Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance.

BACKGROUND: Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome. RESULTS: We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data. CONCLUSIONS: The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

The Quartet Data Portal: integration of community-wide resources for multiomics quality control.

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling.

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.

Trends in the approval of cancer therapies by the FDA in the twenty-first century.

The cancer treatment landscape has changed dramatically since the turn of the century, resulting in substantial improvements in outcomes for patients. This Review summarizes trends in the approval of oncology therapeutic products by the United States Food and Drug Administration (FDA) from January 2000 to October 2022, based on a categorization of these products by their mechanism of action and primary target. Notably, the rate of oncology indication approvals has increased in this time, driven by approvals for targeted therapies, as has the rate of introduction of new therapeutic approaches. Kinase inhibitors are the dominant product class by number of approved products and indications, yet immune checkpoint inhibitors have the second most approvals despite not entering the market until 2011. Other trends include a slight increase in the share of approvals for biomarker-defined populations and the emergence of tumour-site-agnostic approvals. Finally, we consider the implications of the trends for the future of oncology therapeutic product development, including the impact of novel therapeutic approaches and technologies.

Analysis and therapeutic targeting of the IL-1R pathway in anaplastic large cell lymphoma.

Anaplastic large cell lymphoma (ALCL), a subgroup of mature T-cell neoplasms with an aggressive clinical course, is characterized by elevated expression of CD30 and anaplastic cytology. To achieve a comprehensive understanding of the molecular characteristics of ALCL pathology and to identify therapeutic vulnerabilities, we applied genome-wide CRISPR library screenings to both anaplastic lymphoma kinase positive (ALK+) and primary cutaneous (pC) ALK- ALCLs and identified an unexpected role of the interleukin-1R (IL-1R) inflammatory pathway in supporting the viability of pC ALK- ALCL. Importantly, this pathway is activated by IL-1α in an autocrine manner, which is essential for the induction and maintenance of protumorigenic inflammatory responses in pC-ALCL cell lines and primary cases. Hyperactivation of the IL-1R pathway is promoted by the A20 loss-of-function mutation in the pC-ALCL lines we analyze and is regulated by the nonproteolytic protein ubiquitination network. Furthermore, the IL-1R pathway promotes JAK-STAT3 signaling activation in ALCLs lacking STAT3 gain-of-function mutation or ALK translocation and enhances the sensitivity of JAK inhibitors in these tumors in vitro and in vivo. Finally, the JAK2/IRAK1 dual inhibitor, pacritinib, exhibited strong activities against pC ALK- ALCL, where the IL-1R pathway is hyperactivated in the cell line and xenograft mouse model. Thus, our studies revealed critical insights into the essential roles of the IL-1R pathway in pC-ALCL and provided opportunities for developing novel therapeutic strategies.

Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies.

BACKGROUND: The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples. RESULTS: We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy. CONCLUSIONS: A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods.

Personalized genome assembly for accurate cancer somatic mutation discovery using tumor-normal paired reference samples.

BACKGROUND: The use of a personalized haplotype-specific genome assembly, rather than an unrelated, mosaic genome like GRCh38, as a reference for detecting the full spectrum of somatic events from cancers has long been advocated but has never been explored in tumor-normal paired samples. Here, we provide the first demonstrated use of de novo assembled personalized genome as a reference for cancer mutation detection and quantifying the effects of the reference genomes on the accuracy of somatic mutation detection. RESULTS: We generate de novo assemblies of the first tumor-normal paired genomes, both nuclear and mitochondrial, derived from the same individual with triple negative breast cancer. The personalized genome was chromosomal scale, haplotype phased, and annotated. We demonstrate that it provides individual specific haplotypes for complex regions and medically relevant genes. We illustrate that the personalized genome reference not only improves read alignments for both short-read and long-read sequencing data but also ameliorates the detection accuracy of somatic SNVs and SVs. We identify the equivalent somatic mutation calls between two genome references and uncover novel somatic mutations only when personalized genome assembly is used as a reference. CONCLUSIONS: Our findings demonstrate that use of a personalized genome with individual-specific haplotypes is essential for accurate detection of the full spectrum of somatic mutations in the paired tumor-normal samples. The unique resource and methodology established in this study will be beneficial to the development of precision oncology medicine not only for breast cancer, but also for other cancers.

