Delineating the molecular and morphological changes that cancer cells undergo in response to extracellular stimuli is crucial for identifying factors that promote tumor progression. Label-free optical imaging offers a potentially promising route for retrieving such single-cell information by generating detailed visualization of the morphology and determining alterations in biomolecular composition. The potential of such nonperturbative morphomolecular microscopy for analyzing microbiota-cancer cell interactions has been surprisingly underappreciated, despite the growing evidence of the critical role of dysbiosis in malignant transformations. Here, using a model system of breast cancer cells, we show that label-free Raman microspectroscopy and quantitative phase microscopy can detect biomolecular and morphological changes in single cells exposed to toxin (BFT), a toxin secreted by enterotoxigenic. Remarkably, using machine learning to elucidate subtle, but consistent, cellular differences, we found that the morphomolecular differences between BFT-exposed and control breast cancer cells became more accentuated after passage, corroborating our findings that a short-term BFT exposure imparts a long-term effect on cancer cells and promotes a more invasive phenotype. Complementing more classical labeling techniques, our label-free platform offers a global detection approach with measurements representative of the overall cellular phenotype, paving the way for further investigations into the multifaceted interactions between the cancer cell and the microbiota.