Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab

Human NK cells and cancer

The long story of NK cells started about 50 y ago with the first demonstration of a natural cytotoxic activity within an undefined subset of circulating leukocytes, has involved an ever-growing number of researchers, fascinated by the apparently easy-to-reach aim of getting a "universal anti-tumor immune tool". In fact, in spite of the impressive progress obtained in the first decades, these cells proved far more complex than expected and, paradoxically, the accumulating findings have continuously moved forward the attainment of a complete control of their function for immunotherapy. The refined studies of these latter years have indicated that NK cells can epigenetically calibrate their functional potential, in response to specific environmental contexts, giving rise to extraordinarily variegated subpopulations, comprehensive of memory-like cells, tissue-resident cells, or cells in various differentiation stages, or distinct functional states. In addition, NK cells can adapt their activity in response to a complex body of signals, spanning from the interaction with either suppressive or stimulating cells (myeloid-derived suppressor cells or dendritic cells, respectively) to the engagement of various receptors (specific for immune checkpoints, cytokines, tumor/viral ligands, or mediating antibody-dependent cell-mediated cytotoxicity). According to this picture, the idea of an easy and generalized exploitation of NK cells is changing, and the way is opening toward new carefully designed, combined and personalized therapeutic strategies, also based on the use of genetically modified NK cells and stimuli capable of strengthening and redirecting their effector functions against cancer.

Programmed Death Ligand-1 and Tumor Mutation Burden Testing of Patients With Lung Cancer for Selection of Immune Checkpoint Inhibitor Therapies: Guideline From the College of American Pathologists, Association for Molecular Pathology, International Association for the Study of Lung Cancer, Pulmonary Pathology Society, and LUNGevity Foundation

CONTEXT.—

Rapid advancements in the understanding and manipulation of tumor-immune interactions have led to the approval of immune therapies for patients with non-small cell lung cancer. Certain immune checkpoint inhibitor therapies require the use of companion diagnostics, but methodologic variability has led to uncertainty around test selection and implementation in practice.

OBJECTIVE.—

To develop evidence-based guideline recommendations for the testing of immunotherapy/immunomodulatory biomarkers, including programmed death ligand-1 (PD-L1) and tumor mutation burden (TMB), in patients with lung cancer.

DESIGN.—

The College of American Pathologists convened a panel of experts in non-small cell lung cancer and biomarker testing to develop evidence-based recommendations in accordance with the standards for trustworthy clinical practice guidelines established by the National Academy of Medicine. A systematic literature review was conducted to address 8 key questions. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach, recommendations were created from the available evidence, certainty of that evidence, and key judgments as defined in the GRADE Evidence to Decision framework.

RESULTS.—

Six recommendation statements were developed.

CONCLUSIONS.—

This guideline summarizes the current understanding and hurdles associated with the use of PD-L1 expression and TMB testing for immune checkpoint inhibitor therapy selection in patients with advanced non-small cell lung cancer and presents evidence-based recommendations for PD-L1 and TMB testing in the clinical setting.

Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond

The integration of computer vision into pathology through slide digitalization represents a transformative leap in the field's evolution. Traditional pathology methods, while reliable, are often time-consuming and susceptible to intra- and interobserver variability. In contrast, computer vision, empowered by artificial intelligence (AI) and machine learning (ML), promises revolutionary changes, offering consistent, reproducible, and objective results with ever-increasing speed and scalability. The applications of advanced algorithms and deep learning architectures like CNNs and U-Nets augment pathologists' diagnostic capabilities, opening new frontiers in automated image analysis. As these technologies mature and integrate into digital pathology workflows, they are poised to provide deeper insights into disease processes, quantify and standardize biomarkers, enhance patient outcomes, and automate routine tasks, reducing pathologists' workload. However, this transformative force calls for cross-disciplinary collaboration between pathologists, computer scientists, and industry innovators to drive research and development. While acknowledging its potential, this chapter addresses the limitations of AI in pathology, encompassing technical, practical, and ethical considerations during development and implementation.

A pathologist-AI collaboration framework for enhancing diagnostic accuracies and efficiencies

In pathology, the deployment of artificial intelligence (AI) in clinical settings is constrained by limitations in data collection and in model transparency and interpretability. Here we describe a digital pathology framework, nuclei.io, that incorporates active learning and human-in-the-loop real-time feedback for the rapid creation of diverse datasets and models. We validate the effectiveness of the framework via two crossover user studies that leveraged collaboration between the AI and the pathologist, including the identification of plasma cells in endometrial biopsies and the detection of colorectal cancer metastasis in lymph nodes. In both studies, nuclei.io yielded considerable diagnostic performance improvements. Collaboration between clinicians and AI will aid digital pathology by enhancing accuracies and efficiencies.

