Faculty Bibliography

BACKGROUND:Oncologic outcomes with nipple-sparing mastectomy (NSM) continue to be established. We examine oncologic trends, outcomes, and risk factors, including tumor-to-nipple distance (TND), in therapeutic NSMs. METHODS:Demographics, outcomes, and overall trends for all NSMs undertaken for a therapeutic indication from 2006 to 2017 were analyzed. Oncologic outcomes were investigated with specific focus on recurrence and associated factors, including TND. RESULTS:A total of 496 therapeutic NSMs were performed with average follow-up time of 48.25 months. The most common tumor types were invasive carcinoma (52.4%) and ductal carcinoma in situ (50.4%). Sentinel lymph node sampling was performed in 79.8% of NSMs; 4.1% had positive frozen sentinel lymph node biopsies while 15.7% had positive nodal status on permanent pathologic examination. The most common pathologic cancer stage was stage IA (42.5%) followed by Stage 0 (31.3%).Per NSM, the rate of local recurrence was 1.6% (N=8); the rate of regional recurrence was 0.6% (N=3). In all, 171 NSMs had magnetic resonance imaging available to assess tumor-to-nipple distance (TND). NSMs with TND ≤1 centimeter (25.0% versus 2.4%, p=0.0031/p=0.1129) and ≤2 centimeters (8.7% versus 2.0%; p=0.0218/p=0.1345) trended to higher rates of locoregional recurrence. In univariate analysis, TND ≤1 centimeter was the only significant risk factor for recurrence (OR=13.5833, p=0.0385). No factors were significant in regression analysis. CONCLUSIONS:In this group of early stage and in situ breast carcinoma, therapeutic NSM appears oncologically safe with a locoregional recurrence rate of 2.0%. Tumor-to-nipple distances of ≤1 centimeter and ≤2 centimeters trended to higher rates of recurrence.

INTRODUCTION/BACKGROUND:Accurate assessment of estrogen receptor (ER) expression is crucial to ensure that patients with early breast cancer are accurately identified for appropriate treatment with endocrine therapy. Reverse transcriptase polymerase chain reaction (RT-PCR), compared with immunohistochemistry (IHC), may provide a more precise indication of ER status. Data were pooled and analyzed from two independent, but similarly designed, studies that examined ER status by IHC and the 21-gene Recurrence Score that employs RT-PCR-based methodology. METHODS:Tumor tissue from patients with early stage breast cancer where ER status could be determined by both IHC and RT-PCR was included. ER status by IHC staining was defined as ER-negative (< 1%), ER-low+ (1-10%), or ER+ (> 10%). ER status by RT-PCR was defined as ER-negative (≤ 6.5) or ER+ (> 6.5). Recurrence Score results from the 21-gene assay were reported on a continuous scale from 0 to 100. A sub-analysis examined the association between ER expression (Allred score 2-7) and response to a 14-day pre-surgery pulse with an aromatase inhibitor. A separate sub-analysis examined the association between ER expression and human epidermal growth factor receptor 2 (HER2) expression. RESULTS:Tumor specimens from 192 patients (aged 25-92 years) were included in the pooled analysis. Correlation between IHC- and RT-PCR-measured ER was strong for IHC-defined ER-negative and ER+ samples (r = 0.646 [95% CI 0.553-0.720]). There was 100% concordance for ER+ tumors; however, 56% of the ER-low+ tumors were negative by RT-PCR. Allred score correlated better with ER status measured by RT-PCR at pre-treatment (r = 0.83) than at post-treatment (r = 0.76). The majority (77%) of ER-negative and ER-low+ tumors were HER2-negative. CONCLUSIONS:RT-PCR provided a more accurate assessment of ER expression in patients with ER-low+ tumors, and data support dual testing for patients with ER-low+ status to ensure appropriate treatment planning as it pertains to endocrine therapy. FUNDING/BACKGROUND:Genomic Health, Inc.

