ClinGen Variant Curation Interface: a variant classification platform for the application of evidence criteria from ACMG/AMP guidelines

BACKGROUND

Identification of clinically significant genetic alterations involved in human disease has been dramatically accelerated by developments in next-generation sequencing technologies. However, the infrastructure and accessible comprehensive curation tools necessary for analyzing an individual patient genome and interpreting genetic variants to inform healthcare management have been lacking.

RESULTS

Here we present the ClinGen Variant Curation Interface (VCI), a global open-source variant classification platform for supporting the application of evidence criteria and classification of variants based on the ACMG/AMP variant classification guidelines. The VCI is among a suite of tools developed by the NIH-funded Clinical Genome Resource (ClinGen) Consortium and supports an FDA-recognized human variant curation process. Essential to this is the ability to enable collaboration and peer review across ClinGen Expert Panels supporting users in comprehensively identifying, annotating, and sharing relevant evidence while making variant pathogenicity assertions. To facilitate evidence-based improvements in human variant classification, the VCI is publicly available to the genomics community. Navigation workflows support users providing guidance to comprehensively apply the ACMG/AMP evidence criteria and document provenance for asserting variant classifications.

CONCLUSIONS

The VCI offers a central platform for clinical variant classification that fills a gap in the learning healthcare system, facilitates widespread adoption of standards for clinical curation, and is available at https://curation.clinicalgenome.org.

Comprehensive Multi-omics Analysis Reveals Mitochondrial Stress as a Central Biological Hub for Spaceflight Impact

Spaceflight is known to impose changes on human physiology with unknown molecular etiologies. To reveal these causes, we used a multi-omics, systems biology analytical approach using biomedical profiles from fifty-nine astronauts and data from NASA's GeneLab derived from hundreds of samples flown in space to determine transcriptomic, proteomic, metabolomic, and epigenetic responses to spaceflight. Overall pathway analyses on the multi-omics datasets showed significant enrichment for mitochondrial processes, as well as innate immunity, chronic inflammation, cell cycle, circadian rhythm, and olfactory functions. Importantly, NASA's Twin Study provided a platform to confirm several of our principal findings. Evidence of altered mitochondrial function and DNA damage was also found in the urine and blood metabolic data compiled from the astronaut cohort and NASA Twin Study data, indicating mitochondrial stress as a consistent phenotype of spaceflight.

NASA GeneLab RNA-seq consensus pipeline: standardized processing of short-read RNA-seq data

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility, and reusability of pipeline data; to provide a template for data processing of future spaceflight-relevant datasets; and to encourage cross-analysis of data from other databases with the data available in GeneLab.

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Analysis of omics data from different spaceflight studies presents unique challenges

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A standardized pipeline for RNA-seq analysis eliminates data processing variation

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The GeneLab RNA-seq pipeline includes QC, trimming, mapping, quantification, and DGE

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Space-relevant data processed with this pipeline are available at genelab.nasa.gov

Clinical Accuracy and Impact of Plasma Cell-Free DNA Fungal PCR Panel for Non-Invasive Diagnosis of Fungal Infection

BACKGROUND

Invasive fungal infection (IFI) is a growing cause of morbidity and mortality in oncology and transplant patients. Diagnosis of IFI is often delayed due to need for invasive biopsy and low sensitivity of conventional diagnostic methods. Fungal cell-free DNA (cfDNA) detection in plasma is a novel testing modality for the non-invasive diagnosis of IFI.

METHODS

A novel bioinformatic pipeline was created to interrogate fungal genomes and identify multicopy sequences for cfDNA PCR targeting. A real-time PCR panel was developed for 12 genera and species most commonly causing IFI. Sensitivity and specificity of the fungal PCR panel were determined using plasma samples from patients with IFI and non-IFI controls. Clinical impact of fungal PCR panel was evaluated prospectively based on the treating team's interpretation of the results.

RESULTS

Overall, the sensitivity and specificity were 56.5% (65/115, 95% confidence interval [CI], 47.4%-65.2%) and 99.5% (2064/2075; 95% CI, 99.0%-99.7%), respectively. In the subset of patients with an optimized plasma volume (2mL), sensitivity was 69.6% (48/69; 95% CI, 57.9%-79.2%). Sensitivity was 91.7% (11/12; 95% CI, 62.5%-100%) for detection of Mucorales agents, 56.3% (9/16; 95% CI, 33.2%-76.9%) for Aspergillus species, and 84.6% (11/13; 95% CI, 56.5%-96.9%) for Candida albicans. In a prospective evaluation of 226 patients with suspected IFI, cfDNA testing was positive in 47 (20.8%) patients and resulted in a positive impact on clinical management in 20/47 (42.6%).

