Only Slight Impact of Predicted Replicative Capacity for Therapy Response Prediction

BACKGROUND: Replication capacity (RC) of specific HIV isolates is occasionally blamed for unexpected treatment responses. However, the role of viral RC in response to antiretroviral therapy is not yet fully understood. MATERIALS AND METHODS: We developed a method for predicting RC from genotype using support vector machines (SVMs) trained on about 300 genotype-RC pairs. Next, we studied the impact of predicted viral RC (pRC) on the change of viral load (VL) and CD4(+) T-cell count (CD4) during the course of therapy on about 3,000 treatment change episodes (TCEs) extracted from the EuResist integrated database. Specifically, linear regression models using either treatment activity scores (TAS), the drug combination, or pRC or any combination of these covariates were trained to predict change in VL and CD4, respectively. RESULTS: The SVM models achieved a Spearman correlation (rho) of 0.54 between measured RC and pRC. The prediction of change in VL (CD4) was best at 180 (360) days, reaching a correlation of rho = 0.45 (rho = 0.27). In general, pRC was inversely correlated to drug resistance at treatment start (on average rho = -0.38). Inclusion of pRC in the linear regression models significantly improved prediction of virological response to treatment based either on the drug combination or on the TAS (t-test; p-values range from 0.0247 to 4 10(-6)) but not for the model using both TAS and drug combination. For predicting the change in CD4 the improvement derived from inclusion of pRC was not significant. CONCLUSION: Viral RC could be predicted from genotype with moderate accuracy and could slightly improve prediction of virological treatment response. However, the observed improvement could simply be a consequence of the significant correlation between pRC and drug resistance

Bridging the gap: Short structural variants in the genetics of anorexia nervosa

Abstract: Anorexia nervosa (AN) is a devastating disorder with evidence of underexplored heritability. Twin and family studies estimate heritability (h2) to be 57%–64%, and genome-wide association studies (GWAS) reveal significant genetic correlations with psychiatric and anthropometric traits and a total of nine genome-wide significant loci. Whether significantly associated single nucleotide polymorphisms identified by GWAS are causal or tag true causal variants, remains to be elucidated. We propose a novel method for bridging this knowledge gap by fine-mapping short structural variants (SSVs) in and around GWAS-identified loci. SSV fine-mapping of loci associated with complex disorders such as schizophrenia, amyotrophic lateral sclerosis, and Alzheimer’s disease has uncovered genetic risk markers, phenotypic variability between patients, new pathological mechanisms, and potential therapeutic targets. We analyze previous investigations’ methods and propose utilizing an evaluation algorithm to prioritize 10 SSVs for each of the top two AN GWAS-identified loci followed by Sanger sequencing and fragment analysis via capillary electrophoresis to characterize these SSVs for case/control association studies. Success of previous SSV analyses in complex disorders and effective utilization of similar methodologies supports our proposed method. Furthermore, the structural and spatial properties of the 10 SSVs identified for each of the top two AN GWAS-associated loci, cell adhesion molecule 1 (CADM1) and NCK interacting protein with SH3 domain (NCKIPSD), are similar to previous studies. We propose SSV fine-mapping of AN-associated loci will identify causal genetic architecture. Deepening understandings of AN may lead to novel therapeutic targets and subsequently increase quality-of-life for individuals living with the illness. Public Significance Statement: Anorexia nervosa is a severe and complex illness, arising from a combination of environmental and genetic factors. Recent studies estimate the contribution of genetic variability; however, the specific DNA sequences and how they contribute remain unknown. We present a novel approach, arguing that the genetic variant class, short structural variants, could answer this knowledge gap and allow development of biologically targeted therapeutics, improving quality-of-life and patient outcomes for affected individuals

Spearman correlations between clinical markers (top: viral load, bottom: CD4+ T-cell count) and potential predictors of clinical response (predicted RC and TAS) at different points in time during therapy.

<p>Correlation coefficients were computed from the TCE dataset.</p

The figure depicts the p-values obtained from a one-sided t-test testing whether predicted RC is lower in patients succeeding a treatment than in patients failing a treatment.

<p>Patients are grouped according to the predicted activity (TAS) of the regimen. Virologic success (left) was defined as reaching the limit of detection, i.e. 50 copies of HIV RNA per one ml blood. Immunologic success (right) was defined as an increase of CD4+ T-cells per one µl blood by 50% or more. Different symbols and colors are used to indicate different time-points of the follow-up measurement and different RC prediction models, respectively. The red horizontal dashed bar represents the 5% significance threshold.</p

Leave-one-out cross-validation results for the Monogram data (left) and Erlangen data (right).

<p>Spearman correlations of true and predicted RC values are ρ = 0.546 (Monogram) and ρ = 0.542 (Erlangen). For the Erlangen data, seven outliers with very high measured RC would appear further to the right side of the plot, but are not shown.</p

Number of viral load and CD4⁺ T-cell count measurements at different points in time in the main clinical dataset.

<p>Number of viral load and CD4⁺ T-cell count measurements at different points in time in the main clinical dataset.</p

Correlation coefficients of predicted RC and predicted resistance against different antiretroviral drugs.

<p>(PIs: NFV - nelfinavir, ATV - atazanavir, LPV - lopinavir, RTV - ritonavir, IDV - indinavir, APV - amprenavir, SQV - saquinavir; NRTIs: 3TC - lamivudine, ddI - didanosine, ABC - abacavir, ddC - zalcitabine, ZDV - zidovudine, TDF - tenofovir, d4T - stavudine; NNRTIs: DLV - delavirdine, NVP - nevirapine, EFV - efavirenz). For the HIV genotypes in the main clinical dataset, drug resistances with geno2pheno, as well as RC values based on the Monogram dataset and Erlangen dataset, respectively, were predicted. In each case, two RC predictions were made: one using only the protease sequence of the sample, and one using only the reverse transcriptase sequence. (The remaining sequence positions were padded according to the wild-type strain HXB2, in order to comply with our prediction models that were derived from experimental data incorporating both PR and RT.) Spearman correlations between the protease-based RC predictions and the predicted resistances against the different protease inhibitors, as well as between RT-based RC predictions and resistance against the RT inhibitors were computed. The results are shown in the plot, with drugs ordered by increasing correlation according to RC predictions based on Erlangen data.</p

Relationship between drug resistance, predicted RC and treatment experience.

<p>Based on 5475 genotypes from the EuResist database, the distribution of resistance scores and predicted viral RC for different numbers of previous therapies (completed before the date of genotyping) is shown. In the middle box plot, RC was predicted from a model trained on Monogram data; below, RC predictions are based on Erlangen data. The overall Spearman correlation between the number of previous therapies and the resistance score is 0.560; for the number of therapies and predicted RC, we obtain correlation coefficients of −0.336 (Monogram predictions) and −0.231 (Erlangen predictions).</p