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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.

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.

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.

e20587 Background: The combined use of molecular biomarker testing and targeted precision therapeutics has led to improved survival in lung cancer. Broadening access to national guideline recommended comprehensive molecular testing requires overcoming the challenges of inadequate tissue biopsies, which can lead to the need for additional procedures and ultimately, delays in initiation of care. We show Percepta Genomic Atlas identifies key molecular alterations in transbronchial needle aspirate (TBNA) and transbronchial biopsy (TBB) samples of lung or lymph node collected during the initial diagnostic bronchoscopy. Methods: Percepta Genomic Atlas combines the whole exome TruSeq RNA Exome and targeted AmpliSeq Focus DNA assays (Illumina) for a comprehensive gene panel including ALK, RET, ROS1, NTRK1/3, MET, EGFR, BRAF, KRAS and HER2. TruSight Oncology 500 DNA and AmpliSeq Focus RNA assays (Illumina) were used as reference assays. DNA and RNA were extracted from samples with the AllPrep Micro kit (Qiagen) and analyzed by Percepta Genomic Atlas and reference assays. 94 biopsy samples (73 TBNA and 21 TBB from 71 patients undergoing a diagnostic bronchoscopy for suspected lung cancer were collected into RNAprotect (Qiagen) under an IRB approved protocol. Local molecular testing results from FFPE biopsy samples taken during the same bronchoscopy procedure were collected. Results: RNA and DNA in sufficient amounts to run the Percepta Genomic Atlas and reference assays was obtained from 85 of 94 lung biopsies from 63 of 71 patients. Percepta Genomic Atlas identified pathogenic variants in 29 bronchoscopy biopsy samples from 23 patients including single nucleotide variants in EGFR, KRAS, BRAF and PIK3CA, an EGFR exon 19 deletion/insertion and copy number amplications in AR, EGFR, CDK4, CCND1, MYC, MYCN and PIK3CA. No fusions were identified. This results in a 100% sensitivity for detecting pathogenic alterations when compared to reference assay results. When comparing Percepta Genomic Atlas results to local molecular testing of bronchoscopy samples performed as part of routine clinical care, we found 100% concordance with 7 of 7 alterations detected in 7 patients, including mutations in EGFR, KRAS and BRAF. In a further 4 patients, Percepta Genomic Atlas and local multi-gene NGS testing results agreed, with neither assay identifying guideline recommended alterations. Conclusions: Using a combination of whole exome RNA and targeted DNA sequencing, Percepta Genomic Atlas detects the clinically actionable mutations in patients with non-small cell lung cancer using fresh tissue specimens collected during bronchoscopic tissue sampling, with high concordance to standard of care testing. By initiating broad molecular testing at the time of the bronchoscopy, Percepta Genomic Atlas may provide timelier results for patients with lung cancer.

Additional file 1.

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.

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.

Additional file 1 Fig. S1: Consort diagram of training and validation sets. Fig. S2: DE analysis on B vs M, smoking status, specimen collection timing, and cohort. Fig. S3: Smoking index score distribution on test set. (a) Box plots of smoking index for smoking status. Scatter plots of smoking index distribution vs smoking-related variables, (b) pack-years and (c) years since quitting smoking. The smoking index is positively correlated with pack-years (Pearson’s correlation = 0.23) and negatively correlated with years since quitting smoking (Pearson’s correlation = − 0.61). Fig. S4: Cross validated model score distribution and model performance by inhaled medication. Model GLM(i + I) is similar to GLM(i), except that the additional clinical feature -- subject currently taking inhaled medication -- is included in the input feature set as main effect and for interactions with genomic features. (a) CV score distribution for within-indication low/intermediate pre-test risk samples, shown separately by subject label and inhaled medication. (b) CV performance for down-classification of within-indication low/intermediate pre-test risk samples. Fig. S5: Pairwise scatter plot of individual model in the final ensemble classifier for samples in test set. Benign and malignant samples are colored in blue and red respectively. The four models are (1) clinical heavy logistic regression model (2) genomic-only SVM model (3) clinical-genomic logistic regression model and (4) hierarchical logistic regression model.

