VarScan2 analysis of de novo variants in monozygotic twins discordant for schizophrenia

Two genetic variants in the HIBCH and FTCDNL1 genes are associated with susceptibility to developmental dysplasia of the hips among the Han Chinese population of Southwest China

BACKGROUND

Developmental dysplasia of the hip (DDH) is a common cause of childhood disability, and the incidence of DDH shows significant familial aggregation. As the genetic factors of DDH remain unknown, the correlation between five candidate single nucleotide polymorphisms (SNPs) and DDH was evaluated in the Han Chinese population of Southwest China.

METHODS

A case‒control association study was conducted in 276 patients with DDH and 318 healthy controls. SNP genotyping in the case and control groups was performed by SNPshot and multiple PCR. SNPs were genotyped in the case and control groups by multiplex PCR. The relationship between DDH and candidate SNPs was evaluated using the χ2 test.

RESULTS

The genotype distributions of rs291412 in HIBCH and rs769956 in FTCDNL1 were different between the case and control groups (P < 0.05). After genetic model analysis, logistic regression analysis revealed that the C allele of rs291412 had a protective effect on DDH (OR = 0.605, P = 0.010) and that the G allele of rs769956 was a risk factor (OR = 2.939, P = 0.010).s.

CONCLUSION

These SNPs could be associated with susceptibility to DDH but larger population-based studies should confirm the current results.

De novo detection of somatic mutations in high-throughput single-cell profiling data sets

Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.

Colorectal Cancer Organoid-Stroma Biobank Allows Subtype-Specific Assessment of Individualized Therapy Responses

In colorectal cancers, the tumor microenvironment plays a key role in prognosis and therapy efficacy. Patient-derived tumor organoids (PDTO) show enormous potential for preclinical testing; however, cultured tumor cells lose important characteristics, including the consensus molecular subtypes (CMS). To better reflect the cellular heterogeneity, we established the colorectal cancer organoid-stroma biobank of matched PDTOs and cancer-associated fibroblasts (CAF) from 30 patients. Context-specific phenotyping showed that xenotransplantation or coculture with CAFs improves the transcriptomic fidelity and instructs subtype-specific stromal gene expression. Furthermore, functional profiling in coculture exposed CMS4-specific therapeutic resistance to gefitinib and SN-38 and prognostic expression signatures. Chemogenomic library screening identified patient- and therapy-dependent mechanisms of stromal resistance including MET as a common target. Our results demonstrate that colorectal cancer phenotypes are encrypted in the cancer epithelium in a plastic fashion that strongly depends on the context. Consequently, CAFs are essential for a faithful representation of molecular subtypes and therapy responses ex vivo.

SIGNIFICANCE

Systematic characterization of the organoid-stroma biobank provides a resource for context dependency in colorectal cancer. We demonstrate a colorectal cancer subtype memory of PDTOs that is independent of specific driver mutations. Our data underscore the importance of functional profiling in cocultures for improved preclinical testing and identification of stromal resistance mechanisms. This article is featured in Selected Articles from This Issue, p. 2109.

Advances in vaccine development for cancer prevention and treatment in Lynch Syndrome

Lynch Syndrome (LS) is one of the most common hereditary cancer syndromes, and is caused by mutations in one of the four DNA mismatch repair (MMR) genes, namely MLH1, MSH2, MSH6 and PMS2. Tumors developed by LS carriers display high levels of microsatellite instability, which leads to the accumulation of large numbers of mutations, among which frameshift insertion/deletions (indels) within microsatellite (MS) loci are the most common. As a result, MMR-deficient (MMRd) cells generate increased rates of tumor-specific neoantigens (neoAgs) that can be recognized by the immune system to activate cancer cell killing. In this context, LS is an ideal disease to leverage immune-interception strategies. Therefore, the identification of these neoAgs is an ongoing effort for the development of LS cancer preventive vaccines. In this review, we summarize the computational methods used for in silico neoAg prediction, including their challenges, and the experimental techniques used for in vitro validation of their immunogenicity. In addition, we outline results from past and on-going vaccine clinical trials and highlight avenues for improvement and future directions.

