Multiparametric immune profiling in HPV- oral squamous cell cancer

HIV status alters immune cell infiltration and activation profile in women with breast cancer

The breast cancer (BC)-related mortality is higher and the immunity is altered in women living with HIV (WLWH) compared to HIV-negative women. Therefore, tumor samples of 296 black BC patients from South Africa and Namibia with known age, HIV status, tumor stage, hormone receptor and HER2 status and overall survival (OS) are analyzed for components of the tumor microenvironment (TME). WLWH (n = 117), either with suppressed viral activity (HR = 1.25) or with immune suppression (HR = 2.04), have a shorter OS. HIV status is associated with increased numbers of CD8+ T cells in the TME compared to HIV-negative patients; no correlation is found with CD4+ T cell numbers in the blood. Moreover, an increased expression of CD276/B7-H3 and a more pronounced IFN-γ signaling in the tumors are found in WLWH, independent of age, stage, and BC subtypes. In conclusion, altered T cell composition and CD276 expression in WLWH may contribute to inferior survival and can be used for targeted treatment.

Perspectives on the role of "-Omics" in predicting response to immunotherapy

The annual Immuno-Oncology "Think Tank" held in October 2023 in Siena reviewed the rapidly evolving systems-biological approaches which are now providing a deeper understanding of tumor and tumor microenvironment heterogeneity. Based on this understanding opportunities for novel therapies may be identified to overcome resistance to immunotherapy. There is increasing evidence that malignant disease processes are not limited to purely intracellular or genetic events but constitute a dynamic interaction between the host and disease. Tumor responses are influenced by many host tissue determinants across different cellular compartments, which can now be investigated by high-throughput molecular profiling technologies, often labelled with a suffix "-omics". "Omics" together with ever increasing computational power, fast developments in machine learning, and high-resolution detection tools offer an unrivalled opportunity to connect high-dimensional data and create a holistic view of disease processes in cancer. This review describes advances in several state-of-the-art "-omics" approaches with perspectives on how these can be applied to the clinical development of new immunotherapeutic strategies and ultimately adopted in clinical practice.

Assessment of immune infiltrate in oral cancer: An immunohistochemical study

Background

The biomarkers of antitumour immune response provide valuable prognostic information and aid in the stratification and treatment of cancer. Tumour microenvironment (TME) defines the cancer biology, and assessment of tumour-infiltrating lymphocytes (TILs) in oral squamous cell carcinoma is an arena of vigorous research.

Aims and Objectives

The present study is designed to determine the association of CD8+ and CD3+ T lymphocytes with clinicopathological parameters and their role as prognostic biomarkers.

Materials and Methods

This is an observational and institution-based study. Tissue blocks of histologically proven oral squamous cell carcinoma patients were retrieved from archives, and all clinicopathological parameters were noted. The semiquantitative and quantitative methods of TILs assessment were meticulously applied both in the stromal and intratumoural regions using immunohistochemistry. The standard statistical methods were employed for data analysis.

Results

A significant association of CD8+ T lymphocytes with clinical tumour size (P = 0.012), clinical (P = 0.011), and pathological (P = 0.048) staging was observed. CD3+ T lymphocytes were significantly associated with clinical node involvement. However, no survival benefits were observed with both biomarkers.

Conclusion

CD8+ T lymphocytes showed a significant association with clinical tumour size, clinical, and pathological staging. However, the study did not provide evidence for the prognostic value of the presence of CD3+ and CD8+ T lymphocytes in the tumour epithelium and stroma of oral cancer patients.

SLC2A3 is a Potential Factor for Head and Neck Squamous Cancer Development through Tumor Microenvironment Alteration

INTRODUCTION

Tumor immunity has garnered increasing attention in cancer treatment and progression. However, there is still a challenge in understanding the mechanisms of specific molecules affecting the clinical prognosis and tumor microenvironment (TME).

METHODS

Here, we applied the ESTIMATE algorithm to calculate the immune and stromal scores in 504 HNSC cases from TCGA. Patients were grouped according to the median value of the immune and stromal. Clinicopathological characteristics and differentially expressed genes (DEG) were analyzed. Subsequently, LASSO, COX regression, survival analysis, and clinicopathological characteristics were conducted. Subsequently, SLC2A3 was determined as a predictive factor that high expression of SLC2A3 at the mRNA and protein levels predicted a worse clinical prognosis. GSEA25099 was utilized for external validation of immune infiltration, while tissue PCR, IHC, and Western Blot were used to confirm the expression levels of SLC2A3.

RESULTS

A series of immune-infiltration analyses showed that SLC2A3 expression was negatively correlated with CD8+ T cells, significantly affecting the survival prognosis of HNSC. In the GSEA analysis, the high expression of SLC2A3 was mainly enriched for immune-related biological processes. Meanwhile, high expression of SLC2A3 possessed higher TIDE scores and was also strongly positively correlated with a series of immune checkpoints affecting survival prognosis, thus causing greater susceptibility to immune escape.

CONCLUSION

Conclusively, SLC2A3 is a potential oncogene and factor of HNSC development, notably by an altered state of the immune microenvironment, immune-suppressive regulation, and immune escape.

Cancer cell-specific PD-L1 expression is a predictor of poor outcome in patients with locally advanced oral cavity squamous cell carcinoma

BACKGROUND

Locally advanced oral cavity squamous cell carcinoma (OCSCC) presents a significant clinical challenge despite being partially responsive to standard treatment modalities. This study investigates the prognostic implications of programmed death-ligand 1 (PD-L1) expression in these tumors, focusing on its association with treatment outcomes and the immune microenvironment.

METHODS

We assessed tumor-infiltrating lymphocytes (TILs) in 132 patients with OCSCC to evaluate their impact on survival. Multiplex immunohistochemistry staining for CD3, CD68, CD11c, PD-L1, and P40 was used to explore correlations with clinical outcomes in patients with early-stage (n=22) and locally advanced (n=36) OCSCC. These initial findings were validated through differential gene expression analysis, gene set enrichment, and immune cell deconvolution in a The Cancer Genome Atlas cohort of 163 locally advanced OCSCC tumors. Additionally, single-cell RNA sequencing (scRNA-seq) on a smaller cohort (n=10) further characterized the PD-L1hi or PD-L1lo cancer cells in these tumors.

RESULTS

Elevated PD-L1 expression was associated with poor outcomes in patients with locally advanced OCSCC undergoing standard adjuvant therapy, irrespective of "hot" or "cold" classification based on TILs assessment. PD-L1hi tumors exhibited an active immune response phenotype, enriched with M1 macrophages, CD8+ T cells and T regulatory cells in the tumor microenvironment. Notably, the negative impact of PD-L1 expression on outcomes was primarily attributed to its expression by cancer cells, rather than immune cells. Furthermore, scRNA-seq revealed that immune interactions were not essential for PD-L1 upregulation in cancer cells, instead, complex regulatory networks were involved. Additionally, PD-L1lo locally advanced tumors exhibited more complex pathway enrichment and diverse T-cell populations compared with those in the early-stage.

CONCLUSION

Our findings underscore the prognostic significance of PD-L1 expression in locally advanced OCSCC, and unveil the complex interplay between PD-L1 expression, immune responses, and molecular pathways in the tumor microenvironment. This study provides insights that may inform future therapeutic strategies, including the possibility of tailored immunotherapeutic approaches for patients with PD-L1hi locally advanced OCSCC.

A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method

BACKGROUND

Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine.

MATERIAL AND METHODS

We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH).

RESULTS

We identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p < 0.1), such as immune-related features CD68+ and CD8+ cell infiltration. We constructed five HCC prognosis prediction models using five different machine learning methods. Among the five different machine learning models, the CoxPH model achieved the best performance (area under the curve [AUC], 0.839; C-index, 0.779). According to the risk score from CoxPH model, we divided HCC patients into high-risk group/low-risk group. In both discovery cohort and validation cohort, the patients in low-risk group showed longer overall survival compared with those in high-risk group (p = 1.8 × 10-7 and 3.4 × 10-5, respectively). Moreover, our novel scoring system efficiently predicted the 6, 12, and 18 months survival rate of HCC patients with AUC >0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012).

CONCLUSION

The novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.

