Rate of incidental germline findings detected by tumor-normal matched sequencing in cancer types lacking hereditary cancer testing guidelines.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10582-10582
Author(s):  
Timothy A. Yap ◽  
Arya Ashok ◽  
Jessica Stoll ◽  
Anna Ewa Schwarzbach ◽  
Kimberly L. Blackwell ◽  
...  
Keyword(s):  
Bile Duct ◽  
Hereditary Cancer ◽  
Next Generation Sequencing Data ◽  
Cancer Type ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Germline Variants ◽  
Cancer Types ◽  
Germline Testing

10582 Background: Up to 10% of all cancers are associated with hereditary cancer syndromes; however, guidelines for germline testing are currently limited to patients and families with specific cancer types (ovarian, breast, prostate, pancreatic, etc.). Although germline alterations have been shown in genes associated with cancers such as bile-duct, head & neck, brain, bladder, esophageal, and lung cancers, genetic testing is not routinely offered (PMID: 28873162). In such cancers, a guidelines-based approach may fail to detect cancer risk variants found by tumor-normal (T/N) matched sequencing. Here, we report the prevalence of incidental germline findings in patients with the aforementioned 6 cancer types and highlight frequently mutated genes by cancer type. Methods: We retrospectively analyzed next-generation sequencing data from de-identified records of 19,630 patients tested using Tempus|xT T/N matched assay. Incidental germline findings (i.e., single nucleotide variants and small insertions/deletions) detected in 50 hereditary cancer genes were determined for: bile duct (n = 466), head & neck (n = 673), esophageal (n = 395), brain (n = 1,391), bladder (n = 810), and lung (n = 5,544), where n = total patients. For comparison, we also included 4 cancer types that frequently undergo germline testing: ovarian (n = 2,042), breast (n = 3,542), prostate (n = 2,146), and pancreatic (n = 2,621). Results: We detected incidental pathogenic/likely pathogenic germline variants (P/LPV) in 6.5% (601/9,279) of patients diagnosed with the 6 selected cancer types lacking hereditary cancer testing guidelines. The highest prevalence of P/LPV was identified in patients with bladder (8%), brain (6.9%), and lung (6.5%) cancers. Frequently mutated genes (Table) include ATM (n = 62), BRCA2 (n = 60), BRCA1 (n = 33), APC (n = 27), and CHEK2 (n = 21). Of note, the Ashkenazi Jewish variant (p.I1307K) was the most frequent mutation in APC. For cancer types where patients frequently undergo germline testing, the rates of incidental germline findings in descending order were ovarian (15%), breast (12%), prostate (9.4%), and pancreatic (8.5%) cancers. Conclusions: In addition to enhanced variant calling, T/N matched sequencing may identify germline variants missed by a guidelines-based approach to testing. The identification of such germline findings may have clinical implications for the patient, as well as at-risk family members, thereby resulting in the opportunity for genetic counseling and risk-stratified intervention.[Table: see text]

scholarly journals Deep learning for cancer type classification

2019 ◽  
Author(s):  
Zexian Zeng ◽  
Chengsheng Mao ◽  
Andy Vo ◽  
Janna Ore Nugent ◽  
Seema A Khan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Somatic Mutations ◽  
Disease Classification ◽  
Cancer Type ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Germline Variants ◽  
Cancer Types ◽  
Independent Features

ABSTRACTGenetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. To address these limitations, we propose DeepCues, a deep learning model that utilizes convolutional neural networks to derive features from DNA sequencing data for disease classification and relevant gene discovery. Using whole-exome sequencing, germline variants and somatic mutations, including insertions and deletions, are interactively amalgamated as features. In this study, we applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (p=8.8E-25). Using DeepCues, we found that the top 20 genes associated with breast cancer have a 40% overlap with the top 20 breast cancer genes in the COSMIC database. These data support DeepCues as a novel method to improve the representational resolution of both germline variants and somatic mutations interactively and their power in predicting cancer types, as well the genes involved in each cancer.

