Hyperspectral cell sociology reveals spatial tumor-immune cell interactions associated with lung cancer recurrence

Dysfunction of CD8+ T cells around tumor cells leads to occult lymph node metastasis in NSCLC patients

Occult lymph node metastasis (OLNM) is one of the main causes of regional recurrence in inoperable N0 non-small cell lung cancer (NSCLC) patients following stereotactic ablation body radiotherapy (SABR) treatment. The integration of immunotherapy and SABR (I-SABR) has shown preliminary efficiency in mitigating this recurrence. Therefore, it is necessary to explore the functional dynamics of critical immune effectors, particularly CD8+ T cells in the development of OLNM. In this study, tissue microarrays (TMAs) and multiplex immunofluorescence (mIF) were used to identify CD8+ T cells and functional subsets (cytotoxic CD8+ T cells/predysfunctional CD8+ T cells (CD8+ Tpredys)/dysfunctional CD8+ T cells (CD8+ Tdys)/other CD8+ T cells) among the no lymph node metastasis, OLNM, and clinically evident lymph node metastasis (CLNM) groups. As the degree of lymph node metastasis escalated, the density of total CD8+ T cells and CD8+ Tdys cells, as well as their proximity to tumor cells, increased progressively and remarkably in the invasive margin (IM). In the tumor center (TC), both the density and proximity of CD8+ Tpredys cells to tumor cells notably decreased in the OLNM group compared with the group without metastasis. Furthermore, positive correlations were found between the dysfunction of CD8+ T cells and HIF-1α+CD8 and cancer microvessels (CMVs). In conclusion, the deterioration in CD8+ T cell function and interactive dynamics between CD8+ T cells and tumor cells play a vital role in the development of OLNM in NSCLC. Strategies aimed at improving hypoxia or targeting CMVs could potentially enhance the efficacy of I-SABR.

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

Leveraging spatial omics for the development of precision sarcoma treatments

Sarcomas are rare and heterogeneous cancers that arise from bone or soft tissue, and are the second most prevalent solid cancer in children and adolescents. Owing to the complex nature of pediatric sarcomas, the development of therapeutics for pediatric sarcoma has seen little progress in the past decades. Existing treatments are largely limited to chemotherapy, radiation, and surgery. Limited knowledge of the sarcoma tumor microenvironment (TME) and of well-defined target antigens in the different subtypes necessitates an alternative investigative approach to improve treatments. Recent advances in spatial omics technologies have enabled a more comprehensive study of the TME in multiple cancers. In this opinion article we discuss advances in our understanding of the TME of some cancers enabled by spatial omics technologies, and we explore how these technologies might advance the development of precision treatments for sarcoma, especially pediatric sarcoma.

Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer

Precision oncology approaches for patients with colorectal cancer (CRC) continue to lag behind other solid cancers. Functional precision oncology-a strategy that is based on perturbing primary tumor cells from cancer patients-could provide a road forward to personalize treatment. We extend this paradigm to measuring proteome activity landscapes by acquiring quantitative phosphoproteomic data from patient-derived organoids (PDOs). We show that kinase inhibitors induce inhibitor- and patient-specific off-target effects and pathway crosstalk. Reconstruction of the kinase networks revealed that the signaling rewiring is modestly affected by mutations. We show non-genetic heterogeneity of the PDOs and upregulation of stemness and differentiation genes by kinase inhibitors. Using imaging mass-cytometry-based profiling of the primary tumors, we characterize the tumor microenvironment (TME) and determine spatial heterocellular crosstalk and tumor-immune cell interactions. Collectively, we provide a framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno-) oncology in CRC.

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer

Tumor molecular datasets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning to analyze a single-cell, spatial, and highly multiplexed proteomic dataset from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcome. A novel multiplex immunohistochemistry antibody panel was used to audit T cell functionality and spatial localization in resected tumors from treatment-naive patients with localized pancreatic ductal adenocarcinoma (PDAC) compared to a second cohort of patients treated with neoadjuvant agonistic CD40 (αCD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both treatment cohorts were assayed, and more than 1,000 tumor microenvironment (TME) features were quantified. We then trained machine learning models to accurately predict αCD40 treatment status and disease-free survival (DFS) following αCD40 therapy based upon TME features. Through downstream interpretation of the machine learning models' predictions, we found αCD40 therapy to reduce canonical aspects of T cell exhaustion within the TME, as compared to treatment-naive TMEs. Using automated clustering approaches, we found improved DFS following αCD40 therapy to correlate with the increased presence of CD44+ CD4+ Th1 cells located specifically within cellular spatial neighborhoods characterized by increased T cell proliferation, antigen-experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of machine learning in molecular cancer immunology applications, highlight the impact of αCD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for αCD40-treated patients with PDAC.

