Prognostic Significance of Immune Cell Populations Identified by Machine Learning in Colorectal Cancer Using Routine Hematoxylin and Eosin-Stained Sections

The Glasgow Microenvironment Score: an exemplar of contemporary biomarker evolution in colorectal cancer

Colorectal cancer remains a leading cause of mortality worldwide. Significant variation in response to treatment and survival is evident among patients with similar stage disease. Molecular profiling has highlighted the heterogeneity of colorectal cancer but has had limited impact in daily clinical practice. Biomarkers with robust prognostic and therapeutic relevance are urgently required. Ideally, biomarkers would be derived from H&E sections used for routine pathological staging, have reliable sensitivity and specificity, and require minimal additional training. The biomarker targets would capture key pathological features with proven additive prognostic and clinical utility, such as the local inflammatory response and tumour microenvironment. The Glasgow Microenvironment Score (GMS), first described in 2014, combines assessment of peritumoural inflammation at the invasive margin with quantification of tumour stromal content. Using H&E sections, the Klintrup-Mäkinen (KM) grade is determined by qualitative morphological assessment of the peritumoural lymphocytic infiltrate at the invasive margin and tumour stroma percentage (TSP) calculated in a semi-quantitative manner as a percentage of stroma within the visible field. The resulting three prognostic categories have direct clinical relevance: GMS 0 denotes a tumour with a dense inflammatory infiltrate/high KM grade at the invasive margin and improved survival; GMS 1 represents weak inflammatory response and low TSP associated with intermediate survival; and GMS 2 tumours are typified by a weak inflammatory response, high TSP, and inferior survival. The prognostic capacity of the GMS has been widely validated while its potential to guide chemotherapy has been demonstrated in a large phase 3 trial cohort. Here, we detail its journey from conception through validation to clinical translation and outline the future for this promising and practical biomarker.

Improved Risk-Stratification Scheme for Mismatch-Repair Proficient Stage II Colorectal Cancers Using the Digital Pathology Biomarker QuantCRC

PURPOSE

There is a need to improve current risk stratification of stage II colorectal cancer to better inform risk of recurrence and guide adjuvant chemotherapy. We sought to examine whether integration of QuantCRC, a digital pathology biomarker utilizing hematoxylin and eosin-stained slides, provides improved risk stratification over current American Society of Clinical Oncology (ASCO) guidelines.

EXPERIMENTAL DESIGN

ASCO and QuantCRC-integrated schemes were applied to a cohort of 398 mismatch-repair proficient (MMRP) stage II colorectal cancers from three large academic medical centers. The ASCO stage II scheme was taken from recent guidelines. The QuantCRC-integrated scheme utilized pT3 versus pT4 and a QuantCRC-derived risk classification. Evaluation of recurrence-free survival (RFS) according to these risk schemes was compared using the log-rank test and HR.

RESULTS

Integration of QuantCRC provides improved risk stratification compared with the ASCO scheme for stage II MMRP colorectal cancers. The QuantCRC-integrated scheme placed more stage II tumors in the low-risk group compared with the ASCO scheme (62.5% vs. 42.2%) without compromising excellent 3-year RFS. The QuantCRC-integrated scheme provided larger HR for both intermediate-risk (2.27; 95% CI, 1.32-3.91; P = 0.003) and high-risk (3.27; 95% CI, 1.42-7.55; P = 0.006) groups compared with ASCO intermediate-risk (1.58; 95% CI, 0.87-2.87; P = 0.1) and high-risk (2.24; 95% CI, 1.09-4.62; P = 0.03) groups. The QuantCRC-integrated risk groups remained prognostic in the subgroup of patients that did not receive any adjuvant chemotherapy.

CONCLUSIONS

Incorporation of QuantCRC into risk stratification provides a powerful predictor of RFS that has potential to guide subsequent treatment and surveillance for stage II MMRP colorectal cancers.

Quantitative Multiplexed Analysis of Indoleamine 2,3-Dioxygenase (IDO) and Arginase-1 (ARG1) Expression and Myeloid Cell Infiltration in Colorectal Cancer

Indoleamine 2,3-dioxygenase (IDO) and arginase-1 (ARG1) are amino acid-metabolizing enzymes, frequently highly expressed in cancer. Their expression may deplete essential amino acids, lead to immunosuppression, and promote cancer growth. Still, their expression patterns, prognostic significance, and spatial localization in the colorectal cancer microenvironment are incompletely understood. Using a custom 10-plex immunohistochemistry assay and supervised machine learning-based digital image analysis, we characterized IDO and ARG1 expression in monocytic cells, granulocytes, mast cells, and tumor cells in 833 colorectal cancer patients. We evaluated the prognostic value and spatial arrangement of IDO- and ARG1-expressing myeloid and tumor cells. IDO was mainly expressed not only by monocytic cells but also by some tumor cells, whereas ARG1 was predominantly expressed by granulocytes. Higher density of IDO+ monocytic cells was an independent prognostic factor for improved cancer-specific survival both in the tumor center (Ptrend = .0002; hazard ratio [HR] for the highest ordinal category Q4 [vs Q1], 0.51; 95% CI, 0.33-0.79) and the invasive margin (Ptrend = .0015). Higher density of granulocytes was associated with prolonged cancer-specific survival in univariable models, and higher FCGR3+ARG1+ neutrophil density in the tumor center also in multivariable analysis (Ptrend = .0020). Granulocytes were, on average, located closer to tumor cells than monocytic cells. Furthermore, IDO+ monocytic cells and ARG1- granulocytes were closer than IDO- monocytic cells and ARG1+ granulocytes, respectively. The mRNA expression of the IDO1 gene was assessed in myeloid and tumor cells using publicly available single-cell RNA sequencing data for 62 colorectal cancers. IDO1 was mainly expressed in monocytes and dendritic cells, and high IDO1 activity in monocytes was associated with enriched immunostimulatory pathways. Our findings provided in-depth information about the infiltration patterns and prognostic value of cells expressing IDO and/or ARG1 in the colorectal cancer microenvironment, highlighting the significance of host immune response in tumor progression.

