Mammographic Density Reduction Is a Prognostic Marker of Response to Adjuvant Tamoxifen Therapy in Postmenopausal Patients With Breast Cancer
Tamoxifen treatment is associated with a reduction in mammographic density and an improved
survival. However, the extent to which change in mammographic density during adjuvant
tamoxifen therapy can be used to measure response to treatment is unknown.
Overall, 974 postmenopausal patients with breast cancer who had both a baseline and a follow-up
mammogram were eligible for analysis. On the basis of treatment information abstracted from
medical records, 474 patients received tamoxifen treatment and 500 did not. Mammographic
density was measured by using an automated thresholding method and expressed as absolute
dense area. Change in mammographic density was calculated as percentage change from
baseline. Survival analysis was performed by using delayed-entry Cox proportional hazards
regression models, with death as a result of breast cancer as the end point. Analyses were
adjusted for a range of patient and tumor characteristics.
During a 15-year follow-up, 121 patients (12.4%) died from breast cancer. Women treated with
tamoxifen who experienced a relative density reduction of more than 20% between baseline and
first follow-up mammogram had a reduced risk of death as a result of breast cancer of 50% (hazard
ratio, 0.50; 95% CI, 0.27 to 0.93) compared with women with stable mammographic density. In
the no-tamoxifen group, there was no statistically significant association between mammographic
density change and survival. The survival advantage was not observed when absolute dense areas
at baseline or follow-up were evaluated separately.
A decrease in mammographic density after breast cancer diagnosis appears to serve as a
prognostic marker for improved long-term survival in patients receiving adjuvant tamoxifen, and
these data should be externally validated.
Jingmei Li, Keith Humphreys, Louise Eriksson, Gustaf Edgren, Kamila Czene, and Per Hall
Click here for full article in Journal of Clinical Oncology
[Press release, 27 March 2013] More than 80 genetic ’spelling mistakes’ that can increase the risk of breast, prostate and ovarian cancer have been found in a large, international research study within the framework of the EU Network COGS. For the first time, the researchers also have a relatively clear picture of the total number of genetic alterations that can be linked to these cancers. Ultimately the researchers hope to be able to calculate the individual risk of cancer, to better understand how these cancers develop and to be able to generate new treatments.
The main findings are published in five articles in a special issue on genetic risk factors for cancer in the prestigious scientific journal Nature Genetics. The articles originate from COGS (Collaborative Oncological Gene-environment Study), an EU-based consortium where more than 160 research groups from all over the world are included. In the five COGS studies 100,000 patients with breast, ovarian or prostate cancer and 100,000 healthy individuals from the general population were included.
In a substudy to Karma, scientists will study why it is more common among women with high mammographic density to develop breast cancer. The study will investigate breast tissue biopsies from women with low breast density and compare this with biopsies from women with dense breasts.
Karma Normal is a collaboration between researchers at the Karolinska Institute and physicians at the mammography clinic in Helsingborg Hospital. The study will invite randomly selected women in the Helsingborg area who already participates in Karma.
Medical doctors take biopsies from healthy breasts using the same procedures as in investigating a breast lump. The biopsies are analyzed with different methods which will present a picture of what the tissue looks like. The researchers will look into any cell composition that represents density and any composition that represents changes that would lead to a cancer later on in life.
The aim is to understand what gives rise to the density and what cell types and genes are influencing development of cancer, and hopefully to prevent breast cancer in the future.
Breast cancer has steadily increased over the past 40 years. In Sweden, one woman per hour is diagnosed with breast cancer and every six hours a woman dies from the disease. The need to prevent the onset of breast cancer is high.
Therefore researchers at the Karolinska Institute in collaboration with four hospitals in Sweden perform the largest cancer study ever in Sweden – Karma. The objective of this study is to significantly reduce the incidence of breast cancer. Karma will identify high risk women based on lifestyle, genetics, mammographic morphology and other markers. When we can assess the individual risk for breast cancer, the next step is to tailor a preventive treatment for each woman based on the treatments available to them at the time.
Thanks to the 1000 women who voluntarily choose to join the Karma study every week, we have today reached a major milestone with 50,000 participating women!
Karma study has been made possible thanks to a donation of 50 million SEK from Märit and Hans Rausing’s Initiative Against Breast Cancer.
Mammographic density (MD) is a strong, independent risk factor of breast cancer, but measuring MD is time-consuming and reader-dependent. Objective MD measurement in a high-throughput fashion would enable its wider use as a biomarker for breast cancer. We use a public domain image processing software for the fully automated analysis of MD and penalised regression to construct a measure which mimics a well-established semi-automated measure (Cumulus). We also describe measures which incorporate additional features of mammographic images for improving the risk associations of MD and breast cancer risk.
We randomly partitioned our dataset into a training set for model building (733 cases, 748 controls) and a test set for model assessment (765 cases, 747 controls). Pearson’s product moment correlation coefficient (r) was used to compare the MD measurements by Cumulus and our automated measure which mimics Cumulus. The likelihood ratio test was used to validate the performance of logistic regression models for breast cancer risk, which included our measure capturing additional information in mammographic images.
We observed a high correlation between the Cumulus measure and our measure mimicking Cumulus (r = 0.884, 95% CI: 0.872 to 0.894) in an external test set. Adding a variable, which includes extra information to percent density, significantly improved the fit of the logistic regression model of breast cancer risk (P=0.0002).
Our results demonstrate the potential to facilitate the integration of mammographic density measurements into large-scale research studies and subsequently into clinical practice.
Li J, Szekely L, Eriksson L, Heddson B, Sundbom A, Czene K, Hall P, Humphreys K.
Breast Cancer Res. 2012 Jul 30;14(4):R114.