Usual interstitial pneumonia (UIP) is the histopathologic hallmark of idiopathic pulmonary fibrosis. Although UIP can be detected by high-resolution computed tomography of the chest, the results are frequently inconclusive, and pathology from transbronchial biopsy (TBB) has poor sensitivity. Surgical lung biopsy may be necessary for a definitive diagnosis.To develop a genomic classifier in tissue obtained by TBB that distinguishes UIP from non-UIP, trained against central pathology as the reference standard.Exome enriched RNA sequencing was performed on 283 TBBs from 84 subjects. Machine learning was used to train an algorithm with high rule-in (specificity) performance using specimens from 53 subjects. Performance was evaluated by cross-validation and on an independent test set of specimens from 31 subjects. We explored the feasibility of a single molecular test per subject by combining multiple TBBs from upper and lower lobes. To address whether classifier accuracy depends upon adequate alveolar sampling, we tested for correlation between classifier accuracy and expression of alveolar-specific genes.The top-performing algorithm distinguishes UIP from non-UIP conditions in single TBB samples with an area under the receiver operator characteristic curve (AUC) of 0.86, with specificity of 86% (confidence interval = 71-95%) and sensitivity of 63% (confidence interval = 51-74%) (31 test subjects). Performance improves to an AUC of 0.92 when three to five TBB samples per subject are combined at the RNA level for testing. Although we observed a wide range of type I and II alveolar-specific gene expression in TBBs, expression of these transcripts did not correlate with classifier accuracy.We demonstrate proof of principle that genomic analysis and machine learning improves the utility of TBB for the diagnosis of UIP, with greater sensitivity and specificity than pathology in TBB alone. Combining multiple individual subject samples results in increased test accuracy over single sample testing. This approach requires validation in an independent cohort of subjects before application in the clinic.

8551 Background: The goal of lung nodule management is to make an early diagnosis in patients with lung cancer while avoiding unnecessary, costly and potentially harmful procedures in patients with benign lesions. Increased implementation of low dose CT screening will lead to increased numbers of both benign and malignant nodules that will require effective management. We have previously described the feasibility of detecting gene expression changes associated with lung cancer (“field of injury”) in nasal epithelium utilizing whole transcriptome RNA sequencing from non-invasive nasal brush samples. Using this approach, we now report the performance of candidate nasal classifiers that combine both genomic and clinical features to risk stratify lung nodules from ever-smokers to aid in the diagnosis of lung cancer. Methods: Patients from several clinical cohorts who were ever-smokers with a lung nodule < 30 mm and without a history of prior cancer underwent nasal epithelium sampling. All patients had at least one year of follow up or until a final diagnosis of benign or malignant nodule was made. Candidate classifiers were developed using whole-transcriptome RNA sequencing and machine learning. Training of these classifiers included genomic and clinical information (age, sex, pack-years, years-since-quit, nodule size and nodule spiculation). Two decision boundaries were chosen to maximize sensitivity and specificity for low and high-risk nodules, respectively. The performance of these classifiers was evaluated using cross-validation (CV). Results: All candidate nasal classifiers underwent CV assessment on over 700 patients with lung nodules. All candidate classifiers achieved CV performance of > 40% specificity at 95% sensitivity in low- risk nodules and all candidate classifiers achieved CV performance of > 60% sensitivity at 90% specificity in high-risk nodules. The classifiers stratified benign nodules as low- risk with > 95% negative predictive value (NPV), intermediate risk nodules were stratified as 5-65% risk and malignant nodules were stratified as high- risk with > 65% positive predictive value (PPV) in a population with estimated 25% prevalence. This performance was robust across subgroups of age ( < 65 year vs. >65 year), sex, and current versus former smokers. Conclusions: The nasal genomic-clinical candidate classifiers have a high NPV effectively identifying benign nodules when calling them low- risk, thereby, informing decisions to more safely avoid a diagnostic workup. Additionally, the high PPV of these classifiers identifies malignant nodules when calling them high- risk and informs decisions regarding the urgency of a further diagnostic work-up. A nasal genomic-clinical classifier has the potential to serve as a non-invasive tool for lung cancer risk-stratification to help inform decision making in patients with lung nodules.

e20546 Background: Significant progress has been made in improving progression-free survival in lung cancer through the combination of molecular biomarker testing and personalized therapeutics. Broadening access to national guideline recommended comprehensive molecular testing requires overcoming challenges of inadequate tissue biopsies, which can lead to the need for additional procedures and ultimately, delays in obtaining molecular testing. Additionally, there is growing evidence for the use of targeted therapies to treat early stage lung cancer and therefore a need for molecular testing across all stages of lung cancer. We show Percepta Genomic Atlas can identify key molecular alterations in early stage and advanced lung cancers and that it can successfully detect these alterations in surgical lung biopsy (SLB) specimens, transbronchial needle aspirates (TBNA) and bronchial brush specimens obtained during the initial bronchoscopy at the time of diagnosis. Methods: Percepta Genomic Atlas combines the whole exome TruSeq RNA Exome and targeted AmpliSeq Focus DNA assays (Illumina) for a comprehensive gene panel including ALK, RET, ROS1, NTRK1/3, MET, EGFR, BRAF, KRAS and HER2. TruSight Oncology 500 DNA and AmpliSeq Focus RNA assays (Illumina) were used as orthogonal assays. 92 fresh-frozen SLB samples were purchased from BioIVT and biopsies from 25 patients undergoing a diagnostic bronchoscopy during an IRB approved clinical study were collected into RNAprotect (Qiagen). DNA and RNA were extracted from both sample sets with the AllPrep Micro kit (Qiagen) and analyzed by Percepta Genomic Atlas and orthogonal assays. Results: The Percepta Genomic Atlas assay was used to profile small amounts of RNA and DNA from lung cancer SLB tissues, of which 60% were Stage I, 24% Stage II and 16% Stage III. Genomic alterations were observed in 65% of Stage I, 64% of Stage II and 73% of Stage III samples including single nucleotide variants (SNV) in KRAS, EGFR, and PIK3CA, indels in HER2 exon 20, multiple copy number variants, and RET and MET exon 14 skip rearrangements. Percepta Genomic Atlas was used to successfully profile TBNA and bronchial brush specimens providing molecular data from all 25 patients and identifying multiple alterations including KRAS, EGFR and PIK3CA SNVs. Conclusions: Percepta Genomic Atlas detects clinically actionable alterations in both SLB of early stage lung cancer tumors and in specimens collected at the time of diagnostic bronchoscopy or needle aspiration prior to surgery. The early detection of actionable alterations at the time of initial tissue acquisition could minimize need for additional diagnostic procedures and inform earlier treatment decisions with the expanding field of targeted adjuvant therapy.

