Machine Learning and Artificial Intelligence-driven Spatial Analysis of the Tumor Immune Microenvironment in Pathology Slides

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications

Spatial modelling methods have gained prominence with developments in high throughput imaging platforms. Multiplex immunofluorescence (mIF) provides the scope to examine interactions between tumor and immune compartment at single cell resolution using a panel of antibodies that can be chosen based on the cancer type or the clinical interest of the study. The markers can be used to identify the phenotypes and to examine cellular interactions at global and local scales. Several translational studies rely on key understanding of the tumor microenvironment (TME) to identify drivers of immune response in immunotherapy based clinical trials. To improve the success of ongoing trials, a number of retrospective approaches can be adopted to understand differences in response, recurrence and progression by examining the patient's TME from tissue samples obtained at baseline and at various time points along the treatment. The multiplex immunofluorescence (mIF) technique provides insight on patient specific cell populations and their relative spatial distribution as qualitative measures of a favorable treatment outcome. Spatial analysis of these images provides an understanding of the intratumoral heterogeneity and clustering among cell populations in the TME. A number of mathematical models, which establish clustering as a measure of deviation from complete spatial randomness, can be applied to the mIF images represented as spatial point patterns. These mathematical models, developed for landscape ecology and geographic information studies, can be applied to the TME after careful consideration of the tumor type (cold vs. hot) and the tumor immune landscape. The spatial modelling of mIF images can show observable engagement of T cells expressing immune checkpoint molecules and this can then be correlated with single-cell RNA sequencing data.

Epigenetic targeting of autophagy for cancer: DNA and RNA methylation

Autophagy, a crucial cellular mechanism responsible for degradation and recycling of intracellular components, is modulated by an intricate network of molecular signals. Its paradoxical involvement in oncogenesis, acting as both a tumor suppressor and promoter, has been underscored in recent studies. Central to this regulatory network are the epigenetic modifications of DNA and RNA methylation, notably the presence of N6-methyldeoxyadenosine (6mA) in genomic DNA and N6-methyladenosine (m6A) in eukaryotic mRNA. The 6mA modification in genomic DNA adds an extra dimension of epigenetic regulation, potentially impacting the transcriptional dynamics of genes linked to autophagy and, especially, cancer. Conversely, m6A modification, governed by methyltransferases and demethylases, influences mRNA stability, processing, and translation, affecting genes central to autophagic pathways. As we delve deeper into the complexities of autophagy regulation, the importance of these methylation modifications grows more evident. The interplay of 6mA, m6A, and autophagy points to a layered regulatory mechanism, illuminating cellular reactions to a range of conditions. This review delves into the nexus between DNA 6mA and RNA m6A methylation and their influence on autophagy in cancer contexts. By closely examining these epigenetic markers, we underscore their promise as therapeutic avenues, suggesting novel approaches for cancer intervention through autophagy modulation.

Draining Lymph Nodes in Human Kidney Pancreas Transplant: Potential Implications in Alloimmunity and Tolerance

BACKGROUND

Draining lymph nodes (DLNs) are implicated in alloimmunity and tolerance regulation. The role of DLNs in human organ transplant is unknown due to the lack of relevant clinical tissues.

METHODS

During a combined kidney and pancreas transplant, the distal vena cava and both right and left common and external iliac vessels were mobilized and exposed. Draining lymph nodes along these vessels were removed sequentially and archived before the pancreas transplant. Similarly, the DLNs along the left iliac vessels were removed and archived before implantation of the kidney allograft.

RESULTS

Among 13 patients undergoing kidney and pancreas transplants, 6 had pre- and postreperfusion DLNs clinically archived. The remaining 7 either had only prereperfusion DLNs archived or none. Clinical archiving of DLNs did not alter operative times or parameters.

CONCLUSION

This study describes an approach for clinical archiving of DLNs obtained within the operative field during combined kidney-pancreas transplant in humans. The availability of relevant DLNs is essential for investigating fundamental initial primary immune responses potentially important in allograft alloimmunity and tolerance.

