A filter-flow perspective of hematogenous metastasis offers a non-genetic paradigm for personalized cancer therapy

Abstract

Translational RelevanceSince the discovery of circulating tumor cells (CTC), we have struggled for ways to use them to inform treatment. The only currently accepted method for this is a more is worse paradigm by which clinicians measure CTC burden before and after treatment to assess efficacy. Research efforts are currently focused almost entirely on genetic classification of these cells, which has yet to bear any fruit translationally. We suggest that we should shift the focus of our investigation to one driven by a physical sciences perspective. Specifically, by understanding the vascular system as a network of interconnected organs and capillary beds as filters that capture CTCs. By ascertaining the distribution of CTCs in this network for individual patients, information about the existence of subclinical metastatic disease, and therefore metastatic propensity, will come to light, and allow for better staging, prognostication and rational use of organ-directed therapy in the setting of oligometastatic disease.\n\nAbstractO_ST_ABSPurposeC_ST_ABSResearch into mechanisms of hematogenous metastasis has largely become genetic in focus, attempting to understand the molecular basis of seed-soil relationships. However, preceding this biological mechanism is the physical process of dissemination of circulating tumour cells (CTCs) in the circulatory network. We utilize a novel, network perspective of hematogenous metastasis and a large dataset on metastatic patterns to shed new light on this process.\n\nExperimental DesignThe metastatic efficiency index (MEI), previously suggested by Weiss, quantifies the process of hematogenous metastasis by taking the ratio of metastatic incidence for a given primary-target organ pair and the relative blood flow between the two sites. In this paper we extend the methodology by taking into account the reduction in CTC number that occurs in capillary beds and a novel network model of CTC flow.\n\nResultsBy applying this model to a dataset of metastatic incidence, we show that the MEI depends strongly on the assumptions of micrometastatic lesions in the lung and liver. Utilizing this framework we can represent different configurations of metastatic disease and offer a rational method for identifying patients with oligometastatic disease for inclusion in future trials.\n\nConclusionsWe show that our understanding of the dynamics of CTC flow is significantly lacking, and that this specifically precludes our ability to predict metastatic patterns in individual patients. Our formalism suggests an opportunity to go a step further in metastatic disease characterization by including the distribution of CTCs at staging, offering a rational method of trial design for oligometastatic disease.