Profiling the Somatic Mutational Landscape of Breast Tumors from Hispanic/Latina Women Reveals Conserved and Unique Characteristics

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Abstract

Comprehensive characterization of genomic and transcriptomic alterations in breast tumors from Hispanic/Latina patients reveals distinct genetic alterations and signatures, demonstrating the importance of inclusive studies to ensure equitable care for patients.

Abstract

Somatic mutational profiling is increasingly being used to identify potential targets for breast cancer. However, limited tumor-sequencing data from Hispanic/Latinas (H/L) are available to guide treatment. To address this gap, we performed whole-exome sequencing (WES) and RNA sequencing on 146 tumors and WES of matched germline DNA from 140 H/L women in California. Tumor intrinsic subtype, somatic mutations, copy-number alterations, and expression profiles of the tumors were characterized and compared with data from tumors of non-Hispanic White (White) women in The Cancer Genome Atlas (TCGA). Eight genes were significantly mutated in the H/L tumors including PIK3CA, TP53, GATA3, MAP3K1, CDH1, CBFB, PTEN, and RUNX1; the prevalence of mutations in these genes was similar to that observed in White women in TCGA. Four previously reported Catalogue of Somatic Mutations in Cancer (COSMIC) mutation signatures (1, 2, 3, 13) were found in the H/L dataset, along with signature 16 that has not been previously reported in other breast cancer datasets. Recurrent amplifications were observed in breast cancer drivers including MYC, FGFR1, CCND1, and ERBB2, as well as a recurrent amplification in 17q11.2 associated with high KIAA0100 gene expression that has been implicated in breast cancer aggressiveness. In conclusion, this study identified a higher prevalence of COSMIC signature 16 and a recurrent copy-number amplification affecting expression of KIAA0100 in breast tumors from H/L compared with White women. These results highlight the necessity of studying underrepresented populations.

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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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Second-generation PLINK: rising to the challenge of larger and richer datasets

Christopher Chang, Carson Chow, Laurent Tellier … (2015)

PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

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Comprehensive molecular portraits of human breast tumors

Nikolaus Schultz (2013)

Summary We analyzed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing and reverse phase protein arrays. Our ability to integrate information across platforms provided key insights into previously-defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at > 10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the Luminal A subtype. We identified two novel protein expression-defined subgroups, possibly contributed by stromal/microenvironmental elements, and integrated analyses identified specific signaling pathways dominant in each molecular subtype including a HER2/p-HER2/HER1/p-HER1 signature within the HER2-Enriched expression subtype. Comparison of Basal-like breast tumors with high-grade Serous Ovarian tumors showed many molecular commonalities, suggesting a related etiology and similar therapeutic opportunities. The biologic finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biologic subtypes of breast cancer.

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Author and article information

Journal

Journal ID (nlm-ta): Cancer Res

Journal ID (iso-abbrev): Cancer Res

Title: Cancer Research

Publisher: American Association for Cancer Research

ISSN (Print): 0008-5472

ISSN (Electronic): 1538-7445

Publication date (Print): 01 August 2023

Publication date (Electronic): 05 May 2023

Volume: 83

Issue: 15

Pages: 2600-2613

Affiliations

[1 ]Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, California.

[2 ]Division of Hematology/Oncology, Department of Medicine, University of California, San Francisco, San Francisco, California.

[3 ]Department of Pathology, City of Hope Medical Center, Duarte, California.

[4 ]Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California.

[5 ]Integrative Genomics Shared Resource, Beckman Research Institute of City of Hope, Duarte, California.

[6 ]Western University of Health Sciences College of Graduate Nursing, Pomona, California.

[7 ]Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

[8 ]Department of Public Health Sciences and Comprehensive Cancer Center, University of California Davis, Davis, California.

[9 ]Department of Pathology and Cell Biology, Columbia University Irvine Medical Center, New York, New York.

[10 ]Division of Intramural Research, National Institute on Minority and Health Disparities, National Institutes of Health, Bethesda, Maryland.

[11 ]National Institute on Minority and Health Disparities, NIH, Bethesda, Maryland.

[12 ]Latin American School of Oncology, Sierra Madre, California.

[13 ]Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California.

[14 ]Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California.

[15 ]Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California.

[16 ]Institute for Human Genetics, University of California, San Francisco, San Francisco, California.

[17 ]Chan Zuckerberg Biohub, San Francisco, California.

[18 ]Department of Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, California.

Author notes

[#]

Y.C. Ding, H. Song, and A.W Adamson contributed equally to this article.

[* ] Corresponding Authors: Susan L. Neuhausen, City of Hope, 1500 E Duarte, Duarte, CA 91010. Phone: 626-218-5261; E-mail: sneuhausen@ 123456coh.org ; and Elad Ziv, E-mail: elad.ziv@ 123456ucsf.edu

Cancer Res 2023;83:2600–13

Author information

Yuan Chun Ding https://orcid.org/0000-0001-5578-6942

Hanbing Song https://orcid.org/0000-0002-2164-8813

Aaron W. Adamson https://orcid.org/0000-0002-8515-9787

Daniel Schmolze https://orcid.org/0000-0002-6947-2546

Donglei Hu https://orcid.org/0000-0002-0351-001X

Scott Huntsman https://orcid.org/0000-0002-5440-6191

Linda Steele https://orcid.org/0000-0003-3628-2022

Carmina S. Patrick https://orcid.org/0000-0002-8248-818X

Shu Tao https://orcid.org/0000-0001-5177-6771

Natalie Hernandez https://orcid.org/0009-0000-7012-5499

Charleen D. Adams https://orcid.org/0000-0002-5817-7226

Laura Fejerman https://orcid.org/0000-0003-3179-1151

Kevin Gardner https://orcid.org/0000-0001-8018-4353

Anna María Nápoles https://orcid.org/0000-0001-8838-2899

Eliseo J. Pérez-Stable https://orcid.org/0000-0002-1577-2738

Jeffrey N. Weitzel https://orcid.org/0000-0001-6714-092X

Henrik Bengtsson https://orcid.org/0000-0002-7579-5165

Franklin W. Huang https://orcid.org/0000-0001-5447-0436

Susan L. Neuhausen https://orcid.org/0000-0001-5053-0390

Elad Ziv https://orcid.org/0000-0002-2324-2884

Article

Publisher ID: CAN-22-2510

DOI: 10.1158/0008-5472.CAN-22-2510

PMC ID: 10390863

PubMed ID: 37145128

SO-VID: e7a23c04-3913-4284-a938-a43b42ac5af2

License:

This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

History

Date received : 12 August 2022

Date revision received : 16 February 2023

Date accepted : 02 May 2023

Page count

Pages: 14

Funding

Funded by: National Cancer Institute (NCI), https://doi.org/10.13039/100000054;

Award ID: R01CA184585

Award Recipient : Y.C. Ding D. Hu S. Huntsman L. Steele J.N. Weitzel S.L. Neuhausen E. Ziv

Profiling the Somatic Mutational Landscape of Breast Tumors from Hispanic/Latina Women Reveals Conserved and Unique Characteristics

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Most cited references 58

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

Second-generation PLINK: rising to the challenge of larger and richer datasets

Comprehensive molecular portraits of human breast tumors

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