Tridimensional Visualization and Analysis of Early Human Development.

Generating a precise cellular and molecular cartography of the human embryo is essential to our understanding of the mechanisms of organogenesis in normal and pathological conditions. Here, we have combined whole-mount immunostaining, 3DISCO clearing, and light-sheet imaging to start building a 3D cellular map of the human development during the first trimester of gestation. We provide high-resolution 3D images of the developing peripheral nervous, muscular, vascular, cardiopulmonary, and urogenital systems. We found that the adult-like pattern of skin innervation is established before the end of the first trimester, showing important intra- and inter-individual variations in nerve branches. We also present evidence for a differential vascularization of the male and female genital tracts concomitant with sex determination. This work paves the way for a cellular and molecular reference atlas of human cells, which will be of paramount importance to understanding human development in health and disease. PAPERCLIP.

In situ proximity labeling identifies Lewy pathology molecular interactions in the human brain.

The intracellular misfolding and accumulation of alpha-synuclein into structures collectively called Lewy pathology (LP) is a central phenomenon for the pathogenesis of synucleinopathies, including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Understanding the molecular architecture of LP is crucial for understanding synucleinopathy disease origins and progression. Here we used a technique called biotinylation by antibody recognition (BAR) to label total (BAR-SYN1) and pathological alpha-synuclein (BAR-PSER129) in situ for subsequent mass spectrometry analysis. Results showed superior immunohistochemical detection of LP following the BAR-PSER129 protocol, particularly for fibers and punctate pathology within the striatum and cortex. Mass spectrometry analysis of BAR-PSER129-labeled LP identified 261 significantly enriched proteins in the synucleinopathy brain when compared to nonsynucleinopathy brains. In contrast, BAR-SYN1 did not differentiate between disease and nonsynucleinopathy brains. Pathway analysis of BAR-PSER129-enriched proteins revealed enrichment for 718 pathways; notably, the most significant KEGG pathway was PD, and Gene Ontology (GO) cellular compartments were the vesicle, extracellular vesicle, extracellular exosome, and extracellular organelle. Pathway clustering revealed several superpathways, including metabolism, mitochondria, lysosome, and intracellular vesicle transport. Validation of the BAR-PSER129-identified protein hemoglobin beta (HBB) by immunohistochemistry confirmed the interaction of HBB with PSER129 Lewy neurites and Lewy bodies. In summary, BAR can be used to enrich for LP from formalin-fixed human primary tissues, which allowed the determination of molecular signatures of LP. This technique has broad potential to help understand the phenomenon of LP in primary human tissue and animal models.

A Pipeline for Evaluation of Machine Learning/Artificial Intelligence Models to Quantify Programmed Death Ligand 1 Immunohistochemistry.

Immunohistochemistry (IHC) is used to guide treatment decisions in multiple cancer types. For treatment with checkpoint inhibitors, programmed death ligand 1 (PD-L1) IHC is used as a companion diagnostic. However, the scoring of PD-L1 is complicated by its expression in cancer and immune cells. Separation of cancer and noncancer regions is needed to calculate tumor proportion scores (TPS) of PD-L1, which is based on the percentage of PD-L1-positive cancer cells. Evaluation of PD-L1 expression requires highly experienced pathologists and is often challenging and time-consuming. Here, we used a multi-institutional cohort of 77 lung cancer cases stained centrally with the PD-L1 22C3 clone. We developed a 4-step pipeline for measuring TPS that includes the coregistration of hematoxylin and eosin, PD-L1, and negative control (NC) digital slides for exclusion of necrosis, segmentation of cancer regions, and quantification of PD-L1+ cells. As cancer segmentation is a challenging step for TPS generation, we trained DeepLab V3 in the Visiopharm software package to outline cancer regions in PD-L1 and NC images and evaluated the model performance by mean intersection over union (mIoU) against manual outlines. Only 14 cases were required to accomplish a mIoU of 0.82 for cancer segmentation in hematoxylin-stained NC cases. For PD-L1-stained slides, a model trained on PD-L1 tiles augmented by registered NC tiles achieved a mIoU of 0.79. In segmented cancer regions from whole slide images, the digital TPS achieved an accuracy of 75% against the manual TPS scores from the pathology report. Major reasons for algorithmic inaccuracies include the inclusion of immune cells in cancer outlines and poor nuclear segmentation of cancer cells. Our transparent and stepwise approach and performance metrics can be applied to any IHC assay to provide pathologists with important insights on when to apply and how to evaluate commercial automated IHC scoring systems.

