RADIA: RNA and DNA integrated analysis for somatic mutation detection

A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data

Detection of somatic mutations holds great potential in cancer treatment and has been a very active research field in the past few years, especially since the breakthrough of the next-generation sequencing technology. A collection of variant calling pipelines have been developed with different underlying models, filters, input data requirements, and targeted applications. This review aims to enumerate these unique features of the state-of-the-art variant callers, in the hope to provide a practical guide for selecting the appropriate pipeline for specific applications. We will focus on the detection of somatic single nucleotide variants, ranging from traditional variant callers based on whole genome or exome sequencing of paired tumor-normal samples to recent low-frequency variant callers designed for targeted sequencing protocols with unique molecular identifiers. The variant callers have been extensively benchmarked with inconsistent performances across these studies. We will review the reference materials, datasets, and performance metrics that have been used in the benchmarking studies. In the end, we will discuss emerging trends and future directions of the variant calling algorithms.

A Recurrent Mutation in Anaplastic Lymphoma Kinase with Distinct Neoepitope Conformations

The identification of recurrent human leukocyte antigen (HLA) neoepitopes driving T cell responses against tumors poses a significant bottleneck in the development of approaches for precision cancer therapeutics. Here, we employ a bioinformatics method, Prediction of T Cell Epitopes for Cancer Therapy, to analyze sequencing data from neuroblastoma patients and identify a recurrent anaplastic lymphoma kinase mutation (ALK R1275Q) that leads to two high affinity neoepitopes when expressed in complex with common HLA alleles. Analysis of the X-ray structures of the two peptides bound to HLA-B*15:01 reveals drastically different conformations with measurable changes in the stability of the protein complexes, while the self-epitope is excluded from binding due to steric hindrance in the MHC groove. To evaluate the range of HLA alleles that could display the ALK neoepitopes, we used structure-based Rosetta comparative modeling calculations, which accurately predict several additional high affinity interactions and compare our results with commonly used prediction tools. Subsequent determination of the X-ray structure of an HLA-A*01:01 bound neoepitope validates atomic features seen in our Rosetta models with respect to key residues relevant for MHC stability and T cell receptor recognition. Finally, MHC tetramer staining of peripheral blood mononuclear cells from HLA-matched donors shows that the two neoepitopes are recognized by CD8+ T cells. This work provides a rational approach toward high-throughput identification and further optimization of putative neoantigen/HLA targets with desired recognition features for cancer immunotherapy.

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

Punctuated evolution of canonical genomic aberrations in uveal melanoma

Cancer is thought to arise through the accumulation of genomic aberrations evolving under Darwinian selection. However, it remains unclear when the aberrations associated with metastasis emerge during tumor evolution. Uveal melanoma (UM) is the most common primary eye cancer and frequently leads to metastatic death, which is strongly linked to BAP1 mutations. Accordingly, UM is ideally suited for studying the clonal evolution of metastatic competence. Here we analyze sequencing data from 151 primary UM samples using a customized bioinformatic pipeline, to improve detection of BAP1 mutations and infer the clonal relationships among genomic aberrations. Strikingly, we find BAP1 mutations and other canonical genomic aberrations usually arise in an early punctuated burst, followed by neutral evolution extending to the time of clinical detection. This implies that the metastatic proclivity of UM is "set in stone" early in tumor evolution and may explain why advances in primary treatment have not improved survival.

Informatics for cancer immunotherapy

The rapid development of immunomodulatory cancer therapies has led to a concurrent increase in the application of informatics techniques to the analysis of tumors, the tumor microenvironment, and measures of systemic immunity. In this review, the use of tumors to gather genetic and expression data will first be explored. Next, techniques to assess tumor immunity are reviewed, including HLA status, predicted neoantigens, immune microenvironment deconvolution, and T-cell receptor sequencing. Attempts to integrate these data are in early stages of development and are discussed in this review. Finally, we review the application of these informatics strategies to therapy development, with a focus on vaccines, adoptive cell transfer, and checkpoint blockade therapies.

Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas

Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes (TP53, ATRX, RB1) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types.

Comparative genomic analysis of esophageal squamous cell carcinoma between Asian and Caucasian patient populations

Esophageal squamous cell carcinoma is a major histological type of esophageal cancer, with distinct incidence and survival patterns among races. Although previous studies have characterized somatic mutations in this disease, a rigorous comparison between different patient populations has not been conducted. Here we sequence the samples of 316 Chinese patients, combine them with those from The Cancer Genome Atlas, and perform a comparative analysis between Asian and Caucasian patients. We find that mutated CSMD3 is associated with better prognosis in Asian patients. Applying a robust computational strategy that adjusts for both technical and biological confounding factors, we find that TP53, EP300, and NFE2L2 show higher mutational frequencies in Asian patients. Moreover, NFE2L2 mutations correlate with the allele status of a nearby high-Fst SNP, suggesting their potential interaction. Our study provides insights into the molecular basis underlying the striking racial disparities of this disease, and represents a general computational framework for such a cross-population comparison.

Esophageal squamous cell carcinoma (ESCC) exhibits differences in incidence and survival patterns among races. Here, analysis of Chinese and TCGA ESCC patients reveals that Asian patients exhibit higher TP53, EP300 and NFE2L2 mutational frequencies, and mutated CSMD3 associates with better prognosis.

Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma

Comprehensive multiplatform analysis of 80 uveal melanomas (UM) identifies four molecularly distinct, clinically relevant subtypes: two associated with poor-prognosis monosomy 3 (M3) and two with better-prognosis disomy 3 (D3). We show that BAP1 loss follows M3 occurrence and correlates with a global DNA methylation state that is distinct from D3-UM. Poor-prognosis M3-UM divide into subsets with divergent genomic aberrations, transcriptional features, and clinical outcomes. We report change-of-function SRSF2 mutations. Within D3-UM, EIF1AX- and SRSF2/SF3B1-mutant tumors have distinct somatic copy number alterations and DNA methylation profiles, providing insight into the biology of these low- versus intermediate-risk clinical mutation subtypes.

Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma

Liver cancer has the second highest worldwide cancer mortality rate and has limited therapeutic options. We analyzed 363 hepatocellular carcinoma (HCC) cases by whole-exome sequencing and DNA copy number analyses, and we analyzed 196 HCC cases by DNA methylation, RNA, miRNA, and proteomic expression also. DNA sequencing and mutation analysis identified significantly mutated genes, including LZTR1, EEF1A1, SF3B1, and SMARCA4. Significant alterations by mutation or downregulation by hypermethylation in genes likely to result in HCC metabolic reprogramming (ALB, APOB, and CPS1) were observed. Integrative molecular HCC subtyping incorporating unsupervised clustering of five data platforms identified three subtypes, one of which was associated with poorer prognosis in three HCC cohorts. Integrated analyses enabled development of a p53 target gene expression signature correlating with poor survival. Potential therapeutic targets for which inhibitors exist include WNT signaling, MDM4, MET, VEGFA, MCL1, IDH1, TERT, and immune checkpoint proteins CTLA-4, PD-1, and PD-L1.

A Novel Secreted Protein, MYR1, Is Central to Toxoplasma's Manipulation of Host Cells

The intracellular protozoan Toxoplasma gondii dramatically reprograms the transcriptome of host cells it infects, including substantially up-regulating the host oncogene c-myc. By applying a flow cytometry-based selection to infected mouse cells expressing green fluorescent protein fused to c-Myc (c-Myc–GFP), we isolated mutant tachyzoites defective in this host c-Myc up-regulation. Whole-genome sequencing of three such mutants led to the identification of MYR1 (Myc regulation 1; TGGT1_254470) as essential for c-Myc induction. MYR1 is a secreted protein that requires TgASP5 to be cleaved into two stable portions, both of which are ultimately found within the parasitophorous vacuole and at the parasitophorous vacuole membrane. Deletion of MYR1 revealed that in addition to its requirement for c-Myc up-regulation, the MYR1 protein is needed for the ability of Toxoplasma tachyzoites to modulate several other important host pathways, including those mediated by the dense granule effectors GRA16 and GRA24. This result, combined with its location at the parasitophorous vacuole membrane, suggested that MYR1 might be a component of the machinery that translocates Toxoplasma effectors from the parasitophorous vacuole into the host cytosol. Support for this possibility was obtained by showing that transit of GRA24 to the host nucleus is indeed MYR1-dependent. As predicted by this pleiotropic phenotype, parasites deficient in MYR1 were found to be severely attenuated in a mouse model of infection. We conclude, therefore, that MYR1 is a novel protein that plays a critical role in how Toxoplasma delivers effector proteins to the infected host cell and that this is crucial to virulence.

Toxoplasma gondii is an important human pathogen and a model for the study of intracellular parasitism. Infection of the host cell with Toxoplasma tachyzoites involves the introduction of protein effectors, including many that are initially secreted into the parasitophorous vacuole but must ultimately translocate to the host cell cytosol to function. The work reported here identified a novel protein that is required for this translocation. These results give new insight into a very unusual cell biology process as well as providing a potential handle on a pathway that is necessary for virulence and, therefore, a new potential target for chemotherapy.

The impact of the Cancer Genome Atlas on lung cancer

The Cancer Genome Atlas (TCGA) has profiled more than 10,000 samples derived from 33 types of cancer to date, with the goal of improving our understanding of the molecular basis of cancer and advancing our ability to diagnose, treat, and prevent cancer. This review focuses on lung cancer as it is the leading cause of cancer-related mortality worldwide in both men and women. Particularly, non-small cell lung cancers (including lung adenocarcinoma and lung squamous cell carcinoma) were evaluated. Our goal was to demonstrate the impact of TCGA on lung cancer research under 4 themes: diagnostic markers, disease progression markers, novel therapeutic targets, and novel tools. Examples are given related to DNA mutation, copy number variation, messenger RNA, and microRNA expression along with methylation profiling.

SomVarIUS: somatic variant identification from unpaired tissue samples

Motivation: Somatic variant calling typically requires paired tumor-normal tissue samples. Yet, paired normal tissues are not always available in clinical settings or for archival samples.

Results: We present SomVarIUS, a computational method for detecting somatic variants using high throughput sequencing data from unpaired tissue samples. We evaluate the performance of the method using genomic data from synthetic and real tumor samples. SomVarIUS identifies somatic variants in exome-seq data of  ∼150 ×  coverage with at least 67.7% precision and 64.6% recall rates, when compared with paired-tissue somatic variant calls in real tumor samples. We demonstrate the utility of SomVarIUS by identifying somatic mutations in formalin-fixed samples, and tracking clonal dynamics of oncogenic mutations in targeted deep sequencing data from pre- and post-treatment leukemia samples.

Availability and implementation: SomVarIUS is written in Python 2.7 and available at http://www.sjdlab.org/resources/

Contact: subhajyoti.de@ucdenver.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection

The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.