Sarek: A portable workflow for whole-genome sequencing analysis of germline and somatic variants

Characterization of the genomic landscape of canine diffuse large B-cell lymphoma reveals recurrent H3K27M mutations linked to progression-free survival

Diffuse large B-cell lymphoma (DLBCL) is an aggressive hematopoietic neoplasm that affects humans as well as dogs. While previous studies on canine DLBCL (cDLBCL) have significantly advanced our understanding of the disease, the majority of this research has relied on whole-exome sequencing, which is limited in its ability to detect copy number aberrations and other genomic changes beyond coding regions. Furthermore, many of these studies lack sufficient clinical follow-up data, making it difficult to draw meaningful associations between genetic variants and patient outcomes. Our study aimed to characterize the mutational landscape of cDLBCL using whole-genome sequencing of matched tumor-normal samples obtained from a cohort of 43 dogs previously enrolled in a clinical trial for which longitudinal follow-up was available. We focused on identifying genes that were significantly or recurrently mutated with coding point mutations, copy number aberrations, and their associations with patient outcomes. We identified 26 recurrently mutated genes, 18 copy number gains, and 8 copy number losses. Consistent with prior studies, the most commonly mutated genes included TRAF3, FBXW7, POT1, TP53, SETD2, DDX3X and TBL1XR1. The most prominent copy number gain occurred on chromosome 13, overlapping key oncogenes such as MYC and KIT, while the most frequent deletion was a focal loss on chromosome 26, encompassing IGL, PRAME, GNAZ, RAB36, RSPH14, and ZNF280B. Notably, our set of recurrently mutated genes was significantly enriched with genes involved in epigenetic regulation. In particular, we identified hotspot mutations in two histone genes, H3C8, and LOC119877878, resulting in H3K27M alterations predicted to dysregulate gene expression. Finally, a survival analysis revealed that H3K27M mutations in H3C8 were associated with increased hazard ratios for progression-free survival. No copy number aberrations were associated with survival. These findings underscore the critical role of epigenetic dysregulation in cDLBCL and affirm the dog as a relevant large animal model for interrogating the biological activity of novel histone-modifying treatment strategies.

CaClust: linking genotype to transcriptional heterogeneity of follicular lymphoma using BCR and exomic variants

Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor sequencing data to infer clone genotypes, cell-to-clone mapping, and single-cell genotyping. CaClust outperforms a state-of-the-art model on simulated and patient data. In-depth analyses of single cells from four samples showcase effects of driver mutations, follicular lymphoma evolution, possible therapeutic targets, and single-cell genotyping that agrees with an independent targeted resequencing experiment.

Diagnostic challenges in complicated case of glioblastoma

Glioblastoma is the commonest primary malignant brain tumor, with a very poor prognosis and short overall survival. It is characterized by its high intra- and intertumoral heterogeneity, in terms of both the level of single-nucleotide variants, copy number alterations, and aneuploidy. Therefore, routine diagnosis can be challenging in some cases. We present a complicated case of glioblastoma, which was characterized with five cytogenomic methods: interphase fluorescence in situ hybridization, multiplex ligation-dependent probe amplification, comparative genomic hybridization array and single-nucleotide polymorphism, targeted gene panel, and whole-genome sequencing. These cytogenomic methods revealed classical findings associated with glioblastoma, such as a lack of IDH and TERT mutations, gain of chromosome 7, and loss of chromosome 10. At least three pathological clones were identified, including one with whole-genome duplication, and one with loss of 1p and suspected loss of 19q. Deletion and mutation of the TP53 gene were detected with numerous breakends on 17p and 20q. Based on these findings, we recommend a combined approach to the diagnosis of glioblastoma involving the detection of copy number alterations, mutations, and aneuploidy. The choice of the best combination of methods is based on cost, time required, staff expertise, and laboratory equipment. This integrated strategy could contribute directly to tangible improvements in the diagnosis, prognosis, and prediction of the therapeutic responses of patients with brain tumors.

