Stratification of chemotherapy-treated stage III colorectal cancer patients using multiplexed imaging and single-cell analysis of T-cell populations

Prognostic value of neutrophil-to-lymphocyte ratios pre- and post-surgery in stage III CRC: a study of 2,742 patients

PURPOSE

Stage III colorectal cancer (CRC) is typically treated with surgery; however, it has a high recurrence rate and inconsistent benefits from postoperative chemotherapy. Inflammatory markers like the neutrophil-to-lymphocyte ratio (NLR) have shown prognostic value in various cancers. However, the prognostic significance of NLR measured before and after CRC surgery is not clear. This study aims to clarify the prognostic value of the combination of pre- and post-surgery NLR in stage III CRC patients.

METHODS

Patients with stage III CRC treated between 2001 and 2022 were retrospectively analyzed using data from the Chang Gung Medical Research Database. Patients were categorized into 4 groups based on their pre- and post-operative NLR levels. Kaplan-Meier survival analysis and Cox proportional hazard models were used to assess the associations between NLR levels and overall survival (OS), disease-free survival (DFS), and cancer-specific survival (CSS).

RESULTS

Data from 2,742 patients, median age of 62 years and 54% male, were analyzed. After adjustment, patients in Group IV, with high NLR values both before and after surgery, had greater risks of worse DFS (adjusted hazard ratio [aHR] = 1.30, 95% confidence interval [CI]: 1.13-1.50), OS (aHR = 1.36, 95% CI: 1.14-1.63), and CSS (aHR = 1.27, 95% CI: 1.04-1.55) compared to Group I.

CONCLUSIONS

High NLR levels before and after surgery is a strong predictor of poor outcomes in stage III CRC patients. The findings suggest that monitoring NLR at both time points can be a valuable prognostic tool, guiding postoperative care and treatment strategies to improve patient outcomes.

Spatial analysis by current multiplexed imaging technologies for the molecular characterisation of cancer tissues

Tumours are composed of tumour cells and the surrounding tumour microenvironment (TME), and the molecular characterisation of the various elements of the TME and their interactions is essential for elucidating the mechanisms of tumour progression and developing better therapeutic strategies. Multiplex imaging is a technique that can quantify the expression of multiple protein markers on the same tissue section while maintaining spatial positioning, and this method has been rapidly developed in cancer research in recent years. Many multiplex imaging technologies and spatial analysis methods are emerging, and the elucidation of their principles and features is essential. In this review, we provide an overview of the latest multiplex imaging techniques by type of imaging and staining method and an introduction to image analysis methods, primarily focusing on spatial cellular properties, providing deeper insight into tumour organisation and spatial molecular biology in the TME.

The evolution of immune profiling: will there be a role for nanoparticles?

Immune profiling provides insights into the functioning of the immune system, including the distribution, abundance, and activity of immune cells. This understanding is essential for deciphering how the immune system responds to pathogens, vaccines, tumors, and other stimuli. Analyzing diverse immune cell types facilitates the development of personalized medicine approaches by characterizing individual variations in immune responses. With detailed immune profiles, clinicians can tailor treatment strategies to the specific immune status and needs of each patient, maximizing therapeutic efficacy while minimizing adverse effects. In this review, we discuss the evolution of immune profiling, from interrogating bulk cell samples in solution to evaluating the spatially-rich molecular profiles across intact preserved tissue sections. We also review various multiplexed imaging platforms recently developed, based on immunofluorescence and imaging mass spectrometry, and their impact on the field of immune profiling. Identifying and localizing various immune cell types across a patient's sample has already provided important insights into understanding disease progression, the development of novel targeted therapies, and predicting treatment response. We also offer a new perspective by highlighting the unprecedented potential of nanoparticles (NPs) that can open new horizons in immune profiling. NPs are known to provide enhanced detection sensitivity, targeting specificity, biocompatibility, stability, multimodal imaging features, and multiplexing capabilities. Therefore, we summarize the recent developments and advantages of NPs, which can contribute to advancing our understanding of immune function to facilitate precision medicine. Overall, NPs have the potential to offer a versatile and robust approach to profile the immune system with improved efficiency and multiplexed imaging power.

