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Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes.

ABSTRACT: Mutational escape from vaccine-induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Knowing the virus' fitness as a function of its proteomic sequence can enable rational design of potent vaccines, as this information can focus vaccine-induced immune responses to target mutational vulnerabilities of the virus. Spin models have been proposed as a means to infer intrinsic fitness landscapes of HIV proteins from patient-derived viral protein sequences. These sequences are the product of nonequilibrium viral evolution driven by patient-specific immune responses and are subject to phylogenetic constraints. How can such sequence data allow inference of intrinsic fitness landscapes? We combined computer simulations and variational theory á la Feynman to show that, in most circumstances, spin models inferred from patient-derived viral sequences reflect the correct rank order of the fitness of mutant viral strains. Our findings are relevant for diverse viruses.

SUBMITTER: Shekhar K

PROVIDER: S-EPMC5260469 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes.

Shekhar Karthik K Ruberman Claire F CF Ferguson Andrew L AL Barton John P JP Kardar Mehran M Chakraborty Arup K AK

Physical review. E, Statistical, nonlinear, and soft matter physics 20131204 6

Mutational escape from vaccine-induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Knowing the virus' fitness as a function of its proteomic sequence can enable rational design of potent vaccines, as this information can focus vaccine-induced immune responses to target mutational vulnerabilities of the virus. Spin models have been proposed as a means to infer intrinsic fitness landscapes of HIV proteins ...[more]

PMID: 24483484

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Project description:Patient-derived cancer xenografts (PDCX) generally represent more reliable models of human disease in which to evaluate a potential drugs preclinical efficacy. However to date, only a few patient-derived gastric cancer xenograft (PDGCX) models have been reported. In this study, we aimed to establish additional PDGCX models and to evaluate whether these models accurately reflected the histological and genetic diversities of the corresponding patient tumors. By engrafting fresh patient gastric cancer (GC) tissues into immune-compromised mice (SCID and/or nude mice), thirty two PDGCX models were established. Histological features were assessed by a qualified pathologist based on H&E staining. Genomic comparison was performed for several biomarkers including ERBB1, ERBB2, ERBB3, FGFR2, MET and PTEN. These biomarkers were profiled to assess gene copy number by fluorescent in situ hybridization (FISH) and/or protein expression by immunohistochemistry (IHC). All 32 PDGCX models retained the histological features of the corresponding human tumors. Furthermore, among the 32 models, 78% (25/32) highly expressed ERBB1 (EGFR), 22% (7/32) were ERBB2 (HER2) positive, 78% (25/32) showed ERBB3 (HER3) high expression, 66% (21/32) lost PTEN expression, 3% (1/32) harbored FGFR2 amplification, 41% (13/32) were positive for MET expression and 16% (5/32) were MET gene amplified. Between the PDGCX models and their parental tumors, a high degree of similarity was observed for FGFR2 and MET gene amplification, and also for ERBB2 status (agreement rate = 94~100%; kappa value = 0.81~1). Protein expression of PTEN and MET also showed moderate agreement (agreement rate = 78%; kappa value = 0.46~0.56), while ERBB1 and ERBB3 expression showed slight agreement (agreement rate = 59~75%; kappa value = 0.18~0.19). ERBB2 positivity, FGFR2 or MET gene amplification was all maintained until passage 12 in mice. The stability of the molecular profiles observed across subsequent passages within the individual models provides confidence in the utility and translational significance of these models for in vivo testing of personalized therapies.

Dataset Information

Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes.

Publications

Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes.

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