Artificial Intelligence in the Pathology of Gastric Cancer

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

Artificial intelligence in anatomical pathology: building a strong foundation for precision medicine

With the convergence of digital pathology (DP) and artificial intelligence (AI), anatomic pathology practice has been experiencing an exciting paradigm shifting. Pathologists will be provided with an augmented ability to improve diagnostic accuracy, efficiency, and consistency. There will be subvisual morphometric features discovered and multiomics data integrated to provide better prognostic and theragnostic information to guide individual patients' management. The perspective for future precision medicine is promising. However, there are many challenges before AI-assisted DP diagnostic workflows can be successfully implemented. Herein, we briefly review some examples of AI application in anatomic pathology with an emphasis on the subspecialty of gastrointestinal pathology and discuss potential challenges for clinical implementation.

Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer

Although the tumor-stroma ratio (TSR) has prognostic value in many cancers, the traditional semi-quantitative visual assessment method has inter-observer variability, making it impossible for clinical practice. We aimed to develop a machine learning (ML) algorithm for accurately quantifying TSR in hematoxylin-and-eosin (H&E)-stained whole slide images (WSI) and further investigate its prognostic effect in patients with muscle-invasive bladder cancer (MIBC). We used an optimal cell classifier previously built based on QuPath open-source software and ML algorithm for quantitative calculation of TSR. We retrospectively analyzed data from two independent cohorts to verify the prognostic significance of ML-based TSR in MIBC patients. WSIs from 133 MIBC patients were used as the discovery set to identify the optimal association of TSR with patient survival outcomes. Furthermore, we performed validation in an independent external cohort consisting of 261 MIBC patients. We demonstrated a significant prognostic association of ML-based TSR with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TSR associated with better prognosis. Uni- and multivariate Cox regression analyses showed that TSR was independently associated with overall survival (p < 0.001 for all analyses) after adjusting for clinicopathological factors including age, gender, and pathologic stage. TSR was found to be a strong prognostic factor that was not redundant with the existing staging system in different subgroup analyses (p < 0.05 for all analyses). Finally, the expression of six genes (DACH1, DEEND2A, NOTCH4, DTWD1, TAF6L, and MARCHF5) were significantly associated with TSR, revealing possible potential biological relevance. In conclusion, we developed an ML algorithm based on WSIs of MIBC patients to accurately quantify TSR and demonstrated its prognostic validity for MIBC patients in two independent cohorts. This objective quantitative method allows application in clinical practice while reducing the workload of pathologists. Thus, it might be of significant aid in promoting precise pathology services in MIBC.

Identification of Novel Tumor Microenvironment Regulating Factor That Facilitates Tumor Immune Infiltration in Cervical Cancer

Cervical cancer is one of the most common gynecologic malignancies and one of the leading causes of cancer-related deaths in women worldwide. There are more than 30 categories of human papillomavirus infections in the genital tract. The recently discovered immune checkpoint suppression is a potential approach to improve clinical outcomes in these patients by altering immune cell function. However, many questions remain unanswered in terms of this method. For example, the proportion of responders is limited and the exact mechanism of action is uncertain. The tumor microenvironment (TME) has long been regarded as having nonnegligible influence on effectiveness of immunotherapy. The programmed cell death protein 1 (PD-1) pathway has received much attention due to its involvement in activating T-cell immune checkpoint responses. Since tumor cells may evade immune detection and become highly resistant to conventional treatments, anti-PD-1/PD-L1 antibodies are preferred as a kind of cancer treatment and many have just been licensed. To provide a theoretical basis for the development of new therapies, investigating the effect of tumor microenvironment on the prognosis of cervical cancer is necessary. In this work, immunological scores obtained from the ESTIMATE algorithm were used to differentiate between patients with high and low immune cell infiltration. We identified 11 immunologically significant differentially expressed genes (DEGs). For example, CXCR3 was found to be an important factor in CD8⁺ T cell recruitment and tumor immunological infiltration in cervical cancer. These results may lead to novel directions of understanding complex interactions between cancer cells and the tumor microenvironment, as well as new treatment options for cervical cancer.