Nanotechnology and cancer

Abstract

The biological picture of cancer is rapidly advancing from models built from phenomenological descriptions to network models derived from systems biology, which can capture the evolving pathophysiology of the disease at the molecular level. The translation of this (still academic) picture into a clinically relevant framework can be enabling for the war on cancer, but it is a scientific and technological challenge. In this review, we discuss emerging in vitro diagnostic technologies and therapeutic approaches that are being developed to handle this challenge. Our discussion of in vitro diagnostics is guided by the theme of making large numbers of measurements accurately, sensitively, and at very low cost. We discuss diagnostic approaches based on microfluidics and nanotechnology. We then review the current state of the art of nanoparticle-based therapeutics that have reached the clinic. The goal of the presentation is to identify nanotherapeutic strategies that are designed to increase efficacy while simultaneously minimizing the toxic side effects commonly associated with cancer chemotherapies.

Figure 1

These images represent the evolving picture of medicine that is driving the development of nanotechnologies for the investigation, diagnosis, and treatment of cancer. (a) A mammogram exemplifies traditional, phenomenological, single-parameter cancer diagnostic techniques. (b) The “cancer pathways” model for understanding the differential response of certain cancers to molecular therapies. Each pathway is comprised of a cascade of interacting proteins (circled in yellow). Genetic mutation of a protein within a pathway can lead to cancer. Identifying which pathway is altered typically involves the analysis of multiple mRNAs and proteins from the cancerous tissue. If the altered pathway is correctly diagnosed, then that information can lead to the appropriate prescription of drugs. (c) A dynamic network model of disease. This model, compared to the cancer pathways model, more accurately reflects the complex interrelationships between various proteins within a biological system. It is thus a truer reflection of the molecular nature of the disease, but it also can be mined for biomarkers that can be diagnostic for the progression of the disease. Such biomarkers can potentially be harnessed for detecting disease prior to the emergence of clinical symptoms (dynamic network model images courtesy of Leroy Hood.