Multiplexed analysis of macrophage polarisation in pulmonary metastases of microsatellite stable colorectal cancer

Tumour-associated macrophages (TAMs) express a continuum of phenotypes ranging from an anti-tumoural M1-like phenotype to a pro-tumoural M2-like phenotype. During cancer progression, TAMs may shift to a more M2-like polarisation state, but the role of TAMs in CRC metastases is unclear. We conducted a comprehensive spatial and prognostic analysis of TAMs in CRC pulmonary metastases and corresponding primary tumours using multiplexed immunohistochemistry and machine learning-based image analysis. We obtained data from 106 resected pulmonary metastases and 74 corresponding primary tumours. TAMs in the resected pulmonary metastases were located closer to the cancer cells and presented a more M2-like polarised state in comparison to the primary tumours. Higher stromal M2-like macrophage densities in the invasive margin of pulmonary metastases were associated with worse 5-year overall survival (HR 3.19, 95% CI 1.35-7.55, p = 0.008). The results of this study highlight the value of multiplexed analysis of macrophage polarisation in cancer metastases and might have clinical implications in future cancer therapy.

Bifidobacterium longum SX-1326 ameliorates gastrointestinal toxicity after irinotecan chemotherapy via modulating the P53 signaling pathway and brain-gut axis

BACKGROUND

Colorectal cancer (CRC) is a prevalent malignant malignancy affecting the gastrointestinal tract that is usually treated clinically with chemotherapeutic agents, whereas chemotherapeutic agents can cause severe gastrointestinal toxicity, which brings great pain to patients. Therefore, finding effective adjuvant agents for chemotherapy is crucial.

METHODS

In this study, a CRC mouse model was successfully constructed using AOM/DSS, and the treatment was carried out by probiotic Bifidobacterium longum SX-1326 (B. longum SX-1326) in combination with irinotecan. Combining with various techniques of modern biomedical research, such as Hematoxylin and Eosin (H&E), Immunohistochemistry (IHC), Western blotting and 16S rDNA sequencing, we intend to elucidate the effect and mechanism of B. longum SX-1326 in improving the anticancer efficacy and reducing the side effects on the different levels of molecules, animals, and bacteria.

RESULTS

Our results showed that B. longum SX-1326 enhanced the expression of Cleaved Caspase-3 (M vs. U = p < 0.01) and down-regulated the expression level of B-cell lymphoma-2 (Bcl-2) through up-regulation of the p53 signaling pathway in CRC mice, which resulted in an adjuvant effect on the treatment of CRC with irinotecan. Moreover, B. longum SX-1326 was also able to regulate the gut-brain-axis (GBA) by restoring damaged enterochromaffin cells, reducing the release of 5-hydroxytryptamine (5-HT) in brain tissue (I vs. U = 89.26 vs. 75.03, p < 0.05), and further alleviating the adverse effects of nausea and vomiting. In addition, B. longum SX-1326 reversed dysbiosis in CRC model mice by increasing the levels of Dehalobacterium, Ruminnococcus, and Mucispirillum. And further alleviated colorectal inflammation by downregulating the TLR4/MyD88/NF-κB signaling pathway.

CONCLUSIONS

In conclusion, our work reveals that B. longum SX-1326 has a favorable effect in adjuvant irinotecan for CRC and amelioration of post-chemotherapy side effects, and also provides the theoretical basis and data for finding a safe and efficient chemotherapeutic adjuvant.

Machine Learning Quantification of Intraepithelial Tumor-Infiltrating Lymphocytes as a Significant Prognostic Factor in High-Grade Serous Ovarian Carcinomas

The prognostic and predictive role of tumor-infiltrating lymphocytes (TILs) has been demonstrated in various neoplasms. The few publications that have addressed this topic in high-grade serous ovarian carcinoma (HGSOC) have approached TIL quantification from a semiquantitative standpoint. Clinical correlation studies, therefore, need to be conducted based on more accurate TIL quantification. We created a machine learning system based on H&E-stained sections using 76 molecularly and clinically well-characterized advanced HGSOC. This system enabled immune cell classification. These immune parameters were subsequently correlated with overall survival (OS) and progression-free survival (PFI). An intense colonization of the tumor cords by TILs was associated with a better prognosis. Moreover, the multivariate analysis showed that the intraephitelial (ie) TILs concentration was an independent and favorable prognostic factor both for OS (p = 0.02) and PFI (p = 0.001). A synergistic effect between complete surgical cytoreduction and high levels of ieTILs was evidenced, both in terms of OS (p = 0.0005) and PFI (p = 0.0008). We consider that digital analysis with machine learning provided a more accurate TIL quantification in HGSOC. It has been demonstrated that ieTILs quantification in H&E-stained slides is an independent prognostic parameter. It is possible that intraepithelial TIL quantification could help identify candidate patients for immunotherapy.

Deep learning-derived spatial organization features on histology images predicts prognosis in colorectal liver metastasis patients after hepatectomy

Histopathological images of colorectal liver metastases (CRLM) contain rich morphometric information that may predict patients' outcomes. However, to our knowledge, no study has reported any practical deep learning framework based on the histology images of CRLM, and their direct association with prognosis remains largely unknown. In this study, we developed a deep learning-based framework for fully automated tissue classification and quantification of clinically relevant spatial organization features (SOFs) in H&E-stained images of CRLM. The SOFs based risk-scoring system demonstrated a strong and robust prognostic value that is independent of the current clinical risk score (CRS) system in independent clinical cohorts. Our framework enables fully automated tissue classification of H&E images of CRLM, which could significantly reduce assessment subjectivity and the workload of pathologists. The risk-scoring system provides a time- and cost-efficient tool to assist clinical decision-making for patients with CRLM, which could potentially be implemented in clinical practice.

