MRI Distinguishes Tumor Hypoxia Levels of Different Prognostic and Biological Significance in Cervical Cancer.

Tumor hypoxia levels range from mild to severe and have different biological and therapeutical consequences but are not easily assessable in patients. Here we present a method based on diagnostic dynamic contrast enhanced (DCE) MRI that reflects a continuous range of hypoxia levels in patients with tumors of cervical cancer. Hypoxia images were generated using an established approach based on pixel-wise combination of DCE-MRI parameters ν e and K trans, representing oxygen consumption and supply, respectively. Using two tumor models, an algorithm to retrieve surrogate measures of hypoxia levels from the images was developed and validated by comparing the MRI-defined levels with hypoxia levels reflected in pimonidazole-stained histologic sections. An additional indicator of hypoxia levels in patient tumors was established on the basis of expression of nine hypoxia-responsive genes; a strong correlation was found between these indicator values and MRI-defined hypoxia levels in 63 patients. Chemoradiotherapy outcome of 74 patients was most strongly predicted by moderate hypoxia levels, whereas more severe or milder levels were less predictive. By combining gene expression profiles and MRI-defined hypoxia levels in cancer hallmark analysis, we identified a distribution of levels associated with each hallmark; oxidative phosphorylation and G2-M checkpoint were associated with moderate hypoxia, epithelial-to-mesenchymal transition, and inflammatory responses with significantly more severe levels. At the mildest levels, IFN response hallmarks together with HIF1A protein expression by IHC appeared significant. Thus, our method visualizes the distribution of hypoxia levels within patient tumors and has potential to distinguish levels of different prognostic and biological significance. SIGNIFICANCE: These findings present an approach to image a continuous range of hypoxia levels in tumors and demonstrate the combination of imaging with molecular data to better understand the biology behind these different levels. ©2020 American Association for Cancer Research.