Measuring parameters of the cancer ecosystem for cancer ecological staging.

Abstract

e14684 Background: Cancer progression through clonal evolution and emergent phenotypic heterogeneity is thought to reflect stochastic events such as genetic drift. This divergence over time in the character of a neoplasm might also reflect genetic selection, analogous to other populations in nature, to maximize niche resource utilization. We hypothesized that selection pressures operate in patients with cancer to drive cancer evolution, are clinically identifiable, their influence measurable. Methods: To develop a system for cancer ecology staging, a feasibility study recruited 15 patients with active cancer from any site, with expected survival of more than 6 months and providing informed consent. A set of clinical parameters obtained from a patient questionnaire, physical exam and laboratory testing was used to generate eight separate ecological profiles of tumor microenvironment, chronic inflammation, energy balance, psychosocial stress, GI microbiome, endocrine environment, skeletal remodeling and environmental mutagenesis. A scoring system, based on evidence of positive selection was designed to quantitate the individual profiles. Profile scores were then aggregated using a 2-D radar plot to generate a polygon, an ‘ecogram’, whose area, it is hypothesized, corresponds to the net level of selection pressure influencing tumor evolution. Results: Ecological profiles were obtained from each of 15 patients allowing determination of the ecogram area (EA) bounded by the polygon. EA determinations ranged widely among the 15 patient, from 0-12.7 arbitrary units (au, mean 5.01± 0.80). Ecograms from individual patients demonstrated unique shapes suggesting specificity for individual patient ecology. EA measurements were then used to inform an ecological staging system based on a simplified dichotomization, low/high, of ecosystem resources and threats. Of 15 patients, 6 were considered to have high resources (EA > 5au) available to support tumor evolution. High anti-tumor threat, measured by CD3 lymphocyte immunohistochemical scoring, was detected in 11 patients. Conclusions: An ecological assessment of patients with active cancer appears feasible. Inter-patient variation in ecogram area and morphology suggests there are potential important differences in genetic selection found between patients and should be correlated with survival outcomes in future studies, validation offering a target for ecosystem ‘restoration’.