Systems Biology Approach to Predicting and Assessing Response to Chemoradiation for Brain Tumors
Abstract
Clinical Problem in the Topic Area of Brain Tumors: Over 180,000 patients are diagnosed with brain tumors annually in the US. Of these, over 23,000 are diagnosed with glioblastoma multiforme (GBM), the most common and aggressive of all malignant brain tumors, with a 5-year survival rate at less than 5%. Unlike other cancers (e.g., breast cancer), the treatment for GBM has not changed in a few decades and consists of the standard-of-care chemo-radiation therapy. Unfortunately, over 40% of GBM patients will not respond to chemo-radiation and will recur within 6-8 months, suggesting that they could have been better candidates for newer experimental therapies such as monoclonal antibodies, immuno-, gene-, or combination therapies. Unfortunately, the lack of information apriori regarding patient’s response to chemo-radiation treatment makes clinical decisions for a neuro-oncologist extremely challenging and leads to patient anxiety regarding opting for the “standard-of-care” treatment, as against a potentially more responsive alternative treatment. There is hence a need for non-invasive tools that could apriori predict patient’s response to chemo-radiation, so an appropriate treatment can be planned. A second significant challenge in management of GBM is to distinguish patients with a benign radiation necrosis from those with a tumor recurrence. As many as 30%-40% patients treated with high doses of radiation will show signs of suspected tumor recurrence on post-treatment MRI (magnetic resonance imaging). Unfortunately, radiation necrosis, a benign treatment effect that manifests itself 6-12 months after chemo-radiation therapy, mimics recurrent GBM on conventional imaging. Currently, invasive surgical biopsy at the site of suspicion remains the only resort for disease confirmation. This is an agonizing choice for patients and their doctors, however. While there is obvious worry about subjecting the patients to yet another surgical intervention, clinicians and patients are rightly concerned about early identification and management of recurrent cancer as soon as possible. Conversely, of the patients who do undergo invasive biopsy, 5%-10% will suffer morbidity and between 1%-3% will succumb to biopsy-related complications. There is therefore an unmet clinical need for developing accurate, non-invasive image-based methods for reliably distinguishing tumor recurrence from radiation necrosis. Contributions: In this work, the Principal Investigator (PI) will develop non-invasive and clinically actionable “predictive” image-based markers for identifying (a) patients who will respond to chemo-radiation from patients who will not, on pre-treatment MRI and (b) patients who have radiation necrosis from tumor recurrence, on post-treatment MRI. • Identifying chemo-radiation resistant tumors on pre-treatment MRI will impact 180,000 brain tumor patients (including 23,000 GBM) diagnosed annually by allowing them early on to identify if they may be potential candidates for other drug targets such as gene/immunotherapy or combination therapies. • Identifying patients with tumor recurrence from those with radiation necrosis will avoid over 18,000 (over 9,000 in GBM) unnecessary and harmful surgical interventions costing over $50,000/surgery, in patients with a benign radiation necrosis. Early identification of patients with radiation necrosis could help alleviate patient anxiety, while sparing these patients who have a benign condition to undergo unnecessary and morbid biopsy. Career Goals: The PI began her tenure track faculty position in biomedical engineering at Case Western Reserve University in 2016, but has been working in the area of brain tumors for the last 5 years. She will utilize this award to develop an independent program while establishing herself as a successful brain tumor researcher. Within a year into her tenure track position, she has already published over 8 articles and 20 abstracts, along with
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 29, 2018
- Source ID
- W81XWH1810404
Entities
People
- Pallavi Tiwari
Organizations
- Case Western Reserve University
- United States Army