Genomic Sequencing Classifier (GSC) was developed to assist with patient management in cases where bronchoscopy is inconclusive. Studies have shown that exposure to tobacco smoke alters gene expression in airway epithelial cells in a way that indicates an increased risk of developing lung cancer. Percepta GSC leverages this idea of a molecular "field of injury" from smoking and was developed using RNA sequencing data generated from lung bronchial brushings of the upper airway. A Percepta GSC score is calculated from an ensemble of machine learning algorithms utilizing clinical and genomic features and is used to refine a patient's risk stratification. METHODS: The objective of the analysis described and reported here is to validate the analytical performance of Percepta GSC. Analytical performance studies characterized the sensitivity of Percepta GSC test results to input RNA quantity, the potentially interfering agents of blood and genomic DNA, and the reproducibility of test results within and between processing runs and between laboratories. RESULTS: Varying the amount of input RNA into the assay across a nominal range had no significant impact on Percepta GSC classifier results. Bronchial brushing RNA contaminated with up to 10% genomic DNA by nucleic acid mass also showed no significant difference on classifier results. The addition of blood RNA, a potential contaminant in the bronchial brushing sample, caused no change to classifier results at up to 11% contamination by RNA proportion. Percepta GSC scores were reproducible between runs, within runs, and between laboratories, varying within less than 4% of the total score range (standard deviation of 0.169 for scores on 4.57 scale). CONCLUSIONS: The analytical sensitivity, analytical specificity, and reproducibility of Percepta GSC laboratory results were successfully demonstrated under conditions of expected day to day variation in testing. Percepta GSC test results are analytically robust and suitable for routine clinical use.

A comparison of the degradation behaviour of 3D printed PDLGA scaffolds incorporating bioglass or biosilica.

Silica incorporation into biomaterials, such as Bioglass and Si-substituted calcium phosphate ceramics has received significant attention in bone tissue engineering over the last few decades. This study aims to explore the dissolution behaviour of natural biosilica isolated from a freshwater diatom, Cyclotella meneghiniana, that has been incorporated into 3D printed poly (DL-lactide -co - glycolide) (PDLGA) scaffolds using extrusion and additive manufacturing. In the study, two different dry weight percentage (1 wt% & 5 wt%) of diatom-silica were incorporated into PDLGA scaffolds that were then degraded in phosphate buffered saline (PBS) cell free media. In addition, pure PDLGA scaffolds and 5 wt% Bioglass scaffolds were used as control groups. The degradation study was performed over 26-weeks. The release rate of Si4+ ions from diatom-PDLGA scaffolds was found to increase exponentially with respect to time. The compressive strength of scaffolds was also measured with the Diatom-PDLGA scaffolds found to maintain their strength for longer than either pure PDLGA scaffolds or 5 wt% Bioglass scaffolds. 13C NMR data showed that diatom biosilica containing scaffolds had less degradation than pure or bioglass-containing scaffolds at comparable time-points. Overall, the Diatom-PDLGA scaffolds were found to have more desirable physiochemical properties for bone repair compared to Bioglass.

Filament extrusion of bioresorbable PDLGA for additive manufacturing utilising diatom biosilica to inhibit process-induced thermal degradation.

Bone scaffolds are often fabricated by initially producing custom-made filaments by twin-screw extruder and subsequently fabricating into 3D scaffolds using fused deposition modelling. This study aims to directly compare the effect of two alternative silica-rich filler materials on the thermo-mechanical properties of such scaffolds after extrusion and printing. Poly (DL-lactide-co-glycolide) (PDLGA) was blended with either 45S5 Bioglass (5 wt %) or Biosilica (1 and 5 wt%) isolated from Cyclotella meneghiniana a freshwater diatom were tested. Diatom-PDLGA was found to have similar mechanical strength and ductility to pure-PDLGA, whereas Bioglass-PDLGA was found induce a more brittle behaviour. Bioglass-PDLGA was also found to have the lowest toughness in terms of energy absorption to failure. The TGA results suggested that significant thermal degradation in both the Bioglass filaments and scaffolds had occurred as a result of processing. However, diatom biosilica was found to inhibit thermal degradation of the PDLGA. Furthermore, evidence suggested the agglomeration of Bioglass particles occurred during processing the Bioglass-PDLGA filaments. Overall, diatom biosilica was found to be a promising candidate as a bone filler additive in 3D printed PDLGA scaffolds, whereas Bioglass caused some potentially detrimental effects on performance.