Rapid survey of de novo mutations in naturally growing tree species following the March 2011 disaster in Fukushima: The effect of low-dose-rate radiation

The impact of low-dose-rate radiation on genetics is largely unknown, particularly in natural environments. The Fukushima Dai-ich Nuclear Power Plant disaster resulted in the creation of contaminated natural lands. In this study, de novo mutations (DNMs) in germ line cells were surveyed from double-digest RADseq fragments in Japanese cedar and flowering cherry trees exposed to ambient dose rates ranging from 0.08 to 6.86 μGy h^-1. These two species are among the most widely cultivated Japanese gymnosperm and angiosperm trees for forestry and horticultural purpose, respectively. For Japanese flowering cherry, open crossings were performed to produce seedlings, and only two candidate DNMs were detected from uncontaminated area. For Japanese cedar, the haploid megagametophytes were used as next generation samples. The use of megagametophytes from open crossing for next generation mutation screening had many advantages such as reducing exposure to radiation in contaminated areas because artificial crossings are not needed and the ease of data analysis owing to the haploid nature of megagametophytes. A direct comparison of the nucleotide sequences of parents and megagametophytes revealed an average of 1.4 candidate DNMs per megagametophyte sample (range: 0-40) after filtering procedures were optimized based on the validation of DNMs via Sanger sequencing. There was no relationship between the observed mutations and the ambient dose rate in the growing area or the concentration of ¹³⁷Cs in cedar branches. The present results also suggest that mutation rates differ among lineages and that the growing environment has a relatively large influence on these mutation rates. These results suggested there was no significant increase in the mutation rate of the germplasm of Japanese cedar and flowering cherry trees growing in the contaminated areas.

Synthetic enforcement of STING signaling in cancer cells appropriates the immune microenvironment for checkpoint inhibitor therapy

Immune checkpoint inhibitors (ICIs) enhance anticancer immunity by releasing repressive signals into tumor microenvironments (TMEs). To be effective, ICIs require preexisting immunologically "hot" niches for tumor antigen presentation and lymphocyte recruitment. How the mutational landscape of cancer cells shapes these immunological niches remains poorly defined. We found in human and murine colorectal cancer (CRC) models that the superior antitumor immune response of mismatch repair (MMR)-deficient CRC required tumor cell-intrinsic activation of cGAS-STING signaling triggered by genomic instability. Subsequently, we synthetically enforced STING signaling in CRC cells with intact MMR signaling using constitutively active STING variants. Even in MMR-proficient CRC, genetically encoded gain-of-function STING was sufficient to induce cancer cell-intrinsic interferon signaling, local activation of antigen-presenting cells, recruitment of effector lymphocytes, and sensitization of previously "cold" TMEs to ICI therapy in vivo. Thus, our results introduce a rational strategy for modulating cancer cell-intrinsic programs via engineered STING enforcement to sensitize resistant tumors to ICI responsiveness.

Mutagenic Characteristics of Six Heavy Metals in Escherichia coli: The Commonality and Specificity

The history of long-term environmental exposure to heavy metals can be recorded in the genome as sporadic and specific mutations. Variable environments introduce diverse and adaptive mutations to organisms. To reveal the information hidden in genomes about environmental exposure to heavy metals, we performed long-term mutation accumulation (MA) experiments with Escherichia coli, analyzed genomes from 36 populations across 1650 generations with 6 heavy metal exposure regimes (arsenic, cadmium, chromium, copper, nickel, and lead), and inferred metal-specific evolution modes at the genomic level. All heavy metals induced genetic mutations with a mean rate of 3.459 × 10-9 per nucleotide per generation. The mutational spectrum exhibited distinct signatures; however, heavy metals also shared common mutation signatures prominently associated with all cancer types. The mutated genes showed an average similarity of 54.4% within the same exposure regime, whereas only 38.8% between exposure regimes. In terms of biological insights, mutated genes were enriched to fundamental cellular processes such as metabolism, motility, and transport. Our study elucidates the mutagenic commonality and specificity of environmental heavy metals, which are highly specific at mutational features and locus, but conserved at gene and functional levels, and may play crucial roles in the convergence of adaptation to heavy metals.