Spatial effects of infiltrating T cells on neighbouring cancer cells and prognosis in stage III CRC patients

Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single-cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil (5FU)-based chemotherapy. Images underwent segmentation for tumour, stroma, and immune cells, and cancer cell 'state' protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell-T-cell interactions at single-cell level. In our discovery cohort (Memorial Sloan Kettering samples), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (Huntsville Clearview Cancer Center samples) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between the percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (discovery cohort: p = 0.07; validation cohort: p = 0.19). We next utilised our region-based nearest neighbour approach to determine the spatial relationships between cytotoxic T cells, helper T cells, and cancer cell clusters. In both cohorts, we found that shorter distance between cytotoxic T cells, T helper cells, and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (discovery cohort: p = 0.01; validation cohort: p = 0.003). © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

From Crypts to Cancer: A Holistic Perspective on Colorectal Carcinogenesis and Therapeutic Strategies

Colorectal cancer (CRC) represents a significant global health burden, with high incidence and mortality rates worldwide. Recent progress in research highlights the distinct clinical and molecular characteristics of colon versus rectal cancers, underscoring tumor location's importance in treatment approaches. This article provides a comprehensive review of our current understanding of CRC epidemiology, risk factors, molecular pathogenesis, and management strategies. We also present the intricate cellular architecture of colonic crypts and their roles in intestinal homeostasis. Colorectal carcinogenesis multistep processes are also described, covering the conventional adenoma-carcinoma sequence, alternative serrated pathways, and the influential Vogelstein model, which proposes sequential APC, KRAS, and TP53 alterations as drivers. The consensus molecular CRC subtypes (CMS1-CMS4) are examined, shedding light on disease heterogeneity and personalized therapy implications.

A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes

The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for treating patients with CRC. While clinical studies showed that TILs improve the host immune response, leading to a better prognosis, inter-observer agreement for quantifying TILs is not perfect. Incorporating machine learning (ML) based applications in clinical routine may promote diagnosis reliability. Recently, ML has shown potential for making progress in routine clinical procedures. We aim to systematically review the TILs analysis based on ML in CRC histological images. Deep learning (DL) and non-DL techniques can aid pathologists in identifying TILs, and automated TILs are associated with patient outcomes. However, a large multi-institutional CRC dataset with a diverse and multi-ethnic population is necessary to generalize ML methods.

Spatial Effects of Infiltrating T cells on Neighbouring Cancer Cells and Prognosis in Stage III CRC patients

Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks, as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil-based chemotherapy. Images underwent segmentation for tumour, stroma and immune cells, and cancer cell 'state' protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell - T cell interactions at single-cell level. In our discovery cohort (MSK), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (HV) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (Discovery cohort: p = 0.07, Validation cohort: p = 0.19). We next utilized our region-based nearest neighbourhood approach to determine the spatial relationships between cytotoxic T cells, helper T cells and cancer cell clusters. In the both cohorts, we found that lower distance between cytotoxic T cells, T helper cells and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (Discovery cohort: p = 0.01, Validation cohort: p = 0.003).

Pathobiology of Candida auris infection analyzed by multiplexed imaging and single cell analysis

Fungal organisms contribute to significant human morbidity and mortality and Candida auris (C. auris) infections are of utmost concern due to multi-drug resistant strains and persistence in critical care and hospital settings. Pathogenesis and pathology of C. auris is still poorly understood and in this study, we demonstrate how the use of multiplex immunofluorescent imaging (MxIF) and single-cell analysis can contribute to a deeper understanding of fungal infections within organs. We used two different neutrophil depletion murine models (treated with either 1A8-an anti-Ly6G antibody, or RB6-8C5-an anti-Ly6G/Ly6C antibody; both 1A8 and RB6-8C5 antibodies have been shown to deplete neutrophils) and compared to wildtype, non-neutropenic mice. Following pathologist assessment, fixed samples underwent MxIF imaging using a C. albicans antibody (shown to be cross-reactive to C. auris) and immune cell biomarkers-CD3 (T cells), CD68 (macrophages), B220 (B cells), CD45 (monocytes), and Ly6G (neutrophils) to quantify organ specific immune niches. MxIF analysis highlighted the heterogenous distribution of C. auris infection within heart, kidney, and brain 7 days post-infection. Size and number of fungal abscesses was greatest in the heart and lowest in brain. Infected mice had an increased count of CD3+, CD68+, B220+, and CD45+ immune cells, concentrated around C. auris abscesses. CD68+ cells were predominant in wildtype (non-neutropenic mice) and CD3+/CD45+ cells were predominant in neutropenic mice, with B cells being the least abundant. These findings suggest a Th2 driven immune response in neutropenic C. auris infection mice models. This study demonstrates the value of MxIF to broaden understanding of C. auris pathobiology, and mechanistic understanding of fungal infections.