Reliability and Variability of Ki-67 Digital Image Analysis Methods for Clinical Diagnostics in Breast Cancer.

Ki-67 is a nuclear protein associated with proliferation, and a strong potential biomarker in breast cancer, but is not routinely measured in current clinical management owing to a lack of standardization. Digital image analysis (DIA) is a promising technology that could allow high-throughput analysis and standardization. There is a dearth of data on the clinical reliability as well as intra- and interalgorithmic variability of different DIA methods. In this study, we scored and compared a set of breast cancer cases in which manually counted Ki-67 has already been demonstrated to have prognostic value (n = 278) to 5 DIA methods, namely Aperio ePathology (Lieca Biosystems), Definiens Tissue Studio (Definiens AG), Qupath, an unsupervised immunohistochemical color histogram algorithm, and a deep-learning pipeline piNET. The piNET system achieved high agreement (interclass correlation coefficient: 0.850) and correlation (R = 0.85) with the reference score. The Qupath algorithm exhibited a high degree of reproducibility among all rater instances (interclass correlation coefficient: 0.889). Although piNET performed well against absolute manual counts, none of the tested DIA methods classified common Ki-67 cutoffs with high agreement or reached the clinically relevant Cohen's κ of at least 0.8. The highest agreement achieved was a Cohen's κ statistic of 0.73 for cutoffs 20% and 25% by the piNET system. The main contributors to interalgorithmic variation and poor cutoff characterization included heterogeneous tumor biology, varying algorithm implementation, and setting assignments. It appears that image segmentation is the primary explanation for semiautomated intra-algorithmic variation, which involves significant manual intervention to correct. Automated pipelines, such as piNET, may be crucial in developing robust and reproducible unbiased DIA approaches to accurately quantify Ki-67 for clinical diagnosis in the future.

Fit-for-Purpose Ki-67 Immunohistochemistry Assays for Breast Cancer.

New therapies are being developed for breast cancer, and in this process, some "old" biomarkers are reutilized and given a new purpose. It is not always recognized that by changing a biomarker's intended use, a new biomarker assay is created. The Ki-67 biomarker is typically assessed by immunohistochemistry (IHC) to provide a proliferative index in breast cancer. Canadian laboratories assessed the analytical performance and diagnostic accuracy of their Ki-67 IHC laboratory-developed tests (LDTs) of relevance for the LDTs' clinical utility. Canadian clinical IHC laboratories enrolled in the Canadian Biomarker Quality Assurance Pilot Run for Ki-67 in breast cancer by invitation. The Dako Ki-67 IHC pharmDx assay was employed as a study reference assay. The Dako central laboratory was the reference laboratory. Participants received unstained slides of breast cancer tissue microarrays with 32 cases and performed their in-house Ki-67 assays. The results were assessed using QuPath, an open-source software application for bioimage analysis. Positive percent agreement (PPA, sensitivity) and negative percent agreement (NPA, specificity) were calculated against the Dako Ki-67 IHC pharmDx assay for 5%, 10%, 20%, and 30% cutoffs. Overall, PPA and NPA varied depending on the selected cutoff; participants were more successful with 5% and 10%, than with 20% and 30% cutoffs. Only 4 of 16 laboratories had robust IHC protocols with acceptable PPA for all cutoffs. The lowest PPA for the 5% cutoff was 85%, for 10% was 63%, for 20% was 14%, and for 30% was 13%. The lowest NPA for the 5% cutoff was 50%, for 10% was 33%, for 20% was 50%, and for 30% was 57%. Despite many years of international efforts to standardize IHC testing for Ki-67 in breast cancer, our results indicate that Canadian clinical LDTs have a wide analytical sensitivity range and poor agreement for 20% and 30% cutoffs. The poor agreement was not due to the readout but rather due to IHC protocol conditions. International Ki-67 in Breast Cancer Working Group (IKWG) recommendations related to Ki-67 IHC standardization cannot take full effect without reliable fit-for-purpose reference materials that are required for the initial assay calibration, assay performance monitoring, and proficiency testing.

Comparison of the Response to Neoadjuvant Therapy Between Immunohistochemistry HER2 (3+) and HER2 (2+)/ISH+ Early-Stage Breast Cancer: A Retrospective Multicenter Cohort Study.

