Assessing whole blood predictors of glioblastoma

The National Cancer Institute awarded Baylor Research Institute $186,457 on September 1, 2026, under the Cancer Research Manpower program (CFDA 93.398) to develop and validate a blood-based transcriptomic platform for glioblastoma detection and monitoring. The award supports a K22 career transition grant enabling the principal investigator to advance toward an independent academic research career.

The project pursues two aims: developing a blood gene signature panel for glioblastoma using the whole blood globin reduction (WBGR) platform, which depletes hemoglobin transcripts to improve RNA sequencing sensitivity, and prospectively evaluating the clinical utility of that signature panel in glioblastoma management. The investigator will apply machine learning techniques to longitudinal blood transcriptomic data collected across therapy stages to enhance predictive performance and assess gene expression dynamics. The work addresses the clinical need for noninvasive disease monitoring tools; current imaging techniques often fail to detect early-stage tumors or differentiate true progression from treatment-related effects, sometimes requiring up to four months for recurrence confirmation, while brain biopsy remains invasive and impractical for routine surveillance.

Work is performed in Temple, Texas. The period of performance runs from September 1, 2026, through August 31, 2029.

Abstract

The overarching goal of this NIH K22 career transition award application is to support my progression to an independent academic research career by advancing a novel, noninvasive blood transcriptomic platform for glioblastoma detection and monitoring. Glioblastoma is the most aggressive and deadly primary brain tumor in adults. Despite therapeutic advances, patient survival remains dismal due to delayed diagnosis, high recurrence rates, and the lack of effective tools for noninvasive disease monitoring. Current imaging techniques often fail to detect early-stage tumors or differentiate true tumor progression from treatment-related effects-sometimes requiring up to four months for recurrence confirmation. While brain biopsy remains the gold standard, IT is invasive and impractical for routine surveillance, highlighting the urgent need for blood-based biomarkers to guide clinical decisions. To address this need, i developed the whole blood globin reduction (wbgr) platform, which improves RNA sequencing sensitivity by depleting abundant hemoglobin transcripts that typically obscure disease-relevant signals. Using wbgr, i identified a ten-gene glioblastoma diagnostic panel (GBM-DX) capable of differentiating glioblastoma patients from controls. I hypothesize that transcriptomic alterations in whole blood reflect tumor development, progression, and treatment response. This K22 project will pursue two aims: (1) develop a blood gene signature panel for glioblastoma, and (2) prospectively evaluate the clinical utility of the signature panel in glioblastoma management. To enhance predictive performance and assess gene expression dynamics over time, i will apply AI-powered machine learning techniques to longitudinal blood transcriptomic data collected across therapy stages. My background includes a PH.d. In biology and molecular genetics and postdoctoral training in cancer drug discovery, blood transcriptomics, and translational glioblastoma research. I led the development of wbgr, published in high-impact journals, and collaborate closely with clinical teams and leading experts in neurosurgery, glioblastoma biology, and bioinformatics. The K22 award will provide critical support for completing this project, generating preliminary data for future R01 funding, and launching my independent research program. Ultimately, this work seeks to deliver noninvasive, clinically actionable tools to improve diagnosis, monitoring, and outcomes for glioblastoma patients.