Utility of a Molecular Classifier as a Complement to High-Resolution Computed Tomography to Identify Usual Interstitial Pneumonia.

Rationale: Usual interstitial pneumonia (UIP) is the defining morphology of idiopathic pulmonary fibrosis (IPF). Guidelines for IPF diagnosis conditionally recommend surgical lung biopsy for histopathology diagnosis of UIP when radiology and clinical context are not definitive. A "molecular diagnosis of UIP" in transbronchial lung biopsy, the Envisia Genomic Classifier, accurately predicted histopathologic UIP.Objectives: We evaluated the combined accuracy of the Envisia Genomic Classifier and local radiology in the detection of UIP pattern.Methods: Ninety-six patients who had diagnostic lung pathology as well as a transbronchial lung biopsy for molecular testing with Envisia Genomic Classifier were included in this analysis. The classifier results were scored against reference pathology. UIP identified on high-resolution computed tomography (HRCT) as documented by features in local radiologists' reports was compared with histopathology.Measurements and Main Results: In 96 patients, the Envisia Classifier achieved a specificity of 92.1% (confidence interval [CI],78.6-98.3%) and a sensitivity of 60.3% (CI, 46.6-73.0%) for histology-proven UIP pattern. Local radiologists identified UIP in 18 of 53 patients with UIP histopathology, with a sensitivity of 34.0% (CI, 21.5-48.3%) and a specificity of 96.9% (CI, 83.8-100%). In conjunction with HRCT patterns of UIP, the Envisia Classifier results identified 24 additional patients with UIP (sensitivity 79.2%; specificity 90.6%).Conclusions: In 96 patients with suspected interstitial lung disease, the Envisia Genomic Classifier identified UIP regardless of HRCT pattern. These results suggest that recognition of a UIP pattern by the Envisia Genomic Classifier combined with HRCT and clinical factors in a multidisciplinary discussion may assist clinicians in making an interstitial lung disease (especially IPF) diagnosis without the need for a surgical lung biopsy.

Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts.

BACKGROUND: Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. METHODS: In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates - age, pack-years, inhaled medication use, and specimen collection timing. RESULTS: In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37-53%) and a sensitivity of 91% (95%CI 81-97%), resulting in a negative predictive value of 95% (95% CI 89-98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44-82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78-97%). CONCLUSIONS: The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy.

Analysis of the aetiology of epilepsy in 3,216 adult patients attending a tertiary referral center enabled by an electronic patient record.

PURPOSE: The aim of this study was to review the causes of the epilepsies in our institution, an adult tertiary referral center for neurology and neurosurgery in Dublin, Ireland. Data was obtained from a bespoke epilepsy electronic patient record (EPR). METHODS: Predetermined search parameters of well-established broad categories of epilepsy aetiology were used to identify patients with a diagnosis of epilepsy attending Beaumont Hospital, Dublin. There were 3216 patients that met the inclusion criteria for this study. We included living patients with epilepsy attending our institution. We then excluded patients with a diagnosis of pure non-epileptic attack disorder and patients found to have idiopathic generalised epilepsy (IGE) (n = 382) from our final cohort. We excluded IGE due to the complex polygenic basis underlying this patient group. RESULTS: An aetiology was identified in 54.3 % (n = 1747) of the total number of patients studied. Of the symptomatic epilepsies, 41.08 % (n = 1321) were acquired and 13.3 % (n = 426) were predominantly of genetic or developmental aetiology. The most common causes of the acquired epilepsies were hippocampal sclerosis (n = 380; 28.75 %), cerebral tumor (n = 279; 21.06 %), traumatic brain injury (n = 248; 18.77 %), stroke and cerebrovascular disease (n = 151; 11.43 %) and perinatal causes (n = 138; 10.45 %). The leading causes in the genetic / developmental category included cavernous haemangiomas (n = 62, 22.22 %), arteriovenous malformations (n = 59; 21.15 %) and cortical dysplasia (n = 55; 19.71 %). The aetiology of a patient's epilepsy was undetermined in 45.68 % (n = 1469) of individuals. CONCLUSION: This study emphasizes the clinical utility of the ILAE's 2017 revised classification of the epilepsies and highlights the evolving dynamic nature of attributing causality in epilepsy. This is the largest single centre analysis of the aetiology of the epilepsies described in the literature. It is also the first large scale study examining aetiology utilising a bespoke electronic patient record in epilepsy.

