Abstract

Simple Summary Tumor heterogeneity influences tumor progression and response to therapy, introducing a significant challenge in the treatment of breast cancer. We employed magnetic resonance imaging (MRI) to characterize tumor heterogeneity over time in response to treatment in a mouse model of HER2+ breast cancer. In a two-part approach, we first used quantitative MRI to identify unique subregions of the tumor (i.e., “tumor habitats”, resolving intratumoral heterogeneity), then used the habitats to stratify tumors prior to treatment into two distinct “tumor imaging phenotypes” (resolving intertumoral heterogeneity). The tumor phenotypes exhibited differential response to treatments, suggesting that baseline phenotypes can predict therapy response. Additionally, there were significant correlations between the imaging habitats and histological measures of vascular maturation, hypoxia, and macrophage infiltration, lending ex vivo biological validation to the in vivo imaging habitats. Application of these techniques in the clinical setting could improve understanding of an individual patient’s tumor pathology and potential therapeutic sensitivity. Abstract This study identifies physiological habitats using quantitative magnetic resonance imaging (MRI) to elucidate intertumoral differences and characterize microenvironmental response to targeted and cytotoxic therapy. BT-474 human epidermal growth factor receptor 2 (HER2+) breast tumors were imaged before and during treatment (trastuzumab, paclitaxel) with diffusion-weighted MRI and dynamic contrast-enhanced MRI to measure tumor cellularity and vascularity, respectively. Tumors were stained for anti-CD31, anti-ɑSMA, anti-CD45, anti-F4/80, anti-pimonidazole, and H&E. MRI data was clustered to identify and label each habitat in terms of vascularity and cellularity. Pre-treatment habitat composition was used stratify tumors into two “tumor imaging phenotypes” (Type 1, Type 2). Type 1 tumors showed significantly higher percent tumor volume of the high-vascularity high-cellularity (HV-HC) habitat compared to Type 2 tumors, and significantly lower volume of low-vascularity high-cellularity (LV-HC) and low-vascularity low-cellularity (LV-LC) habitats. Tumor phenotypes showed significant differences in treatment response, in both changes in tumor volume and physiological composition. Significant positive correlations were found between histological stains and tumor habitats. These findings suggest that the differential baseline imaging phenotypes can predict response to therapy. Specifically, the Type 1 phenotype indicates increased sensitivity to targeted or cytotoxic therapy compared to Type 2 tumors.