Multiple cancer type classification by small RNA expression profiles with plasma samples from multiple facilities. Issue 6 (14th March 2022)

• Record Type: Journal Article
• Authors: Suzuki, Kuno; Igata, Hideyoshi; Abe, Motoki; Yamamoto, Yusuke
• Other Names: Iwanaga Terunao investigator.; Kanzaki Hiroaki investigator.; Kato Naoya investigator.; Tanaka Nobuko investigator.; Kawasaki Kenji investigator.; Matsushita Kazuyuki investigator.; Samashima Ryu investigator.; Tsukino Keiji investigator.; Yokomizo Akira investigator.; Miyashita Yosuke investigator.; Sumiyoshi Issei investigator.; Takahashi Kazuhisa investigator.; Serizawa Nobuko investigator.; Tomishima Ko investigator.; Nagahara Akihito investigator.; Ishizuka Yumiko investigator.; Horimoto Yoshiya investigator.; Nagata Masayoshi investigator.; Ishikawa Keisuke investigator.; Horie Shigeo investigator.; Shiina Shuichiro investigator.; Nasu Motomi investigator.; Hashimoto Takashi investigator.; Mine Shinji investigator.; Kawano Shingo investigator.; Sugimoto Kiichi investigator.; Sakamoto Kazuhiro investigator.; Takemura Hiroyuki investigator.; Wakita Mitsuru investigator.; Tabe Yoko investigator.; Kato Shunsuke investigator.; Miyagi Yohei investigator.; Adachi Hiroyuki investigator.; Isaka Tetsuya investigator.; Ito Hiroyuki investigator.; Yamanaka Takashi investigator.; Yoshida Tatsuya investigator.; Yamashita Toshinari investigator.; Ogata Takashi investigator.; Yamada Takanobu investigator.; Oshima Takashi investigator.; Yamamoto Naoto investigator.; Murakawa Masaaki investigator.; Morinaga Soichiro investigator.; Kobayashi Satoshi investigator.; Tezuka Shun investigator.; Ueno Makoto investigator.; Koizumi Mitsuyuki investigator.; Osaka Kimito investigator.; Kishida Takeshi investigator.; Sato Sumito investigator.; Mikayama Yo investigator.; Shiozawa Manabu investigator.; Inokuchi Yasuhiro investigator.; Furuta Mitsuhiro investigator.; Machida Nozomu investigator.; Sato Shinya investigator.; Yano Yoshihiko investigator.; Miwa Atsushi investigator.; Ito Kazuto investigator.; Kurosawa Isao investigator.; Kikuchi Osamu investigator.; Tazawa Hiromitsu investigator.; Muto Manabu investigator.; Honda Takashi investigator.; Kawashima Hiroki investigator.; Ishigami Masatoshi investigator.; Saito Yutaka investigator.; Daiko Hiroyuki investigator.; Yoshikawa Takaki investigator.; Kanemitsu Yukihide investigator.; Kato Ken investigator.; Esaki Minoru investigator.; Okusaka Takuji investigator.; Sakamoto Hiromi investigator.; Yoshida Teruhiko investigator.; Ochiya Takahiro investigator.; Sasaki Mitsuhito investigator.; Ikeda Masafumi investigator.; Kudo Masashi investigator.; Gotohda Naoto investigator.; Mitsunaga Shuichi investigator.; Kuwata Takeshi investigator.; Kojima Takashi investigator.; Murano Tatsuro investigator.; Yano Tomonori investigator.; Yamaji Taiki investigator.; Matsuda Takahisa investigator.; Tsugane Shoichiro investigator.; Hashimoto Kazuki investigator.; Yamada Kazuhiko investigator.; Takemura Nobuyuki investigator.; Ito Kyoji investigator.; Mihara Fuminori investigator.; Shimomura Akihiko investigator.; Shigeyasu Kunitoshi investigator.; Noma Kazuhiro investigator.; Fujiwara Toshiyoshi investigator.; Yamamoto Hideki investigator.; Morita Mizuki investigator.; Toyooka Shinichi investigator.; Tamori Akihiro investigator.; Nakabori Tasuku investigator.; Ikezawa Kenji investigator.; Ohkawa Kazuyoshi investigator.; Kunimasa Kei investigator.; Nishino Kazumi investigator.; Kumagai Toru investigator.; Kudo Toshihiro investigator.; Sugimoto Naotoshi investigator.; Yasui Masayoshi investigator.; Omori Takeshi investigator.; Miyata Hiroshi investigator.; Kimura Toru investigator.; Maniwa Tomohiro investigator.; Okami Jiro investigator.; Kusama Hiroki investigator.; Kittaka Nobuyoshi investigator.; Nakayama Takahiro investigator.; Nakayama Masashi investigator.; Nakai Yasutomo investigator.; Nishimura Kazuo investigator.; Yotsui Shoji investigator.; Yamamoto Takashi investigator.; Yamasaki Tomoyuki investigator.; Yamashita Emi investigator.; Saito Kazune investigator.; Yoshida Keiichi investigator.; Ohue Masayuki investigator.; Koda Masakazu investigator.; Yamaguchi Tatsuya investigator.; Tanaka Masami investigator.; Nishizawa Takashi investigator.; Taira Tetsuhiko investigator.; Kawano Junko investigator.; Sagara Yasuaki investigator.; Horita Yosuke investigator.; Mihara Yoshiaki investigator.; Hamaguchi Tetsuya investigator.; Suzuki Okihide investigator.; Kumagai Yoichi investigator.; Ishida Hideyuki investigator.; Yamagishi Motoki investigator.; Shimoyama Hideaki investigator.; Sasaki Haruaki investigator.; Nakasato Takehiko investigator.; Shichijo Takeshi investigator.; Fukagai Takashi investigator.; Nishimura Kota investigator.; Hirayama Kidai investigator.; Morita Masashi investigator.; Kudo Yujin investigator.; Takeuchi Susumu investigator.; Ikeda Norihiko investigator.; Kamoda Naohiro investigator.; Namiki Kazunori investigator.; Ohno Yoshio investigator.; Umezu Tomohiro investigator.; Murakami Yoshiki investigator.; Kuroda Masahiko investigator.;
• Is Part Of: Cancer science. Volume 113:Issue 6(2022)
• Page Start: 2144
• Page End: 2166
• Publication Date: 2022-03-14
• Subjects: liquid biopsy -- machine learning -- multiple cancer type classification -- multiple facilities -- NGS -- small RNA; Cancer -- Periodicals; Neoplasms -- Periodicals; Research -- Periodicals; Electronic journals; 616.994005
• DOI: 10.1111/cas.15309
• Languages: English
• ISSNs: 1347-9032

Abstract

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.