Observational Study of Responses to Treatments in Advanced Central Nervous System (CNS) Tumors

Overview

Background: Primary central nervous system (CNS) tumors grow in the brain and spinal cord. These tumors are rare, but they are difficult to treat and often fatal. SmartMatch is a new technology that tries to help find the best medicines for the particular tumor by testing how small pieces of surgically removed tumor tissue react to different drugs. The study team wants to see if SmartMatch can complete the analysis and generate a report within 21 days from the time of the surgery. Objective: To better understand CNS tumors so doctors can find better ways to treat them in the future. Eligibility: People aged 15 years and older with recurrent CNS tumors. Patient must already be scheduled for a surgery for the tumor at the NIH. Design: The study team will collect a small amount of tumor samples during the planned surgery. They may also use tumor samples from previous procedures. No new or additional procedures will be done for the purpose of this study. The tumor samples will be sent to a lab for SmartMatch analysis. Once completed, the results will be shared with the patient and his/her local doctor. Together they can decide whether to incorporate the results into the treatment plan. It is important to know that the results may or may not be helpful. There will only be one blood test for research. Blood and tissue samples collected may be used for additional analysis. Tumor tissue may be used to grow additional samples for further study. Participants will receive a pathology diagnosis and mutation profile generated by pathologists who specialize in CNS tumors. The study team will seek updates on participant's health approximately every 6 months for 3 years. Tumor samples may also be collected from any additional surgery done at NIH during this time.

Background: * Primary central nervous system (CNS) tumors are uncommon and are mostly classified as rare diseases. Despite their low incidence, CNS cancers are associated with significant morbidity and mortality across all age groups. * Treatment options for CNS tumors are limited, especially upon disease progression, largely due to the incomplete understanding of disease biology and the challenges in conducting clinical studies, given the rarity of these diseases. * The traditional pathway for oncology drug development, involving several phases of clinical trials, can take many years to a decade and comes with a tremendously high financial cost. To overcome this challenge and speed up improving oncology patient care, drug repurposing has emerged as a useful approach. * Real-time drug screening for rare brain tumors is advancing precision medicine by generating clinical trial concepts aimed at overcoming key challenges in brain tumor management. However, delayed turnaround times remain a significant limitation to their clinical utility. * The Gujral lab at the Fred Hutchinson Cancer Center has developed SmartMatch, a cutting-edge artificial intelligence (AI)-driven system pharmacology-based platform to address the challenges in precision medicine. The platform allows the generation of drug response data using real-time tissue-based screening using freshly resected tumor or biopsy from a patient. The data collected is used to train a machine learning model, allowing precise prediction to a large panel of Food and Drug Administration (FDA)-approved drugs. This is a test that can be ordered by any physician in the US, and results are available upon request. Objective: -To determine the proportion of participants with advanced CNS tumors for whom SmartMatch drug screen analysis results are generated within 21 days from the time of tumor tissue acquisition Eligibility: * Participants \>=15 years with advanced CNS tumors. * Participants must have been scheduled for a brain tumor biopsy or resection planned to take place at NIH. Design: * Tumor and blood samples will be collected for multi-omic analysis. * Fresh tumor samples will be used for drug screen analysis by SmartMatch. * After initial sample collection, participants will be followed remotely every 6 (+/-3) months for 3 years.