Subgroup Identification Using Virtual Twins for Human Microbiome Studies.

Abstract

Even when the same treatment is employed, some patients are cured, while others are not. The patients that are cured may have beneficial microbes in their body that can boost treatment effects, but it is vice versa for the patients that are not cured. That is, treatment effects can vary depending on the patient's microbiome. If the effects of candidate treatments are well-predicted based on the patient's microbiome, we can select a treatment that is suited to the patient's microbiome or alter the patient's microbiome to improve treatment effects. Here, I introduce a streamlined analytic method, microbiome virtual twins (MiVT), to probe for the interplay between microbiome and treatment. MiVT employs a new prediction method, distance-based machine learning (dML), to improve prediction accuracy in microbiome studies and a new significance test, bootstrap-based test for regression tree (BoRT), to test if each subgroup's treatment effect is the same with the overall treatment effect. MiVT will serve as a useful guideline in microbiome-based personalized medicine to select the therapy that is most suited to the patient's microbiome or to tune the patient's microbiome to be suited to the treatment.