The clinical utility of sequencing the entirety of CFTR

BACKGROUND

Cystic fibrosis (CF) is caused by deleterious variants in each CFTR gene. We investigated the utility of whole-gene CFTR sequencing when fewer than two pathogenic or likely pathogenic (P/LP) variants were detected by conventional testing (sequencing of exons and flanking introns) of CFTR.

METHODS

Individuals with features of CF and a CF-diagnostic sweat chloride concentration with zero or one P/LP variants identified by conventional testing enrolled in the CF Mutation Analysis Program (MAP) underwent whole-gene CFTR sequencing. Replication was performed on individuals enrolled in the CF Genome Project (CFGP), followed by phenotype review and interrogation of other genes.

RESULTS

Whole-gene sequencing identified a second P/LP variant in 20/43 MAP enrollees (47 %) and 10/22 CFGP enrollees (45 %) who had one P/LP variant after conventional testing. No P/LP variants were detected when conventional testing was negative (MAP: n = 43; CFGP: n = 13). Genome-wide analysis was unable to find an alternative etiology in CFGP participants with fewer than two P/LP CFTR variants and CF could not be confirmed in 91 % following phenotype re-review.

CONCLUSIONS

Whole-gene CFTR analysis is beneficial in individuals with one previously-identified P/LP variant and a CF-diagnostic sweat chloride. Negative conventional CFTR testing indicates that the phenotype should be re-evaluated.

Genetic Modifiers of Cystic Fibrosis Lung Disease Severity: Whole-Genome Analysis of 7,840 Patients

Rationale: Lung disease is the major cause of morbidity and mortality in persons with cystic fibrosis (pwCF). Variability in CF lung disease has substantial non-CFTR (CF transmembrane conductance regulator) genetic influence. Identification of genetic modifiers has prognostic and therapeutic importance. Objectives: Identify genetic modifier loci and genes/pathways associated with pulmonary disease severity. Methods: Whole-genome sequencing data on 4,248 unique pwCF with pancreatic insufficiency and lung function measures were combined with imputed genotypes from an additional 3,592 patients with pancreatic insufficiency from the United States, Canada, and France. This report describes association of approximately 15.9 million SNPs using the quantitative Kulich normal residual mortality-adjusted (KNoRMA) lung disease phenotype in 7,840 pwCF using premodulator lung function data. Measurements and Main Results: Testing included common and rare SNPs, transcriptome-wide association, gene-level, and pathway analyses. Pathway analyses identified novel associations with genes that have key roles in organ development, and we hypothesize that these genes may relate to dysanapsis and/or variability in lung repair. Results confirmed and extended previous genome-wide association study findings. These whole-genome sequencing data provide finely mapped genetic information to support mechanistic studies. No novel primary associations with common single variants or rare variants were found. Multilocus effects at chr5p13 (SLC9A3/CEP72) and chr11p13 (EHF/APIP) were identified. Variant effect size estimates at associated loci were consistently ordered across the cohorts, indicating possible age or birth cohort effects. Conclusions: This premodulator genomic, transcriptomic, and pathway association study of 7,840 pwCF will facilitate mechanistic and postmodulator genetic studies and the development of novel therapeutics for CF lung disease.

Pleiotropic modifiers of age-related diabetes and neonatal intestinal obstruction in cystic fibrosis

Individuals with cystic fibrosis (CF) develop complications of the gastrointestinal tract influenced by genetic variants outside of CFTR. Cystic fibrosis-related diabetes (CFRD) is a distinct form of diabetes with a variable age of onset that occurs frequently in individuals with CF, while meconium ileus (MI) is a severe neonatal intestinal obstruction affecting ∼20% of newborns with CF. CFRD and MI are slightly correlated traits with previous evidence of overlap in their genetic architectures. To better understand the genetic commonality between CFRD and MI, we used whole-genome-sequencing data from the CF Genome Project to perform genome-wide association. These analyses revealed variants at 11 loci (6 not previously identified) that associated with MI and at 12 loci (5 not previously identified) that associated with CFRD. Of these, variants at SLC26A9, CEBPB, and PRSS1 associated with both traits; variants at SLC26A9 and CEBPB increased risk for both traits, while variants at PRSS1, the higher-risk alleles for CFRD, conferred lower risk for MI. Furthermore, common and rare variants within the SLC26A9 locus associated with MI only or CFRD only. As expected, different loci modify risk of CFRD and MI; however, a subset exhibit pleiotropic effects indicating etiologic and mechanistic overlap between these two otherwise distinct complications of CF.

