Development of a didanosine genotypic resistance interpretation system based on large derivation and validation datasets

Scoring methods for building genotypic scores: an application to didanosine resistance in a large derivation set

Background

Several attempts have been made to determine HIV-1 resistance from genotype resistance testing. We compare scoring methods for building weighted genotyping scores and commonly used systems to determine whether the virus of a HIV-infected patient is resistant.

Methods and Principal Findings

Three statistical methods (linear discriminant analysis, support vector machine and logistic regression) are used to determine the weight of mutations involved in HIV resistance. We compared these weighted scores with known interpretation systems (ANRS, REGA and Stanford HIV-db) to classify patients as resistant or not. Our methodology is illustrated on the Forum for Collaborative HIV Research didanosine database (N = 1453). The database was divided into four samples according to the country of enrolment (France, USA/Canada, Italy and Spain/UK/Switzerland). The total sample and the four country-based samples allow external validation (one sample is used to estimate a score and the other samples are used to validate it). We used the observed precision to compare the performance of newly derived scores with other interpretation systems. Our results show that newly derived scores performed better than or similar to existing interpretation systems, even with external validation sets. No difference was found between the three methods investigated. Our analysis identified four new mutations associated with didanosine resistance: D123S, Q207K, H208Y and K223Q.

Conclusions

We explored the potential of three statistical methods to construct weighted scores for didanosine resistance. Our proposed scores performed at least as well as already existing interpretation systems and previously unrecognized didanosine-resistance associated mutations were identified. This approach could be used for building scores of genotypic resistance to other antiretroviral drugs.

Can linear regression modeling help clinicians in the interpretation of genotypic resistance data? An application to derive a lopinavir-score

BACKGROUND

The question of whether a score for a specific antiretroviral (e.g. lopinavir/r in this analysis) that improves prediction of viral load response given by existing expert-based interpretation systems (IS) could be derived from analyzing the correlation between genotypic data and virological response using statistical methods remains largely unanswered.

METHODS AND FINDINGS

We used the data of the patients from the UK Collaborative HIV Cohort (UK CHIC) Study for whom genotypic data were stored in the UK HIV Drug Resistance Database (UK HDRD) to construct a training/validation dataset of treatment change episodes (TCE). We used the average square error (ASE) on a 10-fold cross-validation and on a test dataset (the EuroSIDA TCE database) to compare the performance of a newly derived lopinavir/r score with that of the 3 most widely used expert-based interpretation rules (ANRS, HIVDB and Rega). Our analysis identified mutations V82A, I54V, K20I and I62V, which were associated with reduced viral response and mutations I15V and V91S which determined lopinavir/r hypersensitivity. All models performed equally well (ASE on test ranging between 1.1 and 1.3, p = 0.34).

CONCLUSIONS

We fully explored the potential of linear regression to construct a simple predictive model for lopinavir/r-based TCE. Although, the performance of our proposed score was similar to that of already existing IS, previously unrecognized lopinavir/r-associated mutations were identified. The analysis illustrates an approach of validation of expert-based IS that could be used in the future for other antiretrovirals and in other settings outside HIV research.

Genotypic resistance testing in routine clinical care

PURPOSE OF REVIEW

Genotypic resistance testing has become part of routine clinical management of HIV-infected patients. Focussing on observational studies, this review looks at recent advances in this area.

RECENT FINDINGS

Translation of the nucleotide sequence generated by the resistance test into clinically useful information remains a major challenge. A recent key development is the availability of therapy optimization tools to predict regimens that are most likely to achieve virological suppression. Standard genotypic resistance testing only examines protease and part of reverse transcriptase; as drugs are licensed to further targets, it has become necessary to expand the repertoire for testing. Traditionally, genotypic testing has not been attempted at viral loads less than 1000 copies/ml, but recent studies indicate that major mutations are often detected at much lower levels. Similarly, various methods have been developed for the detection of minority variants including allele-specific PCR, single-genome sequencing, and ultra-deep sequencing.

SUMMARY

The technology and interpretation of genotypic resistance tests is in a phase of rapid development. It remains uncertain which of these developments will become part of routine clinical practice.