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For many years, patient age, axillary lymph node status, tumor size, histological features (especially histological grade and lymphovascular invasion), hormone receptor status, and HER2 status have been the major factors used to categorize patients with breast cancer in order to assess prognosis and determine the appropriate therapy. These factors are most often viewed in combination to group patients into various risk categories. Although these risk categories are useful for assessing prognosis and risk in groups of patients with breast cancer, their role in determining prognosis and evaluating risk in an individual patient is more limited. Therefore, better methods are required to help assess prognosis and determine the most appropriate treatment for patients on an individual basis. Recently, various molecular techniques, particular gene expression profiling, have been increasingly used to help refine breast cancer classification and to assess prognosis and response to therapy. Although the precise role of these newer techniques in the daily management of patients with breast cancer continues to evolve, it is clear that they have the potential to provide value above and beyond that provided by the traditional clinical and pathological prognostic and predictive factors.

The categorization of intraductal proliferative lesions of the breast based on routine light microscopic examination of histopathologic sections is in many cases challenging, even for experienced pathologists. The development of computational tools to aid pathologists in the characterization of these lesions would have great diagnostic and clinical value. As a first step to address this issue, we evaluated the ability of computational image analysis to accurately classify DCIS and UDH and to stratify nuclear grade within DCIS. Using 116 breast biopsies diagnosed as DCIS or UDH from the Massachusetts General Hospital (MGH), we developed a computational method to extract 392 features corresponding to the mean and standard deviation in nuclear size and shape, intensity, and texture across 8 color channels. We used L1-regularized logistic regression to build classification models to discriminate DCIS from UDH. The top-performing model contained 22 active features and achieved an AUC of 0.95 in cross-validation on the MGH data-set. We applied this model to an external validation set of 51 breast biopsies diagnosed as DCIS or UDH from the Beth Israel Deaconess Medical Center, and the model achieved an AUC of 0.86. The top-performing model contained active features from all color-spaces and from the three classes of features (morphology, intensity, and texture), suggesting the value of each for prediction. We built models to stratify grade within DCIS and obtained strong performance for stratifying low nuclear grade vs. high nuclear grade DCIS (AUC = 0.98 in cross-validation) with only moderate performance for discriminating low nuclear grade vs. intermediate nuclear grade and intermediate nuclear grade vs. high nuclear grade DCIS (AUC = 0.83 and 0.69, respectively). These data show that computational pathology models can robustly discriminate benign from malignant intraductal proliferative lesions of the breast and may aid pathologists in the diagnosis and classification of these lesions.

Multiple independent studies have shown that tumor-infiltrating lymphocytes (TIL) are prognostic in breast cancer with potential relevance for response to immune-checkpoint inhibitor therapy. Although many groups are currently evaluating TIL, there is no standardized system for diagnostic applications. This study reports the results of two ring studies investigating TIL conducted by the International Working Group on Immuno-oncology Biomarkers. The study aim was to determine the intraclass correlation coefficient (ICC) for evaluation of TIL by different pathologists. A total of 120 slides were evaluated by a large group of pathologists with a web-based system in ring study 1 and a more advanced software-system in ring study 2 that included an integrated feedback with standardized reference images. The predefined aim for successful ring studies 1 and 2 was an ICC above 0.7 (lower limit of 95% confidence interval (CI)). In ring study 1 the prespecified endpoint was not reached (ICC: 0.70; 95% CI: 0.62–0.78). On the basis of an analysis of sources of variation, we developed a more advanced digital image evaluation system for ring study 2, which improved the ICC to 0.89 (95% CI: 0.85–0.92). The Fleiss' kappa value for <60 vs ≥60% TIL improved from 0.45 (ring study 1) to 0.63 in RS2 and the mean concordance improved from 88 to 92%. This large international standardization project shows that reproducible evaluation of TIL is feasible in breast cancer. This opens the way for standardized reporting of tumor immunological parameters in clinical studies and diagnostic practice. The software-guided image evaluation approach used in ring study 2 may be of value as a tool for evaluation of TIL in clinical trials and diagnostic practice. The experience gained from this approach might be applicable to the standardization of other diagnostic parameters in histopathology.

