Overview:
Dr. He's lab focuses on the molecular and tissue level control through specialization of Extracelullar Vesicles (EVs), which can be investigated as a driving agent for advancing cancer immunotherapy, regenerative medicine, and therapeutics. The goal is to be able to develop drugs that can utilize EVs to target against cancers and autoimmune disease.
Project:
Generation of EQI for benchmarking performance in biomarker detection
Breast cancer removal is often successful, but can have chances for recurrence (Breast Cancer Recurrence). Detection and methods to understand dormant clones which lead to BCR is often cost ineffective and prohibited. Therefore, the rise of artificial intelligence (AI) to further understand BCR through leveraging the molecular information in circulating extracellular vesicles (EVs). Although there are several studies done on detecting breast cancer biomarkers in EVs, there is no studies relating the impact of EV heterogeneity towards BCR.
Through the help of my sponsor and PI, I propose to study the latter, using ExoQuality Index (EQI), which is an AI approach that accounts for EV heterogeneity based on EV characterization after liquid biopsy. EQI will be used to compare the differential analysis methods such as edgeR for estimating the biological coefficient of variation (BCV). Using HiperGator, I plan to tackle this project through a three step plan.
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1) curate 30-cited EV breast cancer datasets from NCBI's GEO to generate the EQI
2) modify DESeq2 and edgeR with the EQI to identify breast cancer biomarkers
3) experimentally validate EV biomarkers from breast cancer patients
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Through this experiment, the lab and I plan to integrate EQI to maximize analytical methods for breast cancer biomarker discovery and diagnostics. I will advance the feasibility of using EVs for non-invasive clinical applications as well.
under supervision of Dr. He and Zach Greenberg