Mammographic density: use in risk assessment and as a biomarker in prevention trials

Mammographic density, endocrine therapy and breast cancer risk: a prognostic and predictive biomarker review

BACKGROUND

Endocrine therapy is effective at preventing or treating breast cancer. Some forms of endocrine therapy have been shown to reduce mammographic density. Reduced mammographic density for women receiving endocrine therapy could be used to estimate the chance of breast cancer returning or developing breast cancer in the first instance (a prognostic biomarker). In addition, changes in mammographic density might be able to predict how well a woman responds to endocrine therapy (a predictive biomarker). The role of breast density as a prognostic or predictive biomarker could help improve the management of breast cancer.

OBJECTIVES

To assess the evidence that a reduction in mammographic density following endocrine therapy for breast cancer prevention in women without previous breast cancer, or for treatment in women with early-stage hormone receptor-positive breast cancer, is a prognostic or predictive biomarker.

SEARCH METHODS

We searched the Cochrane Breast Cancer Group Specialised Register, CENTRAL, MEDLINE, Embase, and two trials registers on 3 August 2020 along with reference checking, bibliographic searching, and contact with study authors to obtain further data.

SELECTION CRITERIA

We included randomised, cohort and case-control studies of adult women with or without breast cancer receiving endocrine therapy. Endocrine therapy agents included were selective oestrogen receptor modulators and aromatase inhibitors. We required breast density before start of endocrine therapy and at follow-up. We included studies published in English.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures expected by Cochrane. Two review authors independently extracted data and assessed risk of bias using adapted Quality in Prognostic Studies (QUIPS) and Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tools. We used the GRADE approach to evaluate the certainty of the evidence. We did not perform a quantitative meta-analysis due to substantial heterogeneity across studies.

MAIN RESULTS

Eight studies met our inclusion criteria, of which seven provided data on outcomes listed in the protocol (5786 women). There was substantial heterogeneity across studies in design, sample size (349 to 1066 women), participant characteristics, follow-up (5 to 14 years), and endocrine therapy agent. There were five breast density measures and six density change definitions. All studies had at least one domain as at moderate or high risk of bias. Common concerns were whether the study sample reflected the review target population, and likely post hoc definitions of breast density change. Most studies on prognosis for women receiving endocrine therapy reported a reduced risk associated with breast density reduction. Across endpoints, settings, and agents, risk ratio point estimates (most likely value) were between 0.1 and 1.5, but with substantial uncertainty. There was greatest consistency in the direction and magnitude of the effect for tamoxifen (across endpoints and settings, risk ratio point estimates were between 0.3 and 0.7). The findings are summarised as follows. Prognostic biomarker findings: Treatment Breast cancer mortality Two studies of 823 women on tamoxifen (172 breast cancer deaths) reported risk ratio point estimates of ~0.4 and ~0.5 associated with a density reduction. The certainty of the evidence was low. Recurrence Two studies of 1956 women on tamoxifen reported risk ratio point estimates of ~0.4 and ~0.7 associated with a density reduction. There was risk of bias in methodology for design and analysis of the studies and considerable uncertainty over the size of the effect. One study of 175 women receiving an aromatase inhibitor reported a risk ratio point estimate of ~0.1 associated with a density reduction. There was considerable uncertainty about the effect size and a moderate or high risk of bias in all domains. One study of 284 women receiving exemestane or tamoxifen as part of a randomised controlled trial reported risk ratio point estimates of ~1.5 (loco-regional recurrence) and ~1.3 (distance recurrence) associated with a density reduction. There was risk of bias in reporting and study confounding, and uncertainty over the size of the effects. The certainty of the evidence for all recurrence endpoints was very low. Incidence of a secondary primary breast cancer Two studies of 451 women on exemestane, tamoxifen, or unknown endocrine therapy reported risk ratio point estimates of ~0.5 and ~0.6 associated with a density reduction. There was risk of bias in reporting and study confounding, and uncertainty over the effect size. The certainty of the evidence was very low. We were unable to find data regarding the remaining nine outcomes prespecified in the review protocol. Prevention Incidence of invasive breast cancer and ductal carcinoma in situ (DCIS) One study of 507 women without breast cancer who were receiving preventive tamoxifen as part of a randomised controlled trial (51 subsequent breast cancers) reported a risk ratio point estimate of ~0.3 associated with a density reduction. The certainty of the evidence was low. Predictive biomarker findings: One study of a subset of 1065 women from a randomised controlled trial assessed how much the effect of endocrine therapy could be explained by breast density declines in those receiving endocrine therapy. This study evaluated the prevention of invasive breast cancer and DCIS. We found some evidence to support the hypothesis, with a risk ratio interaction point estimate ~0.5. However, the 95% confidence interval included unity, and data were based on 51 women with subsequent breast cancer in the tamoxifen group. The certainty of the evidence was low.

