Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa

Background: Selecting the optimal combination of HIV drugs for an individual in resourcelimited settings is challenging because of the limited availability of drugs and genotyping.Objective: The evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype, using cases from the Phidisa cohort in South Africa.Methods: Cases from Phidisa of treatment change following failure were identified that had the following data available: baseline CD4 count and viral load, details of failing and previous antiretroviral drugs, drugs in new regimen and time to follow-up. The HIV Resistance Response Database Initiative’s (RDI’s) models used these data to predict the probability of a viral load < 50 copies/mL at follow-up. The models were also used to identify effective alternative combinations of three locally available drugs.Results: The models achieved accuracy (area under the receiver–operator  characteristic curve) of 0.72 when predicting response to therapy, which is less accurate than for an independent global test set (0.80) but at least comparable to that of genotyping with rules-based interpretation. The models were able to identify alternative locally available three-drug regimens that were predicted to be effective in 69% of all cases and 62% of those whose new treatment failed in the clinic.Conclusion: The predictive accuracy of the models for these South African patients together with the results of previous studies suggest that the RDI’s models have the potential to optimise treatment selection and reduce virological failure in different patient populations, without the use of a genotype

Human Illness from Avian Influenza H7N3, British Columbia

Avian influenza that infects poultry in close proximity to humans is a concern because of its pandemic potential. In 2004, an outbreak of highly pathogenic avian influenza H7N3 occurred in poultry in British Columbia, Canada. Surveillance identified two persons with confirmed avian influenza infection. Symptoms included conjunctivitis and mild influenzalike illness

Trim28 Haploinsufficiency Triggers Bi-stable Epigenetic Obesity.

This is the final version of the article. It first appeared from Cell Press via http://dx.doi.org/10.1016/j.cell.2015.12.025More than one-half billion people are obese, and despite progress in genetic research, much of the heritability of obesity remains enigmatic. Here, we identify a Trim28-dependent network capable of triggering obesity in a non-Mendelian, "on/off" manner. Trim28(+/D9) mutant mice exhibit a bi-modal body-weight distribution, with isogenic animals randomly emerging as either normal or obese and few intermediates. We find that the obese-"on" state is characterized by reduced expression of an imprinted gene network including Nnat, Peg3, Cdkn1c, and Plagl1 and that independent targeting of these alleles recapitulates the stochastic bi-stable disease phenotype. Adipose tissue transcriptome analyses in children indicate that humans too cluster into distinct sub-populations, stratifying according to Trim28 expression, transcriptome organization, and obesity-associated imprinted gene dysregulation. These data provide evidence of discrete polyphenism in mouse and man and thus carry important implications for complex trait genetics, evolution, and medicine.This work was supported by funding from the Max-Planck Society, ERC (ERC-StG-281641), DFG (SFB992 “MedEp”; SFB 1052 “ObesityMechanisms”), EU_FP7 (NoE ”Epigenesys”; “Beta-JUDO” n° 279153), BMBF (DEEP), MRC (Metabolic Disease Unit - APC, SOR, GSHY, MRC_MC_UU_12012/1), Wellcome Trust (SOR, 095515/Z/11/Z) and the German Research Council (DFG) for the Clinical Research Center "Obesity Mechanisms" CRC1052/1 C05 and the Federal Ministry of Education and Research, Germany, FKZ, 01EO1001 (Integrated Research and Treatment Center (IFB) Adiposity Diseases

Depletion of stromal cells expressing fibroblast activation protein-α from skeletal muscle and bone marrow results in cachexia and anemia.

Fibroblast activation protein-α (FAP) identifies stromal cells of mesenchymal origin in human cancers and chronic inflammatory lesions. In mouse models of cancer, they have been shown to be immune suppressive, but studies of their occurrence and function in normal tissues have been limited. With a transgenic mouse line permitting the bioluminescent imaging of FAP(+) cells, we find that they reside in most tissues of the adult mouse. FAP(+) cells from three sites, skeletal muscle, adipose tissue, and pancreas, have highly similar transcriptomes, suggesting a shared lineage. FAP(+) cells of skeletal muscle are the major local source of follistatin, and in bone marrow they express Cxcl12 and KitL. Experimental ablation of these cells causes loss of muscle mass and a reduction of B-lymphopoiesis and erythropoiesis, revealing their essential functions in maintaining normal muscle mass and hematopoiesis, respectively. Remarkably, these cells are altered at these sites in transplantable and spontaneous mouse models of cancer-induced cachexia and anemia. Thus, the FAP(+) stromal cell may have roles in two adverse consequences of cancer: their acquisition by tumors may cause failure of immunosurveillance, and their alteration in normal tissues contributes to the paraneoplastic syndromes of cachexia and anemia