PURPOSE/OBJECTIVE:To investigate how breast parenchymal uptake (BPU) of 18F-FDG on positron emission tomography/ magnetic resonance imaging (PET/MRI) in patients with breast cancer is related to background parenchymal enhancement (BPE), amount of fibroglandular tissue (FGT), and age, as well as whether BPU varies as a function of distance from the primary breast cancer. MATERIALS AND METHODS/METHODS:volume of interest 1) in the same quadrant of the ipsilateral breast, 5 mm from the index lesion; 2) in the opposite quadrant of the ipsilateral breast; and 3) in contralateral breast, quadrant matched to the opposite quadrant of the ipsilateral breast. The maximum standardized uptake value (SUVmax) of the index cancer was measured using a VOI that included the entire volume of the index lesion. Bleed from the primary tumor was corrected for (PET edge, MIM). FGT and BPE was assessed by 2 readers on a 4-point scale in accordance with BI-RADS lexicon. The Wilcoxon signed rank test and the Spearman rank correlation test were performed. RESULTS:BPU was significantly greater in the same quadrant as the breast cancer as compared with the opposite quadrant of the same breast (p < 0.001 for both readers) and was significantly greater in the opposite quadrant of the same breast compared to the matched quadrant of the contralateral breast (p = 0.002 for reader 1 and <0.001 for reader 2). While the FGT SUVmax in the same quadrant as the cancer correlated significantly with SUVmax of the index lesion, the FGT SUVmax in the opposite quadrant of the same breast and in the matched quadrant of the contralateral breast did not. The FGT SUVmax in the contralateral breast positively correlated with the degree of BPE and negatively correlated with age, but did not show a significant correlation with the amount of FGT for either reader. CONCLUSION/CONCLUSIONS:There appears to be an inverse correlation between metabolic activity of normal breast parenchyma and distance from the index cancer. BPU significantly correlates with BPE.

Positron emission tomography / magnetic resonance imaging (PET/MRI) is an emerging imaging technology that allows for the acquisition of multiple MRI parameters simultaneously with PET data. In this review, we address the technical requirements of PET/MRI including protocols and tracers, the potential of integrated localized breast PET/MRI exams, and possible applications of whole-body PET/MRI in breast cancer patients. Currently, PET/MRI can be performed on sequential and integrated PET/MRI scanners but, as not all practices can access these dedicated machines, several studies look at PET and MRI exams that are performed separately on separate scanners within a short time frame. This practice likely provides similar clinical data, although exact colocalization for iso-voxel analysis, currently performed only in research, is not possible. In PET/MRI, the MRI sequences are flexible and can be customized according to the aim of the exam. The most commonly used radiotracer is ¹⁸ F-FDG; however, tracers that image hypoxia and drug targets such as estrogen receptors and HER2 are in development and may increase the utility of PET/MRI. For dedicated breast PET/MRI, a potential advantage over standard breast MRI alone may be the complementary sensitivities of MRI for extent of disease within the breast and PET for axillary and internal mammary nodal metastases. Moreover, layers of multiparametric MRI and PET metrics derived from the index lesion are being investigated as predictors of response to neoadjuvant therapy. These data may eventually be able to be quantified and mined in a way that furthers radiomics and also precision medicine. Finally, in whole-body imaging of breast cancer patients, single-institution studies have found that PET/MRI detects more metastases than PET at about half the radiation dose, although a survival benefit has not been shown. For now, whole-body PET/MRI in breast cancer patients may be most relevant for young patients who may undergo serial surveillance exams.

BACKGROUND:Cancers induce gene expression alterations in stroma surrounding tumors that supports cancer progression. However, it is actually not at all known the extent of altered stromal gene expression enacted by tumors nor the extent to which altered stromal gene expression penetrates the stromal tissue. Presently, post-surgical "tumor-free" stromal tissue is determined to be cancer-free based on solely on morphological normality-a criteria that has not changed in more than 100 years despite the existence of sophisticated gene expression data to the contrary. We therefore investigated the extent to which breast tumors alter stromal gene expression in three dimensions in women undergoing mastectomy with the intent of providing a genomic determination for development of future risk of recurrence criteria, and to inform the need for adjuvant full-breast irradiation. METHODS AND FINDINGS/RESULTS:Genome-wide gene expression changes were determined in histopathologically normal breast tissue in 33 women undergoing mastectomy for stage II and III primary invasive ductal carcinoma at serial distances in three dimensions from the tumor. Gene expression was determined by genome-wide mRNA analysis and subjected to metagene mRNA characterization. Tumor-like gene expression signatures in stroma were identified that surprisingly transitioned to a plastic, normalizing homeostatic signature with distance from tumor. Stroma closest to tumor displayed a pronounced tumor-like signature enriched in cancer-promoting pathways involved in disruption of basement membrane, cell migration and invasion, WNT signaling and angiogenesis. By 2 cm from tumor in all dimensions, stromal tissues were in transition, displaying homeostatic and tumor suppressing gene activity, while also expressing cancer supporting pathways. CONCLUSIONS:The dynamics of gene expression in the post-tumor breast stroma likely co-determines disease outcome: reversion to normality or transition to transformation in morphologically normal tissue. Our stromal genomic signature may be important for personalizing surgical and adjuvant therapeutic decisions and risk of recurrence.

