Improved prediction of breast cancer risk based on phenotypic DNA damage repair capacity in peripheral blood B cells.

Breast cancer prediction DNA repair capacity Phenotypic assay

Journal

Research square
Titre abrégé: Res Sq
Pays: United States
ID NLM: 101768035

Informations de publication

Date de publication:
27 Jun 2023
Historique:
pubmed: 18 7 2023
medline: 18 7 2023
entrez: 18 7 2023
Statut: epublish

Résumé

Standard Breast Cancer (BC) risk prediction models based only on epidemiologic factors generally have quite poor performance, and there have been a number of risk scores proposed to improve them, such as AI-based mammographic information, polygenic risk scores and pathogenic variants. Even with these additions BC risk prediction performance is still at best moderate. In that decreased DNA repair capacity (DRC) is a major risk factor for development of cancer, we investigated the potential to improve BC risk prediction models by including a measured phenotypic DRC assay. Using blood samples from the Breast Cancer Family Registry we assessed the performance of phenotypic markers of DRC in 46 matched pairs of individuals, one from each pair with BC (with blood drawn before BC diagnosis) and the other from controls matched by age and time since blood draw. We assessed DRC in thawed cryopreserved peripheral blood mononuclear cells (PBMCs) by measuring γ-H2AX yields (a marker for DNA double-strand breaks) at multiple times from 1 to 20 hrs after a radiation challenge. The studies were performed using surface markers to discriminate between different PBMC subtypes. The parameter If replicated in larger studies, these results suggest that inclusion of a fingerstick-based phenotypic DRC blood test has the potential to markedly improve BC risk prediction.

Sections du résumé

Background UNASSIGNED
Standard Breast Cancer (BC) risk prediction models based only on epidemiologic factors generally have quite poor performance, and there have been a number of risk scores proposed to improve them, such as AI-based mammographic information, polygenic risk scores and pathogenic variants. Even with these additions BC risk prediction performance is still at best moderate. In that decreased DNA repair capacity (DRC) is a major risk factor for development of cancer, we investigated the potential to improve BC risk prediction models by including a measured phenotypic DRC assay.
Methods UNASSIGNED
Using blood samples from the Breast Cancer Family Registry we assessed the performance of phenotypic markers of DRC in 46 matched pairs of individuals, one from each pair with BC (with blood drawn before BC diagnosis) and the other from controls matched by age and time since blood draw. We assessed DRC in thawed cryopreserved peripheral blood mononuclear cells (PBMCs) by measuring γ-H2AX yields (a marker for DNA double-strand breaks) at multiple times from 1 to 20 hrs after a radiation challenge. The studies were performed using surface markers to discriminate between different PBMC subtypes.
Results UNASSIGNED
The parameter
Conclusions UNASSIGNED
If replicated in larger studies, these results suggest that inclusion of a fingerstick-based phenotypic DRC blood test has the potential to markedly improve BC risk prediction.

Identifiants

pubmed: 37461559
doi: 10.21203/rs.3.rs-3093360/v1
pmc: PMC10350237
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : NIEHS NIH HHS
ID : P30 ES009089
Pays : United States
Organisme : NIH HHS
ID : S10 OD026845
Pays : United States
Organisme : NIEHS NIH HHS
ID : U01 ES029660
Pays : United States

Auteurs

Hazeem L Okunola (HL)

Columbia University Irving Medical Center.

Igor Shuryak (I)

Columbia University Irving Medical Center.

Mikhail Repin (M)

Columbia University Irving Medical Center.

Hui-Chen Wu (HC)

Columbia University Mailman School of Public Health.

Regina M Santella (RM)

Columbia University Mailman School of Public Health.

Mary Beth Terry (MB)

Columbia University Mailman School of Public Health.

Helen C Turner (HC)

Columbia University Irving Medical Center.

David J Brenner (DJ)

Columbia University Irving Medical Center.

Classifications MeSH