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Performance of a Genomic Sequencing Classifier for the Preoperative Diagnosis of Cytologically Indeterminate Thyroid Nodules

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Performance of a Genomic Sequencing Classifier for the Preoperative Diagnosis of Cytologically Indeterminate Thyroid Nodules Supplementary Online Content Patel KN, Angell TE, Babiarz J, et al. Performance of a genomic sequencing classifier for the preoperative diagnosis of cytologically indeterminate thyroid nodules. JAMA Surg. Published online May 23, 2018. doi:10.1001/jamasurg.2018.1153 eMethods 1. Training cohort eMethods 2. Reference methods eMethods 3. RNA purification eMethods 4. Library preparation eMethods 5. Next-generation sequencing eMethods 6. RNA sequencing pipeline, feature extraction, and quality control eAppendix. Genomic sequence classifier to gene expression classifier comparison on a per- samples basis eTable 1. Blinding of the independent test set eTable 2. Composition of the core ensemble model training set eTable 3. Feature sets used in each classifier within the final ensemble model eTable 4. List of 1115 core genes deriving the ensemble model prediction eTable 5. Histology subtype comparison between validation cohorts eTable 6. Prevalence of malignancy between validation cohorts eTable 7. Performance comparison between the genomic sequence classifier and gene expression classifier eFigure 1. Afirma gene expression classifier system eFigure 2. Standards for Reporting of Diagnostic Accuracy Studies diagram of sample flow through the study eReferences. This supplementary material has been provided by the authors to give readers additional information about their work. © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/28/2021 eMethods 1. Training cohort The following thyroid nodule FNA samples were included in the training set (eTable 2): ENHANCE Arm 1 A dedicated molecular sample was obtained when the cytology specimen was collected from a nodule ≥ 1 cm during clinical care, and subsequently underwent surgery. Two central histopathologists reviewed the histopathology slides while blinded to all molecular information and blinded to the local histopathology diagnosis. A similarly blinded third central histopathologist reviewed the slides if agreement was not achieved between the first two blinded histopathologists. If two out of three agreement was not achieved while blinded to each other’s diagnosis, then all 3 histopathologists re-viewed digitized slide images in open discussion to achieve a diagnosis while blinded to all molecular information. ENHANCE Arm 2 A dedicated molecular sample was obtained when the cytology specimen was collected from a nodule ≥ 1 cm during clinical care. Arm 2 samples were all unoperated, Bethesda II, or Bethesda III/IV and GEC benign, and lacked 2015 American Thyroid Association high suspicion sonographic pattern findings. Additionally, they had clinical follow-up (mean 23 months, range 17-32) and either a repeat FNA that was cytology benign, or had no growth (< 50% increase in volume or < 20% increase in 2 or more dimensions) or development of high suspicion ultrasound findings after the initial FNA. Nodules were excluded from Arm 2 if repeat FNA was Bethesda V or VI, GEC suspicious, or they underwent surgery. Arm 2 nodules served as truly benign samples, recognizing that GEC benign samples were underrepresented among operated Arm 1 samples. VERA-CVP (non Cyto-I) samples Samples described in the clinical validation of the Afirma GEC1 with sufficient materials remaining. Only Bethesda II, V, and VI samples with histopathology labels defined by an expert panel of pathologists were allowed in the training set. 60% of these samples were randomly chosen into the training set. VERA-Train Samples used in the training set of the Afirma GEC.1 VERA-Extra Collected and associated with histopathology labels identically to VERA-CVP, but these samples were not used in the training or validation of the Afirma GEC. CLIA-GEC B Samples from the CLIA stream that are GEC Benign. These samples do not have long term follow-up or a histopathology label. Their benign GEC prediction is used as a surrogate label in algorithm training. © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/28/2021 eMethods 2. Reference methods BRAF V600E status BRAF V600E status was determined from genomic DNA using Competitive Allele Specific Taqman PCR (castPCR™, Thermo Fisher, Waltham, MA) for BRAF 1799T>A mutation, as previously described.2,3 Briefly, genomic DNA was purified with the AllPrep Micro Kit (Qiagen, Hilden, Germany) and quantified with Quanti-iT PicoGreen dsDNA Assay Kit (Thermo Fisher, Waltham, MA). Five ng of DNA was tested with wild-type and mutant assays on an ABI7900HT. Samples