New PET biomarker improves prognosis of breast and lung cancer
Human cancers are naturally constituted by a mix of subpopulations that are heterogeneous in their biology and spatial distribution. This has a reflection in the distribution of metabolic activity within the tumor, which can be assessed in human patients thanks to positron emission tomography (PET). Using continuous and discrete mathematical models of cancer progression, MOLAB researchers identified that the most aggressive clones (which are more metabolically active) experience a spatial drift towards the tumor boundary.
These theoretical results suggested a framework that could be used to extract information from PET images. In collaboration with Hospital General Universitario de Ciudad Real and Hospital General Universitario de Albacete, 18F-fluorodeoxyglucose (18F-FDG) PET images of 175 non-small cell lung cancer patients and 61 breast cancer patients were collected. Those images were processed and a simple measure of the relative position of metabolic active niches was computed: the distance from the centroid of the tumor to the voxel of maximum radiotracer uptake (SUVmax), normalized by a linear metric of the tumor size.
This new measure was named NHOC, from Normalized distance from 18F-FDG Hotspot to Centroid, and was used to identify survival differences in the cohorts of patients. Since the NHOC is a measure of the metabolic displacement that in silico models had associated with more advanced tumors, it was expected that it could give information about prognosis. The analyses gave positive results: it was found that those patients that had a greater value of NHOC were related to a worst outcome or a faster disease progression. Moreover, NHOC outperformed other classical PET-based metrics such as the value of the SUVmax and the metabolic tumor volume (MTV) in both groups of patients.
This work has been recently published in Proceedings of the National Academy of Sciences of the United States of America, placed in Q1 (8/71) in the category of “Multidisciplinary sciences”.
Evolutionary dynamics at the tumor edge reveal metabolic imaging biomarkers
Juan Jiménez-Sánchez, Jesús J. Bosque, Germán A. Jiménez Londoño, David Molina-García, Álvaro Martínez, Julián Pérez-Beteta, Carmen Ortega-Sabater, Antonio F. Honguero Martínez, Ana M. García Vicente, Gabriel F. Calvo, Víctor M. Pérez-García
Proceedings of the National Academy of Sciences Feb 2021, 118 (6) e2018110118; DOI: 10.1073/pnas.2018110118