Person:

Collins, Laura

Advancements in molecular biology have unveiled multiple breast cancer promoting pathways and potential therapeutic targets. Large randomized clinical trials remain the ultimate means of validating therapeutic efficacy, but they require large cohorts of patients and are lengthy and costly. A useful approach is to conduct a window of opportunity study in which patients are exposed to a drug pre-surgically during the interval between the core needle biopsy and the definitive surgery. These are non-therapeutic studies and the end point is not clinical or pathological response but rather evaluation of molecular changes in the tumor specimens that can predict response. However, since the end points of the non-therapeutic studies are biologic, it is critical to first define the biologic changes that occur in the absence of treatment. In this study, we compared the molecular profiles of breast cancer tumors at the time of the diagnostic biopsy versus the definitive surgery in the absence of any intervention using the Nanostring nCounter platform. We found that while the majority of the transcripts did not vary between the two biopsies, there was evidence of activation of immune related genes in response to the first biopsy and further investigations of the immune changes after a biopsy in early breast cancer seem warranted.

The assessment of protein expression in immunohistochemistry (IHC) images provides important diagnostic, prognostic and predictive information for guiding cancer diagnosis and therapy. Manual scoring of IHC images represents a logistical challenge, as the process is labor intensive and time consuming. Since the last decade, computational methods have been developed to enable the application of quantitative methods for the analysis and interpretation of protein expression in IHC images. These methods have not yet replaced manual scoring for the assessment of IHC in the majority of diagnostic laboratories and in many large-scale research studies. An alternative approach is crowdsourcing the quantification of IHC images to an undefined crowd. The aim of this study is to quantify IHC images for labeling of ER status with two different crowdsourcing approaches, image-labeling and nuclei-labeling, and compare their performance with automated methods. Crowdsourcing- derived scores obtained greater concordance with the pathologist interpretations for both image-labeling and nuclei-labeling tasks (83% and 87%), as compared to the pathologist concordance achieved by the automated method (81%) on 5,338 TMA images from 1,853 breast cancer patients. This analysis shows that crowdsourcing the scoring of protein expression in IHC images is a promising new approach for large scale cancer molecular pathology studies.

Although expression of estrogen receptor (ER), progesterone receptor (PR), and cell proliferation marker Ki67 serve as predictive and prognostic factors in breast cancers, little is known about their roles in normal breast tissue. Here in a nested case–control study within the Nurses’ Health Studies (90 cases, 297 controls), we evaluated their expression levels in normal breast epithelium in relation to subsequent breast cancer risk among women with benign breast disease. Tissue microarrays were constructed using cores obtained from benign biopsies containing normal terminal duct lobular units and immunohistochemical stained for these markers. We found PR and Ki67 expression was non-significantly but positively associated with subsequent breast cancer risk, whereas ER expression was non-significantly inversely associated. After stratifying by lesion subtype, Ki67 was significantly associated with higher risk among women with proliferative lesions with atypical hyperplasia. However, given the small sample size, further studies are required to confirm these results.

Introduction: Estrogen receptor (ER) and progesterone receptor (PR) testing are performed in the evaluation of breast cancer. While the clinical utility of ER as a predictive biomarker to identify patients likely to benefit from hormonal therapy is well-established, the added value of PR is less well-defined. The primary goals of our study were to assess the distribution, inter-assay reproducibility, and prognostic significance of breast cancer subtypes defined by patterns of ER and PR expression. Methods: We integrated gene expression microarray (GEM) and clinico-pathologic data from 20 published studies to determine the frequency (n = 4,111) and inter-assay reproducibility (n = 1,752) of ER/PR subtypes (ER+/PR+, ER+/PR-, ER-/PR-, ER-/PR+). To extend our findings, we utilized a cohort of patients from the Nurses’ Health Study (NHS) with ER/PR data recorded in the medical record and assessed on tissue microarrays (n = 2,011). In both datasets, we assessed the association of ER and PR expression with survival. Results: In a genome-wide analysis, progesterone receptor was among the least variable genes in ER- breast cancer. The ER-/PR+ subtype was rare (approximately 1 to 4%) and showed no significant reproducibility (Kappa = 0.02 and 0.06, in the GEM and NHS datasets, respectively). The vast majority of patients classified as ER-/PR+ in the medical record (97% and 94%, in the GEM and NHS datasets) were re-classified by a second method. In the GEM dataset (n = 2,731), progesterone receptor mRNA expression was associated with prognosis in ER+ breast cancer (adjusted P <0.001), but not in ER- breast cancer (adjusted P = 0.21). PR protein expression did not contribute significant prognostic information to multivariate models considering ER and other standard clinico-pathologic features in the GEM or NHS datasets. Conclusion: ER-/PR+ breast cancer is not a reproducible subtype. PR expression is not associated with prognosis in ER- breast cancer, and PR does not contribute significant independent prognostic information to multivariate models considering ER and other standard clinico-pathologic factors. Given that PR provides no clinically actionable information in ER+ breast cancer, these findings question the utility of routine PR testing in breast cancer.

