Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade

Spatial analysis identifies DC niches as predictors of pembrolizumab therapy in head and neck squamous cell cancer

Head and neck squamous cell carcinoma (HNSCC) shows variable response to anti-programmed cell death protein 1 (PD-1) therapy, which can be partially explained by a combined positive score (CPS) of tumor and immune cell expression of programmed death-ligand 1 (PD-L1) within the local tumor microenvironment (TME). To better define TME immune determinants associated with treatment efficacy, we conduct a study of n = 48 HNSCC tumors from patients prior to pembrolizumab therapy. Our investigation combines a rapid bioorthogonal multiplex staining method with computational analysis of whole-slide imaging to capture the single-cell spatial heterogeneity and complexity of the TME. Analyzing 6,316 fields of view (FOVs), we provide comprehensive PD-L1 phenotyping and cell proximity assays across the entirety of tissue sections. While none of the PD-L1 metrics adequately predict response, we find that the spatial organization of CCR7+ dendritic cells (DCs) in niches better predicts overall patient survival than CPS alone. This study highlights the importance of understanding the spatial context of immune networks for immunotherapy.

All-at-once spatial proteome profiling of complex tissue context with single-cell-type resolution by proximity proteomics

Spatial proteomics enables in-depth mapping of tissue architectures, mostly achieved by laser microdissection-mass spectrometry (LMD-MS) and antibody-based imaging. However, trade-offs among sampling precision, throughput, and proteome coverage still limit the applicability of these strategies. Here, we propose proximity labeling for spatial proteomics (PSPro) by combining precise antibody-targeted biotinylation and efficient affinity purification for all-at-once cell-type proteome capture with sub-micrometer resolution from single tissue slice. With fine-tuned labeling parameters, PSPro shows reliable performance in benchmarking against flow cytometry- and LMD-based proteomic workflows. We apply PSPro to tumor and spleen slices, enriching thousands of proteins containing known markers from ten cell types. We further incorporate LMD into PSPro to facilitate comparison of cell subpopulations from the same tissue slice, revealing spatial proteome heterogeneity of cancer cells and immune cells in pancreatic tumor. Collectively, PSPro converts the traditional "antibody-epitope" paradigm to an "antibody-cell-type proteome" for spatial biology in a user-friendly manner. A record of this paper's transparent peer review process is included in the supplemental information.

Quantitatively defined stromal B cell aggregates are associated with response to checkpoint inhibitors in unresectable melanoma

Multiplex immunofluorescence (mIF) is a promising tool for immunotherapy biomarker discovery in melanoma and other solid tumors. mIF captures detailed phenotypic information of immune cells in the tumor microenvironment, as well as spatial data that can reveal biologically relevant interactions among cell types. Given the complexity of mIF data, the development of automated analysis pipelines is crucial for advancing biomarker discovery. In pre-treatment melanoma samples from 50 patients treated with immune checkpoint inhibitors (ICIs), a higher stromal B cell percentage is associated with the clinical benefit of ICI therapy. The automatic detection of B cell aggregates with DBSCAN, a novel application of a computer-aided machine learning algorithm, demonstrates the potential for enhanced accuracy compared to pathologist assessment of lymphoid aggregates. TCF1+ and LAG3- T cell subpopulations are enriched near stromal B cells, suggesting potential functional interactions. These analyses provide a roadmap for the further development of spatial immunotherapy biomarkers in melanoma and other diseases.

Double-Multiplex Immunostainings for Immune Profiling of Invasive Breast Carcinoma: Emerging Novel Immune-Based Biomarkers

The role of tumor microenvironment in invasive breast cancer prognosis and treatment is highly appreciated. With the advent of immunotherapy, immunophenotypic characterization in primary tumors is gaining attention as it can improve patient stratification. Here, we discuss the benefits of spatial analysis employing double and multiplex immunostaining, allowing the simultaneous detection of more than one protein on the same tissue section, which in turn helps us provide functional insight into infiltrating immune cells within tumors. We focus on studies demonstrating the prognostic and predictive impact of distinct tumor-infiltrating lymphocyte subpopulations including different CD8(+) T subsets as well as CD4(+) T cells and tumor-associated macrophages in invasive breast carcinoma. The clinical value of immune cell topography is also appreciated. We further refer to how the integration of digital pathology and artificial intelligence in routine practice could enhance the accuracy of multiplex immunostainings evaluation within the tumor microenvironment, maximizing our perception of host immune response, improving in turn decision-making towards more precise immune-associated therapies.

Society for Immunotherapy of Cancer (SITC) consensus statement on essential biomarkers for immunotherapy clinical protocols

Immunotherapy of cancer is now an essential pillar of treatment for patients with many individual tumor types. Novel immune targets and technical advances are driving a rapid exploration of new treatment strategies incorporating immune agents in cancer clinical practice. Immunotherapies perturb a complex system of interactions among genomically unstable tumor cells, diverse cells within the tumor microenvironment including the systemic adaptive and innate immune cells. The drive to develop increasingly effective immunotherapy regimens is tempered by the risk of immune-related adverse events. Evidence-based biomarkers that measure the potential for therapeutic response and/or toxicity are critical to guide optimal patient care and contextualize the results of immunotherapy clinical trials. Responding to the lack of guidance on biomarker testing in early-phase immunotherapy clinical trials, we propose a definition and listing of essential biomarkers recommended for inclusion in all such protocols. These recommendations are based on consensus provided by the Society for Immunotherapy of Cancer (SITC) Clinical Immuno-Oncology Network (SCION) faculty with input from the SITC Pathology and Biomarker Committees and the Journal for ImmunoTherapy of Cancer readership. A consensus-based selection of essential biomarkers was conducted using a Delphi survey of SCION faculty. Regular updates to these recommendations are planned. The inaugural list of essential biomarkers includes complete blood count with differential to generate a neutrophil-to-lymphocyte ratio or systemic immune-inflammation index, serum lactate dehydrogenase and albumin, programmed death-ligand 1 immunohistochemistry, microsatellite stability assessment, and tumor mutational burden. Inclusion of these biomarkers across early-phase immunotherapy clinical trials will capture variation among trials, provide deeper insight into the novel and established therapies, and support improved patient selection and stratification for later-phase clinical trials.