The Johns Hopkins Molecular Tumor Board Precision Oncology elective for Medical Oncology fellows

11035

Background: The accelerated impact of next generation sequencing (NGS) in clinical decision making requires the integration of cancer genomics and precision oncology focused training into medical oncology education. The Johns Hopkins Molecular Tumor Board (JH MTB) is a multi-disciplinary effort focused on integration of NGS findings with critical evidence interpretation to generate personalized recommendations tailored to the genetic footprint of individual patients. Methods: The JH MTB and the Medical Oncology Fellowship Program have developed a 3-month precision oncology elective for fellows in their research years. Commencing fall of 2020, the goals of this elective are to enhance the understanding of NGS platforms and findings, advance the interpretation and characterization of molecular assay outputs by use of mutation annotators and knowledgebases and ultimately master the art of matching NGS findings with available therapies. Fellow integration into the MTB focuses on mentored case-based learning in mutation characterization and ranking by levels of evidence for actionability, with culmination in form of verbal presentations and written summary reports of final MTB recommendations. A mixed methods questionnaire was administered to evaluate progress since elective initiation. Results: Three learners who have participated as of February 2021 were included. Of the two who had completed the MTB elective, each have presented at least 10 cases, with at least 1 scholarly publication planned. All indicated strong agreement that MTB elective had increased their comfort with interpreting clinical NGS reports as well as the use of knowledgebases and variant annotators. Exposure to experts in the field of molecular precision oncology, identification of resources necessary to interpret clinical NGS reports, development of ability to critically assess various NGS platforms, and gained familiarity with computational analyses relevant to clinical decision making were noted as strengths of the MTB elective. Areas of improvement included ongoing initiatives that involve streamlining variant annotation and transcription of information for written reports. Conclusions: A longitudinal elective in the JHU MTB has been found to be preliminarily effective in promoting knowledge mastery and creating academic opportunities related to the clinical application of precision medicine. Future directions will include leveraging of the MTB infrastructure for research projects, learner integration into computational laboratory meetings, and expansion of the MTB curriculum to include different levels of learners from multiple medical education programs. Continued elective participation will be key to understanding how best to facilitate adaptive expertise in assigning clinical relevance to genomic findings, ultimately improving precision medicine delivery in patient care and trial development.

Abstract A33: High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins is an emerging biomarker for predicting patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low positive predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method to predict peptide-MHC binding. MHCnuggets is the only method to handle binding prediction for common or rare alleles of MHC Class I or II, with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is capable of faster performance than other methods. When compared to methods that integrate binding affinity and HLAp data from mass spectrometry, MHCnuggets yields a fourfold increase in positive predictive value on independent MHC-bound peptide (HLAp) data. We applied MHCnuggets to 26 cancer types in TCGA, processing 52.6 million allele-peptide comparisons in under 2.3 hours, yielding 103,587 candidate immunogenic missense mutations (IMMs). IMM hotspots occurred in 36 genes, including 22 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (p<2e-16), including CD8+ T cells. Notably, only 0.15% of predicted immunogenic missense mutations were observed in >2 patients, with 65% of these derived from driver mutations. Our results provide a new method for neoantigen prediction with high performance characteristics and demonstrate its utility in large data sets across human cancers.

Citation Format: Xiaoshan M. Shao, Rohit Bhattacharya, Justin Huang, Ashok Sivakumar, Collin Tokheim, Lily Zheng, Dylan Hirsch, Ben Kaminow, Ashton Omdahl, Maria Bonsack, Angelika B. Riemer, Victor E. Velculescu, Valsamo Anagnostou, Kymberleigh Pagel, Rachel Karchin. High-throughput prediction of MHC Class I and Class II neoantigens with MHCnuggets [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr A33.

Assessment of patient clinical descriptions and pathogenic variants from gene panel sequences in the CAGI-5 intellectual disability challenge

The Critical Assessment of Genome Interpretation-5 intellectual disability challenge asked to use computational methods to predict patient clinical phenotypes and the causal variant(s) based on an analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental manifestations (i.e. ID, autism, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) have been made available for this challenge. For each patient, predictors had to report the causative variants and which of the seven phenotypes were present. Since neurodevelopmental disorders are characterized by strong comorbidity, tested individuals often present more than one pathological condition. Considering the overall clinical manifestation of each patient, the correct phenotype has been predicted by at least one group for 93 individuals (62%). ID and ASD were the best predicted among the seven phenotypic traits. Also, causative or potentially pathogenic variants were predicted correctly by at least one group. However, the prediction of the correct causative variant seems to be insufficient to predict the correct phenotype. In some cases, the correct prediction has been supported by rare or common variants in genes different from the causative one.

Ion mobility separation of deprotonated oligosaccharide isomers - evidence for gas-phase charge migration

There has been increasing evidence that certain isomeric glycans can be separated efficiently by ion mobility-mass spectrometry when deprotonated ions are analyzed. To better understand the fundamentals behind these separations, we here investigate the impact of ionisation mode and adduct formation using IM-MS, density-functional theory and ab initio molecular dynamics.

Distinguishing N-acetylneuraminic acid linkage isomers on glycopeptides by ion mobility-mass spectrometry

Differentiating the structure of isobaric glycopeptides represents a major challenge for mass spectrometry-based characterisation techniques. Here we show that the regiochemistry of the most common N-acetylneuraminic acid linkages of N-glycans can be identified in a site-specific manner from individual glycopeptides using ion mobility-mass spectrometry analysis of diagnostic fragment ions.

Collision cross sections of high-mannose N-glycans in commonly observed adduct states--identification of gas-phase conformers unique to [M-H](-) ions

We report collision cross sections (CCS) of high-mannose N-glycans as [M + Na](+), [M + K](+), [M + H](+), [M + Cl](-), [M + H2PO4](-) and [M - H](-) ions, measured by drift tube (DT) ion mobility-mass spectrometry (IM-MS) in helium and nitrogen gases. Further analysis using traveling wave (TW) IM-MS reveal the existence of distinct conformers exclusive to [M - H](-) ions.