Prediction of breast cancer risk based on flow variant analysis of circulating peripheral blood mononuclear cells

Loke, Johnny; Alim, Ishraq; Yam, Sarah; Klugman, Susan; Xia, Li C.; Gruber, Dorota; Tegay, David; LaBella, Andrea; Onel, Kenan; Ostrer, Harry

doi:10.1016/j.xhgg.2022.100085

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…A concern with genomic or proteomic approaches is that mammalian DNA damage repair mechanisms are extraordinarily complex, in humans involving ~ 450 genes, 13 different pathways, with over half the proteins interacting with other proteins from different pathways [39]. Most BC risk models now try to capture genetic risk variants in DNA repair but that genotype typically only explains a small portion of the variation in phenotype [40][41][42][43][44]. Consequently, any speci c genomic or proteomic methodology may not re ect overall DRC.…”

Section: Introductionmentioning

confidence: 99%

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

Okunola

Shuryak

Repin

et al. 2023

Preprint

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Background 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 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 The parameter Fres, the residual damage signal in PBMC B cells at 20 hrs post challenge, was the strongest predictor of breast cancer with an AUC (Area Under receiver-operator Curve) of 0.89 [95% Confidence Interval: 0.84–0.93] and a BC status prediction accuracy of 0.80. To illustrate the combined use of a phenotypic predictor with standard BC predictors, we combined Fres in B cells with age at blood draw, and found that the combination resulted in significantly greater BC predictive power (AUC of 0.97 [95% CI: 0.94–0.99]), an increase of 13 percentage points over age alone. Conclusions 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.

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