Interpretation Of Next-generation Sequencing Results Research Articles

Fragmentation assessment of FFPE DNA helps in evaluating NGS library complexity and interpretation of NGS results

Formalin-fixed paraffin-embedded (FFPE) tissue remains the most common source for DNA extraction from human tissue both in research and routine clinical practice. FFPE DNA can be considerably fragmented, and the amount of DNA measured in nanograms may not represent the amount of amplifiable DNA available for next-generation sequencing (NGS). Two samples with similar input DNA amounts in nanograms can yield NGS analyses of considerably different quality. Nevertheless, many protocols for NGS library preparation from FFPE DNA describe input DNA in nanograms without indication of a minimum requirement of amplifiable genome equivalent DNA.An important NGS quality metric is the library complexity, reflecting the number of DNA fragments from the original specimen represented in the final library. Aiming to illustrate the relationship between DNA fragmentation degree and library complexity, we assessed the fragmentation degree of 116 lung cancer FFPE DNA samples to calculate the amount of amplifiable input DNA used for library preparation. Mean unique coverage, coverage uniformity, and mean number of PCR duplicates with the same unique molecular identifier were used to evaluate library complexity.We showed that the amount of amplifiable input DNA predicted library complexity better than the input measured in nanograms. The frequent discrepancy between DNA amount in nanograms and the amount of amplifiable DNA indicate that the fragmentation degree should be considered when performing NGS of FFPE DNA. Importantly, the fragmentation assessment may help when interpreting NGS data and be a useful tool for evaluating library complexity in the absence of unique molecular identifiers.

Feasibility of a Decision Support Software to Aid in the Interpretation of Somatic Mutations Detected in Hematologic Malignancies

Introduction: Genomic characterization is critical for diagnosis, prognosis, and therapeutic decision-making in hematological malignancies. Due to the increasing use of next-generation sequencing (NGS) and evolving medical guidelines, routine clinical interpretation of NGS results is difficult and resource-intensive. Pathologists and oncologists must stay abreast of the latest guidelines (e.g., from WHO, NCCN, CAP/ASH), emerging evidence from clinical or preclinical studies, and new drug approvals in their region. To address these challenges, we explored the feasibility of using a cloud-based CE-IVD somatic variant interpretation tool, NAVIFY® Mutation Profiler*, to provide automated and accurate clinical content for somatic variants detected in 745 hematologic tumor samples from cBioPortal (Cerami et al. Cancer Discovery 2012, Gao et al. Sci Signal 2013). Methods: Whole exome mutation data of acute myeloid leukemia (AML), myeloproliferative neoplasm (MPN), chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), and multiple myeloma (MM) (Figure 1A) were converted to an individual Variant Call Format (VCF) file containing single nucleotide variants (SNVs) and insertion/deletions (indels) for each sample. The VCF files along with fusion data, which was only available for AML samples, were programmatically uploaded to NAVIFY Mutation Profiler through the API. As a proof-of-concept, a random 30% subset of the samples were analyzed with a beta version of the software (v2.0.0.22e0193) with access to preliminary curation content (Roche-2.18.0-VnV released April 3, 