title: Molecular classification of thyroid nodules using high-dimensionality genomic data.
abstract: OBJECTIVE
  We set out to develop a molecular test that distinguishes benign and malignant thyroid nodules using fine-needle aspirates (FNA).
  
  
  METHODS
  We used mRNA expression analysis to measure more than 247,186 transcripts in 315 thyroid nodules, comprising multiple subtypes. The data set consisted of 178 retrospective surgical tissues and 137 prospectively collected FNA samples. Two classifiers were trained separately on surgical tissues and FNAs. The performance was evaluated using an independent set of 48 prospective FNA samples, which included 50% with indeterminate cytopathology.
  
  
  RESULTS
  Performance of the tissue-trained classifier was markedly lower in FNAs than in tissue. Exploratory analysis pointed to differences in cellular heterogeneity between tissues and FNAs as the likely cause. The classifier trained on FNA samples resulted in increased performance, estimated using both 30-fold cross-validation and an independent test set. On the test set, negative predictive value and specificity were estimated to be 96 and 84%, respectively, suggesting clinical utility in the management of patients considering surgery. Using in silico and in vitro mixing experiments, we demonstrated that even in the presence of 80% dilution with benign background, the classifier can correctly recognize malignancy in the majority of FNA samples.
  
  
  CONCLUSIONS
  The FNA-trained classifier was able to classify an independent set of FNAs in which substantial RNA degradation had occurred and in the presence of blood. High tolerance to dilution makes the classifier useful in routine clinical settings where sampling error may be a concern. An ongoing multicenter clinical trial will allow us to validate molecular test performance on a larger independent test set of prospectively collected thyroid FNAs.
authors.affiliation: Veracyte, Inc., 7000 Shoreline Court Suite 250, South San Francisco, California 94080, USA.
authors.forename: Darya
authors.initials: D
authors.lastname: Chudova
authors.forename: Jonathan I
authors.initials: JI
authors.lastname: Wilde
authors.forename: Eric T
authors.initials: ET
authors.lastname: Wang
authors.forename: Hui
authors.initials: H
authors.lastname: Wang
authors.forename: Nusrat
authors.initials: N
authors.lastname: Rabbee
authors.forename: Camila M
authors.initials: CM
authors.lastname: Egidio
authors.forename: Jessica
authors.initials: J
authors.lastname: Reynolds
authors.forename: Ed
authors.initials: E
authors.lastname: Tom
authors.forename: Moraima
authors.initials: M
authors.lastname: Pagan
authors.forename: C Ted
authors.initials: CT
authors.lastname: Rigl
authors.forename: Lyssa
authors.initials: L
authors.lastname: Friedman
authors.forename: C Charles
authors.initials: CC
authors.lastname: Wang
authors.forename: Richard B
authors.initials: RB
authors.lastname: Lanman
authors.forename: Martha
authors.initials: M
authors.lastname: Zeiger
authors.forename: Electron
authors.initials: E
authors.lastname: Kebebew
authors.forename: Juan
authors.initials: J
authors.lastname: Rosai
authors.forename: Giovanni
authors.initials: G
authors.lastname: Fellegara
authors.forename: Virginia A
authors.initials: VA
authors.lastname: LiVolsi
authors.forename: Giulia C
authors.initials: GC
authors.lastname: Kennedy
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D044963:Biopsy, Fine-Needle; D005786:Gene Expression Regulation; D014644:Genetic Variation; D023281:Genomics / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012333:RNA, Messenger / Q000235:genetics; D012372:ROC Curve; D015203:Reproducibility of Results; D016606:Thyroid Nodule / Q000145:classification / Q000235:genetics* / Q000473:pathology / Q000601:surgery*; D014158:Transcription, Genetic
chemical_list: D012333:RNA, Messenger