Artificial intelligence in colorectal surgery: an AI-powered systematic review

Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications are and what can be reasonably expected. This AI-powered review assessed the role of AI in colorectal surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of PubMed, Embase, Scopus, Cochrane Library databases, and gray literature was conducted on all available articles on AI in colorectal surgery (from January 1 1997 to March 1 2021), aiming to define the perioperative applications of AI. Potentially eligible studies were identified using novel software powered by natural language processing (NLP) and machine learning (ML) technologies dedicated to systematic reviews. Out of 1238 articles identified, 115 were included in the final analysis. Available articles addressed the role of AI in several areas of interest. In the preoperative phase, AI can be used to define tailored treatment algorithms, support clinical decision-making, assess the risk of complications, and predict surgical outcomes and survival. Intraoperatively, AI-enhanced surgery and integration of AI in robotic platforms have been suggested. After surgery, AI can be implemented in the Enhanced Recovery after Surgery (ERAS) pathway. Additional areas of applications included the assessment of patient-reported outcomes, automated pathology assessment, and research. Available data on these aspects are limited, and AI in colorectal surgery is still in its infancy. However, the rapid evolution of technologies makes it likely that it will increasingly be incorporated into everyday practice.

High PD-L1 Expression Correlates with an Immunosuppressive Tumour Immune Microenvironment and Worse Prognosis in ALK-Rearranged Non-Small Cell Lung Cancer

High tumour programmed cell death-ligand 1 (PD-L1) expression is associated with poor progression-free survival (PFS) after tyrosine kinase inhibitor (TKI) therapy in ALK-rearranged non-small cell lung cancer (NSCLC). However, the characteristics of the tumour microenvironment (TME) and their prognostic values in ALK-rearranged NSCLC are unknown. Here, we collected tumour tissues from pretreated ALK-rearranged NSCLC patients, immunohistochemical staining was used to assess PD-L1 expression, and tumour-infiltrating immune cells were determined via multiplex immunofluorescence staining (mIF). Our data showed that the median values of PFS for the high PD-L1 group and low PD-L1 group who received ALK-TKI treatment were 4.4 and 16.4 months, respectively (p = 0.008). The median overall survival (OS) of the two groups was 24.0 months and not reached, respectively (p = 0.021). Via univariate and multivariate analyses, a high PD-L1 expression and a worse ECOG PS were determined to be independent prognostic factors of OS (HR = 3.35, 95% CI: 1.23-9.11, p = 0.018; HR = 6.42, 95% CI: 1.45-28.44, p = 0.014, respectively). In addition, the high PD-L1 group had increased Tregs and exhausted CD8+ T cells in both the tumour and stroma (all p < 0.05). High PD-L1 expression was an adverse predictive and prognostic biomarker for ALK-rearranged NSCLC. The characteristics of the TME in patients with high PD-L1 expression were shown to have an immunosuppressive status.

Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients

Histopathologic assessment is indispensable for diagnosing colorectal cancer (CRC). However, manual evaluation of the diseased tissues under the microscope cannot reliably inform patient prognosis or genomic variations crucial for treatment selections. To address these challenges, we develop the Multi-omics Multi-cohort Assessment (MOMA) platform, an explainable machine learning approach, to systematically identify and interpret the relationship between patients' histologic patterns, multi-omics, and clinical profiles in three large patient cohorts (n = 1888). MOMA successfully predicts the overall survival, disease-free survival (log-rank test P-value<0.05), and copy number alterations of CRC patients. In addition, our approaches identify interpretable pathology patterns predictive of gene expression profiles, microsatellite instability status, and clinically actionable genetic alterations. We show that MOMA models are generalizable to multiple patient populations with different demographic compositions and pathology images collected from distinctive digitization methods. Our machine learning approaches provide clinically actionable predictions that could inform treatments for colorectal cancer patients.

Spatially resolved multimarker evaluation of CD274 (PD-L1)/PDCD1 (PD-1) immune checkpoint expression and macrophage polarisation in colorectal cancer

Background

The CD274 (PD-L1)/PDCD1 (PD-1) immune checkpoint interaction may promote cancer progression, but the expression patterns and prognostic significance of PD-L1 and PD-1 in the colorectal cancer microenvironment are inadequately characterised.

Methods

We used a custom 9-plex immunohistochemistry assay to quantify the expression patterns of PD-L1 and PD-1 in macrophages, T cells, and tumour cells in 910 colorectal cancer patients. We evaluated cancer-specific mortality according to immune cell subset densities using multivariable Cox regression models.

Results

Compared to PD-L1– macrophages, PD-L1+ macrophages were more likely M1-polarised than M2-polarised and located closer to tumour cells. PD-L1+ macrophage density in the invasive margin associated with longer cancer-specific survival [Ptrend = 0.0004, HR for the highest vs. lowest quartile, 0.52; 95% CI: 0.34–0.78]. T cell densities associated with longer cancer-specific survival regardless of PD-1 expression (Ptrend < 0.005 for both PD-1+ and PD-1– subsets). Higher densities of PD-1+ T cell/PD-L1+ macrophage clusters associated with longer cancer-specific survival (Ptrend < 0.005).

Conclusions

PD-L1+ macrophages show distinct polarisation profiles (more M1-like), spatial features (greater co-localisation with tumour cells and PD-1+ T cells), and associations with favourable clinical outcome. Our comprehensive multimarker assessment could enhance the understanding of immune checkpoints in the tumour microenvironment and promote the development of improved immunotherapies.

