Sean M. Boyle

Mutations in NGLY1 cause an inherited disorder of the endoplasmic reticulum-associated degradation pathway

Purpose:The endoplasmic reticulum–associated degradation pathway is responsible for the translocation of misfolded proteins across the endoplasmic reticulum membrane into the cytosol for subsequent degradation by the proteasome. To define the phenotype associated with a novel inherited disorder of cytosolic endoplasmic reticulum–associated degradation pathway dysfunction, we studied a series of eight patients with deficiency of N-glycanase 1.Methods:Whole-genome, whole-exome, or standard Sanger sequencing techniques were employed. Retrospective chart reviews were performed in order to obtain clinical data.Results:All patients had global developmental delay, a movement disorder, and hypotonia. Other common findings included hypolacrima or alacrima (7/8), elevated liver transaminases (6/7), microcephaly (6/8), diminished reflexes (6/8), hepatocyte cytoplasmic storage material or vacuolization (5/6), and seizures (4/8). The nonsense mutation c.1201A>T (p.R401X) was the most common deleterious allele.Conclusion:NGLY1 deficiency is a novel autosomal recessive disorder of the endoplasmic reticulum–associated degradation pathway associated with neurological dysfunction, abnormal tear production, and liver disease. The majority of patients detected to date carry a specific nonsense mutation that appears to be associated with severe disease. The phenotypic spectrum is likely to enlarge as cases with a broader range of mutations are detected.Genet Med 16 10, 751–758.

PATH-SCAN: a reporting tool for identifying clinically actionable variants.

The American College of Medical Genetics and Genomics (ACMG) recently released guidelines regarding the reporting of incidental findings in sequencing data. Given the availability of Direct to Consumer (DTC) genetic testing and the falling cost of whole exome and genome sequencing, individuals will increasingly have the opportunity to analyze their own genomic data. We have developed a web-based tool, PATH-SCAN, which annotates individual genomes and exomes for ClinVar designated pathogenic variants found within the genes from the ACMG guidelines. Because mutations in these genes predispose individuals to conditions with actionable outcomes, our tool will allow individuals or researchers to identify potential risk variants in order to consult physicians or genetic counselors for further evaluation. Moreover, our tool allows individuals to anonymously submit their pathogenic burden, so that we can crowd source the collection of quantitative information regarding the frequency of these variants. We tested our tool on 1092 publicly available genomes from the 1000 Genomes project, 163 genomes from the Personal Genome Project, and 15 genomes from a clinical genome sequencing research project. Excluding the most commonly seen variant in 1000 Genomes, about 20% of all genomes analyzed had a ClinVar designated pathogenic variant that required further evaluation.

Abstract 1292: Methods of improving accuracy of neoantigen identification for therapeutic and diagnostic use in immuno-oncology

Background:Neoantigens are increasingly critical in immuno-oncology as a therapeutic target for neoantigen-based personalized cancer vaccines and as a potential biomarker for immunotherapy response. However, the methods for identifying which neoepitopes are more likely to provoke an immune response remains an important challenge for improving both the effectiveness of neoantigen-based vaccines and enabling the potential use of neoantigens as a biomarker in immunotherapy. Methods:We sought to improve overall neoantigen identification performance by systematically improving critical components of our ACE ImmunoID assays and neoantigen pipeline. Personalis9 Accuracy and Content Enhanced (ACE) technology was developed to fill critical gaps in conventional exome and transcriptome sequencing that can lead to missed neoantigens. To improve MHC-epitope binding prediction, we trained neural networks on mass spectrometry derived MHC-epitope binding data. This is in contrast to other MHC binding algorithms that have been primarily trained using in vitro competitive binding data, which suffer from having not been processed, loaded, nor shuttled natively into the HLA binding domain. HLA typing, a key input into the neoantigen prediction algorithms, was improved through exome augmentation of the HLA region with an optimized HLA typing algorithm. Other enhancements include RNA based somatic variant calling, peptide phasing, transcript isoform estimation, and identification of indel and fusion derived neoepitopes. Results:Our ACE augmented exome demonstrates high sensitivity and specificity for SNVs, indels, and fusions at MAF >=10%. These are all variant types that result in putative neoantigens. Further, we show that our augmented ACE transcriptome can achieve high sensitivity for RNA derived variants and can be an important filter for putative neoantigens. When compared with commercially available MHC binding algorithms for specific HLA alleles, our MHC binding prediction algorithm consistently achieves a higher overall sensitivity and specificity than other tools. For example, our MHC class I-epitope binding prediction algorithm demonstrated an aggregative precision value of 0.88 across HLA alleles, as opposed to 0.50 for other widely used tools. To assess overall HLA-typing performance, we performed a blinded clinical HLA typing validation demonstrating 98% and 95% concordance with Class I and II HLA results (respectively) from clinical testing. We also show instances where peptide phasing, SNP, indel and fusion-derived neoepitopes are important for more accurate and comprehensive neoantigen identification. Citation Format: Sean Michael Boyle, Ravi Alla, Ryan Wang, Eric Levy, Gabor Bartha, Jason Harris, Robert McCord, Rena McClory, John West, Richard Chen. Methods of improving accuracy of neoantigen identification for therapeutic and diagnostic use in immuno-oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1292.

