Virtual Library

Start Your Search

C.D. Morrison



Author of

  • +

    MINI 22 - New Technology (ID 134)

    • Event: WCLC 2015
    • Type: Mini Oral
    • Track: Biology, Pathology, and Molecular Testing
    • Presentations: 2
    • +

      MINI22.05 - Quality Control Process for NGS to Minimize False Positives (ID 2989)

      16:45 - 18:15  |  Author(s): C.D. Morrison

      • Abstract
      • Presentation
      • Slides

      Background:
      Next generation sequencing (NGS) has exceptional sensitivity, but at the expense of false positives. This can result in a less than optimal positive predictive value and eventually the futile treatment of patients. We have developed a unique set of quality control filters for both Ion Torrent and Illumina that minimize false positives, but have little negative impact on sensitivity. To address this paradoxical association of sensitivity and false positives, we developed a dual platform methodology of NGS using both the Ion Torrent and Illumina to solve this classical dilemma.

      Methods:
      A series of filters were developed to determine quality cutoffs for variant calls to minimize false positives that included the minimum quality score threshold (QUALT), minimum percent variant reads (MPVR), minimum variant reads (MVR), minimum variant reads threshold (MVRT), minimum variant allelic frequency threshold (MVAF), minimum variant reads positive predictive value (MVR-PPV), and systematic errors (SE). A parallel system of using the MiSeq and PGM to sequence all specimens within an IT systems control and a Classify Callsmatrix solution for mutational analysis was designed. Unique cohorts of patients with prior exome sequencing as part of TCGA were used as gold standard controls with matching fresh frozen and FFPE samples.

      Results:
      Table 1 provides the results of filters developed to maximize sensitivity versus PPV. Using our targeted sequencing panel the PGM consistently outperformed the MiSeq for the standard performance characteristics of sensitivity and PPV for both frozen and FFPE samples. Both platforms have systematic false positives that are unique and gene specific.

      Table 1 Platform Tissue VAF setting QUAL Cutoff MVRT Cutoff MVAF Cutoff Mean Sensitivity Range Sensitivity Mean PPV Range PPV
      PGM FF 0.2% None None None 100% 93-100% 88% 70-96%
      PGM FF 0.2% >99 >=20 >.035 99% 93-100% 95% 78-100%
      PGM FFPE 0.2% None None None 99% 93-100% 58% 2-94%
      PGM FFPE 0.2% >99 >=21 >.018 97% 63-100% 92% 40-100%
      MiSeq FF 1% None None None 97% 79-100% 49% 31-66%
      MiSeq FF 1% >99 >=5 >.017 95% 66-100% 82% 66-95%
      MiSeq FFPE 1% None None None 94% 43-100% 10% 2-37%
      MiSeq FFPE 1% >99 >=10 >.028 92% 39-100% 62% 6-100%
      Table 2 provides the results for dual platform sequencing which show a marked reduction in false positives while maintaining sensitivity.
      Table 2 FF FF FF FF FFPE FFPE FFPE FFPE
      SNV(s) SNV(s) Indels Indels SNV(s) SNV(s) Indels Indels
      Percent VAF Percent VAF Percent VAF Percent VAF
      Assay Sensitivity 99.8% 2.87% 100.0% 2.90% 98.3% 3.56% 100.0% 3.60%
      Assay PPV 97.5% 2.87% 91.0% 2.90% 96.7% 3.56% 91.0% 3.60%


      Conclusion:
      Single platform NGS is plagued by false positives. Dual platform sequencing is a reliable method of diminishing false positives with minimal to no impact on sensitivity.

      Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

    • +

      MINI22.08 - Development of a Protein Viewer for Displaying Variants of Unknown Significance in Relation to Actionable Mutations and Protein Domains (ID 2917)

      16:45 - 18:15  |  Author(s): C.D. Morrison

      • Abstract
      • Presentation
      • Slides

      Background:
      Next-generation sequencing (NGS) can be used to interrogate multiple areas of the tumor genome. Several hot-spot panels have been developed to identify variants amenable to targeted therapies and enrollment into clinical trials. Variants of unknown significance (VUS) in the vicinity of hot-spots are routinely discovered. To better understand these obscure VUS, we built a Protein Viewer that displays the relationship of known actionable variant(s) to the VUS.

      Methods:
      We developed a web-based protein viewer that can be deployed across multiple browsers. The tool supports the visual representation of 23 genes which are interrogated by our NGS platform. We used the longest mRNA transcript (hg19) to define the protein domains. All actionable variants as reported by an knowledge database were included, with the selected VUS differentially highlighted. VUS is defined as a non-actionable variant that is not reported in dbSNP.

      Results:
      Approximately 50% of all stage III and IV lung cancer patients tested by our NGS platform have one or more VUS. After the variant information is loaded in the Protein Viewer, a two-dimensional image of the full length protein with actionable variants and VUS is displayed (Figure 1). The Viewer is utilized at RPCI to present cases at our molecular tumor board for quick visualization and discussion. Figure 1 Figure 1: Protein Viewer with a PIK3CA VUS harboring a Q546H (pink) in a lung adenocarcinoma. Top panel with PIK3CA exons 2-21 boundaries (vertical lines) with protein domains (blue rectangles along axis). Bottom panel with the zoom feature which allows more discreet visualization of the VUS, a neighboring Q546K actionable variant (green), and additional actionable variants for ovarian cancer (green rectangles).