CONCLUSIONS

The fungal cfDNA PCR panel offers a non-invasive approach to early diagnosis of IFI, providing actionable results for personalized care.

Increasing Clinical Trial Accrual via Automated Matching of Biomarker Criteria

Successful implementation of precision oncology requires both the deployment of nucleic acid sequencing panels to identify clinically actionable biomarkers, and the efficient screening of patient biomarker eligibility to on-going clinical trials and therapies. This process is typically performed manually by biocurators, geneticists, pathologists, and oncologists; however, this is a time-intensive, and inconsistent process amongst healthcare providers. We present the development of a feature matching algorithmic pipeline that identifies patients who meet eligibility criteria of precision medicine clinical trials via genetic biomarkers and apply it to patients undergoing treatment at the Stanford Cancer Center. This study demonstrates, through our patient eligibility screening algorithm that leverages clinical sequencing derived biomarkers with precision medicine clinical trials, the successful use of an automated algorithmic pipeline as a feasible, accurate and effective alternative to the traditional manual clinical trial curation.

Gene-specific criteria for PTEN variant curation: Recommendations from the ClinGen PTEN Expert Panel

The ClinGen PTEN Expert Panel was organized by the ClinGen Hereditary Cancer Clinical Domain Working Group to assemble clinicians, researchers, and molecular diagnosticians with PTEN expertise to develop specifications to the 2015 ACMG/AMP Sequence Variant Interpretation Guidelines for PTEN variant interpretation. We describe finalized PTEN-specific variant classification criteria and outcomes from pilot testing of 42 variants with benign/likely benign (BEN/LBEN), pathogenic/likely pathogenic (PATH/LPATH), uncertain significance (VUS), and conflicting (CONF) ClinVar assertions. Utilizing these rules, classifications concordant with ClinVar assertions were achieved for 14/15 (93.3%) BEN/LBEN and 16/16 (100%) PATH/LPATH ClinVar consensus variants for an overall concordance of 96.8% (30/31). The variant where agreement was not reached was a synonymous variant near a splice donor with noncanonical sequence for which in silico models cannot predict the native site. Applying these rules to six VUS and five CONF variants, adding shared internal laboratory data enabled one VUS to be classified as LBEN and two CONF variants to be as classified as PATH and LPATH. This study highlights the benefit of gene-specific criteria and the value of sharing internal laboratory data for variant interpretation. Our PTEN-specific criteria and expertly reviewed assertions should prove helpful for laboratories and others curating PTEN variants.

Structural Variation Detection by Proximity Ligation from Formalin-Fixed, Paraffin-Embedded Tumor Tissue

The clinical management and therapy of many solid tumor malignancies depends on detection of medically actionable or diagnostically relevant genetic variation. However, a principal challenge for genetic assays from tumors is the fragmented and chemically damaged state of DNA in formalin-fixed, paraffin-embedded (FFPE) samples. From highly fragmented DNA and RNA there is no current technology for generating long-range DNA sequence data as is required to detect genomic structural variation or long-range genotype phasing. We have developed a high-throughput chromosome conformation capture approach for FFPE samples that we call Fix-C, which is similar in concept to Hi-C. Fix-C enables structural variation detection from archival FFPE samples. This method was applied to 15 clinical adenocarcinoma- and sarcoma-positive control specimens spanning a broad range of tumor purities. In this panel, Fix-C analysis achieves a 90% concordance rate with fluorescence in situ hybridization assays, the current clinical gold standard. In addition, novel structural variation undetected by other methods could be identified, and long-range chromatin configuration information recovered from these FFPE samples harboring highly degraded DNA. This powerful approach will enable detailed resolution of global genome rearrangement events during cancer progression from FFPE material and will inform the development of targeted molecular diagnostic assays for patient care.

