Boltz et al 2011 on standing genetic variation and HIV drug resistance

14 Jan

I am re-reading and older but beautiful paper on drug resistance evolution and standing genetic variation by Valerie Boltz and colleagues (Boltz et al 2011, PNAS). I wanted to share this story because it is a nice example of population genetics at work in a relevant system: HIV during antiviral treatment.

Boltz and colleagues look at the risk of virology failure or death in women on first-line HIV therapy who had been previously treated with single-dose Nevirapine (sdNVP) and correlate the risk with the observed frequency of drug resistance mutations prior to first-line treatment.

OK, that’s a long and ugly sentence. I’ll try to explain. So there is a group of women who are all HIV-positive. They have recently given birth to a baby and during childbirth, they were treated with sdNVP. This simple (and cheap) treatment reduces the risk that the baby gets HIV infected during birth (it is not recommended any more as better options are now available). One drawback of the sdNVP treatment is that the women end up with an increased frequency of drug resistance mutations in their viral population (specifically mutations K103N, Y181C and G190A in RT). Now, half-a-year or more later, the women start “normal” triple-drug therapy for their own health. They are in a clinical trial and some of them start NNRTI-based treatment whereas others start PI/r-based treatment. Since sdNVP is an NNRTI, the researchers were (rightfully) worried that the viral populations in these women have standing genetic variation for NNRTI resistance – more than other people who had never been treated with sdNVP. The paper looks at the women in both arms of the trial, but I am only interested in the NNRTI arm.

So what Boltz and colleagues did was: use allele-specific PCR to quantify the amount of standing genetic variation for NNRTI drug resistance in these women’s viral populations before they started their first-line therapy (but after the sdNVP) and then correlate with how well the treatment worked in these women. During treatment, the researchers focus on the occurrence of two bad outcomes: death and virologic failure. Death is not so common in this trial (but does occur, 4 of 241 women die in the three years of the trial) – virologic failure is more common (38 women). Virologic failure means that there is detectable viral replication in the blood even though the person is on treatment. Often, virologic failure is caused by drug resistance.

More SGV -> more virologic failure

Not surprisingly (but very cool nevertheless): more standing genetic variation prior to treatment is associated with a higher probability of virologic failure. Their (and my) interpretation: Standing Genetic Variation matters for drug resistance evolution.

This figure (fig 3 in the original paper) shows that women with >1% of the viral population carrying a resistance mutation have a much higher probability of virologic failure or death. The plot (a Kaplan-Meier plot) shows time in weeks on the x-axis and the fraction of women without failure or death on the y-axis. One way to read such a graph is to focus on one point on the x-axis. After 72 weeks, about 85% of the women with no detected drug resistance mutations are doing well on their treatment. 77% of the women with drug resistance mutations at a frequency <1% are doing well. 53% of the women with drug resistance mutations at a frequency between 1-10% are doing well and 49% of women with drug resistance mutations at a frequency of more than 10% are doing well.

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