Sub-type contributions to Tx outcomes

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Variations in drug-resistant mutations appeared to correspond with specific sub-type designations in patients with HIV who failed first-line treatment, according to recent findings. “Sub-type designations are … widely used to represent genetic background variation in HIV sequences,” Healio reports that the researchers wrote. “While high viral genetic diversity and variable ART adherence is known to contribute to drug resistance and ART failure, sub-type contributions to treatment outcomes remains actively researched through in vitro and surveillance methods.”

The report says the researchers evaluated a multi-cohort, multi-sub-type dataset of reverse transcriptase (RT) isolates from patients failing WHO-recommended first-line ART. The analysis included 1,425 sequences, including 202 sub-type B sequences, 696 sub-type C sequences, 44 sub-type G sequences, 351 CRF01_AE sequences, 58 CRF02_AG sequences and 74 other sub-types. They used a hierarchical model to characterise resistance mutation variations as pertained to treatment histories and sub-type genetic backgrounds.

Sub-type B, predominately found in the US and Western Europe, generally demonstrated lower resistance mutation frequencies. In contrast, sub-type C, the most common subtype worldwide (largely found in South Africa and India), as well as sub-type CRF01_AE, commonly found in Southeast Asia, demonstrated higher resistance mutation frequencies to both nucleoside reverse transcriptase inhibitors (NRTI) and non-nucleoside reverse transcriptase inhibitors (NNRTI).

Compared with sub-type B, thymidine analogue mutation (TAM) frequency in sub-type C and CRF01_AE increased by 9% to 20% at RT positions 41L (RR = 1.9-5.2), 67N (RR = 2.2-5.3), 70R/E (RR = 2.4-5.8), 184 V/I (RR = 1.2-1.6), 215 F/Y (RR = 1.9-4.3), and 219Q/E (RR = 1.8-5.6). Furthermore, sub-type C and CRF01_AE were projected to have greater cross-resistance to future treatment approaches vs sub-type B (42% and 48% v. 60%, respectively).

“Given the higher levels of resistance observed in sub-type C and CRF01_AE population, interventions that minimise ecological contributions to resistance such as increased monitoring, viral load testing or use of boosted (protease inhibitor-based) regimens may warrant consideration,” the researchers are quoted in the report as saying.

“Future studies should further disentangle the role of ecological and biological contributors for higher levels of resistance in sub-type C and CRF01_AE. Identifying the correct causal explanations for the patterns of resistance characterised here is crucial to optimising ART among populations infected with non-B HIV-1 sub-types.”

Abstract
Background.HIV-1 drug resistance mutations (DRMs) often accompany treatment failure. Although subtype differences are widely studied, DRM comparisons between subtypes either focus on specific geographic regions or include populations with heterogeneous treatments.
Methods. We characterized DRM patterns following first-line failure and their impact on future treatment in a global, multi-subtype reverse transcriptase sequence dataset. We developed a hierarchical modeling approach to address the high dimensional challenge of modeling and comparing frequencies of multiple DRMs in varying first-line regimens, durations and subtypes. DRM co-occurrence was characterized using a novel application of a statistical network model.
Results. In 1,425 sequences – 202 subtype B, 696 C, 44 G, 351 circulating recombinant forms (CRF)01_AE, 58 CRF02_AG and 74 from other subtypes mutation frequencies were higher in subtypes C and CRF01_AE compared to B overall. Mutation frequency increased by 9-20% at reverse transcriptase positions 41, 67, 70, 184, 215 and 219 in subtype C and CRF01_AE vs. B. Subtype C and CRF01_AE exhibited higher predicted cross-resistance (+12-18%) to future therapy options compared to subtype B. Topologies of subtype mutation networks were mostly similar.
Conclusions. We find clear differences in DRM outcomes following first-line failure suggesting subtype-specific ecological and/or biological factors that determine DRM patterns.

Full Healio report
Open Forum Infectious Diseases abstract


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