Characterizing the subtle divergence of the metastatic lesions from the primary tumor is critical to understanding organ-specific adaptations that regulate further disease progression as well as the development of targeted chemotherapy treatment options. Though genomic assays have provided insights into the aberrant expression of a few biomarkers, dissecting metastatic cancers based on objective molecular markers still remains challenging. I show that the exquisite specificity of Raman microspectroscopy in detecting molecular phenotypes can be harnessed to investigate and differentially identify engineered metastatic breast cancer cellular models in a label-free manner. A Raman microscope is used to acquire spectra from unique organ-specific human metastatic breast cancer cell lines that were established from the outgrowth of metastatic breast cancer cells from explant cultures of each organ. By correlating the Raman spectra with the pathology, I architected partial least squares-discriminant analysis and support vector machine-derived decision algorithms that exhibit significant power in segmenting between the established cell lines in the brain, lung, liver, spine and breast. Using the acquired chemical profiles, I show the robustness of the method to spurious correlations and ascertain the informative spectral bands that hint at organ-specific biomarkers as opposed to the presence of a single universal marker. These findings underscore the significance of tissue-specific microenvironments, especially the lipid phenotype in promoting adaptations in metastatic cancer cells and highlight the potential of Raman spectroscopy for further evaluation of targeted chemotherapeutic approaches in these cellular model systems.
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Raman spectroscopy of isogenic breast cancer cells derived from organ-specific metastases reveals distinct biochemical signatures