A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers

Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics, and clinical information from 465 patients with primary TNBC. To facilitate reusing this unique dataset, we provided a detailed description of the dataset with special attention to data quality in this study. The multiomics data were generally of high quality, but a few sequencing data had quality issues and should be noted in subsequent data reuse. Furthermore, we reconduct data analyses with updated pipelines and the updated version of the human reference genome from hg19 to hg38. The updated profiles were in good concordance with those previously published in terms of gene quantification, variant calling, and copy number alteration. Additionally, we developed a user-friendly web-based database for convenient access and interactive exploration of the dataset. Our work will facilitate reusing the dataset, maximize the values of data and further accelerate cancer research.

Measurement(s)	RNA expression profiling • whole-exome sequencing (WES) • somatic mutations • copy number alterations (CNAs)
Technology Type(s)	RNA sequencing • DNA sequencing • OncoScan CNV assay
Factor Type(s)	Intervention or procedure
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Location	China

Biological Pathway-Derived TMB Robustly Predicts the Outcome of Immune Checkpoint Blockade Therapy

Although immune checkpoint blockade (ICB) therapies have achieved great progress, the patient response varies among cancers. In this study, we analyzed the potential genomic indicators contributing to ICB therapy response. The results showed that high tumor mutation burden (TMB) failed to predict response in anti-PD1 treated melanoma. SERPINB3 was the most significant response-related gene in melanoma and mutations in either SERPINB3 or PEG3 can serve as an independent risk factor in melanoma. Some recurrent mutations in CSMD3 were only in responders or non-responders, indicating their diverse impacts on patient response. Enrichment scores (ES) of gene mutations in 12 biological pathways were significantly higher in responders or non-responders. Next, the P-TMB calculated from genes in these pathways was significantly related to patient response with prediction AUC 0.74–0.82 in all collected datasets. In conclusion, our work provides new insights into the application of TMB in predicting patient response, which will benefit to immunotherapy research.

Comprehensive profiling of 1015 patients' exomes reveals genomic-clinical associations in colorectal cancer

The genetic basis of colorectal cancer (CRC) and its clinical associations remain poorly understood due to limited samples or targeted genes in current studies. Here, we perform ultradeep whole-exome sequencing on 1015 patients with CRC as part of the ChangKang Project. We identify 46 high-confident significantly mutated genes, 8 of which mutate in 14.9% of patients: LYST, DAPK1, CR2, KIF16B, NPIPB15, SYTL2, ZNF91, and KIAA0586. With an unsupervised clustering algorithm, we propose a subtyping strategy that classisfies CRC patients into four genomic subtypes with distinct clinical characteristics, including hypermutated, chromosome instability with high risk, chromosome instability with low risk, and genome stability. Analysis of immunogenicity uncover the association of immunogenicity reduction with genomic subtypes and poor prognosis in CRC. Moreover, we find that mitochondrial DNA copy number is an independent factor for predicting the survival outcome of CRCs. Overall, our results provide CRC-related molecular features for clinical practice and a valuable resource for translational research.

Identifying tumorigenic non-coding mutations through altered cis-regulation

Identification of non-coding mutations driving tumorigenesis requires alternative approaches to coding mutations. Enriched associations between mutated regulatory elements and altered cis-regulation in tumors are a promising approach to stratify candidate non-coding driver mutations. Here we provide a bioinformatics pipeline to mine data from the Cancer Genomic Commons (GDC) for such associations. The pipeline integrates RNA and whole-genome sequencing with genotyping data to reveal putative non-coding driver mutations by cancer type.

For complete information on the generation and use of this protocol, please refer to Cheng et al. (2021).

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Integration of expression, genotyping, and whole-genome sequencing modalities

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RNA-seq profiles resolved to gene-level allele-specific expression (ASE).