DNA Methyltransferase Inhibition Upregulates the Costimulatory Molecule ICAM-1 and the Immunogenic Phenotype of Melanoma Cells

In cutaneous melanoma, epigenetic dysregulation is implicated in drug resistance and tumor immune escape. However, the epigenetic mechanisms that influence immune escape remain poorly understood. To elucidate how epigenetic dysregulation alters the expression of surface proteins that may be involved in drug targeting and immune escape, we performed a 3-dimensional surfaceome screen in primary melanoma cultures and identified the DNA-methyltransferase inhibitor decitabine as significantly upregulating the costimulatory molecule ICAM-1. By analyzing The Cancer Genome Atlas melanoma dataset, we further propose ICAM-1 upregulation on melanoma cells as a biomarker of a proinflammatory and antitumorigenic signature. Specifically, we showed that DNA-methyltransferase inhibitor administration upregulated the expression of the antigen-presenting machinery, HLA class I/II, as well as the secretion of the proinflammatory chemokines CXCL9 and CXCL10. Our in silico analysis on The Cancer Genome Atlas and ex vivo experiments on human primary melanoma samples revealed that increased ICAM-1 expression positively correlated with increased immunogenicity of human melanoma cells and correlated with increased immune cell infiltration. These findings suggest a therapeutic approach to modulate the immunogenic phenotype of melanoma cells, hence supporting the exploration of DNA-methyltransferase inhibitor as a potential inducer of infiltration in immunologically cold tumors.

The Society for Immunotherapy of Cancer Perspective on Tissue-Based Technologies for Immuno-Oncology Biomarker Discovery and Application

With immuno-oncology becoming the standard of care for a variety of cancers, identifying biomarkers that reliably classify patient response, resistance, or toxicity becomes the next critical barrier toward improving care. Multiparametric, multi-omics, and computational platforms generating an unprecedented depth of data are poised to usher in the discovery of increasingly robust biomarkers for enhanced patient selection and personalized treatment approaches. Deciding which developing technologies to implement in clinical settings ultimately, applied either alone or in combination, relies on weighing pros and cons, from minimizing patient sampling to maximizing data outputs, and assessing the reproducibility and representativeness of findings, while lessening data fragmentation toward harmonization. These factors are all assessed while taking into consideration the shortest turnaround time. The Society for Immunotherapy of Cancer Biomarkers Committee convened to identify important advances in biomarker technologies and to address advances in biomarker discovery using multiplexed IHC and immunofluorescence, their coupling to single-cell transcriptomics, along with mass spectrometry-based quantitative and spatially resolved proteomics imaging technologies. We summarize key metrics obtained, ease of interpretation, limitations and dependencies, technical improvements, and outward comparisons of these technologies. By highlighting the most interesting recent data contributed by these technologies and by providing ways to improve their outputs, we hope to guide correlative research directions and assist in their evolution toward becoming clinically useful in immuno-oncology.

Spatial Architecture of Single-Cell and Vasculature in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants the identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between antitumor immunity and clinical outcomes; however, such connections remain underexplored. Here, we employed a data set derived from imaging mass cytometry of 71 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multiscale computational algorithms. The TNBC TME reflected a heterogeneous ecosystem with high spatial and compositional heterogeneity. Spatial analysis identified 10 recurrent cellular neighborhoods-a collection of local TME characteristics with unique cell components. The prevalence of cellular neighborhoods enriched with B cells, fibroblasts, and tumor cells, in conjunction with vascular density and perivasculature immune profiles, could significantly enrich long-term survivors. Furthermore, relative spatial colocalization of SMAhi fibroblasts and tumor cells compared with B cells correlated significantly with favorable clinical outcomes. Using a deep learning model trained on engineered spatial data, we can predict with high accuracy (mean area under the receiver operating characteristic curve of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles and suggest novel imaging-based biomarkers for the treatment development in the context of TNBC.

Society for Immunotherapy of Cancer: updates and best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) image analysis and data sharing

OBJECTIVES

Multiplex immunohistochemistry and immunofluorescence (mIHC/IF) are emerging technologies that can be used to help define complex immunophenotypes in tissue, quantify immune cell subsets, and assess the spatial arrangement of marker expression. mIHC/IF assays require concerted efforts to optimize and validate the multiplex staining protocols prior to their application on slides. The best practice guidelines for staining and validation of mIHC/IF assays across platforms were previously published by this task force. The current effort represents a complementary manuscript for mIHC/IF analysis focused on the associated image analysis and data management.

METHODS

The Society for Immunotherapy of Cancer convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the quantitative image analysis of mIHC/IF output and data management considerations.

RESULTS

Best-practice approaches for image acquisition, color deconvolution and spectral unmixing, tissue and cell segmentation, phenotyping, and algorithm verification are reviewed. Additional quality control (QC) measures such as batch-to-batch correction and QC for assembled images are also discussed. Recommendations for sharing raw outputs, processed results, key analysis programs and source code, and representative photomicrographs from mIHC/IF assays are included. Lastly, multi-institutional harmonization efforts are described.

CONCLUSIONS

mIHC/IF technologies are maturing and are routinely included in research studies and moving towards clinical use. Guidelines for how to perform and standardize image analysis on mIHC/IF-stained slides will likely contribute to more comparable results across laboratories and pave the way for clinical implementation. A checklist encompassing these two-part guidelines for the generation of robust data from quantitative mIHC/IF assays will be provided in a third publication from this task force. While the current effort is mainly focused on best practices for characterizing the tumor microenvironment, these principles are broadly applicable to any mIHC/IF assay and associated image analysis.

