No abstract

Background: A benchmark of near-perfect adherence (≥95%) to antiretroviral therapy (ART) is often cited as necessary for HIV viral suppression. However, given newer, more effective ART medications, the threshold for viral suppression may be lower. We estimated the minimum ART adherence level necessary to achieve viral suppression. Settings: The Patient-centered HIV Care Model demonstration project. Methods: Adherence to ART was calculated using the proportion of days covered measure for the 365-day period before each viral load test result, and grouped into 5 categories (<50%, 50% to <80%, 80% to <85%, 85% to <90%, and ≥90%). Binomial regression analyses were conducted to determine factors associated with viral suppression (HIV RNA <200 copies/mL); demographics, proportion of days covered category, and ART regimen type were explanatory variables. Generalized estimating equations with an exchangeable working correlation matrix accounted for correlation within subjects. In addition, probit regression models were used to estimate adherence levels required to achieve viral suppression in 90% of HIV viral load tests. Results: The adjusted odds of viral suppression did not differ between persons with an adherence level of 80% to <85% or 85% to <90% and those with an adherence level of ≥90%. In addition, the overall estimated adherence level necessary to achieve viral suppression in 90% of viral load tests was 82% and varied by regimen type; integrase inhibitor- and nonnucleoside reverse transcriptase inhibitor-based regimens achieved 90% viral suppression with adherence levels of 75% and 78%, respectively. Conclusions: The ART adherence level necessary to reach HIV viral suppression may be lower than previously thought and may be regimen-dependent.

A major goal of cancer sequencing projects is to identify genetic alterations that determine clinical phenotypes, such as survival time or drug response. Somatic mutations in cancer are typically very diverse, and are found in different sets of genes in different patients. This mutational heterogeneity complicates the discovery of associations between individual mutations and a clinical phenotype. This mutational heterogeneity is explained in part by the fact that driver mutations, the somatic mutations that drive cancer development, target genes in cellular pathways, and only a subset of pathway genes is mutated in a given patient. Thus, pathway-based analysis of associations between mutations and phenotype are warranted. Here, we introduce an algorithm to find groups of genes, or pathways, whose mutational status is associated to a clinical phenotype without prior definition of the pathways. Rather, we find subnetworks of genes in an gene interaction network with the property that the mutational status of the genes in the subnetwork are significantly associated with a clinical phenotype. This new algorithm is built upon HotNet, an algorithm that finds groups of mutated genes using a heat diffusion model and a two-stage statistical test. We focus here on discovery of statistically significant correlations between mutated subnetworks and patient survival data. A similar approach can be used for correlations with other types of clinical data, through use of an appropriate statistical test. We apply our method to simulated data as well as to mutation and survival data from ovarian cancer samples from The Cancer Genome Atlas. In the TCGA data, we discover nine subnetworks containing genes whose mutational status is correlated with survival. Genes in four of these subnetworks overlap known pathways, including the focal adhesion and cell adhesion pathways, while other subnetworks are novel.