MutScan: fast detection and visualization of target mutations by scanning FASTQ data

Integrative genomics identifies SHPRH as a tumor suppressor gene in lung adenocarcinoma that regulates DNA damage response

BACKGROUND

Identification of driver mutations and development of targeted therapies has considerably improved outcomes for lung cancer patients. However, significant limitations remain with the lack of identified drivers in a large subset of patients. Here, we aimed to assess the genomic landscape of lung adenocarcinomas (LUADs) from individuals without a history of tobacco use to reveal new genetic drivers of lung cancer.

METHODS

Integrative genomic analyses combining whole-exome sequencing, copy number, and mutational information for 83 LUAD tumors was performed and validated using external datasets to identify genetic variants with a predicted functional consequence and assess association with clinical outcomes. LUAD cell lines with alteration of identified candidates were used to functionally characterize tumor suppressive potential using a conditional expression system both in vitro and in vivo.

RESULTS

We identified 21 genes with evidence of positive selection, including 12 novel candidates that have yet to be characterized in LUAD. In particular, SNF2 Histone Linker PHD RING Helicase (SHPRH) was identified due to its frequency of biallelic disruption and location within the familial susceptibility locus on chromosome arm 6q. We found that low SHPRH mRNA expression is associated with poor survival outcomes in LUAD patients. Furthermore, we showed that re-expression of SHPRH in LUAD cell lines with inactivating alterations for SHPRH reduces their in vitro colony formation and tumor burden in vivo. Finally, we explored the biological pathways associated SHPRH inactivation and found an association with the tolerance of LUAD cells to DNA damage.

CONCLUSIONS

These data suggest that SHPRH is a tumor suppressor gene in LUAD, whereby its expression is associated with more favorable patient outcomes, reduced tumor and mutational burden, and may serve as a predictor of response to DNA damage. Thus, further exploration into the role of SHPRH in LUAD development may make it a valuable biomarker for predicting LUAD risk and prognosis.

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

Development, Evaluation, and Implementation of a House-Made Targeted Next-Generation Sequencing Spoligotyping in a French Laboratory

Epidemiological studies investigating transmission chains of tuberculosis are undertaken worldwide to tackle its spread. CRISPR locus diversity, called spoligotyping, is a widely used genotyping assay for Mycobacterium tuberculosis complex (MTBC) characterization. Herein, we developed a house-made targeted next-generation sequencing (tNGS) spoligotyping, and compared its outputs with those of membrane-based spoligotyping. A total of 144 clinical MTBC strains were retrospectively selected to be representative of the local epidemiology. Data analysis of a training set allowed for the setting of “presence”/“absence” thresholds for each spacer to maximize the sensibility and specificity related to the membrane-based spoligotyping. The thresholds above, in which the spacer was considered present, were 50 read per millions for spacers 10 and 14, 20,000 for spacers 20, 21, and 31, and 1000 for the other spacers. The confirmation of these thresholds was performed using a validation set. The overall agreement on the training and validation sets was 97.5% and 93.8%, respectively. The discrepancies concerned six strains: Two for spacer 14, two for spacer 31, and two for spacer 32. The tNGS spoligotyping, whose thresholds were finely-tuned during a careful bioinformatics pipeline development process, appears be a technique that is reliable, inexpensive, free of handling errors, and automatable through automatic transfer into the laboratory computer system.

Hepatobiliary Tumor Organoids Reveal HLA Class I Neoantigen Landscape and Antitumoral Activity of Neoantigen Peptide Enhanced with Immune Checkpoint Inhibitors

