Cancer’s staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic and prognostic accuracy when accompanied by rigorous preprocessing and external validation; for example, recent integrated classifiers report AUCs around 0.81–0.87 for difficult early-detection tasks. This review synthesizes how artificial intelligence (AI), particularly deep learning and machine learning, bridges this gap by enabling scalable, non-linear integration of disparate omics layers into clinically actionable insights. We explore cutting-edge AI methodologies, including graph neural networks for biological network modeling, transformers for cross-modal fusion, and explainable AI (XAI) for transparent clinical decision support. Critical applications are highlighted, such as AI-driven therapy selection (e.g., predicting targeted therapy resistance), proteogenomic early detection, and radiogenomic non-invasive diagnostics. We further address translational challenges: data harmonization, batch correction, missing data imputation, and computational scalability. Emerging trends, federated learning for privacy-preserving collaboration, spatial/single-cell omics for microenvironment decoding, quantum computing, and patient-centric “N-of-1” models, signal a paradigm shift toward dynamic, personalized cancer management. Despite persistent hurdles in model generalizability, ethical equity, and regulatory alignment, AI-powered multi-omics integration promises to transform precision oncology from reactive population-based approaches to proactive, individualized care.

Graft-versus-host disease remains one of the most formidable barriers to the complete success of hematopoietic stem cell transplantation that has emerged as the curative approach for many hematopoietic malignancies because it affects quality of life and overall survival. Macrophages are among the important members of the immune system, which perform dual roles in GVHD as both therapeutic tools and targets. This review epitomizes the multifunctional role of macrophages in the pathophysiology of both acute and chronic GVHD. Macrophages play an important role in the early phase of GVHD because of their recruitment and infiltration into target organs. Furthermore, they polarize into two functionally different phenotypes, including M1 and M2. In the case of acute GVHD, most macrophages express the M1 phenotype characterized by the production of pro-inflammatory cytokines that contribute to tissue damage. In contrast, in chronic GVHD, macrophages tend toward the M2 phenotype associated with the repair of tissues and fibrosis. A critical balance among these phenotypes is central to the course and severity of GVHD. Further interactions of macrophages with other lymphocytes such as T cells, B cells, and fibroblast further determine the course of GVHD. Macrophage interaction associated with alloreactive T cells promotes inflammation. This is therefore important in inducing injuries of tissues during acute GVHD. Interaction of macrophages, B cell, fibroblast, and CD4+ T cells promotes fibrosis during chronic GVHD and, hence, the subsequent dysfunction of organs. These are some insights, while several challenges remain. First, the impact of the dominant cytokines in GVHD on the polarization of macrophages is incompletely characterized and sometimes controversial. Second, the development of targeted therapies able to modulate macrophage function without systemic side effects remains an area of ongoing investigation. Future directions involve the exploration of macrophage-targeted therapies, including small molecules, antibodies, and nanotechnology, which modulate macrophage behavior and improve patient outcomes. This underlines the fact that a profound understanding of the dual role of macrophages in GVHD is essential for developing new and more effective therapeutic strategies. Targeting macrophages might represent one avenue for decreasing the incidence and severity of GVHD and improving the success and safety of HSCT.

Acute myeloid leukemia (AML) remains a formidable blood cancer, despite recent advances in treatment. A significant challenge persists in improving patient outcomes, particularly in addressing relapse and treatment resistance. Identifying new therapeutic targets is critical for advancing AML therapy. C-type lectin-like molecule-1 (CLL-1) has emerged as a promising therapeutic target in AML. This cell surface receptor is highly expressed on AML blasts and demonstrates stable expression throughout disease progression. CLL-1’s consistent presence makes it an ideal candidate for monitoring minimal residual disease (MRD), which is a critical indicator for predicting relapse. Beyond its utility as a diagnostic marker, CLL-1 offers exciting potential in the development of immunotherapies. Emerging strategies, such as CAR-T-cell therapy and antibody-drug conjugates (ADCs), are being investigated to leverage the immune system against CLL-1-expressing AML cells. This review examines the structure, function, and expression patterns of CLL-1 in AML and other hematologic malignancies, providing insights into its role in disease pathogenesis and treatment potential. Exploring CLL-1 as a target for diagnosis, MRD monitoring, and immunotherapy opens new avenues for AML treatment. A deeper understanding of its relationship with AML pathogenesis will aid in the development of targeted therapies, offering hope for improved patient outcomes in the future.

Acute myeloid leukemia (AML) with KMT2A rearrangements (KMT2A-r) represents a highly aggressive and prognostically unfavorable subtype of leukemia, often resistant to standard treatments and associated with high relapse rates. KMT2A-r, found in 3–10% of adult AML cases, disrupt epigenetic regulation by forming chimeric proteins that activate oncogenic pathways like HOXA and MEIS1. These fusion proteins recruit cofactors such as Menin and DOT1L, driving leukemogenesis through abnormal histone methylation. Diagnosing KMT2A-r AML requires precision, with traditional methods like FISH and RT-PCR being complemented by advanced technologies such as next-generation sequencing (NGS) and machine learning (ML). ML models, leveraging transcriptomic data, can predict KMT2A-r and identify biomarkers like LAMP5 and SKIDA1, improving risk stratification. Therapeutically, there is a shift from chemotherapy to targeted therapies. Menin inhibitors (e.g., Revumenib, Ziftomenib) disrupt the Menin-KMT2A interaction, suppressing HOXA/MEIS1 and promoting differentiation. DOT1L inhibitors (e.g., Pinometostat) show promise in combination therapies, while novel approaches like WDR5 inhibitors and PROTAC-mediated degradation are expanding treatment options. Despite progress, challenges remain, including optimizing minimal residual disease monitoring, overcoming resistance, and validating biomarkers. This review emphasizes the imperative to translate molecular insights into personalized therapeutic regimens, offering renewed hope for patients afflicted by this historically refractory malignancy.

Leukemia, a group of blood cancers, presents a significant global health challenge. Despite advancements in conventional therapies like chemotherapy and immunotherapy, the need for more effective and less toxic treatments remains. Nanotechnology offers a promising avenue for targeted drug delivery and immune modulation in the fight against leukemia. Through the utilization of nanomaterials’ special qualities, like their small size, large surface area, and capacity to transport a variety of payloads, scientists are creating novel ways to get around the drawbacks of conventional treatments. These strategies include targeted drug delivery, immune cell activation, and overcoming drug resistance. However, challenges remain in translating these promising nanotechnological approaches into clinical applications. Addressing issues such as toxicity, biodistribution, and regulatory hurdles is crucial for the successful development of nanomedicine for leukemia. In conclusion, nanotechnology offers a promising future for the treatment of leukemia. Continued research and development are essential to unlock the full potential of nanomaterials and improve patient outcomes. The potential of nanotechnology-based strategies to improve the effectiveness of leukemia treatments is explored in this review. We go over the function of different nanomaterials in delivering therapeutic agents to leukemia cells, such as liposomes, polymeric nanoparticles, and inorganic anoparticles. We also investigate the engineering of nanomaterials to influence the immune system and promote anti-tumor reactions.