Reversible cerebral vasoconstrictive syndrome preceded by minor head trauma.

Reversible cerebral vasoconstriction syndrome is a heterogeneous and under-recognised neurovascular disorder. Our knowledge with regards to specific syndrome triggers and optimal management is limited. The delay in diagnosis can be deleterious to the patient due to intracerebral sequelae causing temporary or permanent morbidity. Prompt identification of this syndrome is vital to reverse neurological deficits while appropriately managing and supporting patient recovery.

Analytical and Clinical Validation of Expressed Variants and Fusions From the Whole Transcriptome of Thyroid FNA Samples.

Introduction: The Afirma® Xpression Atlas (XA) detects gene variants and fusions in thyroid nodule FNA samples from a curated panel of 511 genes using whole-transcriptome RNA-sequencing. Its intended use is among cytologically indeterminate nodules that are Afirma GSC suspicious, Bethesda V/VI nodules, or known thyroid metastases. Here we report its analytical and clinical validation. Methods: DNA and RNA were purified from the same sample across 943 blinded FNAs and compared by multiple methodologies, including whole-transcriptome RNA-seq, targeted RNA-seq, and targeted DNA-seq. An additional 695 blinded FNAs were used to define performance for fusions between whole-transcriptome RNA-seq and targeted RNA-seq. We quantified the reproducibility of the whole-transcriptome RNA-seq assay across laboratories and reagent lots. Finally, variants and fusions were compared to histopathology results. Results: Of variants detected in DNA at 5 or 20% variant allele frequency, 74 and 88% were also detected by XA, respectively. XA variant detection was 89% when compared to an alternative RNA-based detection method. Low levels of expression of the DNA allele carrying the variant, compared with the wild-type allele, was found in some variants not detected by XA. 82% of gene fusions detected in a targeted RNA fusion assay were detected by XA. Conversely, nearly all variants or fusions detected by XA were confirmed by an alternative method. Analytical validation studies demonstrated high intra-plate reproducibility (89%-94%), inter-plate reproducibility (86-91%), and inter-lab accuracy (90%). Multiple variants and fusions previously described across the spectrum of thyroid cancers were identified by XA, including some with approved or investigational targeted therapies. Among 190 Bethesda III/IV nodules, the sensitivity of XA as a standalone test was 49%. Conclusion: When the Afirma Genomic Sequencing Classifier (GSC) is used first among Bethesda III/IV nodules as a rule-out test, XA supplements genomic insight among those that are GSC suspicious. Our data clinically and analytically validate XA for use among GSC suspicious, or Bethesda V/VI nodules. Genomic information provided by XA may inform clinical decision-making with precision medicine insights across a broad range of FNA sample types encountered in the care of patients with thyroid nodules and thyroid cancer.

Analytical Verification Performance of Afirma Genomic Sequencing Classifier in the Diagnosis of Cytologically Indeterminate Thyroid Nodules.

Background: Fine needle aspiration (FNA) cytology, a diagnostic test central to thyroid nodule management, may yield indeterminate results in up to 30% of cases. The Afirma® Genomic Sequencing Classifier (GSC) was developed and clinically validated to utilize genomic material obtained during the FNA to accurately identify benign nodules among those deemed cytologically indeterminate so that diagnostic surgery can be avoided. A key question for diagnostic tests is their robustness under different perturbations that may occur in the lab. Herein, we describe the analytical performance of the Afirma GSC. Results: We examined the analytical sensitivity of the Afirma GSC to varied input RNA amounts and the limit of detection of malignant signals with heterogenous samples mixed with adjacent normal or benign tissues. We also evaluated the analytical specificity from potential interfering substances such as blood and genomic DNA. Further, the inter-laboratory, intra-run, and inter-run reproducibility of the assay were examined. Analytical sensitivity analysis showed that Afirma GSC calls are tolerant to variation in RNA input amount (5-30 ng), and up to 75% dilution of malignant FNA material. Analytical specificity studies demonstrated Afirma GSC remains accurate in presence of up to 75% blood or 30% genomic DNA. The Afirma GSC results are highly reproducible across different operators, runs, reagent lots, and laboratories. Conclusion: The analytical robustness and reproducibility of the Afirma GSC test support its routine clinical use among thyroid nodules with indeterminant FNA cytology.