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.​.

Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model

Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon's entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as "cold", "compartmentalized" and "mixed", which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes.

Functional status and spatial architecture of tumor-infiltrating CD8+ T cells are associated with lymph node metastases in non-small cell lung cancer

BACKGROUND

Anti-PD-(L)1 immunotherapy has been recommended for non-small cell lung cancer (NSCLC) patients with lymph node metastases (LNM). However, the exact functional feature and spatial architecture of tumor-infiltrating CD8 + T cells remain unclear in these patients.

METHODS

Tissue microarrays (TMAs) from 279 IA-IIIB NSCLC samples were stained by multiplex immunofluorescence (mIF) for 11 markers (CD8, CD103, PD-1, Tim3, GZMB, CD4, Foxp3, CD31, αSMA, Hif-1α, pan-CK). We evaluated the density of CD8 + T-cell functional subsets, the mean nearest neighbor distance (mNND) between CD8 + T cells and neighboring cells, and the cancer-cell proximity score (CCPS) in invasive margin (IM) as well as tumor center (TC) to investigate their relationships with LNM and prognosis.

RESULTS

The densities of CD8 + T-cell functional subsets, including predysfunctional CD8 + T cells (T_predys) and dysfunctional CD8 + T cells (T_dys), in IM predominated over those in TC (P < 0.001). Multivariate analysis identified that the densities of CD8 + T_predys cells in TC and CD8 + T_dys cells in IM were significantly associated with LNM [OR = 0.51, 95%CI (0.29-0.88), P = 0.015; OR = 5.80, 95%CI (3.19-10.54), P < 0.001; respectively] and recurrence-free survival (RFS) [HR = 0.55, 95%CI (0.34-0.89), P = 0.014; HR = 2.49, 95%CI (1.60-4.13), P = 0.012; respectively], independent of clinicopathological factors. Additionally, shorter mNND between CD8 + T cells and their neighboring immunoregulatory cells indicated a stronger interplay network in the microenvironment of NSCLC patients with LNM and was associated with worse prognosis. Furthermore, analysis of CCPS suggested that cancer microvessels (CMVs) and cancer-associated fibroblasts (CAFs) selectively hindered CD8 + T cells from contacting with cancer cells, and were associated with the dysfunction of CD8 + T cells.

CONCLUSION

Tumor-infiltrating CD8 + T cells were in a more dysfunctional status and in a more immunosuppressive microenvironment in patients with LNM compared with those without LNM.

Single-cell spatial landscapes of the lung tumour immune microenvironment

Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution^1-9. Most clinical strategies rely on histopathological stratification of tumour subtypes, yet the spatial context of single-cell phenotypes within these stratified subgroups is poorly understood. Here we apply imaging mass cytometry to characterize the tumour and immunological landscape of samples from 416 patients with lung adenocarcinoma across five histological patterns. We resolve more than 1.6 million cells, enabling spatial analysis of immune lineages and activation states with distinct clinical correlates, including survival. Using deep learning, we can predict with high accuracy those patients who will progress after surgery using a single 1-mm² tumour core, which could be informative for clinical management following surgical resection. Our dataset represents a valuable resource for the non-small cell lung cancer research community and exemplifies the utility of spatial resolution within single-cell analyses. This study also highlights how artificial intelligence can improve our understanding of microenvironmental features that underlie cancer progression and may influence future clinical practice.

Multiplex immunofluorescence and single-cell transcriptomic profiling reveal the spatial cell interaction networks in the non-small cell lung cancer microenvironment

BACKGROUND

Conventional immunohistochemistry technologies were limited by the inability to simultaneously detect multiple markers and the lack of identifying spatial relationships among cells, hindering understanding of the biological processes in cancer immunology.

METHODS

Tissue slices of primary tumours from 553 IA∼IIIB non-small cell lung cancer (NSCLC) cases were stained by multiplex immunofluorescence (mIF) assay for 10 markers, including CD4, CD38, CD20, FOXP3, CD66b, CD8, CD68, PD-L1, CD133 and CD163, evaluating the amounts of 26 phenotypes of cells in tumour nest and tumour stroma. StarDist depth learning model was utilised to determine the spatial location of cells based on mIF graphs. Single-cell RNA sequencing (scRNA-seq) on four primary NSCLC cases was conducted to investigate the putative cell interaction networks.