Suspected and confirmed germline variants from tumor-only somatic sequencing of 864 gastrointestinal malignancies.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13131-e13131
Author(s):  
Shivani Khanna ◽  
Steven Brad Maron ◽  
Leah Chase ◽  
Samantha Lomnicki ◽  
Sonia Kupfer ◽  
...  
Keyword(s):  
Genetic Testing ◽  
Allele Frequency ◽  
Hereditary Cancer ◽  
Univariate Analysis ◽  
Genetic Evaluation ◽  
Pathogenic Variant ◽  
Cancer Genes ◽  
Gastrointestinal Malignancies ◽  
Germline Variants ◽  
Pathogenic Variants

e13131 Background: Targeted tumor-only somatic sequencing informs therapies and is becoming a routine part of cancer care. It also identifies patients with possible germline variants who require confirmatory genetic testing. The aim was to identify patients with suspected and confirmed germline variants whose GI tumors underwent somatic sequencing. Methods: 864 patients with GI tumors who had Foundation One (FO) somatic sequencing from 4/2003-3/2018 were evaluated. Inclusion criteria for suspected germline variants were: a) allele frequency ≥ 35% in hereditary cancer genes and b) pathogenic variants by FO and/or ClinVar. Variants in commonly mutated somatic genes ( TP53, KRAS, CDKN2A) were excluded in patients over age 40. Recommendation of genetic evaluation and germline test results were recorded. Patient, family, and tumor characteristics were compared using univariate analysis. Results: 199 of 864 patients had suspected germline pathogenic variants. 50 patients were recommended genetic evaluation, and 26 patients underwent genetic testing. A germline pathogenic variant was confirmed in 15 patients. Among all patients suspected to have germline variants, 8% were confirmed by genetic testing. Patients under age 40 and those with family cancer history were more often referred for testing (Table). Patients with variants in BRCA1, MLH1, MSH2, PMS2, POLE and TP53 were more often referred for testing. Conclusions: A quarter of patients carried a somatic pathogenic variant with allele frequency ≥35% in a hereditary cancer gene. 25% of these patients were recommended for genetic evaluation. Younger patients and those with family history were more often referred. 8% of patients with suspected germline variants were confirmed by genetic testing. These results provide “real world” experience in using somatic only tumor testing to identify patients with germline pathogenic variants who then might benefit from future cancer screening and genetic testing in family members. Comparison of characteristics by recommendation to undergo genetic testing based on somatic tumor sequencing results. [Table: see text]

Confirmation of germline variants identified by tumor testing: A population-based study.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e13021-e13021
Author(s):  
Alexandra Pender ◽  
Aly Karsan ◽  
Stephen Yip ◽  
Ian Bosdet ◽  
Sean Young ◽  
...  
Keyword(s):  
Hereditary Cancer ◽  
Cancer Susceptibility ◽  
Population Based ◽  
Driver Mutations ◽  
Low Grade ◽  
Germline Variants ◽  
Referral Rates ◽  
Therapeutic Implications ◽  
Cancer Susceptibility Genes ◽  
Germline Testing