Detection and Surveillance of Bladder Cancer Using Urine Tumor DNA

Current regimens for the detection and surveillance of bladder cancer are invasive and have suboptimal sensitivity. Here, we present a novel high-throughput sequencing (HTS) method for detection of urine tumor DNA (utDNA) called utDNA CAPP-Seq (uCAPP-Seq) and apply it to 67 healthy adults and 118 patients with early-stage bladder cancer who had urine collected either prior to treatment or during surveillance. Using this targeted sequencing approach, we detected a median of 6 mutations per patient with bladder cancer and observed surprisingly frequent mutations of the PLEKHS1 promoter (46%), suggesting these mutations represent a useful biomarker for detection of bladder cancer. We detected utDNA pretreatment in 93% of cases using a tumor mutation-informed approach and in 84% when blinded to tumor mutation status, with 96% to 100% specificity. In the surveillance setting, we detected utDNA in 91% of patients who ultimately recurred, with utDNA detection preceding clinical progression in 92% of cases. uCAPP-Seq outperformed a commonly used ancillary test (UroVysion, P = 0.02) and cytology and cystoscopy combined (P ≤ 0.006), detecting 100% of bladder cancer cases detected by cytology and 82% that cytology missed. Our results indicate that uCAPP-Seq is a promising approach for early detection and surveillance of bladder cancer. SIGNIFICANCE: This study shows that utDNA can be detected using HTS with high sensitivity and specificity in patients with early-stage bladder cancer and during post-treatment surveillance, significantly outperforming standard diagnostic modalities and facilitating noninvasive detection, genotyping, and monitoring.This article is highlighted in the In This Issue feature, p. 453.

Identification of a Novel Somatic Mutation Leading to Allele Dropout for EGFR L858R Genotyping in Non-Small Cell Lung Cancer

OBJECTIVE

While PCR-based genotyping methods abound in molecular testing for lung cancer therapy, these approaches may not provide the robust sensitivity to detect accurate genotypes in a variable cancer genomic background.

METHODS

Here, we describe a study of a clinical tumor specimen containing a novel somatic single nucleotide variant that caused allele drop-out in EGFR L858R genotyping, resulting in a false-negative interpretation and impacting patient clinical management.

RESULTS

We demonstrate that a subsequent unbiased next-generation sequencing approach correctly identified the driver mutation, and therefore may be more reliable for somatic variant detection.

CONCLUSIONS

These findings magnify the potential pitfalls of PCR amplification-based approaches and stress the importance of unbiased and sensitive molecular testing strategies for therapeutic marker detection as molecular testing becomes the standard for determining clinical management of cancer patients.

Transcriptome sequencing from diverse human populations reveals differentiated regulatory architecture

Large-scale sequencing efforts have documented extensive genetic variation within the human genome. However, our understanding of the origins, global distribution, and functional consequences of this variation is far from complete. While regulatory variation influencing gene expression has been studied within a handful of populations, the breadth of transcriptome differences across diverse human populations has not been systematically analyzed. To better understand the spectrum of gene expression variation, alternative splicing, and the population genetics of regulatory variation in humans, we have sequenced the genomes, exomes, and transcriptomes of EBV transformed lymphoblastoid cell lines derived from 45 individuals in the Human Genome Diversity Panel (HGDP). The populations sampled span the geographic breadth of human migration history and include Namibian San, Mbuti Pygmies of the Democratic Republic of Congo, Algerian Mozabites, Pathan of Pakistan, Cambodians of East Asia, Yakut of Siberia, and Mayans of Mexico. We discover that approximately 25.0% of the variation in gene expression found amongst individuals can be attributed to population differences. However, we find few genes that are systematically differentially expressed among populations. Of this population-specific variation, 75.5% is due to expression rather than splicing variability, and we find few genes with strong evidence for differential splicing across populations. Allelic expression analyses indicate that previously mapped common regulatory variants identified in eight populations from the International Haplotype Map Phase 3 project have similar effects in our seven sampled HGDP populations, suggesting that the cellular effects of common variants are shared across diverse populations. Together, these results provide a resource for studies analyzing functional differences across populations by estimating the degree of shared gene expression, alternative splicing, and regulatory genetics across populations from the broadest points of human migration history yet sampled.

Previous gene expression studies have identified factors influencing population-level variation in gene regulation. However, these efforts have been limited to a small set of well-studied populations. By leveraging the high resolution of RNA sequencing and broad population sampling, we survey the landscape of transcriptome variation across a globally distributed set of seven populations that span a breadth of human genetic variation and major dispersal events. We assess differences in gene expression, transcript structure, and regulatory variation. We find only 44 transcripts that show significant differences in expression, likely as a result of the small sample size, but we find that 25% of the variance in gene expression is due to population differences. This is a larger fraction than previously observed, and it is likely due to the greater breadth of human diversity assayed in this study. We also find that population-specific variance is mostly due to transcription variability rather than the configuration of expressed gene products. Additionally, known common regulatory variants have similar effects across populations including those we study here. These data and results serve as a resource cataloging the wide array of gene expression regulation affecting population variation among diverse groups, improving our understanding of transcriptional diversity.