BACKGROUND: According to the American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) criteria, both immunohistochemical HER2 (3+) and HER2 (2+)/in situ hybridization (ISH) amplified [HER2 (2+)/ISH+] breast cancers (BCs) fall under the HER2-positive BC category. However, there is a lack of studies exploring the difference of neoadjuvant therapeutic response between patients with HER2 (3+) and HER2 (2+)/ISH+ early BC. We aimed to evaluate the neoadjuvant therapeutic response, long-term outcome, and intrinsic subtype heterogeneity between HER2 (3+) and HER2 (2+)/ISH+ BC. METHODS: We examined 2 distinct cohorts. Cohort 1 (C1) encompassed 2648 patients with HER2-positive early BC diagnoses, and they received neoadjuvant therapy (NT) and surgery between January 1, 2009 and December 31, 2022, from the Shanghai Jiao Tong University Breast Cancer Data Base. Cohort 2 (C2) comprised 135 patients with early-stage HER2-positive BC who underwent NT and surgery at Henan Cancer Hospital from January 1, 2021, to December 31, 2022. These patients had available genomic and transcriptomic data at their disposal. C1 and C2 were further categorized into 2 patient cohorts as follows: (1) patients with IHC HER2 (3+) early BC [HER2 (3+) group], (2) patients with HER2 (2+)/ISH+ early BC [HER2 (2+)/ISH+ group]. Among those excluded from the analysis were patients < 18 years or >80 years of age. Clinicopathological parameters, long-term outcomes, and intrinsic subtypes were analyzed. RESULTS: In the C1 population, 83.7% had HER2 (3+) BC, while 16.3% had HER2 (2+)/ISH+ BC. Patients with HER2 (3+) had a significantly higher pathological complete response (PCR) rate (38.9%) than patients with HER2 (2+)/ISH+ (18.1%; P < .001), but the disease-free survival (DFS) was comparable after a median follow-up of 29 months (P = .556). The addition of trastuzumab or trastuzumab plus pertuzumab to neoadjuvant chemotherapy (NAC) improved PCR rates and DFS in HER2 (3+) BC but not in HER2 (2+)/ISH+ BC. In the C2 population, 97.75% HER2 (3+) and 52.17% HER2 (2+)/ISH+ were HER2 enriched (HER2E) subtype (P < .001). HER2E showed increased PCR rates compared to non-HER2E (P = .004). CONCLUSIONS: Compared to HER2 (3+) BC, the limited effectiveness of neoadjuvant trastuzumab and pertuzumab therapy for HER2 (2+)/ISH+ BC is due to subtype heterogeneity. Reassessment of targeted therapy efficacy in patients with HER2 (2+)/ISH+ BC is essential.

Immunohistochemical assessment of HER2 low breast cancer: interobserver reproducibility and correlation with digital image analysis.

PURPOSE: The recent findings from the DESTINY-Breast04 trial highlighted the clinical importance of distinguishing between HER2 immunohistochemistry (IHC) scores 0 and 1 + in metastatic breast cancer (BC). However, pathologist interpretation of HER2 IHC scoring is subjective, and standardized methodology is needed. We evaluated the consistency of HER2 IHC scoring among pathologists and the accuracy of digital image analysis (DIA) in interpreting HER2 IHC staining in cases of HER2-low BC. METHODS: Fifty whole-slide biopsies of BC with HER2 IHC staining were evaluated, comprising 25 cases originally reported as IHC score 0 and 25 as 1 +. These slides were digitally scanned. Six pathologists with breast expertise independently reviewed and scored the scanned images, and DIA was applied. Agreement among pathologists and concordance between pathologist scores and DIA results were statistically analyzed using Kendall coefficient of concordance (W) tests. RESULTS: Substantial agreement among at least five of the six pathologists was found for 18 of the score 0 cases (72%) and 15 of the score 1 + cases (60%), indicating excellent interobserver agreement (W = 0.828). DIA scores were highly concordant with pathologist scores in 96% of cases (47/49), indicating excellent concordance (W = 0.959). CONCLUSION: Although breast subspecialty pathologists were relatively consistent in evaluating BC with HER2 IHC scores of 0 and 1 +, DIA may be a reliable supplementary tool to enhance the standardization and quantification of HER2 IHC assessment, especially in challenging cases where results may be ambiguous (i.e., scores 0-1 +). These findings hold promise for improving the accuracy and consistency of HER2 testing.

