Multi-omics-guided dynamic precision medicine in leukemia: An AML-centered framework integrating genotype, cellular state, bone marrow pathology, and the immune ecosystem.

Multi-omics approaches for deciphering disease evolution and identifying therapeutic targets in acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy characterized by dynamic clonal evolution, diverse molecular alterations, and variable therapeutic responses. Although advances in genomic profiling have substantially improved disease classification and risk stratification, many patients continue to experience relapse and treatment resistance, highlighting the need for a more comprehensive understanding of AML biology. Recent developments in multi-omics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and single-cell approaches, have enabled unprecedented insights into the molecular mechanisms underlying AML initiation, progression, and therapeutic failure. Integrative multi-omics analyses have revealed complex interactions among genetic mutations, epigenetic remodeling, metabolic reprogramming, and leukemia microenvironmental adaptations that collectively drive disease evolution. These approaches have also facilitated the identification of novel biomarkers, lineage-specific dependencies, and therapeutically actionable vulnerabilities that are not apparent from single-layer analyses. Furthermore, multi-omics-guided studies are accelerating the development of precision medicine strategies by improving patient stratification and uncovering mechanisms of drug resistance. In this review, we summarize recent advances in multi-omics technologies and discuss how integrative analyses are reshaping our understanding of AML evolution. We highlight emerging therapeutic targets identified through multi-omics investigations and examine their translational potential for innovative drug development. Finally, we discuss current challenges and future opportunities for implementing multi-omics-guided precision medicine in AML.

Integrative precision oncology in neuroblastoma: multi-omics biomarkers, molecular targets, and immunotherapeutic strategies

Neuroblastoma is the most frequent extracranial solid tumor of young children and demonstrates a wide range of biological behavior, from spontaneous regression to highly aggressive metastatic disease. Recent advances in genomics, epigenetics, tumor microenvironment biology, and multi-omics technologies have greatly enhanced understanding of neuroblastoma pathogenesis and enabled the development of precision diagnostic and prognostic approaches. This review examines current evidence for molecular and cellular mechanisms underlying neuroblastoma initiation, progression, and therapeutic resistance, with a focus on MYCN amplification, ALK pathway dysregulation, chromosomal instability, telomere maintenance mechanisms, epigenetic remodeling, and neural crest developmental biology. This review primarily emphasizes that these characteristics should be interpreted within a unified developmental framework. In this context, age serves more as an indicator of the differentiation status of the sympathoadrenal progenitor pool rather than merely a timeline for the accumulation of somatic mutations. This viewpoint connects the epigenetic, genetic, and clinical aspects of the disease, providing insight into its distinctive range of behaviors, from spontaneous regression in infants to fatal progression in older children. The promising role of integrative diagnostic platforms — such as genomic profiling, liquid biopsy, radiomics, metabolomics, single-cell sequencing, and artificial intelligence-aided imaging approaches — in early detection and disease characterization is explored in detail. The evaluation of both existing and emerging prognostic models involves a critical analysis of molecular biomarkers, tumor microenvironment signatures, circulating tumor DNA, immune profiling, and machine-learning-based stratification systems. Additionally, the translational potential of systems biology approaches is examined, along with future directions for integrating multi-omics data into clinically relevant precision oncology frameworks. Integrative approaches are collectively reshaping neuroblastoma research by connecting molecular pathogenesis with advanced diagnostic and prognostic tools. These developments are expected to facilitate enhanced risk-adapted therapeutic strategies, personalized medicine, and ultimately, improved survival rates in children with neuroblastoma.

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

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. 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. 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. UMIN000056873, approved by the Institutional Review Board of the National Cancer Center Hospital East.