Immune cell topography of head and neck cancer

BACKGROUND

Approximately 50% of head and neck squamous cell carcinomas (HNSCC) recur after treatment with curative intent. Immune checkpoint inhibitors are treatment options for recurrent/metastatic HNSCC; however, less than 20% of patients respond. To increase this response rate, it is fundamental to increase our understanding of the spatial tumor immune microenvironment (TIME).

METHODS

In total, 53 HNSCC specimens were included. Using a seven-color multiplex immunohistochemistry panel we identified tumor cells, CD163+macrophages, B cells, CD8+T cells, CD4+T helper cells and regulatory T cells (Tregs) in treatment-naive surgical resection specimens (n=29) and biopsies (n=18). To further characterize tumor-infiltrating CD8+T cells, we stained surgical resection specimens (n=12) with a five-color tumor-resident panel including CD103, Ki67, CD8 and pan-cytokeratin. Secretome analysis was performed on matched tumor suspensions (n=11) to measure protein levels.

RESULTS

Based on CD8+T cell infiltrates, we identified four different immunotypes: fully infiltrated, stroma-restricted, immune-excluded, and immune-desert. We found higher cytokine levels in fully infiltrated tumors compared with other immunotypes. While the highest immune infiltrates were observed in the invasive margin for all immune cells, CD163+macrophages and Tregs had the highest tendency to infiltrate the tumor center. Within the tumor center, especially B cells stayed at the tumor stroma, whereas CD163+macrophages, followed by T cells, were more often localized within tumor fields. Also, B cells were found further away from other cells and often formed aggregates while T cells and CD163+macrophages tended to be more closely located to each other. Across resection specimens from various anatomical sites within the head and neck, oral cavity tumors exhibited the highest densities of Tregs. Moreover, the distance from B cells and T cells to tumor cells was shortest in oral cavity squamous cell carcinoma (OCSCC), suggesting more interaction between lymphocytes and tumor cells. Also, the fraction of T cells within 10 µm of CD163+macrophages was lowest in OCSCC, indicating fewer myeloid/T-cell suppressive interactions in OCSCC.

CONCLUSIONS

We comprehensively described the TIME of HNSCC using a unique data set of resection specimens. We discovered that the composition, as well as the relative localization of immune cells in the TIME, differed in distinct anatomical sites of the head and neck.

Development and validation of a predictive model based on β-Klotho for head and neck squamous cell carcinoma

Head and neck epithelial tissue tumors may be identified as head and neck squamous cell carcinoma (HNSC). Numerous malignancies are encouraged by dysregulation of the FGF19-β-Klotho (KLB) axis in the tumor microenvironment. Using protein databases and RT-qPCR, we examined KLB expression in HNSC. In HNSC, higher KLB expression was linked to longer survival times and better prognoses. Furthermore, variations in drug susceptibility and immunological infiltration were noted according to KLB expression levels. These results underscore the importance of KLB in the course and management of HNSC by indicating that it may function as a possible prognostic marker and influence immunological and therapeutic responses in these individuals. Further study on HNSC is necessary to investigate KLB's potential as a therapeutic target and prognostic indicator.

Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry

Artificial intelligence (AI) is a reality of our times, and it has been successfully implemented in all fields, including medicine. As a relatively new domain, all efforts are directed towards creating algorithms applicable in most medical specialties. Pathology, as one of the most important areas of interest for precision medicine, has received significant attention in the development and implementation of AI algorithms. This focus is especially important for achieving accurate diagnoses. Moreover, immunohistochemistry (IHC) serves as a complementary diagnostic tool in pathology. It can be further augmented through the application of deep learning (DL) and machine learning (ML) algorithms for assessing and analyzing immunohistochemical markers. Such advancements can aid in delineating targeted therapeutic approaches and prognostic stratification. This article explores the applications and integration of various AI software programs and platforms used in immunohistochemical analysis. It concludes by highlighting the application of these technologies to pathologies such as breast, prostate, lung, melanocytic proliferations, and hematologic conditions. Additionally, it underscores the necessity for further innovative diagnostic algorithms to assist physicians in the diagnostic process.

Spatial relationships in the urothelial and head and neck tumor microenvironment predict response to combination immune checkpoint inhibitors

Immune checkpoint inhibitors (ICI) can achieve remarkable responses in urothelial cancer (UC), which may depend on tumor microenvironment (TME) characteristics. However, the relationship between the TME, usually characterized by immune cell density, and response to ICI is unclear. Here, we quantify the TME immune cell densities and spatial relationships (SRs) of 24 baseline UC samples, obtained before pre-operative combination ICI treatment, using multiplex immunofluorescence. We describe SRs by approximating the first nearest-neighbor distance distribution with a Weibull distribution and evaluate the association between TME metrics and ipilimumab+nivolumab response. Immune cell density does not discriminate between response groups. However, the Weibull SR metrics of CD8+ T cells or macrophages to their closest cancer cell positively associate with response. CD8+ T cells close to B cells are characteristic of non-response. We validate our SR response associations in a combination ICI cohort of head and neck tumors. Our data confirm that SRs, in contrast to density metrics, are strong biomarkers of response to pre-operative combination ICIs.

Comparative analysis of immune infiltrates in head and neck cancers across anatomical sites

BACKGROUND

The response rate to immune checkpoint inhibitors targeting programmed cell death 1 (PD-1) receptor is 13%-18% for patients with recurrent or metastatic head and neck squamous cell carcinoma (HNSCC). Detailed understanding of the tumor immune microenvironment (TIME) is crucial in order to explain and improve this response rate. HNSCCs arise at various anatomical locations including the oral cavity, hypopharynx, larynx and oropharynx. Studies directly comparing immune infiltration between anatomical sites are scarce. Since the distinct locations could drive deviating microenvironments, we questioned whether the immune composition varies across these HNSCC sites.

METHODS

Here, we characterized the TIME of 76 fresh tumor specimens using flow cytometry and performed single-cell RNA-sequencing on nine head and neck tumor samples.

RESULTS

We found major differences in the composition of the TIME between patients. When comparing anatomical sites: tumors originating from the oral cavity had higher T cell infiltrates than tumors from other anatomical sites. The percentage of tumor-infiltrating T-lymphocytes positive for the immune checkpoint PD-1 varied considerably between patients, with the highest fraction of PD-1+ T cells found in larynx squamous cell carcinomas (SCCs). While we had hypothesized that the anatomical sites of tumor origin would drive sample clustering, our data showed that the type of TIME was more dominant and was particularly driven by the fraction of T cells positive for PD-1. Moreover, a high proportion of PD-1+ CD8+ T cells associated with an improved overall survival. Using single-cell RNA-sequencing, we observed that PD-1 expression was highest in the CD8-ENTPD1 tissue resident memory T cell/exhausted T cell and CD4-CXCL13 type 1 T helper cell clusters.

CONCLUSIONS

We found that oral cavity SCCs had the highest frequencies of T cells. We also observed considerable interpatient heterogeneity for PD-1 on T cells, with noticeably higher frequencies of PD-1+ CD4+ T helper cells in larynx SCCs. Within the entire cohort, a higher fraction of CD8+ T cells positive for PD-1 was linked to improved overall survival. Whether the fraction of PD-1+ T cells within the TIME enables immune checkpoint inhibitor response prediction for patients with head and neck cancer remains to be determined.

Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate

The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.

Components of the tumor immune microenvironment based on m-IHC correlate with prognosis and subtype of triple-negative breast cancer

BACKGROUND AND AIM

The spatial distribution and interactions of cells in the tumor immune microenvironment (TIME) might be related to the different responses of triple-negative breast cancer (TNBC) to immunomodulators. The potential of multiplex IHC (m-IHC) in evaluating the TIME has been reported, but the efficacy is insufficient. We aimed to research whether m-IHC results could be used to reflect the TIME, and thus to predict prognosis and complement the TNBC subtyping system.

METHODS

The clinical, imaging, and prognosis data for 86 TNBC patients were retrospectively reviewed. CD3, CD4, CD8, Foxp3, PD-L1, and Pan-CK markers were stained by m-IHC. Particular cell spatial distributions and interactions in the TIME were evaluated with the HALO multispectral analysis platform. Then, we calculated the prognostic value of components of the TIME and their correlations with TNBC transcriptomic subtypes and MRI radiomic features reflecting TNBC subtypes.