Background/UNASSIGNED:In the digital era when mHealth has emerged as an important venue for health care, the application of computer science, such as machine learning, has proven to be a powerful tool for health care in detecting or predicting various medical conditions by providing improved accuracy over conventional statistical or expert-based systems. Symptoms are often indicators for abnormal changes in body functioning due to illness or side effects from medical treatment. Real-time symptom report refers to the report of symptoms that patients are experiencing at the time of reporting. The use of machine learning integrating real-time patient-centered symptom report and real-time clinical analytics to develop real-time precision prediction may improve early detection of lymphedema and long term clinical decision support for breast cancer survivors who face lifelong risk of lymphedema. Lymphedema, which is associated with more than 20 distressing symptoms, is one of the most distressing and dreaded late adverse effects from breast cancer treatment. Currently there is no cure for lymphedema, but early detection can help patients to receive timely intervention to effectively manage lymphedema. Because lymphedema can occur immediately after cancer surgery or as late as 20 years after surgery, real-time detection of lymphedema using machine learning is paramount to achieve timely detection that can reduce the risk of lymphedema progression to chronic or severe stages. This study appraised the accuracy, sensitivity, and specificity to detect lymphedema status using machine learning algorithms based on real-time symptom report. Methods/UNASSIGNED:A web-based study was conducted to collect patients' real-time report of symptoms using a mHealth system. Data regarding demographic and clinical information, lymphedema status, and symptom features were collected. A total of 355 patients from 45 states in the US completed the study. Statistical and machine learning procedures were performed for data analysis. The performance of five renowned classification algorithms of machine learning were compared: Decision Tree of C4.5, Decision Tree of C5.0, gradient boosting model (GBM), artificial neural network (ANN), and support vector machine (SVM). Each classification algorithm has certain user-definable hyper parameters. Five-fold cross validation was used to optimize these hyper parameters and to choose the parameters that led to the highest average cross validation accuracy. Results/UNASSIGNED:Using machine leaning procedures comparing different algorithms is feasible. The ANN achieved the best performance for detecting lymphedema with accuracy of 93.75%, sensitivity of 95.65%, and specificity of 91.03%. Conclusions/UNASSIGNED:A well-trained ANN classifier using real-time symptom report can provide highly accurate detection of lymphedema. Such detection accuracy is significantly higher than that achievable by current and often used clinical methods such as bio-impedance analysis. Use of a well-trained classification algorithm to detect lymphedema based on symptom features is a highly promising tool that may improve lymphedema outcomes.

Purpose: Well-established risk factors for breast cancer include family history (FH), BRCA mutations and biopsies with atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS). Several mathematical models, including the Gail and Tyrer-Cuzick models, have been developed to quantify a patient's risk for developing breast cancer. These models all differ in the list of variables and risk factors that are included in risk calculations. As a result, there is no single model that best estimates the risk for all high risk patients. The purpose of this study is to examine the application of the Gail and Tyrer-Cuzick models in a contemporary cohort of women who are enrolled in a comprehensive high-risk breast cancer database. Methods: The institutional High Risk Breast Cancer Consortium (HRBCC) was established in January 2011. Patients who were at high risk for developing breast cancer based on family history (maternal and paternal), BRCA mutations, AH and LCIS were eligible to enroll in the database. The following variables were included in this analysis: age, family history, genetic testing results, reproductive history, AH, LCIS, Gail and Tyrer-Cuzick scores, risk reduction strategies, and outcomes. All clinical data are obtained from detailed questionnaires filled out by patients who consent to the database studies and from a review of electronic medical records. Descriptive statistics were performed. Results: A total of 604 women were enrolled between 1/2011-2/2016. The median age was 51 years (range 20-87). The majority of women were Caucasian (83%). 52% had a strong FH, 13% were BRCA1 and 2 positive, 48% had AH, and 22% had LCIS. 47% of patients in our high risk program were not eligible for Gail model analysis (age <35 years, BRCA mutation carriers, history of LCIS). Only one patient was not eligible for Tyrer-Cuzick model calculation based on age >84 years. For patients who were eligible for Gail model analysis, 26 (8%) women did not meet criteria (5-year risk >=1.7%) for being designated as high risk for breast cancer. 34 (6%) of our patients did not have Tyrer- Cuzick scores over 20% (criterion for high risk). Notably, majority of the patients (69%) who were not defined as high-risk based on Gail scores >=1.7% or Tyrer-Cuzick scores >=20%, had a strong family history of breast cancer. Only 14 (2%) patients developed breast cancer during our study period, and the majority (93%) of the cancers were early stage (stage 0, I). Conclusions: Our institutional high-risk database includes women who are at high risk based on well-established risk factors for developing breast cancer (FH, BRCA mutations, AH, LCIS). Current mathematical models including the Gail and Tyrer-Cuzick models did not capture the increased risk of breast cancer in 8% of our population. While the models are helpful, in clinical practice they are not necessarily the be-all and end-all. Using heuristic risk factors is more time efficient and comprehensive risk assessment allows the clinicians and patients to better understand risk. Identifying patients as high risk and enrolling them in a high-risk database and program allow us to capture long term follow up, recommend surveillance for early detection, and better understand the effectiveness of different risk reduction and management strategies for this population