Molecular characteristics of breast tumors in patients screened for germline predisposition from a population-based observational study.
Clinical screening
Gene expression
Gene variants
Hereditary breast cancer
Molecular subtypes
Journal
Genome medicine
ISSN: 1756-994X
Titre abrégé: Genome Med
Pays: England
ID NLM: 101475844
Informations de publication
Date de publication:
14 04 2023
14 04 2023
Historique:
received:
26
10
2022
accepted:
05
04
2023
medline:
18
4
2023
entrez:
14
4
2023
pubmed:
15
4
2023
Statut:
epublish
Résumé
Pathogenic germline variants (PGVs) in certain genes are linked to higher lifetime risk of developing breast cancer and can influence preventive surgery decisions and therapy choices. Public health programs offer genetic screening based on criteria designed to assess personal risk and identify individuals more likely to carry PGVs, dividing patients into screened and non-screened groups. How tumor biology and clinicopathological characteristics differ between these groups is understudied and could guide refinement of screening criteria. Six thousand six hundred sixty breast cancer patients diagnosed in South Sweden during 2010-2018 were included with available clinicopathological and RNA sequencing data, 900 (13.5%) of which had genes screened for PGVs through routine clinical screening programs. We compared characteristics of screened patients and tumors to non-screened patients, as well as between screened patients with (n = 124) and without (n = 776) PGVs. Broadly, breast tumors in screened patients showed features of a more aggressive disease. However, few differences related to tumor biology or patient outcome remained significant after stratification by clinical subgroups or PAM50 subtypes. Triple-negative breast cancer (TNBC), the subgroup most enriched for PGVs, showed the most differences between screening subpopulations (e.g., higher tumor proliferation in screened cases). Significant differences in PGV prevalence were found between clinical subgroups/molecular subtypes, e.g., TNBC cases were enriched for BRCA1 PGVs. In general, clinicopathological differences between screened and non-screened patients mimicked those between patients with and without PGVs, e.g., younger age at diagnosis for positive cases. However, differences in tumor biology/microenvironment such as immune cell composition were additionally seen within PGV carriers/non-carriers in ER + /HER2 - cases, but not between screening subpopulations in this subgroup. Characterization of molecular tumor features in patients clinically screened and not screened for PGVs represents a relevant read-out of guideline criteria. The general lack of molecular differences between screened/non-screened patients after stratification by relevant breast cancer subsets questions the ability to improve the identification of screening candidates based on currently used patient and tumor characteristics, pointing us towards universal screening. Nevertheless, while that is not attained, molecular differences identified between PGV carriers/non-carriers suggest the possibility of further refining patient selection within certain patient subsets using RNA-seq through, e.g., gene signatures. The Sweden Cancerome Analysis Network - Breast (SCAN-B) was prospectively registered at ClinicalTrials.gov under the identifier NCT02306096.
Sections du résumé
BACKGROUND
Pathogenic germline variants (PGVs) in certain genes are linked to higher lifetime risk of developing breast cancer and can influence preventive surgery decisions and therapy choices. Public health programs offer genetic screening based on criteria designed to assess personal risk and identify individuals more likely to carry PGVs, dividing patients into screened and non-screened groups. How tumor biology and clinicopathological characteristics differ between these groups is understudied and could guide refinement of screening criteria.
METHODS
Six thousand six hundred sixty breast cancer patients diagnosed in South Sweden during 2010-2018 were included with available clinicopathological and RNA sequencing data, 900 (13.5%) of which had genes screened for PGVs through routine clinical screening programs. We compared characteristics of screened patients and tumors to non-screened patients, as well as between screened patients with (n = 124) and without (n = 776) PGVs.
RESULTS
Broadly, breast tumors in screened patients showed features of a more aggressive disease. However, few differences related to tumor biology or patient outcome remained significant after stratification by clinical subgroups or PAM50 subtypes. Triple-negative breast cancer (TNBC), the subgroup most enriched for PGVs, showed the most differences between screening subpopulations (e.g., higher tumor proliferation in screened cases). Significant differences in PGV prevalence were found between clinical subgroups/molecular subtypes, e.g., TNBC cases were enriched for BRCA1 PGVs. In general, clinicopathological differences between screened and non-screened patients mimicked those between patients with and without PGVs, e.g., younger age at diagnosis for positive cases. However, differences in tumor biology/microenvironment such as immune cell composition were additionally seen within PGV carriers/non-carriers in ER + /HER2 - cases, but not between screening subpopulations in this subgroup.
CONCLUSIONS
Characterization of molecular tumor features in patients clinically screened and not screened for PGVs represents a relevant read-out of guideline criteria. The general lack of molecular differences between screened/non-screened patients after stratification by relevant breast cancer subsets questions the ability to improve the identification of screening candidates based on currently used patient and tumor characteristics, pointing us towards universal screening. Nevertheless, while that is not attained, molecular differences identified between PGV carriers/non-carriers suggest the possibility of further refining patient selection within certain patient subsets using RNA-seq through, e.g., gene signatures.
TRIAL REGISTRATION
The Sweden Cancerome Analysis Network - Breast (SCAN-B) was prospectively registered at ClinicalTrials.gov under the identifier NCT02306096.
Identifiants
pubmed: 37060015
doi: 10.1186/s13073-023-01177-4
pii: 10.1186/s13073-023-01177-4
pmc: PMC10103478
doi:
Banques de données
ClinicalTrials.gov
['NCT02306096']
Types de publication
Observational Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
25Informations de copyright
© 2023. The Author(s).