The aim of this study was to demonstrate the analytical validity of an RNA classifier for medullary thyroid carcinoma (MTC).Fresh-frozen tissue specimens were obtained from commercial sources, and MTC diagnoses were confirmed by histopathology review. De-identified patient fine-needle aspiration biopsies (FNABs) and whole blood from normal donors were obtained. Total RNA was extracted, amplified, and hybridized to custom microarrays for gene expression analysis. Gene expression data were normalized and classified via a machine learning algorithm. Positive control materials were produced from MTC tissues and tested across multiple experiments and laboratories. Twenty-seven MTC tissue specimens were used to evaluate the sensitivity of the MTC classifier. Gene expression data from tissues and FNABs were used to model classifier response to mixtures of MTC samples with normal thyroid tissue, a benign thyroid nodule, a Hürthle cell adenoma, and whole blood. Select mixture conditions were confirmed in vitro. Assay tolerance to RNA input variation (5-25 ng) and genomic DNA contamination (30% by mass) was evaluated. The intra- and inter-run reproducibility and inter-laboratory accuracy of MTC classifier results were characterized.The MTC classifier sensitivity of 96.3% [confidence interval 81.0-99.9%] was determined retrospectively using 27 MTC confirmed tissue specimens. One false-negative result in a necrotic tissue implicated sample necrosis in reduced classifier sensitivity. Dilution modeling of MTC samples with normal or benign tissues showed consistent detection of MTC down to 20% sample proportions, with in vitro confirmation of 20% analytical sensitivity. Classifier tolerance to RNA input variation (5-25 ng), genomic DNA contamination (30% by mass), and an interfering substance (blood) was demonstrated with 100% accurate classifier results under all tested conditions. The maximum observed run-to-run score difference for a single FNAB sample was ∼1 unit compared with the average score difference between 38 MTC and non-MTC FNABs of ∼32 units. MTC classifier results for 20 tissues processed from total RNA in two different laboratories showed 100% concordance.The MTC classifier, offered as part of the routine molecular testing of cytology-indeterminate thyroid nodules, demonstrates robust analytical sensitivity, specificity, accuracy, and reproducibility.

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.

Background Identification of Hürthle cell cancers by non-operative fine-needle aspiration biopsy (FNAB) of thyroid nodules is challenging. Resultingly, non-cancerous Hürthle lesions were conventionally distinguished from Hürthle cell cancers by histopathological examination of tissue following surgical resection. Reliance on histopathological evaluation requires patients to undergo surgery to obtain a diagnosis despite most being non-cancerous. It is highly desirable to avoid surgery and to provide accurate classification of benignity versus malignancy from FNAB preoperatively. In our first-generation algorithm, Gene Expression Classifier (GEC), we achieved this goal by using machine learning (ML) on gene expression features. The classifier is sensitive, but not specific due in part to the presence of non-neoplastic benign Hürthle cells in many FNAB. Results We sought to overcome this low-specificity limitation by expanding the feature set for ML using next-generation whole transcriptome RNA sequencing and called the improved algorithm the Genomic Sequencing Classifier (GSC). The Hürthle identification leverages mitochondrial expression and we developed novel feature extraction mechanisms to measure chromosomal and genomic level loss-of-heterozygosity (LOH) for the algorithm. Additionally, we developed a multi-layered system of cascading classifiers to sequentially triage Hürthle cell-containing FNAB, including: 1. presence of Hürthle cells, 2. presence of neoplastic Hürthle cells, and 3. presence of benign Hürthle cells. The final Hürthle cell Index utilizes 1048 nuclear and mitochondrial genes; and Hürthle cell Neoplasm Index leverages LOH features as well as 2041 genes. Both indices are Support Vector Machine (SVM) based. The third classifier, the GSC Benign/Suspicious classifier, utilizes 1115 core genes and is an ensemble classifier incorporating 12 individual models. Conclusions The accurate algorithmic depiction of this complex biological system among Hürthle subtypes results in a dramatic improvement of classification performance; specificity among Hürthle cell neoplasms increases from 11.8% with the GEC to 58.8% with the GSC, while maintaining the same sensitivity of 89%. Electronic supplementary material The online version of this article (10.1186/s12918-019-0693-z) contains supplementary material, which is available to authorized users.