Validation of an expanded neoantigen identification platform for therapeutic and diagnostic use in immuno-oncology.

To assess the e ectiveness of this pipeline in predicting immunogenic neoantigens, we assembled a gold-set of 23 known, previously experimentally-validated immunogenic neoantigens from the literature. We spiked in these neoepitopes into exome data and assessed the ability of our neoantigen pipeline to find and rank these immunogenic known neoantigens. Preliminary results show our neoantigen pipeline is able to accurately identify 22 out of 23 (~96%) of the spiked in neoantigens as being potentially immunogenic. Within our neoantigen pipeline, variants that are detected by our DNA and RNA cancer analysis pipelines are processed for antigen identification, including SNVs, indels, and fusion events. Importantly, both in-frame and out-of-frame events are accurately considered by transcript, allowing for detection of a wealth of candidate neoantigens. Our pipeline includes assessment of important immunologic components including HLA prediction, MHC binding (class I and II), immunogenicity, similarity to self, and similarity to known antigens. Additionally, peptides are evaluated for variant allele frequency in both the RNA and DNA of the tumor sample and gene expression level is considered. Collectively, our ImmunoID product provides a comprehensive assessment of features that may be used for identifying and ranking potentially immunogenic neoantigens. Validation of an Expanded Neoantigen Identification Platform for Therapeutic and Diagnostic Use in Immuno-oncology

Abstract 554: Accurately identifying neoantigens utilizing both DNA and RNA somatic variants in an enhanced platform

The identification of neoantigens is a crucial step in the development of neoantigen-based personalized cancer vaccines and other immunotherapies. Accurately predicting which neoantigens are likely to be immunogenic remains a key challenge owing to the complex processes involved in determining neoantigen immunogenicity including the antigen presenting machinery, likelihood of MHC class I and II binding, similarity to self, and ability to interact with the TCR. We have developed a neoantigen detection pipeline built upon our analytically validated Accuracy and Content Enhanced (ACE) Exome and Transcriptome sequencing platform and somatic variants calling pipeline through combined DNA and RNA analysis. The analytical performance of these pipelines is greater than >97% sensitivity for small variants (RNA and DNA) with a specificity of >98% (DNA) and a fusion sensitivity of >99% (RNA). Within our neoantigen pipeline, variants that are detected by our DNA and RNA cancer analysis pipelines are processed for antigen identification, including SNVs, indels, and fusion events. Importantly, both in-frame and out-of-frame events are accurately considered by transcript, allowing for detection of a wealth of candidate neoantigens. Our pipeline includes assessment of important immunologic components including HLA prediction, MHC binding (class I and II), immunogenicity, similarity to self, and similarity to known antigens. Additionally, peptides are evaluated for variant allele frequency in both the RNA and DNA of the tumor sample and gene expression level is considered. Collectively, our ImmunoID product provides a comprehensive assessment of features that may be used for identifying and ranking potentially immunogenic neoantigens. To assess the effectiveness of this pipeline in predicting immunogenic neoantigens, we assembled a gold-set of 23 known, previously experimentally-validated immunogenic neoantigens from the literature. We spiked in these neoepitopes into exome data and assessed the ability of our neoantigen pipeline to find and rank these immunogenic known neoantigens. Preliminary results show our neoantigen pipeline is able to accurately identify 22 out of 23 (~96%) of the spiked in neoantigens as being potentially immunogenic. Citation Format: Sean M. Boyle, Jason Harris, Gabor Bartha, Ravi Alla, Mirian Karbelashvili, Steve Chervitz, Aldrin Montana, Craig Rowell, Patrick Jonganeel, Scott Kirk, Rena McClory, John West, Rich Chen. Accurately identifying neoantigens utilizing both DNA and RNA somatic variants in an enhanced platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 554. doi:10.1158/1538-7445.AM2017-554