2020) that contained interpretations for hematological cancers. Using the curated knowledge base, NAVIFY Mutation Profiler automatically assigned tier classifications for variants based on consensus recommendations from AMP, ASCO, CAP, and ACMG (Li et al. JMD 2017). Results: Of the 12,254 somatic mutations assessed with NAVIFY Mutation Profiler, 791 were classified as variants of strong or potential clinical significance (Tier I or II) (Figure 1B). We found 53% of cases had a Tier I or II interpretation, with the highest rate in MPN (96%) and lowest in DLBCL (28%). Given that no copy number variants and limited fusion data were included, these numbers might be an underestimate of the percentage of cases with clinically significant variants. The most frequently mutated genes harboring Tier I or II variants were consistent with known genes with variants of diagnostic, prognostic or therapeutic value in hematological cancers (Taylor et al. Blood 2017). Since interpretations for MYD88 had not been released in the content version used, we manually re-classified MYD88 SNVs during the first review of the report in a specific cancer type (such as classifying MYD88 p.L265P as Tier IA in DLBCL and Tier IIC in CLL); the software then automatically reflected the new classifications in subsequent cases with the same variant in the same cancer type. We also compared tier classifications to a public knowledge base, OncoKB (Chakravarty et al. JCO PO 2017, Suehnholz et al. Blood 2019). In OncoKB, we only found information for AML. FLT3, IDH1, and IDH2, were classified with Level of Evidence 1 which corresponds to AMP Tier IA. NAVIFY Mutation Profiler also classified FLT3 mutations as Tier IA, but classified IDH1 and IDH2 as Tier IB since we used the European Union region for analysis, and ivosidenib and enasidenib are approved by the FDA but not the EMA for IDH1 and IDH2 mutated AML, respectively. OncoKB classified SRSF2, SF3B1, and U2AF1 as Level of Evidence 4, which were consistent with Tier IIC. Conclusions: NAVIFY Mutation Profiler can help prioritize NGS variants and streamline the clinical laboratory workflow, thereby reducing manual errors and maintaining consistency in variant interpretation across samples. While the tool provided an automated tier classification, it was possible to apply custom classifications that were propagated to later cases to reliably manage the knowledge. This study demonstrates the feasibility of using decision support software to automatically curate, annotate, interpret, and report somatic variants in hematological cancers identified by NGS. * This product has not been evaluated by the Food and Drug Administration and is not commercially available in the United States. Disclosures Yaung: Roche Sequencing Solutions, Inc.: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties. Pek:Roche Sequencing Solutions, Inc.: Current Employment. Niu:Roche Sequencing Solutions, Inc.: Current Employment, Current equity holder in publicly-traded company. Li:Roche Sequencing Solutions, Inc.: Current Employment, Current equity holder in publicly-traded company. Palma:Clovis Oncology: Current equity holder in publicly-traded company; Editas Medicine: Current equity holder in publicly-traded company; Exact Sciences: Current equity holder in publicly-traded company; Roche Sequencing Solutions, Inc.: Current Employment, Current equity holder in publicly-traded company; Johnson & Johnson: Current equity holder in publicly-traded company. Schmid:Roche Sequencing Solutions, Inc.: Current Employment, Current equity holder in publicly-traded company.

