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          Institute: MPI für Informatik     Collection: Computational Biology and Applied Algorithmics     Display Documents

ID: 618821.0, MPI für Informatik / Computational Biology and Applied Algorithmics
HIV-1 mutational pathways under multidrug therapy
Authors:Lawyer, Glenn; Altmann, André; Thielen, Alex; Zazzi, Maurizio; Sönnerborg, Anders; Lengauer, Thomas
Date of Publication (YYYY-MM-DD):2011
Title of Journal:AIDS Research and Therapy
Issue / Number:1
Start Page:1
End Page:13
Sequence Number of Article:26
Review Status:Peer-review
Audience:Experts Only
Intended Educational Use:No
Abstract / Description:\textbf{Background} Genotype-derived drug resistance profiles are a valuable
asset in HIV-1 therapy decisions. Therapy decisions could be further improved,
both in terms of predicting length of current therapy success and in preserving
followup therapy options, through better knowledge of mutational pathways- here
defined as specific locations on the viral genome which, when mutant, alter the
risk that additional specific mutations arise. We limit the search to locations
in the reverse transcriptase region of the HIV-1 genome which host resistance
mutations to nucleoside (NRTI) and non-nucleoside (NNRTI) reverse transcriptase
inhibitors (as listed in the 2008 International AIDS Society report), or which
were mutant at therapy start in 5% or more of the therapies studied.

\textbf{Methods} A Cox proportional hazards model was fit to each location with
the hazard of a mutation at that location during therapy proportional to the
presence/absence of mutations at the remaining locations at therapy start. A
pathway from preexisting to occurring mutation was indicated if the covariate
was both selected as important via smoothly clipped absolute deviation (a form
of regularized regression) and had a small p-value. The Cox model also allowed
controlling for non-genetic parameters and potential nuisance factors such as
viral resistance and number of previous therapies. Results were based on 1981
therapies given to 1495 distinct patients drawn from the EuResist database.

\textbf{Results} The strongest influence on the hazard of developing NRTI
resistance was having more than four previous therapies, not any one existing
resistance mutation. Known NRTI resistance pathways were shown, and previously
speculated inhibition between the thymidine analog pathways was evidenced.
Evidence was found for a number of specific pathways between NRTI and NNRTI
resistance sites. A number of common mutations were shown to increase the
hazard of developing both NRTI and NNRTI resistance. Viral resistance to the
therapy compounds did not materially effect the hazard of mutation in our model.

\textbf{Conclusions} The accuracy of therapy outcome prediction tools may be
increased by including the number of previous treatments, and by considering
locations in the HIV genome which increase the hazard of developing resistance
Last Change of the Resource (YYYY-MM-DD):2012-03-20
External Publication Status:published
Document Type:Article
Communicated by:Thomas Lengauer
Affiliations:MPI für Informatik/Computational Biology and Applied Algorithmics
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