ESTER J. KWON
University of California, San Diego
Critical to the clinical management of cancer is the ability to detect disease and provide molecular information that can be acted on by therapeutics. Current clinical methods in cancer diagnostics include invasive biopsies of tumor tissue, imaging, and measurement of biomarkers in the blood. These tools can be enhanced by technology that is minimally invasive and could produce functional readouts on tumor activity. Exciting progress toward this goal includes activity-based probes to create “synthetic” biomarkers, methods to image metabolic activity of tumors, and devices for drug micro-dosing. Future goals are to coordinate the readouts from these diagnostic methods with molecular targeted therapeutics for a unified precision medicine approach for cancer treatment.
HETEROGENEITY DRIVES CHALLENGES IN CANCER TREATMENT
Cancer is a leading cause of death worldwide, and in the United States alone cancer care was estimated to cost $147 billion in 2017 (National Cancer Institute). Despite promising recent advances in cancer treatment, the response of patients to therapies is heterogeneous. The evidence for the heterogeneity of human cancer between individuals, tumors, and even within the same tumor is growing as more samples are sequenced and novel methods are developed. Adding to the complexity is the fact that heterogeneity not only is encoded by genetics but can be influenced by the microenvironment of the cancer cell.
At the same time, an increasing number of therapeutics target cancer cells on a molecular basis. This presents the challenge of how to guide clinical decisions
for patient populations on a quantitative basis to match therapeutics to an individual’s cancer in order to maximize efficacy and minimize treatment side effects.
Furthermore, technology that is time-resolved would allow longitudinal monitoring of patients to track their response and capture the development of therapeutic resistance.
All these challenges are embodied in the National Institutes of Health Precision Medicine Initiative, an effort to understand “how a person’s genetics, environment, and lifestyle can help determine the best approach to prevent or treat disease.”
CANCER BIOMARKERS CAN GUIDE CLINICAL CARE
The timeline of cancer progression is complicated and includes risk assessment, screening, diagnosis of disease onset, response to treatment, and monitoring for relapse after treatment. Biomarkers can provide information about disease status and answer important questions such as, Who has cancer and how advanced is it? What is the best available treatment for an individual and his or her cancer? Is the patient responding to treatment? Is there relapse of disease? The answers to these questions can provide valuable insight to inform clinical actions and improve the clinical management of cancer for reduced side effects and better treatment outcomes.
Biomarkers come in many forms and can include genetic sequencing information, such as the increased risk for breast and ovarian cancer in individuals with BRCA1 mutations (Shattuck-Eidens et al. 1995); analysis of nanometer-scale extracellular vesicles from the urine, such as prostate cancer prediction through sequencing of exosomes (McKiernan et al. 2016); and aberrant overexpression of proteins, such as measuring expression levels of HER2 in biopsies to guide treatment of antibody-based therapies (Slamon et al. 2001).
Given the large diversity of individual patients, the future of cancer diagnostics will likely integrate many inherently noisy biomarker signals using methods developed for “big data.”
ACTIVITY-BASED NANOSENSORS AS THERANOSTICS
Although there are promising biomarkers for the diagnosis and monitoring of disease, not all biomarkers are created equal. For example, the serum biomarker prostate-specific antigen (PSA) was widely adopted to screen men over the age of 50 for prostate cancer starting in the 1990s. Although PSA increased diagnostic sensitivity for prostate cancer, many of the cancers were benign. Unfortunately, clinical actions based on the detection of elevated PSA levels increase the risk for complications associated with follow-up procedures, such as biopsies and treatment, leading to what has been recognized as “overtreatment” (Prensner et
al. 2012). Furthermore, the mechanism for the rise of PSA protein levels in the bloodstream remains unclear, and therefore its link to disease progression is not well understood.
This outcome with PSA reveals a need to reenvision biomarkers as tools that encode information relevant for clinical decisions for treatment, such as how a particular tumor might respond to drug treatment. The pairing of diagnostic information with treatment is embodied in the term “theranostic,” a portmanteau of therapy and diagnostic.
Enzymes are often the target of cancer therapeutics. A particularly important class of enzymes are proteases, which are known to play critical roles in the progression of cancer (Koblinski et al. 2000). Fluorescence-based small-molecule probes, developed to functionally image protease activity, are important tools to study biology and can also be used in surgical navigation for total tumor resection (Yim et al. 2018). Other groups have engineered imaging probes that, activated by protease activity, fluoresce in the visible and near-infrared wavelengths (Jiang et al. 2004; Weissleder et al. 1999). However, these technologies rely on optical imaging that is limited by imaging depth.
As a complementary technology, we have engineered an exogenously administered nanoscale sensor that is targeted to tumor cells and sheds analytical fragments in response to tumor-specific proteases that can be detected in the urine (Kwon et al. 2017). The advantage of an exogenously administered synthetic system over a naturally occurring blood biomarker is that analyte generation can be engineered for optimal kinetics, specificity, and amplitude using material design. Furthermore, we found that analyte generation was also dependent on ligand–receptor matching between the nanosensor and tumors.
This technology can be developed to include a wider array of protease-sensitive analytes and tumor-specific receptors to precisely stratify patients into treatment groups based on receptor expression that can be matched to therapies such as integrin-targeted therapeutics. Urine-based readouts can be paired with spatial information if nanosensors are built with superparamagnetic nanoparticle cores for magnetic resonance imaging (Kwong et al. 2013).
A growing number of molecularly targeted therapeutics are available for the treatment of cancer. Diagnostics with activity-based readouts are promising tools to stratify patients for these increasingly precise treatment regimes. The future of clinical management of cancer will include the integration of diagnostic information with molecularly targeted therapeutics.
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