A Deep Learning-Based Assay for Programmed Death Ligand 1 Immunohistochemistry Scoring in Non-Small Cell Lung Carcinoma: Does it Help Pathologists Score?

Several studies have developed various artificial intelligence (AI) models for immunohistochemical analysis of programmed death ligand 1 (PD-L1) in patients with non-small cell lung carcinoma; however, none have focused on specific ways by which AI-assisted systems could help pathologists determine the tumor proportion score (TPS). In this study, we developed an AI model to calculate the TPS of the PD-L1 22C3 assay and evaluated whether and how this AI-assisted system could help pathologists determine the TPS and analyze how AI-assisted systems could affect pathologists' assessment accuracy. We assessed the 4 methods of the AI-assisted system: (1 and 2) pathologists first assessed and then referred to automated AI scoring results (1, positive tumor cell percentage; 2, positive tumor cell percentage and visualized overlay image) for final confirmation, and (3 and 4) pathologists referred to the automated AI scoring results (3, positive tumor cell percentage; 4, positive tumor cell percentage and visualized overlay image) while determining TPS. Mixed-model analysis was used to calculate the odds ratios (ORs) with 95% CI for AI-assisted TPS methods 1 to 4 compared with pathologists' scoring. For all 584 samples of the tissue microarray, the OR for AI-assisted TPS methods 1 to 4 was 0.94 to 1.07 and not statistically significant. Of them, we found 332 discordant cases, on which the pathologists' judgments were inconsistent; the ORs for AI-assisted TPS methods 1, 2, 3, and 4 were 1.28 (1.06-1.54; P = .012), 1.29 (1.06-1.55; P = .010), 1.28 (1.06-1.54; P = .012), and 1.29 (1.06-1.55; P = .010), respectively, which were statistically significant. For discordant cases, the OR for each AI-assisted TPS method compared with the others was 0.99 to 1.01 and not statistically significant. This study emphasized the usefulness of the AI-assisted system for cases in which pathologists had difficulty determining the PD-L1 TPS.

Enhanced prognostic evaluation of diffuse large B-cell lymphoma: A comprehensive surveillance study incorporating Epstein-Barr virus infection status and immunohistochemical markers.

Emerging biologic subsets and new prognostic markers are significantly important for aggressive diffuse large B-cell lymphoma (DLBCL). Nevertheless, the high cost of testing limits the availability of these tests in most hospitals, thus making prognostic judgment based on basic immunohistochemical testing, whole blood Epstein-Barr virus DNA (WBEBV) surveillance and clinical features advantageous for hospitals and patients with poor medical conditions. We included 647 DLBCL patients treated in our hospital from January 2009 to March 2023. Non-germinal center B-cell like, Ki-67, and International Prognostic Index (IPI) scores were related to cMYC/B-cell lymphoma 2 (Bcl-2)-double expression. Age, Epstein-Barr virus-encoded small RNA (EBER) positivity, and IPI scores were associated with mortality. The cutoffs for differential overall survival (OS) of age, WBEBV, Bcl-2, and cMYC were 57 years, 1514 copies/mL (baseline), 5.89 × 104 copies/mL (treatment), 40%, and 55%, respectively. EBER positivity was significantly associated with a worse OS. Patients with newly defined DE (Bcl-2 ≥ 40 and cMYC > 55) had a worse prognosis than controls (p = 0.04). We found that cMYC with an optimal cutoff of 47.5 could effectively predict high-grade DLBCL with an area under the curve of 0.912, and the specificity and sensitivity were 70.7% and 100%, respectively. Our study provides valuable insights into the prognostic factors and biomarker cutoffs that influence OS in DLBCL patients, which may guide clinicians in tailoring treatment strategies and improving patient outcomes.

Identification of high-performing antibodies for the reliable detection of Tau proteoforms by Western blotting and immunohistochemistry.