Background: This study aimed to determine if associations of pre-diagnostic percent breast density, absolute dense area, and non-dense area with subsequent breast cancer risk differ by the tumour's molecular marker status. Methods: We included 1010 postmenopausal women with breast cancer and 2077 matched controls from the Nurses' Health Study (NHS) and the Nurses' Health Study II (NHS II) cohorts. Breast density was estimated from digitised film mammograms using computer-assisted thresholding techniques. Information on breast cancer risk factors was obtained prospectively from biennial questionnaires. Polychotomous logistic regression was used to assess associations of breast density measures with tumour subtypes by the status of selected tissue markers. All tests of statistical significance were two sided. Results: The association of percent density with breast cancer risk appeared to be stronger in ER− as compared with ER+ tumours, but the difference did not reach statistical significance (density ⩾50% vs <10% odds ratio (OR)=3.06, 95% confidence interval (CI) 2.17–4.32 for ER+ OR=4.61, 95% CI 2.36–9.03 for ER−, Pheterogeneity=0.08). Stronger positive associations were found for absolute dense area and CK5/6− and EGFR− as compared with respective marker-positive tumours (Pheterogeneity=0.002 and 0.001, respectively). Stronger inverse associations of non-dense area with breast cancer risk were found for ER− as compared with ER+ tumours (Pheterogeneity=0.0001) and for AR+, CK5/6+, and EGFR+ as compared with respective marker-negative tumours (Pheterogeneity=0.03, 0.005, and 0.009, respectively). The associations of density measures with breast cancer did not differ by progesterone receptor and human epidermal growth factor receptor 2 status. Conclusions: Breast density influences the risk of breast cancer subtypes by potentially different mechanisms.

Studies have suggested that hormone receptor and Ki67 expression in normal breast tissue are associated with subsequent breast cancer risk. We examined the associations of breast cancer risk factors with estrogen receptor (ER), progesterone receptor (PR), insulin-like growth factor-1 receptor (IGF-1R), and Ki67 expression in normal breast tissue. This analysis included 388 women with benign breast disease (ages 17–67 years) in the Nurses’ Health Studies. Immunohistochemical staining was performed on tissue microarrays constructed from benign biopsies containing normal breast epithelium and scored as the percentage of epithelial cells that were positively stained. Ordinal logistic regression (outcomes in tertiles), adjusting for age and potential confounders, was performed to estimate odds ratios (OR) and 95% confidence intervals (CI) for the associations with risk factors. Alcohol consumption was positively associated (≥2.5 vs.<0.4 drink/wk: OR = 2.69, 95% CI = 1.26–5.75, p-trend = 0.008) and breastfeeding was inversely associated (≥6 months vs. never: OR = 0.11, 95% CI = 0.04–0.35, p-trend = 0.0003) with ER expression. Height (≥66 vs.<64 inches: OR = 2.50, 95% CI = 1.34–4.67, p-trend = 0.005) and BMI at age 18 (≥22 vs.<20 kg/m2: OR = 2.33, 95% CI = 1.18–4.62, p-trend = 0.01) were positively associated with PR expression. Body size at age 5–10 years was inversely associated with Ki67 (Level ≥ 2.5 vs. 1: OR = 0.55, 95% CI = 0.30–1.01, p-trend = 0.03). Premenopausal BMI (≥25 vs.<20 kg/m2) was positively associated with cytoplasmic IGF-1R (OR = 5.06, 95% CI = 1.17–21.8, p-trend = 0.04). Our data suggest that anthropometrics, breastfeeding, and alcohol intake may influence the molecular characteristics of normal breast tissue, elucidating the mechanisms by which these risk factors operate. However, larger studies are required to confirm these results.