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Mutated cis-regulatory features associated with gene-level ASE by cancer type

Identification of Tumor Antigens and Immune Landscape in Glioblastoma for mRNA Vaccine Development

Background: Clinical benefits from standard therapies against glioblastoma (GBM) are limited in part due to the intrinsic radio- and chemo-resistance. As an essential part of tumor immunotherapy for adjunct, therapeutic tumor vaccines have been effective against multiple solid cancers, while their efficacy against GBM remains undefined. Therefore, this study aims to find the possible tumor antigens of GBM and identify the suitable population for cancer vaccination through immunophenotyping. Method: The genomic and responding clinical data of 169 GBM samples and five normal brain samples were obtained from The Cancer Genome Atlas (TCGA). The mRNA_seq data of 940 normal brain tissue were downloaded from Genotype-Tissue Expression (GTEx). Potential GBM mRNA antigens were screened out by differential expression, copy number variant (CNV), and mutation analysis. K-M survival and Cox analysis were carried out to investigate the prognostic association of potential tumor antigens. Tumor Immune Estimation Resource (TIMER) was used to explore the association between the antigens and tumor immune infiltrating cells (TIICs). Immunophenotyping of 169 samples was performed through consensus clustering based on the abundance of 22 kinds of immune cells. The characteristics of the tumor immune microenvironment (TIME) in each cluster were explored through single-sample gene set enrichment analysis based on 29 kinds of immune-related hallmarks and pathways. Weighted gene co-expression network analysis (WGCNA) was performed to cluster the genes related to immune subtypes. Finally, pathway enrichment analyses were performed to annotate the potential function of modules screened through WGCNA. Results: Two potential tumor antigens selected were significantly positively associated with the antigen-presenting immune cells (APCs) in GBM. Furthermore, the expression of antigens was verified at the protein level by Immunohistochemistry. Two robust immune subtypes, immune subtype 1 (IS1) and immune subtype 2 (IS2), representing immune status "immune inhibition" and "immune inflamed", respectively, had distinct clinical outcomes in GBM. Conclusion: ARPC1B and HK3 were potential mRNA antigens for developing GBM mRNA vaccination, and the patients in IS2 were considered the most suitable population for vaccination in GBM.

Identification of tumor antigens and immune subtypes in lower grade gliomas for mRNA vaccine development

BACKGROUND

As an important part of tumor immunotherapy for adjunct, therapeutic tumor vaccines have been effective against multiple solid cancers, while their efficacy against lower grade glioma (LGG) remains undefined. Immunophenotyping of tumors is an essential tool to evaluate the immune function of patients with immunodeficiency or autoimmunity. Therefore, this study aims to find the potential tumor antigen of LGG and identify the suitable population for cancer vaccination based on the immune landscape.

METHOD

The genomic and clinical data of 529 patients with LGG were obtained from TCGA, the mRNA_seq data of normal brain tissue were downloaded from GTEx. Differential expression gene and mutation analysis were performed to screen out potential antigens, K-M curves were carried out to investigate the correlation between the level of potential antigens and OS and DFS of patients. TIMER dataset was used to explore the correlation between genes and immune infiltrating cells. Immunophenotyping of 529 tumor samples was based on the single-sample gene sets enrichment analysis. Cibersort and Estimate algorithm were used to explore the tumor immune microenvironment characteristics in each immune subtype. Weighted gene co-expression network analysis (WGCNA) clustered immune-related genes and screened the hub genes, and pathway enrichment analyses were performed on the hub modules related to immune subtype in the WGCNA.

RESULTS

Selecting for the mutated, up-regulated, prognosis- and immune-related genes, four potential tumor antigens were identified in LGG. They were also significantly positively associated with the antigen-presenting immune cells (APCs). Three robust immune subtypes, IS1, IS2 and IS3, represented immune status "desert", "immune inhibition", and "inflamed" respectively, which might serve as a predictive parameter. Subsequently, clinicopathological features, including the codeletion status of 1p19q, IDH mutation status, tumor mutation burden, tumor stemness, etc., were significantly different among subtypes.

