Advancing Cancer Patient Care by Integrating Circulating Tumor Cell Technology to Understand the Spatial and Temporal Dynamics of Cancer. Issue 6 (September 2014)

• Record Type: Journal Article
• Authors: Rodriguez‐Lee, Mariam; Kuhn, Peter; Webb, David R.; Gurwitz, David
• Is Part Of: Drug development research. Volume 75:Issue 6(2014)
• Page Start: 384
• Page End: 392
• Publication Date: 2014-09
• Subjects: Drug development -- Periodicals; Drugs -- Research -- Periodicals; 615.19
• DOI: 10.1002/ddr.21225
• Languages: English
• ISSNs: 0272-4391

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p> <table-wrap position="anchor" orientation="portrait"> <table border="1"> <colgroup span="1"> <col align="left" span="1" /> </colgroup> <tbody> <tr> <td align="left" rowspan="1" colspan="1">Preclinical Research</td> </tr> </tbody> </table> </table-wrap> </p> <p>Spatial and temporal dynamics of cancer, studied with physical science approaches at critical transition points of the disease can provide insight into the biology of cancer and the evolutionary changes that occur both naturally and in response to therapy. A very promising development in translational cancer medicine has been the emergence of circulating tumor cells (CTC) as minimally invasive "liquid biopsies." We envision that the future utility of CTC will not simply be confined to enumeration, but also include their routine characterization using a high‐content approach that investigates morphometrics, protein expression and genomic profiling. This novel approach guided by mathematical models to predict the spread of disease from the primary site to secondary site can bring the bench to the bedside for cancer patients. It is agnostic with reference to drug choice and treatment regimen, which also means that each patient is unique. The approach is Bayesian from a data collection perspective and is patient‐centric rather than drug or new chemical entity–centric. The analysis of data comes from an understanding of commonalities and differences that are detected among patients with a given cancer type. Thus, patients are treated over the course of their disease with various drug regimens that reflects our real‐time understanding of their evolving tumor genomics and response to treatment. This likely means that smaller cohorts of patients receive any given regimen but we hypothesize that it would lead to better patient outcomes than with the current classic approach to drug testing and development.</p> </abstract>