Quantitative Analysis of Inflammatory Uterine Lesions of Pregnant Gilts with Digital Image Analysis Following Experimental PRRSV-1 Infection

Reproductive disorders caused by porcine reproductive and respiratory syndrome virus-1 are not yet fully characterized. We report QuPath-based digital image analysis to count inflammatory cells in 141 routinely, and 35 CD163 immunohistochemically stained endometrial slides of vaccinated or unvaccinated pregnant gilts inoculated with a high or low virulent PRRSV-1 strain. To illustrate the superior statistical feasibility of the numerical data determined by digital cell counting, we defined the association between the number of these cells and endometrial, placental, and fetal features. There was strong concordance between the two manual scorers. Distributions of total cell counts and endometrial and placental qPCR results differed significantly between examiner1's endometritis grades. Total counts' distribution differed significantly between groups, except for the two unvaccinated. Higher vasculitis scores were associated with higher endometritis scores, and higher total cell counts were expected with high vasculitis/endometritis scores. Cell number thresholds of endometritis grades were determined. A significant correlation between fetal weights and total counts was shown in unvaccinated groups, and a significant positive correlation was found between these counts and endometrial qPCR results. We revealed significant negative correlations between CD163+ counts and qPCR results of the unvaccinated group infected with the highly virulent strain. Digital image analysis was efficiently applied to assess endometrial inflammation objectively.

Analysis of Spatial Molecular Data

Digital analysis of pathology whole-slide images has been recently gaining interest in the context of cancer diagnosis and treatment. In particular, deep learning methods have demonstrated significant potential in supporting pathology analysis, recently detecting molecular traits never before recognized in pathology H&E whole-slide images (WSIs). Alongside these advancements in the digital analysis of WSIs, it is becoming increasingly evident that both spatial and overall tumor heterogeneity may be significant determinants of cancer prognosis and treatment outcome. In this chapter, we describe methods that aim to support these two elements. We describe both an end-to-end deep learning pipeline for producing limited spatial transcriptomics from WSIs with associated bulk gene expression data, as well as an algorithm for quantifying spatial tumor heterogeneity based on the results of this pipeline.

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.

Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC

Importance

Currently, predictive biomarkers for response to immune checkpoint inhibitor (ICI) therapy in lung cancer are limited. Identifying such biomarkers would be useful to refine patient selection and guide precision therapy.

Objective

To develop a machine-learning (ML)-based tumor-infiltrating lymphocytes (TILs) scoring approach, and to evaluate TIL association with clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC).

Design, Setting, and Participants

This multicenter retrospective discovery-validation cohort study included 685 ICI-treated patients with NSCLC with median follow-up of 38.1 and 43.3 months for the discovery (n = 446) and validation (n = 239) cohorts, respectively. Patients were treated between February 2014 and September 2021. We developed an ML automated method to count tumor, stroma, and TIL cells in whole-slide hematoxylin-eosin-stained images of NSCLC tumors. Tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) expression were assessed separately, and clinical response to ICI therapy was determined by medical record review. Data analysis was performed from June 2021 to April 2022.

Exposures

All patients received anti-PD-(L)1 monotherapy.

Main Outcomes and Measures

Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were determined by blinded medical record review. The area under curve (AUC) of TIL levels, TMB, and PD-L1 in predicting ICI response were calculated using ORR.

Results

Overall, there were 248 (56%) women in the discovery cohort and 97 (41%) in the validation cohort. In a multivariable analysis, high TIL level (≥250 cells/mm2) was independently associated with ICI response in both the discovery (PFS: HR, 0.71; P = .006; OS: HR, 0.74; P = .03) and validation (PFS: HR = 0.80; P = .01; OS: HR = 0.75; P = .001) cohorts. Survival benefit was seen in both first- and subsequent-line ICI treatments in patients with NSCLC. In the discovery cohort, the combined models of TILs/PD-L1 or TMB/PD-L1 had additional specificity in differentiating ICI responders compared with PD-L1 alone. In the PD-L1 negative (<1%) subgroup, TIL levels had superior classification accuracy for ICI response (AUC = 0.77) compared with TMB (AUC = 0.65).

Conclusions and Relevance

In these cohorts, TIL levels were robustly and independently associated with response to ICI treatment. Patient TIL assessment is relatively easily incorporated into the workflow of pathology laboratories at minimal additional cost, and may enhance precision therapy.

Tumor-Associated Neutrophils in Colorectal Cancer Development, Progression and Immunotherapy

The colorectal-cancer (CRC) incidence rate and mortality have remained high for several years. In recent years, immune-checkpoint-inhibitor (ICI) therapy has rapidly developed. However, it is only effective in a few CRC patients with microsatellite-instability-high (MSI-H) or mismatch-repair-deficient (dMMR) CRC. How to improve the efficiency of ICI therapy in CRC patients with microsatellite stability (MSS) remains a huge obstacle. Tumor-associated neutrophils (TANs), which are similar to macrophages, also have N1 and N2 phenotypes. They can be recruited and polarized through different cytokines or chemokines, and then play an antitumor or tumor-promoting role. In CRC, we find that the prognostic significance of TANs is still controversial. In this review, we describe the antitumor regulation of TANs, and their mechanism of promoting tumor progression by boosting the transformation of inflammation into tumors, facilitating tumor-cell proliferation, metastasis and angiogenesis. The targeting of TANs combined with ICIs may be a new treatment model for CRC. Relevant animal experiments have shown good responses, and clinical trials have also been carried out in succession. TANs, as “assistants” of ICI treatment, may become the key to the success of CRC immunotherapy, although no significant results have been obtained.