CONCLUSION

FCGBP, FLNC, TLR7, and CSF2RA were potential antigens for developing cancer vaccination, and the patients in IS3 were considered the most suitable for vaccination in LGG.

Unraveling the chaotic genomic landscape of primary and metastatic canine appendicular osteosarcoma with current sequencing technologies and bioinformatic approaches

Osteosarcoma is a rare disease in children but is one of the most common cancers in adult large breed dogs. The mutational landscape of both the primary and pulmonary metastatic tumor in two dogs with appendicular osteosarcoma (OSA) was comprehensively evaluated using an automated whole genome sequencing, exome and RNA-seq pipeline that was adapted for this study for use in dogs. Chromosomal lesions were the most common type of mutation. The mutational landscape varied substantially between dogs but the lesions within the same patient were similar. Copy number neutral loss of heterozygosity in mutant TP53 was the most significant driver mutation and involved a large region in the middle of chromosome 5. Canine and human OSA is characterized by loss of cell cycle checkpoint integrity and DNA damage response pathways. Mutational profiling of individual patients with canine OSA would be recommended prior to targeted therapy, given the heterogeneity seen in our study and previous studies.

Accuracy and efficiency of germline variant calling pipelines for human genome data

Advances in next-generation sequencing technology have enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data. However, there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN and DeepVariant) using the Genome in a Bottle Consortium, "synthetic-diploid" and simulated WGS datasets. DRAGEN and DeepVariant show better accuracy in SNP and indel calling, with no significant differences in their F1-score. DRAGEN platform offers accuracy, flexibility and a highly-efficient execution speed, and therefore superior performance in the analysis of WGS data on a large scale. The combination of DRAGEN and DeepVariant also suggests a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical applications.

Accuracy and efficiency of germline variant calling pipelines for human genome data

Advances in next-generation sequencing technology have enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data. However, there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN and DeepVariant) using the Genome in a Bottle Consortium, "synthetic-diploid" and simulated WGS datasets. DRAGEN and DeepVariant show better accuracy in SNP and indel calling, with no significant differences in their F1-score. DRAGEN platform offers accuracy, flexibility and a highly-efficient execution speed, and therefore superior performance in the analysis of WGS data on a large scale. The combination of DRAGEN and DeepVariant also suggests a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical applications.

Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays

CONTEXT.—

Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical laboratory setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories.

OBJECTIVE.—

To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipeline as a part of a new clinical NGS assay.

DATA SOURCES.—

This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation.

CONCLUSIONS.—

This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.

inCNV: An Integrated Analysis Tool for Copy Number Variation on Whole Exome Sequencing

The detection of copy number variations (CNVs) on whole-exome sequencing (WES) represents a cost-effective technique for the study of genetic variants. This approach, however, has encountered an obstacle with high false-positive rates due to biases from exome sequencing capture kits and GC contents. Although plenty of CNV detection tools have been developed, they do not perform well with all types of CNVs. In addition, most tools lack features of genetic annotation, CNV visualization, and flexible installation, requiring users to put much effort into CNV interpretation. Here, we present "inCNV," a web-based application that can accept multiple CNV-tool results, then integrate and prioritize them with user-friendly interfaces. This application helps users analyze the importance of called CNVs by generating CNV annotations from Ensembl, Database of Genomic Variants (DGV), ClinVar, and Online Mendelian Inheritance in Man (OMIM). Moreover, users can select and export CNVs of interest including their flanking sequences for primer design and experimental verification. We demonstrated how inCNV could help users filter and narrow down the called CNVs to a potentially novel CNV, a common CNV within a group of samples of the same disease, or a de novo CNV of a sample within the same family. Besides, we have provided in CNV as a docker image for ease of installation (https://github.com/saowwapark/inCNV).