Tumor-Infiltrating Immune Cells and HLA Expression as Potential Biomarkers Predicting Response to PD-1 Inhibitor Therapy in Stage IV Melanoma Patients

PD-1 inhibitors are known to be effective in melanoma; however, a considerable proportion of patients fail to respond to therapy, necessitating the identification of predictive markers. We examined the predictive value of tumor cell HLA class I and II expression and immune cell infiltration in melanoma patients treated with PD-1 inhibitors. Pretreatment surgical samples from 40 stage IV melanoma patients were studied immunohistochemically for melanoma cell expression of HLA class I molecules (using four antibody clones with different specificities), HLA-II, and immune cell infiltration (using a panel of 10 markers). Among the responders, the ratio of patients showing melanoma cell HLA-II expression was higher compared to non-responders (p = 0.0158), and similar results were obtained in the case of two anti-HLA-I antibodies. A combined score of HLA-I/II expression also predicted treatment response (p = 0.0019). Intratumoral infiltration was stronger in the responders for most immune cell types. Progression-free survival showed an association with HLA-II expression, the combined HLA score, and the density of immune cells expressing CD134 and PD-1, while overall survival was significantly associated only with HLA class II expression. Our findings corroborate previous results indicating the importance of immune cell infiltration and tumor cell HLA-II expression in the efficacy of PD-1 inhibitor treatment in a "real world" patient cohort and suggest the potential predictive role of HLA class I expression.

Spatial organization and stochastic fluctuations of immune cells impact clinical responsiveness to immunotherapy in melanoma patients

High-dimensional, spatial single-cell technologies, such as CyTOF imaging mass cytometry (IMC), provide detailed information regarding locations of a large variety of cancer and immune cells in microscopic scales in tumor microarray slides obtained from patients prior to immune checkpoint inhibitor (ICI) therapy. An important question is how the initial spatial organization of these cells in the tumor microenvironment (TME) changes with time and regulates tumor growth and eventually outcomes as patients undergo ICI therapy. Utilizing IMC data of melanomas of patients who later underwent ICI therapy, we develop a spatially resolved interacting cell system model that is calibrated against patient response data to address the above question. We find that the tumor fate in these patients is determined by the spatial organization of activated CD8+ T cells, macrophages, and melanoma cells and the interplay between these cells that regulate exhaustion of CD8+ T cells. We find that fencing of tumor cell boundaries by exhausted CD8+ T cells is dynamically generated from the initial conditions that can play a protumor role. Furthermore, we find that specific spatial features such as co-clustering of activated CD8+ T cells and macrophages in the pretreatment samples determine the fate of the tumor progression, despite stochastic fluctuations and changes over the treatment course. Our framework enables the determination of mechanisms of interplay between a key subset of tumor and immune cells in the TME that regulate clinical response to ICIs.

T lymphocyte recruitment to melanoma brain tumors depends on distinct venous vessels

To improve immunotherapy for brain tumors, it is important to determine the principal intracranial site of T cell recruitment from the bloodstream and their intracranial route to brain tumors. Using intravital microscopy in mouse models of intracranial melanoma, we discovered that circulating T cells preferably adhered and extravasated at a distinct type of venous blood vessel in the tumor vicinity, peritumoral venous vessels (PVVs). Other vascular structures were excluded as alternative T cell routes to intracranial melanomas. Anti-PD-1/CTLA-4 immune checkpoint inhibitors increased intracranial T cell motility, facilitating migration from PVVs to the tumor and subsequently inhibiting intracranial tumor growth. The endothelial adhesion molecule ICAM-1 was particularly expressed on PVVs, and, in samples of human brain metastases, ICAM-1 positivity of PVV-like vessels correlated with intratumoral T cell infiltration. These findings uncover a distinct mechanism by which the immune system can access and control brain tumors and potentially influence other brain pathologies.

Highly Multiplexed Tissue Imaging in Precision Oncology and Translational Cancer Research

Precision oncology tailors treatment strategies to a patient's molecular and health data. Despite the essential clinical value of current diagnostic methods, hematoxylin and eosin morphology, immunohistochemistry, and gene panel sequencing offer an incomplete characterization. In contrast, highly multiplexed tissue imaging allows spatial analysis of dozens of markers at single-cell resolution enabling analysis of complex tumor ecosystems; thereby it has the potential to advance our understanding of cancer biology and supports drug development, biomarker discovery, and patient stratification. We describe available highly multiplexed imaging modalities, discuss their advantages and disadvantages for clinical use, and potential paths to implement these into clinical practice. Significance: This review provides guidance on how high-resolution, multiplexed tissue imaging of patient samples can be integrated into clinical workflows. It systematically compares existing and emerging technologies and outlines potential applications in the field of precision oncology, thereby bridging the ever-evolving landscape of cancer research with practical implementation possibilities of highly multiplexed tissue imaging into routine clinical practice.

Seeing or believing in hyperplexed spatial proteomics via antibodies: New and old biases for an image-based technology

Hyperplexed in-situ targeted proteomics via antibody immunodetection (i.e., >15 markers) is changing how we classify cells and tissues. Differently from other high-dimensional single-cell assays (flow cytometry, single-cell RNA sequencing), the human eye is a necessary component in multiple procedural steps: image segmentation, signal thresholding, antibody validation, and iconographic rendering. Established methods complement the human image evaluation, but may carry undisclosed biases in such a new context, therefore we re-evaluate all the steps in hyperplexed proteomics. We found that the human eye can discriminate less than 64 out of 256 gray levels and has limitations in discriminating luminance levels in conventional histology images. Furthermore, only images containing visible signals are selected and eye-guided digital thresholding separates signal from noise. BRAQUE, a hyperplexed proteomic tool, can extract, in a marker-agnostic fashion, granular information from markers which have a very low signal-to-noise ratio and therefore are not visualized by traditional visual rendering. By analyzing a public human lymph node dataset, we also found unpredicted staining results by validated antibodies, which highlight the need to upgrade the definition of antibody specificity in hyperplexed immunostaining. Spatially hyperplexed methods upgrade and supplant traditional image-based analysis of tissue immunostaining, beyond the human eye contribution.