RESULTS

The components of the TIME score were established by m-IHC and demonstrated positive prognostic value for TNBC (p = 0.0047, 0.039, <0.0001 for DMFS, RFS, and OS). The score was calculated from several indicators, including Treg% in the tumor core (TC) or stromal area (SA), PD-L1+ cell% in the SA, CD3 + cell% in the TC, and PD-L1+ /CD8+ cells in the invasive margin and SA. According to the TNBC subtyping system, a few TIME indicators were significantly different in different subtypes and significantly correlated with MRI radiomic features reflecting TNBC subtypes.

CONCLUSION

We demonstrated that the m-IHC-based quantitative score and indicators related to the spatial distribution and interactions of cells in the TIME can aid in the accurate diagnosis of TNBC in terms of prognosis and classification.

Delineating spatial cell-cell interactions in the solid tumour microenvironment through the lens of highly multiplexed imaging

The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (<10) markers, limiting the capacity for cell phenotyping. Recently developed digital pathology technologies, together with sophisticated bioimage analysis programs, now enable the high-resolution, highly-multiplexed analysis of diverse immune and stromal cell markers within the TME of clinical specimens. In this article, we review the tumour-promoting non-cell autonomous interactions in the TME and their impact on tumour behaviour. We additionally survey commonly used image analysis programs and highly-multiplexed spatial imaging technologies, and we discuss their relative advantages and limitations. The spatial organization of the TME varies enormously between patients, and so leveraging these technologies in future studies to further characterize how non-cell autonomous interactions impact tumour behaviour may inform the personalization of cancer treatment.​.

Combination of multiple omics techniques for a personalized therapy or treatment selection

Despite targeted therapies and immunotherapies have revolutionized the treatment of cancer patients, only a limited number of patients have long-term responses. Moreover, due to differences within cancer patients in the tumor mutational burden, composition of the tumor microenvironment as well as of the peripheral immune system and microbiome, and in the development of immune escape mechanisms, there is no "one fit all" therapy. Thus, the treatment of patients must be personalized based on the specific molecular, immunologic and/or metabolic landscape of their tumor. In order to identify for each patient the best possible therapy, different approaches should be employed and combined. These include (i) the use of predictive biomarkers identified on large cohorts of patients with the same tumor type and (ii) the evaluation of the individual tumor with "omics"-based analyses as well as its ex vivo characterization for susceptibility to different therapies.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Whole-Slide Imaging, Mutual Information Registration for Multiplex Immunohistochemistry and Immunofluorescence

Multiplex immunohistochemistry/immunofluorescence (mIHC/mIF) is a developing technology that facilitates the evaluation of multiple, simultaneous protein expressions at single-cell resolution while preserving tissue architecture. These approaches have shown great potential for biomarker discovery, yet many challenges remain. Importantly, streamlined cross-registration of multiplex immunofluorescence images with additional imaging modalities and immunohistochemistry (IHC) can help increase the plex and/or improve the quality of the data generated by potentiating downstream processes such as cell segmentation. To address this problem, a fully automated process was designed to perform a hierarchical, parallelizable, and deformable registration of multiplexed digital whole-slide images (WSIs). We generalized the calculation of mutual information as a registration criterion to an arbitrary number of dimensions, making it well suited for multiplexed imaging. We also used the self-information of a given IF channel as a criterion to select the optimal channels to use for registration. Additionally, as precise labeling of cellular membranes in situ is essential for robust cell segmentation, a pan-membrane immunohistochemical staining method was developed for incorporation into mIF panels or for use as an IHC followed by cross-registration. In this study, we demonstrate this process by registering whole-slide 6-plex/7-color mIF images with whole-slide brightfield mIHC images, including a CD3 and a pan-membrane stain. Our algorithm, WSI, mutual information registration (WSIMIR), performed highly accurate registration allowing the retrospective generation of an 8-plex/9-color, WSI, and outperformed 2 alternative automated methods for cross-registration by Jaccard index and Dice similarity coefficient (WSIMIR vs automated WARPY, P < .01 and P < .01, respectively, vs HALO + transformix, P = .083 and P = .049, respectively). Furthermore, the addition of a pan-membrane IHC stain cross-registered to an mIF panel facilitated improved automated cell segmentation across mIF WSIs, as measured by significantly increased correct detections, Jaccard index (0.78 vs 0.65), and Dice similarity coefficient (0.88 vs 0.79).

Perspectives in Melanoma: meeting report from the Melanoma Bridge (December 1st-3rd, 2022-Naples, Italy)

Outcomes for patients with melanoma have improved over the past decade with the clinical development and approval of immunotherapies targeting immune checkpoint receptors such as programmed death-1 (PD-1), programmed death ligand 1 (PD-L1) or cytotoxic T lymphocyte antigen-4 (CTLA-4). Combinations of these checkpoint therapies with other agents are now being explored to improve outcomes and enhance benefit-risk profiles of treatment. Alternative inhibitory receptors have been identified that may be targeted for anti-tumor immune therapy, such as lymphocyte-activation gene-3 (LAG-3), as have several potential target oncogenes for molecularly targeted therapy, such as tyrosine kinase inhibitors. Unfortunately, many patients still progress and acquire resistance to immunotherapy and molecularly targeted therapies. To bypass resistance, combination treatment with immunotherapies and single or multiple TKIs have been shown to improve prognosis compared to monotherapy. The number of new combinations treatment under development for melanoma provides options for the number of patients to achieve a therapeutic benefit. Many diagnostic and prognostic assays have begun to show clinical applicability providing additional tools to optimize and individualize treatments. However, the question on the optimal algorithm of first- and later-line therapies and the search for biomarkers to guide these decisions are still under investigation. This year, the Melanoma Bridge Congress (Dec 1st-3rd, 2022, Naples, Italy) addressed the latest advances in melanoma research, focusing on themes of paramount importance for melanoma prevention, diagnosis and treatment. This included sessions dedicated to systems biology on immunotherapy, immunogenicity and gene expression profiling, biomarkers, and combination treatment strategies.

Comparing and Correcting Spectral Sensitivities between Multispectral Microscopes: A Prerequisite to Clinical Implementation

Multispectral, multiplex immunofluorescence (mIF) microscopy has been used to great effect in research to identify cellular co-expression profiles and spatial relationships within tissue, providing a myriad of diagnostic advantages. As these technologies mature, it is essential that image data from mIF microscopes is reproducible and standardizable across devices. We sought to characterize and correct differences in illumination intensity and spectral sensitivity between three multispectral microscopes. We scanned eight melanoma tissue samples twice on each microscope and calculated their average tissue region flux intensities. We found a baseline average standard deviation of 29.9% across all microscopes, scans, and samples, which was reduced to 13.9% after applying sample-specific corrections accounting for differences in the tissue shown on each slide. We used a basic calibration model to correct sample- and microscope-specific effects on overall brightness and relative brightness as a function of the image layer. We tested the generalizability of the calibration procedure and found that applying corrections to independent validation subsets of the samples reduced the variation to 2.9 ± 0.03%. Variations in the unmixed marker expressions were reduced from 15.8% to 4.4% by correcting the raw images to a single reference microscope. Our findings show that mIF microscopes can be standardized for use in clinical pathology laboratories using a relatively simple correction model.

Regional Variation in the Tumor Microenvironment, Immune Escape and Prognostic Factors in Breast Cancer in Sub-Saharan Africa

The low overall survival rates of patients with breast cancer in sub-Saharan Africa (SSA) are driven by regionally differing tumor biology, advanced tumor stages at diagnosis, and limited access to therapy. However, it is not known whether regional differences in the composition of the tumor microenvironment (TME) exist and affect patients' prognosis. In this international, multicentre cohort study, 1,237 formalin-fixed, paraffin-embedded breast cancer samples, including samples of the "African Breast Cancer-Disparities in Outcomes (ABC-DO) Study," were analyzed. The immune cell phenotypes, their spatial distribution in the TME, and immune escape mechanisms of breast cancer samples from SSA and Germany (n = 117) were investigated using histomorphology, conventional and multiplex IHC, and RNA expression analysis. The data revealed no regional differences in the number of tumor-infiltrating lymphocytes (TIL) in the 1,237 SSA breast cancer samples, while the distribution of TILs in different breast cancer IHC subtypes showed regional diversity, particularly when compared with German samples. Higher TIL densities were associated with better survival in the SSA cohort (n = 400), but regional differences concerning the predictive value of TILs existed. High numbers of CD163+ macrophages and CD3+CD8+ T cells accompanied by reduced cytotoxicity, altered IL10 and IFNγ levels and downregulation of MHC class I components were predominantly detected in breast cancer samples from Western SSA. Features of nonimmunogenic breast cancer phenotypes were associated with reduced patient survival (n = 131). We therefore conclude that regional diversity in the distribution of breast cancer subtypes, TME composition, and immune escape mechanisms should be considered for therapy decisions in SSA and the design of personalized therapies. See related Spotlight by Bergin et al., p. 705.