AUTHORS' CONCLUSIONS

There is low-/very low-certainty evidence to support the hypothesis that breast density change following endocrine therapy is a prognostic biomarker for treatment or prevention. Studies suggested a potentially large effect size with tamoxifen, but the evidence was limited. There was less evidence that breast density change following tamoxifen preventive therapy is a predictive biomarker than prognostic biomarker. Evidence for breast density change as a prognostic treatment biomarker was stronger for tamoxifen than aromatase inhibitors. There were no studies reporting mammographic density change following endocrine therapy as a predictive biomarker in the treatment setting, nor aromatase inhibitor therapy as a prognostic or predictive biomarker in the preventive setting. Further research is warranted to assess mammographic density as a biomarker for all classes of endocrine therapy and review endpoints.

The effect of hormone therapy on breast density following risk-reducing salpingo-oophorectomy in women with an increased risk for breast and ovarian cancer

OBJECTIVE

To compare the effect of tibolone to conjugated estrogens with medroxyprogesterone-acetate (CEE + MPA) on breast density, as a predictor for breast cancer risk, in women with a high risk of breast and ovarian cancer.

METHODS

Women aged 30-50 (N = 114) who had undergone risk-reducing salpingo-oophorectomy (RRSO) were randomized to tibolone or CEE + MPA.

RESULTS

Breast density decreased 46% after RRSO in untreated women, 39% after treatment with tibolone, and 17% after treatment with CEE + MPA; the decrease in breast density after CEE + MPA was significantly different compared with that of untreated women (P = 0.017).

CONCLUSIONS

A decline in breast density is seen after premenopausal RRSO despite the use of both CEE + MPA or tibolone, although lower breast density is seen after tibolone use.

Incorporating Biomarkers in Studies of Chemoprevention

Despite Food and Drug Administration approval of tamoxifen and raloxifene for breast cancer risk reduction and endorsement by multiple agencies, uptake of these drugs for primary prevention in the United States is only 4% for risk eligible women likely to benefit from their use. Side effects coupled with incomplete efficacy and lack of a survival advantage are the likely reasons. This disappointing uptake, after the considerable effort and expense of large Phase III cancer incidence trials required for approval, suggests that a new paradigm is required. Current prevention research is focused on (1) refining risk prediction, (2) exploring behavioral and natural product interventions, and (3) utilizing novel translational trial designs for efficacy. Risk biomarkers will play a central role in refining risk estimates from traditional models and selecting cohorts for prevention trials. Modifiable risk markers called surrogate endpoint or response biomarkers will continue to be used in Phase I and II prevention trials to determine optimal dose or exposure and likely effectiveness from an intervention. The majority of Phase II trials will continue to assess benign breast tissue for response and mechanism of action biomarkers. Co-trials are those in which human and animal cohorts receive the same effective dose and the same tissue biomarkers are assessed for modulation due to the intervention, but then additional animals are allowed to progress to cancer development. These collaborations linking biomarker modulation and cancer prevention may obviate the need for cancer incidence trials for non-prescription interventions.

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices

An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges-Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., [Formula: see text]) and with a larger offset length (i.e., [Formula: see text]), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

Biomarkers for early detection and as surrogate endpoints in cancer prevention trials: issues and opportunities

In order to improve the early detection and diagnosis of cancer, give more accurate prognoses, stratify individuals by risk, predict response to treatment, and help the transition of basic research into clinical application, biomarkers are needed that accurately represent or predict clinical outcomes. To be useful in trials for chemopreventive agent development, biomarkers must be subject to modulation, easy to obtain and quantify, and have biological meaning, ideally representing steps in well-understood carcinogenic pathways. Though difficult to validate fully, wisely chosen biomarkers in early-phase trials can inform the prioritization of large-scale, long-term trials that measure clinical outcomes. When well-designed, smaller trials using biomarkers as surrogate endpoints should promote faster decisions regarding which targeted preventive agents to pursue, promising greater progress in the personalization of medicine. Biomarkers could become useful in distinguishing indolent from aggressive forms of ductal carcinoma in situ as well as localized invasive breast and prostate cancer, lesions that are often overtreated. Chemopreventive strategies that reduce the progression of early forms of premalignancy can benefit patients not only by reducing their risk of cancer and death from cancer but also by reducing their need for invasive interventions. Genomic and proteomic methods offer the possibility of revealing new potential markers, especially for diseases whose biology is complex or not well understood. Panels of markers may be used to accommodate the molecular heterogeneity of cancers. Biomarkers in phase 2 prevention trials of combinations of chemopreventive drugs have been used to demonstrate synergistic action of multiple agents, allowing use of lower doses, with less toxicity, a critical feature of interventions intended for cancer prevention.

Mammographic density change with 1 year of aerobic exercise among postmenopausal women: a randomized controlled trial

BACKGROUND

The Alberta Physical Activity and Breast Cancer Prevention (ALPHA) Trial examined the influence of aerobic exercise on biological factors that are associated with breast cancer risk. Mammographic density, a secondary outcome, is reported here.

