Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor–normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

Freeze-frame synthetic proteins trap DNA reaction intermediates in single live cells, revealing origins of genome instability.

Graphical Abstract Highlights d E. coli carcinogen-like proteins cause DNA damage and mutation when upregulated d Human homologs form a cancer-predictive network, promote DNA damage and mutation d Conserved endogenous DNA damage-promoting mechanisms identified d DNA damage-up proteins (DDPs): a broad class of cancer gene function

Purpose Following automated variant calling, manual review of aligned read sequences is required to identify a high-quality list of somatic variants. Despite widespread use in analyzing sequence data, methods to standardize manual review have not been described, resulting in high inter- and intralab variability. Methods This manual review standard operating procedure (SOP) consists of methods to annotate variants with four different calls and 19 tags. The calls indicate a reviewer’s confidence in each variant and the tags indicate commonly observed sequencing patterns and artifacts that inform the manual review call. Four individuals were asked to classify variants prior to, and after, reading the SOP and accuracy was assessed by comparing reviewer calls with orthogonal validation sequencing. Results After reading the SOP, average accuracy in somatic variant identification increased by 16.7% ( p value = 0.0298) and average interreviewer agreement increased by 12.7% ( p value < 0.001). Manual review conducted after reading the SOP did not significantly increase reviewer time. Conclusion This SOP supports and enhances manual somatic variant detection by improving reviewer accuracy while reducing the interreviewer variability for variant calling and annotation.

The interpretation of variants in cancer is frequently focused on direct protein coding alterations. However, this analysis strategy excludes somatic mutations in non-coding regions of the genome and even exonic mutations may have unidentified non-coding consequences. Here we present RegTools (www.regtools.org), a free, open-source software package designed to integrate analysis of somatic variant calls from genomic data with splice junctions extracted from transcriptomic data in order to efficiently identify variants that may cause aberrant splicing in tumors.ContributionsY.-Y.F. was involved in all aspects of this study, including designing methodology, developing and testing the tool software, analyzing and interpreting data, and writing the manuscript, with input from A.R., K.C.C, Z.L.S., J.K., D.F.C., O.L.G., and M.G. A.R. designed the tool and led software development efforts. Y.L., W.C.C., R.U., and R.G. provided unpublished tumor datasets and provided critical feedback on the manuscript. O.G. and M.G. supervised the study. All authors read and approved the final manuscript