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.

Scoring PD-L1 Expression in Urothelial Carcinoma: An International Multi-Institutional Study on Comparison of Manual and Artificial Intelligence Measurement Model (AIM-PD-L1) Pathology Assessments

Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can guide the use of immune checkpoint inhibitor (ICI) therapy in cancer treatment. However, substantial interobserver variability has been reported using these immunoassays. Artificial intelligence (AI) has the potential to accurately measure biomarker expression in tissue samples, but its reliability and comparability to standard manual scoring remain to be evaluated. This multinational study sought to compare the %TC scoring of PD-L1 expression in advanced urothelial carcinoma, assessed by either an AI Measurement Model (AIM-PD-L1) or expert pathologists. The concordance among pathologists and between pathologists and AIM-PD-L1 was determined. The positivity rate of ≥ 1%TC PD-L1 was between 20-30% for 8/10 pathologists, and the degree of agreement and scoring distribution for among pathologists and between pathologists and AIM-PD-L1 was similar both scored as a continuous variable or using the pre-defined cutoff. Numerically higher score variation was observed with the 22C3 assay than with the 28-8 assay. A 2-h training module on the 28-8 assay did not significantly impact manual assessment. Cases exhibiting significantly higher variability in the assessment of PD-L1 expression (mean absolute deviation > 10) were found to have patterns of PD-L1 staining that were more challenging to interpret. An improved understanding of sources of manual scoring variability can be applied to PD-L1 expression analysis in the clinical setting. In the future, the application of AI algorithms could serve as a valuable reference guide for pathologists while scoring PD-L1.

Immunotherapy and lung cytopathology: Overview and possibilities

Immunotherapy has become a promising cancer treatment in the past decade, and IHC is the most commonly used testing method for PDL-1/PD1 evaluation. In general, PD-L1 assays can be performed on both FFPE specimens and cytological samples. However, their use on smears is not yet well-established or validated. Nowadays, digital images and advanced algorithms can aid in interpreting PD-L1 in cytological samples. Understanding the immune environment of non-small cell lung cancer (NSCLC) is critical in developing successful anticancer immunotherapies. The use of a multiplexed immunofluorescence (mIF) assay on cytological samples obtained through minimally invasive methods appears to be a viable option for investigating the immune environment of NSCLC. This review aims to briefly summarize the knowledge of the role of cytopathology in the analysis of PD-L1 by immunocytochemistry (ICC) and future directions of cytopathology in the immunotherapy setting.

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

BACKGROUND

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.

MATERIALS AND METHODS

We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.

RESULTS

A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.

CONCLUSION

AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

Improved concordance of challenging human epidermal growth factor receptor 2 dual in-situ hybridisation cases with the use of a digital image analysis algorithm in breast cancer

AIMS

Accurate assessment of human epidermal growth factor receptor 2 (HER2) expression by HER2 immunohistochemistry and in-situ hybridisation (ISH) is critical for the management of patients with breast cancer. The revised 2018 ASCO/CAP guidelines define 5 groups based on HER2 expression and copy number. Manual pathologist quantification by light microscopy of equivocal and less common HER2 ISH groups (groups 2-4) can be challenging, and there are no data on interobserver variability in reporting of these cases. We sought to determine whether a digital algorithm could improve interobserver variability in the assessment of difficult HER2 ISH cases.

METHODS AND RESULTS

HER2 ISH was evaluated in a cohort enriched for less common HER2 patterns using standard light microscopy versus analysis of whole slide images using the Roche uPath HER2 dual ISH image analysis algorithm. Standard microscopy demonstrated significant interobserver variability with a Fleiss's kappa value of 0.471 (fair-moderate agreement) improving to 0.666 (moderate-good) with the use of the algorithm. For HER2 group designation (groups 1-5), there was poor-moderate reliability between pathologists by microscopy [intraclass correlation coefficient (ICC) = 0.526], improving to moderate-good agreement (ICC = 0.763) with the use of the algorithm. In subgroup analysis, the algorithm improved concordance particularly in groups 2, 4 and 5. Time to enumerate cases was also significantly reduced.

CONCLUSION

This work demonstrates the potential of a digital image analysis algorithm to improve the concordance of pathologist HER2 amplification status reporting in less common HER2 groups. This has the potential to improve therapy selection and outcomes for patients with HER2-low and borderline HER2-amplified breast cancers.

Artificial Intelligence in the Pathology of Gastric Cancer

Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.