e13021 Background: Multi-gene panel tumour testing (TT) has been available in British Columbia since mid-2016 for metastatic non-small cell lung cancer (NSCLC), colorectal cancer (CRC), melanoma (MEL), low-grade glioma (LGG), and gastro-intestinal stromal tumours (GIST). TT can detect somatic driver mutations and potential pathogenic germline variants (pPGVs) associated with hereditary cancer susceptibility. We reviewed the frequency of pPGVs identified by TT and examined referral rates to the Hereditary Cancer Program (HCP) for confirmatory germline testing (GT) and therapeutic implications of PGV findings. Methods: All patients (pts) undergoing TT testing from October 1, 2016 to December 31, 2018 were identified. Diagnosis, age, gender, family history and treatment data were obtained. TT was performed by next-generation sequencing for all/selected regions of the following genes: AKT1, ALK, BRAF, BRCA1, BRCA2, CCND1, CCND3, CIC, EGFR, ERBB2, ERBB3, FUBP1, HRAS, IDH1, IDH2, KIT, KRAS, MAP2K1, MET, NRAS, PDGFRA, PIK3CA, PTEN, ROS1, SDHA, SDHB, SDHC, SDHD. Results: Among 2937 TTs, pPGVs were identified in 83 pts (2.8%) [Table 1]. 50 pts (57%) were referred to HCP, 41 had germline testing, and 14 PGV were confirmed. PGVs were most commonly identified in BRCA1/2 and SDHA and these findings did not influence oncologic treatments. Conclusions: TT detected pPGVs in 2.8% of unselected pts with metastatic cancers. Among 41 pts undergoing germline testing, 34% who would not have otherwise met testing criteria, had a confirmed PGV. Referral rates were low due to lack of patient and clinician awareness and poor health status. Although PGV findings did not directly impact treatment, TT identified 14 new families with hereditary cancer who can benefit from early detection and screening. Future directions include expansion of TT to include additional hereditary cancer susceptibility genes and development of digital tools for pts and clinicians. [Table: see text]

scholarly journals Genomic basis for RNA alterations revealed by whole-genome analyses of 27 cancer types

2017 ◽  
Author(s):  
◽  
Claudia Calabrese ◽  
Natalie R. Davidson ◽  
Nuno A. Fonseca ◽  
Yao He ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Specific Expression ◽  
Alu Elements ◽  
Cancer Types ◽  
Genome Analyses ◽  
Gene Alterations

AbstractWe present the most comprehensive catalogue of cancer-associated gene alterations through characterization of tumor transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes project. Using matched whole-genome sequencing data, we attributed RNA alterations to germline and somatic DNA alterations, revealing likely genetic mechanisms. We identified 444 associations of gene expression with somatic non-coding single-nucleotide variants. We found 1,872 splicing alterations associated with somatic mutation in intronic regions, including novel exonization events associated with Alu elements. Somatic copy number alterations were the major driver of total gene and allele-specific expression (ASE) variation. Additionally, 82% of gene fusions had structural variant support, including 75 of a novel class called “bridged” fusions, in which a third genomic location bridged two different genes. Globally, we observe transcriptomic alteration signatures that differ between cancer types and have associations with DNA mutational signatures. Given this unique dataset of RNA alterations, we also identified 1,012 genes significantly altered through both DNA and RNA mechanisms. Our study represents an extensive catalog of RNA alterations and reveals new insights into the heterogeneous molecular mechanisms of cancer gene alterations.

scholarly journals Copy number variation analysis increases the diagnostic yield in muscle diseases

2017 ◽  
Vol 3 (6) ◽  
pp. e204 ◽  
Author(s):  
Salla Välipakka ◽  
Marco Savarese ◽  
Mridul Johari ◽  
Lydia Sagath ◽  
Meharji Arumilli ◽  
...  
Keyword(s):  
Muscular Dystrophy ◽  
Copy Number ◽  
Diagnostic Yield ◽  
Dystrophin Gene ◽  
Final Diagnosis ◽  
Next Generation Sequencing Data ◽  
Comparative Genomic ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Muscle Diseases

Objective:Copy number variants (CNVs) were analyzed from next-generation sequencing data, with the aim of improving diagnostic yield in skeletal muscle disorder cases.Methods:Four publicly available bioinformatic analytic tools were used to analyze CNVs from sequencing data from patients with muscle diseases. The patients were previously analyzed with a targeted gene panel for single nucleotide variants and small insertions and deletions, without achieving final diagnosis. Variants detected by multiple CNV analysis tools were verified with either array comparative genomic hybridization or PCR. The clinical significance of the verified CNVs was interpreted, considering previously identified variants, segregation studies, and clinical information of the patient cases.Results:Combining analysis of all different mutation types enabled integration of results and identified the final cause of the disease in 9 myopathy cases. Complex effects like compound heterozygosity of different mutation types and compound disease arising from variants of different genes were unraveled. We identified the first large intragenic deletion of the titin (TTN) gene implicated in the pathogenesis of a severe form of myopathy. Our work also revealed a “double-trouble” effect in a patient carrying a single heterozygous insertion/deletion mutation in the TTN gene and a Becker muscular dystrophy causing deletion in the dystrophin gene.Conclusions:Causative CNVs were identified proving that analysis of CNVs is essential for increasing the diagnostic yield in muscle diseases. Complex severe muscular dystrophy phenotypes can be the result of different mutation types but also of the compound effect of 2 different genetic diseases.

