Canonical correlation analysis for multi-omics: Application to cross-cohort analysis.

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

Real-world efficacy and safety of switching to bictegravir/emtricitabine/tenofovir alafenamide in older people living with HIV.

ABSTRACT: Approximately 50% of people living with HIV (PLWH) in the United States are ≥50 years old. Clinical trials of bictegravir/emtricitabine/tenofovir alafenamide (B/F/TAF) demonstrated potent efficacy and favorable safety in older PLWH; however, real-world data would be useful to validate these results.Retrospective cohort study.We evaluated records from PLWH aged ≥50 years at the Orlando Immunology Center who were switched to B/F/TAF between February 2018 and August 2019. Eligible patients had baseline HIV-1 RNA <50 copies/mL and 48 weeks of follow-up data. The primary endpoint was maintenance of HIV-1 RNA <50 copies/mL at Week 48. The impact of switching to B/F/TAF on drug-drug interactions (DDIs) and safety parameters were also assessed.Three-hundred and fifty patients met inclusion criteria, median age was 57 years, 20% were women, and 43% were non-White. Fifty-five percent of patients switched from integrase inhibitor-based regimens; the most common reason for switch was simplification. At Week 48, 330 (94%) patients maintained an HIV-1 RNA <50 copies/mL and 20 (6%) had an HIV-1 RNA between 50 and 400 copies/mL. One-hundred and forty potential DDIs were identified in 121 (35%) patients taking a boosting agent or rilpivirine at baseline that were resolved after switching to B/F/TAF. Treatment-related adverse events occurred in 51 (15%) patients (all Grade 1-2) and led to 8 discontinuations.In this real-world cohort, switching to B/F/TAF was associated with maintenance of virologic control, and avoidance of DDIs in a large proportion of patients. These data support use of B/F/TAF as a treatment option in older PLWH.