RESULTS

Spatial proximity among CD20+ B cells, CD4+ T cells and CD38+ T cells (r2  = 0.41) was observed, whereas the distribution of regulatory T cells was associated with decreased infiltration levels of CD20+ B cells and CD38+ T cells (r2  = -0.45). Univariate Cox analyses identified closer proximity between CD8+ T cells predicted longer disease-free survival (DFS). In contrast, closer proximity between CD133+ cancer stem cells (CSCs), longer distances between CD4+ T cells and CD20+ B cells, CD4+ T cells and neutrophils, and CD20+ B cells and neutrophils were correlated with dismal DFS. Data from scRNA-seq further showed that spatially adjacent N1-like neutrophils could boost the proliferation and activation of T and B lymphocytes, whereas spatially neighbouring M2-like macrophages showed negative effects. An immune-related risk score (IRRS) system aggregating robust quantitative and spatial prognosticators showed that high-IRRS patients had significantly worse DFS than low-IRRS ones (HR 2.72, 95% CI 1.87-3.94, p < .001).

CONCLUSIONS

We developed a framework to analyse the cell interaction networks in tumour microenvironment, revealing the spatial architecture and intricate interplays between immune and tumour cells.

Tumor Immune Microenvironment and Immunotherapy in Non-Small Cell Lung Cancer: Update and New Challenges

Non-small cell lung cancer (NSCLC) is a serious threat to the health of older adults. Despite the significant progress in immunotherapy, effective treatments for NSCLC remain limited. The development of tumors indicates failure in immune surveillance and the successful immune escape of tumor cells. Research on the tumor immune microenvironment (TIME) revealed these opposing immune processes and contributed to the discovery of new methods to suppress the immune escape and restore the immune surveillance functions. This paper aimed to provide updates on the current findings regarding the relevance of TIME in NSCLC treatment. It also aimed to introduce the TIME, immune editing, cancer immunotherapy, and new challenges. Based on the clinical data, the combination of neoadjuvant chemotherapy and immune checkpoint inhibitor (ICI) therapy is suitable for patients with NSCLC who are not eligible to undergo surgery. Combined ICI therapy after epidermal growth factor receptor (EGFR)/tyrosine kinase inhibitor (TKI) therapy should be considered in patients with EGFR mutations. Chemoradiotherapy may increase the density of CD8⁺ lymphocytes, which is significantly associated with better prognosis. For older patients and those with advanced-stage disease, regional tumor treatments, such as stereotactic radiation therapy and percutaneous cryoablation, may be more suitable, but further studies are needed to confirm this. In conclusion, restoring immune surveillance is as important as removing cancerous tissues; further studies that include the use of combined treatment methods, individualized treatment plans, and immunonutrition are warranted.

Spatially variant immune infiltration scoring in human cancer tissues

The Immunoscore is a method to quantify the immune cell infiltration within cancers to predict the disease prognosis. Previous immune profiling approaches relied on limited immune markers to establish patients’ tumor immunity. However, immune cells exhibit a higher-level complexity that is typically not obtained by the conventional immunohistochemistry methods. Herein, we present a spatially variant immune infiltration score, termed as SpatialVizScore, to quantify immune cells infiltration within lung tumor samples using multiplex protein imaging data. Imaging mass cytometry (IMC) was used to target 26 markers in tumors to identify stromal, immune, and cancer cell states within 26 human tissues from lung cancer patients. Unsupervised clustering methods dissected the spatial infiltration of cells in tissue using the high-dimensional analysis of 16 immune markers and other cancer and stroma enriched labels to profile alterations in the tumors’ immune infiltration patterns. Spatially resolved maps of distinct tumors determined the spatial proximity and neighborhoods of immune-cancer cell pairs. These SpatialVizScore maps provided a ranking of patients’ tumors consisting of immune inflamed, immune suppressed, and immune cold states, demonstrating the tumor’s immune continuum assigned to three distinct infiltration score ranges. Several inflammatory and suppressive immune markers were used to establish the cell-based scoring schemes at the single-cell and pixel-level, depicting the cellular spectra in diverse lung tissues. Thus, SpatialVizScore is an emerging quantitative method to deeply study tumor immunology in cancer tissues.

Spatial distribution and functional analysis define the action pathway of Tim-3/Tim-3 ligands in tumor development

The spatial organization of immune cells within the tumor microenvironment (TME) largely determines the anti-tumor immunity and also highly predicts tumor progression and therapeutic response. Tim-3 is a well-accepted immune checkpoint and plays multifaceted immunoregulatory roles via interaction with distinct Tim-3 ligands (Tim-3L), showing great potential as an immunotherapy target. However, the cell sociology mediated by Tim-3/Tim-3L and their contribution to tumor development remains elusive. Here, we analyzed the spatial distribution of Tim-3/Tim-3L in TME using multiplex fluorescence staining and revealed that despite the increased Tim-3 expression in various tumor-infiltrated lymphocytes, Tim-3⁺CD4⁺ cells were more accumulated in parenchymal/tumor region compared with stromal region and exhibited more close association with patient survival. Strikingly, CD4 T cells surrounding Tim-3L⁺ cells expressed higher Tim-3 than other cells in cancerous tissues. In vivo studies confirmed that depletion of CD4 T cells completely abrogated the inhibition of tumor growth and metastasis, as well as the functional improvement of CD8 T and NK, mediated by Tim-3 blockade, which was further validated in peripheral lymphocytes from patients with hepatocellular carcinoma. In conclusion, our findings unravel the importance of CD4 T cells in Tim-3/Tim-3L-mediated immunosuppression and provide new thoughts for Tim-3 targeted cancer immunotherapy.