Identification of potential inhibitors for drug-resistant EGFR mutations in non-small cell lung cancer using whole exome sequencing data

Epidermal growth factor receptor (EGFR) gene mutations are prevalent in about 50% of lung adenocarcinoma patients. Highly effective tyrosine kinase inhibitors (TKIs) targeting the EGFR protein have revolutionized treatment for the prevalent and aggressive lung malignancy. However, the emergence of new EGFR mutations and the rapid development of additional drug resistance mechanisms pose substantial challenge to the effective treatment of NSCLC. To investigate the underlying causes of drug resistance, we utilized next-generation sequencing data to analyse the genetic alterations in different tumor genomic states under the pressure of drug selection. This study involved a comprehensive analysis of whole exome sequencing data (WES) from NSCLC patients before and after treatment with afatinib and osimertinib with a goal to identify drug resistance mutations from the post-treatment WES data. We identified five EGFR single-point mutations (L718A, G724E, G724K, K745L, V851D) and one double mutation (T790M/L858R) associated with drug resistance. Through molecular docking, we observed that mutations, G724E, K745L, V851D, and T790M/L858R, have negatively affected the binding affinity with the FDA-approved drugs. Further, molecular dynamic simulations revealed the detrimental impact of these mutations on the binding efficacy. Finally, we conducted virtual screening against structurally similar compounds to afatinib and osimertinib and identified three compounds (CID 71496460, 73292362, and 73292545) that showed the potential to selectively inhibit EGFR despite the drug-resistance mutations. The WES-based study provides additional insight to understand the drug resistance mechanisms driven by tumor mutations and helps develop potential lead compounds to inhibit EGFR in the presence of drug resistance mutations.

NFTest: automated testing of Nextflow pipelines

MOTIVATION

The ongoing expansion in the volume of biomedical data has contributed to a growing complexity in the tools and technologies used in research with an increased reliance on complex workflows written in orchestration languages such as Nextflow to integrate algorithms into processing pipelines. The growing use of workflows involving various tools and algorithms has led to increased scrutiny of software development practices to avoid errors in individual tools and in the connections between them.

RESULTS

To facilitate test-driven development of Nextflow pipelines, we created NFTest, a framework for automated pipeline testing and validation with customizability options for Nextflow features. It is open-source, easy to initialize and use, and customizable to allow for testing of complex workflows with test success configurable through a broad range of assertions. NFTest simplifies the testing burden on developers by automating tests once defined and providing a flexible interface for running tests to validate workflows. This reduces the barrier to rigorous biomedical workflow testing and paves the way toward reducing computational errors in biomedicine.

AVAILABILITY AND IMPLEMENTATION

NFTest is an open-source Python framework under the GPLv2 license and is freely available at https://github.com/uclahs-cds/tool-NFTest. The call-sSNV Nextflow pipeline is available at: https://github.com/uclahs-cds/pipeline-call-sSNV.

Divergent HLA variations and heterogeneous expression but recurrent HLA loss-of- heterozygosity and common HLA-B and TAP transcriptional silencing across advanced pediatric solid cancers

The human leukocyte antigen (HLA) system is a major factor controlling cancer immunosurveillance and response to immunotherapy, yet its status in pediatric cancers remains fragmentary. We determined high-confidence HLA genotypes in 576 children, adolescents and young adults with recurrent/refractory solid tumors from the MOSCATO-01 and MAPPYACTS trials, using normal and tumor whole exome and RNA sequencing data and benchmarked algorithms. There was no evidence for narrowed HLA allelic diversity but discordant homozygosity and allele frequencies across tumor types and subtypes, such as in embryonal and alveolar rhabdomyosarcoma, neuroblastoma MYCN and 11q subtypes, and high-grade glioma, and several alleles may represent protective or susceptibility factors to specific pediatric solid cancers. There was a paucity of somatic mutations in HLA and antigen processing and presentation (APP) genes in most tumors, except in cases with mismatch repair deficiency or genetic instability. The prevalence of loss-of-heterozygosity (LOH) ranged from 5.9 to 7.7% in HLA class I and 8.0 to 16.7% in HLA class II genes, but was widely increased in osteosarcoma and glioblastoma (~15-25%), and for DRB1-DQA1-DQB1 in Ewing sarcoma (~23-28%) and low-grade glioma (~33-50%). HLA class I and HLA-DR antigen expression was assessed in 194 tumors and 44 patient-derived xenografts (PDXs) by immunochemistry, and class I and APP transcript levels quantified in PDXs by RT-qPCR. We confirmed that HLA class I antigen expression is heterogeneous in advanced pediatric solid tumors, with class I loss commonly associated with the transcriptional downregulation of HLA-B and transporter associated with antigen processing (TAP) genes, whereas class II antigen expression is scarce on tumor cells and occurs on immune infiltrating cells. Patients with tumors expressing sufficient HLA class I and TAP levels such as some glioma, osteosarcoma, Ewing sarcoma and non-rhabdomyosarcoma soft-tissue sarcoma cases may more likely benefit from T cell-based approaches, whereas strategies to upregulate HLA expression, to expand the immunopeptidome, and to target TAP-independent epitopes or possibly LOH might provide novel therapeutic opportunities in others. The consequences of HLA class II expression by immune cells remain to be established. Immunogenetic profiling should be implemented in routine to inform immunotherapy trials for precision medicine of pediatric cancers.

