The immune score as a new possible approach for the classification of cancer

A novel immune-related genes prognosis biomarker for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is closely associated with the immune microenvironment. To identify the effective population before administering treatment, the establishment of prognostic immune biomarkers is crucial for early HCC diagnosis and treatment.

RESULTS

A total of 335 IRGs identified from 788 overlapping IRGs were associated with the survival of HCC. A prognostic immunoscore model was identified. The Kaplan-Meier survival curves and time-dependent ROC analysis revealed a powerful prognostic performance of immunoscore signature via multi validation. Besides, the immunoscore signature exhibited a better predictive power compared to other prognostic signatures. Gene set enrichment analysis showed multiple signaling differences between the high and low immunoscore group. Furthermore, immunoscore was significantly associated with multiple immune cells and immune infiltration in the tumor microenvironment.

CONCLUSIONS

We identified the immunoscore as a robust marker for predicting HCC patient survival.

METHODS

Three sets of immune-related genes (IRGs) were integrated to identify the overlapping IRGs. Weighted gene co-expression network analysis was performed to obtain the survival-related IRGs. Further, the prognostic immunoscore model was constructed via LASSO-penalized Cox regression analysis. Then the prognostic performance of immunoscore was evaluated. In addition, ESTIMATE and CIBERSORT algorithms were applied to explore the relationship between immunoscore and tumor immune microenvironment.

Interaction network of immune-associated genes affecting the prognosis of patients with glioblastoma

Glioblastoma multiforme (GBM) is a common malignant tumor type of the nervous system. The purpose of the present study was to establish a regulatory network of immune-associated genes affecting the prognosis of patients with GBM. The GSE4290, GSE50161 and GSE2223 datasets from the Gene Expression Omnibus database were screened to identify common differentially expressed genes (co-DEGs). A functional enrichment analysis indicated that the co-DEGs were mainly enriched in cell communication, regulation of enzyme activity, immune response, nervous system, cytokine signaling in immune system and the AKT signaling pathway. The co-DEGs accumulated in immune response were then further investigated. For this, the intersection of those co-DEGs and currently known immune-regulatory genes was obtained and a differential expression analysis of these overlapping immune-associated genes was performed. A risk model was established using immune-regulatory genes that affect the prognosis of patients with GBM. The risk score was significantly associated with the prognosis of patients with GBM and had a significant independent predictive value. The risk model had high accuracy in predicting the prognosis of patients with GBM [area under the receiver operating characteristic curve (AUC)=0.764], which was higher than that of a previously reported model of prognosis-associated biomarkers (AUC=0.667). Furthermore, an interaction network was constructed by using immune-regulatory genes and transcription factors affecting the prognosis of patients with GBM and the University of California Santa Cruz database was used to perform a preliminary analysis of the transcription factors and immune genes of interest. The interaction network of immune-regulatory genes constructed in the present study enhances the current understanding of mechanisms associated with poor prognosis of patients with GBM. The risk score model established in the present study may be used to evaluate the prognosis of patients with GBM.

Associations between tissue-based CD3+ T-lymphocyte count and colorectal cancer survival in a prospective cohort of older women

Tumor-infiltrating lymphocytes in colorectal cancer (CRC) predict better survival. However, associations between T-lymphocyte count in histologically normal tissues from patients with CRC and survival remain uncertain. We examined associations of CD3+ T-cells in colorectal tumor and histologically normal tissues with CRC-specific and all-cause mortality in the prospective Iowa Women's Health Study. Tissue microarrays were constructed using paraffin-embedded colorectal tissue samples from 464 women with tumor tissues and 314 women with histologically normal tissues (55-69 years at baseline) diagnosed with incident CRC from 1986 to 2002 and followed through 2014 (median follow-up 20.5 years). Three tumor and two histologically normal tissue cores for each patient were immunostained using CD3+ antibody and quantified, and the counts were averaged across the cores in each tissue. Cox proportional hazards regression estimated hazard ratios (HR) and 95% confidence interval (CI) for CRC-specific and all-cause mortality. After adjustment for age at diagnosis, body mass index, smoking status, tumor grade, and stage, HRs (95% CI) for the highest versus lowest tertile of tumor CD3+ score were 0.59 (0.38-0.89) for CRC-specific mortality and 0.82 (0.63-1.05) for all-cause mortality; for histologically normal CD3+ score, the corresponding HRs (95% CI) were 0.47 (0.19-1.17) and 0.50 (0.27-0.90), respectively. The CD3+ score combining the tumor and histologically normal scores was inversely associated with CRC-specific and all-cause mortality. Although the association between tumor CD3+ score and all-cause mortality was not significant, both higher CD3+ T-lymphocyte counts in tumor and histologically normal scores tended to be associated with lower CRC-specific and all-cause mortality.

