Citations

Eosinophilic disorders and related syndromes represent a heterogeneous group of neoplastic and nonneoplastic conditions, characterized by more eosinophils in the peripheral blood, and may involve eosinophil-induced organ damage. In the WHO classification of myeloid and lymphoid neoplasms, eosinophilic disorders characterized by dysregulated tyrosine kinase (TK) fusion genes are recognized as a new category termed, myeloid/lymphoid neoplasms with eosinophilia and rearrangement of PDGFRA , PDGFRB or FGFR1 or with PCM1-JAK2 . In addition to these aforementioned TK fusion genes, rearrangements involving FLT3 and ABL1 genes have also been described. These new NCCN Guidelines include recommendations for the diagnosis, staging, and treatment of any one of the myeloid/lymphoid neoplasms with eosinophilia (MLN-Eo) and a TK fusion gene included in the 2017 WHO Classification, as well as MLN-Eo and a FLT3 or ABL1 rearrangement.

Abstract Comprehensive interpretation of a patient's cancer genome is a cross-disciplinary process requiring the expertise of oncologists, molecular pathologists, genetic counselors, genomic scientists, and bioinformaticians. The Variant Interpretation for Cancer Consortium (VICC) has created a non-clinical forum for the variant interpretation process by connecting experts from multiple academic institutions and industry to create a Virtual Molecular Tumor Board (VMTB). During VICC-VMTB sessions, experts retrospectively discuss de-identified, challenging patient cases in the context of genomic interpretation guidelines to evaluate a patient's profile across several distinct components: genomic variants; disease context; therapeutic history; family history; demographic information; genomic assay information; and variant allele frequency. These components are processed through the prism of publically available knowledge and expertise of VMTB members, resulting in a broad spectrum of clinically relevant assertions about the patient's disease that may be of prognostic, diagnostic, or predictive relevance. Through these evaluations, the VICC-VMTB primarily works as a means of illuminating challenges in genomic interpretation and advancing the objectives of domain-specific VICC working groups. Examples of challenges that have been highlighted by the VICC-VMTB forum include: 1) inconsistent disease and therapy ontologies across resources are being addressed by Disease Harmonization & Drug Harmonization working groups, respectively; 2) use of machine-learning and natural language processing into clinical-grade variant interpretation are being addressed by the AI-Assisted Curation working group; 3) aggregation and harmonization of knowledgebase data into a centralized resource is being addressed by the Data Licensing and Variant Harmonization working groups; 4) inefficiencies in knowledgebase search functions are being addressed by the Search working group; 5) determination of accurate prediction of biological consequences of rare variants is being addressed by the In Silico Interpretation working group; and 6) standardization of genomic variant curation is being addressed by the Knowledge Curation and Interpretation Standards working group. These VICC working groups provide dedicated expert assemblies intended to specifically tackle these challenges, further increasing the breadth for which genomic information can be made useful in cancer variant interpretation. The VICC-VMTB has been a pertinent link between cancer genomes and clinical interpretation. We are confident VICC-VMTB will continue to be a gateway of acceleration and progress needed in the field of cancer interpretation to provide accurate and efficient answers for our patients in the future. Citation Format: Beth A. Pitel, Shruti Rao, Catherine Del Vecchio Fitz, Subha Madhavan, Rodrigo Dientsmann, Peter Horak, Ian King, Susan M. Mockus, Gordana Raca, Damian T. Rieke, Peter Rogan, Dmitriy Sonkin, David Tamborero, Ioannis S. Vlachos, Brian Walsh, Jeremy L. Warner, Malachi Griffith, Obi L. Griffith, Debyani Chakravarty, Alex H. Wagner. The Virtual Molecular Tumor Board of the Variant Interpretation for Cancer Consortium: A systematic gateway connecting cancer genome interpretation and progress in genomic knowledgebases in cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3222.