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample.

BACKGROUND: Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. RESULTS: In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. CONCLUSIONS: The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions.

The SEQC2 epigenomics quality control (EpiQC) study.

BACKGROUND: Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA's Epigenomics Quality Control Group. RESULTS: Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms. CONCLUSIONS: The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research.

Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study.

With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing.

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

Hepatic encephalopathy induced by Lenvatinib and anti-PD-1 mAb in a patient with advanced hepatocellular carcinoma: A case report.

Previous studies have reported that the combinational therapy of Lenvatinib and anti-programmed cell death-1 (PD-1) monoclonal antibody (mAb) produced a longer overall survival in patients with hepatocellular carcinoma (HCC). The current case report presented a a patient with HCC who had hepatic encephalopathy (HE) following treatment with Lenvatinib and anti-PD-1 mAb. The 42-year-old patient was diagnosed with stage IVa HCC accompanied with cirrhosis and Child-Pugh C. Computed tomography (CT) imaging revealed collateral circulation of the portal vein, causing significant varicose veins in the gastric fundus, mesenteric varices and colon edema. The patient was administered 12 mg Lenvatinib once daily combined with 240 mg anti-PD-1 mAb. After 3 days of treatment, he presented with a disorder of psychoneurosis and blood ammonia (248 µg/dl; normal levels, 40-80 µg/dl). A cranial CT scan exhibited no significant abnormalities. The patient rapidly progressed from grade 1 to grade 3 HE. Lenvatinib treatment was discontinued. After admission to the intensive care unit, the patient's blood ammonia level dropped to 132 µg/dl, after which he was discharged. It was concluded that the portal vein collateral circulation in the patient with HCC may have caused HE development whilst receiving Lenvatinib and anti-PD-1 mAb combinational therapy.

A multi-center cross-platform single-cell RNA sequencing reference dataset.

Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmark scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. We believe the datasets we describe here will provide a resource that meets this need by allowing evaluation of various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.

Chemopreventive effect of dieckol against 7,12-dimethylbenz(a)anthracene induced skin carcinogenesis model by modulatory influence on biochemical and antioxidant biomarkers.

Skin cancer is the commonly found type, which contributes to 40% of whole cancer incidences worldwide. Dieckol is an active compound occurs in the marine algae with many biological benefits. In this exploration, we intended to investigate the therapeutic potency of dieckol against the 7,12-dimethylbenz(a)anthracene (DMBA)-triggered skin carcinogenesis in mice. The skin cancer was stimulated to the animals via injecting the 25 µg of DMBA in 100 µL of acetone in shaved dorsal portion along with the 30 mg/kg of dieckol supplementation for 25 week. The antioxidant enzymes and phase-I and -II detoxifying enzymes in the test animals were inspected via standard protocols. Pro-inflammatory markers (IL-6, IL-1ß, and TNF-α) level was examined via ELISA kits and the expression of inflammatory molecular markers like p-NF-ƙB, IƙBα and p-IƙBα were studied through western blotting. The expression status of pro- and anti-apoptotic proteins (p53, Bax, Bcl-2, caspase-3, caspase-9, COX-2, TGF-ß1) was investigated via real-time polymerase chain reaction (RT-PCR). Our results revealed that the 30 mg/kg of dieckol supplementation noticeably regained the body and liver weight and also diminished the tumor incidence in the DMBA-incited animals. Dieckol treatment exhibited an enhanced antioxidants (SOD, CAT, GPx, and GSH) and reduced phase-I enzymes Cyt-p450 and Cyt-b5 in the DMBA-induced animals. Dieckol also diminished the pro-inflammatory modulators like IL-6, IL-1ß and TNF-α. Western blotting result evidenced that the dieckol was inhibited the IƙB/NF-ƙB signaling pathway. RT-PCR study proved the enhanced expression of pro-apoptotic protein (p53, Bax, caspase-3 and -9) in the dieckol treated animals. Histological study also confirmed the therapeutic benefits of Dieckol. Altogether with these findings, it was clear that the dieckol has appreciably allayed the DMBA activated skin tumorigenesis in the mice and it could be a promising agent to treat the human skin cancer in future.

