Abstract

Chemotherapy has significantly improved cancer survival rates; however, many anticancer agents induce severe acute and chronic side effects that substantially reduce patient quality of life and limit therapeutic efficacy. Despite their clinical importance, the molecular mechanisms underlying chemotherapy-induced toxicities across different organs remain incompletely understood, in part due to the lack of integrated, scalable, and accessible analytical platforms capable of synthesizing large transcriptomic datasets across tissues and experimental models.

This thesis describes the development of a cloud-deployed RNA-seq analytics platform designed to identify candidate imaging biomarkers and therapeutic targets associated with chemotherapy induced toxicities. The platform integrates large-scale transcriptomic datasets derived from more many organs collected from oxaliplatin-treated animal models as an example and is architected to support extensible, community-driven data contributions from both animal and human studies.

Using modern web technologies and cloud infrastructure, the system enables structured data ingestion, interactive gene- and pathway-level visualization, and reproducible cross-study comparisons without requiring specialized local computational resources.

To enhance biological interpretability and analysis of the datasest, the platform incorporates complementary analytical frameworks. ThematicGO is an AI-assisted, keyword-driven method that organizes Gene Ontology enrichment results into biologically intuitive themes, reducing redundancy and enabling rapid identification of dominant biological programs across tissues.

Inter-Variability Cross-Correlation Analysis (IVCCA) is a correlation-based approach that prioritizes genes and pathways based on coordinated expression patterns across individual samples, enabling discovery of regulatory hubs and targetable pathways that may be overlooked by traditional differential expression analyses.

These deployed tools provide a unified, scalable framework for transcriptomic analysis that bridges raw RNA-seq data and actionable biological insight. Using cloud deployment this work establishes a foundation for a comprehensive and analyzable database of chemotherapy-induced side effects and offers a versatile platform for translational target discovery applicable to diverse therapeutic and imaging applications.

Committee Chair

Mikhail Berezin, PhD, Chair - Radiology

Committee Members

Song Hu, PhD - BME Monica Shokeen, PhD - Radiology Victor Brodsky, MD - Pathology & Immunology

Degree

Master of Science (MS)

Author's Department

Biomedical Engineering

Author's School

McKelvey School of Engineering

Document Type

Thesis

Date of Award

Spring 5-7-2026

Language

English (en)

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