scholarly journals dv-trio: a family-based variant calling pipeline using DeepVariant

2020 ◽  
Vol 36 (11) ◽  
pp. 3549-3551 ◽  
Author(s):  
Eddie K K Ip ◽  
Clinton Hadinata ◽  
Joshua W K Ho ◽  
Eleni Giannoulatou
Keyword(s):  
Genetic Model ◽  
Variant Calling ◽  
Supplementary Information ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Mutation Discovery ◽  
Family Trio ◽  
Sequencing Studies ◽  
Family Based

Abstract Motivation In 2018, Google published an innovative variant caller, DeepVariant, which converts pileups of sequence reads into images and uses a deep neural network to identify single-nucleotide variants and small insertion/deletions from next-generation sequencing data. This approach outperforms existing state-of-the-art tools. However, DeepVariant was designed to call variants within a single sample. In disease sequencing studies, the ability to examine a family trio (father-mother-affected child) provides greater power for disease mutation discovery. Results To further improve DeepVariant’s variant calling accuracy in family-based sequencing studies, we have developed a family-based variant calling pipeline, dv-trio, which incorporates the trio information from the Mendelian genetic model into variant calling based on DeepVariant. Availability and implementation dv-trio is available via an open source BSD3 license at GitHub (https://github.com/VCCRI/dv-trio/). Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Germline Sequencing Improves Tumor-Only Sequencing Interpretation in a Precision Genomic Study of Patients With Pediatric Solid Tumor

2021 ◽  
pp. 1840-1852
Author(s):  
Jaclyn Schienda ◽  
Alanna J. Church ◽  
Laura B. Corson ◽  
Brennan Decker ◽  
Catherine M. Clinton ◽  
...  
Keyword(s):  
Copy Number ◽  
Integrated Analysis ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Clinical Interpretation ◽  
Germline Variants ◽  
Panel Testing ◽  
Pathogenic Variants ◽  
Genomic Study

PURPOSE Molecular tumor profiling is becoming a routine part of clinical cancer care, typically involving tumor-only panel testing without matched germline. We hypothesized that integrated germline sequencing could improve clinical interpretation and enhance the identification of germline variants with significant hereditary risks. MATERIALS AND METHODS Tumors from pediatric patients with high-risk, extracranial solid malignancies were sequenced with a targeted panel of cancer-associated genes. Later, germline DNA was analyzed for a subset of these genes. We performed a post hoc analysis to identify how an integrated analysis of tumor and germline data would improve clinical interpretation. RESULTS One hundred sixty participants with both tumor-only and germline sequencing reports were eligible for this analysis. Germline sequencing identified 38 pathogenic or likely pathogenic variants among 35 (22%) patients. Twenty-five (66%) of these were included in the tumor sequencing report. The remaining germline pathogenic or likely pathogenic variants were single-nucleotide variants filtered out of tumor-only analysis because of population frequency or copy-number variation masked by additional copy-number changes in the tumor. In tumor-only sequencing, 308 of 434 (71%) single-nucleotide variants reported were present in the germline, including 31% with suggested clinical utility. Finally, we provide further evidence that the variant allele fraction from tumor-only sequencing is insufficient to differentiate somatic from germline events. CONCLUSION A paired approach to analyzing tumor and germline sequencing data would be expected to improve the efficiency and accuracy of distinguishing somatic mutations and germline variants, thereby facilitating the process of variant curation and therapeutic interpretation for somatic reports, as well as the identification of variants associated with germline cancer predisposition.

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