Background The Afirma Genomic Sequencing Classifier (GSC) uses RNA sequencing to assess FNA specimens from cytologically indeterminate thyroid nodules, which are also tested for specific molecular aberrations associated with thyroid cancer via a suite of highly accurate malignancy classifiers. This suite can also be applied independently to Bethesda V/VI nodules. The Afirma Xpression Atlas (XA) is an optional add-on test to GSC and the malignancy classifiers that reports nucleotide variants and fusions across 511 cancer-associated genes. Medullary thyroid cancer (MTC) is a rare subtype of thyroid cancer that can appear in every Bethesda category. Herein, we report the prevalence and genomic landscape of MTC classifier positive (MTC+) FNA samples in a CLIA certified clinical laboratory. Methods All Afirma GSC and malignancy classifier tests run between July 2017 and November 2018 were deidentified and examined for MTC+ cases. Afirma XA was run on all MTC cases and all variants and fusions were tabulated. Results Examination of 22,130 FNAs revealed 77 MTC cases. Among consecutive GSC tests, 28 were Bethesda III (0.16% out of 17,245) and 27 were Bethesda IV (0.65% out of 4,182). Provider-ordered testing was done on an additional 14 and 8 MTC cases from Bethesda V and VI nodules, respectively. Examining all MTC+ samples revealed that 55.8% harbored a RET variant (+/- others), 9.1% contained a KRAS variant (+/- others), 6.5% had an HRAS variant, 2.6% possessed fusions, and 26.0% included no variant/fusion. Conclusions In indeterminate FNA samples, the Afirma GSC can help to clarify the risk of MTC. In our cohort, the Afirma XA identified a variant or fusion in 74.0% of MTC+ FNAs. Limitations of this study include the lack of knowledge regarding germline RET status (which should be checked in all patients with an identified RET variant or confirmed MTC) and final pathology on MTC+ samples. Future studies may investigate how the preoperative identification of a known MTC driver mutation in a biopsy sample can inform the pre-operative evaluation, the surgical plan, and the potential role of targeted therapy. Unless otherwise noted, all abstracts presented at ENDO are embargoed until the date and time of presentation. For oral presentations, the abstracts are embargoed until the session begins. Abstracts presented at a news conference are embargoed until the date and time of the news conference. The Endocrine Society reserves the right to lift the embargo on specific abstracts that are selected for promotion prior to or during ENDO.

© 2018 American Medical Association. All rights reserved. IMPORTANCE Use of next-generation sequencing of RNA and machine learning algorithms can classify the risk of malignancy in cytologically indeterminate thyroid nodules to limit unnecessary diagnostic surgery. OBJECTIVE To measure the performance of a genomic sequencing classifier for cytologically indeterminate thyroid nodules. DESIGN, SETTING, AND PARTICIPANTS A blinded validation study was conducted on a set of cytologically indeterminate thyroid nodules collected by fine-needle aspiration biopsy between June 2009 and December 2010 from 49 academic and community centers in the United States. All patients underwent surgery without genomic information and were assigned a histopathology diagnosis by an expert panel blinded to all genomic information. There were 210 potentially eligible thyroid biopsy samples with Bethesda III or IV indeterminate cytopathology that constituted a cohort previously used to validate the gene expression classifier. Of these, 191 samples (91.0%) had adequate residual RNA for validation of the genomic sequencing classifier. Algorithm development and independent validation occurred between August 2016 and May 2017. EXPOSURES Thyroid nodule surgical histopathology diagnosis by an expert panel blinded to all genomic data. MAIN OUTCOMES AND MEASURES The primary end point was measurement of genomic sequencing classifier sensitivity, specificity, and negative and positive predictive values in biopsies from Bethesda III and IV nodules. The secondary end point was measurement of classifier performance in biopsies from Bethesda II, V, and VI nodules. RESULTS Of the 183 included patients, 142 (77.6%) were women, and the mean (range) age was 51.7 (22.0-85.0) years. The genomic sequencing classifier had a sensitivity of 91% (95% CI, 79-98) and a specificity of 68% (95% CI, 60-76). At 24% cancer prevalence, the negative predictive value was 96% (95% CI, 90-99) and the positive predictive value was 47% (95% CI, 36-58). CONCLUSIONS AND RELEVANCE The genomic sequencing classifier demonstrates high sensitivity and accuracy for identifying benign nodules. Its 36% increase in specificity compared with the gene expression classifier potentially increases the number of patients with benign nodules who can safely avoid unnecessary diagnostic surgery.

