Molecular classification of thyroid nodules using high-dimensionality genomic data.
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.
Chudova
Darya
D
Veracyte, Inc., 7000 Shoreline Court Suite 250, South San Francisco, California 94080, USA.
Wilde
Jonathan I
JI
Wang
Eric T
ET
Wang
Hui
H
Rabbee
Nusrat
N
Egidio
Camila M
CM
Reynolds
Jessica
J
Tom
Ed
E
Pagan
Moraima
M
Rigl
C Ted
CT
Friedman
Lyssa
L
Wang
C Charles
CC
Lanman
Richard B
RB
Zeiger
Martha
M
Kebebew
Electron
E
Rosai
Juan
J
Fellegara
Giovanni
G
LiVolsi
Virginia A
VA
Kennedy
Giulia C
GC
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
D012333:RNA, Messenger