The failure of antiangiogenic therapy in the treatment of metastatic breast cancer is due to the development of resistance to inhibition of vascular endothelial growth factor (VEGF), and our inability to identify patients most likely to respond to such therapies. To elucidate the factors underlying 'antiangiogenic resistance' and develop translational biomarkers for identifying resistance in patients, it is necessary to relate molecular/structural/functional changes in the tumor microenvironment to changes measured with in vivo imaging, and develop well-?validated computational biology models of antiangiogenic resistance in breast cancer. Since such multiscale angiogenesis data and models do not currently exist, we have proposed to develop them in this application. Therefore, our goal is to elucidate the mechanisms of antiangiogenic resistance in breast cancer and develop imaging biomarkers to identify patients that could benefit most from antiangiogenic agents. Guided by compelling preliminary data, we will pursue three Specific Aims: (1) To characterize angiogenesis and metastatic burden in a human breast cancer model with multiscale imaging; (2) To develop a multiscale image-based computational model of antiangiogenic resistance in breast cancer; and (3) To determine if treatment with a non-VEGF targeted antiangiogenic/anti-tumorigenic peptide can circumvent resistance.
Under Aim1, we will create co-registered, quantitative angiogenesis data at complementary spatial scales, over time, by combining in vivo MRI, ex vivo magnetic resonance microscopy (?MRI) ex vivo micro-CT (?CT) and laser scanning confocal microscopy (LSCM). Multiscale imaging will relate in vivo angiogenic changes to alterations in tumor microvasculature and angiogenic protein expression.
Under Aim2, we will use multiscale imaging data from Aim1 to develop a computational model of antiangiogenic resistance that is comprehensively validated in an experimental breast cancer xenograft. We will vary model parameters implicated in antiangiogenic resistance to identify changes in the tumor microenvironment that can be exploited as clinical biomarkers of antiangiogenic resistance in breast cancer.
Under Aim3, we will treat a human breast cancer model with a non-VEGF targeted multimodal antiangiogenic/anti-tumorigenic peptide to determine if targeting multiple pathways can overcome antiangiogenic resistance in breast cancer. The approach is innovative because it blends cutting-edge advances in multiscale imaging, multiscale computational modeling and biomimetic peptides. This approach has the potential to impact 'systems biology' investigations of other cancers and diseases involving the pathological vasculature. The proposed research is significant because we expect to: (i) elucidate the mechanisms via which antiangiogenic resistance develops in breast cancer patients; (ii) identify potential imaging-based biomarker of antiangiogenic resistance, and (iii) test therapies that circumvent antiangiogenic resistance in breast cancer. Ultimately, such knowledge has the potential to identify new therapeutic strategies and reduce mortality from metastatic breast cancer.
The proposed research is relevant to public health because there is a crucial need to identify those patients with metastatic breast cancer that are most likely to respond to antiangiogenic drugs. The proposed research is relevant to the NIH's mission because elucidating the mechanisms of antiangiogenic drug resistance in metastatic breast cancer will help develop new therapies and clinical biomarkers for ameliorating the suffering of these patients Our multiscale image-based computational modeling approach is consistent with NIH's commitment to transforming medicine through discovery.
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