Survey on Mental Health Status and Quality of Life and Correlation among Patients with Permanent Stoma of Colorectal Tumor

Background

Colorectal cancer is one of the malignant tumors of the digestive system relatively hidden onset with unobvious early clinical symptoms. Most patients have developed into middle and late stages when they were diagnosed, missing the best period of operation. Advanced colorectal cancer has strong diffusion and metastasis with short survival time, which seriously threatens the life safety of patients.

Objective

To investigate the mental health status and quality of life and the relationship between them in patients with permanent stoma of colorectal cancer.

Methods

In this study, a case-control study was conducted to select 80 patients (stoma group) with colorectal cancer treated by permanent stoma surgery in our hospital from January 2020 to June 2021 and 80 patients (control group) with colorectal cancer treated by sphincter-preserving surgery at the same time. The psychological health degree and quality of life of the two groups of patients were evaluated by the psychological resilience scale (CD-RISC), the positive psychological capital questionnaire (PPQ), and the cancer patient quality of life-specific scale (QOL-LC), and a linear correlation model was used to analyze the correlation of CD-RISC score, PPQ score, and QOL-LC score.

Results

The total scores of tenacity, optimism, self-improvement, and resilience of the patients in the stoma group were significantly lower than those in the control one, and the difference between them was statistically significant (P < 0.05); the four dimensions of self-efficacy, optimism, hope, and resilience and the total score of PPQ of patients in the stoma group were significantly lower than those in the control group, and all of the differences were statistically significant (P < 0.05); the somatic function, psychological function, symptoms of side effects, social function, and the total QOL-LC score of patients in the stoma one were significantly lower than those in the control one, and all of the differences were statistically significant (P < 0.05); the total QOL-LC score of patients in the stoma group showed a significant positive correlation with PPQ score and CD-RISC score (r = 0.511 and r = 0.608, P < 0.01).

Conclusion

The overall level of mental health and life quality of patients with permanent stoma of colorectal cancer was worse than that of patients without stoma measures, and there was a certain correlation between patients' mental health and quality of life.

Prognostic significance of spatial and density analysis of T lymphocytes in colorectal cancer

BACKGROUND

Although high T cell density is a strong favourable prognostic factor in colorectal cancer, the significance of the spatial distribution of T cells is incompletely understood. We aimed to evaluate the prognostic significance of tumour cell-T cell co-localisation and T cell densities.

METHODS

We analysed CD3 and CD8 immunohistochemistry in a study cohort of 983 colorectal cancer patients and a validation cohort (N = 246). Individual immune and tumour cells were identified to calculate T cell densities (to derive T cell density score) and G-cross function values, estimating the likelihood of tumour cells being co-located with T cells within 20 µm radius (to derive T cell proximity score).

RESULTS

High T cell proximity score associated with longer cancer-specific survival in both the study cohort [adjusted HR for high (vs. low) 0.33, 95% CI 0.20-0.52, P_trend < 0.0001] and the validation cohort [adjusted HR for high (vs. low) 0.15, 95% CI 0.05-0.45, P_trend < 0.0001] and its prognostic value was independent of T cell density score.

CONCLUSIONS

The spatial point pattern analysis of tumour cell-T cell co-localisation could provide detailed information on colorectal cancer prognosis, supporting the value of spatial measurement of T cell infiltrates as a novel, robust tumour-immune biomarker.

An integrative transcriptome analysis reveals potential predictive, prognostic biomarkers and therapeutic targets in colorectal cancer

BACKGROUND

A deep understanding of potential molecular biomarkers and therapeutic targets related to the progression of colorectal cancer (CRC) from early stages to metastasis remain mostly undone. Moreover, the regulation and crosstalk among different cancer-driving molecules including messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs) and micro-RNAs (miRNAs) in the transition from stage I to stage IV remain to be clarified, which is the aim of this study.

METHODS

We carried out two separate differential expression analyses for two different sets of samples (stage-specific samples and tumor/normal samples). Then, by the means of robust dataset analysis we identified distinct lists of differently expressed genes (DEGs) for Robust Rank Aggregation (RRA) and weighted gene co-expression network analysis (WGCNA). Then, comprehensive computational systems biology analyses including mRNA-miRNA-lncRNA regulatory network, survival analysis and machine learning algorithms were also employed to achieve the aim of this study. Finally, we used clinical samples to carry out validation of a potential and novel target in CRC.

RESULTS

We have identified the most significant stage-specific DEGs by combining distinct results from RRA and WGCNA. After finding stage-specific DEGs, a total number of 37 DEGs were identified to be conserved across all stages of CRC (conserved DEGs). We also found DE-miRNAs and DE-lncRNAs highly associated to these conserved DEGs. Our systems biology approach led to the identification of several potential therapeutic targets, predictive and prognostic biomarkers, of which lncRNA LINC00974 shown as an important and novel biomarker.

CONCLUSIONS

Findings of the present study provide new insight into CRC pathogenesis across all stages, and suggests future assessment of the functional role of lncRNA LINC00974 in the development of CRC.

Understanding Tricky Cellular and Molecular Interactions in Pancreatic Tumor Microenvironment: New Food for Thought

Pancreatic ductal adenocarcinoma (PDAC) represents 90% of all pancreatic cancer cases and shows a high mortality rate among all solid tumors. PDAC is often associated with poor prognosis, due to the late diagnosis that leads to metastasis development, and limited efficacy of available treatments. The tumor microenvironment (TME) represents a reliable source of novel targets for therapy, and even if many of the biological interactions among stromal, immune, and cancer cells that populate the TME have been studied, much more needs to be clarified. The great limitation in the efficacy of current standard chemoterapy is due to both the dense fibrotic inaccessible TME barrier surrounding cancer cells and the immunological evolution from a tumor-suppressor to an immunosuppressive environment. Nevertheless, combinatorial therapies may prove more effective at overcoming resistance mechanisms and achieving tumor cell killing. To achieve this result, a deeper understanding of the pathological mechanisms driving tumor progression and immune escape is required in order to design rationale-based therapeutic strategies. This review aims to summarize the present knowledge about cellular interactions in the TME, with much attention on immunosuppressive functioning and a specific focus on extracellular matrix (ECM) contribution.

