Abstract and Introduction
Abstract
Background: Predictive assays for cancer treatment are not new technology, but they have failed to meet the criteria necessary for standardized use in clinical decision-making.
Methods: The authors summarize the use of predictive assays and the challenges and values associated with these assays in the clinical setting.
Results: Predictive assays commercially available in the clinical setting are not standardized, have significant obstacles to overcome, and cannot be relied upon by health care professionals due to the limited value these assays provide to the decision-making process for the treatment of patients.
Conclusions: A method that more closely recapitulates the human tumor microenvironment and accurately predicts response with high reproducibility would be beneficial to patient outcomes and quality of life.
Introduction
Cancer is the second leading cause of death in the United States, despite the many chemotherapy agents and hundreds of combinations available for treatment. However, health care professionals generally make treatment decisions based on standard protocols developed in clinical trials to successfully match a patient to a treatment regimen. This generalized approach can lead to a response, but oftentimes patients fail to show improvement. A predictive assay to help guide treatment decisions has the potential to lead to improved outcomes and fewer unnecessary adverse events endured by patients.
The treatment of cancer currently relies on classical chemotherapy regimens based on histology and targeted agents for patients who carry specific genomic alterations. In the era of genomics, we are learning that cancer is an individual disease and, with a push toward personalized medicine, treatment is shifting to a more tailored approach. However, genomic testing still fails to capture the factors that ultimately determine how tumor cells will behave inside the body.
One of these factors is intratumoral heterogeneity. Large-scale sequencing of solid cancers has revealed that different cells within a tumor can have distinct profiles, including in gene expression, proliferation, and metastatic potential, among others. These differences can contribute to treatment failures and have consequences related to personalized medicine. The impact on predictive assays lies in the sampling of tumors that commonly rely on biopsy specimens or small resections that do not fully represent the complexity of the tumor, thus resulting in the failure of drugs selected through personalized screening based on a small population of biologically identical cells. The information gained from studying tumor heterogeneity will be invaluable in optimizing predictive assays, such as serial biopsies, to gain a broader picture of individual tumor response to drug therapy.
Functional predictive assays providing information on personalized responses to drugs are needed to help guide treatment decisions and improve outcomes. Chemosensitivity assays offer potential in predicting treatment response; however, after more than 20 years, controversy still exists on the predictive value of such assays.