Multiplexed electrochemical assays for clinical applications

Rapid, accurate diagnoses are central to future efficient healthcare to identify diseases at early stages, avoid unnecessary treatment, and improve outcomes. Electrochemical techniques have been applied in many ways to support clinical applications by enabling the analysis of relevant disease biomarkers in user-friendly, sensitive, low-cost assays. Electrochemistry offers a launchpad for multiplexed biomarker assays that offer more accurate and precise diagnostics compared to single biomarker assays. In this short review, we underpin the importance of multiplexed analyses and provide a universal overview of current electrochemical assay strategies for multiple biomarkers. We highlight relevant examples of electrochemical methods that successfully quantify important disease biomarkers. Finally, we offer a future outlook on possible strategies that can be employed to increase throughput, sensitivity, and specificity of multiplexed electrochemical assays.

High density and proximity of CD8+ T cells to tumor cells are correlated with better response to nivolumab treatment in metastatic pleural mesothelioma

BACKGROUND

The efficacy of immune checkpoint inhibitors (ICIs) in pleural mesothelioma has recently been established. The response to ICIs can be predicted by quantitative analysis of cells and their spatial distribution in the tumor microenvironment (TME). However, the detailed composition of the TME in pleural mesothelioma has not been reported. We evaluated the association between the TME and response to ICIs in this cancer.

METHODS

A retrospective analysis of 22 pleural mesothelioma patients treated with nivolumab in different centers was performed using surgical specimens. Four patients had a partial response to nivolumab (response group) and 18 patients had stable or progressive disease (nonresponse group). The number of CD4, CD8, FoxP3, CK, and PD-L1 positive cells, cell density, and cell-to-cell distance were analyzed by multiplex immunofluorescence.

RESULTS

PD-L1 expression did not differ significantly between the response and nonresponse groups. The density of total T cells and of CD8⁺ T cells was significantly higher in the response than in the nonresponse group. CD8⁺ T cells were more clustered and located closer to tumor cells, whereas regulatory T cells were located further from tumor cells in the response than in the nonresponse group.

CONCLUSIONS

High density and spatial proximity of CD8⁺ T cells to tumor cells were associated with better response to nivolumab, whereas the proximity of regulatory T cells to tumor cells was associated with worse response, suggesting that the distinct landscape of the TME could be a potential predictor of ICI efficacy in pleural mesothelioma.

Multiomics technologies for comprehensive tumor microenvironment analysis in triple-negative breast cancer under neoadjuvant chemotherapy

Triple-negative breast cancer (TNBC) is one of the most aggressive breast cancer subtypes and is characterized by abundant infiltrating immune cells within the microenvironment. As standard care, chemotherapy remains the fundamental neoadjuvant treatment in TNBC, and there is increasing evidence that supplementation with immune checkpoint inhibitors may potentiate the therapeutic efficiency of neoadjuvant chemotherapy (NAC). However, 20-60% of TNBC patients still have residual tumor burden after NAC and require additional chemotherapy; therefore, it is critical to understand the dynamic change in the tumor microenvironment (TME) during treatment to help improve the rate of complete pathological response and long-term prognosis. Traditional methods, including immunohistochemistry, bulk tumor sequencing, and flow cytometry, have been applied to elucidate the TME of breast cancer, but the low resolution and throughput may overlook key information. With the development of diverse high-throughput technologies, recent reports have provided new insights into TME alterations during NAC in four fields, including tissue imaging, cytometry, next-generation sequencing, and spatial omics. In this review, we discuss the traditional methods and the latest advances in high-throughput techniques to decipher the TME of TNBC and the prospect of translating these techniques to clinical practice.

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI

Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.

The role of dendritic cells in radiation-induced immune responses

Dendritic cells perform critical functions in bridging innate and adaptive immunity. Their ability to sense adjuvant signals in their environment, migrate on maturation, and cross-present cell-associated antigens enables these cells to carry antigen from tissue sites to lymph nodes, and thereby prime naïve T cells that cannot enter tissues. Despite being an infrequent cell type in tumors, we discuss how dendritic cells impact the immune environment of tumors and their response to cancer therapies. We review how radiation therapy of tumors can impact dendritic cells, through transfer of cell associated antigens to dendritic cells and the release of endogenous adjuvants, resulting in increased antigen presentation in the tumor-draining lymph nodes. We explore how tumor specific factors can result in negative regulation of dendritic cell function in the tumor, and the impact of direct radiation exposure to dendritic cells in the treatment field. These data suggest an important role for dendritic cell subpopulations in activating new T cell responses and boosting existing T cell responses to tumor associated antigens in tumor draining lymph nodes following radiation therapy. It further justifies a focus on the needs of the lymph node T cells to improve systemic anti-immunity following radiation therapy.

Companion diagnostic requirements for spatial biology using multiplex immunofluorescence and multispectral imaging

Immunohistochemistry has long been held as the gold standard for understanding the expression patterns of therapeutically relevant proteins to identify prognostic and predictive biomarkers. Patient selection for targeted therapy in oncology has successfully relied upon standard microscopy-based methodologies, such as single-marker brightfield chromogenic immunohistochemistry. As promising as these results are, the analysis of one protein, with few exceptions, no longer provides enough information to draw effective conclusions about the probability of treatment response. More multifaceted scientific queries have driven the development of high-throughput and high-order technologies to interrogate biomarker expression patterns and spatial interactions between cell phenotypes in the tumor microenvironment. Such multi-parameter data analysis has been historically reserved for technologies that lack the spatial context that is provided by immunohistochemistry. Over the past decade, technical developments in multiplex fluorescence immunohistochemistry and discoveries made with improving image data analysis platforms have highlighted the importance of spatial relationships between certain biomarkers in understanding a patient's likelihood to respond to, typically, immune checkpoint inhibitors. At the same time, personalized medicine has instigated changes in both clinical trial design and its conduct in a push to make drug development and cancer treatment more efficient, precise, and economical. Precision medicine in immuno-oncology is being steered by data-driven approaches to gain insight into the tumor and its dynamic interaction with the immune system. This is particularly necessary given the rapid growth in the number of trials involving more than one immune checkpoint drug, and/or using those in combination with conventional cancer treatments. As multiplex methods, like immunofluorescence, push the boundaries of immunohistochemistry, it becomes critical to understand the foundation of this technology and how it can be deployed for use as a regulated test to identify the prospect of response from mono- and combination therapies. To that end, this work will focus on: 1) the scientific, clinical, and economic requirements for developing clinical multiplex immunofluorescence assays; 2) the attributes of the Akoya Phenoptics workflow to support predictive tests, including design principles, verification, and validation needs; 3) regulatory, safety and quality considerations; 4) application of multiplex immunohistochemistry through lab-developed-tests and regulated in vitro diagnostic devices.

Multiparametric immune profiling of advanced cervical cancer to predict response to programmed death-1 inhibitor combination therapy: an exploratory study of the CLAP trial

PURPOSE

Checkpoint immunotherapy is a promising treatment option for advanced cervical cancer. To aid in selecting patients for this treatment, we identified potential predictors of the response to anti-PD-1 combination therapy.

METHODS

We simultaneously characterized CD8⁺, FoxP3⁺, PD-L1⁺, CD68⁺, CD31⁺, PANCK⁺, and PANCK^-PD-L1⁺ cells at the invasive margin (IM) of tumor by multispectral imaging of tissue sections from 37 patients with advanced cervical cancer in our previous trial cohort. The densities of each cell and cell-to-cell topography were compared between the responder and non-responder groups and evaluated for their predictive value in clinical response and survival.

RESULTS

CD8⁺ T cells, PD-L1⁺ cells, and PANCK^-PD-L1⁺ immune cells showed higher densities at the IM in the responders than in the non-responders (P = 0.022, 0.0094, and 0.049, respectively). A higher density of CD8⁺ T cells at the IM was related to prolonged progression-free survival (PFS; P = 0.031). A higher ratio of CD68⁺/CD8⁺ cells was found in the non-responder group (P = 0.003) and related to poor PFS (P = 0.016). A higher density of PANCK^-PD-L1⁺ immune cells within 20, 30, and 45 µm of PANCK⁺ tumor cells was correlated with better clinical response (P = 0.017, 0.017, and 0.02, respectively).