Spatial oncology: Translating contextual biology to the clinic

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Distinguishing of Histopathological Staging Features of H-E Stained Human cSCC by Microscopical Multispectral Imaging

Cutaneous squamous cell carcinoma (cSCC) is the second most common malignant skin tumor. Early and precise diagnosis of tumor staging is crucial for long-term outcomes. While pathological diagnosis has traditionally served as the gold standard, the assessment of differentiation levels heavily depends on subjective judgments. Therefore, how to improve the diagnosis accuracy and objectivity of pathologists has become an urgent problem to be solved. We used multispectral imaging (MSI) to enhance tumor classification. The hematoxylin and eosin (H&E) stained cSCC slides were from Shanghai Ruijin Hospital. Scale-invariant feature transform was applied to multispectral images for image stitching, while the adaptive threshold segmentation method and random forest segmentation method were used for image segmentation, respectively. Synthetic pseudo-color images effectively highlight tissue differences. Quantitative analysis confirms significant variation in the nuclear area between normal and cSCC tissues (p < 0.001), supported by an AUC of 1 in ROC analysis. The AUC within cSCC tissues is 0.57. Further study shows higher nuclear atypia in poorly differentiated cSCC tissues compared to well-differentiated cSCC (p < 0.001), also with an AUC of 1. Lastly, well differentiated cSCC tissues show more and larger keratin pearls. These results have shown that combined MSI with imaging processing techniques will improve H&E stained human cSCC diagnosis accuracy, and it will be well utilized to distinguish histopathological staging features.

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.

Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives

Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.

Mapping the single cell spatial immune landscapes of the melanoma microenvironment

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

Machine learning models for predicting of PD-1 treatment efficacy in Pan-cancer patients based on routine hematologic and biochemical parameters

Immune checkpoint blockade therapy targeting the programmed death-1(PD-1) pathway has shown remarkable efficacy and durable response in patients with various cancer types. Early prediction of therapeutic efficacy is important for optimizing treatment plans and avoiding potential side effects. In this work, we developed an efficient machine learning prediction method using routine hematologic and biochemical parameters to predict the efficacy of PD-1 combination treatment in Pan-Cancer patients. A total of 431 patients with nasopharyngeal carcinoma, esophageal cancer and lung cancer who underwent PD-1 checkpoint inhibitor combination therapy were included in this study. Patients were divided into two groups: progressive disease (PD) and disease control (DC) groups. Hematologic and biochemical parameters were collected before and at the third week of PD-1 therapy. Six machine learning models were developed and trained to predict the efficacy of PD-1 combination therapy at 8-12 weeks. Analysis of 57 blood biomarkers before and after three weeks of PD-1 combination therapy through statistical analysis, heatmaps, and principal component analysis did not accurately predict treatment outcome. However, with machine learning models, both the AdaBoost classifier and GBDT demonstrated high levels of prediction efficiency, with clinically acceptable AUC values exceeding 0.7. The AdaBoost classifier exhibited the highest performance among the 6 machine learning models, with a sensitivity of 0.85 and a specificity of 0.79. Our study demonstrated the potential of machine learning to predict the efficacy of PD-1 combination therapy based on changes in hematologic and biochemical parameters.

Biomarker development for PD-(L)1 axis inhibition: a consensus view from the SITC Biomarkers Committee

Therapies targeting the programmed cell death protein-1/programmed death-ligand 1 (PD-L1) (abbreviated as PD-(L)1) axis are a significant advancement in the treatment of many tumor types. However, many patients receiving these agents fail to respond or have an initial response followed by cancer progression. For these patients, while subsequent immunotherapies that either target a different axis of immune biology or non-immune combination therapies are reasonable treatment options, the lack of predictive biomarkers to follow-on agents is impeding progress in the field. This review summarizes the current knowledge of mechanisms driving resistance to PD-(L)1 therapies, the state of biomarker development along this axis, and inherent challenges in future biomarker development for these immunotherapies. Innovation in the development and application of novel biomarkers and patient selection strategies for PD-(L)1 agents is required to accelerate the delivery of effective treatments to the patients most likely to respond.

Engineering cancer's end: An interdisciplinary approach to confront the complexities of cancer

Cancer engineering is an interdisciplinary approach that promises to confront the complexities of cancer and accelerate transformative discoveries by integrating innovative fields across engineering and the physical sciences with a focus on cancer. We offer a conceptual framework for the hallmarks of cancer engineering, integrating 12 fields: system dynamics; imaging, radiation, and spectroscopy; robotics and controls; solid mechanics; fluid mechanics; chemistry and nanomaterials; mathematics and simulation; cellular and protein engineering; kinetics and thermodynamics; materials science; manufacturing and biofabrication; and microsystems.

Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours

Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.

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.

Exploring immune status in peripheral blood and tumor tissue in association with survival in patients with multi-organ metastatic colorectal cancer

Colorectal cancer (CRC) raises considerable clinical challenges, including a high mortality rate once the tumor spreads to distant sites. At this advanced stage, more accurate prediction of prognosis and treatment outcome is urgently needed. The role of cancer immunity in metastatic CRC (mCRC) is poorly understood. Here, we explore cellular immune cell status in patients with multi-organ mCRC. We analyzed T cell infiltration in primary tumor sections, surveyed the lymphocytic landscape of liver metastases, and assessed circulating mononuclear immune cells. Besides asking whether immune cells are associated with survival at this stage of the disease, we investigated correlations between the different tissue types; as this could indicate a dominant immune phenotype. Taken together, our analyses corroborate previous observations that higher levels of CD8+ T lymphocytes link to better survival outcomes. Our findings therefore extend evidence from earlier stages of CRC to indicate an important role for cancer immunity in disease control even after metastatic spreading to multiple organs. This finding may help to improve predicting outcome of patients with mCRC and suggests a future role for immunotherapeutic strategies.

An On-Treatment Decreased Trend of Serum IL-6 and IL-8 as Predictive Markers Quickly Reflects Short-Term Efficacy of PD-1 Blockade Immunochemotherapy in Patients with Advanced Gastric Cancer

Objective

Immunotherapy has proven effective in treating advanced gastric cancer (AGC), yet its benefits are limited to a subset of patients. Our aim is to swiftly identify prognostic biomarkers using cytokines to improve the precision of clinical guidance and decision-making for PD-1 inhibitor-based cancer immunotherapy in AGC.

