Increasing the Number of Thyroid Lesions Classes in Microarray Analysis Improves the Relevance of Diagnostic Markers

BackgroundGenetic markers for thyroid cancers identified by microarray analysis have offered limited predictive accuracy so far because of the few classes of thyroid lesions usually taken into account. To improve diagnostic relevance, we have simultaneously analyzed microarray data from six public datasets covering a total of 347 thyroid tissue samples representing 12 histological classes of follicular lesions and normal thyroid tissue. Our own dataset, containing about half the thyroid tissue samples, included all categories of thyroid lesions. Methodology/Principal Findings Classifier predictions were strongly affected by similarities between classes and by the number of classes in the training sets. In each dataset, sample prediction was improved by separating the samples into three groups according to class similarities. The cross-validation of differential genes revealed four clusters with functional enrichments. The analysis of six of these genes (APOD, APOE, CLGN, CRABP1, SDHA and TIMP1) in 49 new samples showed consistent gene and protein profiles with the class similarities observed. Focusing on four subclasses of follicular tumor, we explored the diagnostic potential of 12 selected markers (CASP10, CDH16, CLGN, CRABP1, HMGB2, ALPL2, ADAMTS2, CABIN1, ALDH1A3, USP13, NR2F2, KRTHB5) by real-time quantitative RT-PCR on 32 other new samples. The gene expression profiles of follicular tumors were examined with reference to the mutational status of the Pax8-PPARγ, TSHR, GNAS and NRAS genes. Conclusion/Significance We show that diagnostic tools defined on the basis of microarray data are more relevant when a large number of samples and tissue classes are used. Taking into account the relationships between the thyroid tumor pathologies, together with the main biological functions and pathways involved, improved the diagnostic accuracy of the samples. Our approach was particularly relevant for the classification of microfollicular adenomas

Datasets, classes and samples.

<p>Datasets, classes and samples.</p

Hierarchical clustering of cross-validated differential genes.

<p>The heatmap shows gene-expression levels in the Fontaine dataset for the tissue classes common to the Giordano dataset (104 genes). Functional enrichments are shown for five identified gene clusters on the right, followed by p-values and the genes involved. The black text represents Level 3 Gene Ontology terms; the blue text represents canonical Ingenuity pathways. Gene profiles of the differential genes reflect the class similarities observed. WT: wild type tissue; PTC: papillary thyroid carcinoma; FTA: macrofollicular thyroid adenoma; FTC: follicular thyroid carcinoma; OTA: oncocytic thyroid adenoma; and OTC: oncocytic thyroid carcinoma.</p

Cross validated differential genes from the two main datasets (n = 104).

<p>Cross validated differential genes from the two main datasets (n = 104).</p

Primer sequences used for real-time quantitative RT-PCR.

<p>Primer sequences used for real-time quantitative RT-PCR.</p

Classifier genes from the Fontaine dataset (n = 220 genes).

<p>AT: autoimmune thyroiditis; FTA: macrofollicular thyroid adenoma; FTAb: microfollicular thyroid adenoma; FTC: follicular thyroid carcinoma; GD, Graves' disease; MNG: multinodular goiter; OTA: oncocytic thyroid adenoma; OTC: oncocytic thyroid carcinoma; PTC: papillary thyroid carcinoma; TUM: tumor of uncertain malignancy; and OTUM:, oncocytic tumor of uncertain malignancy.</p

Inter-dataset cross-validations. Positive accuracies of the classifiers defined by the Fontaine dataset in five other datasets.

<p>FTA: macrofollicular thyroid adenoma; FTC: follicular thyroid carcinoma; OTA: oncocytic thyroid adenoma; OTC: oncocytic thyroid carcinoma; and PTC: papillary thyroid carcinoma.</p

Gene expression of selected markers.

<p>Differential expression of 12 genes measured by real-time quantitative RT-PCR on 32 new follicular thyroid tumors (8 FTA, 8 FTAb, 8 OTA and 8 FTC). The upper and the lower limits of each box represent the upper and the lower quartiles, respectively. The bold lines represent medians. The expression of ADAMTS2, CABIN1, ADLH1A3, USP13, NR2F2, KRTHB5, CASP10, CDH16, CLGN, CRABP1, HMGB2 and ALPL2 genes was referred to the β-globin expression level. Significances of differential expression between the classes were assessed by t-tests and those over the classes by F-tests. FTA: macrofollicular thyroid adenoma; FTAb: microfollicular thyroid adenoma; FTC: follicular thyroid carcinoma; and OTA: oncocytic thyroid adenoma.</p

Molecular classification of thyroid tissues.

<p>The centroid signatures of the thyroid tissues were compared to each other in the Fontaine dataset generated by our laboratory <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007632#pone.0007632-Fontaine1" target="_blank">[15]</a>, and compared to the Giordano dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007632#pone.0007632-Giordano1" target="_blank">[5]</a>. Centroids were defined as the mean gene-expression signature of each tissue class over all the genes. Panel A shows the hierarchical clustering of the tissues in the Fontaine dataset. The dendrogram can be cut into three robust branches (black circles) defining three groups of classes. The groups contain benign lesions and normal tissue (green boxes), malignant tumors (red boxes), or oncocytic tumors and microfollicular adenomas (blue boxes). Panel B shows pairwise correlation coefficients between the tissue classes from the Fontaine dataset. The heatmap represents a symmetrical matrix of color-coded correlation coefficients. Tissue classes are ordered as in the hierarchical clustering and marked with the same three colors (green, red and blue). Panel C shows the hierarchical clustering of the tissues in the Giordano dataset. The dendrogram can be cut into three robust branches (black circles) defining three groups of classes. The groups contain heterogeneous lesions and normal tissue (green boxes), malignant tumors (red boxes), or oncocytic tumors (blue boxes). The two datasets show three groups containing similar tissue classes. WT: wild type tissue; PTC: papillary thyroid carcinoma; FTA: macrofollicular thyroid adenoma; FTAb: microfollicular thyroid adenoma; GMN, multinodular goiter; FTC: follicular thyroid carcinoma (FTC+: with Pax8/PPARγ translocation; FTC-: without Pax8/PPARγ translocation); OTA: oncocytic thyroid adenoma; OTC: oncocytic thyroid carcinoma; TUM: tumor of uncertain malignancy; OTUM: oncocytic tumor of uncertain malignancy; GD: Grave's disease; and AT: autoimmune thyroïditis.</p

Protein expression of selected genes.

<p>The protein expression of six selected differential genes was measured on 49 new tissue samples. These independent samples represent six tissue classes that are common to the two main datasets (WT, PTC, FTA, FTC, OTA, and OTC). The AT class was added to compare the expression levels with a non-tumoral lesion. Seven samples were used for each class. Protein expression profiles of APOD, CLGN, SDHA, APOE, CRABP1 and TIMP1 are shown by box-plots for the different classes of tissue. Significances of differential expression over the classes were assessed by F-tests. P-values are shown between brackets. WT: wild type tissue; PTC: papillary thyroid carcinoma; FTA: macrofollicular thyroid adenoma; FTC: follicular thyroid carcinoma; OTA: oncocytic thyroid adenoma; and OTC: oncocytic thyroid carcinoma.</p