      Conclusion:
      Understanding the relationship of VUS to protein domains and proximity to previously known actionable sites is a potentially powerful way to evaluate and determine whether a patient might be a candidate for targeted therapy. Because the exact effect of the VUS on the function of the protein is still impossible to discern (tyrosine kinase inhibitor sensitivity/ resistance/no effect), the next generation of protein viewers should incorporate 3D and protein folding/domain interaction prediction capabilities.

      Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

  • +

    P3.04 - Poster Session/ Biology, Pathology, and Molecular Testing (ID 235)

    • Event: WCLC 2015
    • Type: Poster
    • Track: Biology, Pathology, and Molecular Testing
    • Presentations: 2
    • +

      P3.04-025 - DNA Extraction of Lung Cancer Samples for Advanced Diagnostic Testing (ID 3205)

      09:30 - 17:00  |  Author(s): C.D. Morrison

      • Abstract
      • Slides

      Background:
      Tumor specimens are routinely formalin fixed and paraffin embedded (FFPE) prior to histologic evaluation. This process preserves the morphology and cellular features required for proper staining and microscopic review. However, this practice presents numerous challenges for the extraction of high quality DNA for advanced diagnostic testing that include nanoString and Next-Generation Sequencing (NGS) technologies. An extraction process that consistently produces sufficient DNA yield and fragment size from these difficult but most precious tissue samples is a requirement for any Molecular Pathology laboratory utilizing these platforms. The data presented here will compare the quantity and quality of DNA extracted using two methods, QIAGEN and Covaris, and success of downstream testing.

      Methods:
      FFPE tumor samples from a variety of tumor types, including lung, were macro-dissected using 14-guage needles, with 1 core extracted using the Covaris truXtract FFPE DNA isolation method and the other matched core using the QIAGEN DNeasy tissue kit. All samples were processed using manufacturer’s recommended instructions. DNA metrics were measured using Qubit (picogreen) and NanoDrop for yield and purity, followed by fragment size estimation on a 2100 BioAnalyzer (Agilent Technologies). A subset of matched DNA sample pairs were used as template for PGM AmpliSeq and MiSeq TSCA library preparation, followed by NGS. A subset of DNA sample pairs were also analyzed for copy number using the nanoString nCounter system.

      Results:
      DNA yields and fragment lengths were substantially higher for truXtract samples as compared to DNeasy when measured by picogreen quantitation and Bioanalyzer electrophoresis (Figure 1). A higher degree of successful advanced molecular diagnostic test results was also observed for the truXtract DNA samples, especially for the Illumina NGS system (improved clustering and coverage) and nCounter platform (improved counts) that prefer longer fragment lengths than Ion Torrent NGS. Figure 1Figure 1: 2100 Bioanalyzer traces of DNA prepared from three lung cancer FFPE samples; DNeasy (left) and TruXtract (right).



      Conclusion:
      FFPE tumor samples prepared using the truXtract FFPE DNA isolation kit provides an efficient system for generating high quality DNA samples from even the most difficult lung cancer specimens. The combination of improved yield and fragment size measured for nearly every sample tested suggests that even smaller biopsies can now be collected for advanced diagnostic testing.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

    • +

      P3.04-057 - Pyrosequencing VS NGS KRAS and EGFR Mutation Detection: A Head to Head Comparison in Lung Adenocarcinomas (ID 2712)

      09:30 - 17:00  |  Author(s): C.D. Morrison

      • Abstract

      Background:
      Pyrosequencing is a popular method for detecting actionable somatic mutations. Most labs use pyrosequencing at an analytical sensitivity of 10%, potentially missing actionable mutations that have a low variant allele frequency (VAF) due to low neoplastic nuclear content or due to neoplastic heterogeneity. Furthermore, the cost-effectiveness of pyrosequencing rapidly decreases when numerous hotspots are interrogated simultaneously and scaleability is limited. Next-generation sequencing (NGS) is scaleable and has the capacity to detect mutations at VAFs less than 10%. The goals of this study were to perform NGS on a series of KRAS and EGFR cases that were “mutation negative” but had suspicious pyrosequencing peaks which were insufficient for a definite determination, and to review EGFR exon 19 deletion cases that were detected by NGS but missed by pyrosequencing.

      Methods:
      All the KRAS and EGFR pyrosequencing runs performed at Roswell Park Cancer Institute between July 2011 and November 2014 were manually reviewed. All actionable KRAS and EGFR variants that were found at a VAF of 4% or more and less than 10% and had remnant DNA were tested by a dual MiSeq/PGM platform NGS pipeline with a 3.6% VAF analytic sensitivity for FFPE tissues. We also included EGFR exon 19 cases that had discrepant findings between NGS and pyrosequencing.

      Results:
      Six lung adenocarcinomas with suspicious KRAS pyrograms were reviewed. By NGS, 4/6 were found to have activating codon 12 and 13 KRAS mutations (NGS VAF range 6-14%). Twelve lung adenocarcinomas with suspicious EGFR pyrograms or discrepant EGFR exon 19 pyrosequencing/NGS results were reviewed. By NGS, 4/12 were found to have actionable mutations, including 3 exon 19 deletions (NGS VAF range 5-24%) and 2 T790M resistance mutations (NGS VAF range 4-5%).

      Conclusion:
      Pyrosequencing lacks the analytic sensitivity to detect actionable KRAS and EGFR mutations with very low VAF and can entirely miss EGFR exon 19 deletions, even at a high VAF. NGS and can be optimized to detect single nucleotide alterations with a VAF less than 5% and can reliably detect EGFR exon 19 deletions. The capabilities of NGS can translate into improved clinical validity and clinical utility.