CONCLUSIONS

Multiparametric immune profiling of CD8⁺ T cells, PD-L1⁺ cells, CD68⁺ macrophages and PANCK^-PD-L1⁺ immune cells at the invasive margin may help identify patients with cervical cancer who may benefit from anti-PD-1 combination therapy.

CLINICAL TRIAL REGISTRATION

ClinicalTrials. gov identifier: NCT03816553, January 25, 2019.

High Expression of E2F4 Is an Adverse Prognostic Factor and Related to Immune Infiltration in Oral Squamous Cell Carcinoma

Background

We aimed to evaluate the prognostic value of E2F4 expression in oral squamous cell carcinoma (OSCC) and clarify its influence on immune cell infiltration and biological functions.

Methods

The Cancer Genome Atlas (TCGA) database, the STRING database, and related online tools as well as single-sample gene set enrichment analysis (ssGSEA) were used for the analyses in our study.

Results

The E2F4 expression was elevated in OSCC tumor tissue compared with paracancerous tissues. The high expression of E2F4 was closely related to the poorer overall survival, disease-free survival, and progression-free interval of OSCC. In addition, pathway enrichment analyses revealed that the top 49 genes most closely related to E2F4 were strongly associated with the cell cycle. E2F4-related proteins were closely related to the following KEGG pathways: cell cycle, cellular senescence, PI3K-Akt signaling pathway, Wnt signaling pathway, and notch signaling pathway. It was also demonstrated that the E2F4 expression was negatively correlated with immune purity and strongly related to immune cell infiltration in OSCC.

Conclusions

E2F4 can be used as a novel biomarker for the diagnosis and prognosis of OSCC.

How Risk Factors Affect Head and Neck Squamous Cell Carcinoma (HNSCC) Tumor Immune Microenvironment (TIME): Their Influence on Immune Escape Mechanisms and Immunotherapy Strategy

Most head and neck squamous cell carcinomas (HNSCCs) are caused by lifestyle, such as cigarette smoking, or by viruses, such as human papillomavirus (HPV) and Epstein–Barr virus (EBV). HNSCC remains a clinical challenge, notwithstanding the improvements observed in the past years, involving surgery, radiotherapy, and chemotherapy. Recurrent/metastatic (R/M) disease represents an unmet clinical need. Immunotherapy has improved the prognosis of a small proportion of these patients, but most still do not benefit. In the last decade, several preclinical and clinical studies have explored the HNSCC tumor immune microenvironment (TIME), identifying important differences between smoking-associated and virus-associated HNSCCs. This review aims to present how different etiologies affect the HNSCC TIME, affecting immune escape mechanisms and sensitivity to immunotherapy.

Prognostic value of FOXP3+ regulatory T cells for patients with locally advanced oropharyngeal squamous cell carcinoma

BACKGROUND

Oropharyngeal squamous cell carcinoma (OPSCC) is the most common neoplasm originating at the base of the tongue or in the tonsils or soft palate. In this study, we investigated the prognostic value of FOXP3+ regulatory T cells in OPSCC.

METHODS

Tumor tissues of patients with locally advanced OPSCC were analyzed using quantitative multiplex immunohistochemistry. Staining of CD8+ T cells, conventional CD4+FOXP3- T cells (Tconv cells), CD4+FOXP3+ regulatory T cells (Treg cells), CD20+ B cells, and CD68+ macrophages was performed, and cell density was evaluated in both the tumor and its stroma.

RESULTS

Among the 71 patients included in this study, males constituted 93.0% of the cohort, and the median age was 59 years (range: 42-80 years). A total of 56 patients (78.9%) had a smoking history, and 53 (74.6%) patients were positive for human papillomavirus (HPV). The most frequent site of OPSCC was the tonsils (70.4%), followed by the base of the tongue (25.4%). The proportion of Treg cells was lower in the tumors of patients with HPV than in those of patients without HPV. Patients with OPSCC whose tumor Treg cell levels were above the median had longer relapse-free survival (RFS) periods than those with tumor Treg cell levels below the median (HR, 0.12; 95% CI, 0.03-0.46; p = 0.02). Our multivariate analysis identified high Treg levels (HR, 0.13; 95% CI, 0.02-1.00; p = 0.05) as an RFS factor that predicted a good prognosis.

CONCLUSIONS

Our results demonstrated that high Treg cell density in locally advanced OPSCC tumors was correlated with longer RFS.

The PD-1/PD-L1 Pathway: A Perspective on Comparative Immuno-Oncology

Simple Summary

The programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) pathway inhibits the function of activated immune cells. This mediates immune tolerance and prevents immune-mediated tissue destruction. The malfunction of this pathway is involved in the pathogenesis of chronic infections, autoimmunity, and cancer. The PD-1/PD-L1 pathway is an excellent example of the research benefits of comparative pathology and attests to the importance of the “one health one medicine” concept. Pioneering research was mainly focused on the examination of cells and tissues of human and mouse origin. It mainly revealed that PD-L1-positive tumor cells can paralyze PD-1-bearing immune cells, which prevents immunological destruction of cancer cells. This led to a major breakthrough in cancer treatment, i.e., the use of antibodies that block the interaction of these molecules and restore anti-cancer immune defense (immune checkpoint therapy). Further studies provided more detailed information on the tissue-specific context and fine-tuning of this pathway. The most recent research has extended the investigations to the examination of several animal species with the aim of improving disease diagnostics and treatment for certain animal diseases, in particular cancer, which is a major cause of disease and death in companion animals.

Abstract

The programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) pathway mainly attracted attention in immuno-oncology, leading to the development of immune checkpoint therapy. It has, however, much broader importance for tissue physiology and pathology. It mediates basic processes of immune tolerance and tissue homeostasis. In addition, it is involved in the pathogenesis of chronic infectious diseases, autoimmunity, and cancer. It is also an important paradigm for comparative pathology as well as the “one health one medicine” concept. The aim of this review is to provide an overview of novel research into the diverse facets of the PD-1/PD-L1 pathway and to give insights into its fine-tuning homeostatic role in a tissue-specific context. This review details early translational research from the discovery phase based on mice as animal models for understanding pathophysiological aspects in human tissues to more recent research extending the investigations to several animal species. The latter has the twofold goal of comparing this pathway between humans and different animal species and translating diagnostic tools and treatment options established for the use in human beings to animals and vice versa.

CD8+ and FoxP3+ T-Cell Cellular Density and Spatial Distribution After Programmed Death-Ligand 1 Check Point Inhibition

OBJECTIVES

To analyze CD8+ and FoxP3+ T-cell cellular density (CD) and intercellular distances (ID) in head and neck squamous cell carcinoma (HNSCC) samples from a neoadjuvant trial of durvalumab +/- metformin.

METHODS

Paired pre- and post-treatment primary HNSCC tumor samples were stained for CD8+ and FoxP3+. Digital image analysis was used to determine estimated mean CD8+ and FoxP3+ CDs and CD8+-FoxP3+ IDs in the leading tumor edge (LTE) and tumor adjacent stroma (TAS) stratified by treatment arm, human papillomavirus (HPV) status, and pathologic treatment response. A subset of samples was characterized for T-cell related signatures using digital spatial genomic profiling.

RESULTS

Post-treatment analysis revealed a significant decrease in FoxP3+ CD and an increase in CD8+ CDs in the TAS between patients receiving durvalumab and metformin versus durvlaumab alone. Both treatment arms demonstrated significant post-treatment increases in ID. Although HPV+ and HPV- had similar immune cell CDs in the tumor microenvironment, HPV+ pre-treatment samples had 1.60 times greater ID compared with HPV- samples, trending toward significance (p = 0.05). At baseline, pathologic responders demonstrated a 1.16-fold greater CD8+ CDs in the LTE (p = 0.045) and 2.28-fold greater ID (p = 0.001) than non-responders. Digital spatial profiling revealed upregulation of FoxP3+ and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) in the TAS (p = 0.006, p = 0.026) in samples from pathologic responders.

