A general method to derive robust organ-specific gene expression-based differentiation indices: application to thyroid cancer diagnostic.

Differentiation is central to development, while dedifferentiation is central to cancer progression. Hence, a quantitative assessment of differentiation would be most useful. We propose an unbiased method to derive organ-specific differentiation indices from gene expression data and demonstrate its usefulness in thyroid cancer diagnosis. We derived a list of thyroid-specific genes by selecting automatically those genes that are expressed at higher level in the thyroid than in any other organ in a normal tissue's genome-wide gene expression compendium. The thyroid index of a tissue was defined as the median expression of these thyroid-specific genes in that tissue. As expected, the thyroid index was inversely correlated with meta-PCNA, a proliferation metagene, across a wide range of thyroid tumors. By contrast, the two indices were positively correlated in a time course of thyroid-stimulating hormone (TSH) activation of primary thyrocytes. Thus, the thyroid index captures biological information not integrated by proliferation rates. The differential diagnostic of follicular thyroid adenomas and follicular thyroid carcinoma is a notorious challenge for pathologists. The thyroid index discriminated them as accurately as did machine-learning classifiers trained on the genome-wide cancer data. Hence, although it was established exclusively from normal tissue data, the thyroid index integrates the relevant diagnostic information contained in tumoral transcriptomes. Similar results were obtained for the classification of the follicular vs classical variants of papillary thyroid cancers, that is, tumors dedifferentiating along a different route. The automated procedures demonstrated in the thyroid are applicable to other organs.