A Novel Signature of Lipid Metabolism-Related Gene Predicts Prognosis and Response to Immunotherapy in Lung Adenocarcinoma

Background: Lipid metabolism disorder, a new hallmark of cancer initiation, has been involved in lung adenocarcinoma (LUAD). However, few biomarkers about lipid metabolism-related genes (LMRGs) have been developed for prognosis prediction and clinical treatment of LUAD patients.

Methods: In this study, we constructed and validated an effective prognostic prediction model for LUAD patients depending on LMRGs. Subsequently, we investigated the prediction model from immune microenvironment, genomic changes, and immunotherapy.

Results: Then, eleven LMRGs were identified and applied to LUAD subtyping. In comparison with the high-risk group, the low-risk group exhibited a remarkably favorable prognosis, along with a higher immune score and lower tumor purity. Moreover, the low-risk group presented higher levels of immune checkpoint molecules, lower tumor immune dysfunction and exclusion (TIDE) score and tumor mutation burden (TMB), and higher likelihood of benefiting from immunotherapy. Furthermore, the genomic changes of six LMRGs (CD79A, HACD1, CYP17A1, SLCO1B3, ANGPTL4, and LDHA) were responsible for the difference in susceptibility to LUAD by greatly influencing B-cell activation.

Conclusion: Generally speaking, the LMRG model is a reliable independent biomarker for predicting adverse outcomes in LUAD patients and has the potential to facilitate risk-stratified immunotherapy.

Single-cell spatial architectures associated with clinical outcome in head and neck squamous cell carcinoma

There is increasing evidence that the spatial organization of cells within the tumor-immune microenvironment (TiME) of solid tumors influences survival and response to therapy in numerous cancer types. Here, we report results and demonstrate the applicability of quantitative single-cell spatial proteomics analyses in the TiME of primary and recurrent human papillomavirus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) tumors. Single-cell compositions of a nine patient, primary and recurrent (n = 18), HNSCC cohort is presented, followed by deeper investigation into the spatial architecture of the TiME and its relationship with clinical variables and progression free survival (PFS). Multiple spatial algorithms were used to quantify the spatial landscapes of immune cells within TiMEs and demonstrate that neoplastic tumor-immune cell spatial compartmentalization, rather than mixing, is associated with longer PFS. Mesenchymal (αSMA⁺) cellular neighborhoods describe distinct immune landscapes associated with neoplastic tumor-immune compartmentalization and improved patient outcomes. Results from this investigation are concordant with studies in other tumor types, suggesting that trends in TiME cellular heterogeneity and spatial organization may be shared across cancers and may provide prognostic value in multiple cancer types.

Spatial Metrics of Interaction between CD163-Positive Macrophages and Cancer Cells and Progression-Free Survival in Chemo-Treated Breast Cancer

Tumor-associated macrophages (TAMs) promote progression of breast cancer and other solid malignancies via immunosuppressive, pro-angiogenic and pro-metastatic effects. Tumor-promoting TAMs tend to express M2-like macrophage markers, including CD163. Histopathological assessments suggest that the density of CD163-positive TAMs within the tumor microenvironment is associated with reduced efficacy of chemotherapy and unfavorable prognosis. However, previous analyses have required research-oriented pathologists to visually enumerate CD163+ TAMs, which is both laborious and subjective and hampers clinical implementation. Objective, operator-independent image analysis methods to quantify TAM-associated information are needed. In addition, since M2-like TAMs exert local effects on cancer cells through direct juxtacrine cell-to-cell interactions, paracrine signaling, and metabolic factors, we hypothesized that spatial metrics of adjacency of M2-like TAMs to breast cancer cells will have further information value. Immunofluorescence histo-cytometry of CD163+ TAMs was performed retrospectively on tumor microarrays of 443 cases of invasive breast cancer from patients who subsequently received adjuvant chemotherapy. An objective and automated algorithm was developed to phenotype CD163+ TAMs and calculate their density within the tumor stroma and derive several spatial metrics of interaction with cancer cells. Shorter progression-free survival was associated with a high density of CD163+ TAMs, shorter median cancer-to-CD163+ nearest neighbor distance, and a high number of either directly adjacent CD163+ TAMs (within juxtacrine proximity <12 μm to cancer cells) or communicating CD163+ TAMs (within paracrine communication distance <250 μm to cancer cells) after multivariable adjustment for clinical and pathological risk factors and correction for optimistic bias due to dichotomization.