Abstract The use of clinical gene sequencing is now commonplace, and genome analysts and molecular pathologists are often tasked with the labor-intensive process of interpreting the clinical significance of large numbers of tumor variants. Numerous independent knowledgebases have been constructed to alleviate this manual burden, however these knowledgebases are non-interoperable. As a result, the analyst is left with a difficult tradeoff: for each knowledgebase used the analyst must understand the nuances particular to that resource and integrate its evidence accordingly when generating the clinical report, but for each knowledgebase omitted there is increased potential for missed findings of clinical significance.The Variant Interpretation for Cancer Consortium (VICC; cancervariants.org) was formed as a driver project of the Global Alliance for Genomics and Health (GA4GH; ga4gh.org) to address this concern. VICC members include representatives from several major somatic interpretation knowledgebases including CIViC, OncoKB, Jax-CKB, the Weill Cornell PMKB, the IRB-Barcelona Cancer Biomarkers Database, and others. Previously, the VICC built and reported on a harmonized meta-knowledgebase of 19,551 biomarker associations of harmonized variants, diseases, drugs, and evidence across the constituent resources.In that study, we analyzed the frequency with which the tumor samples from the AACR Project GENIE cohort would match to harmonized associations. Variant matches increased dramatically from 57% to 86% when broader matching to regions describing categorical variants were allowed. Unlike precise sequence variants with specified alternate alleles, categorical variants describe a collection of potential variants with a common feature, such as “V600” (non-valine alleles at the 600 residue), “Exon 20 mutations” (all non-silent mutations in exon 20), or “Gain-of-function” (hypermorphic alterations that activate or amplify gene activity). However, matching observed sequence variants to categorical variants is challenging, as the latter are typically only described as unstructured text. Here we describe the expressive and computational GA4GH Variation Representation specification (vr-spec.readthedocs.io), which we co-developed as members of the GA4GH Genomic Knowledge Standards work stream. This specification provides a schema for common, precise forms of variation (e.g. SNVs and Indels) and the method for computing identifiers from these objects. We highlight key aspects of the specification and our work to apply it to the characterization of categorical variation, showcasing the variant terminology and classification tools developed by the VICC to support this effort. These standards and tools are free, open-source, and extensible, overcoming barriers to standardized variant knowledge sharing and search. Citation Format: Alex H. Wagner, Reece K. Hart, Larry Babb, Robert R. Freimuth, Adam Coffman, Yonghao Liang, Beth Pitel, Angshumoy Roy, Matthew Brush, Jennifer Lee, Anna Lu, Thomas Coard, Shruti Rao, Deborah Ritter, Brian Walsh, Susan Mockus, Peter Horak, Ian King, Dmitriy Sonkin, Subha Madhavan, Gordana Raca, Debyani Chakravarty, Malachi Griffith, Obi L. Griffith. Harmonization standards from the Variant Interpretation for Cancer Consortium [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1096.

Abstract Rationale Usual interstitial pneumonia (UIP) is the defining morphology of idiopathic pulmonary fibrosis (IPF). Guidelines for IPF diagnosis conditionally recommend surgical lung biopsy for histopathology diagnosis of UIP when radiology and clinical context are not definitive. A “molecular diagnosis of UIP” in transbronchial lung biopsy, the Envisia Genomic Classifier, accurately predicted histopathologic UIP. Objectives We evaluated the combined accuracy of the Envisia Genomic Classifier and local radiology in the detection of UIP pattern. Methods Ninety-six patients who had diagnostic lung pathology as well as a transbronchial lung biopsy for molecular testing with Envisia Genomic Classifier were included in this analysis. The classifier results were scored against reference pathology. UIP identified on high-resolution computed tomography (HRCT) as documented by features in local radiologists’ reports was compared with histopathology. Measurements and Main Results In 96 patients, the Envisia Classifier achieved a specificity of 92.1% (confidence interval [CI],78.6–98.3%) and a sensitivity of 60.3% (CI, 46.6–73.0%) for histology-proven UIP pattern. Local radiologists identified UIP in 18 of 53 patients with UIP histopathology, with a sensitivity of 34.0% (CI, 21.5–48.3%) and a specificity of 96.9% (CI, 83.8–100%). In conjunction with HRCT patterns of UIP, the Envisia Classifier results identified 24 additional patients with UIP (sensitivity 79.2%; specificity 90.6%). Conclusions In 96 patients with suspected interstitial lung disease, the Envisia Genomic Classifier identified UIP regardless of HRCT pattern. These results suggest that recognition of a UIP pattern by the Envisia Genomic Classifier combined with HRCT and clinical factors in a multidisciplinary discussion may assist clinicians in making an interstitial lung disease (especially IPF) diagnosis without the need for a surgical lung biopsy.

