Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers

Abstract

Significance Through the use of different in silico modeling approaches capturing tumor heterogeneity, we postulated that areas of high metabolic activity would shift toward the periphery as tumors become more aggressive. To confirm the hypothesis and provide clinical value for the finding, we collected 18F-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, normalizing it by a surrogate of the volume. The metric, NHOC, showed higher prognostic value than other classical PET-based metabolic biomarkers used in oncology, evidencing that the shift of the hotspot of activity from the center of the tumor to its periphery correlates with a poor prognosis. Human cancers are biologically and morphologically heterogeneous. A variety of clonal populations emerge within these neoplasms and their interaction leads to complex spatiotemporal 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, normalized distance from 18F-fluorodeoxyglucose (18F-FDG) hotspot to centroid (NHOC), based on the separation from the location of the activity (proliferation) hotspot to the tumor centroid. The NHOC metric can be computed for patients using 18F-FDG PET–computed tomography (PET/CT) images where the voxel of maximum uptake (standardized uptake value [SUV]max) is taken as the activity hotspot. Two datasets of 18F-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 NHOC 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 additional insights into the evolutionary mechanisms behind tumor progression, provides a different PET/CT-based biomarker, and reveals that an activity hotspot closer to the tumor periphery is associated to a worst patient outcome.