Single-cell sequencing in precision management of chronic myeloid leukemia

Single-cell sequencing technology provides a high-resolution perspective for the precision diagnosis and treatment of chronic myeloid leukemia (CML). Cellular and molecular heterogeneity in CML is closely associated with tyrosine kinase inhibitor resistance, residual disease, and disease progression. However, the averaged signals generated by conventional bulk sequencing limit the identification of minor resistant clones, state-dependent leukemia stem cell (LSC) programs, and complex microenvironmental interactions. This review summarizes frontier applications of single-cell RNA sequencing and related single-cell omics technologies in CML, including molecular mechanism analysis, tumor microenvironment profiling, resistance mechanism exploration, biomarker discovery, and the translation of these findings into risk stratification, treatment monitoring, and individualized therapeutic strategies. By integrating recent global research findings, this review discusses the value of single-cell technology in revealing the functional heterogeneity of LSCs and provides a balanced perspective on the future integration of spatial multi-omics, artificial intelligence-assisted analysis, and clinical decision systems. The aim is to clarify how CML diagnosis and treatment may move from empiric disease monitoring toward a more precise, single-cell-informed model while acknowledging current technical, economic, regulatory, and validation barriers.

Integrated Multi-Omics Profiling Identifies Prognostic Biomarkers Predictive of Early Disease Progression and Therapeutic Response in Solid Malignancies: A Prospective Multicenter Cohort Study

Background Solid malignancies exhibit substantial molecular and clinical heterogeneity, limiting the prognostic accuracy of conventional clinicopathological variables and single-biomarker approaches. Integrated multi-omics profiling may provide a more comprehensive assessment of tumor biology by combining genomic, transcriptomic, proteomic, and metabolomic information. This study evaluated whether an integrated multi-omics model could identify prognostic biomarkers associated with early disease progression and therapeutic response across diverse solid tumors. Method This prospective multicenter cohort study enrolled 612 adults with histologically confirmed solid malignancies from five tertiary oncology centers in Armenia between January 2023 and December 2025. After prespecified exclusions, 588 patients were included in the final analysis. Pretreatment tumor tissue and peripheral blood samples underwent whole-exome sequencing, RNA sequencing, liquid chromatography–tandem mass spectrometry-based proteomics, and untargeted metabolomic profiling. Multi-omics datasets were normalized, corrected for batch effects, and integrated using Multi-Omics Factor Analysis and Similarity Network Fusion. Machine-learning models were developed to predict early disease progression within 12 months and therapeutic response. Clinical outcomes included objective response rate, progression-free survival, and overall survival. Results The median age of the cohort was 61 years, and 54.8% of patients were male. Lung, colorectal, and breast cancers were the most frequently represented malignancies. The objective response rate was 42.0%, and the disease control rate was 71.9%. During a median follow-up of 24.8 months, 39.6% of patients experienced disease progression and 29.9% died. Genomic profiling identified recurrent alterations in TP53, KRAS, PIK3CA, APC, EGFR, PTEN, BRAF, and ARID1A. Early disease progression was associated with increased expression of inflammatory, angiogenic, epithelial–mesenchymal transition, extracellular matrix remodeling, glycolytic, glutamine, and kynurenine-pathway signatures. Durable responders demonstrated enrichment of interferon signaling, antigen presentation, cytotoxic T-cell activity, and preserved mitochondrial metabolism. Integration of all molecular platforms identified a 32-marker prognostic signature comprising genomic, transcriptomic, proteomic, and metabolomic features. The integrated model achieved an area under the receiver operating characteristic curve of 0.93 and a Harrell concordance index of 0.91, outperforming clinical variables and each individual omics platform. High-risk classification was independently associated with shorter progression-free survival (adjusted hazard ratio, 2.84; 95% CI, 2.18–3.70; P < 0.001) and overall survival (adjusted hazard ratio, 2.61; 95% CI, 1.94–3.52; P < 0.001). Conclusion Integrated multi-omics profiling provided superior prognostic and predictive performance compared with conventional clinical factors or single-omics analyses. The identified 32-marker signature accurately stratified patients according to risk of early progression, therapeutic response, and survival across multiple solid malignancies. External validation and prospective clinical utility studies are required before routine implementation. Keywords: Multi-omics; solid malignancies; prognostic biomarkers; precision oncology; therapeutic response.

Cellular subpopulation remodeling and biphasic molecular dynamics in glioma recurrence: a multi-omics analysis and diagnostic markers screening using liquid biopsy samples.