Mayo Clinic researchers have discovered a new immunotherapy target, a cryptic antigen, may be key in helping the immune system fight tumors in ovarian cancer. 

Free radicals are produced in the body during normal cellular metabolic activities, and their excessive accumulation can overwhelm the natural antioxidant mechanisms. This leads to oxidative stress, which is associated with the development and progression of non-communicable diseases (NCDs) such as liver and kidney diseases, cardiovascular diseases, neurodegenerative diseases, cancer, and diabetes. Enzymes play a significant role in maintaining a balance between antioxidants and free radicals by either enhancing the production of antioxidants or slowing down the generation of free radicals in the body. There is no up-to-date review on how antioxidant-enzyme interactions modulate the development and progression of NCDs. This review, therefore, discusses the mechanisms of antioxidant-enzyme interactions in the control of oxidative stress, as well as the implications and prospects of these interactions in the management of NCDs. Therapeutic strategies targeting antioxidant-enzyme interactions in the natural defense mechanisms of the body against oxidative stress can provide targeted benefits in the management of various NCDs. The mechanisms of interaction of some antioxidants with catalase, superoxide dismutase, glutathione reductase, glutathione peroxidase, glutathione S-transferases, thioredoxin protein, and thioredoxin reductase suggest their strong involvement in mitigating the development and progression of NCDs. Moreover, understanding the specific interactions and signaling pathways involved in antioxidant-enzyme interactions could facilitate the emergence of novel and effective therapeutic strategies for the management of NCDs and should be considered a primary goal of future studies. This study provides the necessary template, encourages discussion, and creates more opportunities for the next stage in the development of antioxidant therapies.

Researchers identify predictive markers of response to immunotherapy treatment in a study involving data from over 700 patients across six different cohorts, published in Nature Communications. The study has significant relevance, impacting the management of bladder cancer patients. Machine learning tools enabled the identification of key variables for the success of immunotherapy treatment. This innovative methodology has allowed researchers to pinpoint which tumor subtypes respond best to immunotherapy.