A B S T R A C T PurposeRecent misclassification (false negative) incidents have raised awareness concerning limitations of immunohistochemistry (IHC) in assessment of estrogen receptor (ER) in breast cancer. Here we define a new method for standardization of ER measurement and then examine both change in percentage and threshold of intensity (immunoreactivity) to assess sources for test discordance. MethodsAn assay was developed to quantify ER by using a control tissue microarray (TMA) and a series of cell lines in which ER immunoreactivity was analyzed by quantitative immunoblotting in parallel with the automated quantitative analysis (AQUA) method of quantitative immunofluorescence (QIF). The assay was used to assess the ER protein expression threshold in two independent retrospective cohorts from Yale and was compared with traditional methods. ResultsTwo methods of analysis showed that change in percentage of positive cells from 10% to 1% did not significantly affect the overall number of ER-positive patients. The standardized assay for ER on two Yale TMA cohorts showed that 67.9% and 82.5% of the patients were above the 2-pg/g immunoreactivity threshold. We found 9.1% and 19.7% of the patients to be QIF-positive/IHCnegative, and 4.0% and 0.4% to be QIF-negative/IHC-positive for a total of 13.1% and 20.1% discrepant cases when compared with pathologists' judgment of threshold. Assessment of survival for both cohorts showed that patients who were QIF-positive/pathologist-negative had outcomes similar to those of patients who had positive results for both assays. ConclusionAssessment of intensity threshold by using a quantitative, standardized assay on two independent cohorts suggests discordance in the 10% to 20% range with current IHC methods, in which patients with discrepant results have prognostic outcomes similar to ER-positive patients with concordant results.

Context.—In 2007 the American Society of Clinical Oncology/College of American Pathologists made new recommendations for HER2 testing and redefined HER2 positivity. Objective.—To analyze results from simultaneous HER2 testing with immunohistochemistry and fluorescence in situ hybridization (FISH) in 2590 invasive breast carcinomas between 2002 and 2010, using 2 scoring systems. Design.—Cases from between 2002 and 2006 were scored by using original US Food and Drug Administration criteria (N = 1138) and those from between 2007 and 2010 were evaluated according to American Society of Clinical Oncology/College of American Pathologists criteria (N = 1452). Concordance between testing methods and clinicopathologic associations were determined. Results.—Overall concordance between immunohistochemistry/FISH in the 9-year period was 96.2% (κ = 0.82), and positive concordance was lower. After 2007, the proportion of HER2/neu-positive and HER2/neu-negative cases was not significantly changed when using immunohistochemistry (10.5% versus 8.9%, P = .22 and 69.4% versus 63%, P = .13, respectively), but the number of equivocal cases was higher (19.9% versus 28%, P < .001). While the proportion of negative cases by FISH remained unchanged after 2007 (86.5% versus 88.2%, P = .76), the number of positive cases was lower (13.4% versus 9.2%, P < .001). In addition, 38 cases (2.6%) were FISH equivocal, 16 of which were also equivocal by immunohistochemistry. Overall, immunohistochemistry/FISH concordance was 95.9% between 2002 and 2006 (κ = 0.82) and 96.4% after 2007 (κ = 0.82). However, an approximately 13% lower positive assay concordance was noted in the last period. Conclusions.—Application of American Society of Clinical Oncology/College of American Pathologists recommendations is associated with comparable overall immunohistochemistry/FISH concordance, reduced positive concordance, and increased equivocal results.

Prostate cancer is a leading cause of morbidity and mortality for adult males in the US. The diagnosis of prostate carcinoma is usually made on prostate core needle biopsies obtained through a transrectal approach. These biopsies may account for a significant portion of the pathologists’ workload, yet variability in the experience and expertise, as well as fatigue of the pathologist may adversely affect the reliability of cancer detection. Machine-learning algorithms are increasingly being developed as tools to aid and improve diagnostic accuracy in anatomic pathology. The Paige Prostate AI-based digital diagnostic is one such tool trained on the digital slide archive of New York’s Memorial Sloan Kettering Cancer Center (MSKCC) that categorizes a prostate biopsy whole-slide image as either “Suspicious” or “Not Suspicious” for prostatic adenocarcinoma. To evaluate the performance of this program on prostate biopsies secured, processed, and independently diagnosed at an unrelated institution, we used Paige Prostate to review 1876 prostate core biopsy whole-slide images (WSIs) from our practice at Yale Medicine. Paige Prostate categorizations were compared to the pathology diagnosis originally rendered on the glass slides for each core biopsy. Discrepancies between the rendered diagnosis and categorization by Paige Prostate were each manually reviewed by pathologists with specialized genitourinary pathology expertise. Paige Prostate showed a sensitivity of 97.7% and positive predictive value of 97.9%, and a specificity of 99.3% and negative predictive value of 99.2% in identifying core biopsies with cancer in a data set derived from an independent institution. Areas for improvement were identified in Paige Prostate’s handling of poor quality scans. Overall, these results demonstrate the feasibility of porting a machine-learning algorithm to an institution remote from its training set, and highlight the potential of such algorithms as a powerful workflow tool for the evaluation of prostate core biopsies in surgical pathology practices.

Cells respond to many stressors by senescing, acquiring stable growth arrest, morphologic and metabolic changes, and a proinflammatory senescence-associated secretory phenotype. The heterogeneity of senescent cells (SnCs) and senescence-associated secretory phenotype are vast, yet ill characterized. SnCs have diverse roles in health and disease and are therapeutically targetable, making characterization of SnCs and their detection a priority. The Cellular Senescence Network (SenNet), a National Institutes of Health Common Fund initiative, was established to address this need. The goal of SenNet is to map SnCs across the human lifespan to advance diagnostic and therapeutic approaches to improve human health. State-of-the-art methods will be applied to identify, define and map SnCs in 18 human tissues. A common coordinate framework will integrate data to create four-dimensional SnC atlases. Other key SenNet deliverables include innovative tools and technologies to detect SnCs, new SnC biomarkers and extensive public multi-omics datasets. This Perspective lays out the impetus, goals, approaches and products of SenNet.