Clinical and Economic Value of Genetic Sequencing for Personalized Therapy in Non–small-cell Lung Cancer

Two recent studies examining the clinical and economic value of next-generation sequencing (NGS)-based diagnostic testing (multi-gene panel examining ≥ 30 genes) for non–small-cell lung cancer therapy compared with single gene ALK, EGFR testing to select therapy demonstrated statistically insignificant improvement in population-level overall survival and only a moderate incremental cost-effectiveness ratio associated with the NGS testing approach. The data, however, revealed a key practice gap: many patients with actionable mutations did not receive targeted therapies. This gap is attributed, in part, to limitations in the availability and interpretation of NGS results, sample processing constraints, limited access to targeted therapies, and lagging awareness of the rapidly evolving field of personalized medicine, all of which result in “clinical inertia,” (ie, suboptimal use of targeted therapy against an actionable driver alteration identified by NGS testing). Additional analysis estimated that cost-effectiveness would improve significantly if a higher percentage of patients received testing and if all patients who were eligible for targeted therapies received them. Strategies to address implementation barriers will help to realize the full value of NGS testing in cancer care.

Evolving Landscape of Molecular Prescreening Strategies for Oncology Early Clinical Trials.

Most academic precision oncology programs have been designed to facilitate enrollment of patients in early clinical trials with matched targeted agents. Over the last decade, major changes were seen both in the targetable molecular alteration landscape and in drug development trends. In this article, we describe how the Vall d’Hebron Institute of Oncology molecular prescreening program adapted to a dynamic model of biomarker-drug codevelopment. We started with a tumor-agnostic hotspot mutation panel plus in situ hybridization and immunohistochemistry of selected markers and subsequently transitioned to tumor-specific amplicon-based next-generation sequencing (NGS) tests together with custom copy number, fusion, and outlier gene expression panels. All assays are optimized for archived formalin-fixed paraffin-embedded tumor tissues without matched germline sequencing. In parallel, biomarker-matched trials evolved from a scenario of few targets and large populations (such as PI3K inhibitors in PIK3CA mutants) to a complex situation with many targets and small populations (such as multiple targetable fusion events). Recruitment rates in clinical trials with mandatory biomarkers decreased over the last 3 years. Molecular tumor board meetings proved critical to guide oncologists on emerging biomarkers for clinical testing and interpretation of NGS results. The substantial increase of immunotherapy trials had a major impact in target prioritization and guided clinical implementation of new markers, such as tumor mutational burden, with larger exon-based NGS assays and gene expression signatures to capture microenvironment infiltration patterns. This new multiomics era of precision oncology is expected to increase the opportunities for early clinical trial matching.

Clinical and neuroimaging phenotypes of genetic parkinsonism from infancy to adolescence.

Genetic early-onset parkinsonism presenting from infancy to adolescence (≤21 years old) is a clinically diverse syndrome often combined with other hyperkinetic movement disorders, neurological and imaging abnormalities. The syndrome is genetically heterogeneous, with many causative genes already known. With the increased use of next-generation sequencing in clinical practice, there have been novel and unexpected insights into phenotype-genotype correlations and the discovery of new disease-causing genes. It is now recognized that mutations in a single gene can give rise to a broad phenotypic spectrum and that, conversely different genetic disorders can manifest with a similar phenotype. Accurate phenotypic characterization remains an essential step in interpreting genetic findings in undiagnosed patients. However, in the past decade, there has been a marked expansion in knowledge about the number of both disease-causing genes and phenotypic spectrum of early-onset cases. Detailed knowledge of genetic disorders and their clinical expression is required for rational planning of genetic and molecular testing, as well as correct interpretation of next-generation sequencing results. In this review we examine the relevant literature of genetic parkinsonism with ≤21 years onset, extracting data on associated movement disorders as well as other neurological and imaging features, to delineate syndromic patterns associated with early-onset parkinsonism. Excluding PRKN (parkin) mutations, >90% of the presenting phenotypes have a complex or atypical presentation, with dystonia, abnormal cognition, pyramidal signs, neuropsychiatric disorders, abnormal imaging and abnormal eye movements being the most common features. Furthermore, several imaging features and extraneurological manifestations are relatively specific for certain disorders and are important diagnostic clues. From the currently available literature, the most commonly implicated causes of early-onset parkinsonism have been elucidated but diagnosis is still challenging in many cases. Mutations in ∼70 different genes have been associated with early-onset parkinsonism or may feature parkinsonism as part of their phenotypic spectrum. Most of the cases are caused by recessively inherited mutations, followed by dominant and X-linked mutations, and rarely by mitochondrially inherited mutations. In infantile-onset parkinsonism, the phenotype of hypokinetic-rigid syndrome is most commonly caused by disorders of monoamine synthesis. In childhood and juvenile-onset cases, common genotypes include PRKN, HTT, ATP13A2, ATP1A3, FBX07, PINK1 and PLA2G6 mutations. Moreover, Wilson's disease and mutations in the manganese transporter are potentially treatable conditions and should always be considered in the differential diagnosis in any patient with early-onset parkinsonism.

Collaboration of colorado cancer genetic counselors to integrate next generation sequencing panels into clinical practice.

The recent introduction of clinically available next generation sequencing (NGS) cancer panels has presented new challenges for genetic counselors. Determining which patients are appropriate for NGS panel testing is complex. Due to the large number of genes included in the NGS panels, thorough and appropriate pre-test counseling and interpretation of NGS results can be a time-consuming and difficult process. Many of the genes associated with increased cancer risk lack published clinical management guidelines and estimates of cancer risk for individuals with deleterious mutations. In order to efficiently and effectively review the clinical utility of NGS panels, Colorado cancer genetic counselors formed a working group to gain a better understanding of the genes included in NGS cancer panels. This publication reports on the approach of this group, the process used to evaluate a selected NGS panel, future directions for this collaboration, and ideas for other genetic counselors to form similar groups to efficiently evaluate new technologies and improve practice.