Novel insights into breast cancer copy number genetic heterogeneity revealed by single-cell genome sequencing.


Journal

eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614

Informations de publication

Date de publication:
13 05 2020
Historique:
received: 30 08 2019
accepted: 03 04 2020
entrez: 14 5 2020
pubmed: 14 5 2020
medline: 16 3 2021
Statut: epublish

Résumé

Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse. Cells in the body remain healthy by tightly preventing and repairing random changes, or mutations, in their genetic material. In cancer cells, however, these mechanisms can break down. When these cells grow and multiply, they can then go on to accumulate many mutations. As a result, cancer cells in the same tumor can each contain a unique combination of genetic changes. This genetic heterogeneity has the potential to affect how cancer responds to treatment, and is increasingly becoming appreciated clinically. For example, if a drug only works against cancer cells carrying a specific mutation, any cells lacking this genetic change will keep growing and cause a relapse. However, it is still difficult to quantify and understand genetic heterogeneity in cancer. Copy number alterations (or CNAs) are a class of mutation where large and small sections of genetic material are gained or lost. This can result in cells that have an abnormal number of copies of the genes in these sections. Here, Baslan et al. set out to explore how CNAs might vary between individual cancer cells within the same tumor. To do so, thousands of individual cancer cells were isolated from human breast tumors, and a technique called single-cell genome sequencing used to screen the genetic information of each of them. These experiments confirmed that CNAs did differ – sometimes dramatically – between patients and among cells taken from the same tumor. For example, many of the cells carried extra copies of well-known cancer genes important for treatment, but the exact number of copies varied between cells. This heterogeneity existed for individual genes as well as larger stretches of DNA: this was the case, for instance, for an entire section of chromosome 8, a region often affected in breast and other tumors. The work by Baslan et al. captures the sheer extent of genetic heterogeneity in cancer and in doing so, highlights the power of single-cell genome sequencing. In the future, a finer understanding of the genetic changes present at the level of an individual cancer cell may help clinicians to manage the disease more effectively.

Autres résumés

Type: plain-language-summary (eng)
Cells in the body remain healthy by tightly preventing and repairing random changes, or mutations, in their genetic material. In cancer cells, however, these mechanisms can break down. When these cells grow and multiply, they can then go on to accumulate many mutations. As a result, cancer cells in the same tumor can each contain a unique combination of genetic changes. This genetic heterogeneity has the potential to affect how cancer responds to treatment, and is increasingly becoming appreciated clinically. For example, if a drug only works against cancer cells carrying a specific mutation, any cells lacking this genetic change will keep growing and cause a relapse. However, it is still difficult to quantify and understand genetic heterogeneity in cancer. Copy number alterations (or CNAs) are a class of mutation where large and small sections of genetic material are gained or lost. This can result in cells that have an abnormal number of copies of the genes in these sections. Here, Baslan et al. set out to explore how CNAs might vary between individual cancer cells within the same tumor. To do so, thousands of individual cancer cells were isolated from human breast tumors, and a technique called single-cell genome sequencing used to screen the genetic information of each of them. These experiments confirmed that CNAs did differ – sometimes dramatically – between patients and among cells taken from the same tumor. For example, many of the cells carried extra copies of well-known cancer genes important for treatment, but the exact number of copies varied between cells. This heterogeneity existed for individual genes as well as larger stretches of DNA: this was the case, for instance, for an entire section of chromosome 8, a region often affected in breast and other tumors. The work by Baslan et al. captures the sheer extent of genetic heterogeneity in cancer and in doing so, highlights the power of single-cell genome sequencing. In the future, a finer understanding of the genetic changes present at the level of an individual cancer cell may help clinicians to manage the disease more effectively.

