Abstract PL01-01: Physicist vs. physician: Digitizing clinical assessment and using it for evidence-based prediction of outcomes
Authors: Peter Kuhn, Jorge J. Nieva
Journal: Cancer Research (2019)

Abstract The Current Status of Tumor Biomarkers The past decade has seen tremendous advances in cancer care driven largely by better understanding of cancer genetics and biology. Characterization of the DNA changes driving cancer formation is now routine and it seems the only questions left to answer are how many genes do we need to characterize? And what will it cost to do so? Despite the ability to sequence the whole DNA of a tumor, we find that cancer remains difficult to treat. Some of this difficulty stems from heterogeneity of the tumor and its evolution under therapeutic selection driving drug resistance. This of course, also seems to be an “easy” problem to solve. One can simply sequence the cancer more often. Yet, despite our ability to sequence tumor DNA, patients continue to die at an alarming rate. Perhaps the problem is one of gene expression? Surely if we measure the DNA and the RNA, we can know everything there is to know about the cancer. Or do we? The Problem: Complexity of Human Biology Cancer does not occur merely as an isolated event in polystyrene well, or on the hindlimb of an inbred mouse. It occurs in people, and people are unique. We are missing key details if we lack the context of human biology, physiology, and metabolism. Not just the tumor microenvironment, but also the macroenvironment. Herein, the cancer biologist, geneticist, or physicist, must rely on the physician to give key details about the patient. Are they old or young? Are they sick or healthy? Where has the cancer spread? From what continent where the patient’s ancestors raised? To tackle this problem, the cancer researcher must integrate a whole series of non-quantitative data. For physicians this is not a problem, physicians are categorizers and non-quantitative information that allows for algorithm-based treatment is quite suitable. For a quantitative scientist, a physicist or mathematician who studies cancer, these data present real problems, not the least of which is that they are often subjective. The Status Quo: Clinical Trials as a Controlled Laboratory So when translating preclinical findings to the clinic, the solution for cancer researchers has been to simply request a homogenous group of patients to test our discoveries. Just enroll 100 or so patients to a clinical trial, be sure that none of them have hepatitis, HIV, heart, lung, liver or kidney problems. Don’t enroll anyone with brain metastasis, and be certain that all of them are gainfully employed with all the other laboratory values being normal, and we’ll test our discoveries on them. Open up 90 clinical trial sites around the world to find enough of these perfect physical specimens and if the treatment works on them, then we’ll have something that can be given to general patient populations. In short, we have developed a system of cancer research and discovery that seeks to replicate the laboratory within the clinic rather than operating a laboratory that reflects the heterogeneity of cancer in humans. An Alternative Approach: Uniting the Laboratory with the Clinic As we move forward in developing new cancer therapy, we need to decide which discipline should drive cancer research. Is it the physicians? Who want to try out every new therapy in every possible patient and then determine the groups that benefit? Or should it be the physicists, cancer biologists and geneticists who want to perform experiments in tightly controlled populations? Can we do both?This presentation will describe collaborative efforts to create a laboratory capable of studying cancer in the context of the complexity of human life. We will describe models made to quantify the pathways of disease in multiple dimensions and reflect the complexity of disease spread. Additionally, we will describe a series of efforts to quantify elements of the physical examination of cancer patients to begin to quantify frailty and illness in a reproducible numeric way that contextualizes biomarker information derived from the primary tumor and its metastasis. These collaborative efforts serve to move the physicist closer to the clinic and the physician closer to the quantitative science that we will need to bring the next decade of cancer discoveries to patients in the fastest way possible. Citation Format: Peter Kuhn, Jorge J. Nieva. Physicist vs. physician: Digitizing clinical assessment and using it for evidence-based prediction of outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr PL01-01.