Implications of single-cell immune landscape of tumor microenvironment for the colorectal cancer diagnostics and therapy

Colorectal cancer (CRC) originates from the polyps lining the colon and is among the most common types of cancer. With the increasing popularity of single-cell sequencing technologies, researchers have been able to better understand the immune landscape of colorectal cancer, by analyzing their expression and interactions in detail with the tumor microenvironment (TME) at single-cell level. Since the tumor-immune cell interactions play a critical part in the advancement as well as treatment response in colorectal cancer, the release of inhibitory factors such as T cells are important for recognizing and destroying cancer cells. Such information is vital to identify immunotherapeutic targets for cure and monitoring response to treatments. Therefore, a comprehensive single-cell studies-based overview of key immunogenic agents regulating the TME of CRC is provided in this review. Tumor-associated macrophages can promote tumor growth and resistance to treatment by releasing factors that inhibit the function of other immune cells. Additionally, colorectal cancer cells can express programmed cell death protein 1 and its ligand, which can also inhibit T-cell function. Researchers have found that certain types of immune cells, prominently T cells, natural killer, and dendritic cells, can have a positive impact on the prognosis of colorectal cancer patients. Treatments like immune checkpoint inhibitors and CAR-T therapies that help to release the inhibitory signals from the cancer cells allow the immune cells to function more effectively.

Fibroblast Activation Protein-α (FAP) Identifies Stromal Invasion in Colorectal Neoplasia

The increasing detection of colorectal adenomas and early adenocarcinomas (ADCs) in the context of nationwide screening programs has led to a significant increase in the incidence of inconclusive diagnoses in which histopathologic analysis of endoscopic biopsies does not allow pathologists to provide a reliable diagnosis of stromal invasion. The objective of this study was to analyze the discriminative capacity of the immunohistochemical expression of fibroblast activation protein-α (FAP) in distinguishing colorectal adenomas with low-grade dysplasia (LGD) and high-grade dysplasia (HGD) from invasive intestinal-type ADCs. The study analyzed the first endoscopic biopsies from a series of patients classified as inconclusive or conclusive for stromal invasion based on the pathologic report. In total, 30 ADCs, 52 HGDs, and 15 LGDs were included in the study. FAP expression was detected in 23/30 ADCs and was negative in all adenomas with either LGD or HGD features (100% specificity and 76.7% sensitivity, area under the curve=0.883, CI=0.79-0.98). Considering these findings, we conclude that FAP is a potentially useful tool for helping pathologists identify invasive lesions in colorectal endoscopic biopsies, avoiding unnecessary biopsy repetitions.

Recent advancements in tumour microenvironment landscaping for target selection and response prediction in immune checkpoint therapies achieved through spatial protein multiplexing analysis

Immune checkpoint therapies have significantly advanced cancer treatment. Nevertheless, the high costs and potential adverse effects associated with these therapies highlight the need for better predictive biomarkers to identify patients who are most likely to benefit from treatment. Unfortunately, the existing biomarkers are insufficient to identify such patients. New high-dimensional spatial technologies have emerged as a valuable tool for discovering novel biomarkers by analysing multiple protein markers at a single-cell resolution in tissue samples. These technologies provide a more comprehensive map of tissue composition, cell functionality, and interactions between different cell types in the tumour microenvironment. In this review, we provide an overview of how spatial protein-based multiplexing technologies have fuelled biomarker discovery and advanced the field of immunotherapy. In particular, we will focus on how these technologies contributed to (i) characterise the tumour microenvironment, (ii) understand the role of tumour heterogeneity, (iii) study the interplay of the immune microenvironment and tumour progression, (iv) discover biomarkers for immune checkpoint therapies (v) suggest novel therapeutic strategies.