Identifiants

pubmed: 32401198
doi: 10.7554/eLife.51480
pii: 51480
pmc: PMC7220379
doi:
pii:

Substances chimiques

Biomarkers, Tumor 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA045508
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA182514
Pays : United States
Organisme : Department of Defense
ID : W81XWH-11-10747
Pays : International
Organisme : Susan G. Komen
ID : IIR13265578
Pays : United States

Déclaration de conflit d'intérêts

TB, JK, KV, KM, HC, SD, FA, MR, LR, AL, JS, YM, RS, RG, KC, GN, VV, CC, AK, LH, MW, JH No competing interests declared, JW, ND is an employee of Philips Research North America. AG is affiliated with House Gordon Software Company LTD. The author has no other competing interests to declare.

Références

Clin Cancer Res. 2016 Jul 1;22(13):3249-59
pubmed: 26842237
Nat Commun. 2016 Aug 09;7:12498
pubmed: 27502118
Science. 2014 Oct 10;346(6206):256-9
pubmed: 25301631
Nature. 2019 May;569(7757):560-564
pubmed: 31118521
Science. 2006 Oct 13;314(5797):268-74
pubmed: 16959974
Nat Genet. 2013 Dec;45(12):1439-45
pubmed: 24185512
Nature. 2015 Feb 19;518(7539):422-6
pubmed: 25470049
Cancer Res. 2018 Jan 15;78(2):348-358
pubmed: 29180472
Cancer Cell. 2016 May 9;29(5):751-766
pubmed: 27165746
Cancer Cell. 2006 Dec;10(6):529-41
pubmed: 17157792
Nat Rev Cancer. 2017 Aug 24;17(9):557-569
pubmed: 28835719
Nature. 2019 Mar;567(7748):399-404
pubmed: 30867590
Sci Transl Med. 2010 Jun 30;2(38):38ra47
pubmed: 20592421
Nature. 2014 Oct 2;514(7520):54-8
pubmed: 25079331
Cell. 2002 Nov 1;111(3):393-405
pubmed: 12419249
Genome Biol. 2016 Dec 9;17(1):250
pubmed: 27931250
Science. 2018 Nov 30;362(6418):1060-1063
pubmed: 30498128
J Clin Oncol. 2016 Jul 20;34(21):2460-7
pubmed: 27138582
J Clin Oncol. 2015 Apr 20;33(12):1334-9
pubmed: 25559818
BMC Cancer. 2006 Apr 18;6:96
pubmed: 16620391
EMBO J. 2013 Mar 6;32(5):617-28
pubmed: 23395906
Nat Genet. 2019 May;51(5):824-834
pubmed: 31036964
Nature. 2012 Oct 4;490(7418):61-70
pubmed: 23000897
Nature. 2016 May 02;534(7605):47-54
pubmed: 27135926
Genes Chromosomes Cancer. 2002 Jan;33(1):1-16
pubmed: 11746982
Nature. 2012 Jun 20;486(7403):405-9
pubmed: 22722202
Cancer Genet Cytogenet. 2003 Nov;147(1):62-7
pubmed: 14580772
Cancer Cell. 2014 Mar 17;25(3):282-303
pubmed: 24651011
Cell. 2019 Nov 14;179(5):1207-1221.e22
pubmed: 31730858
Nat Genet. 2014 Oct;46(10):1051-9
pubmed: 25151356
Am J Hum Genet. 1990 Jun;46(6):1101-11
pubmed: 2339703
Nature. 2015 Jun 4;522(7554):106-110
pubmed: 26017313
Nat Protoc. 2012 May 03;7(6):1024-41
pubmed: 22555242
Genome Res. 2020 Jan;30(1):49-61
pubmed: 31727682
Genome Res. 2015 May;25(5):714-24
pubmed: 25858951
Nature. 2017 Mar 2;543(7643):122-125
pubmed: 28178237
J Clin Oncol. 2010 Jul 10;28(20):3366-79
pubmed: 20530283
Nature. 2011 Apr 7;472(7341):90-4
pubmed: 21399628
Nature. 2015 Apr 16;520(7547):358-62
pubmed: 25855289
Nat Med. 2015 Jul;21(7):751-9
pubmed: 26099045
Nature. 2012 Apr 04;486(7403):395-9
pubmed: 22495314
Nat Biotechnol. 2015 Mar;33(3):285-289
pubmed: 25599178
Nat Med. 2017 Mar;23(3):376-385
pubmed: 28165479
Cancer Discov. 2014 Aug;4(8):956-71
pubmed: 24893890
J Clin Oncol. 2016 Feb 20;34(6):542-9
pubmed: 26527775
Nature. 2012 Jun 10;486(7403):353-60
pubmed: 22722193
N Engl J Med. 2015 Jul 16;373(3):209-19
pubmed: 26030518
Neuron. 2012 Apr 26;74(2):285-99
pubmed: 22542183
Nat Rev Cancer. 2015 May;15(5):261-75
pubmed: 25907219
Proc Natl Acad Sci U S A. 2012 Feb 21;109(8):2796-801
pubmed: 21825174
Oncol Lett. 2017 Apr;13(4):2027-2033
pubmed: 28454358
Nature. 2015 Dec 3;528(7580):142-6
pubmed: 26605532
Nat Cell Biol. 2015 May;17(5):651-64
pubmed: 25866923
Science. 2014 Oct 10;346(6206):251-6
pubmed: 25301630
Cell. 2012 May 25;149(5):994-1007
pubmed: 22608083
JAMA. 2006 Jun 7;295(21):2492-502
pubmed: 16757721
Cell. 2014 Feb 27;156(5):1002-16
pubmed: 24581498
Cell. 2015 Oct 8;163(2):506-19
pubmed: 26451490
Nat Med. 2017 Aug;23(8):929-937
pubmed: 28714990
Nature. 2012 Apr 18;486(7403):346-52
pubmed: 22522925
Science. 2015 Apr 3;348(6230):56-61
pubmed: 25838373
Nat Methods. 2015 Jun;12(6):519-22
pubmed: 25915121

