High–spatiotemporal resolution deuterium metabolic imaging enables in vivo phenotyping of intra- and intertumoral heterogeneity
TL;DR
In vivo DMI data acquired with sufficiently high spatiotemporal resolution can distinguish metabolically distinct subregions within viable tumors and between tumor models, demonstrating that in vivo DMI-based metabolic maps could provide the means to characterize intra- and intertumoral heterogeneities.
Abstract
Intra- and intertumoral heterogeneities raise fundamental biological questions and have important implications for accurate cancer diagnosis, prognosis, and treatment. These heterogeneities reflect tumor complexity, including a pronounced diversity in metabolic phenotypes and profiles. This study demonstrates that in vivo deuterium metabolic imaging (DMI) data acquired with sufficiently high spatiotemporal resolution provide a minimally invasive approach to assess these heterogeneities. A multifrequency DMI approach was used to examine tumor heterogeneities within and between colon cancer models. Regions of high and low glucose enrichment and labeled lactate accumulation could thus be detected; these were analyzed using unsupervised clustering strategies based on k-means clustering of area-under-the-curve information, and principal components analysis with Gaussian mixture modeling. Spatial alignment of these imaging-derived clusters for 2H-glucose and 2H-lactate showed good agreement with each other as well as with histological sections, validating the biological relevance of the identified subregions. Immunohistochemical analyses showed that glucose-enriched subregions were positively correlated with the expression of GLUT1 and DLAT, while lactate-enriched areas showed elevated expression of LDHA and MCT4. These results demonstrate that in vivo DMI can distinguish metabolically distinct subregions within viable tumors and between tumor models. DMI-based metabolic maps could thus provide the means to characterize intra- and intertumoral heterogeneities, paving the way for imaging-based metabolic phenotyping in precision oncology.