METHODS

The ALPHA Trial was a parallel group randomized controlled trial conducted between May 2003 and July 2007. Postmenopausal, sedentary women ages 50 to 74 years (n = 320) were evenly randomized to aerobic exercise (45 minutes, 5 days per week) or control (usual life-style) for 1 year. Dense fibroglandular tissue and nondense fatty tissue were measured from mammograms at baseline and 1 year using computer-assisted thresholding software for area measurements and a new technique that relies on the calibration of mammography units with a tissue-equivalent phantom for volumetric measurements.

RESULTS

Nondense volume decreased in the exercise group relative to the control group (difference between groups = -38.5 cm(3); 95% confidence interval, -61.6 to 15.4; P = 0.001). Changes in total body fat accounted for this decrease. Changes in dense area and dense volume, measures that have previously been associated with breast cancer risk, were not significantly different between the groups (P > or = 0.26).

CONCLUSIONS

Achieving changes in mammographic measures may require more exercise or a study population with higher baseline levels of sex hormones or a wider range of mammographic density. The data from this study, however, suggest that the protective effect of exercise on breast cancer risk may operate through a mechanism other than mammographic density.

Effects of arzoxifene on bone mineral density and endometrium in postmenopausal women with normal or low bone mass

INTRODUCTION

Arzoxifene, a benzothiophene estrogen agonist/antagonist, is being developed for prevention and treatment of osteoporosis and for risk reduction of invasive breast cancer in postmenopausal women.

METHODS

The effects of arzoxifene 20 mg/d on bone mineral density (BMD), uterine safety, and overall safety were studied in the FOUNDATION study, a 2-yr randomized, placebo-controlled trial including 331 postmenopausal women with normal to low bone mass.

RESULTS

Compared to placebo, arzoxifene significantly increased lumbar spine (+2.9%) and total hip (+2.2%) BMD. Arzoxifene decreased biochemical markers of bone metabolism compared to placebo. Changes in breast density were neutral or slightly decreased in the arzoxifene vs. placebo group. There was no evidence of endometrial hyperplasia or carcinoma in the arzoxifene group as assessed by central review of baseline and follow-up endometrial biopsies. There was no significant change between the groups in endometrial thickness assessed by transvaginal ultrasound. The incidence of uterine polyps and vaginal bleeding was not significantly different between the groups. Vulvovaginal mycotic infection was the only adverse event significantly increased in the arzoxifene vs. placebo group. Hot flushes were not significantly different between the groups.

CONCLUSION

In postmenopausal women with normal to low bone mass, arzoxifene 20 mg/d increased BMD at the spine and hip and had a neutral effect on the uterus and endometrium.

Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study

RATIONALE AND OBJECTIVES

Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superposition, offering superior parenchymal texture visualization compared to mammography. The aim of this study was to investigate the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation.

MATERIALS AND METHODS

DBT and digital mammographic (DM) images of 39 women were analyzed. Texture features, shown in previous studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. The relative performances of the DBT and DM texture features were compared in correlating with two measures of breast cancer risk: (1) the Gail and Claus risk estimates and (2) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk.

RESULTS

No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density than DM features (P < or = .05). When dividing the study population into groups of increasing breast percent density, the DBT texture features appeared to be more discriminative, having regression lines with overall lower P values, steeper slopes, and higher R(2) estimates.

CONCLUSION

Although preliminary, the results of this study suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation.

Role of dietary lignans in the reduction of breast cancer risk

Lignans are a large group of fiber-associated phenolic compounds widely distributed in edible plants. Some of the ingested plant lignans are converted by intestinal microbiota to enterolignans, enterodiol (END) and enterolactone (ENL), the latter of which has been thought to be the major biologically active lignan, and suggested to be associated with low risk of breast cancer. In line with this, administration of plant lignans which are further metabolized to ENL, or ENL as such, have been shown to inhibit or delay the growth of experimental mammary cancer. The mechanism of anticarcinogenic action of ENL is not yet fully understood, but there is intriguing evidence for ENL as a modulator of estrogen signaling. These findings have generated interest in the use of lignans as components of breast cancer risk reducing functional foods. Identification of target groups, who would benefit most, is of pivotal importance. Therefore, further identification and validation of relevant biomarkers, which can be used as indicators of lignan or ENL action and breast cancer risk reduction at different stages of the disease, are of importance.

Use of biomarkers for breast cancer risk assessment and prevention

Epidemiologic models used for cancer risk prediction, such as the Gail model, are validated for populations undergoing regular screening but often have suboptimal individual predictive accuracy. Risk biomarkers may be employed to improve predictive accuracy based on the Gail or other epidemiologic models and, to the extent that they are reversible, may be used to assess response in phase I-II prevention trials. Risk biomarkers used as intermediate response endpoints include high mammographic breast density, intra-epithelial neoplasia, and cytomorphology with associated molecular markers such as Ki-67. At the present time these biomarkers may not be used to predict or monitor individual response to standard prevention interventions but are used in early phase clinical trials as preliminary indicators of efficacy.