Researchers at the Johns Hopkins Kimmel Cancer Center have discovered how certain pathogenic bacteria in gut and breast tissue can promote breast cancer development and progression by hijacking a key metabolic enzyme known as spermine oxidase (SMOX).

Researchers at MUSC Hollings Cancer Center have developed an AI-based tool called BIOPREVENT that uses blood biomarkers and clinical data to predict serious complications after stem cell and bone marrow transplants months before symptoms appear. In a study of more than 1,300 transplant recipients, the tool outperformed standard clinical models, identifying patients at higher risk for chronic graft-versus-host disease and transplant-related death. By providing earlier, personalized risk information through a free web-based application, BIOPREVENT could support closer monitoring and future prevention strategies in transplant care.

The association between chronic inflammation and cancer has reshaped our understanding of tumorigenesis and cancer therapy. Inflammatory responses can both promote and suppress cancer, depending on the context and timing. Key molecular players, such as nuclear factor kappa-light-chain-enhancer of activated B cells, interleukin-6, signal transducer and activator of transcription 3, and a variety of immune cell types, including tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells, orchestrate an environment conducive to tumor survival, angiogenesis, metastasis, and immune evasion. In recent years, immunotherapy, particularly immune checkpoint inhibitors, has revolutionized cancer treatment. However, its success varies across tumor types and patients, underscoring the need to understand the tumor microenvironment and inflammatory context. This review examines the mechanistic underpinnings of inflammation-driven cancer, discusses translational research efforts targeting inflammatory pathways, and explores clinical applications, including the integration of immunotherapy with anti-inflammatory agents and biomarkers for personalized treatment. Future directions in the field include the application of artificial intelligence, microbiome research, single-cell technologies, and gene editing tools to further tailor therapies and overcome resistance mechanisms.

Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of pulmonary nodules is crucial for timely diagnosis and effective treatment. Conventional computer-aided detection systems have shown limitations, including high false-positive rates and low sensitivity. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown great potential in improving the accuracy and reliability of nodule detection and classification. This study aimed to develop and evaluate an automatic method for lung nodule detection and classification using a CNN-based architecture applied to computed tomography images from the publicly available LIDC-IDRI database.

Many diseases are driven by proteins that are difficult or impossible to inhibit with conventional drugs. Instead of blocking their activity, an emerging therapeutic strategy aims to remove these proteins entirely from the cell by harnessing the cell’s own degradation machinery. In a new study, researchers at CeMM, AITHYRA and the Scripps Research Institute have now developed a systematic method to discover such protein-degrading compounds on a large scale. The approach, published in Nature Chemical Biology (DOI: 10.1038/s41589-025-02137-2) provides a powerful new route toward therapies for diseases such as certain aggressive forms of leukemia.Many diseases are driven by proteins that are difficult or impossible to inhibit with conventional drugs. Instead of blocking their activity, an emerging therapeutic strategy aims to remove these proteins entirely from the cell by harnessing the cell’s own degradation machinery. In a new study, researchers at CeMM, AITHYRA and the Scripps Research Institute have now developed a systematic method to discover such protein-degrading compounds on a large scale. The approach, published in Nature Chemical Biology (DOI: 10.1038/s41589-025-02137-2) provides a powerful new route toward therapies for diseases such as certain aggressive forms of leukemia.

Insulin resistance — when the body doesn't properly respond to insulin, a hormone that helps control blood glucose levels — is one of the fundamental causes of diabetes. In addition to diabetes, it is widely known that insulin resistance can lead to cardiovascular, kidney and liver diseases. While insulin resistance is tightly associated with obesity, it has been difficult to evaluate insulin resistance itself in the clinic. For the first time, researchers including those from the University of Tokyo applied a machine learning-based prediction model of insulin resistance to half a million participants from the UK Biobank and demonstrated that insulin resistance is a risk factor for 12 types of cancer.