Breast cancer is the most common malignant disease worldwide, with over 2.26 million new cases in 2020. Its diagnosis is determined by a histological review of breast biopsy specimens, which can be labor-intensive, subjective, and error-prone. Artificial Intelligence (AI)—based tools can support cancer detection and classification in breast biopsies ensuring rapid, accurate, and objective diagnosis. We present here the development, external clinical validation, and deployment in routine use of an AI-based quality control solution for breast biopsy review. The underlying AI algorithm is trained to identify 51 different types of clinical and morphological features, and it achieves very high accuracy in a large, multi-site validation study. Specifically, the area under the receiver operating characteristic curves (AUC) for the detection of invasive carcinoma and of ductal carcinoma in situ (DCIS) are 0.99 (specificity and sensitivity of 93.57 and 95.51%, respectively) and 0.98 (specificity and sensitivity of 93.79 and 93.20% respectively), respectively. The AI algorithm differentiates well between subtypes of invasive and different grades of in situ carcinomas with an AUC of 0.97 for invasive ductal carcinoma (IDC) vs. invasive lobular carcinoma (ILC) and AUC of 0.92 for DCIS high grade vs. low grade/atypical ductal hyperplasia, respectively, as well as accurately identifies stromal tumor-infiltrating lymphocytes (TILs) with an AUC of 0.965. Deployment of this AI solution as a real-time quality control solution in clinical routine leads to the identification of cancers initially missed by the reviewing pathologist, demonstrating both clinical utility and accuracy in real-world clinical application.

Background The androgen receptor (AR) is an emerging prognostic marker and therapeutic target in breast cancer. AR is expressed in 60–80% of breast cancers, with higher prevalence among estrogen receptor-positive (ER+) tumors. Androgen treatment inhibits ER signaling in ER+/AR+ breast cancer cell lines, and AR expression is associated with improved survival for this subtype in epidemiologic studies. However, whether AR expression modifies the efficacy of selective ER modulators or aromatase inhibitors for ER+ cancers remains unclear. Methods We evaluated the prognostic and predictive value of AR expression among 3021 postmenopausal ER+ breast cancer patients in the Breast International Group (BIG) trial 1–98. The BIG 1–98 study was a four-armed, double-blind, phase III randomized clinical trial that compared 5 years of tamoxifen or letrozole monotherapy, or sequences of 2 years and 3 years treatment with one drug and then the other. AR expression was measured by immunohistochemistry and the percentage of AR-positive nuclei was quantified. The association between AR expression and prognosis was evaluated using Cox proportional hazards models. Continuous AR-by-treatment interactions were assessed using Subpopulation Treatment Effect Pattern Plots (STEPP). Results Eighty-two percent of patients had AR+ (≥ 1%) tumors. Patients with AR+ cancers were more likely to have smaller, lower-grade tumors, with higher expression of ER and PR. AR expression was not associated with breast cancer-free interval (BCFI) (415 events) over a median 8.0 years of follow-up ( p  = 0.12, log-rank test). In multivariable-adjusted models, AR expression was not associated with BCFI (HR = 1.07, 95% CI 0.83–1.36, p  = 0.60). The letrozole versus tamoxifen monotherapy treatment effect did not significantly differ for AR+ tumors (HR = 0.63, 95% CI 0.44–0.75, p  = 0.003) and AR− tumors (HR = 0.39, 95% CI 0.21–0.72, p  = 0.002) ( p -heterogeneity = 0.16). STEPP analysis also suggested no heterogeneity of the treatment effect across the continuum of AR expression. Conclusions AR expression was not associated with prognosis, nor was there heterogeneity of the letrozole versus tamoxifen treatment effect by AR expression. These findings suggest that AR expression may not be an informative biomarker for the selection of adjuvant endocrine therapy for postmenopausal women with ER+ breast cancers. Trial registration ClinicalTrials.gov , NCT00004205, Registered 27 January 2003—Retrospectively registered, https://clinicaltrials.gov/ct2/show/study/NCT00004205 .

