Cancer cells within a single tumor exhibit significant morphological, genetic, and metabolic variability, posing challenges for identifying and treating carcinoma by selecting suitable biomarkers. This complexity necessitates multiparametric characterization, such as next-generation sequencing and proteomics, which complicates their practical clinical management and the implementation of precision medicine. Here, we propose a new analytical approach for phenotyping cancer heterogeneity using hyperspectral IR images of tumors and maps of frequency shifts. For this purpose, we focus on the molecular drivers underlying the features of cancer cells and their metabolism, which are unequivocally identified in FTIR spectra, including the DNA and protein conformational landscape, carbohydrate metabolism, and extracellular matrix digestion. Since intratumor heterogeneity is a dynamic process and depends on the microenvironment and expanding tumor mass, we tested our method on the murine model of pulmonary metastasis of mammary gland carcinoma, delivering primary and secondary tumors. The frequency maps captured inter- and intratumor diversity, providing molecular information which can be linked to metastatic evolution.
Chrabaszcz et al. (Mon,) studied this question.