New insights into the tumour immune microenvironment of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is unique among head and neck cancers for its strong causative association with Epstein Barr-Virus and high levels of immune infiltration that play a role in pathogenesis. As such, immunotherapy for the treatment of NPC is a promising area of research in the pursuit of improving patient outcomes. Understanding the tumour immune microenvironment (TIME) of NPC is the key to developing targeted immunotherapies and stratifying patients to determine optimal treatment regimens. Recent research has uncovered distinct characteristics of the TIME in NPC as well as important differences between the different disease subtypes; however, reviewing the state of the field reveals a further need for the application of novel techniques like multiplexed hyperspectral imaging and mass cytometry. These techniques can be used to identify spatial, compositional, and functional aspects of the TIME in NPC such as immune cell sociology, novel immune populations, and differences in immune-related signalling pathways in NPC in order to identify clinically relevant characteristics for targeted immunotherapy development and biomarker discovery.

Spatial analysis and CD25-expression identify regulatory T cells as predictors of a poor prognosis in colorectal cancer

Regulatory T cells (Tregs) are a heterogeneous cell population that can either suppress or stimulate immune responses. Tumor-infiltrating Tregs are associated with an adverse outcome from most cancer types, but have generally been found to be associated with a good prognosis in colorectal cancer (CRC). We investigated the prognostic heterogeneity of Tregs in CRC by co-expression patterns and spatial analyses with diverse T cell markers, using multiplex fluorescence immunohistochemistry and digital image analysis in two consecutive series of primary CRCs (total n = 1720). Treg infiltration in tumors, scored as FOXP3⁺ or CD4⁺/CD25⁺/FOXP3⁺ (triple-positive) cells, was strongly correlated to the overall amount of CD3⁺ and CD8⁺ T cells, and consequently associated with a favorable 5-year relapse-free survival rate among patients with stage I-III CRC who underwent complete tumor resection. However, high relative expression of the activation marker CD25 in triple-positive Tregs was independently associated with an adverse outcome in a multivariable model incorporating clinicopathological and known molecular prognostic markers (hazard ratio = 1.35, p = 0.028). Furthermore, spatial marker analysis based on Voronoi diagrams and permutation testing of cellular neighborhoods revealed a statistically significant proximity between Tregs and CD8⁺-cells in 18% of patients, and this was independently associated with a poor survival (multivariable hazard ratio = 1.36, p = 0.017). These results show prognostic heterogeneity of different Treg populations in primary CRC, and highlight the importance of multi-marker and spatial analyses for accurate immunophenotyping of tumors in relation to patient outcome.

Tumor-immune landscape patterns before and after chemoradiation in resectable esophageal adenocarcinomas

Immunotherapy is a new anti‐cancer treatment option, showing promising results in clinical trials. To investigate potential immune biomarkers in esophageal adenocarcinoma (EAC), we explored immune landscape patterns in the tumor microenvironment before and after neoadjuvant chemoradiation (nCRT). Sections from matched pretreatment biopsies and post‐nCRT resection specimens (n = 188) were stained for (1) programmed death‐ligand 1 (PD‐L1, CD274); (2) programmed cell death protein 1 (PD‐1, CD279), forkhead box P3 (FOXP3), CD8, pan‐cytokeratin multiplex; and (3) an MHC class I, II duplex. The densities of tumor‐associated immune cells (TAICs) were calculated using digital image analyses and correlated to histopathological nCRT response [tumor regression grade (TRG)], survival, and post‐nCRT immune patterns. PD‐L1 positivity defined by a combined positive score of >1 was associated with a better response post‐nCRT (TRG 1–3 versus 4, 5, p = 0.010). In addition, high combined mean densities of CD8⁺, FOXP3⁺, and PD‐1⁺ TAICs in the tumor epithelium and stroma of biopsies were associated with a better response (TRG 1–3 versus 4, 5, p = 0.025 and p = 0.044, respectively). Heterogeneous TAIC density patterns were observed post‐nCRT, with significantly higher CD8⁺ and PD‐1⁺ TAIC mean densities compared with biopsies (both p = 0.000). Three immune landscape patterns were defined post‐nCRT: ‘inflamed’, ‘invasive margin’, and ‘desert’, of which ‘inflamed’ was the most frequent (57%). Compared with matched biopsies, resection specimens with ‘inflamed’ tumors showed a significantly higher increase in CD8⁺ density compared with non‐inflamed tumors post‐nCRT (p = 0.000). In this cohort of EAC patients, higher TAIC densities in pretreatment biopsies were associated with response to nCRT. This warrants future research into the potential of the tumor‐immune landscape for patient stratification and novel (immune) therapeutic strategies. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Targeting Neoepitopes to Treat Solid Malignancies: Immunosurgery