Tumor Immunogenomic Features Determine Outcomes in Patients with Metastatic Colorectal Cancer Treated with Standard-of-Care Combinations of Bevacizumab and Cetuximab

PURPOSE

CALGB/SWOG 80405 was a randomized phase III trial in first-line patients with metastatic colorectal cancer treated with bevacizumab, cetuximab, or both, plus chemotherapy. We tested the effect of tumor immune features on overall survival (OS).

EXPERIMENTAL DESIGN

Primary tumors (N = 554) were profiled by RNA sequencing. Immune signatures of macrophages, lymphocytes, TGFβ, IFNγ, wound healing, and cytotoxicity were measured. CIBERSORTx scores of naive and memory B cells, plasma cells, CD8+ T cells, resting and activated memory CD4+ T cells, M0 and M2 macrophages, and activated mast cells were measured.

RESULTS

Increased M2 macrophage score [HR, 6.30; 95% confidence interval (CI), 3.0-12.15] and TGFβ signature expression (HR, 1.35; 95% CI, 1.05-1.77) were associated with shorter OS. Increased scores of plasma cells (HR, 0.55; 95% CI, 0.38-0.87) and activated memory CD4+ T cells (HR, 0.34; 95% CI, 0.16-0.65) were associated with longer OS. Using optimal cutoffs from these four features, patients were categorized as having either 4, 3, 2, or 0-1 beneficial features associated with longer OS, and the median (95% CI) OS decreased from 42.5 (35.8-47.8) to 31.0 (28.8-34.4), 25.2 (20.6-27.9), and 17.7 (13.5-20.4) months respectively (P = 3.48e-11).

CONCLUSIONS

New immune features can be further evaluated to improve patient response. They provide the rationale for more effective immunotherapy strategies.

SERPINH1 is a Potential Prognostic Biomarker and Correlated With Immune Infiltration: A Pan-Cancer Analysis

Background: Serpin peptidase inhibitor clade H, member 1 (SERPINH1) is a gene encoding a member of the serpin superfamily of serine proteinase inhibitors. The upregulated of SERPINH1 was associated with poor prognosis in breast cancer, stomach adenocarcinoma, and esophageal carcinoma. However, the role of SERPINH1 in pan-cancer is largely unexplored.

Methods: SERPINH1 expression and the correlation with prognosis in human pan-cancer were analyzed by the Cancer Genome Atlas and the Genotype-Tissue Expression dataset. Pearson correlation analysis was applied to evaluate the role of SERPINH1 expression in tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), DNA methyltransferase, and common immunoregulators. Spearman’s correlation test was used to analysis SERPINH1 expression in tumor immune infiltration and infiltrating immune cells via the Tumor Immune Evaluation Resource database. Furtherly, immunohistochemistry staining of SERPINH1 was acquired from the Human Protein Atlas database for validation.

Results: SERPINH1 was abnormally expressed in fourteen cancers. The high expression of SERPINH1 significantly reduced the overall survival (OS), disease-specific survival, and progression free interval in eleven cancers. Moreover, SERPINH1 expression was correlated with MMR, MSI, TMB, and DNA methylation in multiple types of cancer. Also, SERPINH1 expression showed strong association with immunoregulators and immune checkpoint markers in testicular germ cell tumors, brain lower grade glioma (LGG), pheochromocytoma and paraganglioma. In addition, SERPINH1 expression was related to immune cell infiltration in multiple cancers, particularly in breast invasive carcinoma, LGG, and liver hepatocellular carcinoma. The result of immunohistochemistry verification shown that SERPINH1 staining was higher in tumor samples than in normal tissue in colon adenocarcinoma, head and neck squamous cell carcinoma, kidney renal papillary cell carcinoma and cervical squamous cell carcinoma, which was consistent with the result of OS.

Conclusion: Overall, these results indicate that SERPINH1 may serve as an important prognostic biomarker and correlate with tumor immunity in human pan-cancer.

Desmoplastic Reaction, Immune Cell Response, and Prognosis in Colorectal Cancer

Background

The relationships between tumor stromal features (such as desmoplastic reaction, myxoid stroma, and keloid-like collagen bundles) and immune cells in the colorectal carcinoma microenvironment have not yet been fully characterized.

Methods

In 908 tumors with available tissue among 4,465 incident colorectal adenocarcinoma cases in two prospective cohort studies, we examined desmoplastic reaction, myxoid stroma, and keloid-like collagen bundles. We conducted multiplex immunofluorescence for T cells [CD3, CD4, CD8, CD45RO (PTPRC), and FOXP3] and for macrophages [CD68, CD86, IRF5, MAF, and MRC1 (CD206)]. We used the inverse probability weighting method and the 4,465 incident cancer cases to adjust for selection bias.

Results

Immature desmoplastic reaction was associated with lower densities of intraepithelial CD3⁺CD8⁺CD45RO⁺ cells [multivariable odds ratio (OR) for the highest (vs. lowest) density category, 0.43; 95% confidence interval (CI), 0.29-0.62; P_trend <0.0001] and stromal M1-like macrophages [the corresponding OR, 0.44; 95% CI, 0.28-0.70; P_trend = 0.0011]. Similar relations were observed for myxoid stroma [intraepithelial CD3⁺CD8⁺CD45RO⁺ cells (P_trend <0.0001) and stromal M1-like macrophages (P_trend = 0.0007)] and for keloid-like collagen bundles (P_trend <0.0001 for intraepithelial CD3⁺CD8⁺CD45RO⁺ cells). In colorectal cancer-specific survival analyses, multivariable-adjusted hazard ratios (with 95% confidence intervals) were 0.32 (0.23-0.44; P_trend <0.0001) for mature (vs. immature) desmoplastic reaction, 0.25 (0.16-0.39; P_trend <0.0001) for absent (vs. marked) myxoid stroma, and 0.12 (0.05-0.28; P_trend <0.0001) for absent (vs. marked) keloid-like collagen bundles.

