Risk
prediction models always have very important public health implications by
identifying high risk population for that disease and targeting them for increased screening , chemoprevention or
other risk reducing life style management regimens.
Various
models have been in use to calculate the risk score of developing breast cancer
in women. Two most frequently used and well validated models are Gail and Rosner-Colditz.
The Gail score includes: age at menarche,
number of previous breast biopsies, presence of atypical hyperplasia at biopsy,
age at first birth, number of first-degree relatives with a history of breast
cancer, and age.
In
clinical studies the Rosner-Colditz model slightly outperforms Gail model and additionally
includes: premenopausal duration (age at menopause minus age at menarche),
postmenopausal duration (current age minus age at menopause), type of
menopause, age at first birth minus age at menarche, birth index, history of
benign breast disease, duration of PMH use by type (estrogen, estrogen plus
progesterone, or other) and timing (current v past), body-mass
index (BMI; trajectory from age 18 years to current), height, alcohol intake
(from age 18 years to current).
Even
combined, these models include only what are called the traditional risk
factors and does not include genetic risk score (GRS), mammographic density
(MD), and postmenopausal endogenous hormone levels collectively called as biological
risk factors.
Addition
of the biological risk factors to the existing models improve the risk prediction
especially in postmenopausal women not using hormone therapy
(HT), according to research paper presented here at the AACR Annual Meeting 2016, April 16-20. Subsequently this improves
the tailoring of chemoprevention and screening strategies in women opined Xuehong Zhang, MD, ScD, lead author of the study and assistant professor of medicine at Harvard Medical School and associate epidemiologist at Brigham and Women's Hospital in Boston.
“We conducted the first comprehensive
evaluation of the independent and joint contribution of several biological markers
of risk in the two validated breast cancer risk prediction models [Gail and
Rosner-Colditz models] using data from up to 10,052 breast cancer cases and
12,575 controls of European ancestry from the Nurses’ Health Study (NHS) and NHS II,” he added.
“A genetic risk score can summarize in a single
number an individual’s genetic predisposition to a certain disease outcome
[e.g., breast cancer in this study] based on multiple risk alleles,” Zhang
explained. He and colleagues calculated a breast cancer genetic risk score
based on 67 single-nucleotide polymorphisms (SNPs) identified from a recently published meta-analysis of nine genome-wide association
(GWAS) studies.
The data was stratified according to menopausal
status and age and area under the curve (AUC) was calculated for the 5-year
risk of invasive breast cancer and estrogen receptor (ER) and progesterone
receptor (PR) positive disease (ER+PR+) after adding the biological risk
factors to the prevailing models.
In both models, about 45% of women were
premenopausal, 25% were postmenopausal and not using hormone therapy, and 30%
were postmenopausal and using hormone therapy.
"The improvement in risk prediction was
greatest in postmenopausal women not taking hormone therapy, the group where
all three hormones could be measured and hence contribute to the model,"
Dr Zhang said in news release.
The AUC improved by 11.7 units and 9.4 units
for Gail and Rosner-Colditz models, respectively in risk prediction for ER+PR+
breast cancer development in post-menopausal women adding the biological markers.
These results have tremendous significance because recent data from the US
National Health and Nutrition Examination Survey shows that 90% of
postmenopausal women are not on hormone therapy, thus the improvements seen for
this subgroup would apply to the majority of postmenopausal women in the
U.S.," Zhang said in a statement released by AACR. "An important next
step in this research will be to validate these initial findings in other study
populations."
References:
Zang
X, Rice M, Tworoger SS, et al. Zhang, Xuehong, Breast Cancer Risk Prediction
Models Improved by Adding Multiple Biological Markers of Risk. Presented at:
AACR 2016 Annual Meeting; New Orleans, Louisiana, April 16-20, 2016. Abstract
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