<p>Assessment of tumor mutation burden calculation from gene panel sequencing data</p>

Abstract The large variety of CNS tumor entities affecting children and adolescents, some of which are exceedingly rare, results in very diverging patient outcomes and renders accurate diagnosis challenging. To assess the diagnostic utility of routine DNA methylation-based CNS tumor classification and gene panel sequencing, the Molecular Neuropathology 2.0 study prospectively integrated these (epi-)genetic analyses with reference neuropathological diagnostics as an international trial for newly-diagnosed pediatric patients. In a four-year period, 1,215 patients with sufficient tissue were enrolled from 65 centers, receiving a reference neuropathological diagnosis according to the WHO classification in >97%. Using 10 FFPE sections as input, DNA methylation analysis was successfully performed in 95% of cases, of which 78% with sufficient tumor cell content were assigned to a distinct epigenetic tumor class. The remaining 22% did not match any of 82 represented classes, indicating novel rare tumor entities. Targeted gene panel sequencing of >130 genes performed for 96% of patients with matched blood samples detected diagnostically, prognostically, or therapeutically relevant somatic alterations in 48%. Germline DNA sequencing data indicated potential predisposition syndromes in ~10% of patients. Discrepant results by neuropathological and epigenetic classification (29%) were enriched in histological high-grade gliomas and implicated clinical relevance in 5% of all cases. Clinical follow-up suggests improved survival for some patients with high-grade glioma histology and lower-grade molecular profiles. Routine (epi-)genetic profiling at the time of primary diagnosis adds a valuable layer of information to neuropathological diagnostics and will improve clinical management of CNS tumors.

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Generalized Additive Models for the Detection of Copy Number Variations (CNVs) Using Multi-gene Panel Sequencing Data

Studies in Classification, Data Analysis, and Knowledge Organization - Advanced Studies in Classification and Data Science ◽

10.1007/978-981-15-3311-2_16 ◽

2020 ◽

pp. 199-213

Author(s):

Corinna Ernst ◽

Rita K. Schmutzler ◽

Eric Hahnen

Keyword(s):

Copy Number ◽

Generalized Additive Models ◽

Copy Number Variations ◽

Gene Panel ◽

Additive Models ◽

Sequencing Data ◽

Gene Panel Sequencing ◽

Panel Sequencing

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A Method to Evaluate the Quality of Clinical Gene-Panel Sequencing Data for Single-Nucleotide Variant Detection

Journal of Molecular Diagnostics ◽

10.1016/j.jmoldx.2017.06.001 ◽

2017 ◽

Vol 19 (5) ◽

pp. 651-658 ◽

Cited By ~ 11

Author(s):

Chung Lee ◽

Joon S. Bae ◽

Gyu H. Ryu ◽

Nayoung K.D. Kim ◽

Donghyun Park ◽

...

Keyword(s):

Single Nucleotide Variant ◽

Gene Panel ◽

Sequencing Data ◽

Single Nucleotide ◽

Variant Detection ◽

Gene Panel Sequencing ◽

Panel Sequencing

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Comprehensive genomic profiling to merge precision medicine with immunotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.e23108 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. e23108-e23108

Author(s):

Stephen Lyle ◽

Julie Y Tse ◽

Ruobai Sun ◽

Xiu Huang ◽

Meaghan Russell ◽

...

Keyword(s):

Genetic Variation ◽

Copy Number ◽

Treatment Strategies ◽

Gene Panel ◽

Next Generation Sequencing Data ◽

Patient Treatment ◽

Clinical Samples ◽

Sequencing Data ◽

Tumor Mutation Burden ◽

Mutation Burden

e23108 Background: Immunotherapy is rapidly emerging as one of the most promising therapeutic options in clinical oncology. However, not all patients will respond to immune-oncology drugs and diagnostic assays are urgently needed to identify biomarkers that predict response to these therapies. We developed an assay that utilizes next generation sequencing data to simultaneously determine different types of somatic changes in tumors. The assay was designed, in part, to facilitate identification of cancer patients most likely to respond to immunotherapies by detecting 1) CD274 (PD-L1) copy number alterations, 2) viral infections (HPV16/18 and EBV), 3) microsatellite instability (MSI), and 4) tumor mutation burden. Methods: We developed a 435-gene panel assay (CancerPlex) with the goal of identifying all the genomic changes that inform treatment decisions with the highest possible accuracy. We thoroughly evaluated the performance of the assay using reference samples that represent all classes of genetic variation, including SNPs, indels, copy number changes, rearrangements, HPV/EBV infection and MSI. FFPE clinical samples were also evaluated to assess the ability of the assay to detect genetic variation in complex, heterogeneous tumors. Results: The assay has excellent performance, with a mean sensitivity of 99.4% and specificity of 99.9% for detecting somatic mutations with an allele fraction as low as 10%. The assay identified the MSI status of colorectal tumors with the same sensitivity as immunohistochemistry but with greater sensitivity than PCR. We also showed that calculating tumor mutation burden using the 435-gene panel predicts response to pembrolizumab as effectively as using whole exome sequencing. Among 892 patients across all tumor types, 6.8% were identified as candidates for immunotherapy based upon high tumor mutation burden and/or MSI status. Conclusions: The capacity of the 435-gene panel to determine all of the critical genetic changes, tumor mutation burden, MSI status, CD274 (PD-L1) CNVs, and HPV/EBV status has important ramifications for patient treatment strategies, including identification of patients who are more likely to benefit from immune checkpoint inhibitor therapies.

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