Background We developed a classifier using RNA sequencing data that identifies the usual interstitial pneumonia (UIP) pattern for the diagnosis of idiopathic pulmonary fibrosis. We addressed significant challenges, including limited sample size, biological and technical sample heterogeneity, and reagent and assay batch effects. Results We identified inter- and intra-patient heterogeneity, particularly within the non-UIP group. The models classified UIP on transbronchial biopsy samples with a receiver-operating characteristic area under the curve of ~ 0.9 in cross-validation. Using in silico mixed samples in training, we prospectively defined a decision boundary to optimize specificity at ≥85%. The penalized logistic regression model showed greater reproducibility across technical replicates and was chosen as the final model. The final model showed sensitivity of 70% and specificity of 88% in the test set. Conclusions We demonstrated that the suggested methodologies appropriately addressed challenges of the sample size, disease heterogeneity and technical batch effects and developed a highly accurate and robust classifier leveraging RNA sequencing for the classification of UIP. Electronic supplementary material The online version of this article (10.1186/s12864-018-4467-6) contains supplementary material, which is available to authorized users.

Background Clinical guidelines specify that diagnosis of interstitial pulmonary fibrosis (IPF) requires identification of usual interstitial pneumonia (UIP) pattern. While UIP can be identified by high resolution CT of the chest, the results are often inconclusive, making surgical lung biopsy necessary to reach a definitive diagnosis (Raghu et al., Am J Respir Crit Care Med 183(6):788–824, 2011). The Envisia genomic classifier differentiates UIP from non-UIP pathology in transbronchial biopsies (TBB), potentially allowing patients to avoid an invasive procedure (Brown et al., Am J Respir Crit Care Med 195:A6792, 2017). To ensure patient safety and efficacy, a laboratory developed test (LDT) must meet strict regulatory requirements for accuracy, reproducibility and robustness. The analytical characteristics of the Envisia test are assessed and reported here. Methods The Envisia test utilizes total RNA extracted from TBB samples to perform Next Generation RNA Sequencing. The gene count data from 190 genes are then input to the Envisia genomic classifier, a machine learning algorithm, to output either a UIP or non-UIP classification result. We characterized the stability of RNA in TBBs during collection and shipment, and evaluated input RNA mass and proportions on the limit of detection of UIP. We evaluated potentially interfering substances such as blood and genomic DNA. Intra-run, inter-run, and inter-laboratory reproducibility of test results were also characterized. Results RNA content within TBBs preserved in RNAprotect is stable for up to 14 days with no detectable change in RNA quality. The Envisia test is tolerant to variation in RNA input (5 to 30 ng), with no impact on classifier results. The Envisia test can tolerate dilution of non-UIP and UIP classification signals at the RNA level by up to 60% and 20%, respectively. Analytical specificity studies utilizing UIP and non-UIP samples mixed with genomic DNA (up to 30% relative input) demonstrated no impact to classifier results. The Envisia test tolerates up to 22% of blood contamination, well beyond the level observed in TBBs. The test is reproducible from RNA extraction through to Envisia test result (standard deviation of 0.20 for Envisia classification scores on > 7-unit scale). Conclusions The Envisia test demonstrates the robust analytical performance required of an LDT. Envisia can be used to inform the diagnoses of patients with suspected IPF. Electronic supplementary material The online version of this article (doi:10.1186/s12890-017-0485-4) contains supplementary material, which is available to authorized users.

6087 Background: Systemic therapies targeting specific genomic alterations in advanced MTC are available or under investigation. The Afirma Genomic Sequencing Classifier (GSC) uses RNA sequencing to assess FNA specimens from cytologically indeterminate thyroid nodules, which are also tested for specific molecular aberrations associated with thyroid cancer via a suite of highly accurate malignancy classifiers. This suite can be applied independently to Bethesda V/VI nodules. The Afirma Xpression Atlas (XA) is an additional test that can be combined with Afirma GSC to report nucleotide variants and fusions across 511 cancer-associated genes. Here we report the prevalence and genomic landscape of MTC classifier positive (MTC+) FNA samples. Methods: All Afirma GSC and malignancy classifier tests run in the Veracyte Clinical Laboratory between July 2017 and January 2019 were deidentified and examined for MTC+ cases. Afirma XA was run on all such cases, and all variants and fusions were tabulated. Results: Examination of 29,895 FNAs revealed 90 MTC cases. Of 22,793 Bethesda III cases, 32 (0.14%) were MTC+. Of 5,491 Bethesda IV cases, 33 (0.60%) were MTC+. Provider-ordered testing was done on an additional 16 and 9 MTC cases from Bethesda V and VI nodules, respectively. 58% of all MTC+ samples harbored a RET variant (+/- others), 9% contained a KRAS variant (+/- others), 6% included an HRAS variant, 1% had a BRAF fusion, 1% demonstrated other fusions, and 26% held no variant/fusion. Conclusions: In our cohort, Afirma XA identified a variant or fusion in 74% of MTC+ FNAs. Currently approved or investigational therapies exist for cancers with RET, BRAF and HRAS alterations, suggesting that 64% of our series might be eligible for treatment based on genomic information from FNA. In advanced MTC, noninvasive FNA sample collection at the time of diagnosis may ultimately impact on targeted therapy selection, with the option to repeat FNA testing should the disease progress. Future studies may investigate how finding a genomic alteration by FNA can inform the management of MTC and, in the case of progressive disease, improve our understanding of the mechanisms of disease progression and drug resistance.

