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.

CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC’s functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.

The interpretation of variants in cancer is frequently focused on direct protein coding alterations. However, most somatic mutations are in noncoding regions of the genome, and even exonic mutations may have unidentified noncoding consequences. Here we present Regtools, a software package designed to efficiently identify variants that may cause aberrant splicing in tumors. Our tool integrates variant calls from genomic data with junctions extracted from transcriptomic data in order to examine potential cis alterations to splicing near a somatic variant. Based on user-defined parameters and position relative to known exons, variants are first annotated as splicing relevant or not. Splicing junctions are inferred from transcriptomic sequencing data, and comparison of junctions to a reference transcriptome allows for identification and annotation of novel junctions and nearby regulatory or splicing motifs. From there, mutations are associated with junctions that overlap with a flanking region. In order to evaluate Regtools, we used it to analyze the transcriptional output of tumor-sequencing data from two cohorts of cancer patients, one of hepatocellular carcinoma and one of small cell lung cancer with 28 and 21 samples, respectively. We performed whole-exome and RNA sequencing on each sample. Somatic variants were called on whole-exome alignment data. For each cohort, we compared the junctional profiles between tumors and identified numerous examples of variants for which there are elevated levels of proximal novel or known junctions. Moreover, out of 754 (153 in HCC; 601 in SCLC) variants identified as splicing relevant by our approach, only 165 (20 in HCC; 145 in SCLC) were annotated as splicing relevant by Ensembl's Variant Effect Predictor, using the recommended “per_gene” option. This preliminary analysis illustrates the importance of an efficient, user-friendly computational tool for identifying important noncoding variants that would otherwise be undervalued or perhaps even completely ignored by traditional methods and annotators. Regtools is freely available and open source (https://github.com/griffithlab/regtools). Citation Format: Yang-Yang Feng, Avinash Ramu, Zachary L. Skidmore, Jason Kunisaki, Kelsy C. Cotto, Obi L. Griffith, Malachi Griffith. Regtools: Integrated analysis of genomic and transcriptomic data for discovery of mutations associated with aberrant splicing in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2285.

Patients with multiple myeloma (MM) who are treated with lenalidomide rarely develop a secondary B-cell acute lymphoblastic leukemia (B-ALL). The clonal and biological relationship between these sequential malignancies is not yet clear. We identified 17 patients with MM treated with lenalidomide, who subsequently developed B-ALL. Samples were evaluated with sequencing, cytogenetics/FISH, immunohistochemical staining (IHC), and IgH clonality assessment. Samples were assessed for shared mutations and recurrently mutated genes. Through whole exome sequencing and cytogenetics/FISH analysis of 7 paired samples (MM versus matched B-ALL), no mutational overlap between samples was observed. Unique dominant IgH clonotypes between the tumors were observed in 5 paired MM / B-ALL samples. Across all 17 B-ALL samples, 14 (83%) had a TP53 variant detected. Three MM samples with sufficient sequencing depth (>500X) revealed rare cells (average of 0.6% VAF, or 1.2% of cells) with the same TP53 variant identified in the subsequent B-ALL sample. A lack of mutational overlap between MM and B-ALL samples shows that B-ALL developed as a second malignancy arising from a founding population of cells that probably represented unrelated clonal hematopoiesis caused by a TP53 mutation. The recurrent variants in TP53 in the B-ALL samples suggest a common path for malignant transformation that may be similar to that of TP53-mutant, treatment-related acute myeloid leukemia. The presence of rare cells containing TP53 variants in bone marrow at the initiation of lenalidomide treatment suggests that cellular populations containing TP53 variants expand in the presence of lenalidomide to increase the likelihood of B-ALL development.