Webinar: Combination Therapy for Glioblastoma: A Personalized, AI-Driven Approach by InsideScientific

Combination Therapy for Glioblastoma: A Personalized, AI-Driven Approach

About

Glioblastoma (GBM) is a devastating primary brain cancer with approximately 10,000 new US diagnoses annually. The current standard of care (SOC) for GBM includes surgical resection followed by radiation therapy (RT) and temozolomide (TMZ), however, there is near universal recurrence and development of resistance after treatment. Relapse in disease is tightly linked with dynamic changes in gene expression during tumor evolution, highlighting the need for stronger preclinical GBM models. Recent studies have suggested the utilization of combination therapy approaches for GBM patients to address TMZ resistance.

During this webcast, Certis Oncology researcher, Emily Eastwood, will present a personalized, AI-based approach to predict and test the efficacy of combination therapies in GBM. She will share a longitudinal study featuring a unique pair of patient-derived xenograft (PDX) models originating from the same patient: one from a surgical resection at initial GBM diagnosis, and another from a subsequent recurrence—providing a unique opportunity to study disease progression, resistance, and novel treatment strategies.

You’ll also learn about:

How predictive AI/ML modeling can inform personalized treatment decisions.

The application of “matched pair” in vitro and in vivo models to optimize translatability in preclinical development.

Employing targeted radiation in preclinical modeling to mimic clinical scenarios.

Differences in drug response observed in subcutaneous and orthotopic models, including a deep dive into intracranial implantation by stereotactic surgery.

The advantages of optical bioluminescence imaging (BLI) and the murine-scale MRI in assessing therapeutic response.

Presenter

Emily Eastwood, PhD

Principal Scientist, Certis Oncology Solutions

Emily Eastwood, PhD, is a Principal Scientist at Certis Oncology Solutions, where she drives the development of innovative preclinical models from clinical cancer biopsies to advance precision oncology. With more than ten years of experience in neuroscience, pharmacology, and oncology research, she has led cross-functional teams in designing complex in vivo studies, managing molecular diagnostic operations, and integrating AI-driven models to improve predictions of drug efficacy.
Dr. Eastwood began her scientific career with a PhD in Behavioral Neuroscience from Oregon Health & Science University, where her research centered on opioid pathways and genetic influences on addiction. She then completed a postdoctoral fellowship at the Fred Hutchinson Cancer Center, where she employed genetically engineered mouse models, liquid biopsies, and functional genomics to uncover vulnerabilities in lung cancer. Her work has been published in leading journals, including Cancer Discovery, Science Signaling, and Genes & Development. Dr. Eastwood is dedicated to bridging laboratory discoveries with clinical application to develop effective therapies for patients with challenging and rare cancers.

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