Eric Walk

Biospecimens acquired during routine medical practice are the primary sources of molecular information about patients and their diseases that underlies precision medicine and translational research. In cancer care, molecular analysis of biospecimens is especially common because it often determines treatment choices and may be used to monitor therapy in real time. However, patient specimens are collected, handled, and processed according to routine clinical procedures during which they are subjected to factors that may alter their molecular quality and composition. Such artefactual alteration may skew data from molecular analyses, render analysis data uninterpretable, or even preclude analysis altogether if the integrity of a specimen is severely compromised. As a result, patient care and safety may be affected, and medical research dependent on patient samples may be compromised. Despite these issues, there is currently no requirement to control or record preanalytical variables in clinical practice with the single exception of breast cancer tissue handled according to the guideline jointly developed by the American Society of Clinical Oncology and College of American Pathologists (CAP) and enforced through the CAP Laboratory Accreditation Program. Recognizing the importance of molecular data derived from patient specimens, the CAP Personalized Healthcare Committee established the Preanalytics for Precision Medicine Project Team to develop a basic set of evidence-based recommendations for key preanalytics for tissue and blood specimens. If used for biospecimens from patients, these preanalytical recommendations would ensure the fitness of those specimens for molecular analysis and help to assure the quality and reliability of the analysis data.

BACKGROUND: Human epidermal growth factor receptor 2 (HER2) fluorescence in situ hybridization (FISH) is a quantitative assay for selecting breast cancer patients for trastuzumab therapy. However, current HER2 FISH procedures are labor intensive, manual methods that require skilled technologists and specialized fluorescence microscopy. Furthermore, FISH slides cannot be archived for long term storage and review. Our objective was to develop an automated brightfield double in situ hybridization (BDISH) application for HER2 gene and chromosome 17 centromere (CEN 17) and test the assay performance with dual color HER2 FISH evaluated breast carcinomas. METHODS: The BDISH assay was developed with the nick translated dinitrophenyl (DNP)-labeled HER2 DNA probe and DNP-labeled CEN 17 oligoprobe on the Ventana BenchMark(R) XT slide processing system. Detection of HER2 and CEN 17 signals was accomplished with the silver acetate, hydroquinone, and H2O2 reaction with horseradish peroxidase (HRP) and the fast red and naphthol phosphate reaction with alkaline phosphatase (AP), respectively. The BDISH specificity was optimized with formalin-fixed, paraffin-embedded xenograft tumors, MCF7 (non-amplified HER2 gene) and BT-474 (amplified HER2 gene). Then, the BDISH performance was evaluated with 94 routinely processed breast cancer tissues. Interpretation of HER2 and CEN 17 BDISH slides was conducted by 4 observers using a conventional brightfield microscope without oil immersion objectives. RESULTS: Sequential hybridization and signal detection for HER2 and CEN 17 ISH demonstrated both DNA targets in the same cells. HER2 signals were visualized as discrete black metallic silver dots while CEN 17 signals were detected as slightly larger red dots. Our study demonstrated a high consensus concordance between HER2 FISH and BDISH results of clinical breast carcinoma cases based on the historical scoring method (98.9%, Simple Kappa = 0.9736, 95% CI = 0.9222 - 1.0000) and the ASCO/CAP scoring method with the FISH equivocal cases (95.7%, Simple Kappa = 0.8993%, 95% CI = 0.8068 - 0.9919) and without the FISH equivocal cases (100%, Simple Kappa = 1.0000%, 95% CI = 1.0000 - 1.0000). CONCLUSION: Automated BDISH applications for HER2 and CEN 17 targets were successfully developed and it might be able to replace manual two-color HER2 FISH methods. The application also has the potential to be used for other gene targets. The use of BDISH technology allows the simultaneous analyses of two DNA targets within the context of tissue morphological observation.