The Swedish childhood tumor biobank: systematic collection and molecular characterization of all pediatric CNS and other solid tumors in Sweden

The Swedish Childhood Tumor Biobank (BTB) is a nonprofit national infrastructure for collecting tissue samples and genomic data from pediatric patients diagnosed with central nervous system (CNS) and other solid tumors. The BTB is built on a multidisciplinary network established to provide the scientific community with standardized biospecimens and genomic data, thereby improving knowledge of the biology, treatment and outcome of childhood tumors. As of 2022, over 1100 fresh-frozen tumor samples are available for researchers. We present the workflow of the BTB from sample collection and processing to the generation of genomic data and services offered. To determine the research and clinical utility of the data, we performed bioinformatics analyses on next-generation sequencing (NGS) data obtained from a subset of 82 brain tumors and patient blood-derived DNA combined with methylation profiling to enhance the diagnostic accuracy and identified germline and somatic alterations with potential biological or clinical significance. The BTB procedures for collection, processing, sequencing, and bioinformatics deliver high-quality data. We observed that the findings could impact patient management by confirming or clarifying the diagnosis in 79 of the 82 tumors and detecting known or likely driver mutations in 68 of 79 patients. In addition to revealing known mutations in a broad spectrum of genes implicated in pediatric cancer, we discovered numerous alterations that may represent novel driver events and specific tumor entities. In summary, these examples reveal the power of NGS to identify a wide number of actionable gene alterations. Making the power of NGS available in healthcare is a challenging task requiring the integration of the work of clinical specialists and cancer biologists; this approach requires a dedicated infrastructure, as exemplified here by the BTB.

GenomeChronicler: The Personal Genome Project UK Genomic Report Generator Pipeline

In recent years, there has been a significant increase in whole genome sequencing data of individual genomes produced by research projects as well as direct to consumer service providers. While many of these sources provide their users with an interpretation of the data, there is a lack of free, open tools for generating reports exploring the data in an easy to understand manner. GenomeChronicler was developed as part of the Personal Genome Project UK (PGP-UK) to address this need. PGP-UK provides genomic, transcriptomic, epigenomic and self-reported phenotypic data under an open-access model with full ethical approval. As a result, the reports generated by GenomeChronicler are intended for research purposes only and include information relating to potentially beneficial and potentially harmful variants, but without clinical curation. GenomeChronicler can be used with data from whole genome or whole exome sequencing, producing a genome report containing information on variant statistics, ancestry and known associated phenotypic traits. Example reports are available from the PGP-UK data page (personalgenomes.org.uk/data). The objective of this method is to leverage existing resources to find known phenotypes associated with the genotypes detected in each sample. The provided trait data is based primarily upon information available in SNPedia, but also collates data from ClinVar, GETevidence, and gnomAD to provide additional details on potential health implications, presence of genotype in other PGP participants and population frequency of each genotype. The analysis can be run in a self-contained environment without requiring internet access, making it a good choice for cases where privacy is essential or desired: any third party project can embed GenomeChronicler within their off-line safe-haven environments. GenomeChronicler can be run for one sample at a time, or in parallel making use of the Nextflow workflow manager. The source code is available from GitHub (https://github.com/PGP-UK/GenomeChronicler), container recipes are available for Docker and Singularity, as well as a pre-built container from SingularityHub (https://singularity-hub.org/collections/3664) enabling easy deployment in a variety of settings. Users without access to computational resources to run GenomeChronicler can access the software from the Lifebit CloudOS platform (https://lifebit.ai/cloudos) enabling the production of reports and variant calls from raw sequencing data in a scalable fashion.