Evolutionary dynamics at the tumor edge reveals metabolic imaging biomarkers

Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatio-temporal dynamics during tumor growth. We studied the reshaping of metabolic activity in human cancers by means of continuous and discrete mathematical models, and matched the results to positron emission tomography (PET) imaging data. Our models revealed that the location of increasingly active proliferative cellular spots progressively drifted from the center of the tumor to the periphery, as a result of the competition between gradually more aggressive phenotypes. This computational finding led to the development of a metric, the NPAC, based on the distance from the location of peak activity (proliferation) to the tumor centroid. The NPAC metric can be computed for human patients using 18 F-FDG PET/CT images where the voxel of maximum uptake (SUV max ) is taken as the point of peak activity. Two datasets of 18 F-FDG PET/CT images were collected, one from 61 breast cancer patients and another from 161 non-small-cell lung cancer patients. In both cohorts, survival analyses were carried out for the NPAC and for other classical PET/CT-based biomarkers, finding that the former had a high prognostic value, outperforming the latter. In summary, our work offers new insights into the evolutionary mechanisms behind tumor progression and provides a PET/CT-based biomarker with clinical applicability. Significance Statement Through the use of different in silico modeling approaches capturing tumor heterogeneity, we predicted that areas of high metabolic activity would shift towards the periphery as tumors become more malignant. To confirm the prediction and provide clinical value for the finding, we took 18 F-FDG PET images of breast cancers and non-small-cell lung cancers, where we measured the distance from the point of maximum activity to the tumor centroid, and normalized it by a surrogate of the volume. We show that this metric has a high prognostic value for both malignancies and outperforms other classical PET-based metabolic biomarkers used in oncology.