P16/Ki-67 Immunostaining is Useful in Stratification of Atypical Metaplastic Epithelium of the Cervix

Cervical metaplastic squamous epithelium exhibiting atypia insufficient for a diagnosis of cervical intraepithelial neoplasia (CIN) is usually reported as “atypical squamous metaplasia” (ASM). Stratification impacts treatment since the differential is often between reactive and high grade CIN (CIN II, III). Diagnosis with H&E is associated with low intra/interobserver concurrence. P16/Ki-67 immunostains are helpful to assess cervical biopsies for HPV-associated lesions but staining in metaplastic squamous epithelium has received little attention. This study aims to establish staining characteristics of metaplastic squamous epithelium and determine if p16/Ki-67 is useful in ASM stratification. 80 cervical biopsies containing morphologically normal and dysplastic squamous metaplasia were retrieved to determine the staining characteristics of metaplastic epithelium utilizing p16/Ki-67 immunostains. These included 21 benign squamous metaplasia (BSM) from benign cervices, 15 BSM present adjacent to HPV/CIN lesions, and 44 CIN involving squamous metaplasia. Serial sections with controls were stained for p16 and Ki-67 and in-situ hybridization (ISH) for low-risk (LR) and high-risk (HR) HPV was performed. P16 was recorded as negative, spotty, or band-like. Ki-67 was recorded as positive when present in >50% of lesional nuclei. Results were correlated with H&E diagnosis. 95% of the BSMs, whether from normal cervices or adjacent to HPV/CIN were p16/Ki-67 negative. 81% HG CINs involving squamous metaplasia were p16 band/Ki-67 positive. Low grade CIN (CIN I) involving metaplastic epithelium showed a broad distribution of p16/Ki-67 staining patterns. Based on these criteria, 20 ASM were evaluated. 10% of the ASM cases were p16 band/Ki-67 positive indicating HG CIN. 60% of the ASMs were p16/Ki-67 negative indicating reactive change (all with the exception of one case being HPV negative). The remaining 30% of the ASM cases showed variable positivity for p16 and Ki-67 and could not be stratified into the two categories. Thus p16/Ki-67 staining is helpful in stratification of ASM as reactive or CIN

P16 and Ki67 Immunostains Decrease Intra- and Interobserver Variability in the Diagnosis and Grading of Anal Intraepithelial Neoplasia (AIN)

Background Significant variation is reported in the diagnosis of HPV-associated AIN. We previously observed that band-like positivity for p16 in >90% of contiguous cells coupled with Ki67 positivity in >50% of lesional cells is strongly associated with high grade AIN. This study was undertaken to determine if addition of p16 and Ki67 immunostaining would reduce inter- and intraobserver variability in diagnosis and grading of AIN. Design H&E stained slides of 60 anal biopsies were reviewed by three pathologists and consensus diagnoses were achieved: 25 negative, 12 low (condyloma and/or AIN I) and 23 high (9 AIN II and 14 AIN III) grade lesions. The H&E stained slides were diagnosed independently by three additional (“participant”) pathologists. Several weeks later they re-examined these slides in conjunction with corresponding p16 and Ki67 immunostains. Results Addition of p16 and Ki67 immunostains reduced intra- and interobserver variability, improved concurrence with consensus diagnoses and reduced two-step differences in diagnosis. Negative and high grade AIN diagnoses showed the most improvement in concurrence levels. Conclusion Addition of p16 and Ki67 immunostains is helpful in the diagnosis and grading of AIN

Computational pathology in ovarian cancer

Histopathologic evaluations of tissue sections are key to diagnosing and managing ovarian cancer. Pathologists empirically assess and integrate visual information, such as cellular density, nuclear atypia, mitotic figures, architectural growth patterns, and higher-order patterns, to determine the tumor type and grade, which guides oncologists in selecting appropriate treatment options. Latent data embedded in pathology slides can be extracted using computational imaging. Computers can analyze digital slide images to simultaneously quantify thousands of features, some of which are visible with a manual microscope, such as nuclear size and shape, while others, such as entropy, eccentricity, and fractal dimensions, are quantitatively beyond the grasp of the human mind. Applications of artificial intelligence and machine learning tools to interpret digital image data provide new opportunities to explore and quantify the spatial organization of tissues, cells, and subcellular structures. In comparison to genomic, epigenomic, transcriptomic, and proteomic patterns, morphologic and spatial patterns are expected to be more informative as quantitative biomarkers of complex and dynamic tumor biology. As computational pathology is not limited to visual data, nuanced subvisual alterations that occur in the seemingly “normal” pre-cancer microenvironment could facilitate research in early cancer detection and prevention. Currently, efforts to maximize the utility of computational pathology are focused on integrating image data with other -omics platforms that lack spatial information, thereby providing a new way to relate the molecular, spatial, and microenvironmental characteristics of cancer. Despite a dire need for improvements in ovarian cancer prevention, early detection, and treatment, the ovarian cancer field has lagged behind other cancers in the application of computational pathology. The intent of this review is to encourage ovarian cancer research teams to apply existing and/or develop additional tools in computational pathology for ovarian cancer and actively contribute to advancing this important field

Androgen receptor positive triple negative breast cancer: Clinicopathologic, prognostic, and predictive features.

INTRODUCTION:Overexpression of the androgen receptor (AR) characterizes a distinct molecular subset of triple negative breast carcinomas (TNBC). The role of AR as a prognostic/predictive biomarker in TNBC is controversial, but increasing evidence suggests that this subset may respond to therapeutic agents targeting AR. Evaluation of AR has not been standardized, and criteria for selection of patients for antiandrogen therapy remain controversial. In this study we determine the appropriate threshold of AR immunoreactivity to define AR positive (AR+) TNBC, describe the clinicopathologic features of AR+ TNBC, and discuss the utility of AR positivity as a prognostic and predictive marker in TNBC. MATERIALS AND METHODS:135 invasive TNBC processed in accordance with ASCO/CAP guidelines, were immunostained for AR. Clinicopathologic features of AR+ TNBC were analyzed and compared to AR negative (AR-) TNBC. Patients' age, tumor size, tumor grade, lymph node status, proliferation rate, immunopositivity for EGFR, CK5/6, Ki-67, and disease free survival (DFS) were evaluated statistically. RESULTS:A 1% cutpoint was confirmed as the appropriate threshold for AR positivity. Using this cutpoint 41% of 135 TNBC were AR+. AR+ TNBC occurred in older women, were larger, had lower mean proliferation rate and increased incidence of axillary metastasis than AR- TNBC. 76% of TNBC with apocrine morphology were AR+. A subset of AR+TNBC expressed basal markers (EGFR and CK5/6). A prognostic model was created. SUMMARY:AR identifies a heterogeneous group of TNBC. Additional evaluation of EGFR expression allowed us to stratify TNBCs into 3 risk groups with significant differences in DFS and therapeutic implications: low-risk (AR+ EGFR-) which represents the LAR molecular subtype with the best prognosis and may benefit the most from anti-androgen therapies; high-risk (AR- EGFR+) which represents the basal molecular subtype with the worst prognosis and may benefit the most from chemotherapy regimens; intermediate-risk (AR+EGFR+ and AR-EGFR-) TNBC with an intermediate prognosis. Prospective trials are required to further validate this prognostic and predictive grouping