Neoantigen-directed therapy lacks preclinical models recapitulating neoantigen characteristics of original tumors. It is urgent to develop a platform to assess T cell response for neoantigen screening. Here, immunogenic potential of neoantigen-peptides of tumor tissues and matched organoids (n = 27 pairs) are analyzed by Score tools with whole genome sequencing (WGS)-based human leukocyte antigen (HLA)-class-I algorithms. The comparisons between 9203 predicted neoantigen-peptides from 2449 mutations of tumor tissues and 9991 ones from 2637 mutations of matched organoids demonstrate that organoids preserved majority of genetic features, HLA alleles, and similar neoantigen landscape of original tumors. Higher neoantigen load is observed in tumors with early stage. Multiomics analysis combining WGS, RNA-seq, single-cell RNA-seq, mass spectrometry filters out 93 candidate neoantigen-peptides with strong immunogenic potential for functional validation in five organoids. Immunogenic peptides are defined by inducing increased CD107aCD137IFN-γ expressions and IFN-γ secretion of CD8 cells in flow cytometry and enzyme-linked immunosorbent assay assays. Nine immunogenic peptides shared by at least two individuals are validated, including peptide from TP53R90S . Organoid killing assay confirms the antitumor activity of validated immunogenic peptide-reactive CD8 cells, which is further enhanced in the presence of immune checkpoint inhibitors. The study characterizes HLA-class-I neoantigen landscape in hepatobiliary tumor, providing practical strategy with tumor organoid model for neoantigen-peptide identification in personalized immunotherapy.

KmerKeys: a web resource for searching indexed genome assemblies and variants

K-mers are short DNA sequences that are used for genome sequence analysis. Applications that use k-mers include genome assembly and alignment. However, the wider bioinformatic use of these short sequences has challenges related to the massive scale of genomic sequence data. A single human genome assembly has billions of k-mers. As a result, the computational requirements for analyzing k-mer information is enormous, particularly when involving complete genome assemblies. To address these issues, we developed a new indexing data structure based on a hash table tuned for the lookup of short sequence keys. This web application, referred to as KmerKeys, provides performant, rapid query speeds for cloud computation on genome assemblies. We enable fuzzy as well as exact sequence searches of assemblies. To enable robust and speedy performance, the website implements cache-friendly hash tables, memory mapping and massive parallel processing. Our method employs a scalable and efficient data structure that can be used to jointly index and search a large collection of human genome assembly information. One can include variant databases and their associated metadata such as the gnomAD population variant catalogue. This feature enables the incorporation of future genomic information into sequencing analysis. KmerKeys is freely accessible at https://kmerkeys.dgi-stanford.org.

CleanSeq: A Pipeline for Contamination Detection, Cleanup, and Mutation Verifications from Microbial Genome Sequencing Data

Contaminations frequently occur in bacterial cultures, which significantly affect the reproducibility and reliability of the results from whole-genome sequencing (WGS). Decontaminated WGS data with clean reads is the only desirable source for detecting possible variants correctly. Improvements in bioinformatics are essential to analyze the contaminated WGS dataset. Existing pipelines usually contain contamination detection, decontamination, and variant calling separately. The efficiency and results from existing pipelines fluctuate since distinctive computational models and parameters are applied. It is then promising to develop a bioinformatical tool containing functions to discriminate and remove contaminated reads and improve variant calling from clean reads. In this study, we established a Python-based pipeline named CleanSeq for automatic detection and removal of contaminating reads, analyzing possible genome variants with proper verifications via local re-alignments. The application and reproducibility are proven in either simulated, publicly available datasets or actual genome sequencing reads from our experimental evolution study in Escherichia coli. We successfully obtained decontaminated reads, called out all seven consistent mutations from the contaminated bacterial sample, and derived five colonies. Collectively, the results demonstrated that CleanSeq could effectively process the contaminated samples to achieve decontaminated reads, based on which reliable results (i.e., variant calling) could be obtained.

Truncated titin proteins and titin haploinsufficiency are targets for functional recovery in human cardiomyopathy due to TTN mutations

[Figure: see text].

A computational toolset for rapid identification of SARS-CoV-2, other viruses and microorganisms from sequencing data

In this paper, we present a toolset and related resources for rapid identification of viruses and microorganisms from short-read or long-read sequencing data. We present fastv as an ultra-fast tool to detect microbial sequences present in sequencing data, identify target microorganisms and visualize coverage of microbial genomes. This tool is based on the k-mer mapping and extension method. K-mer sets are generated by UniqueKMER, another tool provided in this toolset. UniqueKMER can generate complete sets of unique k-mers for each genome within a large set of viral or microbial genomes. For convenience, unique k-mers for microorganisms and common viruses that afflict humans have been generated and are provided with the tools. As a lightweight tool, fastv accepts FASTQ data as input and directly outputs the results in both HTML and JSON formats. Prior to the k-mer analysis, fastv automatically performs adapter trimming, quality pruning, base correction and other preprocessing to ensure the accuracy of k-mer analysis. Specifically, fastv provides built-in support for rapid severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) identification and typing. Experimental results showed that fastv achieved 100% sensitivity and 100% specificity for detecting SARS-CoV-2 from sequencing data; and can distinguish SARS-CoV-2 from SARS, Middle East respiratory syndrome and other coronaviruses. This toolset is available at: https://github.com/OpenGene/fastv.