GEAMP, a novel gastroesophageal junction carcinoma cell line derived from a malignant pleural effusion

Gastroesophageal junction (GEJ) cancer remains a clinically significant disease in Western countries due to its increasing incidence, which mirrors that of esophageal cancer, and poor prognosis. To develop novel and effective approaches for prevention, early detection, and treatment of patients with GEJ cancer, a better understanding of the mechanisms driving pathogenesis and malignant progression of this disease is required. These efforts have been limited by the small number of available cell lines and appropriate preclinical animal models for in vitro and in vivo studies. We have established and characterized a novel GEJ cancer cell line, GEAMP, derived from the malignant pleural effusion of a previously treated GEJ cancer patient. Comprehensive genetic analyses confirmed a clonal relationship between GEAMP cells and the primary tumor. Targeted next-generation sequencing identified 56 nonsynonymous alterations in 51 genes including TP53 and APC, which are commonly altered in GEJ cancer. In addition, multiple copy-number alterations were found including EGFR and K-RAS gene amplifications and loss of CDKN2A and CDKN2B. Histological examination of subcutaneous flank xenografts in nude and NOD-SCID mice showed a carcinoma with mixed squamous and glandular differentiation, suggesting GEAMP cells contain a subpopulation with multipotent potential. Finally, pharmacologic inhibition of the EGFR signaling pathway led to downregulation of key downstream kinases and inhibition of cell proliferation in vitro. Thus, GEAMP represents a valuable addition to the limited number of bona fide GEJ cancer cell lines.

A machine learning approach for somatic mutation discovery

Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality somatic mutation calls improved tumor mutation load-based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of cancer patients.

Engineering Genetic Predisposition in Human Neuroepithelial Stem Cells Recapitulates Medulloblastoma Tumorigenesis

Human neural stem cell cultures provide progenitor cells that are potential cells of origin for brain cancers. However, the extent to which genetic predisposition to tumor formation can be faithfully captured in stem cell lines is uncertain. Here, we evaluated neuroepithelial stem (NES) cells, representative of cerebellar progenitors. We transduced NES cells with MYCN, observing medulloblastoma upon orthotopic implantation in mice. Significantly, transcriptomes and patterns of DNA methylation from xenograft tumors were globally more representative of human medulloblastoma compared to a MYCN-driven genetically engineered mouse model. Orthotopic transplantation of NES cells generated from Gorlin syndrome patients, who are predisposed to medulloblastoma due to germline-mutated PTCH1, also generated medulloblastoma. We engineered candidate cooperating mutations in Gorlin NES cells, with mutation of DDX3X or loss of GSE1 both accelerating tumorigenesis. These findings demonstrate that human NES cells provide a potent experimental resource for dissecting genetic causation in medulloblastoma.

Genomic sequencing and editing revealed the GRM8 signaling pathway as potential therapeutic targets of squamous cell lung cancer

The study sought to explore novel genetic aberration driving squamous cell lung carcinoma (LUSC). The whole exome (WES), whole genome (WGS) and target region (TS) sequencings and CRISPR-Cas9 genome editing techniques were integrated to explore and validate novel targeting candidates from LUSC primary tumors and corresponding patient-derived xenografts (PDXs). Seven genes (FGFR2, GRM1,PIK3CG, PIK3CA,ZFHX4, CSMD3, GRM8) with high frequencies of both single nucleotide variants (SNVs) and copy number variants (CNVs), and two genes (CLDN1 and RIT1) only with CNVs were identified by bioinformatics analysis. The functions of these candidates were validated through CRISPR-Cas9 system in primary PDX cells. Furthermore, we focused on the genetic and functional analysis of Metabotropic glutamate receptor 8 (GRM8), whose transcriptional activation was elucidated to promote the survival of LUSC tumor cell through inhibiting cAMP pathway and activating MAPK pathway. The SNV identified in GRM8, A112G, activated downstream signaling pathway and induced cell proliferation, which could be reversed by cAMP stimulator and MEK inhibitor. In conclusion, the components of GRM8 signaling pathway could serve as potential targets of squamous cell lung cancer carrying GRM8 activating variants.