A new review by researchers from Universiti Putra Malaysia explores the emerging role of extracellular RNAs (exRNAs), tiny molecules carried in exosomes as powerful tools for cancer diagnosis, prognosis, and treatment. These natural nanocarriers can be detected in blood and other body fluids, offering a non-invasive alternative to traditional biopsies. With the ability to reflect tumor behavior in real time and even deliver targeted therapies, exRNAs are shaping the future of precision oncology. While technical and regulatory hurdles remain, the study highlights how advances in RNA science, bioengineering, and AI could soon make exRNA-based "theranostics" a clinical reality.

Recent advances in spatial and single-cell omics have significantly revolutionized biomarker discovery in tumor immunotherapy by addressing critical challenges such as tumor heterogeneity, immune evasion, and variability within the tumor microenvironment (TME). Immunotherapeutic strategies, including immune checkpoint inhibitors and adoptive T-cell transfer, have demonstrated promising clinical outcomes; however, their efficacy is limited by low response rates and the incidence of immune-related adverse events (irAEs). Therefore, the identification of reliable biomarkers is essential for predicting treatment efficacy, minimizing irAEs, and facilitating patient stratification. Spatial omics integrates molecular profiling with spatial localization, thereby providing comprehensive insights into the cellular organization and functional states within the TME. By elucidating the spatial patterns of immune cell infiltration and tumor heterogeneity, this approach enhances the prediction of therapeutic responses. Similarly, single-cell omics enables high-resolution analysis of cellular heterogeneity by capturing transcriptomic, epigenomic, and metabolic signatures at the single-cell level. The integrated application of spatial and single-cell omics has enabled the identification of previously undetected biomarkers, including rare immune cell subsets implicated in resistance mechanisms. In addition to spatial transcriptomics (ST), this technological landscape also includes spatial proteomics (SP) and spatial metabolomics, which further facilitate the study of dynamic tumor-immune interactions. Multi-omics integration provides a comprehensive overview of biomarker landscapes, while the rapid evolution of artificial intelligence (AI)-based approaches enhances the analysis of complex, multidimensional datasets to ultimately enhance predictive potential and clinical utility. Despite substantial progress, several challenges remain in the context of standardization, data integration, and real-time monitoring. Nevertheless, the incorporation of spatial and single-cell omics into biomarker research holds transformative potential for advancing personalized cancer immunotherapy. These emerging strategies pave the way for the development of innovative diagnostic and therapeutic interventions, thereby enabling precision oncology and improving treatment outcomes across a wide range of tumor profiles.

This review aims to provide a comprehensive overview of the integration of spatial omics with single-cell omics in the discovery of biomarkers for tumor immunotherapy. Specifically, it examines the strategies by which these emerging technologies address the challenges related to tumor heterogeneity, immune evasion, and the dynamic nature of the TME. By elaborating on the principles, applications, and clinical potential of these technologies, this review also critically evaluates their limitations, challenges, and the current gaps in clinical translation.

Researchers have discovered a protein which is critical for steering melanoma cancer cells as they spread throughout the body. The malignant cells become dependent on eIF2A to migrate, pointing to new strategies for impeding metastasis. Dependence only emerges after malignant transformation, suggesting a therapeutic window that might spare healthy tissues.

A new radioimmunotherapy approach has been shown to successfully eliminate cancer stem cells (CSCs) in preclinical models of ovarian cancer, outperforming the current gold standard. This research, published in the July issue of The Journal of Nuclear Medicine, lays the foundation for further development of radionuclide therapies targeting CSCs, offering renewed hope for more effective treatment options and improved outcomes for patients.

A team led by researchers at MUSC Hollings Cancer Center developed an artificial intelligence tool that uses natural language processing to identify the original cancer type of patients with brain metastases by analyzing their doctors’ clinical notes. The model outperformed standard diagnostic codes, accurately determining the primary cancer type in over 90% of cases, which can improve research efforts and enable more precise care. The lightweight, scalable tool could streamline cancer care and research by unlocking critical insights buried in electronic health records.

A research team from Kumamoto University has made a groundbreaking discovery that reveals how the human T-cell leukemia virus type 1 (HTLV-1) silently persists in the body, potentially laying the foundation for new therapeutic approaches. Their findings, published on May 13, 2025, in Nature Microbiology, identify a previously unknown genetic “silencer” element that keeps the virus in a dormant, undetectable state.

Salk Institute researchers launch machine learning framework ShortStop, which explores overlooked DNA regions in the "dark side" of the human genome in search of microproteins that may play roles in health or disease. They already used ShortStop to analyze a lung cancer dataset and find microproteins that may serve as biomarkers or therapeutic targets.