CONCLUSIONS

Analysis of CD8+ and FoxP3+ detected population differences according to HPV status, pathologic response, and treatment. Greater CD8+-FoxP3+ ID was associated with pathologic response. CD8+ and FoxP3+ T-cell distributions may be predictive of response to immune checkpoint inhibition.

CLINICALTRIALS

gov (Identifier NCT03618654).

LEVEL OF EVIDENCE

Level 3 Laryngoscope, 2022.

Moving beyond the T cell synapse for combination neoadjuvant immunotherapy in head and neck cancer

Patients with HPV-unrelated head and neck squamous cell carcinoma (HPV-unrelated HNSCC) show only modest benefit from treatment with PD-1 inhibitors (PD-1i). Targeting transforming growth factor β (TGF-β) may make PD-1i more effective by inducing T cell responses. In this issue of the JCI, Redman et al. performed a clinical trial in 14 patients with HPV-unrelated HNSCC using bintrafusp alfa, a bifunctional fusion protein that blocks PD-L1 and TGF-β. Primary tumors displayed pathologic responses with 5 of 14 patients having at least a partial response. While no primary tumor or metastatic lymph node demonstrated a complete pathologic response, the findings suggest that concurrent neoadjuvant inhibition of PD-L1 and TGF-β may provide a rational strategy to improve pathologic response and clinical outcome in patients with HPV-unrelated HNSCC.

Hyperspectral three-dimensional absorption imaging using snapshot optical tomography

Hyperspectral imaging (HSI) records a series of two-dimensional (2D) images for different wavelengths to provide the chemical fingerprint at each pixel. Combining HSI with a tomographic data acquisition method, we can obtain the chemical fingerprint of a sample at each point in three-dimensional (3D) space. The so-called 3D HSI typically suffers from low imaging throughput due to the requirement of scanning the wavelength and rotating the beam or sample. In this paper we present an optical system which captures the entire four-dimensional (4D), i.e., 3D structure and 1D spectrum, dataset of a sample with the same throughput of conventional HSI systems. Our system works by combining snapshot projection optical tomography (SPOT) which collects multiple projection images with a single snapshot, and Fourier-transform spectroscopy (FTS) which results in superior spectral resolution by collecting and processing a series of interferogram images. Using this hyperspectral SPOT system we imaged the volumetric absorbance of dyed polystyrene microbeads, oxygenated red blood cells (RBCs), and deoxygenated RBCs. The 4D optical system demonstrated in this paper provides a tool for high-throughput chemical imaging of complex microscopic specimens.

Data-Rich Spatial Profiling of Cancer Tissue: Astronomy Informs Pathology

Astronomy was among the first disciplines to embrace Big Data and use it to characterize spatial relationships between stars and galaxies. Today, medicine, in particular pathology, has similar needs with regard to characterizing the spatial relationships between cells, with an emphasis on understanding the organization of the tumor microenvironment. In this article, we chronicle the emergence of data-intensive science through the development of the Sloan Digital Sky Survey and describe how analysis patterns and approaches similarly apply to multiplex immunofluorescence (mIF) pathology image exploration. The lessons learned from astronomy are detailed, and the new AstroPath platform that capitalizes on these learnings is described. AstroPath is being used to generate and display tumor-immune maps that can be used for mIF immuno-oncology biomarker development. The development of AstroPath as an open resource for visualizing and analyzing large-scale spatially resolved mIF datasets is underway, akin to how publicly available maps of the sky have been used by astronomers and citizen scientists alike. Associated technical, academic, and funding considerations, as well as extended future development for inclusion of spatial transcriptomics and application of artificial intelligence, are also addressed.

Development and therapeutic manipulation of the head and neck cancer tumor environment to improve clinical outcomes

The clinical response to cancer therapies involves the complex interplay between the systemic, tumoral, and stromal immune response as well as the direct impact of treatments on cancer cells. Each individual's immunological and cancer histories are different, and their carcinogen exposures may differ. This means that even though two patients with oral tumors may carry an identical mutation in TP53, they are likely to have different pre-existing immune responses to their tumors. These differences may arise due to their distinct accessory mutations, genetic backgrounds, and may relate to clinical factors including previous chemotherapy exposure and concurrent medical comorbidities. In isolation, their cancer cells may respond similarly to cancer therapy, but due to their baseline variability in pre-existing immune responses, patients can have different responses to identical therapies. In this review we discuss how the immune environment of tumors develops, the critical immune cell populations in advanced cancers, and how immune interventions can manipulate the immune environment of patients with pre-malignancies or advanced cancers to improve therapeutic outcomes.

Immunohistochemistry scoring of breast tumor tissue microarrays: A comparison study across three software applications

Digital pathology can efficiently assess immunohistochemistry (IHC) data on tissue microarrays (TMAs). Yet, it remains important to evaluate the comparability of the data acquired by different software applications and validate it against pathologist manual interpretation. In this study, we compared the IHC quantification of 5 clinical breast cancer biomarkers-estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), and cytokeratin 5/6 (CK5/6)-across 3 software applications (Definiens Tissue Studio, inForm, and QuPath) and benchmarked the results to pathologist manual scores. IHC expression for each marker was evaluated across 4 TMAs consisting of 935 breast tumor tissue cores from 367 women within the Nurses' Health Studies; each women contributing three 0.6-mm cores. The correlation and agreement between manual and software-derived results were primarily assessed using Spearman's ρ, percentage agreement, and area under the curve (AUC). At the TMA core-level, the correlations between manual and software-derived scores were the highest for HER2 (ρ ranging from 0.75 to 0.79), followed by ER (0.69-0.71), PR (0.67-0.72), CK5/6 (0.43-0.47), and EGFR (0.38-0.45). At the case-level, there were good correlations between manual and software-derived scores for all 5 markers (ρ ranging from 0.43 to 0.82), where QuPath had the highest correlations. Software-derived scores were highly comparable to each other (ρ ranging from 0.80 to 0.99). The average percentage agreements between manual and software-derived scores were excellent for ER (90.8%-94.5%) and PR (78.2%-85.2%), moderate for HER2 (65.4%-77.0%), highly variable for EGFR (48.2%-82.8%), and poor for CK5/6 (22.4%-45.0%). All AUCs across markers and software applications were ≥0.83. The 3 software applications were highly comparable to each other and to manual scores in quantifying these 5 markers. QuPath consistently produced the best performance, indicating this open-source software is an excellent alternative for future use.

Modulation of Lymphocyte Functions in the Microenvironment by Tumor Oncogenic Pathways

Despite the broad application of different immunotherapeutic strategies for the treatment of solid as well as hematopoietic cancers, the efficacy of these therapies is still limited, with only a minority of patients having a long-term benefit resulting in an improved survival rate. In order to increase the response rates of patients to the currently available immunotherapies, a better understanding of the molecular mechanisms underlying the intrinsic and/or extrinsic resistance to treatment is required. There exist increasing evidences that activation of different oncogenic pathways as well as inactivation of tumor suppressor genes (TSG) in tumor cells inhibit the immune cell recognition and influegnce the composition of the tumor microenvironment (TME), thus leading to an impaired anti-tumoral immune response. A deeper understanding of the link between the tumor milieu and genomic alterations of TSGs and oncogenes is indispensable for the optimization of immunotherapies and to predict the patients’ response to these treatments. This review summarizes the role of different cancer-related, oncogene- and TSG-controlled pathways in the context of anti-tumoral immunity and response to different immunotherapies.

Impact of HPV status on immune responses in head and neck squamous cell carcinoma

The main objective of our study was to understand the impact of immune cell composition and the tumor-reactivity of tumor infiltrating lymphocytes (TIL) in HPV-positive (HPV⁺) and HPV-negative (HPV^-) head and neck squamous cell carcinoma (HNSCC). TIL cultures were established from primary HNSCC tumors, the T cell subsets were phenotypically characterized using flow cytometry, and Interferon (IFN)-γ ELISA assay was used to determine TIL function. NanoString Immune Profiler was used to determine an immune signature by HPV-status, and multiplex immunohistochemistry (MIHC) was used to quantify immune cell distributions and their spatial relationships. Results showed that HPV⁺ and HPV^- HNSCC had similar capacity to expand IFN-γ reactive TIL populations, and these TIL populations had similar characteristics. NanoString analysis revealed increased differential expression of genes related to B cell functions in HPV⁺ HNSCC, which were significant at a Benjamini-Yekutieli adjusted p-value of < 0.001. MIHC also displayed increased CD8⁺ T cell and CD19/CD20⁺ B cell densities in the tumor region of HPV⁺ HNSCC as opposed to HPV^- HNSCC (p < 0.01). Increases in a combined metric of tumor B cell content and stromal plasma cell content was associated with increased progression-free survival in HPV^- HNSCC patients treated with immune checkpoint inhibitor therapy (p = 0.03). In summary, TIL populations expanded from HPV⁺ and HPV^- HNSCC displayed similar IFN-γ reactivity. However, we identified a strong B-cell signature present within HPV⁺ HNSCC, and higher B and plasma cell content associated with improved PFS in HPV^- HNSCC patients treated with immune checkpoint inhibitors.

