Inferring HIV escape rates from multi-locus genotype data

TL;DR

This paper introduces a new method for estimating HIV escape mutation rates from serial sequence data, accounting for multiple competing mutations and stochastic effects, leading to higher estimated escape rates than previous studies.

Contribution

The paper presents a novel approach to infer HIV escape rates that explicitly models competition and stochasticity, improving accuracy over prior methods.

Findings

Estimated HIV escape rates are substantially higher than previous reports.

The method effectively models competition between multiple escape mutations.

Applicable to other contexts like cancer evolution with time series data.

Abstract

Cytotoxic T-lymphocytes (CTLs) recognize viral protein fragments displayed by major histocompatibility complex (MHC) molecules on the surface of virally infected cells and generate an anti-viral response that can kill the infected cells. Virus variants whose protein fragments are not efficiently presented on infected cells or whose fragments are presented but not recognized by CTLs therefore have a competitive advantage and spread rapidly through the population. We present a method that allows a more robust estimation of these escape rates from serially sampled sequence data. The proposed method accounts for competition between multiple escapes by explicitly modeling the accumulation of escape mutations and the stochastic effects of rare multiple mutants. Applying our method to serially sampled HIV sequence data, we estimate rates of HIV escape that are substantially larger than those previously reported. The method can be extended to complex escapes that require compensatory mutations. We expect our method to be applicable in other contexts such as cancer evolution where time series data is also available.