Ramesh, Archana

Abstract Objective Differentiating forms of autoimmune encephalitis (AE) from other causes of seizures helps expedite immunotherapies in AE patients and informs studies regarding their contrasting pathophysiology. We aimed to investigate whether and how Nuclear Magnetic Resonance (NMR)‐based metabolomics could differentiate AE from drug‐resistant epilepsy (DRE), and stratify AE subtypes. Methods This study recruited 238 patients: 162 with DRE and 76 AE, including 27 with contactin‐associated protein‐like 2 (CASPR2), 29 with leucine‐rich glioma inactivated 1 (LGI1) and 20 with N‐methyl‐ d ‐aspartate receptor (NMDAR) antibodies. Plasma samples across the groups were analyzed using NMR spectroscopy and compared with multivariate statistical techniques, such as orthogonal partial least squares discriminant analysis (OPLS‐DA). Results The OPLS‐DA model successfully distinguished AE from DRE patients with a high predictive accuracy of 87.0 ± 3.1% (87.9 ± 3.4% sensitivity and 86.3 ± 3.6% specificity). Further, pairwise OPLS‐DA models were able to stratify the three AE subtypes. Plasma metabolomic signatures of AE included decreased high‐density lipoprotein (HDL, −(C H 2 ) n −, –C H 3 ), phosphatidylcholine and albumin (lysyl moiety). AE subtype‐specific metabolomic signatures were also observed, with increased lactate in CASPR2, increased lactate, glucose, and decreased unsaturated fatty acids (UFA, –C H 2 CH=) in LGI1, and increased glycoprotein A (GlycA) in NMDAR‐antibody patients. Interpretation This study presents the first non‐antibody‐based biomarker for differentiating DRE, AE and AE subtypes. These metabolomics signatures underscore the potential relevance of lipid metabolism and glucose regulation in these neurological disorders, offering a promising adjunct to facilitate the diagnosis and therapeutics.

Abstract Biomarkers that select patients for therapeutics would benefit clinical trial design and patient care. The Akt pathway is a therapeutic target in Glioblastoma Multiforme (GBM) and an important determinant of patient outcome. However, it is not known whether activity of this pathway varies among GBM tumors. To investigate we used Akt pathway genes from published GBM expression datasets. We detected 5 patterns of Akt pathway gene expression and these Akt subgroups correlated with prognosis. We analyzed networks within subgroups using both gene set enrichment analysis and a novel method that scores relevance based on both expression and local network connectivity (Komurov et al., PLoS Comput Biol. 2010 6:8, 1-10). The results suggest therapeutic targets within the individual subgroups. Furthermore, preliminary analysis indicates that human GBM xenograft models and primary human GBM manifest similar Akt subgroups. We are investigating rodent xenograft models of Akt subgroups to evaluate subgroup-specific drug sensitivity, and will present a method and examples of analysis integrating gene expression data from human GBM xenografts and primary human GBM. We hypothesize that Akt subgroups will help personalize treatment for GBM therapeutics. Supported by Barrow Neurological Foundation, Halle Family Foundation, and NIH 1 K01 NS064952-01A1 (AMJ). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4138. doi:10.1158/1538-7445.AM2011-4138