Conclusions

Immature desmoplastic reaction and myxoid stroma were associated with lower densities of tumor intraepithelial memory cytotoxic T cells and stromal M1-like macrophages, likely reflecting interactions between tumor, immune, and stromal cells in the colorectal tumor microenvironment.

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence

Immunotherapies play critical roles in cancer treatment. However, given that only a few patients respond to immune checkpoint blockades and other immunotherapeutic strategies, more novel technologies are needed to decipher the complicated interplay between tumor cells and the components of the tumor immune microenvironment (TIME). Tumor immunomics refers to the integrated study of the TIME using immunogenomics, immunoproteomics, immune-bioinformatics, and other multi-omics data reflecting the immune states of tumors, which has relied on the rapid development of next-generation sequencing. High-throughput genomic and transcriptomic data may be utilized for calculating the abundance of immune cells and predicting tumor antigens, referring to immunogenomics. However, as bulk sequencing represents the average characteristics of a heterogeneous cell population, it fails to distinguish distinct cell subtypes. Single-cell-based technologies enable better dissection of the TIME through precise immune cell subpopulation and spatial architecture investigations. In addition, radiomics and digital pathology-based deep learning models largely contribute to research on cancer immunity. These artificial intelligence technologies have performed well in predicting response to immunotherapy, with profound significance in cancer therapy. In this review, we briefly summarize conventional and state-of-the-art technologies in the field of immunogenomics, single-cell and artificial intelligence, and present prospects for future research.

Refining the Molecular Framework for Pancreatic Cancer with Single-cell and Spatial Technologies

Pancreatic ductal adenocarcinoma (PDAC) is a treatment-refractory malignancy in urgent need of a molecular framework for guiding therapeutic strategies. Bulk transcriptomic efforts over the past decade have yielded two broad consensus subtypes: classical pancreatic/epithelial versus basal-like/squamous/quasi-mesenchymal. Although this binary classification enables prognostic stratification, it does not currently inform the administration of treatments uniquely sensitive to either subtype. Furthermore, bulk mRNA studies are challenged by distinguishing contributions from the neoplastic compartment versus other cell types in the microenvironment, which is accentuated in PDAC given that neoplastic cellularity can be low. The application of single-cell transcriptomics to pancreatic tumors has generally lagged behind other cancer types due in part to the difficulty of extracting high-quality RNA from enzymatically degradative tissue, but emerging studies have and will continue to shed light on intratumoral heterogeneity, malignant-stromal interactions, and subtle transcriptional programs previously obscured at the bulk level. In conjunction with insights provided by single-cell/nucleus dissociative techniques, spatially resolved technologies should also facilitate the contextualization of gene programs and inferred cell-cell interactions within the tumor architecture. Finally, given that patients often receive neoadjuvant chemotherapy and/or chemoradiotherapy even in resectable disease, deciphering the gene programs enriched in or induced by cytotoxic therapy will be crucial for developing insights into complementary treatments aimed at eradicating residual cancer cells. Taken together, single-cell and spatial technologies provide an unprecedented opportunity to refine the foundations laid by prior bulk molecular studies and significantly augment precision oncology efforts in pancreatic cancer.

Prognostic significance of myeloid immune cells and their spatial distribution in the colorectal cancer microenvironment

Background

Myeloid cells represent an abundant yet heterogeneous cell population in the colorectal cancer microenvironment, and their roles remain poorly understood.

Methods

We used multiplexed immunofluorescence combined with digital image analysis to identify CD14⁺ monocytic and CD15⁺ granulocytic cells and to evaluate their maturity (HLA-DR and CD33), immunosuppressive potential (ARG1) and proximity to cytokeratin (KRT)-positive tumor cells in 913 colorectal carcinomas. Using covariate data of 4465 incident colorectal cancers in two prospective cohort studies, the inverse probability weighting method was used with multivariable-adjusted Cox proportional hazards models to assess cancer-specific mortality according to ordinal quartiles (Q1–Q4) of myeloid cell densities. Immune cell–tumor cell proximity was measured with the nearest neighbor method and the G-cross function, which determines the likelihood of any tumor cell having at least one immune cell of the specified type within a certain radius.

Results

Higher intraepithelial (P_trend=0.0002; HR for Q4 (vs Q1), 0.48, 95% CI 0.31 to 0.76) and stromal (P_trend <0.0001; HR for Q4 (vs Q1), 0.42, 95% CI 0.29 to 0.63) densities of CD14⁺HLA-DR⁺ cells were associated with lower colorectal cancer-specific mortality while, conversely, higher intraepithelial densities of CD14⁺HLA-DR⁻ cells were associated with higher colorectal cancer-specific mortality (P_trend=0.0003; HR for Q4 (vs Q1), 1.78, 95% CI 1.25 to 2.55). Spatial analyses indicated that CD15⁺ cells were located closer to tumor cells than CD14⁺ cells, and CD14⁺HLA-DR⁺ cells were closer to tumor than CD14⁺HLA-DR⁻ cells (p<0.0001). The G-cross proximity measurement, evaluating the difference in the likelihood of any tumor cell being colocated with at least one CD14⁺HLA-DR⁺ cell versus CD14⁺HLA-DR⁻ cell within a 20 µm radius, was associated with lower colorectal cancer-specific mortality (P_trend <0.0001; HR for Q4 (vs Q1), 0.37, 95% CI 0.24 to 0.57).