Heterogeneity of programmed death-ligand 1 expression and infiltrating lymphocytes in paired resected primary and metastatic non-small cell lung cancer

Metastatic tumors (MTs) may show different characteristics of the immune microenvironment from primary tumors (PTs) in non-small cell lung cancer (NSCLC). The heterogeneity of immune markers in metastatic NSCLC and its associated factors has not been well demonstrated. In this study, 64 surgically resected specimens of paired PTs and MTs were obtained from 28 patients with NSCLC. Multiplex immunofluorescence (mIF; panel including programmed death-ligand 1 (PD-L1), Cytokeratin, CD8, and CD68) was performed on whole sections. The heterogeneity of the immune contexture of PD-L1 expression, infiltrating lymphocytes, and immune-to-tumor cell distances was quantified via digital image analysis. In a quantitative comparison of MTs and corresponding PTs, MTs showed higher PD-L1 expression levels, lower density of CD8+ cytotoxic T lymphocytes (CTLs), and longer spatial distance between CTLs and tumor cells. Subgroup analysis, which associated clinical factors, revealed that the heterogeneity of immune markers was more obvious in extrapulmonary, metachronous, and treated MTs, while fewer differences were observed in intrapulmonary, synchronous, and untreated MTs. In particular, MTs showed significantly higher PD-L1 expression and lower lymphocyte infiltration in metastatic NSCLC with EGFR mutations. Prognosis analysis showed that an increased density of CD8+ CTLs in MTs was associated with better overall survival (OS). Therefore, significant discrepancies in PD-L1 expression and lymphocyte infiltration in metastatic NSCLC are most likely associated with temporal heterogeneity with a history of anti-treatment and correlated with EGFR mutations. The detection of immune markers in re-obtained metastatic specimens may be required for immunotherapy prediction in these patients with metastatic NSCLC.

Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data

Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.

Spatial UMAP and Image Cytometry for Topographic Immuno-oncology Biomarker Discovery

Multiplex immunofluorescence (mIF) can detail spatial relationships and complex cell phenotypes in the tumor microenvironment (TME). However, the analysis and visualization of mIF data can be complex and time-consuming. Here, we used tumor specimens from 93 patients with metastatic melanoma to develop and validate a mIF data analysis pipeline using established flow cytometry workflows (image cytometry). Unlike flow cytometry, spatial information from the TME was conserved at single-cell resolution. A spatial uniform manifold approximation and projection (UMAP) was constructed using the image cytometry output. Spatial UMAP subtraction analysis (survivors vs. nonsurvivors at 5 years) was used to identify topographic and coexpression signatures with positive or negative prognostic impact. Cell densities and proportions identified by image cytometry showed strong correlations when compared with those obtained using gold-standard, digital pathology software (R2 > 0.8). The associated spatial UMAP highlighted "immune neighborhoods" and associated topographic immunoactive protein expression patterns. We found that PD-L1 and PD-1 expression intensity was spatially encoded-the highest PD-L1 expression intensity was observed on CD163+ cells in neighborhoods with high CD8+ cell density, and the highest PD-1 expression intensity was observed on CD8+ cells in neighborhoods with dense arrangements of tumor cells. Spatial UMAP subtraction analysis revealed numerous spatial clusters associated with clinical outcome. The variables represented in the key clusters from the unsupervised UMAP analysis were validated using established, supervised approaches. In conclusion, image cytometry and the spatial UMAPs presented herein are powerful tools for the visualization and interpretation of single-cell, spatially resolved mIF data and associated topographic biomarker development.

A nomogram-based immunoprofile predicts clinical outcomes for stage II and III human colorectal cancer

An immunoscore for colorectal cancer (CRC) has higher prognostic significance than the TNM staging system. However, the tumor immune microenvironment contains various components that affect clinical prognosis. Therefore, a broader range of immune markers is required to establish an accurate immunoprofile to assess the prognosis of patients with CRC. Using immunohistochemistry combined with multispectral immunohistochemistry and objective assessments, the infiltration of four immune cell types (CD4⁺/CD8⁺/forkhead box p3⁺/CD33⁺ cells), as well as the expression of six co-signaling molecules [programmed cell death 1 (PD1) ligand 1/PD1/T-cell immunoglobulin mucin family member 3/lymphocyte-activating 3/tumor necrosis factor receptor superfamily, member 4/inducible T-cell costimulator] and indoleamine 2,3-dioxygenase 1 were investigated in two independent cohorts of CRC. The patients' overall survival (OS) was evaluated using the Kaplan-Meier method. Using the Cox proportional hazards model, independent prognostic factors of patients were assessed and a nomogram-based immunoprofile system was developed. The predictive ability of the nomogram was determined using a concordance index (C-index) and calibration curve. To facilitate clinical application, a simplified nomogram-based immunoprofile was constructed. Using receiver operating characteristic (ROC) analysis, the predictive accuracy for OS was compared between the immunoprofile and the TNM staging system for patients with stage II/III CRC. According to multivariate analysis for the primary cohort, independent prognostic factors for OS were CD8⁺ tumor-infiltrating lymphocytes, CD33⁺ myeloid-derived suppressor cells and TNM stage, which were included in the nomogram. The C-index of the nomogram for predicting OS was 0.861 (95% CI: 0.796-0.925) for the internal validation and 0.759 (95% CI: 0.714-0.804) for the external validation cohort. The simplified nomogram-based immunoprofile system was able to separate same-stage patients into different risk subgroups, particularly for TNM stage II (P<0.0001) and III (P=0.0002) patients. Pairwise comparison of ROC curves for the immunoprofile and TNM stage systems for patients with stage II/III CRC revealed statistically significant differences (P=0.046) and the Z-statistic value was 1.995. In conclusion, the nomogram-based immunoprofile system provides prognostic accuracy regarding clinical outcomes and is a useful supplement to the TNM staging system for patients with stage II/III CRC.

Human tissue cultures of lung cancer predict patient susceptibility to immune-checkpoint inhibition

Despite novel immunotherapies being approved and established for the treatment of non-small cell lung cancer (NSCLC), ex vivo models predicting individual patients' responses to immunotherapies are missing. Especially immune modulating therapies with moderate response rates urge for biomarkers and/or assays to determine individual prediction of treatment response and investigate resistance mechanisms. Here, we describe a standardized ex vivo tissue culture model to investigate individual tumor responses. NSCLC tissue cultures preserve morphological characteristics of the baseline tumor specimen for up to 12 days ex vivo and also maintain T-cell function for up to 10 days ex vivo. A semi-automated analysis of proliferating and apoptotic tumor cells was used to evaluate tissue responses to the PD-1 inhibitor nivolumab (n = 12), from which two cases could be successfully correlated to the clinical outcome. T-cell responses upon nivolumab treatment were investigated by flow cytometry and multispectral imaging. Alterations in the frequency of the Treg population and reorganization of tumor tissues could be correlated to nivolumab responsiveness ex vivo. Thus, our findings not only demonstrate the functionality of T cells in NSCLC slice cultures up to 10 days ex vivo, but also suggests this model for stratifying patients for treatment selection and to investigate in depth the tumor-associated T-cell regulation.

