Best practices for bioinformatic characterization of neoantigens for clinical utility

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

Personalized ctDNA micro-panels can monitor and predict clinical outcomes for patients with triple-negative breast cancer

Circulating tumor DNA (ctDNA) in peripheral blood has been used to predict prognosis and therapeutic response for triple-negative breast cancer (TNBC) patients. However, previous approaches typically use large comprehensive panels of genes commonly mutated across all breast cancers. Given the reduction in sequencing costs and decreased turnaround times associated with panel generation, the objective of this study was to assess the use of custom micro-panels for tracking disease and predicting clinical outcomes for patients with TNBC. Paired tumor-normal samples from patients with TNBC were obtained at diagnosis (T0) and whole exome sequencing (WES) was performed to identify somatic variants associated with individual tumors. Custom micro-panels of 4-6 variants were created for each individual enrolled in the study. Peripheral blood was obtained at baseline, during Cycle 1 Day 3, at time of surgery, and in 3-6 month intervals after surgery to assess variant allele fraction (VAF) at different timepoints during disease course. The VAF was compared to clinical outcomes to evaluate the ability of custom micro-panels to predict pathological response, disease-free intervals, and patient relapse. A cohort of 50 individuals were evaluated for up to 48 months post-diagnosis of TNBC. In total, there were 33 patients who did not achieve pathological complete response (pCR) and seven patients developed clinical relapse. For all patients who developed clinical relapse and had peripheral blood obtained ≤ 6 months prior to relapse (n = 4), the custom ctDNA micro-panels identified molecular relapse at an average of 4.3 months prior to clinical relapse. The custom ctDNA panel results were moderately associated with pCR such that during disease monitoring, only 11% of patients with pCR had a molecular relapse, whereas 47% of patients without pCR had a molecular relapse (Chi-Square; p-value = 0.10). In this study, we show that a custom micro-panel of 4-6 markers can be effectively used to predict outcomes and monitor remission for patients with TNBC. These custom micro-panels show high sensitivity for detecting molecular relapse in advance of clinical relapse. The use of these panels could improve patient outcomes through early detection of relapse with preemptive intervention prior to symptom onset

Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools ( www.regtools.org ), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes