The interindividual genetic variations in drug metabolizing enzymes and transporters influence the efficacy and toxicity of numerous drugs. As a fundamental element in precision medicine, pharmacogenomics, the study of responses of individuals to medication based on their genomic information, enables the evaluation of some specific genetic variants responsible for an individual’s particular drug response. In this article, we review the contributions of genetic polymorphisms to major individual variations in drug pharmacotherapy, focusing specifically on the pharmacogenomics of phase-I drug metabolizing enzymes and transporters. Substantial frequency differences in key variants of drug metabolizing enzymes and transporters, as well as their possible functional consequences, have also been discussed across geographic regions. The current effort illustrates the common presence of variability in drug responses among individuals and across all geographic regions. This information will aid health-care professionals in prescribing the most appropriate treatment aimed at achieving the best possible beneficial outcomes while avoiding unwanted effects for a particular patient.

DIVERGE is a software system for phylogeny-based analyses of protein family evolution and functional divergence. It provides a suite of statistical tools for selection and prioritization of the amino acid sites that are responsible for the functional divergence of a gene family. The synergistic efforts of DIVERGE and other methods have convincingly demonstrated that the pattern of rate change at a particular amino acid site may contain insightful information about the underlying functional divergence following gene duplication. These predicted sites may be used as candidates for further experiments. We are now releasing an updated version of DIVERGE with the following improvements: 1) a feasible approach to examining functional divergence in nearly complete sequences by including deletions and insertions (indels); 2) the calculation of the false discovery rate of functionally diverging sites; 3) estimation of the effective number of functional divergence-related sites that is reliable and insensitive to cutoffs; 4) a statistical test for asymmetric functional divergence; and 5) a new method to infer functional divergence specific to a given duplicate cluster. In addition, we have made efforts to improve software design and produce a well-written software manual for the general user.

Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.

Tumor-specific neoantigens have attracted much attention since they can be used as biomarkers to predict therapeutic effects of immune checkpoint blockade therapy and as potential targets for cancer immunotherapy. In this study, we developed a comprehensive tumor-specific neoantigen database (TSNAdb v1.0), based on pan-cancer immunogenomic analyses of somatic mutation data and human leukocyte antigen (HLA) allele information for 16 tumor types with 7748 tumor samples from The Cancer Genome Atlas (TCGA) and The Cancer Immunome Atlas (TCIA). We predicted binding affinities between mutant/wild-type peptides and HLA class I molecules by NetMHCpan v2.8/v4.0, and presented detailed information of 3,707,562/1,146,961 potential neoantigens generated by somatic mutations of all tumor samples. Moreover, we employed recurrent mutations in combination with highly frequent HLA alleles to predict potential shared neoantigens across tumor patients, which would facilitate the discovery of putative targets for neoantigen-based cancer immunotherapy. TSNAdb is freely available at http://biopharm.zju.edu.cn/tsnadb.

Purpose: Because of their high tumor specificity and immunogenicity, neoantigens have been considered as ultimate targets for cancer immunotherapy. Neoantigen-based vaccines have demonstrated promising efficacy for several cancer types. To further investigate the antitumor potentials for other types of solid tumors, we designed a peptide-based neoantigen vaccine, iNeo-Vac-P01, and conducted a single-arm, open-labeled, investigator-initiated clinical trial (NCT03662815). Patients and Methods: Personalized neoantigen vaccines were designed and manufactured according to our bioinformatics analysis results from the whole-exome sequencing of tumor and peripheral blood cell DNAs. Patients were scheduled to be vaccinated subcutaneously with adjuvant on days 1, 4, 8, 15, and 22 (prime phase), and days 78 and 162 (boost phase). Additional immunizations were administrated every 2–3 months as per patient's potential benefit. The safety and efficacy were assessed through adverse events (AE), progression-free survival (PFS), overall survival (OS), and other parameters. Results: Of the 22 patients enrolled with advanced malignancies, 20 had no or mild AEs, while 2 had grade 3 or 4 acute allergic reactions only after their sixth boost vaccination. The disease control rate was 71.4%. The median PFS was 4.6 months, whereas the median OS was not reached (12-month OS = 55.1%). Around 80% of individual peptides or peptide pools elicited measurable specific immune response. In addition, our findings revealed several potential biomarkers for the prediction of better response. Conclusions: iNeo-Vac-P01 as monotherapy is feasible and safe for patients with advanced solid tumors. It could elicit T-cell–mediated immune response targeting tumor neoantigens, and might have promising antitumor efficacy. See related commentary by Filderman and Storkus, p. 4429