Multiple Germline Events Contribute to Cancer Development in Patients with Li-Fraumeni Syndrome

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Abstract

Li-Fraumeni syndrome (LFS) is an autosomal dominant cancer-predisposition disorder. Approximately 70% of individuals who fit the clinical definition of LFS harbor a pathogenic germline variant in the TP53 tumor suppressor gene. However, the remaining 30% of patients lack a TP53 variant and even among variant TP53 carriers , approximately 20% remain cancer-free. Understanding the variable cancer penetrance and phenotypic variability in LFS is critical to developing rational approaches to accurate, early tumor detection and risk-reduction strategies. We leveraged family-based whole-genome sequencing and DNA methylation to evaluate the germline genomes of a large, multi-institutional cohort of patients with LFS ( n = 396) with variant ( n = 374) or wildtype TP53 ( n = 22). We identified alternative cancer-associated genetic aberrations in 8/14 wildtype TP53 carriers who developed cancer. Among variant TP53 carriers, 19/49 who developed cancer harbored a pathogenic variant in another cancer gene. Modifier variants in the WNT signaling pathway were associated with decreased cancer incidence. Furthermore, we leveraged the noncoding genome and methylome to identify inherited epimutations in genes including ASXL1, ETV6, and LEF1 that confer increased cancer risk. Using these epimutations, we built a machine learning model that can predict cancer risk in patients with LFS with an area under the receiver operator characteristic curve (AUROC) of 0.725 (0.633–0.810).

Significance:

Our study clarifies the genomic basis for the phenotypic variability in LFS and highlights the immense benefits of expanding genetic and epigenetic testing of patients with LFS beyond TP53. More broadly, it necessitates the dissociation of hereditary cancer syndromes as single gene disorders and emphasizes the importance of understanding these diseases in a holistic manner as opposed to through the lens of a single gene.

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Most cited references 83

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Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

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Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

Sue Richards, Nazneen Aziz, Sherri Bale … (2015)

The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.

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The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Aaron McKenna, Matthew Hanna, Eric R. Banks … (2010)

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

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Author and article information

Journal

Journal ID (nlm-ta): Cancer Res Commun

Journal ID (iso-abbrev): Cancer Res Commun

Title: Cancer Research Communications

Publisher: American Association for Cancer Research

ISSN (Electronic): 2767-9764

Publication date Collection: May 2023

Publication date (Electronic): 01 May 2023

Volume: 3

Issue: 5

Pages: 738-754

Affiliations

[1 ]Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.

[2 ]Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.

[3 ]Vector Institute, Toronto, Ontario, Canada.

[4 ]Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada.

[5 ]Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada.

[6 ]Division of Haematology/Oncology, The Hospital for Sick Children, Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada.

[7 ]Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.

[8 ]Children's Cancer Centre, Royal Children's Hospital, Melbourne, Victoria, Australia.

[9 ]Murdoch Children's Research Institute, Parkville, Victoria, Australia.

[10 ]Department of Pediatrics, University of Melbourne, Melbourne, Australia.

[11 ]Michael Rice Cancer Centre, Women's and Children's Hospital, North Adelaide, South Australia, Australia.

[12 ]South Australia Health and Medical Research Institute, Adelaide, South Australia, Australia.

[13 ]South Australia Immunogenomics Cancer Institute, University of Adelaide, Adelaide, Australia.

[14 ]Department of Paediatrics, London Health Sciences Centre and Western University, London, Ontario, Canada.

[15 ]Department of Paediatrics, McMaster University, Hamilton, Ontario, Canada.

[16 ]Department of Medical Oncology, Princess Margaret Hospital and Mount Sinai Hospital, Toronto, Ontario, Canada.

[17 ]Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.

[18 ]Neuro-Oncology Program, Nationwide Children's Hospital and The Ohio State University, Columbus, Ohio.

[19 ]Department of Oncology, St Jude Children's Research Hospital, Memphis, Tennessee.

[20 ]Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.

[21 ]Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland.

[22 ]Department of Pediatrics, University of Utah, Salt Lake City, Utah.

[23 ]PEEL Therapeutics, Inc., Salt Lake City, Utah.

[24 ]Ontario Institute for Cancer Research, Toronto, Ontario, Canada.

[25 ]CIFAR: Child and Brain Development, Toronto, Ontario, Canada.

[26 ]Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.

Author notes

Corresponding Author: David Malkin, The Hospital for Sick Children, 555 University Avenue, Toronto, ON M5G 1X8, Canada. Phone: 416-813-5348; E-mail: david.malkin@ 123456sickkids.ca