Antibodies are essential research tools whose performance directly impacts research conclusions and reproducibility. Owing to its central role in Alzheimer's disease and other dementias, hundreds of distinct antibody clones have been developed against the microtubule-associated protein Tau and its multiple proteoforms. Despite this breadth of offer, limited understanding of their performance and poor antibody selectivity have hindered research progress. Here, we validate a large panel of Tau antibodies by Western blot (79 reagents) and immunohistochemistry (35 reagents). We address the reagents' ability to detect the target proteoform, selectivity, the impact of protein phosphorylation on antibody binding and performance in human brain samples. While most antibodies detected Tau at high levels, many failed to detect it at lower, endogenous levels. By WB, non-selective binding to other proteins affected over half of the antibodies tested, with several cross-reacting with the related MAP2 protein, whereas the "oligomeric Tau" T22 antibody reacted with monomeric Tau by WB, thus calling into question its specificity to Tau oligomers. Despite the presumption that "total" Tau antibodies are agnostic to post-translational modifications, we found that phosphorylation partially inhibits binding for many such antibodies, including the popular Tau-5 clone. We further combine high-sensitivity reagents, mass-spectrometry proteomics and cDNA sequencing to demonstrate that presumptive Tau "knockout" human cells continue to express residual protein arising through exon skipping, providing evidence of previously unappreciated gene plasticity. Finally, probing of human brain samples with a large panel of antibodies revealed the presence of C-term-truncated versions of all main Tau brain isoforms in both control and tauopathy donors. Ultimately, we identify a validated panel of Tau antibodies that can be employed in Western blotting and/or immunohistochemistry to reliably detect even low levels of Tau expression with high selectivity. This work represents an extensive resource that will enable the re-interpretation of published data, improve reproducibility in Tau research, and overall accelerate scientific progress.

Artificial intelligence enhances whole-slide interpretation of PD-L1 CPS in triple-negative breast cancer: A multi-institutional ring study.

BACKGROUND AND AIMS: Evaluation of the programmed cell death ligand-1 (PD-L1) combined positive score (CPS) is vital to predict the efficacy of the immunotherapy in triple-negative breast cancer (TNBC), but pathologists show substantial variability in the consistency and accuracy of the interpretation. It is of great importance to establish an objective and effective method which is highly repeatable. METHODS: We proposed a model in a deep learning-based framework, which at the patch level incorporated cell analysis and tissue region analysis, followed by the whole-slide level fusion of patch results. Three rounds of ring studies (RSs) were conducted. Twenty-one pathologists of different levels from four institutions evaluated the PD-L1 CPS in TNBC specimens as continuous scores by visual assessment and our artificial intelligence (AI)-assisted method. RESULTS: In the visual assessment, the interpretation results of PD-L1 (Dako 22C3) CPS by different levels of pathologists have significant differences and showed weak consistency. Using AI-assisted interpretation, there were no significant differences between all pathologists (P = 0.43), and the intraclass correlation coefficient (ICC) value was increased from 0.618 [95% confidence interval (CI) = 0.524-0.719] to 0.931 (95% CI = 0.902-0.955). The accuracy of interpretation result is further improved to 0.919 (95% CI = 0.886-0.947). Acceptance of AI results by junior pathologists was the highest among all levels, and 80% of the AI results were accepted overall. CONCLUSION: With the help of the AI-assisted diagnostic method, different levels of pathologists achieved excellent consistency and repeatability in the interpretation of PD-L1 (Dako 22C3) CPS. Our AI-assisted diagnostic approach was proved to strengthen the consistency and repeatability in clinical practice.

Diagnosis of verruciform acanthotic vulvar intra-epithelial neoplasia (vaVIN) using CK17, SOX2 and GATA3 immunohistochemistry.