Successful outcome of immune checkpoint blockade in patients with solid cancers is in part associated with a high tumor mutational burden (TMB) and the recognition of private neoantigens by T-cells. The quality and quantity of target recognition is determined by the repertoire of ‘neoepitope’-specific T-cell receptors (TCRs) in tumor-infiltrating lymphocytes (TIL), or peripheral T-cells. Interferon gamma (IFN-γ), produced by T-cells and other immune cells, is essential for controlling proliferation of transformed cells, induction of apoptosis and enhancing human leukocyte antigen (HLA) expression, thereby increasing immunogenicity of cancer cells. TCR αβ-dependent therapies should account for tumor heterogeneity and availability of the TCR repertoire capable of reacting to neoepitopes and functional HLA pathways. Immunogenic epitopes in the tumor-stroma may also be targeted to achieve tumor-containment by changing the immune-contexture in the tumor microenvironment (TME). Non protein-coding regions of the tumor-cell genome may also contain many aberrantly expressed, non-mutated tumor-associated antigens (TAAs) capable of eliciting productive anti-tumor immune responses. Whole-exome sequencing (WES) and/or RNA sequencing (RNA-Seq) of cancer tissue, combined with several layers of bioinformatic analysis is commonly used to predict possible neoepitopes present in clinical samples. At the ImmunoSurgery Unit of the Champalimaud Centre for the Unknown (CCU), a pipeline combining several tools is used for predicting private mutations from WES and RNA-Seq data followed by the construction of synthetic peptides tailored for immunological response assessment reflecting the patient’s tumor mutations, guided by MHC typing. Subsequent immunoassays allow the detection of differential IFN-γ production patterns associated with (intra-tumoral) spatiotemporal differences in TIL or peripheral T-cells versus TIL. These bioinformatics tools, in addition to histopathological assessment, immunological readouts from functional bioassays and deep T-cell ‘adaptome’ analyses, are expected to advance discovery and development of next-generation personalized precision medicine strategies to improve clinical outcomes in cancer in the context of i) anti-tumor vaccination strategies, ii) gauging mutation-reactive T-cell responses in biological therapies and iii) expansion of tumor-reactive T-cells for the cellular treatment of patients with cancer.

Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response

Tumors are not only aggregates of malignant cells but also well-organized complex ecosystems. The immunological components within tumors, termed the tumor immune microenvironment (TIME), have long been shown to be strongly related to tumor development, recurrence and metastasis. However, conventional studies that underestimate the potential value of the spatial architecture of the TIME are unable to completely elucidate its complexity. As innovative high-flux and high-dimensional technologies emerge, researchers can more feasibly and accurately detect and depict the spatial architecture of the TIME. These findings have improved our understanding of the complexity and role of the TIME in tumor biology. In this review, we first epitomized some representative emerging technologies in the study of the spatial architecture of the TIME and categorized the description methods used to characterize these structures. Then, we determined the functions of the spatial architecture of the TIME in tumor biology and the effects of the gradient of extracellular nonspecific chemicals (ENSCs) on the TIME. We also discussed the potential clinical value of our understanding of the spatial architectures of the TIME, as well as current limitations and future prospects in this novel field. This review will bring spatial architectures of the TIME, an emerging dimension of tumor ecosystem research, to the attention of more researchers and promote its application in tumor research and clinical practice.

Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data

Simple Summary

Immune modulation is considered a hallmark of cancer initiation and progression, and has offered promising opportunities for therapeutic manipulation. Multiplex immunofluorescence (mIF) technology has enabled the tumor immune microenvironment (TIME) to be studied at an increased scale, in terms of both the number of markers and the number of samples. Another benefit of mIF technology is the ability to measure not only the abundance but also the spatial location of multiple cells types within a tissue sample simultaneously, allowing for assessment of the co-localization of different types of immune markers. Thus, the use of mIF technologies have enable researchers to characterize patient, clinical, and tumor characteristics in the hope of identifying patients whom might benefit from immunotherapy treatments. In this review we outline some of the challenges and opportunities in the statistical analyses of mIF data to study the TIME.

