Citations

Abstract Background: Biopsy diagnosis of benign breast disease (BBD) based on the most severe lesion in a sample predicts future breast cancer risk and has implications for screening and management. Lobules are the functional unit of the breast and the structures from which BBD arises. We developed and preliminarily validated an automated computational pathology algorithm to discriminate normal from BBD breast lobules as a step toward automated comprehensive characterization of benign biopsies. Methods: 152 BBD biopsies (27 training, 125 validation) from the Mayo Clinic were examined using scanned digital images of H&E stained sections. For each image, a pathologist annotated up to 10 representative lobules as normal or BBD using standard pathology criteria. A deep learning algorithm to quantify lobular features was developed using 129 lobules from 27 subjects. Nine features were identified that discriminate normal vs BBD lobules, expressing lobule size, acini size and number, acinar lumen size, proportion of lobular stroma, and capillaries. Here, we validate their performance to discriminate normal vs BBD lobules in a set of 1250 lobules from 125 subjects using area under the ROC curve (AUC) analysis. Random forest analysis was used for multivariable modeling; model performance was assessed with a tenfold cross-validation approach. Results: Median subject age was 52 years. Among the 125 validation subjects, BBD findings were nonproliferative in 39%, proliferative in 45%, and atypical hyperplasia in 16%. Sections included 552 (44%) normal lobules and 698 BBD (56%) lobules, with representation of both lobule types on each section. In univariate analyses, four individual features showed good discrimination between normal vs. BBD lobules, yielding the following AUCs: lobule size= 0.74, mean acini size= 0.75, epithelial area= 0.75, and number of acini with large lumens= 0.76. Lobule size and epithelial area were highly correlated (Spearman rank correlation r = 0.95), but both of these features showed lower correlation with mean acini size and number of acini with large lumens (each r < 0.5). With random forest modeling, number of acini with large lumens was the strongest discriminating feature, followed by mean acini size, lobule size, and epithelial area. With ten-fold cross validation of the multivariable random forest model, the overall AUC was 0.82 (95% CI: 0.79-0.85). Conclusion: Our validation showed that automated quantitative computational pathology assessment of breast lobules can discriminate normal versus BBD on a per lobule basis. This finding supports the feasibility of developing automated algorithms to classify every lesion in breast biopsies, expanding beyond visual assessment. Further studies using deep learning may reveal novel pathology features for classifying BBD biopsies with the potential to strengthen estimation of breast cancer risk. Citation Format: Amy C. Degnim, Thomas de Bel, Mark E. Sherman, Derek C. Radisky, Stacey J. Winham, Tanya L. Hoskin, Melody L. Stallings Mann, Marlene Frost, Robert A. Vierkant, Brendan T. Broderick, Ethan P. Heinzen, Rushin Brahmbhatt, Muhammad Arshad, Celine M. Vachon, Jodi M. Carter, Lori A. Denison, Daniel W. Visscher, Jeroen van der Laak. Discrimination of benign breast disease from normal lobules using an automated computational pathology algorithm [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2113.

Abstract Background: The intrinsic immune response in triple negative breast cancer (TNBC) has both prognostic and predictive utility, and immune-related biomarkers are now actionable targets in TNBC. Current modalities for immune profiling are limited by lack of spatial context and precise quantitation. We used quantitative digital spatial profiling (DSP) to 1) characterize the immune architecture of tumoral and stromal regions in TNBC and 2) identify prognostic immune signatures. Methods: From the FinXX trial of adjuvant capecitabine therapy, tumor sections (FFPE) matched for patient characteristics, treatment arm and RFS-based outcome (N=44) were assessed with Nanostring GeoMxTM DSP. Briefly, with 3-plex immunofluorescence (pan-cytokeratin, CD45, CD68 with SYTO-13 dye for nuclei), spatially-defined CD45-enriched or CD68-enriched stromal segments and adjacent tumor segments (N=950) were selected for digital quantitation (NanoString nCounter) of 39 immune proteins (e.g. immune cell profiling proteins: CD3, CD4, CD8, CD11c, CD20, CD56, CD68, HLA-DR, PD-1, PD-L1, Granzyme B), immune drug targets (e.g. VISTA, STING, IDO1), and activation status-related proteins (e.g. ICOS, PD-L2, CD40, CD40L, CD44). Normalized, differentially-expressed (DE) proteins were evaluated, and weighted gene co-expression network analysis (WGCNA) was performed to identify modules of highly co-expressing immune proteins, and association with RFS as a categorical variable was determined. Results: 20 DE proteins in tumor segments were associated with RFS. Among them, those with the highest RFS-associated fold changes (FC) included CD20, CD40, CD56, HLA-DR, Granzyme B, and PD-L2 (FC): 2.1-4.4) (p < 0.05). In contrast, only 4 DE proteins were associated with RFS in stromal segments (Granzyme B, IDO-1, PD-L1, and PD-L2 (FC: 1.5-3.3) (p < 0.05). By WGCNA, 2 tumor-based immune modules [IM1 (lymphocyte infiltration) and IM2 (lymphocyte activation)] were identified based on their co-expression. The top three co-expressed proteins in IM1 were CD4, CD40, and CD45 (r >0.9). The three top co-expressed proteins in IM2 were PD-L1, PD-L2, and Granzyme B (r > 0.85). Together with CD56 expression, IM1 and IM2 comprised a set of 3 intratumoral immune features that associated with RFS. For recurring tumors, all 3 immune features clustered with low scores . Among non-recurrent tumors, two distinct, non-overlapping clusters with inverse characteristics were identified: high IM2, low IM1, low CD56 vs. low IM2, high IM1, and high CD56. Overall, across all 3 immune features, PDL2, CD40 and CD56 expression most strongly associated with RFS. Conclusion: With spatially-defined, precise quantification of immune proteins, we identified 3 immune features, including two distinct immune modules, IM1 and IM2 that correlate with durable RFS. These immune modules of highly co-expressing proteins were associated with RFS only when present in the tumor compartment but not tumor-adjacent, immune cell-rich stroma. Citation Format: Jodi M. Carter, Saranya Chumsri, Yaohua Ma, Xue Wang, Jennifer M. Kachergus, Douglas Hinerfeld, Heather Ann Brauer, Sarah Warren, Heikki Joensuu, Edith A. Perez, Aubrey Thompson. Quantitative digital spatial profiling reveals novel prognostic intratumoral immune signatures in triple-negative breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1004.