Previous studies have demonstrated that androgen receptor is expressed in many breast cancers, but its expression in relation to the various breast cancer subtypes as defined by molecular profiling has not been studied in detail. We constructed tissue microarrays from 3093 breast cancers that developed in women enrolled in the Nurses' Health Study. Tissue microarray sections were immunostained for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), cytokeratin 5/6, epidermal growth factor receptor (EGFR) and androgen receptor (ER). Immunostain results were used to categorize each cancer as luminal A or B, HER2 and basal like. The relationships between androgen receptor expression and molecular subtype were analyzed. Overall, 77% of the invasive breast carcinomas were androgen receptor positive. Among 2171 invasive cancers, 64% were luminal A, 15% luminal B, 6% HER2 and 11% basal like. The frequency of androgen receptor expression varied significantly across the molecular phenotypes ( P <0.0001). In particular, androgen receptor expression was commonly observed in luminal A (91%) and B (68%) cancers, but was less frequently seen in HER2 cancers (59%). Despite being defined by the absence of ER and PR expression and being considered hormonally unresponsive, 32% of basal-like cancers expressed androgen receptor. Among 246 cases of ductal carcinoma in situ , 86% were androgen receptor positive, but the frequency of androgen receptor expression differed significantly across the molecular phenotypes ( P =0.001), and high nuclear grade lesions were less likely to be androgen receptor positive compared with lower-grade lesions. Androgen receptor expression is most commonly seen in luminal A and B invasive breast cancers. However, expression of androgen receptor is also seen in approximately one-third of basal-like cancers, providing further evidence that basal-like cancers represent a heterogeneous group. Our findings raise the possibility that targeting the androgen receptor pathway may represent a novel therapeutic approach to the management of patients with basal-like cancers.

Heat-shock factor 1 (HSF1) is the master transcriptional regulator of the cellular response to heat and a wide variety of other stressors. We previously reported that HSF1 promotes the survival and proliferation of malignant cells. At this time, however, the clinical and prognostic significance of HSF1 in cancer is unknown. To address this issue breast cancer samples from 1,841 participants in the Nurses’ Health Study were scored for levels of nuclear HSF1. Associations of HSF1 status with clinical parameters and survival outcomes were investigated by Kaplan–Meier analysis and Cox proportional hazard models. The associations were further delineated by Kaplan–Meier analysis using publicly available mRNA expression data. Our results show that nuclear HSF1 levels were elevated in ∼80% of in situ and invasive breast carcinomas. In invasive carcinomas, HSF1 expression was associated with high histologic grade, larger tumor size, and nodal involvement at diagnosis (P < 0.0001). By using multivariate analysis to account for the effects of covariates, high HSF1 levels were found to be independently associated with increased mortality (hazards ratio: 1.62; 95% confidence interval: 1.21–2.17; P < 0.0013). This association was seen in the estrogen receptor (ER)-positive population (hazards ratio: 2.10; 95% confidence interval: 1.45–3.03; P < 0.0001). In public expression profiling data, high HSF1 mRNA levels were also associated with an increase in ER-positive breast cancer-specific mortality. We conclude that increased HSF1 is associated with reduced breast cancer survival. The findings indicate that HSF1 should be evaluated prospectively as an independent prognostic indicator in ER-positive breast cancer. HSF1 may ultimately be a useful therapeutic target in cancer.