Abstract

Immune modulation is considered a hallmark of cancer initiation and progression. The recent development of immunotherapies has ushered in a new era of cancer treatment. These therapeutics have led to revolutionary breakthroughs; however, the efficacy of immunotherapy has been modest and is often restricted to a subset of patients. Hence, identification of which cancer patients will benefit from immunotherapy is essential. Multiplex immunofluorescence (mIF) microscopy allows for the assessment and visualization of the tumor immune microenvironment (TIME). The data output following image and machine learning analyses for cell segmenting and phenotyping consists of the following information for each tumor sample: the number of positive cells for each marker and phenotype(s) of interest, number of total cells, percent of positive cells for each marker, and spatial locations for all measured cells. There are many challenges in the analysis of mIF data, including many tissue samples with zero positive cells or “zero-inflated” data, repeated measurements from multiple TMA cores or tissue slides per subject, and spatial analyses to determine the level of clustering and co-localization between the cell types in the TIME. In this review paper, we will discuss the challenges in the statistical analysis of mIF data and opportunities for further research.

Spatial molecular profiling: platforms, applications and analysis tools

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

Click CAR-T cell engineering for robustly boosting cell immunotherapy in blood and subcutaneous xenograft tumor

The adoptive transfer of chimeric antigen receptor-T (CAR-T) cells has shown remarkable clinical responses in hematologic malignancies. However, unsatisfactory curative results and side effects for tumor treatment are still unsolved problems. Herein we develop a click CAR-T cell engineering strategy via cell glycometabolic labeling for robustly boosting their antitumor effects and safety in vivo. Briefly, paired chemical groups (N3/BCN) are separately incorporated into CAR-T cell and tumor via nondestructive intrinsic glycometabolism of exogenous Ac4GalNAz and Ac4ManNBCN, serving as an artificial ligand-receptor. Functional groups anchored on cell surface strengthen the interaction of CAR-T cell and tumor via bioorthogonal click chemistry, further enhancing specific recognition, migration and selective antitumor effects of CAR-T cells. In vivo, click CAR-T cell completely removes lymphoma cells and minimizes off-target toxicity via selective and efficient bioorthogonal targeting in blood cancer. Surprisingly, compared to unlabeled cells, artificial bioorthogonal targeting significantly promotes the accumulation, deep penetration and homing of CAR-T cells into tumor tissues, ultimately improving its curative effect for solid tumor. Click CAR-T cell engineering robustly boosts selective recognition and antitumor capabilities of CAR T cells in vitro and in vivo, thereby holding a great potential for effective clinical cell immunotherapy with avoiding adverse events in patients.

Prognostic markers in pT3 bladder cancer: A study from the international bladder cancer tissue microarray project

PURPOSE

We evaluated the prognostic value of 10 putative tumor markers by immunohistochemistry in a large multi-institutional cohort of patients with locally advanced urothelial cancer of the bladder (UCB) with the aim to validate their clinical value and to harmonize protocols for their evaluation.

MATERIALS AND METHODS

Primary tumor specimens from 576 patients with pathologic (p)T3 UCB were collected from 24 institutions in North America and Europe. Three replicate 0.6-mm core diameter samples were collected for the construction of a tissue microarray (TMA). Immunohistochemistry (IHC) for 10 previously described tumor markers was performed and scored at 3 laboratories independently according to a standardized protocol. Associations between marker positivity and freedom from recurrence (FFR) or overall survival (OS) were analyzed separately for each individual laboratory using Cox regression analysis.

RESULTS

The overall agreement of the IHC scoring among laboratories was poor. Correlation among the 3 laboratories varied across the 10 markers. There was generally a lack of association between the individual markers and FFR or OS. The number of altered cell cycle regulators (p53, Rb, and p21) was associated with increased risk of cancer recurrence (P < 0.032). There was no clear pattern in the relationship between the percentage of markers altered in an 8-marker panel and FFR or OS.

CONCLUSIONS

This large international TMA of locally advanced (pT3) UCB suggests that altered expression of p53, Rb, and p21 is associated with worse outcome. However this study also highlights limitations in the reproducibility of IHC even in the most expert hands.

[Overview of Multiplex Immunohistochemistry and Immunofluorescence Techniques in the Lung Cancer Immunotherapy]

肺癌是目前临床上最常见的恶性肿瘤，严重威胁着患者的生命健康及生活质量。程序性细胞死亡受体1（programmed cell death receptor 1, PD-1）及其配体（programmed cell death ligand 1, PD-L1）抑制剂为非小细胞肺癌（non-small cell lung cancer, NSCLC）患者提供了新的治疗策略。现有的生物标志物检测对准确选择免疫治疗受益的患者均有一定的价值，但都存在着局限性。多标记免疫组织化学/免疫荧光（multiplex immunohistochemistry/immunofluorescence, mIHC/IF）技术允许在单一组织切片上同时检测多个抗体，并对细胞组成、细胞功能和细胞-细胞相互作用进行全面研究。国内外已有大量研究使用mIHC/IF技术对肿瘤免疫微环境（tumor immune microenvironment, TIME）下特异性免疫细胞群进行了探索，发现其有助于肺癌患者临床预后判断及疗效预测。肺癌免疫治疗时代，这项技术在转化研究和临床实践中均具有良好的应用前景。本文就mIHC/IF检测方法在肺癌免疫治疗中的研究进展进行了总结和展望。

