Evidence map›Paper›PMID 42509447›Full record

ReviewInternational journal of clinical oncology2026

AI-integrated multi-omics platform to revolutionize anti-metastatic therapy development through circulating tumor cell profiling: SCRUM-MONSTAR-CTC.

Tadayoshi Hashimoto, Taro Shibuki, Takao Fujisawa, Shogen Boku, Mizuho Kikuchi, Atsuko Shimizu, Tomoe Murakami, Yasutoshi Sakamoto, Izumi Miki, Naoko Iida and 17 more

Abstract readReview
In one paragraph

Review in International journal of clinical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

27 authors.

Tadayoshi HashimotoTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.ORCID http://orcid.org/0000-0003-3759-1214
Taro ShibukiTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Takao FujisawaTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Shogen BokuDepartment of Clinical Oncology, Kansai Medical University Hospital, Hirakata, Japan.
Mizuho KikuchiTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Atsuko ShimizuTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Tomoe MurakamiTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Yasutoshi SakamotoTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Izumi MikiTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Naoko IidaTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Haruki KuranoTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Riu YamashitaDivision of Translational Informatics, Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa, Japan.
Jun MasudaBreast Medical Oncology, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan.
Kensuke MatsudaDepartment of Hematology, National Cancer Center Hospital East, Kashiwa, Japan.
Junichiro YudaDepartment of Hematology, National Cancer Center Hospital East, Kashiwa, Japan.
Shugo YajimaPerioperative Treatment Development Promotion Office, National Cancer Center Hospital East, Kashiwa, Japan.
Shin KobayashiPerioperative Treatment Development Promotion Office, National Cancer Center Hospital East, Kashiwa, Japan.
Zedao LiuTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Masataka AmisakiTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Mitsuho ImaiTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Yoshiaki NakamuraTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Hideaki BandoTranslational Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan.
Hyoju KimDepartment of Biochemistry, College of Life Science and Biotechnology, Brain Korea 21 Four Project, Yonsei University, Seoul, Republic of Korea.
Heon Yung GeeDepartment of Pharmacology, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University college of medicine, Brain Korea 21 Project, Seoul, Republic of Korea.
Hee Seung LeeDivision of Gastroenterology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
Hyun Woo Park *Department of Biochemistry, College of Life Science and Biotechnology, Brain Korea 21 Four Project, Yonsei University, Seoul, Republic of Korea. hwp003@yonsei.ac.kr.
Takayuki Yoshino *Department of Gastrointestinal Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577, Chiba, Japan. tyoshino@east.ncc.go.jp.

Funding

Japan Agency for Medical Research and Development 24ck0106971h0001, 25ck0106971h0002, 26ck0106971h0003Kaken Pharmaceutical
6 · The paper itself

Abstract

backgroundMetastatic 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.

methodsThe 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 registrationUMIN000056873, approved by the Institutional Review Board of the National Cancer Center Hospital East.

Indexed as

Artificial IntelligenceNeoplasmsNeoplastic Cells, CirculatingAnimalsBiomarkers, TumorCirculating Tumor DNAHumansMiceMultiomicsNeoplasm MetastasisPrecision MedicineBiomarkers, TumorCirculating Tumor DNAAnti-metastatic therapyCirculating tumor cellsLiquid biopsyMetabolitesMetastatic plasticityPrecision Oncology

Identifiers

PMID42509447
PMCPMC13506547

What Socratic holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.