Abstract 546: Supporting neoantigen identification for personalized cancer vaccines trough analytical validation of an augmented content enhanced (ACE) transcriptome

The identification of neoantigens has become a critical step in the development of neoantigen-based personalized cancer vaccines and other immunotherapy applications. Since neoantigens can be generated from tumor specific mutations in any expressed gene, the first step in identification of neoantigens typically involves deep exome and transcriptome sequencing on the tumor and exome sequencing of the matched normal. As personalized vaccines enter clinical trials with the potential for clinical use, there is a growing need for strong analytical validation of these platforms. To address this we have developed our ACE Exome (~200X) and Transcriptome platforms for neoantigen identification which utilitize an augmented exome approach designed to increase sensitivity for neoantigens in low complexity, traditionally hard to sequencing regions. To enable this platform for neoantigen based personalized cancer vaccines, we have performed a validation of both our ACE Exome (tumor and normal) and ACE transcriptome platforms for detecting DNA-based SNVs and Indels, as well as for RNA based small variant and fusion calls. These are variant types are especially important for neoantigen identification. In this abstract we describe the ACE Exome validation. We used 11 cancer cell lines and their matched normals to assess analytical sensitivity and limits of detection (LOD) for small variant (SNV and Indel) detection using our ACE exome and Tumor Normal bioinformatics pipeline. We identified a gold set of variants, 875 SNVs and 19 Indels that were previously validated in these 11 cell lines (COSMIC, CCLE and Sanger Sequencing confirmed variants). These gold set variants were used to calculate our analytical sensitivity (percent of gold variants detected across the 11 cell line pairs using our assay). To determine our LOD, we chose 3 of the 11 cancer cell lines and created 6 dilutions (5%, 10%, 20%, 30%, 50% and 80% tumor purity) with their matched normal. We then determined Positive Predictive Agreement (PPA, percent of pure cell line variants detected in a diluted samples) and False Discovery Rate (FDR, percent of erroneously detected variants in the diluted sample that were not detected in the pure cell lines) metrics for variants across different minor allelic frequencies (MAF) in the diluted samples. The ACE “Tumor Normal” Exome assay had a high sensitivity of 98% for SNVs and 95% for Indels. The assay also showed robust PPA (sensitivity) of 97% and FDR (specificity) of 2% for SNVs with MAF g= 10% and PPA of 87% and FDR of 3% for Indels with MAF g= 10%. We demonstrate that the ACE “Tumor Normal” Exome assay is highly accurate for identification of SNVs and Indels in cancer exomes. With high analytical sensitivity, PPA and low FDR we believe this assay provides augmented ability to detect cancer driver and potential neoantigen generating mutations across various tumor types. Citation Format: Ravi K. Alla, Jennifer Yen, Sean M. Boyle, Richard Chen. Supporting neoantigen identification for personalized cancer vaccines through analytical validation of an augmented content enhanced (ACE) exome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 428. doi:10.1158/1538-7445.AM2017-428

Effect of assaying the matched normal on clinical cancer sequencing results.