This combined American Neurotology Society, American Otological Society, and American Academy of Otolaryngology – Head and Neck Surgery Foundation document aims to provide guidance during the coronavirus disease of 2019 (COVID-19) on 1) “priority” of care for otologic and neurotologic patients in the office and operating room, and 2) optimal utilization of personal protective equipment. Given the paucity of evidence to inform otologic and neurotologic best practices during COVID-19, the recommendations herein are based on relevant peer-reviewed articles, the Centers for Disease Control and Prevention COVID-19 guidelines, United States and international hospital policies, and expert opinion. The suggestions presented here are not meant to be definitive, and best practices will undoubtedly change with increasing knowledge and high-quality data related to COVID-19. Interpretation of this guidance document is dependent on local factors including prevalence of COVID-19 in the surgeons’ local community. This is not intended to set a standard of care, and should not supersede the clinician's best judgement when managing specific clinical concerns and/or regional conditions. Access to otologic and neurotologic care during and after the COVID-19 pandemic is dependent upon adequate protection of physicians, audiologists, and ancillary support staff. Otolaryngologists and associated staff are at high risk for COVID-19 disease transmission based on close contact with mucosal surfaces of the upper aerodigestive tract during diagnostic evaluation and therapeutic procedures. While many otologic and neurotologic conditions are not imminently life threatening, they have a major impact on communication, daily functioning, and quality of life. In addition, progression of disease and delay in treatment can result in cranial nerve deficits, intracranial and life-threatening complications, and/or irreversible consequences. In this regard, many otologic and neurotologic conditions should rightfully be considered “urgent,” and almost all require timely attention to permit optimal outcomes. It is reasonable to proceed with otologic and neurotologic clinic visits and operative cases based on input from expert opinion of otologic care providers, clinic/hospital administration, infection prevention and control specialists, and local and state public health leaders. Significant regional variations in COVID-19 prevalence exist; therefore, physicians working with local municipalities are best suited to make determinations on the appropriateness and timing of otologic and neurotologic care.

Only 50% to 70% of patients with mesothelioma report asbestos exposure. Other exposures (eg, radiation) play a role in some cases, but some patients have no obvious cause. We describe a series of patients with long-standing indwelling intra-abdominal shunt catheters who developed malignant peritoneal mesothelioma, suggesting a novel association. We identified 7 patients who had shunts and subsequently developed mesothelioma (5 women; median age: 31 y, range: 18 to 45 y). Clinical history and pathology materials were reviewed, and RNA sequencing was performed. Clinical presentations varied; 6 patients had hydrocephalus and a ventriculoperitoneal shunt, and 1 patient had portal hypertension and a portoatrial shunt. The median duration of shunt therapy in 5 cases was 29 years (range: 12 to 35 y); the remaining 2 patients also had shunts for many years, but specific details were unavailable. Two patients had radiotherapy for malignancies in childhood. One had an alleged exposure to asbestos and 1 had prior exposure to talc. The rest had no known risk factors. Histologically, all tumors were purely epithelioid. Treatments included surgical debulking, chemotherapy, and palliative care. All 7 died of disease (median survival: 7 mo, range: 1 to 18 mo). Molecular testing showed loss of NF2 and CDKN2A/B and a BAP1 mutation in 1 case, and no genomic alterations associated with mesothelioma in 2 cases. Peritoneal mesothelioma may represent a complication of long-standing indwelling shunt catheters. The mechanism is unknown, but chronic peritoneal irritation may play a role. Albeit rare, mesothelioma should be considered in patients with a shunt who present with new ascites.