Accurate classification is essential for understanding the pathophysiology of a disease and can inform therapeutic choices. For hematopoietic malignancies, a classification scheme based on the phenotypic similarity between tumor cells and normal cells has been successfully used to define tumor subtypes; however, use of normal cell types as a reference by which to classify solid tumors has not been widely emulated, in part due to more limited understanding of epithelial cell differentiation compared with hematopoiesis. To provide a better definition of the subtypes of epithelial cells comprising the breast epithelium, we performed a systematic analysis of a large set of breast epithelial markers in more than 15,000 normal breast cells, which identified 11 differentiation states for normal luminal cells. We then applied information from this analysis to classify human breast tumors based on normal cell types into 4 major subtypes, HR0-HR3, which were differentiated by vitamin D, androgen, and estrogen hormone receptor (HR) expression. Examination of 3,157 human breast tumors revealed that these HR subtypes were distinct from the current classification scheme, which is based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Patient outcomes were best when tumors expressed all 3 hormone receptors (subtype HR3) and worst when they expressed none of the receptors (subtype HR0). Together, these data provide an ontological classification scheme associated with patient survival differences and provides actionable insights for treating breast tumors.

Active surveillance trials for low-risk ductal carcinoma in situ (DCIS) are in progress in the United States and Europe. In some of these trials, the presence of comedo necrosis in the DCIS has been an exclusion criterion for trial entry. However, the minimum amount of necrosis required by pathologists for a diagnosis of comedo necrosis is not well-defined. We surveyed 35 experienced breast pathologists to assess their diagnostic threshold for comedo necrosis. Pink circles representing necrosis ranging in extent from 10 to 80% of the duct diameter were superimposed on eight replicate histologic images of a single duct involved by low nuclear grade, solid pattern DCIS. These images were circulated by e-mail to the participating pathologists who were asked to select the image that represents the minimum amount of necrosis that they require for a diagnosis of comedo necrosis. Among the 35 participants, the minimum extent of the duct diameter required for a diagnosis of comedo necrosis was 10% for 4 pathologists, 20% for 5, 30% for 11, 40% for 7, 50% for 6, 60% for 1 and 70% for 1. There was no single threshold about which more than one-third of the pathologists agreed met the minimal criteria for comedo necrosis. We conclude that even among experienced breast pathologists, the threshold for comedo necrosis is highly variable. Our findings highlight the need for a standardized definition of comedo necrosis as a trial criterion, and more generally where it may be used as a marker of increased risk of recurrence for therapeutic decision making.

At least four major categories of invasive breast cancer have been reproducibly identified by gene expression profiling: luminal A, luminal B, HER2-type, and basal-like. These subtypes have been shown to differ in their outcome and response to treatment. Whether this heterogeneity reflects the evolution of these subtypes through distinct etiologic pathways has not been clearly defined. We evaluated the association between traditional breast cancer risk factors and risk of previously defined molecular subtypes of breast cancer in the Nurses’ Health Study. This analysis included 2,022 invasive breast cancer cases for whom we were able to obtain archived breast cancer tissue specimens. Tissue microarrays (TMAs) were constructed, and slides were immunostained for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), cytokeratin 5/6 (CK5/6), and epidermal growth factor receptor (EGFR). Using immunostain results in combination with histologic grade, cases were grouped into molecularly defined subtypes. We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). We observed differences in the association between risk factors and subtypes of breast cancer. In general, many reproductive factors were most strongly associated with the luminal A subtype, although these differences were not statistically significant. Weight gain since age 18 showed significant differences in its association with molecular subtypes ( P -heterogeneity = 0.05) and was most strongly associated with the luminal B subtype ( P -trend 0.001). Although there was not significant heterogeneity for lactation across subtypes, an inverse association was strongest for basal-like tumors (HR = 0.6, 95% CI 0.4–0.8; P -heterogeneity = 0.88). These results support the hypothesis that different subtypes of breast cancer have different etiologies and should not be considered as a single group. Identifying risk factors for less common subtypes such as luminal B, HER2-type and basal-like tumors has important implications for prevention of these more aggressive subtypes.