AIMS: Verruciform acanthotic vulvar intra-epithelial neoplasia (vaVIN) is an HPV-independent, p53 wild-type lesion with distinct morphology and documented risk of recurrence and cancer progression. vaVIN is rare, and prospective distinction from non-neoplastic hyperplastic lesions can be difficult. CK17, SOX2 and GATA3 immunohistochemistry has emerging value in the diagnosis of HPV-independent lesions, particularly differentiated VIN. We aimed to test the combined value of these markers in the diagnosis of vaVIN versus its non-neoplastic differentials in the vulva. METHODS AND RESULTS: CK17, SOX2 and GATA3 immunohistochemistry was evaluated on 16 vaVINs and 34 mimickers (verruciform xanthoma, lichen simplex chronicus, lichen sclerosus, psoriasis, pseudo-epitheliomatous hyperplasia). CK17 was scored as 3+ = full-thickness, 2+ = partial-thickness, 1+ = patchy, 0 = absent; SOX2 as 3+ = strong staining ≥ 10% cells, 2+ = moderate, 1 + =weak, 0 = staining in < 10% cells; and GATA3 as pattern 0 = loss in < 25% basal cells, 1 = loss in 25-75% basal cells, 2 = loss in > 75% basal cells. For analysis, results were recorded as positive (CK17 = 3+, SOX2 = 3+, GATA3 = patterns 1/2) or negative (CK17 = 2+/1+/0, SOX2 = 2+/1+/0, GATA3 = pattern 0). CK17, SOX2 and GATA3 positivity was documented in 81, 75 and 58% vaVINs, respectively, versus 32, 17 and 22% of non-neoplastic mimickers, respectively; ≥ 2 marker positivity conferred 83 sensitivity, 88 specificity and 86% accuracy in vaVIN diagnosis. Compared to vaVIN, SOX2 and GATA3 were differentially expressed in lichen sclerosus, lichen simplex chronicus and pseudo-epitheliomatous hyperplasia, whereas CK17 was differentially expressed in verruciform xanthoma and adjacent normal mucosa. CONCLUSIONS: CK17, SOX2 and GATA3 can be useful in the diagnosis of vaVIN and its distinction from hyperplastic non-neoplastic vulvar lesions. Although CK17 has higher sensitivity, SOX2 and GATA3 are more specific, and the combination of all markers shows optimal diagnostic accuracy.

An artificial intelligence-powered PD-L1 combined positive score (CPS) analyser in urothelial carcinoma alleviating interobserver and intersite variability.

AIMS: Immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) have shown promising clinical outcomes in urothelial carcinoma (UC). The combined positive score (CPS) quantifies PD-L1 22C3 expression in UC, but it can vary between pathologists due to the consideration of both immune and tumour cell positivity. METHODS AND RESULTS: An artificial intelligence (AI)-powered PD-L1 CPS analyser was developed using 1,275,907 cells and 6175.42 mm2 of tissue annotated by pathologists, extracted from 400 PD-L1 22C3-stained whole slide images of UC. We validated the AI model on 543 UC PD-L1 22C3 cases collected from three institutions. There were 446 cases (82.1%) where the CPS results (CPS ≥10 or <10) were in complete agreement between three pathologists, and 486 cases (89.5%) where the AI-powered CPS results matched the consensus of two or more pathologists. In the pathologist's assessment of the CPS, statistically significant differences were noted depending on the source hospital (P = 0.003). Three pathologists reevaluated discrepancy cases with AI-powered CPS results. After using the AI as a guide and revising, the complete agreement increased to 93.9%. The AI model contributed to improving the concordance between pathologists across various factors including hospital, specimen type, pathologic T stage, histologic subtypes, and dominant PD-L1-positive cell type. In the revised results, the evaluation discordance among slides from different hospitals was mitigated. CONCLUSION: This study suggests that AI models can help pathologists to reduce discrepancies between pathologists in quantifying immunohistochemistry including PD-L1 22C3 CPS, especially when evaluating data from different institutions, such as in a telepathology setting.

Best practices for achieving consensus in HER2-low expression in breast cancer: current perspectives from practising pathologists.

AIMS: Human epidermal growth factor receptor 2 (HER2) expression is an important biomarker in breast cancer (BC). Most BC cases categorised as HER2-negative (HER2-) express low levels of HER2 [immunohistochemistry (IHC) 1+ or IHC 2+/in-situ hybridisation not amplified (ISH-)] and represent a clinically relevant therapeutic category that is amenable to targeted therapy using a recently approved HER2-directed antibody-drug conjugate. A group of practising pathologists, with expertise in breast pathology and BC biomarker testing, outline best practices and guidance for achieving consensus in HER2 IHC scoring for BC. METHODS AND RESULTS: The authors describe current knowledge and challenges of IHC testing and scoring of HER2-low expressing BC and provide best practices and guidance for accurate identification of BCs expressing low levels of HER2. These expert pathologists propose an algorithm for assessing HER2 expression with validated IHC assays and incorporate the 2023 American Society of Clinical Oncology and College of American Pathologist guideline update. The authors also provide guidance on when to seek consensus for HER2 IHC scoring, how to incorporate HER2-low into IHC reporting and present examples of HER2 IHC staining, including challenging cases. CONCLUSIONS: Awareness of BC cases that are negative for HER protein overexpression/gene amplification and the related clinical relevance for targeted therapy highlight the importance of accurate HER2 IHC scoring for optimal treatment selection.