Unique k-mer sequences for validating cancer-related substitution, insertion and deletion mutations

Cancer genome sequencing has led to important discoveries such as the identification of cancer genes. However, challenges remain in the analysis of cancer genome sequencing. One significant issue is that mutations identified by multiple variant callers are frequently discordant even when using the same genome sequencing data. For insertion and deletion mutations, oftentimes there is no agreement among different callers. Identifying somatic mutations involves read mapping and variant calling, a complicated process that uses many parameters and model tuning. To validate the identification of true mutations, we developed a method using k-mer sequences. First, we characterized the landscape of unique versus non-unique k-mers in the human genome. Second, we developed a software package, KmerVC, to validate the given somatic mutations from sequencing data. Our program validates the occurrence of a mutation based on statistically significant difference in frequency of k-mers with and without a mutation from matched normal and tumor sequences. Third, we tested our method on both simulated and cancer genome sequencing data. Counting k-mer involving mutations effectively validated true positive mutations including insertions and deletions across different individual samples in a reproducible manner. Thus, we demonstrated a straightforward approach for rapidly validating mutations from cancer genome sequencing data.

Distinct Myocardial Transcriptomic Profiles of Cardiomyopathies Stratified by the Mutant Genes

Cardiovascular diseases are the number one cause of morbidity and mortality worldwide, but the underlying molecular mechanisms remain not well understood. Cardiomyopathies are primary diseases of the heart muscle and contribute to high rates of heart failure and sudden cardiac deaths. Here, we distinguished four different genetic cardiomyopathies based on gene expression signatures. In this study, RNA-Sequencing was used to identify gene expression signatures in myocardial tissue of cardiomyopathy patients in comparison to non-failing human hearts. Therefore, expression differences between patients with specific affected genes, namely LMNA (lamin A/C), RBM20 (RNA binding motif protein 20), TTN (titin) and PKP2 (plakophilin 2) were investigated. We identified genotype-specific differences in regulated pathways, Gene Ontology (GO) terms as well as gene groups like secreted or regulatory proteins and potential candidate drug targets revealing specific molecular pathomechanisms for the four subtypes of genetic cardiomyopathies. Some regulated pathways are common between patients with mutations in RBM20 and TTN as the splice factor RBM20 targets amongst other genes TTN, leading to a similar response on pathway level, even though many differentially expressed genes (DEGs) still differ between both sample types. The myocardium of patients with mutations in LMNA is widely associated with upregulated genes/pathways involved in immune response, whereas mutations in PKP2 lead to a downregulation of genes of the extracellular matrix. Our results contribute to further understanding of the underlying molecular pathomechanisms aiming for novel and better treatment of genetic cardiomyopathies.

Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing

Analysis of circulating cell-free DNA (cfDNA) has opened new opportunities for characterizing tumour mutational landscapes with many applications in genomic-driven oncology. We developed a customized targeted cfDNA sequencing approach for breast cancer (BC) using unique molecular identifiers (UMIs) for error correction. Our assay, spanning a 284.5 kb target region, is combined with a novel freely-licensed bioinformatics pipeline that provides detection of low-frequency variants, and reliable identification of copy number variations (CNVs) directly from plasma DNA. We first evaluated our pipeline on reference samples. Then in a cohort of 35 BC patients our approach detected actionable driver and clonal variants at low variant frequency levels in cfDNA that were concordant (77%) with sequencing of primary and/or metastatic solid tumour sites. We also detected ERRB2 gene CNVs used for HER2 subtype classification with 80% precision compared to immunohistochemistry. Further, we evaluated fragmentation profiles of cfDNA in BC and observed distinct differences compared to data from healthy individuals. Our results show that the developed assay addresses the majority of tumour associated aberrations directly from plasma DNA, and thus may be used to elucidate genomic alterations in liquid biopsy studies.