With the widespread application of systemic treatments for hepatocellular carcinoma, liver injury caused by molecular targeted drugs and immune checkpoint inhibitors has become a common clinical problem. The Chinese Society of Hepatology, Chinese Medical Association, organized domestic experts to summarize and analyze adverse liver reactions, as well as advances in the diagnosis and treatment related to systemic therapy for liver cancer, both domestically and internationally. Based on this work, we formulated the “Consensus on the Management of Liver Injury Associated with Targeted Drugs and Immune Checkpoint Inhibitors for Hepatocellular Carcinoma”, aiming to provide practical recommendations and decision-making guidance for clinicians in hepatology and related specialties. This guidance focuses on the monitoring, diagnosis, prevention, and treatment of liver injury during targeted and immune checkpoint inhibitor therapy, ultimately helping more liver cancer patients benefit from targeted immunotherapy.

In a major step forward for cancer care, researchers at ChristianaCare’s Gene Editing Institute have shown that disabling the NRF2 gene with CRISPR technology can reverse chemotherapy resistance in lung cancer. The approach restores drug sensitivity and slows tumor growth. The findings appear today in the journal Molecular Therapy Oncology.

UC San Diego researchers have developed a new targeted therapy for triple-negative breast cancer, the most difficult-to-treat subtype of the disease.

Breast cancer is a highly heterogeneous malignancy among women worldwide. Traditional prognostic models relying solely on clinicopathological features offer limited predictive accuracy and lack molecular-level insights. Unlike such conventional approaches, this study integrates proteomic and clinical data within an interpretable deep learning framework to improve prognostic precision and biological interpretability. We aimed to develop a more reliable model to accurately predict the 5-year survival status of patients with breast cancer using multi-omics data. The model integrating proteomics and clinical features demonstrated superior performance (AUC = 0.8136) compared to other feature combination models. The optimized model with 13 key features (4 clinical features and 9 proteins) achieved an AUC of 0.864 with the precision of 0.970, the recall of 0.810, and F1-score of 0.883. SHapley Additive exPlanations analysis identified MPHOSPH10, EGFR, ARL3, KRT18, lymph node status, and HER2 status as the most influential features, while Kolmogorov–Arnold Network analysis provided explicit mathematical relationships between key contributors and prediction outcomes. Collectively, our interpretable multi-modal model demonstrates robust performance in predicting 5-year survival in breast cancer patients and offers mechanistic insights, thereby enhancing its potential for clinical translation through the development of an accessible prediction tool.

NUS Medicine-led study shows extracellular vesicles carrying gene-targeting antisense oligonucleotides (ASOs) and immune boosters shrink metastatic pancreatic cancer