An accurate, clinically feasible multi-gene expression assay for predicting metastasis in uveal melanoma

Abstract

Uveal (ocular) melanoma is an aggressive cancer that often forms undetectable micrometastases before diagnosis of the primary tumor. These micrometastases later multiply to generate metastatic tumors that are resistant to therapy and are uniformly fatal. We have previously identified a gene expression profile derived from the primary tumor that is extremely accurate for identifying patients at high risk of metastatic disease. Development of a practical clinically feasible platform for analyzing this expression profile would benefit high-risk patients through intensified metastatic surveillance, earlier intervention for metastasis, and stratification for entry into clinical trials of adjuvant therapy. Here, we migrate the expression profile from a hybridization-based microarray platform to a robust, clinically practical, PCR-based 15-gene assay comprising 12 discriminating genes and three endogenous control genes. We analyze the technical performance of the assay in a prospective study of 609 tumor samples, including 421 samples sent from distant locations. We show that the assay can be performed accurately on fine needle aspirate biopsy samples, even when the quantity of RNA is below detectable limits. Preliminary outcome data from the prospective study affirm the prognostic accuracy of the assay. This prognostic assay provides an important addition to the armamentarium for managing patients with uveal melanoma, and it provides a proof of principle for the development of similar assays for other cancers.

Figure 1

Flow diagram summarizing the process for migrating the gene expression profile from a hybridization-based microarray platform to a 15-gene qPCR-based assay performed on a microfluidics card.

Figure 2

Kaplan-Meier analysis showing metastasis-free survival as a function of molecular class in 172 uveal melanoma patients. Statistical significance determined by the log rank test is shown.

Figure 3

Analysis of top genes. A: Heatmap showing normalized expression of the 12 discriminating genes compared with SVM discriminant score in a set of prospectively collected samples (blue = low relative expression, red = high relative expression, gray = intermediate expression). B: Classification errors as a function of the number of discriminating genes in the assay. Genes were removed stepwise according to mRMR rank. See text for details.

Figure 4

Relationship between RNA concentration and RNA quality. A: Scatter plot of RNA concentration versus the geometric mean of the C_t values for the three endogenous control genes. Black line indicates linear regression of values within the expected range; dashed lines indicate 90% prediction interval for regression. B: The same plot as in A, showing samples that were handled/shipped incorrectly and samples that failed the assay (defined as one or more endogenous controls undetectable after 40 qPCR cycles). C: The same plot as in A, showing samples according to their molecular class assignment. D: A subset of 148 cases from the Washington University center in which cytologic diagnosis was available. QNS indicates quantity not sufficient (too few cells to make a diagnosis).

Figure 5

Dilution series of RNA from class 1 and class 2 tumors analyzed by the 15-gene assay. Discriminant scores for SVM (y axis) are plotted against contribution of class 1 RNA (x axis). The 15-gene assay classified mixtures as class 2 even when class 2 RNA was in the minority. Negative scores indicate class 1; positives scores, class 2. Closest fit trendline included for clarity.