Using single cell atlas data to reconstruct regulatory networks

Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)-gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.

High-multiplex tissue imaging in routine pathology-are we there yet?

High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.

The association between the tumor immune microenvironments and clinical outcome in low-grade, early-stage endometrial cancer patients

Endometrial tumors show substantial heterogeneity in their immune microenvironment. This heterogeneity could be used to improve the accuracy of current outcome prediction tools. We assessed the immune microenvironment of 235 patients diagnosed with low-grade, early-stage endometrial cancer. Multiplex quantitative immunofluorescence was carried out to measure CD8, CD68, FOXP3, PD-1, and PD-L1 markers, as well as cytokeratin (CK), on tissue microarrays. Clustering results revealed five robust immune response patterns, each associated with specific immune populations, cell phenotypes, and cell spatial clustering. Most samples (69%) belonged to the immune-desert subtype, characterized by low immune cell densities. Tumor-infiltrating lymphocyte (TIL)-rich samples (4%) displayed high CD8⁺ T-cell infiltration, as well as a high percentage of CD8/PD-1⁺ cells. Immune-exclusion samples (19%) displayed the lowest CD8⁺ infiltration combined with high PD-L1 expression levels in CK⁺ tumor cells. In addition, they demonstrated high tumor cell spatial clustering as well as increased spatial proximity of CD8⁺ /PD-1⁺ and CK/PD-L1⁺ cells. FOXP3 and macrophage-rich phenotypes (3% and 4% of total samples) displayed relatively high levels of FOXP3⁺ regulatory T-cells and CD68⁺ macrophages, respectively. These phenotypes correlated with clinical outcomes, with immune-exclusion tumors showing an association with tumor relapse. When compared with prediction models built using routine pathological variables, models optimized with immune variables showed increased outcome prediction capacity (AUC = 0.89 versus 0.78) and stratification potential. The improved prediction capacity was independent of mismatch repair protein status and adjuvant radiotherapy treatment. Further, immunofluorescence results could be partially recapitulated using single-marker immunohistochemistry (IHC) performed on whole tissue sections. TIL-rich tumors demonstrated increased CD8⁺ T-cells by IHC, while immune-exclusion tumors displayed a lack of CD8⁺ T-cells and frequent expression of PD-L1 in tumor cells. Our results demonstrate the capability of the immune microenvironment to improve standard prediction tools in low-grade, early-stage endometrial carcinomas. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Cutting-Edge Platforms for Analysis of Immune Cells in the Hepatic Microenvironment-Focus on Tumor-Associated Macrophages in Hepatocellular Carcinoma

The role of tumor-associated macrophages (TAMs) in the pathogenesis of hepatocellular carcinoma (HCC) is poorly understood. Most studies rely on platforms that remove intrahepatic macrophages from the microenvironment prior to evaluation. Cell isolation causes activation and phenotypic changes that may not represent their actual biology and function in situ. State-of-the-art methods provides new strategies to study TAMs without losing the context of tissue architecture and spatial relationship with neighboring cells. These technologies, such as multispectral imaging (e.g., Vectra Polaris), mass cytometry by time-of-flight (e.g., Fluidigm CyTOF), cycling of fluorochromes (e.g., Akoya Biosciences CODEX/PhenoCycler-Fusion, Bruker Canopy, Lunaphore Comet, and CyCIF) and digital spatial profiling or transcriptomics (e.g., GeoMx or Visium, Vizgen Merscope) are being utilized to accurately assess the complex cellular network within the tissue microenvironment. In cancer research, these platforms enable characterization of immune cell phenotypes and expression of potential therapeutic targets, such as PDL-1 and CTLA-4. Newer spatial profiling platforms allow for detection of numerous protein targets, in combination with whole transcriptome analysis, in a single liver biopsy tissue section. Macrophages can also be specifically targeted and analyzed, enabling quantification of both protein and gene expression within specific cell phenotypes, including TAMs. This review describes the workflow of each platform, summarizes recent research using these approaches, and explains the advantages and limitations of each.