Abstract 4241: Predicting clinical response in breast cancer using cellular-resolution optical metabolic imaging
Authors: Sharick Joe T. , Walsh Alex J. , Sanders Melinda E. , Meszoely Ingrid , Hooks Mary A. , Kelley Mark C. , Skala Melissa C. 
Published: July 2016

Abstract
While over 50 drugs have been approved by the FDA to treat breast cancer, there are no reliable methods for optimizing treatment regimens for individual patients. Currently, oncologists choose drug treatments based on expression levels of tumor cell signaling receptors (i.e. HER2, ER) and assess whether the treatment is effective after weeks or months of precious time have passed. Unfortunately, over one third of patients exhibit resistance to their initial treatment. The toxic side effects and morbidities resulting from suboptimal drug regimens could be eradicated by applying a personalized medicine approach to breast cancer treatment. This approach would allow clinicians to determine the optimal treatment plan for individual patients early on, at the time of diagnosis.
While current methods track therapy response via changes in tumor size (i.e. MRI, mammography, ultrasound), changes in cell metabolism precede changes in tumor size and thus present an earlier marker of treatment response. Optical metabolic imaging (OMI) is sensitive to these early changes in metabolism by exploiting the intrinsic fluorescent properties of NAD(P)H and FAD, coenzymes of metabolic reactions. OMI endpoints include the optical redox ratio (the fluorescence intensity of NAD(P)H divided by the fluorescence intensity of FAD), as well as the fluorescence lifetimes of NAD(P)H and FAD. The redox ratio reflects the cellular redox state, and the fluorescence lifetimes of NAD(P)H and FAD report on the binding activity of these coenzymes. Additionally, OMI has the unique ability to measure these endpoints in individual cells, which allows for the detection of heterogeneous subpopulations of responsive or resistant cells within a tumor. OMI also allows for high-throughput screening of potential cancer drugs and drug combinations on patient biopsy samples cultured ex vivo. These samples are grown as “organoids” in a 3D matrix that mimics the natural tumor environment.
We have demonstrated that OMI accurately predicts treatment response in organoids derived from breast cancer xenografts compared with gold standard tumor growth curves in vivo. We have also shown that OMI can measure drug response and detect heterogeneous cell populations in organoids derived from triple negative, ER+, and HER2+ human breast tumors. The ability of OMI to predict treatment response has also been demonstrated in the polyoma middle-T mouse model of breast cancer, which exhibits more cellular heterogeneity than cell line xenografts and also incorporates the influence of the immune system on cancer drug response.
Preliminary data shows that OMI of organoids generated from biopsies of newly diagnosed breast cancer patients can accurately predict how the patient clinically responds to neoadjuvant treatment. This methodology could allow oncologists to determine the ideal treatment regimen for their patients at the time of diagnosis.
Citation Format: Joe T. Sharick, Alex J. Walsh, Melinda E. Sanders, Ingrid Meszoely, Mary A. Hooks, Mark C. Kelley, Melissa C. Skala. Predicting clinical response in breast cancer using cellular-resolution optical metabolic imaging. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4241.