Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: A nested case–control study

Zhang, Xuehong; Rice, Megan S.; Tworoger, Shelley S.; Rosner, Bernard; Eliassen, A. Heather; Tamimi, Rulla M.; Joshi, Amit D.; Lindström, Sara; Qian, Jing; Colditz, Graham A.; Willett, Walter C.; Kraft, Peter; Hankinson, Susan E.

doi:10.1371/journal.pmed.1002644

Cited by 99 publications

(98 citation statements)

References 59 publications

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“…The precision of the multivariable model increased when we added two statistically significant interaction terms associated with women’s age: menopause and mammographic density. Such interactions have previously been observed, and we detected an increase of AUC-ROC from 0.74 (95% CI: 0.71–0.77) to 0.80 (95% CI: 0.77–0.83) (Table 2 ), a rise that was statistically significant and offered a final value slightly higher than those of other similar multivariable studies [ 39 ].…”

Section: Discussionsupporting

confidence: 82%

Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study

et al. 2020

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Background In recent years, the identification of genetic and phenotypic biomarkers of cancer for prevention, early diagnosis and patient stratification has been a main objective of research in the field. Different multivariable models that use biomarkers have been proposed for the evaluation of individual risk of developing breast cancer. Methods This is a case control study based on a population-based cohort. We describe and evaluate a multivariable model that incorporates 92 Single-nucleotide polymorphisms (SNPs) (Supplementary Table S1) and five different phenotypic variables and which was employed in a Spanish population of 642 healthy women and 455 breast cancer patients. Results Our model allowed us to stratify two groups: high and low risk of developing breast cancer. The 9th decile included 1% of controls vs 9% of cases, with an odds ratio (OR) of 12.9 and a p-value of 3.43E-07. The first decile presented an inverse proportion: 1% of cases and 9% of controls, with an OR of 0.097 and a p-value of 1.86E-08. Conclusions These results indicate the capacity of our multivariable model to stratify women according to their risk of developing breast cancer. The major limitation of our analysis is the small cohort size. However, despite the limitations, the results of our analysis provide proof of concept in a poorly studied population, and opens up the possibility of using this method in the routine screening of the Spanish population.

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Section: Discussionsupporting

confidence: 82%

Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study

et al. 2020

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“…These interactions have been previously observed and we found an increase of AUC-ROC from 0.74 (95% CI: 0.71-0.77) to 0.80 (95% CI: 0.77-0.83) ( Table 2). This increase was statistically signi cant and offered a nal value slightly higher than other similar multivariable studies [40].…”

Section: Discussionmentioning

confidence: 51%

Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population

Triviño

Ceba

Rubio-Solsona

et al. 2020

Preprint

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Background: In the last years, the identification of different genetic and phenotypic biomarkers for prevention, early diagnostics and patient stratification have been a main objective in cancer research. Different proposals for multivariable models using biomarkers have been presented for the evaluation of individual risk of women to develop breast cancer. Methods: In this study, we describe and evaluate a multivariable model using 92 Single-nucleotide polymorphisms (SNPs) and five different phenotypic variables in a Spanish population of 642 healthy women and 455 breast cancer patients. Results: We were able to stratify both groups with our model. The 9th decile included 1% of controls vs 9% of cases, with an Odds ratio (OR) of 12.9 and a p-value of 3.43E-07. The first decile presented an inverse proportion: 1% of cases and 9% of controls, with an OR of 0.097 and a p-value of 1.86E-08. Conclusions: These results indicate the ability of the multivariable model to stratify women according to their risk to develop breast cancer over a maximum period of 5 years. The analysis present a proof of concept in a poorly studied population and it opens the possibility of using this type of method for routine screening in the Spanish population.

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“…Of note, several studies in European populations with higher than Slovenian breast cancer incidence have shown that both factors independently increase the sensitivity of IBIS. [27][28][29][30] Due to technical limitations both mammographic density and PRS are not routinely included in S-IBIS calculations throughout Slovenia, therefore currently no data on value of mammographic density and PRS in Slovenian population is available. Data are available for selected breast centres and are yet to be analysed at the time writing this article.…”

Section: Discussionmentioning

confidence: 99%

Breast cancer risk based on adapted IBIS prediction model in Slovenian women aged 40–49 years - could it be better?

Oblak

Zadnik

Krajc

et al. 2020

Radiology and Oncology

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AbstractBackgroundThe aim of the study was to assess the proportion of women that would be classified as at above-average risk of breast cancer based on the 10 year-risk prediction of the Slovenian breast cancer incidence rate (S-IBIS) program in two presumably above-average breast cancer risk populations in age group 40-49 years: (i) women referred for any reason to diagnostic breast centres and (ii) women who were diagnosed with breast cancer aged 40–49 years. Breast cancer is the commonest female cancer in Slovenia, with an incidence rate below European average. The Tyrer-Cuzick breast cancer risk assessment algorithm was recently adapted to S-IBIS. In Slovenia a tailored mammographic screening for women at above average risk in age group 40–49 years is considered in the future. S-IBIS is a possible tool to select population at above-average risk of breast cancer for tailored screening.Patients and methodsIn 357 healthy women aged 40–49 years referred for any reason to diagnostic breast centres and in 367 female breast cancer patients aged 40–49 years at time of diagnosis 10-years breast cancer risk was calculated using the S-IBIS software. The proportion of women classified as above-average risk of breast cancer was calculated for each subgroup of the study population.Results48.7% of women in the Breast centre group and 39.2% of patients in the breast cancer group had above-average 10-year breast cancer risk. Positive family history of breast cancer was more prevalent in the Breast centre group (p < 0.05).ConclusionsInclusion of additional risk factors into the S-IBIS is warranted in the populations with breast cancer incidence below European average to reliably stratify women into breast cancer risk groups.

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Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: A nested case–control study

Cited by 99 publications

References 59 publications

Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study

Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study

Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population

Breast cancer risk based on adapted IBIS prediction model in Slovenian women aged 40–49 years - could it be better?

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