The purpose of this study was to determine the frequency of BRAF mutation in cytologically indeterminate thyroid nodules and to investigate whether adding the BRAF test improves diagnostic accuracy of the Afirma Gene Expression Classifier (GEC).BRAF V600E mutational status was determined for DNA extracted from cytologically benign (n = 40), indeterminate (n = 208), and malignant (n = 48) fine-needle aspiration specimens previously categorized by GEC as molecularly Benign or Suspicious. Analytical performance of the BRAF assay was assessed to establish reproducibility and limits of detection. Molecular testing results were correlated with blinded expert histopathological diagnoses.The BRAF assay detected mutations reproducibly to 2.5% mutant allele frequency. The prevalence of BRAF mutations in cytologically benign specimens was 2 of 40 (5.0%, 95% confidence interval [CI], 0-16) and in cytologically malignant specimens was 36 of 48 (75.0%, 95% CI, 60-86). In the cytologically indeterminate category, 10.1% of specimens were BRAF+: 2 of 95 were subcategorized as atypia of undetermined significance or follicular lesion of undetermined significance (2.1%, 95% CI, 0-7); 1 of 70 as follicular neoplasm or suspicious for follicular neoplasm (1.4%, 95% CI, 0-9); and 18 of 43 as suspicious for malignancy (41.9%, 95% CI, 27-58). All BRAF+ specimens were classified as Suspicious by the GEC.BRAF mutations are uncommon in nodules with atypia of undetermined significance or follicular lesion of undetermined significance or follicular neoplasm or suspicious for follicular neoplasm cytology. Most cytologically indeterminate nodules that proved to be malignant were also BRAF-, and all nodules that were false-negative by GEC were also BRAF-. Similarly, all BRAF+ specimens were also GEC Suspicious. Neither GEC test sensitivity nor specificity was improved by addition of BRAF mutation testing.

Our objective was to verify the analytical performance of the Afirma gene expression classifier (GEC) in the classification of cytologically indeterminate thyroid nodule fine-needle aspirates (FNAs).Analytical performance studies were designed to characterize the stability of RNA in FNAs during collection and shipment, analytical sensitivity as applied to input RNA concentration and malignant/benign FNA mixtures, analytical specificity (i.e. potentially interfering substances) as tested on blood and genomic DNA, and assay performance studies including intra-nodule, intraassay, inter-assay, and inter-laboratory reproducibility.RNA content within FNAs preserved in FNAProtect is stable for up to 6 d at room temperature with no changes in RNA yield (P = 0.58) or quality (P = 0.56). FNA storage and shipping temperatures were found to have no significant effect on GEC scores (P = 0.55) or calls (100% concordance). Analytical sensitivity studies demonstrated tolerance to variation in RNA input (5-25 ng) and to the dilution of malignant FNA material down to 20%. Analytical specificity studies using malignant samples mixed with blood (up to 83%) and genomic DNA (up to 30%) demonstrated negligible assay interference with respect to false-negative calls, although benign FNA samples mixed with relatively high proportions of blood demonstrated a potential for false-positive calls. The test is reproducible from extraction through GEC result, including variation across operators, runs, reagent lots, and laboratories (sd of 0.158 for scores on a6 unit scale).Analytical sensitivity, analytical specificity, robustness, and quality control of the GEC were successfully verified, indicating its suitability for clinical use.

Background The current standard practice of lung lesion diagnosis often leads to inconclusive results, requiring additional diagnostic follow up procedures that are invasive and often unnecessary due to the high benign rate in such lesions (Chest 143:e78S-e92, 2013). The Percepta bronchial genomic classifier was developed and clinically validated to provide more accurate classification of lung nodules and lesions that are inconclusive by bronchoscopy, using bronchial brushing specimens (N Engl J Med 373:243–51, 2015, BMC Med Genomics 8:18, 2015). The analytical performance of the Percepta test is reported here. Methods Analytical performance studies were designed to characterize the stability of RNA in bronchial brushing specimens during collection and shipment; analytical sensitivity defined as input RNA mass; analytical specificity (i.e. potentially interfering substances) as tested on blood and genomic DNA; and assay performance studies including intra-run, inter-run, and inter-laboratory reproducibility. Results RNA content within bronchial brushing specimens preserved in RNAprotect is stable for up to 20 days at 4 °C with no changes in RNA yield or integrity. Analytical sensitivity studies demonstrated tolerance to variation in RNA input (157 ng to 243 ng). Analytical specificity studies utilizing cancer positive and cancer negative samples mixed with either blood (up to 10 % input mass) or genomic DNA (up to 10 % input mass) demonstrated no assay interference. The test is reproducible from RNA extraction through to Percepta test result, including variation across operators, runs, reagent lots, and laboratories (standard deviation of 0.26 for scores on > 6 unit scale). Conclusions Analytical sensitivity, analytical specificity and robustness of the Percepta test were successfully verified, supporting its suitability for clinical use. Electronic supplementary material The online version of this article (doi:10.1186/s12885-016-2153-0) contains supplementary material, which is available to authorized users.