Human epidermal growth factor receptor 2 (HER2) status in breast cancer: practice points and challenges.

Human epidermal growth factor receptor 2 (HER2)-enriched breast cancer benefits significantly from anti-HER2 targeted therapies. This highlights the critical need for precise HER2 immunohistochemistry (IHC) interpretation serving as a triage tool for selecting patients for anti-HER2 regimens. Recently, the emerging eligibility of patients with HER2-low breast cancers for a novel HER2-targeted antibody-drug conjugate (T-DXd) adds challenges to HER2 IHC scoring interpretation, notably in the 0-1+ range, which shows high interobserver and interlaboratory staining platform variability. In this review, we navigate evolving challenges and suggest practical recommendations for HER2 IHC interpretation.

Concordance between ER, PR, Ki67, and HER2-low expression in breast cancer by MammaTyper RT-qPCR and immunohistochemistry: implications for the practising pathologist.

BACKGROUND: There are limited data on the role of multigene tests and their correlation with immunohistochemistry (IHC), especially on core biopsy. MammaTyper is a quantitative conformite Europeeanne (CE) marked, National Institute for Health and Care excellence (NICE) approved, in in vitro diagnostic quantitative real-time polymerase chain reaction (RT-qPCR) test for assessment of mRNA expression of four biomarkers (ESR1, PGR, ERBB2, MKI67). METHODS: We evaluated the concordance of MammaTyper with oestrogen receptor (ER), progesterone receptor (PR), HER2, and Ki67 by IHC on 133 core needle biopsies of breast cancer. HER2 was positive if IHC 3+ or 2+ and fluorescence in situ hybridization (FISH)-amplified. Global and hotspot Ki67 expression was analysed using a cutoff of ≥20% assessed manually and by digital image analysis. Agreements were expressed as overall percent agreement (OPA), positive percent agreement (PPA), negative percent agreement (NPA), and Cohen's kappa. RESULTS: RT-qPCR results of ESR1 were highly concordant with IHC with OPA of 94.7% using 1% cutoff and 91.7% when the low ER-positive category was included. The PPA and NPA between RT-qPCR and IHC for PR was 91.5% and 88.0%, respectively, when using the 1% cutoff. For ERBB2/HER2, the OPA was 95% and the PPA was 84.6%. 40 of 72 HER2 IHC score 0 tumours were classified as ERBB2 low. Best concordance between MKI67 by MammaTyper and Ki67 IHC was achieved using hotspot digital image analysis (OPA: 87.2%, PPA: 90.6%, NPA: 80%). CONCLUSION: RT-qPCR-based assessment of the mRNA expression of ESR1, PGR, ERBB2, and MKI67 showed high concordance with IHC, suggesting that the MammaTyper test on core needle biopsies represents a reliable, efficient, and reproducible alternative for breast cancer classification and refining HER2 low categorisation.

Immunohistochemistry of Lung Cancer Biomarkers.

Immunohistochemical (IHC) staining represents a comparatively inexpensive testing method that is attractive as a potential alternative to molecular sequencing methods or fluorescence in situ hybridization for pulmonary biomarker testing. While a variety of IHC tests directed at actionable genetic alterations have been developed and evaluated since the advent of targeted therapy, specific antibody clones for anaplastic lymphoma kinase, ROS-1, and potentially neurotrophic tropmyosin receptor kinase have been the primary antibodies that provide sufficiently robust results to be utilized as either a primary testing or screening method to direct targeted therapy. Antibodies for a variety of other targets such as epidermal growth factor receptors, for example, have lacked sufficient sensitivity and specificity to cover the range of mutations that may occur and are generally not recommended in lieu of molecular testing with the exception of limited resource settings. IHC is also used as a predictive marker for response to immunotherapy through evaluation of programmed death ligand 1 expression. In addition, multiple antibody-drug conjugates (ADCs) are under investigation, designed to deliver drugs directly to tumor cells through binding to specific target antigens. Some ADCs have already received accelerated FDA approval, and IHC was incorporated in many clinical trials evaluating ADC efficacy. As such, it is anticipated that ADCs may have a companion diagnostic IHC to guide patient selection.

Expression of CD44 is associated with aggressiveness in seminomas.

