Glioblastoma is the most common primary brain tumor and is uniformly fatal despite aggressive surgical and adjuvant therapy. As survival is short, it is critical to determine the value of therapy early on in treatment. Improved early predictive assessment would allow neuro-oncologists to personalize and adjust or change treatment sooner to maximize the use of efficacious therapy. During carcinogenesis, tumor suppressor genes can be silenced by aberrant histone deacetylation. This epigenetic modification has become an important target for tumor therapy. Suberoylanilide hydroxamic acid (SAHA, Vorinostat, Zolinza) is an orally active, potent inhibitor of histone deacetylase (HDAC) activity. A major shortcoming of the use of HDAC inhibitors in the treatment of patients with brain tumors is the lack of reliable biomarkers to predict and determine response. Histological evaluation may reflect tumor viability following treatment, but is an invasive procedure and impractical for glioblastoma. Another problem is that response to SAHA therapy is associated with tumor redifferentiation and cytostasis rather than tumor size reduction, thus limiting the use of traditional imaging methods. A noninvasive method to assess drug delivery and efficacy is needed. Here, we investigated whether changes in (1)H MRS metabolites could render reliable biomarkers for an early response to SAHA treatment in an orthotopic animal model for glioma. Untreated tumors exhibited significantly elevated alanine and lactate levels and reduced inositol, N-acetylaspartate and creatine levels, typical changes reported in glioblastoma relative to normal brain tissues. The (1)H MRS-detectable metabolites of SAHA-treated tumors were restored to those of normal-like brain tissues. In addition, reduced inositol and N-acetylaspartate were found to be potential biomarkers for mood alteration and depression, which may also be alleviated with SAHA treatment. Our study suggests that (1)H MRS can provide reliable metabolic biomarkers at the earliest stage of SAHA treatment to predict the therapeutic response.