Background Thyroid carcinomas are known to harbor oncogenic driver mutations and advances in sequencing technology now allow the detection of these in fine needle aspiration biopsies (FNA). Recent work by The Cancer Genome Atlas (TCGA) Research Network has expanded the number of genetic alterations detected in papillary thyroid carcinomas (PTC). We sought to investigate the prevalence of these and other genetic alterations in diverse subtypes of thyroid nodules beyond PTC, including a variety of samples with benign histopathology. This is the first clinical evaluation of a large panel of TCGA-reported genomic alterations in thyroid FNAs. Results In FNAs, genetic alterations were detected in 19/44 malignant samples (43 % sensitivity) and in 7/44 histopathology benign samples (84 % specificity). Overall, after adding a cohort of tissue samples, 38/76 (50 %) of histopathology malignant samples were found to harbor a genetic alteration, while 15/75 (20 %) of benign samples were also mutated. The most frequently mutated malignant subtypes were medullary thyroid carcinoma (9/12, 75 %) and PTC (14/30, 47 %). Additionally, follicular adenoma, a benign subtype of thyroid neoplasm, was also found to harbor mutations (12/29, 41 %). Frequently mutated genes in malignant samples included BRAF (20/76, 26 %) and RAS (9/76, 12 %). Of the TSHR variants detected, (6/7, 86 %) were in benign nodules. In a direct comparison of the same FNA also tested by an RNA-based gene expression classifier (GEC), the sensitivity of genetic alterations alone was 42 %, compared to the 91 % sensitivity achieved by the GEC. The specificity based only on genetic alterations was 84 %, compared to 77 % specificity with the GEC. Conclusions While the genomic landscape of all thyroid neoplasm subtypes will inevitably be elucidated, caution should be used in the early adoption of published mutations as the sole predictor of malignancy in thyroid. The largest set of such mutations known to date detects only a portion of thyroid carcinomas in preoperative FNAs in our cohort and thus is not sufficient to rule out cancer. Due to the finding that variants are also found in benign nodules, testing only GEC suspicious nodules may be helpful in avoiding false positives and altering the extent of treatment when selected mutations are found. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0849-9) contains supplementary material, which is available to authorized users.

BACKGROUND Approximately 15 to 30% of thyroid nodules evaluated by means of fine-needle aspiration are not clearly benign or malignant. Patients with cytologically indeterminate nodules are often referred for diagnostic surgery, though most of these nodules prove to be benign. A novel diagnostic test that measures the expression of 167 genes has shown promise in improving preoperative risk assessment. METHODS We performed a 19-month, prospective, multicenter validation study involving 49 clinical sites, 3789 patients, and 4812 fine-needle aspirates from thyroid nodules 1 cm or larger that required evaluation. We obtained 577 cytologically indeterminate aspirates, 413 of which had corresponding histopathological specimens from excised lesions. Results of a central, blinded histopathological review served as the reference standard. After inclusion criteria were met, a gene-expression classifier was used to test 265 indeterminate nodules in this analysis, and its performance was assessed. RESULTS Of the 265 indeterminate nodules, 85 were malignant. The gene-expression classifier correctly identified 78 of the 85 nodules as suspicious (92% sensitivity; 95% confidence interval [CI], 84 to 97), with a specificity of 52% (95% CI, 44 to 59). The negative predictive values for “atypia (or follicular lesion) of undetermined clinical significance,” “follicular neoplasm or lesion suspicious for follicular neoplasm,” or “suspicious cytologic findings” were 95%, 94%, and 85%, respectively. Analysis of 7 aspirates with false negative results revealed that 6 had a paucity of thyroid follicular cells, suggesting insufficient sampling of the nodule. CONCLUSIONS These data suggest consideration of a more conservative approach for most patients with thyroid nodules that are cytologically indeterminate on fine-needle aspiration and benign according to gene-expression classifier results. (Funded by Veracyte.)