Materials and Methods

The retrospective study compared 36 patients with AGC who received combined anti-PD-1 immunotherapy and chemotherapy (immunochemotherapy) with a control group of 20 patients who received chemotherapy alone. The concentrations of TNF-α, IL-1β, IL-2R, IL-6, IL-8, IL-10, and IL-17 in the serum were assessed using chemiluminescence immunoassay at three distinct time intervals following the commencement of immunochemotherapy.

Results

When compared to controls, patients undergoing immunochemotherapy demonstrated a generalized rise in cytokine levels after the start of treatment. However, patients who benefited from immunochemotherapy showed a decrease in IL-6 or IL-8 concentrations throughout treatment (with varied trends observed for IL-1β, IL-2R, IL-10, IL-17, and TNF-α) was evident in patients benefiting from immunochemotherapy but not in those who did not benefit. Among these markers, the combination of IL-6, IL-8, and CEA showed optimal predictive performance for short-term efficacy of immunochemotherapy in AGC patients.

Conclusion

Reductions in IL-6/IL-8 levels observed during immunochemotherapy correlated with increased responsiveness to treatment effectiveness. These easily accessible blood-based biomarkers are predictive and rapid and may play a crucial role in identifying individuals likely to derive benefits from PD-1 blockade immunotherapy.

ILT2 and ILT4 Drive Myeloid Suppression via Both Overlapping and Distinct Mechanisms

Solid tumors are dense three-dimensional (3D) multicellular structures that enable efficient receptor-ligand trans interactions via close cell-cell contact. Immunoglobulin-like transcript (ILT)2 and ILT4 are related immune-suppressive receptors that play a role in the inhibition of myeloid cells within the tumor microenvironment. The relative contribution of ILT2 and ILT4 to immune inhibition in the context of solid tumor tissue has not been fully explored. We present evidence that both ILT2 and ILT4 contribute to myeloid inhibition. We found that although ILT2 inhibits myeloid cell activation in the context of trans-engagement by MHC-I, ILT4 efficiently inhibits myeloid cells in the presence of either cis- or trans-engagement. In a 3D spheroid tumor model, dual ILT2/ILT4 blockade was required for the optimal activation of myeloid cells, including the secretion of CXCL9 and CCL5, upregulation of CD86 on dendritic cells, and downregulation of CD163 on macrophages. Humanized mouse tumor models showed increased immune activation and cytolytic T-cell activity with combined ILT2 and ILT4 blockade, including evidence of the generation of immune niches, which have been shown to correlate with clinical response to immune-checkpoint blockade. In a human tumor explant histoculture system, dual ILT2/ILT4 blockade increased CXCL9 secretion, downregulated CD163 expression, and increased the expression of M1 macrophage, IFNγ, and cytolytic T-cell gene signatures. Thus, we have revealed distinct contributions of ILT2 and ILT4 to myeloid cell biology and provide proof-of-concept data supporting the combined blockade of ILT2 and ILT4 to therapeutically induce optimal myeloid cell reprogramming in the tumor microenvironment.

PDL1 immunohistochemistry in canine neoplasms: Validation of commercial antibodies, standardization of evaluation, and scoring systems

Immuno-oncology research has brought to light the paradoxical role of immune cells in the induction and elimination of cancer. Programmed cell death protein 1 (PD1), expressed by tumor-infiltrating lymphocytes, and programmed cell death ligand 1 (PDL1), expressed by tumor cells, are immune checkpoint proteins that regulate the antitumor adaptive immune response. This study aimed to validate commercially available PDL1 antibodies in canine tissue and then, applying standardized methods and scoring systems used in human pathology, evaluate PDL1 immunopositivity in different types of canine tumors. To demonstrate cross-reactivity, a monoclonal antibody (22C3) and polyclonal antibody (cod. A1645) were tested by western blot. Cross-reactivity in canine tissue cell extracts was observed for both antibodies; however, the polyclonal antibody (cod. A1645) demonstrated higher signal specificity. Canine tumor histotypes were selected based on the human counterparts known to express PDL1. Immunohistochemistry was performed on 168 tumors with the polyclonal anti-PDL1 antibody. Only membranous labeling was considered positive. PDL1 labeling was detected both in neoplastic and infiltrating immune cells. The following tumors were immunopositive: melanomas (17 of 17; 100%), renal cell carcinomas (4 of 17; 24%), squamous cell carcinomas (3 of 17; 18%), lymphomas (2 of 14; 14%), urothelial carcinomas (2 of 18; 11%), pulmonary carcinomas (2 of 20; 10%), and mammary carcinomas (1 of 31; 3%). Gastric (0 of 10; 0%) and intestinal carcinomas (0 of 24; 0%) were negative. The findings of this study suggest that PDL1 is expressed in some canine tumors, with high prevalence in melanomas.

Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies

The field of oncology has been transformed by immune checkpoint inhibitors (ICI) and other immune-based agents; however, many patients do not receive a durable benefit. While biomarker assessments from pivotal ICI trials have uncovered certain mechanisms of resistance, results thus far have only scraped the surface. Mechanisms of resistance are as complex as the tumor microenvironment (TME) itself, and the development of effective therapeutic strategies will only be possible by building accurate models of the tumor-immune interface. With advancement of multi-omic technologies, high-resolution characterization of the TME is now possible. In addition to sequencing of bulk tumor, single-cell transcriptomic, proteomic, and epigenomic data as well as T-cell receptor profiling can now be simultaneously measured and compared between responders and nonresponders to ICI. Spatial sequencing and imaging platforms have further expanded the dimensionality of existing technologies. Rapid advancements in computation and data sharing strategies enable development of biologically interpretable machine learning models to integrate data from high-resolution, multi-omic platforms. These models catalyze the identification of resistance mechanisms and predictors of benefit in ICI-treated patients, providing scientific foundation for novel clinical trials. Moving forward, we propose a framework by which in silico screening, functional validation, and clinical trial biomarker assessment can be used for the advancement of combined immunotherapy strategies.

Artificial Intelligence-Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study

INTRODUCTION

Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate.