A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples.

Comparing diverse single-cell RNA sequencing (scRNA-seq) datasets generated by different technologies and in different laboratories remains a major challenge. Here we address the need for guidance in choosing algorithms leading to accurate biological interpretations of varied data types acquired with different platforms. Using two well-characterized cellular reference samples (breast cancer cells and B cells), captured either separately or in mixtures, we compared different scRNA-seq platforms and several preprocessing, normalization and batch-effect correction methods at multiple centers. Although preprocessing and normalization contributed to variability in gene detection and cell classification, batch-effect correction was by far the most important factor in correctly classifying the cells. Moreover, scRNA-seq dataset characteristics (for example, sample and cellular heterogeneity and platform used) were critical in determining the optimal bioinformatic method. However, reproducibility across centers and platforms was high when appropriate bioinformatic methods were applied. Our findings offer practical guidance for optimizing platform and software selection when designing an scRNA-seq study.

A novel model of alternative NF-κB pathway activation in anaplastic large cell lymphoma.

Aberrant activation of NF-κB is the most striking oncogenic mechanism in B-cell lymphoma; however, its role in anaplastic large cell lymphomas (ALCL) has not been fully established and its activation mechanism(s) remain unclear. Using ALCL cell line models, we revealed the supporting roles for NFKB2 and the NIK pathway in some ALCL lines. To investigate the detailed activation mechanisms for this oncogenic pathway, we performed specifically designed alternative NF-κB reporter CRISPR screens followed by the RNA-seq analysis, which led us to identify STAT3 as the major mediator for NIK-dependent NF-κB activation in ALCL. Consistently, p-STAT3 level was correlated with NFKB2 nuclear accumulation in primary clinical samples. Mechanistically, we found that in NIK-positive ALK- ALCL cells, common JAK/STAT3 mutations promote transcriptional activity of STAT3 which directly regulates NFKB2 and CD30 expression. Endogenous expression of CD30 induces constitutive NF-κB activation through binding and degrading of TRAF3. In ALK+ ALCL, the CD30 pathway is blocked by the NPM-ALK oncoprotein, but STAT3 activity and resultant NFKB2 expression can still be induced by NPM-ALK, leading to minimal alternative NF-κB activation. Our data suggest combined NIK and JAK inhibitor therapy could benefit patients with NIK-positive ALK- ALCL carrying JAK/STAT3 somatic mutations.

Essential role of the linear ubiquitin chain assembly complex and TAK1 kinase in A20 mutant Hodgkin lymphoma.

More than 70% of Epstein-Barr virus (EBV)-negative Hodgkin lymphoma (HL) cases display inactivation of TNFAIP3 (A20), a ubiquitin-editing protein that regulates nonproteolytic protein ubiquitination, indicating the significance of protein ubiquitination in HL pathogenesis. However, the precise mechanistic roles of A20 and the ubiquitination system remain largely unknown in this disease. Here, we performed high-throughput CRISPR screening using a ubiquitin regulator-focused single-guide RNA library in HL lines carrying either wild-type or mutant A20. Our CRISPR screening highlights the essential oncogenic role of the linear ubiquitin chain assembly complex (LUBAC) in HL lines, which overlaps with A20 inactivation status. Mechanistically, LUBAC promotes IKK/NF-κB activity and NEMO linear ubiquitination in A20 mutant HL cells, which is required for prosurvival genes and immunosuppressive molecule expression. As a tumor suppressor, A20 directly inhibits IKK activation and HL cell survival via its C-terminal linear-ubiquitin binding ZF7. Clinically, LUBAC activity is consistently elevated in most primary HL cases, and this is correlated with high NF-κB activity and low A20 expression. To further understand the complete mechanism of NF-κB activation in A20 mutant HL, we performed a specifically designed CD83-based NF-κB CRISPR screen which led us to identify TAK1 kinase as a major mediator for NF-κB activation in cells dependent on LUBAC, where the LUBAC-A20 axis regulates TAK1 and IKK complex formation. Finally, TAK1 inhibitor Takinib shows promising activity against HL in vitro and in a xenograft mouse model. Altogether, these findings provide strong support that targeting LUBAC or TAK1 could be attractive therapeutic strategies in A20 mutant HL.