The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
Though overall death from opioid overdose are increasing in the United States, the death rate in some states and population groups is stabilizing or even decreasing. Several states have enacted a Naloxone Accessibility Laws to increase naloxone availability as an opioid antidote. The extent to which these laws permit layperson distribution and possession varies. The aim of this study is to investigate differences in provisions of Naloxone Accessibility Laws by states mainly in the Northeast and West regions, and the impact of naloxone availability on the rates of drug overdose deaths. This cross-sectional study was based on the National Vital Statistics System multiple cause-of-death mortality files. The average changes in drug overdose death rates between 2013 and 2017 in relevant states of the Northeast and West regions were compared according to availability of naloxone to laypersons. Seven states in the Northeast region and 10 states in the Western region allowed layperson distribution of naloxone. Layperson possession of naloxone was allowed in 3 states each in the Northeast and the Western regions. The average drug overdose death rates increased in many states in the both regions regardless of legalization of layperson naloxone distribution. The average death rates of 3 states that legalized layperson possession in the West region decreased (-0.33 per 100,000 person); however, in states in the West region that did not allow layperson possession and states in the Northeast region regardless of layperson possession increased between 2013 and 2017. The provision to legalize layperson possession of naloxone was associated with decreased average opioid overdose death rates in 3 states of the West region.
Immunotherapy has fundamentally changed the landscape of cancer treatment. However, only a subset of patients respond to immunotherapy, and a significant portion experience immune-related adverse events (irAEs). In addition, the predictive ability of current biomarkers such as programmed death-ligand 1 (PD-L1) remains unreliable and establishing better potential candidate markers is of great importance in selecting patients who would benefit from immunotherapy. Here, we focus on the role of serum-based proteomic tests in predicting the response and toxicity of immunotherapy. Serum proteomic signatures refer to unique patterns of proteins which are associated with immune response in patients with cancer. These protein signatures are derived from patient serum samples based on mass spectrometry and act as biomarkers to predict response to immunotherapy. Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). The tests effectively stratified patients into groups with good and poor treatment outcomes independent of PD-L1 expression. Here, we review current evidence in the published literature on three liquid biopsy tests that use biomarkers derived from proteomics and machine learning for use in immuno-oncology. We discuss how these tests may inform patient prognosis as well as guide treatment decisions and predict irAE of immunotherapy. Thus, mass spectrometry-based serum proteomics signatures play an important role in predicting clinical outcomes and toxicity.
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