METHODS

We determined PathR in primary tumor resection specimens using guidelines-based visual techniques and developed a convolutional neural network model using the same criteria to digitally measure the percent viable tumor on whole-slide images. Concordance was evaluated between visual determination of percent viable tumor (n = 151) performed by one of the 47 local pathologists and three central pathologists.

RESULTS

For concordance among visual determination of percent viable tumor, the interclass correlation coefficient was 0.87 (95% confidence interval [CI]: 0.84-0.90). Agreement for visually assessed 10% or less viable tumor (major PathR [MPR]) in the primary tumor was 92.1% (Fleiss kappa = 0.83). Digitally assessed percent viable tumor (n = 136) correlated with visual assessment (Pearson r = 0.73; digital/visual slope = 0.28). Digitally assessed MPR predicted visually assessed MPR with outstanding discrimination (area under receiver operating characteristic curve, 0.98) and was associated with longer disease-free survival (hazard ratio [HR] = 0.30; 95% CI: 0.09-0.97, p = 0.033) and overall survival (HR = 0.14, 95% CI: 0.02-1.06, p = 0.027) versus no MPR. Digitally assessed PathR strongly correlated with visual measurements.

CONCLUSIONS

Artificial intelligence-powered digital pathology exhibits promise in assisting pathologic assessments in neoadjuvant NSCLC clinical trials. The development of artificial intelligence-powered approaches in clinical settings may aid pathologists in clinical operations, including routine PathR assessments, and subsequently support improved patient care and long-term outcomes.

Digital Pathology for Better Clinical Practice

(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.

Redefining clinical practice through spatial profiling: a revolution in tissue analysis

Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.

Embracing cancer complexity: Hallmarks of systemic disease

The last 50 years have witnessed extraordinary developments in understanding mechanisms of carcinogenesis, synthesized as the hallmarks of cancer. Despite this logical framework, our understanding of the molecular basis of systemic manifestations and the underlying causes of cancer-related death remains incomplete. Looking forward, elucidating how tumors interact with distant organs and how multifaceted environmental and physiological parameters impinge on tumors and their hosts will be crucial for advances in preventing and more effectively treating human cancers. In this perspective, we discuss complexities of cancer as a systemic disease, including tumor initiation and promotion, tumor micro- and immune macro-environments, aging, metabolism and obesity, cancer cachexia, circadian rhythms, nervous system interactions, tumor-related thrombosis, and the microbiome. Model systems incorporating human genetic variation will be essential to decipher the mechanistic basis of these phenomena and unravel gene-environment interactions, providing a modern synthesis of molecular oncology that is primed to prevent cancers and improve patient quality of life and cancer outcomes.

Single-cell transcriptomes reveal the heterogeneity and microenvironment of vestibular schwannoma

BACKGROUND

Vestibular schwannoma (VS) is the most common benign tumor in the cerebellopontine angle and internal auditory canal. Illustrating the heterogeneous cellular components of VS could provide insights into its various growth patterns.

METHODS

Single-cell RNA sequencing was used to profile transcriptomes from 7 VS samples and 2 normal nerves. Multiplex immunofluorescence was employed to verify the data set results. Bulk RNA sequencing was conducted on 5 normal nerves and 44 VS samples to generate a prediction model for VS growth.

RESULTS

A total of 83 611 cells were annotated as 14 distinct cell types. We uncovered the heterogeneity in distinct VS tumors. A subset of Schwann cells with the vascular endothelial growth factor biomarker was significantly associated with fast VS growth through mRNA catabolism and peptide biosynthesis. The macrophages in the normal nerves were largely of the M2 phenotype, while no significant differences in the proportions of M1 and M2 macrophages were found between slow-growing and fast-growing VS. The normal spatial distribution of fibroblasts and vascular cells was destroyed in VS. The communications between Schwann cells and vascular cells were strengthened in VS compared with those in the normal nerve. Three cell clusters were significantly associated with fast VS growth and could refine the growth classification in bulk RNA.

CONCLUSIONS

Our findings offer novel insights into the VS microenvironment at the single-cell level. It may enhance our understanding of the different clinical phenotypes of VS and help predict growth characteristics. Molecular subtypes should be included in the treatment considerations.

A perspective on FAIR quality control in multiplexed imaging data processing

Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.

Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities

Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.

Emerging prognostic biomarkers in advanced cutaneous melanoma: a literature update

INTRODUCTION

Over the past two years, the scientific community has witnessed an exponential growth in research focused on identifying prognostic biomarkers for melanoma, both in pre-clinical and clinical settings. This surge in studies reflects the need of developing effective prognostic indicators in the field of melanoma.

AREAS COVERED

The aim of this work is to review the scientific literature on the most recent findings on the development or validation of prognostic biomarkers in melanoma, in the attempt of providing both clinicians and researchers with an updated broad synopsis of prognostic biomarkers in cutaneous melanoma.

EXPERT OPINION

While the field of prognostic biomarkers in melanoma appears promising, there are several complexities and limitations to address. The interdependence of clinical, histological, and molecular features requires accurate classification of different biomarker families. Correlation does not imply causation, and adjustments for confounding factors are often overlooked. In this scenario, large-scale studies based on high-quality clinical trial data can provide more reliable evidence. It is essential to avoid oversimplification by focusing on a single biomarker, as the interactions among multiple factors contribute to define the disease course and patient's outcome. Furthermore, implementing well-supported evidence in real-life settings can help advance prognostic biomarker research in melanoma.

