Kengo Gotoh#, Eiji Shinto#, Yuichiro Yoshida*, Hideki Ueno,Yoshiki Kajiwara, Masato Yamadera, Ken Nagata, Hitoshi Tsuda, Junji Yamamoto and Kazuo Hase
Objectives: Cancer subtypes classified according to DNA microarray data predict prognosis with high accuracy. Here we constructed a new colon cancer (CC) subtype classification based on only of genes with known biological functions with the aim of establishing a new prognostic model for clinical use.
Methods: We performed an expression correlation analysis using data for 73 primary CC cases in the public dataset (learning set), focusing on genes located on the long arms of chromosomes 18 and 20 and stromal-related genes. We determined the representation of each gene in the modules with closely correlated expression levels in the same module. Mutations in KRAS, BRAF and TP53 were assessed using direct sequencing. Microsatellite instability (MSI) was analyzed using the Bethesda reference panel.
Results: We constructed a discriminant model with a view to classifying CC into three subtypes (“stromal”, “chromosomal instability [CIN]-like”, “MSI-like”) based on the expression levels of 55 genes of the Learning set. When we applied this predictor to microarray data from other patients with stage II/III colon cancer (n=258, test set), we discovered a significant difference in diseasefree survival between the stromal subtype and the other subtypes (p=1.25e-03). Accordingly, we created an integrated prognostic model for classifying the patients into high- and low-risk groups according to the expression levels of the 55 genes and KRAS mutations (p=1.56e-06). Analysis of independent specimens from patients with stage II/III colon cancer who underwent radical resection (n=59, validation set) confirmed the prognostic value of our model (p=4.75e-02).
Conclusion: The model produced a biologically discriminatory classifier that associated MSI status with the risk of recurrence that may be clinically applicable to the selection of patients with Stage II/ III CC for adjuvant therapy.
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