International journal of clinical oncology · 2026
AI-integrated multi-omics platform to revolutionize anti-metastatic therapy development through circulating tumor cell profiling: SCRUM-MONSTAR-CTC.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Hashimoto Tadayoshi, Shibuki Taro, Fujisawa Takao, Boku Shogen, Kikuchi Mizuho, Shimizu Atsuko, Murakami Tomoe, Sakamoto Yasutoshi, Miki Izumi, Iida Naoko, Kurano Haruki, Yamashita Riu, Masuda Jun, Matsuda Kensuke, Yuda Junichiro, Yajima Shugo, Kobayashi Shin, Liu Zedao, Amisaki Masataka, Imai Mitsuho, Nakamura Yoshiaki, Bando Hideaki, Kim Hyoju, Gee Heon Yung, Lee Hee Seung, Park Hyun Woo, Yoshino Takayuki
Аннотация
BACKGROUND: Metastatic disease remains the leading cause of cancer-related death, yet most precision oncology strategies still emphasize profiling primary tumors and tracking cell-free tumor DNA (ctDNA). Although ctDNA has transformed genomic profiling, molecular residual disease monitoring, and early cancer detection, it cannot directly capture viable tumor cell states, phenotypic plasticity, or functional adaptations that drive metastatic spread. We propose that the next phase of precision oncology should integrate the cellular dimension of metastasis through systematic circulating tumor cell (CTC) profiling.
METHODS: The SCRUM-MONSTAR platform, one of the largest pan-cancer molecular profiling initiatives in Japan, offers an exceptional foundation for this transition through its nationwide infrastructure for multi-omics analysis, longitudinal biospecimen collection, and artificial intelligence-enabled clinical interpretation. By combining matched tissue profiling, serial ctDNA analysis, single-cell CTC transcriptomics, metabolomics, and organoid- and mouse-based functional modeling, SCRUM-MONSTAR-CTC could evolve into a translational ecosystem for anti-metastatic drug discovery. Within this framework, we highlight adherent-to-suspension transition (AST) as one representative, experimentally tractable plasticity program that enables tumor cells to survive in circulation and subsequently colonize distant organs.
DISCUSSION: We envision that identifying and therapeutically targeting AST-related and other metastatic plasticity programs across tumor types will provide a path toward clinically actionable anti-metastatic therapies. More broadly, this framework could enable the identification of metastatic vulnerabilities, the development of biomarker-guided anti-metastatic trials, and the reverse translation of patient-derived discoveries into early-phase clinical testing. Precision oncology must move beyond cataloging tumor genomes and begin targeting metastasis as a dynamic biological process.
TRIAL REGISTRATION: UMIN000056873, approved by the Institutional Review Board of the National Cancer Center Hospital East.
METHODS: The SCRUM-MONSTAR platform, one of the largest pan-cancer molecular profiling initiatives in Japan, offers an exceptional foundation for this transition through its nationwide infrastructure for multi-omics analysis, longitudinal biospecimen collection, and artificial intelligence-enabled clinical interpretation. By combining matched tissue profiling, serial ctDNA analysis, single-cell CTC transcriptomics, metabolomics, and organoid- and mouse-based functional modeling, SCRUM-MONSTAR-CTC could evolve into a translational ecosystem for anti-metastatic drug discovery. Within this framework, we highlight adherent-to-suspension transition (AST) as one representative, experimentally tractable plasticity program that enables tumor cells to survive in circulation and subsequently colonize distant organs.
DISCUSSION: We envision that identifying and therapeutically targeting AST-related and other metastatic plasticity programs across tumor types will provide a path toward clinically actionable anti-metastatic therapies. More broadly, this framework could enable the identification of metastatic vulnerabilities, the development of biomarker-guided anti-metastatic trials, and the reverse translation of patient-derived discoveries into early-phase clinical testing. Precision oncology must move beyond cataloging tumor genomes and begin targeting metastasis as a dynamic biological process.
TRIAL REGISTRATION: UMIN000056873, approved by the Institutional Review Board of the National Cancer Center Hospital East.