High PD-L1 Expression on Tumor Cells Indicates Worse Overall Survival in Advanced Oral Squamous Cell Carcinomas of the Tongue and the Floor of the Mouth but Not in Other Oral Compartments

The markers of the tumor microenvironment (TME) are promising prognostic and predictive factors in oral squamous cell carcinoma (OSCC). The current study aims to analyze the immunohistochemical expression of programmed cell death-ligand 1 (PD-L1) and interleukin-33 (IL-33) in a cohort of 95 chemonaïve OSCCs. PD-L1 and IL-33 were assessed separately in tumor cells (TCs) and tumor-infiltrating lymphocytes (TILs). High PD-L1 expression in TILs was associated with better overall survival (OS) in univariate analysis. Tumors localized in the floor of the oral cavity and tongue tended to have a lower percentage of PD-L1-positive TCs when compared to other locations. PD-L1 expression on TCs had no prognostic significance when the whole cohort was analyzed. However, along with the T descriptor (TNM 8th), it was included in the multivariable model predicting death in carcinomas of the floor of the oral cavity and tongue (HR = 2.51, 95% CI = 1.97-5.28). In other locations, only nodal status was identified as an independent prognostic factor in multivariate analysis (HR = 0.24, 95% CI = 0.08-0.70). Expression of IL-33 had no impact on survival, but it was differently expressed in various locations. In conclusion, the prognostic significance of PD-L1 in oral cancer depends on the tumor site and type of cell expressing immune checkpoint receptor (TCs vs. TILs).

Multi-institutional TSA-amplified Multiplexed Immunofluorescence Reproducibility Evaluation (MITRE) Study

Background

Emerging data suggest predictive biomarkers based on the spatial arrangement of cells or coexpression patterns in tissue sections will play an important role in precision immuno-oncology. Multiplexed immunofluorescence (mIF) is ideally suited to such assessments. Standardization and validation of an end-to-end workflow that supports multisite trials and clinical laboratory processes are vital. Six institutions collaborated to: (1) optimize an automated six-plex assay focused on the PD-1/PD-L1 axis, (2) assess intersite and intrasite reproducibility of staining using a locked down image analysis algorithm to measure tumor cell and immune cell (IC) subset densities, %PD-L1 expression on tumor cells (TCs) and ICs, and PD-1/PD-L1 proximity assessments.

Methods

A six-plex mIF panel (PD-L1, PD-1, CD8, CD68, FOXP3, and CK) was rigorously optimized as determined by quantitative equivalence to immunohistochemistry (IHC) chromogenic assays. Serial sections from tonsil and breast carcinoma and non-small cell lung cancer (NSCLC) tissue microarrays (TMAs), TSA-Opal fluorescent detection reagents, and antibodies were distributed to the six sites equipped with a Leica Bond Rx autostainer and a Vectra Polaris multispectral imaging platform. Tissue sections were stained and imaged at each site and delivered to a single site for analysis. Intersite and intrasite reproducibility were assessed by linear fits to plots of cell densities, including %PDL1 expression by TCs and ICs in the breast and NSCLC TMAs.

Results

Comparison of the percent positive cells for each marker between mIF and IHC revealed that enhanced amplification in the mIF assay was required to detect low-level expression of PD-1, PD-L1, FoxP3 and CD68. Following optimization, an average equivalence of 90% was achieved between mIF and IHC across all six assay markers. Intersite and intrasite cell density assessments showed an average concordance of R²=0.75 (slope=0.92) and R²=0.88 (slope=0.93) for breast carcinoma, respectively, and an average concordance of R²=0.72 (slope=0.86) and R²=0.81 (slope=0.68) for NSCLC. Intersite concordance for %PD-L1+ICs had an average R² value of 0.88 and slope of 0.92. Assessments of PD-1/PD-L1 proximity also showed strong concordance (R²=0.82; slope=0.75).

Conclusions

Assay optimization yielded highly sensitive, reproducible mIF characterization of the PD-1/PD-L1 axis across multiple sites. High concordance was observed across sites for measures of density of specific IC subsets, measures of coexpression and proximity with single-cell resolution.

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

Next-generation tissue-based biomarkers for immunotherapy will likely include the simultaneous analysis of multiple cell types and their spatial interactions, as well as distinct expression patterns of immunoregulatory molecules. Here, we introduce a comprehensive platform for multispectral imaging and mapping of multiple parameters in tumor tissue sections with high-fidelity single-cell resolution. Image analysis and data handling components were drawn from the field of astronomy. Using this "AstroPath" whole-slide platform and only six markers, we identified key features in pretreatment melanoma specimens that predicted response to anti-programmed cell death-1 (PD-1)-based therapy, including CD163⁺PD-L1^- myeloid cells and CD8⁺FoxP3⁺PD-1^low/mid T cells. These features were combined to stratify long-term survival after anti-PD-1 blockade. This signature was validated in an independent cohort of patients with melanoma from a different institution.

Explant Modeling of the Immune Environment of Head and Neck Cancer

Patients exhibit distinct responses to immunotherapies that are thought to be linked to their tumor immune environment. However, wide variations in outcomes are also observed in patients with matched baseline tumor environments, indicating that the biological response to treatment is not currently predictable using a snapshot analysis. To investigate the relationship between the immune environment of tumors and the biological response to immunotherapies, we characterized four murine head and neck squamous cell carcinoma (HNSCC) models on two genetic backgrounds. Using tumor explants from those models, we identified correlations between the composition of infiltrating immune cells and baseline cytokine profiles prior to treatment. Following treatment with PD-1 blockade, CTLA-4 blockade, or OX40 stimulation, we observed inter-individual variability in the response to therapy between genetically identical animals bearing the same tumor. These distinct biological responses to treatment were not linked to the initial tumor immune environment, meaning that outcome would not be predictable from a baseline analysis of the tumor infiltrates. We similarly performed the explant assay on patient HNSCC tumors and found significant variability between the baseline environment of the tumors and their response to therapy. We propose that tumor explants provide a rapid biological assay to assess response to candidate immunotherapies that may allow matching therapies to individual patient tumors. Further development of explant approaches may allow screening and monitoring of treatment responses in HNSCC.

Immune landscape and subtypes in primary resectable oral squamous cell carcinoma: prognostic significance and predictive of therapeutic response

BACKGROUND

Immune landscape of cancer has been increasingly recognized as a key feature affecting disease progression, prognosis and therapeutic response. Here, we sought to comprehensively characterize the patterns of tumor-infiltrating immune cells (TIIs) in primary oral squamous cell carcinoma (OSCC) and develop immune features-derived models for prognostication and therapeutic prediction.

METHODS

A total number of 392 patients with OSCC receiving ablative surgery at three independent centers were retrospectively enrolled and defined as training, testing and validation cohorts. Detailed features of 12 types of TIIs at center of tumor and invasive margin were assessed by immunohistochemistry coupled with digital quantification. TIIs abundance in OSCC was also estimated by bioinformatics approaches using multiple publicly available data sets. Prognostic models based on selected immune features were trained via machine learning approach, validated in independent cohorts and evaluated by time-dependent area under the curves and concordance index (C-index). Immune types of OSCC were further identified by consensus clustering and their associations with genetic, molecular features and patient survival were clarified.

RESULTS

Patterns of TIIs infiltration varied among patients and dynamically evolved along with tumor progression. Prognostic models based on selected TIIs were identified as efficient and sensitive biomarkers to stratify patients into subgroups with favorable or inferior survival as well as responders or non-responders to postoperative radiotherapy or immunotherapy. These models outperformed multiple conventional biomarkers and immune-related scores in prognostic prediction. Furthermore, we identified two main immune subtypes of OSCC (immune-hot and immune-cold) which harbored characteristic TIIs infiltrations and genomic and molecular features, and associated with patient survival.

CONCLUSIONS

Our results delineated immune landscape and subtypes in OSCC, consolidated their clinical values as robust biomarkers to predict patient survival and therapeutic benefits and reinforced key roles of TIIs and tumor-immune interactions underlying oral tumorigenesis, ultimately facilitating development of tailed immunotherapeutic strategies.

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

Next-generation tissue-based biomarkers for immunotherapy will likely include the simultaneous analysis of multiple cell types and their spatial interactions, as well as distinct expression patterns of immunoregulatory molecules. Here, we introduce a comprehensive platform for multispectral imaging and mapping of multiple parameters in tumor tissue sections with high-fidelity single-cell resolution. Image analysis and data handling components were drawn from the field of astronomy. Using this "AstroPath" whole-slide platform and only six markers, we identified key features in pretreatment melanoma specimens that predicted response to anti-programmed cell death-1 (PD-1)-based therapy, including CD163⁺PD-L1^- myeloid cells and CD8⁺FoxP3⁺PD-1^low/mid T cells. These features were combined to stratify long-term survival after anti-PD-1 blockade. This signature was validated in an independent cohort of patients with melanoma from a different institution.