Auteurs

Timour Baslan (T)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.
Department of Molecular and Cellular Biology, Stony Brook University, Stony Brook, United States.

Jude Kendall (J)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.

Konstantin Volyanskyy (K)

Philips Research North America, Biomedical Informatics, Cambridge, United States.

Katherine McNamara (K)

Department of Genetics, Stanford University School of Medicine, Stanford, United States.

Hilary Cox (H)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.

Sean D'Italia (S)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.

Frank Ambrosio (F)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.

Michael Riggs (M)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.

Linda Rodgers (L)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.

Anthony Leotta (A)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.

Junyan Song (J)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, United States.

Yong Mao (Y)

Philips Research North America, Biomedical Informatics, Cambridge, United States.

Jie Wu (J)

Philips Research North America, Biomedical Informatics, Cambridge, United States.

Ronak Shah (R)

Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, United States.

Rodrigo Gularte-Mérida (R)

Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, United States.

Kalyani Chadalavada (K)

Molecular Cytogenetics Core Facility, Memorial Sloan Kettering Cancer Center, New York, United States.

Gouri Nanjangud (G)

Molecular Cytogenetics Core Facility, Memorial Sloan Kettering Cancer Center, New York, United States.

Vinay Varadan (V)

Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, United States.

Assaf Gordon (A)

House Gordon Software Company LTD, Calgary, Canada.

Christina Curtis (C)

Department of Genetics, Stanford University School of Medicine, Stanford, United States.

Alex Krasnitz (A)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.

Nevenka Dimitrova (N)

Philips Research North America, Biomedical Informatics, Cambridge, United States.

Lyndsay Harris (L)

Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, United States.
Division of Hematology/Oncology, Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, United States.
Seidman Cancer Center, University Hospitals of Case Western, Cleveland, United States.

Michael Wigler (M)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.

James Hicks (J)

Cold Spring Harbor Laboratory, Cold Spring Harbor, United States.

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