Abstract Registry-based epidemiologic studies suggest associations between chronic inflammatory intestinal diseases and pancreatic ductal adenocarcinoma (PDAC). As genetic susceptibility contributes to a large proportion of chronic inflammatory intestinal diseases, we hypothesize that the genomic regions surrounding established genome-wide associated variants for these chronic inflammatory diseases are associated with PDAC. We examined the association between PDAC and genomic regions (±500 kb) surrounding established common susceptibility variants for ulcerative colitis, Crohn's disease, inflammatory bowel disease, celiac disease, chronic pancreatitis, and primary sclerosing cholangitis. We analyzed summary statistics from genome-wide association studies data for 8,384 cases and 11,955 controls of European descent from two large consortium studies using the summary data-based adaptive rank truncated product method to examine the overall association of combined genomic regions for each inflammatory disease group. Combined genomic susceptibility regions for ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis were associated with PDAC at P values < 0.05 (0.0040, 0.0057, 0.011, and 3.4 × 10−6, respectively). After excluding the 20 PDAC susceptibility regions (±500 kb) previously identified by GWAS, the genomic regions for ulcerative colitis, Crohn disease, and inflammatory bowel disease remained associated with PDAC (P = 0.0029, 0.0057, and 0.0098, respectively). Genomic regions for celiac disease (P = 0.22) and primary sclerosing cholangitis (P = 0.078) were not associated with PDAC. Our results support the hypothesis that genomic regions surrounding variants associated with inflammatory intestinal diseases, particularly, ulcerative colitis, Crohn disease, inflammatory bowel disease, and chronic pancreatitis are associated with PDAC. Significance: The joint effects of common variants in genomic regions containing susceptibility loci for inflammatory bowel disease and chronic pancreatitis are associated with PDAC and may provide insights to understanding pancreatic cancer etiology.

BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disease that affects aging populations. Current MRI techniques are often limited in their sensitivity to underlying neuropathological changes.PurposeTo characterize differences in voxel‐based morphometry (VBM), apparent diffusion coefficient (ADC), and intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) metrics in aging populations. Additionally, to investigate the connection between cognitive assessments and neuroimaging metrics.Study TypeProspective/cross‐sectional.PopulationIn all, 49 subjects, including 13 with AD dementia, 12 with mild cognitive impairment (MCI), and 24 healthy controls (HC).Field Strength/Sequence3T/magnetization‐prepared rapid acquisition gradient echo (MP‐RAGE) and IVIM‐DWIAssessmentAll participants completed a cognitive screening battery prior to MRI. IVIM‐DWI maps (pure diffusion coefficient [D], pseudodiffusion coefficient [D*], and perfusion fraction [f]) were generated from a biexponential fit of diffusion MRI data. VBM was performed on the standard T1‐weighted MP‐RAGE structural images. Group‐wise templates were used to compare across groups.Statistical TestsAnalysis of covariance (ANCOVA) with gender and age as covariates (familywise error [FWE] corrected, post‐hoc comparisons using Bonferroni correction) for group comparisons. Partial‐η2 and Hedges' g were used for effect‐size analysis. Spearman's correlations (false discovery rate [FDR]‐corrected) for the relationship between cognitive scores and imaging.ResultsClusters of significant group‐wise differences were found mainly in the temporal lobe, hippocampus, and amygdala using all VBM and IVIM methods (P < 0.05 FWE). While VBM showed significant changes between MCI and AD groups and between HC and AD groups, no significant clusters were observed between HC and MCI using VBM. ADC and IVIM‐D demonstrated significant changes, at P < 0.05 FWE, between HC and MCI, notably in the amygdala and hippocampus. Several voxel‐based correlations were observed between neuroimaging metrics and cognitive tests within the cognitively impaired groups (P < 0.05 FDR).Data ConclusionThese findings suggest that IVIM‐DWI metrics may be earlier biomarkers for AD‐related changes than VBM. The use of these techniques may provide novel insight into subvoxel neurodegenerative processes.Level of Evidence2Technical Efficacy Stage2 J. MAGN. RESON. IMAGING 2020;52:1811–1826.

