Despite improvements in the ability to treat cancer, first-line chemotherapy in standard-of-care treatments still fail to elicit a response from about half of cancer patients. This limitation signals a pressing need for practical methods to select personalized cancer therapies. Biodynamic imaging (BDI; a form of dynamic-contrast OCT with low-coherence digital holography) on living cancer biopsies from patients shows potential as a method to guide personalized selection of cancer therapy. However, building a library of signatures for different types of cancer has been a practical obstacle. Here, a comparative preclinical/clinical trial with two-species (human and canine) and two-cancers (esophageal carcinoma and B-cell lymphoma) demonstrates the general applicability of BDI as a method of chemoresistance prediction and as a viable tool for personalized medicine. This study identifies a set of drug response phenotypes that span species and cancer type, suggesting the existence of universal characteristics, which would reduce the burden of library construction needed for the method to be useful for doctors and their patients.
Tumor heterogeneity is one of the greatest obstacles standing in the way of successful cancer therapy. Cancer in a single patient is not a single disease, but is a host of related diseases, all of which need to respond to a single treatment regimen. We have completed the first human clinical trial in esophageal cancer using dynamic-contrast OCT (DC-OCT) based on full-frame digital holography to assess the spatial heterogeneity of biopsy response to platinum-based chemotherapy. A deep twin neural network successfully identified biopsy sub-phenotypes in the dynamic tissue response that enabled accurate prediction of patient treatment success.
Significance: Tumor heterogeneity poses a challenge for the chemotherapeutic treatment of cancer. Tissue dynamics spectroscopy captures dynamic contrast and can capture the response of living tissue to applied therapeutics, but the current analysis averages over the complicated spatial response of living biopsy samples.
Aim: To develop tissue dynamics spectroscopic imaging (TDSI) to map the heterogeneous spatial response of tumor tissue to anticancer drugs.
Approach: TDSI is applied to tumor spheroids grown from cell lines and to ex vivo living esophageal biopsy samples. Doppler fluctuation spectroscopy is performed on a voxel basis to extract spatial maps of biodynamic biomarkers. Functional images and bivariate spatial maps are produced using a bivariate color merge to represent the spatial distribution of pairs of signed drug-response biodynamic biomarkers.
Results: We have mapped the spatial variability of drug responses within biopsies and have tracked sample-to-sample variability. Sample heterogeneity observed in the biodynamic maps is associated with histological heterogeneity observed using inverted selective-plane illumination microscopy.
Conclusion: We have demonstrated the utility of TDSI as a functional imaging method to measure tumor heterogeneity and its potential for use in drug-response profiling.
Biodynamic imaging of chemotherapy response in ex vivo patient biopsies measures changes of intracellular dynamics in response to applied therapeutics. The technique is based on near-IR low-coherence digital holography of Dopplerbroadened light scattering from intracellular dynamics. Three clinical trials of this diagnostic biophotonic technology are nearing completion in esophageal cancer, epithelial ovarian cancer and breast cancer.
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