Quantitative Oncology Research

Two providers staring at image scans on two computer monitors.

Understanding tumor behavior to improve outcomes for cancer patients

The Quantitative Oncology (QO) Program advances the translation of discovery into clinical and translational impact through technology development, measurement science, computational modeling, and AI-enabled integration. The Program unites investigators in engineering, biology, and data science, and applies novel imaging, multi-omic, and analytical technologies to transform discovery into actionable clinical insights. By embedding quantitative science across the cancer continuum, QO provides analytical foundations for KCI's Strategic Plan, designed to achieve measurable advances in Basic Biological Research, Early Detection, Precision Oncology, and Patient-Centered Care focused on our catchment area. QO is co-led by Laura Heiser, Ph.D., a cancer systems biologist whose research uses integrative approaches to identify novel therapeutic strategies for early-stage and advanced cancers, and Young Hwan Chang, Ph.D., a machine learning and AI expert specializing in large-scale multimodal dataset integration and algorithmic discovery. Hisham Mohammed, Ph.D., serves as Assistant Program Leader, bringing expertise in state-of-the-art sequencing technologies. 

In alignment with the KCI Strategic Plan, program members develop cutting-edge laboratory- and computationally-based methods that are deployed in basic and translational cancer research. The ultimate goal is to advance understanding of cancer initiation and evolution; enable early detection and interception through biomarker discovery; expand precision oncology via innovative measurement technologies, integrative analytics, and interpretable AI; and translate these advances into improved clinical care.

Aim 1. To develop and deploy novel quantitative measurement technologies and assays to guide the detection, characterization, and management of cancers. Investigators advance multimodal imaging, spatial proteomics, single-cell and multi-omic sequencing technologies, to enable quantitative analyses of premalignant and malignant tissue architectures, immune contexture, and therapy response. 

Aim 2. To develop and deploy data-intensive computational strategies designed to uncover disease mechanisms, stratify patients, and identify optimal therapeutic interventions. This aim leverages advanced systems biology and AI/ML frameworks to reveal mechanisms underlying neoplastic onset, progression, and therapeutic response, generating actionable insights for biomarker discovery, therapy selection, and adaptive trial design. 

Program Leaders

Program Members