I want to thank the organizers of AANS for giving me the opportunity to present. I'm Anudeep Ekula, and I'm a postdoctoral fellow in Dr. Carter's lab and an aspiring neurosurgeon. Today, I will focus on an RNA sequencing-based liquid biopsy strategy for monitoring response to target therapy in patients with recurrent glioblastoma. I want to thank the organizers of AANS. I have no commercial disclosures. Our work is supported by the NIH, and we are a part of NCI's liquid biopsy consortium. What if we can diagnose a brain tumor at a molecular level, irrespective of its location, using a blood test? What if we can stratify patients to therapies based on a blood test? What if we can assess response to therapies using a blood test? What if we can monitor for recurrence using a simple blood test? Yes, liquid biopsy could be the answer. Liquid biopsy refers to the analysis of patients' biofluids, including blood, CSF, urine, and saliva, to diagnose and monitor diseases. There are three main areas of interest in liquid biopsy. One, the circulating tumor cell fraction. Two, the cell tree fraction. And three, and the focus of our group, the extracellular vesicle fraction. EVs are membrane-bound vesicles released by all the cells of the body, including cancer cells. This is an SEM image of a glioblastoma cell, showing a large number of vesicles on its surface. EVs contain genetic and proteomic cargo derived from the parent cell. They can influence the surrounding cells and potentiate tumor growth and proliferation. EVs are highly stable in the biofluids, and thus the EV RNA and protein cargo is protected, making them suitable as biomarkers. We sought to evaluate the potential of EV RNA-based liquid biopsy to determine tumor presence, assess patient stratification, and predict response to therapy. A clinical trial to evaluate the efficacy of drug docomitinib, an irreversible EGFR tyrosine kinase inhibitor, in patients with recurrent EGFR-amplified glioblastoma was undertaken. We chose 14 patients with recurrent EGFR-amplified GBM undergoing treatment with docomitinib. They were classified as responders and non-responders, based on six-month progression-free survival. Serum samples were collected pre- and one-month post-treatment. Exsolution kit was used for EV RNA isolation. EV RNA is subjected to total RNA-seq with ribosomal RNA depletion. Average sequencing depth per sample is 10-20 million MAP3. This plot represents the relative abundance of RNA biotypes between patients and between pre- and post-treatment time points within each patient. The green represents the protein-coding RNA, while the gray represents the ribosomal RNA. There is a heterogeneity of protein-coding RNA distribution between patients and is similar in pre- and post-treatment time points of each patient. However, ribosomal RNA is the largest component of the sequencing output, despite our RNA depletion. We were able to reliably detect an excess of 10,000 protein-coding RNA in our samples, which signifies a very good coverage. We used GAPDSH to normalize the reads, so that the signature can easily be transferred to a PCR-based assay. We identified a good correlation between the GAPDSH normalization and global normalization. We first compared serum from pooled healthy controls and pre-treatment GBM patients. We observed a differential EV RNA expression profile in critical pathways including gliogenesis, apoptosis, positive regulation of cell proliferation, cell death, and immune system between GBM patients and healthy controls. Specifically, EV RNA's Crabb binding protein CXCR2 and S100A9 are significantly enriched in serum EV RNA of GBM patients compared to healthy controls and can distinguish GBM patients from healthy controls with a very high sensitivity. Interleukin-8 CXCR2 signaling is implicated in GBM cell proliferation, invasion, neovascularization, and even therapy-induced plasticity. Specifically, constitutively active EGFRV3 is implicated in activating several multiple synergistic pathways to potentiate interleukin-8 CXCR2-mediated angiogenesis. Additionally, S100A9 has also been associated with glioma cell growth. EV RNA profiles of GBM patients pre-treatment and post-treatment also reveal differential expression profiles. Additionally, PCA reveals distinct separation between pre-treatment and post-treatment samples, and this distinction is driven by a small group of RNA. This is indicative of dynamically altering EV RNA landscape in the setting of treatment with decomitinib, possibly reflecting the altering molecular landscape in the tumor with therapy. In pre-treatment serum, EV RNA lambdaR2 is significantly upregulated in non-responders, and ZNF35 is significantly downregulated in non-responders. Thus, an EV RNA signature based on pre-treatment serum consisting of lambdaR2 and ZNF35 can be used to identify patients with recurrent EGFR-amplified GBM who would likely respond to decomitinib therapy. This highlights the potential of EV RNA profiling to assist patient stratification to best treatment strategy. In EGFR pathway, there is downstream activation of RAS signaling, BIK-AKT signaling, mTOR signaling, JAK-STAT signaling, which will eventually lead to GBM growth, proliferation, and survival. Specifically, mTOR-lambdaR signaling is overexposed. Literature suggests that stimulation of mTOR-lambdaR leads to reduced autophagic activity. Additionally, in GBM response to chemotherapeutic agents is likely mediated by autophagy, which could imply that upregulation of mTOR-lambdaR pathway in non-responders could be a result of reduced decomitinib-mediated autophagic activity. EV RNA profiles of responders and non-responders to decomitinib also reveal a distinct differential expression profile in responders compared to non-responders in pre-treatment serum. Specifically, EV RNA dNMT3A is enriched in post-treatment serum of responders compared to non-responders to decomitinib and can be used to predict response to decomitinib therapy. This paper suggests that targeting an NMT pathway can alter metabolism, leading to an increased levels of dNMT3A. Although there is no direct effect of decomitinib on an NMT pathway, increased levels of dNMT3A in EV RNA of responders post-therapy could be indicative of metabolic changes occurring in the primary tumor in response to decomitinib therapy. In conclusion, we developed an EV RNA signature to determine tumor presence, stratify patients, and assess response to therapy in patients with recurrent EGFR-amplified GBM undergoing therapy with decomitinib. I would like to thank my mentors, Dr. Carter and Dr. Bulai, my lab members, and our collaborators at Exosome Diagnostics for their incredible support.

RNA sequencing

liquid biopsy

targeted therapy

recurrent glioblastoma

extracellular vesicles