Glembatumumab vedotin (CDX-011, GV) is a fully human Immunoglobulin G2 monoclonal antibody directed against glycoprotein NMB coupled via a peptide linker to monomethyl auristatin E (MMAE), a potent cytotoxic microtubule inhibitor. This phase II study evaluated the overall response rate and safety of GV, glycoprotein NMB (GPNMB) expression, and survival in patients with metastatic uveal melanoma. Eligible patients with metastatic uveal melanoma who had not previously been treated with chemotherapy received GV 1.9 mg/kg every three weeks. The primary endpoint was the objective response rate (ORR). Secondary endpoints included GPNMB expression, progression-free survival (PFS), overall survival (OS), and toxicity analysis. GPNMB expression was assessed pre- and post-treatment via immunohistochemistry for patients with available tumor tissue. Out of 35 patients who received treatment, two patients had confirmed partial responses (PRs; 6%), and 18 patients had a stable disease (SD; 51%) as the best objective response. 38% of the patients had stable disease >100 days. The grade 3 or 4 toxicities that occurred in two or more patients were neutropenia, rash, hyponatremia, and vomiting. The median progression-free survival was 3.1 months (95% CI: 1.5–5.6), and the median overall survival was 11.9 months (95% CI 9.0–16.9) in the evaluable study population. GV is well-tolerated in metastatic uveal melanoma. The disease control rate was 57% despite a low objective response rate. Exploratory immune correlation studies are underway to provide insight into target saturation, combination strategies, and antigen release.

Background: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). Methods: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83–0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76–0.84). Conclusions: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.

Background: Comorbidity-driven microvascular inflammation is posited as a unifying pathophysiologic mechanism for heart failure with preserved ejection fraction (HFpEF). Obesity is proinflammatory and common in HFpEF. We hypothesized that unique obesity-inflammation HFpEF phenotypes exist and are associated with differences in clinical features, fibrosis biomarkers, and functional performance. Methods: Patients (n=301) from 3 HFpEF clinical trials were studied. Unsupervised machine learning (hierarchical clustering) with obese status and 13 inflammatory biomarkers as input variables was performed. Associations of clusters with HFpEF severity and fibrosis biomarkers (PIIINP [procollagen III N-terminal peptide], CITP [C-telopeptide for type I collagen], IGFBP7 [insulin-like growth factor-binding protein-7], and GAL-3 [galectin-3]) were assessed. Results: Hierarchical clustering revealed 3 phenotypes: pan-inflammatory (n=129; 64% obese), noninflammatory (n=83; 55% obese), and obese high CRP (C-reactive protein; n=89; 98% obese). The pan-inflammatory phenotype had more comorbidities and heart failure hospitalizations; higher left atrial volume, NT-proBNP (N-terminal pro-B-type natriuretic peptide), and fibrosis biomarkers; and lower glomerular filtration rate, peak oxygen consumption, 6-minute walk distance, and active hours/day ( P <0.05 for all). The noninflammatory phenotype had the most favorable values for all measures. The obese high CRP phenotype resembled the noninflammatory phenotype except for isolated elevation of CRP and lower functional performance. Hierarchical cluster assignment was independent of CRP genotype combinations that alter CRP levels and more biologically plausible than other clustering approaches. Multiple traditional analytic techniques confirmed and extended the hierarchical clustering findings. Conclusions: Unique obesity-inflammation phenotypes exist in HFpEF and are associated with differences in comorbidity burden, HFpEF severity, and fibrosis. These data support comorbidity-driven microvascular inflammation as a pathophysiologic mechanism for many but not all HFpEF patients.

Abstract Over one million women in the United States receive biopsy diagnoses of benign breast disease (BBD) each year, which confer a 1.5–4.0-fold increase in breast cancer risk. Studies in the general population suggest that nonsteroidal anti-inflammatory agents (NSAID) lower breast cancer risk; however, associations among women with BBD are unknown. We assessed whether NSAID use among women diagnosed with BBD is associated with lower breast cancer risk. Participants included 3,080 women (mean age = 50.3 ± 13.5 years) in the Mayo BBD surgical biopsy cohort diagnosed between January 1, 1992 and December 31, 2001 who completed breast cancer risk factor questionnaires that assessed NSAID use, and whose biopsies underwent detailed pathology review, masked to outcome. Women were followed from date of BBD biopsy to breast cancer diagnosis (main outcome) or censoring (death, prophylactic mastectomy, reduction mammoplasty, lobular carcinoma in situ or last contact). Median follow-up time was 16.4 ± 6.0 years. Incident breast cancer was diagnosed among 312 women over a median follow-up of 9.9 years. Regular non-aspirin NSAID use was associated with lower breast cancer risk [HR = 0.63; 95% confidence interval (CI) = 0.46–0.85; P = 0.002] with trends of lower risk (highest tertiles of use vs. nonuse) for greater number of years used [HR = 0.55; 95% CI = 0.31–0.97; Ptrend = 0.003), days used per month (HR = 0.51; 95% CI = 0.33–0.80; Ptrend = 0.001) and lifetime number of doses taken (HR = 0.53; 95% CI = 0.31–0.89; Ptrend = 0.003). We conclude that nonaspirin NSAID use is associated with statistically significant lower breast cancer risk after a BBD biopsy, including a dose–response effect, suggesting a potential role for NSAIDs in breast cancer prevention among patients with BBD.