Spatial mapping of human hematopoiesis at single-cell resolution reveals aging-associated topographic remodeling

ABSTRACT

The spatial anatomy of hematopoiesis in the bone marrow (BM) has been extensively studied in mice and other preclinical models, but technical challenges have precluded a commensurate exploration in humans. Institutional pathology archives contain thousands of paraffinized BM core biopsy tissue specimens, providing a rich resource for studying the intact human BM topography in a variety of physiologic states. Thus, we developed an end-to-end pipeline involving multiparameter whole tissue staining, in situ imaging at single-cell resolution, and artificial intelligence-based digital whole slide image analysis and then applied it to a cohort of disease-free samples to survey alterations in the hematopoietic topography associated with aging. Our data indicate heterogeneity in marrow adipose tissue (MAT) content within each age group and an inverse correlation between MAT content and proportions of early myeloid and erythroid precursors, irrespective of age. We identify consistent endosteal and perivascular positioning of hematopoietic stem and progenitor cells (HSPCs) with medullary localization of more differentiated elements and, importantly, uncover new evidence of aging-associated changes in cellular and vascular morphologies, microarchitectural alterations suggestive of foci with increased lymphocytes, and diminution of a potentially active megakaryocytic niche. Overall, our findings suggest that there is topographic remodeling of human hematopoiesis associated with aging. More generally, we demonstrate the potential to deeply unravel the spatial biology of normal and pathologic human BM states using intact archival tissue specimens.

T cell-mediated curation and restructuring of tumor tissue coordinates an effective immune response

Antigen-specific T cells traffic to, are influenced by, and create unique cellular microenvironments. Here we characterize these microenvironments over time with multiplexed imaging in a melanoma model of adoptive T cell therapy and human patients with melanoma treated with checkpoint inhibitor therapy. Multicellular neighborhood analysis reveals dynamic immune cell infiltration and inflamed tumor cell neighborhoods associated with CD8+ T cells. T cell-focused analysis indicates T cells are found along a continuum of neighborhoods that reflect the progressive steps coordinating the anti-tumor immune response. More effective anti-tumor immune responses are characterized by inflamed tumor-T cell neighborhoods, flanked by dense immune infiltration neighborhoods. Conversely, ineffective T cell therapies express anti-inflammatory cytokines, resulting in regulatory neighborhoods, spatially disrupting productive T cell-immune and -tumor interactions. Our study provides in situ mechanistic insights into temporal tumor microenvironment changes, cell interactions critical for response, and spatial correlates of immunotherapy outcomes, informing cellular therapy evaluation and engineering.

Spatial and Compositional Biomarkers in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between anti-tumor immunity and clinical outcomes, yet such connections remain underexplored. Here we employed a dataset derived from imaging mass cytometry of 58 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multi-scale computational algorithms. We detected distinct cell distribution patterns among clinical subgroups, potentially stemming from different infiltration related to tumor vasculature and fibroblast heterogeneity. Spatial analysis also identified ten recurrent cellular neighborhoods (CNs) - a collection of local TME characteristics with unique cell components. Coupling of the prevalence of pan-immune and perivasculature immune hotspot CNs, enrichment of inter-CN interactions was associated with improved survival. Using a deep learning model trained on engineered spatial data, we can with high accuracy (mean AUC of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles, and suggest novel imaging-based biomarkers for treatment development in the context of TNBC.

Decoding the tumor microenvironment with spatial technologies

Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.

pSTAT5 is associated with improved survival in patients with thick or ulcerated primary cutaneous melanoma

Identifying prognostic biomarkers to predict clinical outcomes in stage I and II cutaneous melanomas could guide the clinical application of adjuvant and neoadjuvant therapies. We aimed to investigate the prognostic value of phosphorylated signal transducer and activator of transcription 5 (pSTAT5) as a biomarker in early-stage melanoma. This study evaluated all initially staged Ib and II melanoma patients undergoing sentinel node biopsy at a tertiary centre in Brisbane, Australia between 1994 and 2007, with survival data collected from the Queensland Cancer Registry. Primary melanoma tissue from 189 patients was analysed for pSTAT5 level through immunohistochemistry. Cox regression modelling, with adjustment for sex, age, ulceration, anatomical location, and Breslow depth, was applied to determine the association between pSTAT5 detection and melanoma-specific survival. Median duration of follow-up was 7.4 years. High pSTAT5 detection was associated with ulceration and increased tumour thickness. However, multivariate analysis indicated that high pSTAT5 detection was associated with improved melanoma-specific survival (hazard ratio: 0.15, 95% confidence interval: 0.03-0.67) as compared to low pSTAT5 detection. This association persisted when pSTAT5 detection was limited to immune infiltrate or the vasculature, as well as when sentinel node positivity was accounted for. In this cohort, staining for high-pSTAT5 tumours identified a subset of melanoma patients with increased survival outcomes as compared to low-pSTAT5 tumours, despite the former having higher-risk clinicopathological characteristics at diagnosis. pSTAT5 is likely an indicator of local immune activation, and its detection could represent a useful tool to stratify the risk of melanoma progression.

Biomaterial-Mediated Genetic Reprogramming of Merkel Cell Carcinoma and Melanoma Leads to Targeted Cancer Cell Killing In Vitro and In Vivo

Tumor immunotherapy is a promising anticancer strategy; however, tumor cells may employ resistance mechanisms, including downregulation of major histocompatibility complex (MHC) molecules to avoid immune recognition. Here, we investigate reprogramming nanoparticles (NPs) that deliver immunostimulatory genes to enhance immunotherapy and address defective antigen presentation in skin cancer in vitro and in vivo. We use a modular poly(beta-amino ester) (PBAE)-based NP to deliver DNA encoding 4-1BBL, IL-12, and IFNγ to reprogram human Merkel cell carcinoma (MCC) cells in vitro and mouse melanoma tumors in vivo to drive adaptive antitumor immune responses. Optimized NP formulations delivering 4-1BBL/IL-12 or 4-1BBL/IL-12/IFNγ DNA successfully transfect MCC and melanoma cells in vitro and in vivo, respectively, resulting in IFNγ-driven upregulation of MHC class I and II molecules on cancer cells. These NPs reprogram the tumor immune microenvironment (TIME) and elicit strong T-cell-driven immune responses, leading to cancer cell killing and T-cell proliferation in vitro and slowing tumor growth and improving survival rates in vivo. Based on expected changes to the tumor immune microenvironment, particularly the importance of IFNγ to the immune response and driving both T-cell function and exhaustion, next-generation NPs codelivering IFNγ were designed. These offered mixed benefits, exchanging improved polyfunctionality for increased T-cell exhaustion and demonstrating higher systemic toxicity in vivo. Further profiling of the immune response with these NPs provides insight into T-cell exhaustion and polyfunctionality induced by different formulations, providing a greater understanding of this immunotherapeutic strategy.