Understanding the role of the tumor microenvironment (TME) in lung cancer is critical to improving patient outcomes. We identified four histology-independent archetype TMEs in treatment-naïve early-stage lung cancer using imaging mass cytometry in the TRACERx study (n = 81 patients/198 samples/2.3 million cells). In immune-hot adenocarcinomas, spatial niches of T cells and macrophages increased with clonal neoantigen burden, whereas such an increase was observed for niches of plasma and B cells in immune-excluded squamous cell carcinomas (LUSC). Immune-low TMEs were associated with fibroblast barriers to immune infiltration. The fourth archetype, characterized by sparse lymphocytes and high tumor-associated neutrophil (TAN) infiltration, had tumor cells spatially separated from vasculature and exhibited low spatial intratumor heterogeneity. TAN-high LUSC had frequent PIK3CA mutations. TAN-high tumors harbored recently expanded and metastasis-seeding subclones and had a shorter disease-free survival independent of stage. These findings delineate genomic, immune, and physical barriers to immune surveillance and implicate neutrophil-rich TMEs in metastasis. SIGNIFICANCE: This study provides novel insights into the spatial organization of the lung cancer TME in the context of tumor immunogenicity, tumor heterogeneity, and cancer evolution. Pairing the tumor evolutionary history with the spatially resolved TME suggests mechanistic hypotheses for tumor progression and metastasis with implications for patient outcome and treatment. This article is featured in Selected Articles from This Issue, p. 897.

CONTEXT.—: Cancer immunotherapy provides unprecedented rates of durable clinical benefit to late-stage cancer patients across many tumor types, but there remains a critical need for biomarkers to accurately predict clinical response. Although some cancer immunotherapy tests are associated with approved therapies and considered validated, other biomarkers are still emerging and at various states of clinical and translational exploration. OBJECTIVE.—: To provide pathologists with a current and practical update on the evolving field of cancer immunotherapy testing. The scientific background, clinical data, and testing methodology for the following cancer immunotherapy biomarkers are reviewed: programmed death ligand-1 (PD-L1), mismatch repair, microsatellite instability, tumor mutational burden, polymerase δ and ε mutations, cancer neoantigens, tumor-infiltrating lymphocytes, transcriptional signatures of immune responsiveness, cancer immunotherapy resistance biomarkers, and the microbiome. DATA SOURCES.—: Selected scientific publications and clinical trial data representing the current field of cancer immunotherapy. CONCLUSIONS.—: The cancer immunotherapy field, including the use of biomarker testing to predict patient response, is still in evolution. PD-L1, mismatch repair, and microsatellite instability testing are helping to guide the use of US Food and Drug Administration-approved therapies, but there remains a need for better predictors of response and resistance. Several categories of tumor and patient characteristics underlying immune responsiveness are emerging and may represent the next generation of cancer immunotherapy predictive biomarkers. Pathologists have important roles and responsibilities as the field of cancer immunotherapy continues to develop, including leadership of translational studies, exploration of novel biomarkers, and the accurate and timely implementation of newly approved and validated companion diagnostics.

CONTEXT.—: Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities. OBJECTIVE.—: To explore the applications, benefits, and challenges of generative AI in anatomic pathology, with a focus on its impact on diagnostic processes, workflow efficiency, education, and research. DATA SOURCES.—: A comprehensive review of current literature and recent advancements in the application of generative AI within anatomic pathology, categorized into unimodal and multimodal applications, and evaluated for clinical utility, ethical considerations, and future potential. CONCLUSIONS.—: Generative AI demonstrates significant promise in various domains of anatomic pathology, including diagnostic accuracy enhanced through AI-driven image analysis, virtual staining, and synthetic data generation; workflow efficiency, with potential for improvement by automating routine tasks, quality control, and reflex testing; education and research, facilitated by AI-generated educational content, synthetic histology images, and advanced data analysis methods; and clinical integration, with preliminary surveys indicating cautious optimism for nondiagnostic AI tasks and growing engagement in academic settings. Ethical and practical challenges require rigorous validation, prompt engineering, federated learning, and synthetic data generation to help ensure trustworthy, reliable, and unbiased AI applications. Generative AI can potentially revolutionize anatomic pathology, enhancing diagnostic accuracy, improving workflow efficiency, and advancing education and research. Successful integration into clinical practice will require continued interdisciplinary collaboration, careful validation, and adherence to ethical standards to ensure the benefits of AI are realized while maintaining the highest standards of patient care.