Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors.

BACKGROUND: Developing a screening method for identifying individuals at higher risk of elevated brain amyloid burden is important to reduce costs and burden to patients in clinical trials on Alzheimer's disease or the clinical setting. We developed machine learning models using objectively measured lifestyle factors to predict elevated brain amyloid burden on positron emission tomography. METHODS: Our prospective cohort study of non-demented, community-dwelling older adults aged ≥ 65 years was conducted from August 2015 to September 2019 in Usuki, Oita Prefecture, Japan. One hundred and twenty-two individuals with mild cognitive impairment or subjective memory complaints (54 men and 68 women, median age: 75.50 years) wore wearable sensors and completed self-reported questionnaires, cognitive test, and positron emission tomography imaging at baseline. Moreover, 99 individuals in the second year and 61 individuals in the third year were followed up. In total, 282 eligible records with valid wearable sensors, cognitive test results, and amyloid imaging and data on demographic characteristics, living environments, and health behaviors were used in the machine learning models. Amyloid positivity was defined as a standardized uptake value ratio of ≥ 1.4. Models were constructed using kernel support vector machine, Elastic Net, and logistic regression for predicting amyloid positivity. The mean score among 10 times fivefold cross-validation repeats was utilized for evaluation. RESULTS: In Elastic Net, the mean area under the receiver operating characteristic curve of the model using objectively measured lifestyle factors alone was 0.70, whereas that of the models using wearable sensors in combination with demographic characteristics and health and life environment questionnaires was 0.79. Moreover, 22 variables were common to all machine learning models. CONCLUSION: Our machine learning models are useful for predicting elevated brain amyloid burden using readily-available and noninvasive variables without the need to visit a hospital. TRIAL REGISTRATION: This prospective study was conducted in accordance with the Declaration of Helsinki and was approved by the local ethics committee of Oita University Hospital (UMIN000017442). A written informed consent was obtained from all participants. This research was performed based on the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline.

ER-851, a Novel Selective Inhibitor of AXL, Overcomes Resistance to Antimitotic Drugs.

Innate and adaptive resistance to cancer therapies, such as chemotherapies, molecularly targeted therapies, and immune-modulating therapies, is a major issue in clinical practice. Subpopulations of tumor cells expressing the receptor tyrosine kinase AXL become enriched after treatment with antimitotic drugs, causing tumor relapse. Elevated AXL expression is closely associated with drug resistance in clinical samples, suggesting that AXL plays a pivotal role in drug resistance. Although several molecules with AXL inhibitory activity have been developed, none have sufficient activity and selectivity to be clinically effective when administered in combination with a cancer therapy. Here, we report a novel small molecule, ER-851, which is a potent and highly selective AXL inhibitor. To investigate resistance mechanisms and identify driving molecules, we conducted a comprehensive gene expression analysis of chemoresistant tumor cells in mouse xenograft models of genetically engineered human lung cancer and human triple-negative breast cancer. Consistent with the effect of AXL knockdown, cotreatment of ER-851 and antimitotic drugs produced an antitumor effect and prolonged relapse-free survival in the mouse xenograft model of human triple-negative breast cancer. Importantly, when orally administered to BALB/c mice, this compound did not induce retinal toxicity, a known side effect of chronic MER inhibition. Together, these data strongly suggest that AXL is a therapeutic target for overcoming drug resistance and that ER-851 is a promising candidate therapeutic agent for use against AXL-expressing antimitotic-resistant tumors.