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Background <p>Although integrase strand transfer inhibitors (INSTIs) have a high genetic barrier to resistance, cases of virological failure continue to emerge, sometimes in the absence of major resistance-associated mutations. Conventional genotypic and phenotypic resistance testing is costly and time-intensive, especially in resource-limited settings, and is inherently limited in detecting novel resistance pathways in any setting. Machine learning offers a scalable approach to uncover previously unrecognized resistance-associated patterns in HIV-1 genomic data.</p> Results <p>We analyzed 41,247 publicly available HIV-1 integrase sequences from ART-naïve and ART-experienced individuals using interpretable machine learning algorithms. Extreme Gradient Boosting (XGBoost), Gradient Boosting Machines (GBM), Random Forests (RF), Support Vector Machines (SVM), Logistic Regression (LR), and Naïve Bayes classifiers were trained to distinguish treatment status based solely on HIV-1 integrase mutation profiles. XGBoost outperformed other classifiers, with an AUROC of 0.92. Top-ranking mutations identified by the classifiers, including S283G, T112V, D278A, K136Q, T125A, V201I, V31I, I72V, A265V, G134N, and I135V among others, were significantly more prevalent in ART-experienced sequences. Structural analysis showed that most mutations potentially destabilize the three-dimensional structure of HIV-1 integrase. Relative risk (RR) analysis identified twenty three significant co-occurring mutation pairs with major INSTI resistance mutations, including G118R-R269K (RR = 2.3), Q148H-T125A (RR = 1.6), and Y143A-I135V (RR = 2.3). Most of these associations clustered within established resistance pathways (G118R, Q148/G140, Y143, and N155).</p> Conclusions <p>Machine learning identified potential accessory resistance-associated mutations in HIV-1 integrase beyond established INSTI resistance pathways. These mutations may contribute to INSTI resistance via epistatic interactions with known major resistance mutations. Validation of the predictive framework using independent clinical datasets, together with experimental and longitudinal studies, is required to determine the functional impact of these mutations and their relevance to treatment outcomes.</p>

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Machine learning identifies potential accessory resistance-associated mutations in HIV-1 integrase

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Alfred Ssekagiri,
  • Deogratius Ssemwanga,
  • David Patrick Kateete,
  • Daudi Jjingo

摘要

Background

Although integrase strand transfer inhibitors (INSTIs) have a high genetic barrier to resistance, cases of virological failure continue to emerge, sometimes in the absence of major resistance-associated mutations. Conventional genotypic and phenotypic resistance testing is costly and time-intensive, especially in resource-limited settings, and is inherently limited in detecting novel resistance pathways in any setting. Machine learning offers a scalable approach to uncover previously unrecognized resistance-associated patterns in HIV-1 genomic data.

Results

We analyzed 41,247 publicly available HIV-1 integrase sequences from ART-naïve and ART-experienced individuals using interpretable machine learning algorithms. Extreme Gradient Boosting (XGBoost), Gradient Boosting Machines (GBM), Random Forests (RF), Support Vector Machines (SVM), Logistic Regression (LR), and Naïve Bayes classifiers were trained to distinguish treatment status based solely on HIV-1 integrase mutation profiles. XGBoost outperformed other classifiers, with an AUROC of 0.92. Top-ranking mutations identified by the classifiers, including S283G, T112V, D278A, K136Q, T125A, V201I, V31I, I72V, A265V, G134N, and I135V among others, were significantly more prevalent in ART-experienced sequences. Structural analysis showed that most mutations potentially destabilize the three-dimensional structure of HIV-1 integrase. Relative risk (RR) analysis identified twenty three significant co-occurring mutation pairs with major INSTI resistance mutations, including G118R-R269K (RR = 2.3), Q148H-T125A (RR = 1.6), and Y143A-I135V (RR = 2.3). Most of these associations clustered within established resistance pathways (G118R, Q148/G140, Y143, and N155).

Conclusions

Machine learning identified potential accessory resistance-associated mutations in HIV-1 integrase beyond established INSTI resistance pathways. These mutations may contribute to INSTI resistance via epistatic interactions with known major resistance mutations. Validation of the predictive framework using independent clinical datasets, together with experimental and longitudinal studies, is required to determine the functional impact of these mutations and their relevance to treatment outcomes.