Monoclonal antibodies constitute a promising class of targeted anticancer agents that enhance natural immune system functions to suppress cancer cell activity and eliminate cancer cells. The successful application of IgG monoclonal antibodies has inspired the development of various types of therapeutic antibodies, such as antibody fragments, bispecific antibodies, and antibody derivatives (e.g., antibody–drug conjugates and immunocytokines). The miniaturization and multifunctionalization of antibodies are flexible and viable strategies for diagnosing or treating malignant tumors in a complex tumor environment. In this review, we summarize antibodies of various molecular types, antibody applications in cancer therapy, and details of clinical study advances. We also discuss the rationale and mechanism of action of various antibody formats, including antibody–drug conjugates, antibody–oligonucleotide conjugates, bispecific/multispecific antibodies, immunocytokines, antibody fragments, and scaffold proteins. With advances in modern biotechnology, well-designed novel antibodies are finally paving the way for successful treatments of various cancers, including precise tumor immunotherapy, in the clinic.

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.

Dongxin Lin, Philip Taylor, Li-Dong Wang and colleagues have now pooled three genome-wide association analyses of esophageal squamous cell carcinoma, finding two new risk loci at genome-wide significance and an HLA class II locus of significance in high-risk populations. They reanalyze the strength of evidence for previously published risk loci. We conducted a joint (pooled) analysis of three genome-wide association studies (GWAS)1,2,3 of esophageal squamous cell carcinoma (ESCC) in individuals of Chinese ancestry (5,337 ESCC cases and 5,787 controls) with 9,654 ESCC cases and 10,058 controls for follow-up. In a logistic regression model adjusted for age, sex, study and two eigenvectors, two new loci achieved genome-wide significance, marked by rs7447927 at 5q31.2 (per-allele odds ratio (OR) = 0.85, 95% confidence interval (CI) = 0.82–0.88; P = 7.72 × 10−20) and rs1642764 at 17p13.1 (per-allele OR = 0.88, 95% CI = 0.85–0.91; P = 3.10 × 10−13). rs7447927 is a synonymous SNP in TMEM173, and rs1642764 is an intronic SNP in ATP1B2, near TP53. Furthermore, a locus in the HLA class II region at 6p21.32 (rs35597309) achieved genome-wide significance in the two populations at highest risk for ESSC (OR = 1.33, 95% CI = 1.22–1.46; P = 1.99 × 10−10). Our joint analysis identifies new ESCC susceptibility loci overall as well as a new locus unique to the population in the Taihang Mountain region at high risk of ESCC.

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.