Guidelines for visualization and analysis of DC in tissues using multiparameter fluorescence microscopy imaging methods

This article is part of the Dendritic Cell Guidelines article series, which provides a collection of state-of-the-art protocols for the preparation, phenotype analysis by flow cytometry, generation, fluorescence microscopy, and functional characterization of mouse and human dendritic cells (DC) from lymphoid organs and various non-lymphoid tissues. Here, we provide detailed procedures for a variety of multiparameter fluorescence microscopy imaging methods to explore the spatial organization of DC in tissues and to dissect how DC migrate, communicate, and mediate their multiple functional roles in immunity in a variety of tissue settings. The protocols presented here entail approaches to study DC dynamics and T cell cross-talk by intravital microscopy, large-scale visualization, identification, and quantitative analysis of DC subsets and their functions by multiparameter fluorescence microscopy of fixed tissue sections, and an approach to study DC interactions with tissue cells in a 3D cell culture model. While all protocols were written by experienced scientists who routinely use them in their work, this article was also peer-reviewed by leading experts and approved by all co-authors, making it an essential resource for basic and clinical DC immunologists.

An update on methods for detection of prognostic and predictive biomarkers in melanoma

The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.

Quantitative multiplexed imaging technologies for single-cell analysis to assess predictive markers for immunotherapy in thoracic immuno-oncology: promises and challenges

The past decade has witnessed a revolution in cancer treatment by the shift from conventional drugs (chemotherapies) towards targeted molecular therapies and immune-based therapies, in particular the immune-checkpoint inhibitors (ICIs). These immunotherapies selectively release the host immune system against the tumour and have shown unprecedented durable remission for patients with cancers that were thought incurable such as advanced non-small cell lung cancer (aNSCLC). The prediction of therapy response is based since the first anti-PD-1/PD-L1 molecules FDA and EMA approvals on the level of PD-L1 tumour cells expression evaluated by immunohistochemistry, and recently more or less on tumour mutation burden in the USA. However, not all aNSCLC patients benefit from immunotherapy equally, since only around 30% of them received ICIs and among them 30% have an initial response to these treatments. Conversely, a few aNSCLC patients could have an efficacy ICIs response despite low PD-L1 tumour cells expression. In this context, there is an urgent need to look for additional robust predictive markers for ICIs efficacy in thoracic oncology. Understanding of the mechanisms that enable cancer cells to adapt to and eventually overcome therapy and identifying such mechanisms can help circumvent resistance and improve treatment. However, more than a unique universal marker, the evaluation of several molecules in the tumour at the same time, particularly by using multiplex immunostaining is a promising open room to optimise the selection of patients who benefit from ICIs. Therefore, urgent further efforts are needed to optimise to individualise immunotherapy based on both patient-specific and tumour-specific characteristics. This review aims to rethink the role of multiplex immunostaining in immuno-thoracic oncology, with the current advantages and limitations in the near-daily practice use.

Comparison of different predictive biomarker testing assays for PD-1/PD-L1 checkpoint inhibitors response: a systematic review and network meta-analysis

Background

Accurate prediction of efficacy of programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) checkpoint inhibitors is of critical importance. To address this issue, a network meta-analysis (NMA) comparing existing common measurements for curative effect of PD-1/PD-L1 monotherapy was conducted.

Methods

We searched PubMed, Embase, the Cochrane Library database, and relevant clinical trials to find out studies published before Feb 22, 2023 that use PD-L1 immunohistochemistry (IHC), tumor mutational burden (TMB), gene expression profiling (GEP), microsatellite instability (MSI), multiplex IHC/immunofluorescence (mIHC/IF), other immunohistochemistry and hematoxylin-eosin staining (other IHC&HE) and combined assays to determine objective response rates to anti-PD-1/PD-L1 monotherapy. Study-level data were extracted from the published studies. The primary goal of this study was to evaluate the predictive efficacy and rank these assays mainly by NMA, and the second objective was to compare them in subgroup analyses. Heterogeneity, quality assessment, and result validation were also conducted by meta-analysis.

Findings

144 diagnostic index tests in 49 studies covering 5322 patients were eligible for inclusion. mIHC/IF exhibited highest sensitivity (0.76, 95% CI: 0.57-0.89), the second diagnostic odds ratio (DOR) (5.09, 95% CI: 1.35-13.90), and the second superiority index (2.86). MSI had highest specificity (0.90, 95% CI: 0.85-0.94), and DOR (6.79, 95% CI: 3.48-11.91), especially in gastrointestinal tumors. Subgroup analyses by tumor types found that mIHC/IF, and other IHC&HE demonstrated high predictive efficacy for non-small cell lung cancer (NSCLC), while PD-L1 IHC and MSI were highly efficacious in predicting the effectiveness in gastrointestinal tumors. When PD-L1 IHC was combined with TMB, the sensitivity (0.89, 95% CI: 0.82-0.94) was noticeably improved revealed by meta-analysis in all studies.

Interpretation

Considering statistical results of NMA and clinical applicability, mIHC/IF appeared to have superior performance in predicting response to anti PD-1/PD-L1 therapy. Combined assays could further improve the predictive efficacy. Prospective clinical trials involving a wider range of tumor types are needed to establish a definitive gold standard in future.

The role of tumor immune microenvironment in chordoma: promising immunotherapy strategies

Chordoma is a rare malignant bone tumor with limited therapeutic options, which is resistant to conventional chemotherapy and radiotherapy, and targeted therapy is also shown with little efficacy. The long-standing delay in researching its mechanisms of occurrence and development has resulted in the dilemma of no effective treatment targets and no available drugs in clinical practice. In recent years, the role of the tumor immune microenvironment in driving tumor growth has become a hot and challenging topic in the field of cancer research. Immunotherapy has shown promising results in the treatment of various tumors. However, the study of the immune microenvironment of chordoma is still in its infancy. In this review, we aim to present a comprehensive reveal of previous exploration on the chordoma immune microenvironment and propose promising immunotherapy strategies for chordoma based on these characteristics.