Abstract Background: A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening. Methods: United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3–T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups. Results: The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age. Conclusions: PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS. Impact: Personalized genetic risk assessments could inform prostate cancer screening decisions.

The coronavirus disease 2019 (COVID-19) pandemic is taking a massive toll on health care systems globally. We developed the COVID-19 virtual clinic (CVC) in conjunction with drive through testing to cope with this situation. There are two arms of the CVC: (1) a screening arm and (2) positive patient arm. Screening is performed over the phone based on the Centers for Disease Control and Prevention screening guideline. Positive patients are followed at regular intervals by video appointments where concerns can be addressed by a provider while also tracking symptom progression. We enrolled 63 positive patients out of 1,153 screened for COVID-19 as of this writing. The CVC continues to address patients' concerns and symptoms in an effort to minimize emergency department and hospital patient volumes, as incidence increases. Drive through testing in conjunction with a virtual clinic allows us to provide high-quality care in an anxious time without consuming excessive personal protective equipment or unnecessarily exposing health care workers. This article could serve as a model to guide other practices to cope with this and future pandemics.

1508 Background: With the expansion of multigene panel testing for cancer susceptibility, increasing numbers of patients are identified with pathogenic/likely pathogenic variants (P/LP V) in genes which do not have a clearly actionable increased risk of ovarian cancer (OC) (lifetime risk of OC >5%). However, there is concern that patients and/or providers may ascribe OC risk to such genetic findings with the potential for unnecessary oophorectomy (ooph). Methods: The Prospective Registry of Multiplex Testing (PROMPT) is an online registry for individuals with a genetic alteration detected on multiplex panel testing for cancer susceptibility. Participants self-enroll and complete baseline and annual follow-up questionnaires. PROMPT has enrolled 7388 participants (6936; 93.9% women) since September 2014. Results: 1566 women in the PROMPT registry reported ooph, the indications for which were reported as either cancer treatment (n=481, 30.7%) or benign disease (n=432, 27.6%). An additional 186 (12.8%) reported PV in genes associated with lifetime OC risk >5% ( BRCA1, BRCA2, RAD51C, RAD51D, BRIP, or Lynch syndrome genes). The remaining 467 did not have guideline based indications for ooph due to OC risk and are described further here. 92 (19.7%) had a variant of uncertain significance (VUS) in genes associated with OC, 241 (51.6%) had a personal history of breast cancer (BC) and no VUS in OC genes, and 119 (25.5%) had no personal history of BC and no VUS in OC genes. The majority of women had no family history (FH) of OC in first or second degree relatives (Table). Most ooph occurred prior to age 50. Of the 405 women with CHEK2 P/LP, 11.4% reported ooph (59% under age 50 when age known), as did 13.2% (of 228) with CHEK2 VUS, 8.8% (of 261) with ATM P/LP (66.7% under age 50), and 8.3% (of 387) with ATM VUS. In addition, of the 184 women with PALB2 P/LP, 14.1% reported ooph (35.3% under age 50) as did 11.6% (of 198) with PALB2 VUS. Of those who reported provider discussions, 47.2% stated “my provider recommended this” (including >60% in the OC gene VUS group) and an additional 25.2% stated “my provider presented this as an option, but not a requirement”. In those with no FH of OC, 45.8% stated that their provider recommended ooph. Conclusions: 10-15% of women with PV/VUS in genes not associated with a high risk of OC reported ooph without a clear indication. [Table: see text]