OPTIMIZE: multiomics signature guiding neoadjuvant treatmen

When someone is diagnosed with cancer of the larynx (voice box) or nearby throat areas, doctors face a difficult decision: try to preserve these vital structures or remove them completely. Losing the larynx and surrounding throat tissues compromises the voice and normal swallowing function, resulting in a substantially reduced quality of life. Currently, doctors rely mainly on basic examinations and scans to decide whether a patients larynx can be preserved, but these methods lack precision. Many patients receive chemotherapy before surgery to reduce tumor size, and to preserve throat function, but it is currently not possible to predict with accuracy who will benefit from this approach. Recent advances show that adding immunotherapy treatments that boost the immune systems ability to fight cancer alongside chemotherapy may significantly improve survival rates. However, immunotherapy does not work equally well for everyone. Identifying the subgroup of patients who will respond is paramount but currently challenging. This research project is part of an international consortium effort, that will combine profiling and integration of clinical examinations, medical history, genetic information, and medical imaging, to predict treatment responses. The Austrian team will develop machine learning prediction tools aimed at a personalized, data-driven approach to determine which patients should receive immunotherapy along with chemotherapy, with the goal of improving cancer control while preserving laryngeal and esophageal function. This international research collaboration could lead to better clinical decisions, eventually improving the quality of life and life expectancy of patients with laryngeal or hypopharyngeal cancers. It will also help scientists understand the molecular signatures of immune-refractory tumors, to ultimately devise novel approaches to effectively treat them.