Machine and deep learning in inflammatory bowel disease

PURPOSE OF REVIEW

The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial intelligence technologies like machine learning and deep learning presents another watershed moment in IBD treatment. Interest in these methods in IBD research has increased over the past 10 years, and they offer a promising path to better clinical outcomes for IBD patients.

RECENT FINDINGS

Developing new tools to evaluate IBD and inform clinical management is challenging because of the expansive volume of data and requisite manual interpretation of data. Recently, machine and deep learning models have been used to streamline diagnosis and evaluation of IBD by automating review of data from several diagnostic modalities with high accuracy. These methods decrease the amount of time that clinicians spend manually reviewing data to formulate an assessment.

SUMMARY

Interest in machine and deep learning is increasing in medicine, and these methods are poised to revolutionize the way that we treat IBD. Here, we highlight the recent advances in using these technologies to evaluate IBD and discuss the ways that they can be leveraged to improve clinical outcomes.

Reproducibility in PD-L1 Immunohistochemistry Quantification through the Tumor Proportion Score and the Combined Positive Score: Could Dual Immunostaining Help Pathologists?

We studied the pathologists' agreements in quantifying PD-L1 expression through the tumor proportion score (TPS) and the combined positive score (CPS) using single PD-L1 immunohistochemistry (S-IHC) and double immunohistochemistry (D-IHC) combining PD-L1 staining and tumor cell markers. S-IHC and D-IHC were applied to 15 cancer samples to generate 60 digital IHC slides (30 whole slides images and 30 regions of interest of 1 mm²) for PD-L1 expression quantification using both TPS and CPS, twice by four pathologists. Agreements were estimated calculating intraclass correlation coefficients (ICC). Both S-IHC and D-IHC slides analyses resulted in excellent (for TPS, ICC > 0.9) to good (for CPS, ICC > 0.75) inter- and intra-pathologist agreements with slightly higher ICC with D-IHC than with S-IHC. S-IHC resulted in higher TPS and CPS than D-IHC (+5.6 and +6.1 mean differences, respectively). High reproducibility in the quantification of PD-L1 expression is attainable using S-IHC and D-IHC.

Using less annotation workload to establish a pathological auxiliary diagnosis system for gastric cancer

Pathological diagnosis of gastric cancer requires pathologists to have extensive clinical experience. To help pathologists improve diagnostic accuracy and efficiency, we collected 1,514 cases of stomach H&E-stained specimens with complete diagnostic information to establish a pathological auxiliary diagnosis system based on deep learning. At the slide level, our system achieves a specificity of 0.8878 while maintaining a high sensitivity close to 1.0 on 269 biopsy specimens (147 malignancies) and 163 surgical specimens (80 malignancies). The classified accuracy of our system is 0.9034 at the slide level for 352 biopsy specimens (201 malignancies) from 50 medical centers. With the help of our system, the pathologists' average false-negative rate and average false-positive rate on 100 biopsy specimens (50 malignancies) are reduced to 1/5 and 1/2 of the original rates, respectively. At the same time, the average uncertainty rate and the average diagnosis time are reduced by approximately 22% and 20%, respectively.

Proceedings From the ASCO/College of American Pathologists Immune Checkpoint Inhibitor Predictive Biomarker Summit

PURPOSE

Immune checkpoint inhibition (ICI) therapy represents one of the great advances in the field of oncology, highlighted by the Nobel Prize in 2018. Multiple predictive biomarkers for ICI benefit have been proposed. These include assessment of programmed death ligand-1 expression by immunohistochemistry, and determination of mutational genotype (microsatellite instability or mismatch repair deficiency or tumor mutational burden) as a reflection of neoantigen expression. However, deployment of these assays has been challenging for oncologists and pathologists alike.

METHODS

To address these issues, ASCO and the College of American Pathologists convened a virtual Predictive Factor Summit from September 14 to 15, 2021. Representatives from the academic community, US Food and Drug Administration, Centers for Medicare and Medicaid Services, National Institutes of Health, health insurance organizations, pharmaceutical companies, in vitro diagnostics manufacturers, and patient advocate organizations presented state-of-the-art predictive factors for ICI, associated problems, and possible solutions.

RESULTS

The Summit provided an overview of the challenges and opportunities for improvement in assay execution, interpretation, and clinical applications of programmed death ligand-1, microsatellite instability-high or mismatch repair deficient, and tumor mutational burden-high for ICI therapies, as well as issues related to regulation, reimbursement, and next-generation ICI biomarker development.

CONCLUSION

The Summit concluded with a plan to generate a joint ASCO/College of American Pathologists strategy for consideration of future research in each of these areas to improve tumor biomarker tests for ICI therapy.