The promise of personalized medicine will require rigorously validated molecular diagnostics developed on minimally invasive, clinically relevant samples. Measurement of DNA mutations is increasingly common in clinical settings but only higher-prevalence mutations are cost-effective. Patients with rare variants are at best ignored or, at worst, misdiagnosed. Mutations result in downstream impacts on transcription, offering the possibility of broader diagnosis for patients with rare variants causing similar downstream changes. Use of such signatures in clinical settings is rare as these algorithms are difficult to validate for commercial use. Validation on a test set (against a clinical gold standard) is necessary but not sufficient: accuracy must be maintained amidst interfering substances, across reagent lots and across operators. Here we report the development, clinical validation, and diagnostic accuracy of a pre-operative molecular test (Afirma BRAF) to identify BRAF V600E mutations using mRNA expression in thyroid fine needle aspirate biopsies (FNABs). FNABs were obtained prospectively from 716 nodules and more than 3,000 features measured using microarrays. BRAF V600E labels for training (n=181) and independent test (n=535) sets were established using a sensitive quantitative PCR (qPCR) assay. The resulting 128-gene linear support vector machine was compared to qPCR in the independent test set. Clinical sensitivity and specificity for malignancy were evaluated in a subset of test set samples (n=213) with expert-derived histopathology. We observed high positive- (PPA, 90.4%) and negative (NPA, 99.0%) percent agreement with qPCR on the test set. Clinical sensitivity for malignancy was 43.8% (consistent with published prevalence of BRAF V600E in this neoplasm) and specificity was 100%, identical to qPCR on the same samples. Classification was accurate in up to 60% blood. A double-mutant still resulting in the V600E amino acid change was negative by qPCR but correctly positive by Afirma BRAF. Non-diagnostic rates were lower (7.6%) for Afirma BRAF than for qPCR (24.5%), a further advantage of using RNA in small sample biopsies. Afirma BRAF accurately determined the presence or absence of the BRAF V600E DNA mutation in FNABs, a collection method directly relevant to solid tumor assessment, with performance equal to that of an established, highly sensitive DNA-based assay and with a lower non-diagnostic rate. This is the first such test in thyroid cancer to undergo sufficient analytical and clinical validation for real-world use in a personalized medicine context to frame individual patient risk and inform surgical choice.

We present a genotyping method for simultaneously scoring 116,204 SNPs using oligonucleotide arrays. At call rates >99%, reproducibility is >99.97% and accuracy, as measured by inheritance in trios and concordance with the HapMap Project, is >99.7%. Average intermarker distance is 23.6 kb, and 92% of the genome is within 100 kb of a SNP marker. Average heterozygosity is 0.30, with 105,511 SNPs having minor allele frequencies >5%.

The analysis of single nucleotide polymorphisms (SNPs) is increasingly utilizedto investigate the genetic causes of complex human diseases. Here we present a high-throughput genotyping platform that uses a one-primer assay to genotype over 10,000 SNPs per individual on a single oligonucleotide array. This approach uses restriction digestion to fractionate the genome, followed by amplification of a specific fractionated subset of the genome. The resulting reduction in genome complexity enables allele-specific hybridization to the array. The selection of SNPs was primarily determined by computer-predicted lengths of restriction fragments containing the SNPs, andwas further driven by strict empirical measurements of accuracy, reproducibility, andaverage call rate, which we estimate to be >9.5%, >99.9%, and>95%, respectively. With average heterozygosity of 0.38 andgenome scan resolution of 0.31 cM, the SNP array is a viable alternative to panels of microsatellites (STRs). As a demonstration of the utility of the genotyping platform in whole-genome scans, we have replicated and refined a linkage region on chromosome 2p for chronic mucocutaneous candidiasis and thyroid disease, previously identified using a panel of microsatellite (STR) markers.

Analysis of length polymorphisms at STR loci in the human genome has become a standard approach for comparative genotyping in many areas including disease research and diagnostics, parentage assessment, investigations of human diversity, and forensic science. The simultaneous analysis of multiple STR loci through multiplex PCR and multicolor fluorescence detection offers sample conservation, high throughput, and automated genetic analysis. Careful design and optimization of tetranucleotide STR multiplexes has led to reliable, standardized systems that powerfully differentiate and distinguish individual human DNA profiles. The development of these multiplex systems involved a rigorous experimental strategy that included careful selection of PCR primer sequences (for yield, specificity, and multiplex compatability), along with optimization of PCR component concentrations, thermal cycling parameters, and fluorescence detection conditions. This developmental approach rendered well-characterized DNA typing systems that are high performing (sensitive, specific, and balanced), optimized to universal parameters (same reaction conditions), resilient to fluctuations in reaction conditions, and simple to implement and use routinely.

The polymerase chain reaction (PCR) method of specific gene amplification was used in casework to synthesize millions of copies of the polymorphic second exon of the human leukocyte antigen (HLA)-DQ alpha (or DQA1) locus from a variety of evidence samples. The HLA-DQ alpha allelic variants in the amplified deoxyribonucleic acid (DNA) were determined in a rapid non-radioactive test by hybridization to sequence-specific oligonucleotide probes in both the dot-blot and reverse dot-blot formats. This genetic typing system has been subjected to blind proficiency testing; the performance of this test in the analysis of experimentally mixed samples was also evaluated. As of August 1990, over 250 cases have been tested and more than 2000 individual evidence (bloodstains, semen stains, individual hairs, bone fragments, and tissue sections) and reference samples have been analyzed. The first 198 of these cases are summarized in this paper; in 65% of the cases with conclusive results a suspect was included, and in 35%, all suspects were excluded. Individual cases as well as some of the general issues relating to forensic science analysis and this genetic typing system are discussed. The high rate of exclusion reported here combined with the ability of PCR to type old evidence samples suggests the relevance of this genetic test for postconviction review; two cases in which the convicted suspect was excluded are discussed.