Abstract 3138: Identification of therapy-sensitive tumor phenotypes using quantitative MRI habitats in a preclinical model of HER2+ breast cancer
Authors: Kazerouni Anum S. , Hormuth David A. , Davis Tessa , Bloom Meghan J. , Virostko John , Yankeelov Thomas E. , Sorace Anna G. 
Published: July 2021

Abstract
Introduction: Heterogeneity of the tumor microenvironment influences therapeutic delivery and efficacy, presenting a significant challenge in cancer treatment. Quantitative magnetic resonance imaging (MRI) can spatially resolve intratumoral heterogeneity into physiologically-distinct subregions, or habitats. We use quantitative MRI habitats to elucidate intertumoral heterogeneity through identification of tumor imaging phenotypes, and longitudinally evaluate treatment response in a murine model of HER2+ breast cancer.
Methods: BT474 cells were subcutaneously implanted in athymic nude mice (n = 62). Once tumors reached ~235 mm3, mice were randomly assigned to trastuzumab (10 mg/kg) or saline control groups, and treated on days 0 and 3. Diffusion weighted (DW) and dynamic contrast-enhanced (DCE) MRI data were collected on days 0 (pre-treatment), 1 and 4. Apparent diffusion coefficients (ADC, a measure of cell density) were calculated from DW-MRI. DCE-MRI data was modeled to extract the extravascular, extracellular volume fraction, ve, and the volume transfer coefficients, Ktrans and kep, which correspond to the rate of contrast agent wash-in and wash-out, respectively. Habitats were identified through hierarchical clustering of tumor voxel data (ADC, ve, Ktrans, and kep). Tumor composition was quantified as percent tumor volume comprised by each habitat. Finally, tumors were described by their baseline tumor composition and clustered to identify unique tumor imaging phenotypes. Treatment response was measured using percent change in tumor volume over time.
Results: Clustering of the MRI data yielded three habitats: low-vascularity low-cellularity (LV-LC), low-vascularity high-cellularity (LV-HC), high-vascularity high-cellularity (HV-HC). Two distinct tumor imaging phenotypes were identified from clustering, designated as Type 1 and Type 2. Type 1 tumors showed higher relative HV-HC tumor volume and lower relative LV-HC and LV-LC tumor volume, compared to Type 2 tumors (p &lt; 0.05). Type 1 tumors treated with trastuzumab showed a decrease in tumor volume at day 4 (p &lt; 0.05) compared to control. Type 2 tumors treated with trastuzumab showed no significant changes in tumor volume compared to control. Treated Type 1 tumors showed a significant increase in LV-LC percent tumor volume at day 4, compared to day 0 (30% vs. 21%, p &lt; 0.01). Treated Type 2 tumors showed a significant decrease in LV-HC percent tumor volume at day 4, compared to day 0 (25% vs. 46%, p &lt; 0.05).
Discussion & Conclusions: Quantitative MRI habitats can be used to identify tumor phenotypes with differing response to treatment. The Type 1 phenotype may confer increased therapeutic sensitivity, demonstrated by improved response to trastuzumab. Clinical application of this approach could improve understanding of tumor pathology and therapeutic sensitivity for patients with HER2+ breast cancer.
Citation Format: Anum S. Kazerouni, David A. Hormuth, Tessa Davis, Meghan J. Bloom, John Virostko, Thomas E. Yankeelov, Anna G. Sorace. Identification of therapy-sensitive tumor phenotypes using quantitative MRI habitats in a preclinical model of HER2+ breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3138.