Quantitative MRSI to Predict Early Response to SAHA Therapy in GBM Management
Hyunsuk Shim (email@example.com)
Glioblastoma multiforme (GBM) is the most common primary brain tumor and is uniformly fatal. During carcinogenesis, tumor suppressor genes are silenced by aberrant histone deacetylase (HDAC) activity. Reversing this modification has become a goal for tumor therapy. Suberoylanilide hydroxamic acid (SAHA) is an orally-active, potent inhibitor of HDAC. This agent may not only help control tumors but also alter cerebral biochemistry to improve depressive symptoms afflicting many GBM patients. An NCI-funded multi-institutional trial for GBM combining SAHA with standard chemoradiation is scheduled to open soon. However, the lack of reliable biomarkers to predict early response severely hampers the treatment of GBM patients with HDAC inhibitors. Magnetic resonance imaging (MRI) is the standard tool for monitoring therapeutic response in GBMs. Although useful, conventional MRI has shortcomings including difficulty at distinguishing true tumor progression from “pseudo-progression” that is often seen soon after completion of chemoradiation. MRI may also not be ideal for evaluating new therapies, many of which help only a subset of patients. For GBMs, therapeutic response is mainly evaluated by assessing for tumor changes on conventional MRIs, since repeat surgical biopsy is too invasive and may be prone to sampling error. However, this is not ideal for evaluating response to SAHA since preliminary data indicate that drug response is associated with redifferentiation rather than killing/shrinking tumors, which may normalize cancer cell metabolism. While conventional MRI detects tumor size and location, it cannot detect this type of normalization. This team from Emory University proposes to fill this void by using MR spectroscopic imaging (MRSI), which uses special techniques in an MRI scanner to measure the metabolism of cancer cells as well as normal brain. While MRSI is not new, it has not gained widespread clinical use due to poor resolution, long scan times, and difficulty integrating with other types of brain scans. This team proposes to implement state-of-art MRSI technology that can rapidly generate metabolite maps of the entire brain coupled with introduction of an imaging registration/analysis program that combines MRSI data with other imaging studies in a clinically useful fashion. The long-term goal is to develop MRSI into a practical clinical tool that can be readily implemented at most institutions. The establishment of reliable MRSI metabolic biomarkers to assess early response would be of great value in developing new treatments, especially those such as SAHA which do not work by simply killing cells. By allowing clinicians to detect normalization of cancer metabolism in as little as one week of therapy, patients destined to benefit from treatment may be reassured, while those not showing a metabolic response can be switched from an ineffective treatment without further wasting of time. This would clearly be a highly innovative use of MRSI. Importantly, in addition to monitoring tumor response to SAHA therapy, our MRSI-based tool will allow assessment of the biochemical content of normal brain, and may thus indirectly monitor the subject’s quality-of-life.