A review of the medical hyperspectral imaging systems and unmixing algorithms' in biological tissues

Hyperspectral fluorescence imaging (HFI) is a well-known technique in the medical research field and is considered a non-invasive tool for tissue diagnosis. This review article gives a brief introduction to acquisition methods, including the image preprocessing methods, feature selection and extraction methods, data classification techniques and medical image analysis along with recent relevant references. The process of fusion of unsupervised unmixing techniques with other classification methods, like the combination of support vector machine with an artificial neural network, the latest snapshot Hyperspectral imaging (HSI) and vortex analysis techniques are also outlined. Finally, the recent applications of hyperspectral images in cellular differentiation of various types of cancer are discussed.

Prognostic significance of spatial immune profiles in human solid cancers

Immune-based tumor characteristics in the context of tumor heterogeneity are associated with suppression as well as promotion of cancer progression in various tumor types. As immunity typically functions based on intercellular contacts and short-distance cytokine communications, the location and spatial relationships of the tumor immune microenvironment can provide a framework to understand the biology and potential predictive biomarkers related to disease outcomes. Immune spatial analysis is a newly emerging form of cancer research based on recent methodological advances in in situ single-cell analysis, where cell-cell interaction and the tissue architecture can be analyzed in relation to phenotyping the tumor immune heterogeneity. Spatial characteristics of tumors can be stratified into the tissue architecture level and the single-cell level. At the tissue architecture level, the prognostic significance of the density of immune cell lineages, particularly T cells, is leveraged by understanding longitudinal changes in cell distribution in the tissue architecture such as intra-tumoral and peri-tumoral regions, and invasive margins. At the single-cell level, the proximity of the tumor to the immune cells correlates with disease aggressiveness and therapeutic resistance, providing evidence to understand biological interactions and characteristics of the tumor immune microenvironment. In this review, we summarize recent findings regarding spatial information of the tumor immune microenvironment and review advances and challenges in spatial single-cell analysis toward developing tissue-based biomarkers rooted in the immune spatial landscape.

Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review [Invited]

Hyperspectral imaging (HSI) and multispectral imaging (MSI) technologies have the potential to transform the fields of digital and computational pathology. Traditional digitized histopathological slides are imaged with RGB imaging. Utilizing HSI/MSI, spectral information across wavelengths within and beyond the visual range can complement spatial information for the creation of computer-aided diagnostic tools for both stained and unstained histological specimens. In this systematic review, we summarize the methods and uses of HSI/MSI for staining and color correction, immunohistochemistry, autofluorescence, and histopathological diagnostic research. Studies include hematology, breast cancer, head and neck cancer, skin cancer, and diseases of central nervous, gastrointestinal, and genitourinary systems. The use of HSI/MSI suggest an improvement in the detection of diseases and clinical practice compared with traditional RGB analysis, and brings new opportunities in histological analysis of samples, such as digital staining or alleviating the inter-laboratory variability of digitized samples. Nevertheless, the number of studies in this field is currently limited, and more research is needed to confirm the advantages of this technology compared to conventional imagery.

Microbes as Master Immunomodulators: Immunopathology, Cancer and Personalized Immunotherapies

The intricate interplay between the immune system and microbes is an essential part of the physiological homeostasis in health and disease. Immunological recognition of commensal microbes, such as bacterial species resident in the gut or lung as well as dormant viral species, i.e., cytomegalovirus (CMV) or Epstein-Barr virus (EBV), in combination with a balanced immune regulation, is central to achieve immune-protection. Emerging evidence suggests that immune responses primed to guard against commensal microbes may cause unexpected pathological outcomes, e.g., chronic inflammation and/or malignant transformation. Furthermore, translocation of immune cells from one anatomical compartment to another, i.e., the gut-lung axis via the lymphatics or blood has been identified as an important factor in perpetrating systemic inflammation, tissue destruction, as well as modulating host-protective immune responses. We present in this review immune response patterns to pathogenic as well as non-pathogenic microbes and how these immune-recognition profiles affect local immune responses or malignant transformation. We discuss personalized immunological therapies which, directly or indirectly, target host biological pathways modulated by antimicrobial immune responses.