BACKGROUND: Testicular germ cell tumors (TGCTs) exhibit diverse biological and pathological features and are divided in two main types, seminomas and nonseminomatous germ cell tumors (NSGCTs). CD44 is a cell surface receptor, which is highly expressed in malignancies and is implicated in tumorigenesis affecting cell-matrix interactions and cell signaling. METHODS AND RESULTS: Here, we examined the expression of CD44 in tumor cell lines and in patients' material. We found that CD44 is over-expressed in TGCTs compared to normal tissues. Immunohistochemical staining in 71 tissue specimens demonstrated increased expression of CD44 in some patients, whereas CD44 was absent in normal tissue. In seminomas, a high percentage of tumor and stromal cells showed cytoplasmic and/or cell surface staining for CD44 as well as increased staining for CD44 in the tumor stroma was found in some cases. The increased expression of CD44 either in tumor cells or in stromal components was associated with tumor size, nodal metastasis, vascular/lymphatic invasion, and disease stage only in seminomas. The increased stromal expression of CD44 in TGCTs was positively associated with angiogenesis. CONCLUSIONS: CD44 may exhibit diverse biological functions in seminomas and NSGCTs. The expression of CD44 in tumor cells as well as in tumor stroma fosters an aggressive phenotype in seminomas and should be considered in disease treatment.

A new mode for the diagnosis of angioimmunoblastic T-cell lymphoma.

In order to explore a new mode for the diagnosis of angioimmunoblastic T-cell lymphoma (AITL), 31 cases of AITL and 28 cases of peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) were used as the study subjects. Identifying T follicular helper (TFH) cells with CD4, CD10, Bcl-6, and PD-1, identifying proliferative B cells with CD20 and EZH2, identifying proliferative follicular dendritic cells (FDCs) with CD21 and CD23, and analyzing the value of TFH/B/FDC proliferation and immunolocalization in the diagnosis of AITL. (1) Outside the inherent lymphoid follicles, simultaneous proliferation of TFH/B/FDC (a new diagnostic mode) were observed in AITL [83.87%; 26/31], with their immunolocalizations in the same site [83.87%; 26/31], while this phenomenon was not observed in 28 cases of PTCL-NOS (P<0.05). (2) The sensitivity and specificity of using this new mode to diagnose AITL were both high (83.87%, 100%), which was superior to CD2 (100%, 0%), CD3 (100%, 0%), CD4 (100%, 32.14%), CD5 (100%, 25%), CD10 (61.9%, 100%), Bcl-6 (42.86%, 100%), PD-1 (83.87%, 96.43%), and its Youden Index (0.84) was the highest. The areas under the curve (AUC) of CD10, Bcl-6, PD-1, and new mode to diagnosis AITL were 0.81, 0.71, 0.90, and 0.92, respectively, while the new mode had the highest AUC. The simultaneous proliferation of TFH/B/FDC cells outside the inherent lymphoid follicles can be used to assist in the diagnosis of AITL, and the simultaneous spatiotemporal proliferation of TFH/B/FDC cells is a specific immunomorphology of AITL.

Evaluating the diagnostic performance of a large language model-powered chatbot for providing immunohistochemistry recommendations in dermatopathology.

BACKGROUND: Large language model (LLM)-powered chatbots such as ChatGPT have numerous applications. However, their effectiveness in dermatopathology has not been formally evaluated. Dermatopathological cases often require immunohistochemical workup. Here, we evaluate the performance of a chatbot in providing diagnostically useful information on immunohistochemistry relating to dermatological diseases. METHODS: We queried a commonly used chatbot for the immunophenotypes of 51 cutaneous diseases, including a diverse variety of epidermal, adnexal, hematolymphoid, and soft tissue entities. We requested it to provide references for each diagnosis. All tests were repeated, compiled, quantified, and then compared with established literature standards. RESULTS: Clustering analysis demonstrated that recommendations correlated with tumor type, suggesting chatbots can supply appropriate panels. However, a significant portion of recommendations were factually incorrect (13.9%). Citations were rarely clinically useful (24.5%). Many were confabulated (27.2%). Prompt responses for cutaneous adnexal lesions tended to be less accurate while literature references were less useful. Reference retrieval performance was associated with the number of PubMed entries per entity. CONCLUSIONS: This foundational study suggests that LLM-powered chatbots may be useful for generating immunohistochemical panels for dermatologic diagnoses. However, specific performance capabilities and biases must be considered. In addition, extreme caution is advised regarding the tendencies to fabricate material. Future models intentionally fine-tuned to augment diagnostic medicine may prove to be valuable.