Conclusions

Myeloid cell populations occur in spatially distinct distributions and exhibit divergent, subset-specific prognostic significance in colorectal cancer, with mature CD14⁺HLA-DR⁺ and immature CD14⁺HLA-DR⁻ monocytic phenotypes most notably showing opposite associations. These results highlight the prognostic utility of multimarker evaluation of myeloid cell infiltrates and reveal a previously unrecognized degree of spatial organization for myeloid cells in the immune microenvironment.

Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters

AIMS

To develop and validate a deep learning algorithm to quantify a broad spectrum of histological features in colorectal carcinoma.

METHODS AND RESULTS

A deep learning algorithm was trained on haematoxylin and eosin-stained slides from tissue microarrays of colorectal carcinomas (N = 230) to segment colorectal carcinoma digitised images into 13 regions and one object. The segmentation algorithm demonstrated moderate to almost perfect agreement with interpretations by gastrointestinal pathologists, and was applied to an independent test cohort of digitised whole slides of colorectal carcinoma (N = 136). The algorithm correctly classified mucinous and high-grade tumours, and identified significant differences between mismatch repair-proficient and mismatch repair-deficient (MMRD) tumours with regard to mucin, inflammatory stroma, and tumour-infiltrating lymphocytes (TILs). A cutoff of >44.4 TILs per mm² carcinoma gave a sensitivity of 88% and a specificity of 73% in classifying MMRD carcinomas. Algorithm measures of tumour budding (TB) and poorly differentiated clusters (PDCs) outperformed TB grade derived from routine sign-out, and compared favourably with manual counts of TB/PDCs with regard to lymphatic, venous and perineural invasion. Comparable associations were seen between algorithm measures of TB/PDCs and manual counts of TB/PDCs for lymph node metastasis (all P < 0.001); however, stronger correlations were seen between the proportion of positive lymph nodes and algorithm measures of TB/PDCs. Stronger associations were also seen between distant metastasis and algorithm measures of TB/PDCs (P = 0.004) than between distant metastasis and TB (P = 0.04) and TB/PDC counts (P = 0.06).

CONCLUSIONS

Our results highlight the potential of deep learning to identify and quantify a broad spectrum of histological features in colorectal carcinoma.

The Prognostic Role of Macrophage Polarization in the Colorectal Cancer Microenvironment

Macrophages are among the most common cells in the colorectal cancer microenvironment, but their prognostic significance is incompletely understood. Using multiplexed immunofluorescence for CD68, CD86, IRF5, MAF, MRC1 (CD206), and KRT (cytokeratins) combined with digital image analysis and machine learning, we assessed the polarization spectrum of tumor-associated macrophages in 931 colorectal carcinomas. We then applied Cox proportional hazards regression to assess prognostic survival associations of intraepithelial and stromal densities of M1-like and M2-like macrophages while controlling for potential confounders, including stage and microsatellite instability status. We found that high tumor stromal density of M2-like macrophages was associated with worse cancer-specific survival, whereas tumor stromal density of M1-like macrophages was not significantly associated with better cancer-specific survival. High M1:M2 density ratio in tumor stroma was associated with better cancer-specific survival. Overall macrophage densities in tumor intraepithelial or stromal regions were not prognostic. These findings suggested that macrophage polarization state, rather than their overall density, was associated with cancer-specific survival, with M1- and M2-like macrophage phenotypes exhibiting distinct prognostic roles. These results highlight the utility of a multimarker strategy to assess the macrophage polarization at single-cell resolution within the tumor microenvironment.

Primary tumors from mucosal barrier organs drive unique eosinophil infiltration patterns and clinical associations

Eosinophils are bone marrow-derived granulocytes that display key effector functions in allergic diseases. Nonetheless, recent data highlight important roles for eosinophils in the tumor microenvironment (TME). Eosinophils have been attributed with pleiotropic and perhaps conflicting functions, which may be attributed at least in part to variations in eosinophil quantitation in the TME. Thus, a reliable, quantitative, and robust method for the assessment of eosinophilic infiltration in the TME is required. This type of methodology could standardize the identification of these cells and promote the subsequent generation of hypothesis-driven mechanistic studies. To this end, we conducted a comprehensive analysis of multiple primary tumors from distinct anatomical sites using a standardized method. Bioinformatics analysis of 10,469 genomically profiled primary tumors revealed that eosinophil abundance within different tumors can be categorized into three groups representing tumors with high, intermediate, and low eosinophil levels. Consequently, eosinophil abundance, as well as spatial distribution, was determined in tissue tumor arrays of six tumors representing all three classifications (colon and esophagus - high; lung - intermediate; cervix, ovary, and breast - low). With the exception of breast cancer, eosinophils were mainly localized in the tumor stroma. Importantly, the tumor anatomical site was identified as the primary predictive factor of eosinophil stromal density highlighting a distinction between mucosal-barrier organs versus non-mucosal barrier organs. These findings enhance our understanding of eosinophil diversity in the TME and provide a compelling rationale for future experiments assessing the activity of these cells.

Artificial intelligence for the study of colorectal cancer tissue slides

Artificial intelligence (AI) is gaining incredible momentum as a companion diagnostic in a number of fields in oncology. In the present mini-review, we summarize the main uses and findings of AI applied to the analysis of digital histopathological images of slides from colorectal cancer (CRC) patients. Machine learning tools have been developed to automatically and objectively recognize specific CRC subtypes, such as those with microsatellite instability and high lymphocyte infiltration that would optimally respond to specific therapies. Also, AI-based classification in distinct prognostic groups with no studies of the basic biological features of the tumor have been attempted in a methodological approach that we called “biology-agnostic”.