11561Background: Interpretation of a cancer variant’s origin and therapeutic impact poses analytical challenges. Recent studies have indicated that analyzing a tumor with its matched normal can dis...

Abstract 3616: Fix the fixation: effect of formalin fixation on targeted sequencing, variant calling and gene expression

Tumor biopsies are often Formalin-Fixed and Paraffin-Embedded (FFPE) for histological staining, genetic testing and archival purposes. Formalin treatment preserves tissue by crosslinking proteins, but can lead to mutation of the nucleic acid bases that can be detected by next-generation sequencing (NGS) methods despite being unrelated to the cancer. Studies have shown that certain fixation protocols are compatible with high quality nucleic acid isolation and variant calling by NGS. These studies used tissue fixed with 10% neutral buffered formalin for 24 hours, a standard protocol in the pathology field. In practice, however, FFPE sample handling varies from site-to-site. As we processed FFPE samples from various sites, we found that the quality of the isolated nucleic acids and subsequent sequencing results varied substantially. We hypothesized that this is due to deviations from the standard protocol such as improperly buffered reagents, variation in the fixation time and temperature, microwaving, and varied storage conditions of the samples. To understand the role of formalin fixation on the quality of the isolated nucleic acids and subsequent NGS analyses, we subjected the widely studied cell line NA12878 to various formalin fixation conditions. We then performed an augmented target enrichment and sequencing assay on both the DNA and RNA isolated from fresh frozen (FF) and formalin fixed (FFPE) NA12878 samples. We assessed raw nucleic acid quality, library quality, alignment rate, duplication rate, on-target efficiency and variant concordance between FF and FFPE. We noted substantial negative effects on sequencing library quality associated with sample incubation in unbuffered formalin, at high temperatures, and long periods of time (e.g. 3 days rather than 1 day). These harshly treated samples also showed poor alignment qualities, higher duplication rates, and lower mapping qualities. At the small variant level, they showed an increase in global C>T deamination (CG->TA, 50% FFPE vs 35% in FF) and oxidation (CG->AT, 15% FFPE vs 10% FF) rates. Variants caused by formalin fixation were most commonly detected at low ( Using the results of this study, we intend to provide guidance on optimal fixation conditions for NGS applications. It is crucial to fix tissues in buffered formalin for less than 24hr at room temperature to preserve nucleic acid integrity and amenability for downstream NGS assays. We also demonstrate how a deeper understanding of the effects of formalin can improve variant calling from FFPE tissues, especially at lower AFs where formalin-related errors have the greatest impact. Citation Format: Ravi Alla, Shujun Luo, Elena Helman, Sean M. Boyle, Michael J. Clark, Kirk Scott, Parin Sripakdeevong, Mirian Karbelashvili, Deanna M. Church, Michael Snyder, John West, Richard Chen. Fix the fixation: effect of formalin fixation on targeted sequencing, variant calling and gene expression. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3616.

Abstract 3169: The benefits and burdens of assaying matched normal tissue when sequencing cancer genomes