Références
Medicine (Baltimore). 2016 Oct;95(40):e4975
pubmed: 27749552
J Natl Cancer Inst. 2018 Aug 1;110(8):855-862
pubmed: 30099541
Cell. 2012 May 25;149(5):979-93
pubmed: 22608084
Aging (Albany NY). 2020 Feb 24;12(4):3140-3155
pubmed: 32091409
JAMA. 2010 Sep 1;304(9):967-75
pubmed: 20810374
N Engl J Med. 2021 Feb 4;384(5):440-451
pubmed: 33471974
J Natl Cancer Inst. 2020 Jul 1;112(7):708-719
pubmed: 31665482
Ann Surg Oncol. 2019 Oct;26(10):3025-3031
pubmed: 31342359
Genome Biol. 2017 Nov 15;18(1):220
pubmed: 29141660
Nat Biotechnol. 2019 Jul;37(7):773-782
pubmed: 31061481
Cancer Med. 2018 Apr;7(4):1349-1358
pubmed: 29522266
Cancer. 2017 May 15;123(10):1721-1730
pubmed: 28085182
JAMA. 2019 Aug 20;322(7):652-665
pubmed: 31429903
Cancer Res. 2012 Aug 15;72(16):4028-36
pubmed: 22706203
Cancer Res. 2018 Nov 1;78(21):6329-6338
pubmed: 30385609
NPJ Breast Cancer. 2021 Dec 9;7(1):153
pubmed: 34887416
BMC Cancer. 2018 Mar 22;18(1):315
pubmed: 29566657
Br J Surg. 2018 Jan;105(2):e158-e168
pubmed: 29341157
N Engl J Med. 2021 Feb 4;384(5):428-439
pubmed: 33471991
Nat Med. 2019 Oct;25(10):1526-1533
pubmed: 31570822
Br J Cancer. 2018 Jul;119(2):141-152
pubmed: 29867226
Genome Med. 2015 Feb 02;7(1):20
pubmed: 25722745
Nat Commun. 2018 Oct 4;9(1):4083
pubmed: 30287823
J Clin Oncol. 2020 May 1;38(13):1409-1418
pubmed: 32125938
Nature. 2016 May 02;534(7605):47-54
pubmed: 27135926
Clin Cancer Res. 2005 Jul 15;11(14):5175-80
pubmed: 16033833
J Clin Oncol. 2016 May 1;34(13):1460-8
pubmed: 26976419
Cancer Genet. 2020 Aug;246-247:12-17
pubmed: 32805687
Int J Gynecol Cancer. 2006;16 Suppl 2:552-5
pubmed: 17010071
J Natl Compr Canc Netw. 2020 Apr;18(4):380-391
pubmed: 32259785
Brief Bioinform. 2021 Nov 5;22(6):
pubmed: 33971670
J Natl Cancer Inst. 1998 Aug 5;90(15):1138-45
pubmed: 9701363
Bioinformatics. 2016 Sep 15;32(18):2847-9
pubmed: 27207943
JAMA Oncol. 2017 Feb 01;3(2):262-268
pubmed: 27560719
J Clin Oncol. 2009 Mar 10;27(8):1160-7
pubmed: 19204204
Ann Oncol. 2018 Apr 1;29(4):895-902
pubmed: 29365031
NPJ Breast Cancer. 2022 Aug 16;8(1):94
pubmed: 35974007
Genome Med. 2021 Dec 2;13(1):185
pubmed: 34857041
Cells. 2020 Dec 12;9(12):
pubmed: 33322746
J Natl Compr Canc Netw. 2021 Jan 06;19(1):77-102
pubmed: 33406487
Nucleic Acids Res. 2021 Jan 8;49(D1):D412-D419
pubmed: 33125078
Breast Cancer Res Treat. 2012 Apr;132(3):937-45
pubmed: 21701879
Nat Genet. 2006 Sep;38(9):1043-8
pubmed: 16921376
Int J Cancer. 2019 Mar 1;144(5):1195-1204
pubmed: 30175445
Nat Commun. 2020 Jul 27;11(1):3747
pubmed: 32719340
Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5116-21
pubmed: 11309499
Breast Cancer Res. 2012 Jul 27;14(4):R113
pubmed: 22839103
Breast Cancer Res. 2021 Feb 10;23(1):20
pubmed: 33568222
JAMA Oncol. 2022 Mar 01;8(3):e216744
pubmed: 35084436
J Clin Oncol. 2015 Feb 1;33(4):304-11
pubmed: 25452441
Nat Methods. 2019 Jun;16(6):453-454
pubmed: 31133757
JAMA. 2014 Sep 17;312(11):1091-2
pubmed: 25198398
Proc Natl Acad Sci U S A. 2014 Sep 30;111(39):14205-10
pubmed: 25192939
Fam Cancer. 2017 Apr;16(2):187-193
pubmed: 28120249
EMBO Mol Med. 2020 Oct 7;12(10):e12118
pubmed: 32926574
Int J Cancer. 2019 Nov 15;145(10):2692-2700
pubmed: 30927251
Breast Cancer Res Treat. 2018 Feb;168(1):117-126
pubmed: 29164420
Cancer Epidemiol Biomarkers Prev. 2012 Jan;21(1):134-47
pubmed: 22144499
J Clin Oncol. 2019 May 20;37(15):1305-1315
pubmed: 30964716
J Clin Invest. 2011 Jul;121(7):2750-67
pubmed: 21633166
J Natl Cancer Inst. 2020 Dec 14;112(12):1231-1241
pubmed: 32091585