Associations with other cancer-related biomarkers might contribute to poor outcomes in RAS-altered, younger patients with colorectal cancer.

Colorectal cancer (CRC) is a common cancer in younger adults. In patients undergoing liver resection with RAS-altered CRCs, there is evidence suggesting younger patients have worse outcomes than older patients. To explain this pattern, differences in associations between RAS status and other cancer-related biomarkers in tumors from younger versus older patients with CRC were evaluated in a cohort of 925 patients with CRC, 277 (30.0%) of whom were ≤50 years old, and 454 (49.1%) who had RAS-altered tumors. For 3 biomarkers, RNF43, APC, and microsatellite instability (MSI), the association with RAS status was significantly modified by age after adjustment for multiple testing. Specifically, younger patients with RAS-altered tumors were more likely to be MSI-high, RNF43 mutated, and APC wild type. These differences might contribute to the observed pattern of diminished survival in younger versus older patients with CRC with RAS-mutated tumors undergoing liver metastasis resection.

HIV type 1 polymerase gene polymorphisms are associated with phenotypic differences in replication capacity and disease progression.

BACKGROUND: Determinants of intersubtype differences in human immunodeficiency virus type 1 (HIV-1) clinical disease progression remain unknown. METHODS: HIV-1 subtype was independently determined for 5 separate genomic regions in 396 HIV-1 seroconverters from Rakai, Uganda, using a multiregion hybridization assay. Replication capacities (RC) in samples from a subset of 145 of these subjects were determined. HIV-1 genomic regions and pol RC were examined for association with disease progression. Amino acid polymorphisms were examined for association with pol RC. RESULTS: In multivariate analyses, the hazard for progression to the composite end point (defined as a CD4(+) T-cell count <250 cells/mm(3), antiretroviral therapy initiation, or death) among patients with subtype D pol infection was 2.4 times the hazard for those infected with subtype A pol infection (P = .001). Compared with subtype A pol (the reference group), the hazard for progression to the composite end point for subtype D pol infection with a pol RC >67% (ie, the median pol RC) was significantly greater (HR, 4.6; 95% confidence interval [CI], 1.9-11.0; P = .001), whereas the hazard for progression to the composite end point for subtype D pol infection with a pol RC ≤67% was not significantly different (HR, 2.2; 95% CI, 1.0-4.9; P = .051). Amino acid substitutions at protease positions 62 and 64 and at reverse transcriptase position 272 were associated with significant differences in pol RC. CONCLUSIONS: HIV-1 pol gene intersubtype and RC differences are associated with disease progression and may be influenced by amino acid polymorphisms.

Predicting HIV-1 broadly neutralizing antibody epitope networks using neutralization titers and a novel computational method.

BACKGROUND: Recent efforts in HIV-1 vaccine design have focused on immunogens that evoke potent neutralizing antibody responses to a broad spectrum of viruses circulating worldwide. However, the development of effective vaccines will depend on the identification and characterization of the neutralizing antibodies and their epitopes. We developed bioinformatics methods to predict epitope networks and antigenic determinants using structural information, as well as corresponding genotypes and phenotypes generated by a highly sensitive and reproducible neutralization assay.282 clonal envelope sequences from a multiclade panel of HIV-1 viruses were tested in viral neutralization assays with an array of broadly neutralizing monoclonal antibodies (mAbs: b12, PG9,16, PGT121 - 128, PGT130 - 131, PGT135 - 137, PGT141 - 145, and PGV04). We correlated IC50 titers with the envelope sequences, and used this information to predict antibody epitope networks. Structural patches were defined as amino acid groups based on solvent-accessibility, radius, atomic depth, and interaction networks within 3D envelope models. We applied a boosted algorithm consisting of multiple machine-learning and statistical models to evaluate these patches as possible antibody epitope regions, evidenced by strong correlations with the neutralization response for each antibody. RESULTS: We identified patch clusters with significant correlation to IC50 titers as sites that impact neutralization sensitivity and therefore are potentially part of the antibody binding sites. Predicted epitope networks were mostly located within the variable loops of the envelope glycoprotein (gp120), particularly in V1/V2. Site-directed mutagenesis experiments involving residues identified as epitope networks across multiple mAbs confirmed association of these residues with loss or gain of neutralization sensitivity. CONCLUSIONS: Computational methods were implemented to rapidly survey protein structures and predict epitope networks associated with response to individual monoclonal antibodies, which resulted in the identification and deeper understanding of immunological hotspots targeted by broadly neutralizing HIV-1 antibodies.

Recent advances in immunoinformatics: application of in silico tools to drug development.

Immunoinformatics is an emerging specialization of bioinformatics that focuses upon the structure, function and interactions of the molecules involved in immunity. Two major cell types, T-cells and B-cells, play significant roles in allergy, inflammation, infection and protective immunity. This review examines recently developed in silico tools and databases that can be used to identify, characterize or predict antigen epitopes recognized by T- and B-cells including the latest generation of B-cell epitope prediction tools that employ peptide-binding information derived from peptide phage display experiments. The application of these tools to facilitate drug development efforts is also discussed.

A case-based reasoning system for genotypic prediction of HIV-1 co-receptor tropism.

Accurate co-receptor tropism (CRT) determination is critical for making treatment decisions in HIV management. We created a genotypic tropism prediction tool by utilizing the case-based reasoning (CBR) technique that attempts to solve new problems through applying the solution from similar past problems. V3 loop sequences from 732 clinical samples with diverse characteristics were used to build a case library. Additional sequence and molecular properties of the V3 loop were examined and used for similarity assessment. A similarity metric was defined based on each attribute's frequency in the CXCR4-using viruses. We implemented three other genotype-based tropism predictors, support vector machines (SVM), position specific scoring matrices (PSSM), and the 11/25 rule, and evaluated their performance as the ability to predict CRT compared to Monogram's enhanced sensitivity Trofile(®) assay (ESTA). Overall concordance of the CBR based tropism prediction algorithm was 81%, as compared to ESTA. Sensitivity to detect CXCR4 usage was 90% and specificity was at 73%. In comparison, sensitivity of the SVM, PSSM, and the 11/25 rule were 85%, 81%, and 36% respectively while achieving a specificity of 90% by SVM, 75% by PSSM, and 97% by the 11/25 rule. When we evaluated these predictors in an unseen dataset, higher sensitivity was achieved by the CBR algorithm (87%), compared to SVM (82%), PSSM (76%), and the 11/25 rule (33%), while maintaining similar level of specificity. Overall this study suggests that CBR can be utilized as a genotypic tropism prediction tool, and can achieve improved performance in independent datasets compared to model or rule based methods.