Clinical Evaluation of the Potential Utility of Computational Modeling as an HIV Treatment Selection Tool by Physicians with Considerable HIV Experience

The HIV Resistance Response Database Initiative (RDI), which comprises a small research team in the United Kingdom and collaborating clinical centers in more than 15 countries, has used antiretroviral treatment and response data from thousands of patients around the world to develop computational models that are highly predictive of virologic response. The potential utility of such models as a tool for assisting treatment selection was assessed in two clinical pilot studies: a prospective study in Canada and Italy, which was terminated early because of the availability of new drugs not covered by the system, and a retrospective study in the United States. For these studies, a Web-based user interface was constructed to provide access to the models. Participating physicians entered baseline data for cases of treatment failure and then registered their treatment intention. They then received a report listing the five alternative regimens that the models predicted would be most effective plus their own selection, ranked in order of predicted virologic response. The physicians then entered their final treatment decision. Twenty-three physicians entered 114 cases (75 unique cases with 39 entered twice by different physicians). Overall, 33% of treatment decisions were changed following review of the report. The final treatment decisions and the best of the RDI alternatives were predicted to produce greater virologic responses and involve fewer drugs than the original selections. Most physicians found the system easy to use and understand. All but one indicated they would use the system if it were available, particularly for highly treatment-experienced cases with challenging resistance profiles. Despite limitations, the first clinical evaluation of this approach by physicians with substantial HIV-experience suggests that it has the potential to deliver clinical and economic benefits

Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting

Objective. Antiretroviral drug selection in resource-limited settings is often dictated by strict protocols as part of a public health strategy. The objective of this retrospective study was to examine if the HIV-TRePS online treatment prediction tool could help reduce treatment failure and drug costs in such settings. Methods. The HIV-TRePS computational models were used to predict the probability of response to therapy for 206 cases of treatment change following failure in India. The models were used to identify alternative locally available 3-drug regimens, which were predicted to be effective. The costs of these regimens were compared to those actually used in the clinic. Results. The models predicted the responses to treatment of the cases with an accuracy of 0.64. The models identified alternative drug regimens that were predicted to result in improved virological response and lower costs than those used in the clinic in 85% of the cases. The average annual cost saving was $364 USD per year (41%). Conclusions. Computational models that do not require a genotype can predict and potentially avoid treatment failure and may reduce therapy costs. The use of such a system to guide therapeutic decision-making could confer health economic benefits in resource-limited settings

HIV drug susceptibility and treatment response to mega-HAART regimen in patients from the Frankfurt HIV cohort

Objective: To assess the relationship between viral susceptibility at baseline and virological response in human immunodeficiency virus (HIV)-infected patients treated with multi-drug salvage regimens after multiple previous treatment failures. Design: Retrospective analysis of 50 patients from the Frankfurt HIV cohort who had received treatment with a minimum of six drugs, and for whom a sample for baseline viral phenotyping was available. Methods: Viral drug susceptibility was measured retrospectively from stored samples using the Antivirogram, a recombinant virus assay based method. Virological response was defined as a viral load of <400 copies/ml at week 24. For analysis of treatment response, drop-outs were dealt with in two ways, either as failures (DAF) or censored (DAC). Several logistical regression models were applied to identify predictors of response, including baseline virus load, number of new drugs and phenotypic sensitivity scores. Results: At baseline, drug resistance was extensive: 96% of patients had viruses resistant to at least one drug class and 32% had viruses resistant to all three drug classes. In the DAF analysis, 39 patients experienced virological failure. In the DAC analysis, eight were censored and 31 patients experienced virological failure. In multivariate models that adjust for baseline viral load, the number of new drugs and total phenotypic sensitivity scores, the baseline viral load and phenotypic sensitivity score remained significantly associated with virological outcome, whereas in those adjusted for baseline viral load, the number of new drugs, NRTI phenotypic sensitivity score and PI phenotypic sensitivity score, only the latter remained significantly associated with virological outcome. Both the DAF and DAC analyses produced similar results. In all models used, virological failure was shown to be significantly associated with baseline viral load and phenotypic sensitivity score. Conclusions: In this retrospective analysis based on a small number of patients, viral drug susceptibility at baseline was strongly associated with virological outcome at 24 weeks, independent of covariates such as baseline viral load and treatment history. Baseline viral load also maintained a significant, independent association with virological outcome in most models