were labelled BRAF V600E positive if the variant allele frequency was ≥5% and wild type if the allele frequency was <5%. Medullary Thyroid Cancer Histopathology diagnoses, including medullary thyroid cancer, were previously established by an expert panel of thyroid histopathologists while blinded to all clinical and molecular data.1 © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/28/2021 eMethods 3. RNA purification RNA was purified with the AllPrep Micro kit (Qiagen, Hilden, Germany) as previously described.1 RNA was quantified using the QuantiFluor RNA System (Promega, Madison, WI). Fluorescence was read with a Tecan Infinite 200 Pro plate reader (Tecan, Männedorf, Switzerland). RNA Integrity Number was determined with the Bioanalyzer 2100 (Agilent, Santa Clara, CA). © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/28/2021 eMethods 4. Library preparation Samples were randomized and plated into 96 well plates according to their random order. Each plate contained Universal Human Reference RNA (Agilent, Santa Clara, CA), a benign thyroid tissue control sample, a malignant thyroid tissue control sample, a medullary thyroid carcinoma tissue control sample and 6 FNAs that were run on every plate in the study. Additionally, 3 samples from each plate were randomly selected to be included as technical replicates. 15 ng of total RNA was transferred to a 96 well plate. The TruSeq RNA Access Library Preparation Kit (Illumina, San Diego, CA) was adapted for use on the Microlab STAR robotics platform (Hamilton, Reno, NV). During library preparation, total RNA is fragmented, reverse transcribed, end-repaired, A- tailed, and Illumina adapters with individual indexes are ligated. Following PCR and AMpure XP (Beckman Coulter, Indianapolis, IN) cleanup, library size and quantity was determined with the Fragment Analyzer (Advanced Analytical, Ankeny, IA). 250 ng of 4 libraries were combined and sequentially captured with the human exome to remove ribosomal RNA, intronic, and intergenic sequences. Following PCR and AMpure XP (Beckman Coulter, Indianapolis, IN) cleanup, library size and quantity were determined with the Bioanalyzer 2100 (Agilent, Santa Clara, CA). © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/28/2021 eMethods 5. Next-generation sequencing Libraries were normalized to 2 nM, pooled to 16 samples per sequencing run, and denatured according to the manufacturer’s instructions. 1% phiX library (Illumina, San Diego, CA) was spiked into each sequencing run. Denatured and diluted libraries were loaded onto NextSeq 500 machines (Illumina, San Diego, CA) and sequenced with a NextSeq v2 High Output 150 cycle kit (Illumina, San Diego, CA) for paired end 2x76 cycle sequencing. Sequencing runs were required to have >75% of bases ≥Q30 and <1% phiX error rate. © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/28/2021 eMethods 6. RNA sequencing pipeline, feature extraction, and quality control RNA-seq data was used to generate gene expression counts, identify variants, detect fusion-pairs, and calculate loss of heterozygosity (LOH) statistics. Raw sequencing data (FASTQ file) was aligned to human reference genome assembly 37 (Genome Reference Consortium) using STAR RNA-seq aligner.4 Expression counts were obtained by HTSeq5 and normalized using DESeq26 accounting for sequencing depth and gene-wise variability. Variants were identified using GATK variant calling pipeline,7,8 and fusion-pairs detected using STAR-Fusion.9 We developed a loss of heterozygosity (LOH) statistic at chromosome and genome level using variants identified genome-wide. The statistic quantifies the magnitude of LOH by calculating the proportion of variants that have a variant allele frequency (VAF; fraction of reads carrying the alternative allele) away from 0.5 (<0.2 or >0.8) after pre-filtering of variants that has a VAF exactly at zero or one, or is located in cytoband regions exhibiting abnormal excess of LOH signatures across all training samples. To exclude low quality samples from downstream analysis, quality metrics were evaluated against pre- specified acceptance metrics for total numbers of sequenced and uniquely mapped reads, the overall proportion of exonic reads among mapped, the mean per-base coverage, the uniformity of base coverage, and base duplication and mismatch rates. All these QC metrics were generated using RNA-SeQC.10 Any sample that failed a QC metric was reprocessed from total RNA through library preparation and sequencing if sufficient RNA was available. Only samples passing the quality criteria were used for downstream analysis. © 2018 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ on 09/28/2021 eAppendix. Genomic sequence classifier to gene expression classifier comparison on
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