Targeted sequencing assays are increasingly used to identify tumor mutations that guide therapeutic decisions. Interpretation of a cancer variant9s origin and therapeutic impact poses analytical challenges. Recent studies have indicated that jointly analyzing a tumor with its matched normal can accurately discriminate between tumor-specific (somatic) and inherited (germline) mutations. Moreover, a NHGRI/NCI Clinical Sequencing Exploratory Research Consortium Tumor Working Group just released a set of guidelines recommending that laboratories performing cancer sequencing tests should include germline variants. However, procurement of a matched sample is often logistically impractical. In the absence of a matched normal, large databases and analytical techniques are currently used to identify cancer variants in tumor sequencing data. Whether the benefits outweigh the additional burden of sequencing the matched normal for accurate detection of cancer-relevant mutations remains an open question. To compare tumor-only and tumor/normal analysis of cancer samples, we collected a set of >100 formalin-fixed (FFPE) and fresh frozen cancer samples of various tumor types, where matched normal blood or adjacent tissue was available. We performed augmented target enrichment sequencing (exome and large cancer gene panel) of both DNA and RNA. The data was analyzed using cancer bioinformatics pipelines that detect base substitutions, small insertions/deletions, copy number alterations, and gene fusions in both tumor-only and tumor/normal modes. Variants were annotated using described clinical actionability filtering strategies. Analysis of germline variants for secondary findings was performed. We find that 67% of mutations detected in tumor-only mode are reclassified as germline variants when analyzed together with the matched normal sample. These include mutations in hereditary cancer predisposition genes, such as BRCA1, VHL, and other genes with ACMG guidelines that warrant germline variant classification and appropriate management. Clinically actionable mutations may be miscalled as somatic when a matched normal is not available; however, we find the definition of ‘actionable’ can greatly impact the results of this analysis. Finally, the use of newly available large datasets, such as ExAC, substantially decreases the number of miscalled somatic variants in the absence of a matched normal. The effects of administering targeted therapies to patients with germline mutations in the relevant gene are largely unknown. Mutations of putative germline origin may be important for hereditary cancer knowledge and tumor treatment, and should be reported as such. For NGS-based cancer interpretation to guide clinical decisions in a practical and cost-effective manner, highly optimized tumor-only and tumor/normal analyses must be available with proper attention to germline consent, classification and education. Citation Format: Elena Helman, Michael J. Clark, Ravi Alla, Sean M. Boyle, Shujun Luo, Selene Virk, Deanna Church, Parin Sripakdeevong, Jason Harris, Mirian karbelashvili, Christian Haudenschild, John West, Richard Chen. The benefits and burdens of assaying matched normal tissue when sequencing cancer genomes. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3169.

Abstract 533: Accurately identifying expressed somatic variants for neoantigen detection and immuno-oncology

Accurate detection of somatic variants is a staple of both research and clinical cancer analysis, with applications ranging from detecting new common driver mutations in large patient cohorts to selecting therapeutic small molecule treatment courses for an individual patient. Recent research into neoantigens and immunotherapy has shown great promise as a precision therapeutic, and somatic variant detection by next-generation sequencing represents an ideal method of identifying candidate neoantigens. Somatic variant detection typically involves assaying the DNA for changes in gene sequences without assessing whether those variants are actually expressed in RNA. However, the expression of small variants is key because only expressed peptides will be displayed as neoantigens on the cell surface. From a technical standpoint, detection of somatic variants in the RNA represents additional challenges above and beyond those of somatic detection in DNA. The widely varying expression levels of cancer genes, alternative splicing, and RNA editing are all features that make somatic variant calling in RNA uniquely challenging. However, accurately detecting variants directly from expressed transcripts is beneficial to neoantigen prediction, and therefore we sought to create and validate a method for somatic variant calling in RNA. We have designed a highly accurate expression-based somatic variant detection pipeline utilizing extensive discovery and filtering methods to overcome the challenges inherent in RNA somatic variant calling. We validated our pipeline using a combination of well-characterized cell lines, commercially available reference standards, and real world FFPE patient samples. To our knowledge, this is the most extensive validation of its kind to date, representing over 29,158 small variants across 39 samples. In testing, we measured our detection method at >99% sensitivity and >99% PPV using a combination of gold set small variants and orthogonal validation. This method, in combination with our validated DNA somatic variant calling pipeline (>99% sensitivity and >99% PPV), enables precise detection of variant expression levels in a given sample, even at low allele frequency (5%). After validating our RNA somatic variant calling method, we applied it to detect candidate neoantigens in patient tumor samples. We performed HLA typing for each sample using HLAssign software and predicted MHC presentation of the expressed somatic variants. In ongoing studies, we are validating our most promising putative neoantigens using orthogonal technologies and demonstrating our ability to detect the most promising clinically effective peptides for therapy. Citation Format: Sean M. Boyle, Michael J. Clark, Ravi Alla, Shujun Luo, Deanna M. Church, Elena Helman, Parin Sripakdeevong, John West, Rich Chen. Accurately identifying expressed somatic variants for neoantigen detection and immuno-oncology. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 533.