Multiple cancer type classification by small RNA expression profiles with plasma samples from multiple facilities. Issue 6 (14th March 2022) - Suzuki, Kuno; Igata, Hideyoshi; Abe, Motoki; Yamamoto, Yusuke; ,
Abstract: Liquid biopsy is expected to be a promising cancer screening method because of its low invasiveness and the possibility of detecting multiple types in a single test. In the last decade, many studies on cancer detection using small RNAs in blood have been reported. To put small RNA tests into practical use as a multiple cancer type screening test, it is necessary to develop a method that can be applied to multiple facilities. We collected samples of eight cancer types and healthy controls from 20 facilities to evaluate the performance of cancer type classification. A total of 2, 475 cancer samples and 496 healthy control samples were collected using a standardized protocol. After obtaining a small RNA expression profile, we constructed a classification model and evaluated its performance. First, we investigated the classification performance using samples from five single facilities. Each model showed areas under the receiver curve (AUC) ranging from 0.67 to 0.89. Second, we performed principal component analysis (PCA) to examine the characteristics of the facilities. The degree of hemolysis and the data acquisition period affected the expression profiles. Finally, we constructed the classification model by reducing the influence of these factors, and its performance had an AUC of 0.76. The results reveal that small RNA can be used for the classification of cancer types in samples from a single facility. However, interfacility biases will affect the classification of samples from multiple facilities. These findings will provide important insights to improve the performance of multiple cancer type classifications using small RNA expression profiles acquired from multiple facilities. Abstract : Liquid biopsy is expected to be a promising cancer screening method. A total of 2, 475 cancer samples and 496 healthy control samples were collected from 20 facilities. We constructed the multiple cancer type classification model using selected small RNAs and found that its performance had an AUC of 0.76.