Faculty Bibliography

OBJECTIVE. The purpose of our study was to evaluate the upgrade rates of high-risk lesions (HRLs) diagnosed by MRI-guided core biopsy and to assess which clinical and imaging characteristics are predictive of upgrade to malignancy. MATERIALS AND METHODS. A retrospective review was performed of all women who presented to an academic breast radiology center for MRI-guided biopsy between January 1, 2015, and November 30, 2018. Histopathologic results from each biopsy were extracted. HRLs-that is, atypical ductal hyperplasia (ADH), lobular carcinoma in situ (LCIS), atypical lobular hyperplasia (ALH), radial scar, papilloma, flat epithelial atypia (FEA), benign vascular lesion (BVL), and mucocelelike lesion-were included for analysis. Clinical history, imaging characteristics, surgical outcome, and follow-up data were recorded. Radiologic-pathologic correlation was performed. RESULTS. Of 810 MRI-guided biopsies, 189 cases (23.3%) met the inclusion criteria for HRLs. Of the 189 HRLs, 30 cases were excluded for the following reasons: 15 cases were lost to follow-up, six cases were in patients who received neoadjuvant chemotherapy after biopsy, two lesions that were not excised had less than 2 years of imaging follow-up, and seven lesions had radiologic-pathologic discordance at retrospective review. Of the 159 HRLs in our study cohort, 13 (8.2%) were upgraded to carcinoma. Surgical upgrade rates were high for ADH (22.5%, 9/40) and FEA (33.3%, 1/3); moderate for LCIS (6.3%, 3/48); and low for ALH (0.0%, 0/11), radial scar (0.0%, 0/28), papilloma (0.0%, 0/26), and BVL (0.0%, 0/3). Of the upgraded lesions, 69.2% (9/13) were upgraded to ductal carcinoma in situ (DCIS) or well-differentiated carcinoma. ADH lesions were significantly more likely to be upgraded than non-ADH lesions (p = .005). CONCLUSION. ADH diagnosed by MRI-guided core biopsy warrants surgical excision. The other HRLs, however, may be candidates for imaging follow-up rather than excision, especially after meticulous radiologic-pathologic correlation.

Lung neuroendocrine neoplasms (NENs) can be challenging to classify due to subtle histologic differences between pathological types. MicroRNAs (miRNAs) are small RNA molecules that are valuable markers in many neoplastic diseases. To evaluate miRNAs as classificatory markers for lung NENs, we generated comprehensive miRNA expression profiles from 14 typical carcinoid (TC), 15 atypical carcinoid (AC), 11 small cell lung carcinoma (SCLC), and 15 large cell neuroendocrine carcinoma (LCNEC) samples, through barcoded small RNA sequencing. Following sequence annotation and data preprocessing, we randomly assigned these profiles to discovery and validation sets. Through high expression analyses, we found that miR-21 and -375 are abundant in all lung NENs, and that miR-21/miR-375 expression ratios are significantly lower in carcinoids (TC and AC) than in neuroendocrine carcinomas (NECs; SCLC and LCNEC). Subsequently, we ranked and selected miRNAs for use in miRNA-based classification, to discriminate carcinoids from NECs. Using miR-18a and -155 expression, our classifier discriminated these groups in discovery and validation sets, with 93% and 100% accuracy. We also identified miR-17, -103, and -127, and miR-301a, -106b, and -25, as candidate markers for discriminating TC from AC, and SCLC from LCNEC, respectively. However, these promising findings require external validation due to sample size.

Adenomyoepitheliomas (AMEs) of the breast are uncommon and span the morphologic spectrum of benign, atypical, in situ, and invasive forms. In exceptionally rare cases, these tumors metastasize to regional lymph nodes or distant sites. In the era of genomic characterization, data is limited regarding AMEs. The aim of this study was to provide insight into the molecular underpinnings of a spectrum of AMEs. Seven cases of AMEs of the breast (benign-1, atypical-2, in situ-1, invasive-3) were identified in our files. The seven samples were interrogated using the Oncomine Comprehensive Assay v3 (ThermoFisher). Two atypical AMEs and the malignant in situ AME harbored the same gain-of-function PIK3CA mutation. The malignant in situ AME also showed EGFR amplification, not described previously. Both a benign AME and a malignant invasive AME shared the same gain-of-function AKT1 variant. The benign AME also showed a GNAS mutation. Moreover, the same gain-of-function HRAS mutation was present in an atypical AME and a malignant invasive AME. We also identified co-occurring HRAS and PIK3CA mutations in an ER-positive atypical AME, which has not been previously described. No fusion drivers were detected. We describe the molecular characteristics of the spectrum of AME tumors of the breast, which harbor alterations in the PI3K/AKT pathway. Our findings are clinically relevant with respect to the current options of targeted therapy in the rare instances where malignant AME tumors of the breast progress.

Neuroendocrine neoplasms (NENs) are clinically diverse and incompletely characterized cancers that are challenging to classify. MicroRNAs (miRNAs) are small regulatory RNAs that can be used to classify cancers. Recently, a morphology-based classification framework for evaluating NENs from different anatomical sites was proposed by experts, with the requirement of improved molecular data integration. Here, we compiled 378 miRNA expression profiles to examine NEN classification through comprehensive miRNA profiling and data mining. Following data preprocessing, our final study cohort included 221 NEN and 114 non-NEN samples, representing 15 NEN pathological types and 5 site-matched non-NEN control groups. Unsupervised hierarchical clustering of miRNA expression profiles clearly separated NENs from non-NENs. Comparative analyses showed that miR-375 and miR-7 expression is substantially higher in NEN cases than non-NEN controls. Correlation analyses showed that NENs from diverse anatomical sites have convergent miRNA expression programs, likely reflecting morphological and functional similarities. Using machine learning approaches, we identified 17 miRNAs to discriminate 15 NEN pathological types and subsequently constructed a multilayer classifier, correctly identifying 217 (98%) of 221 samples and overturning one histological diagnosis. Through our research, we have identified common and type-specific miRNA tissue markers and constructed an accurate miRNA-based classifier, advancing our understanding of NEN diversity.

Mucinous lesions of the breast include a variety of benign and malignant epithelial processes that display intracytoplasmic or extracellular mucin, including mucocele-like lesions, mucinous carcinoma, solid papillary carcinoma, and other rare subtypes of mucin-producing carcinoma. The finding of free-floating or stromal mucin accumulations is a diagnostic challenge of which the significance depends on the clinical, radiologic, and pathologic context. This article emphasizes the differential diagnosis between benign and malignant mucin-producing lesions, with a brief consideration of potential mimics, such as biphasic and mesenchymal lesions with associated with mucinous, myxoid, or matrix material.

Metastasis causes ~90% of breast cancer mortality. However, standard prognostic tests based mostly on proliferation genes do not measure metastatic potential. Tumor MicroEnvironment of Metastasis (TMEM), an immunohistochemical biomarker for doorways on blood vessels that support tumor cell dissemination is prognostic for metastatic outcome in breast cancer patients. Studies quantifying TMEM doorways have involved manual scoring by pathologists utilizing static digital microscopy: a labor-intensive process unsuitable for use in clinical practice. We report here a validation study evaluating a new quantitative digital pathology (QDP) tool (TMEM-DP) for identification and quantification of TMEM doorways that closely mimics pathologists' workflow and reduces pathologists' variability to levels suitable for use in a clinical setting. Blinded to outcome, QDP was applied to a nested case-control study consisting of 259 matched case-control pairs. Sixty subjects of these were manually scored by five pathologists, digitally recorded using whole slide imaging (WSI), and then used for algorithm development and optimization. Validation was performed on the remainder of the cohort. TMEM-DP shows excellent reproducibility and concordance and reduces pathologist time from ~60 min to ~5 min per case. Concordance between manual scoring and TMEM-DP was found to be >0.79. These results show that TMEM-DP is capable of accurately identifying and scoring TMEM doorways (also known as MetaSite score) equivalent to pathologists.