Identification of immune-related genes with prognostic significance in the microenvironment of cutaneous melanoma

Cutaneous melanoma is one of the most aggressive cancers characterized by increasing incidence and mortality. In recent years, the emergence of immunotherapy has greatly raised the survival rate of patients suffering from cutaneous melanoma, yet some sufferers remain to have poor outcomes after treatment mainly due to the tumor microenvironment (TME). In this study, cutaneous melanoma-associated TME was systematically analyzed using the ESTIMATE algorithm based on the gene transcriptome data obtained from the TCGA database. Totally, 471 patients were included and 553 TME-related genes were screened. Afterwards, a 3-gene signature-based model (CLEC4A, GBP4, KIR2DL4) was constructed via univariate Cox, LASSO, and multivariate Cox regression analyses. To validate the validity of this model, ROC analysis was conducted, and the model was further validated to be an independent prognostic biomarker through univariate and multivariate regression analyses. Finally, the three genes in the model were studied by GSEA and GSVA for their biological significance. We found that the three genes could promote cancer immune response predominantly through affecting immune-related pathways such as antigen processing and presentation, and they may help tumor cells in escaping from surveillance of the immune system when their expression levels were decreased. Additionally, we as well discovered that the expression of the three genes was significantly and positively correlated with the infiltration of related immune cells, but negatively associated with tumor purity. Overall, this study comprehensively analyzed the TME of cutaneous melanoma, identified related biomarkers, and discovered their association with immune system.

Chondrogenic differentiation of Wharton's Jelly mesenchymal stem cells on silk spidroin-fibroin mix scaffold supplemented with L-ascorbic acid and platelet rich plasma

In this research, hWJ-MSCs were grown on silk scaffolds and induced towards chondrogenesis by supplementation with L-ascorbic acid (LAA) or platelet rich plasma (PRP). Silk scaffolds were fabricated with salt leaching method by mixing silk fibroin (SF) with silk spidroin (SS). The silk fibroin was obtained from Bombyx mori cocoon that had been degummed, and the silk spidroin was obtained from wild-type spider Argiope appensa. The effect of scaffold composition and inducer on cell proliferation was observed through MTT assay. The most optimal treatment then continued to be used to induce hWJ-MSC towards chondrogenic differentiation for 7 and 21 days. Scaffolds characterization showed that the scaffolds produced had 3D structure with interconnected pores, and all were biocompatible with hWJ-MSCs. Scaffold with the addition of 10% SS + 90% SF showed higher compressive strength and better pore interconnectivity in comparison to 100% silk fibroin scaffold. After 48 h, cells seeded on scaffold with spidroin and fibroin mix had flattened morphology in comparison to silk fibroin scaffold which appeared to be more rounded on the scaffold surface. Scaffold with 10% (w/w) of silk spidroin (SS) + 90% (w/w) of silk fibroin (SF) was the most optimal composition for cell proliferation. Immunocytochemistry of integrin β1 and RGD sequence, showed that scaffold with SS 10% provide better cell attachment with the presence of RGD sequence from the spidroin silk which could explain the higher cell proliferation than SF100% scaffold. Based on Alcian Blue staining and Collagen Type II immunocytochemistry (ICC), cells grown on 10% SS + 90% SF scaffold with 10% PRP supplementation were the most optimal to support chondrogenesis of hWJ-MSCs. These results showed that the addition of spidroin silk from A. appensa. had impact on scaffold compressive strength and chondrogenic differentiation of hWJ-MSC and had the potential for further development of bio-based material scaffold in cartilage tissue engineering.

The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy

The international American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) tumour-node-metastasis (TNM) staging system provides the current guidelines for the classification of cancer. However, among patients within the same stage, the clinical outcome can be very different. More recently, a novel definition of cancer has emerged, implicating at all stages a complex and dynamic interaction between tumour cells and the immune system. This has enabled the definition of the immune contexture, representing the pre-existing immune parameters associated with patient survival. Even so, the role of distinct immune cell types in modulating cancer progression is increasingly emerging. An immune-based assay named the 'Immunoscore' was defined to quantify the in situ T cell infiltrate and was demonstrated to be superior to the AJCC/UICC TNM classification for patients with colorectal cancer. This Review provides a broad overview of the main immune parameters positively or negatively shaping cancer development, including the Immunoscore, and their prognostic and predictive value. The importance of the immune system in cancer control is demonstrated by the requirement for a pre-existing intratumour adaptive immune response for effective immunotherapies, such as checkpoint inhibitors. Finally, we discuss how the combination of multiple immune parameters, rather than individual ones, might increase prognostic and/or predictive power.

Hist-Immune signature: a prognostic factor in colorectal cancer using immunohistochemical slide image analysis

Computerized image analysis for whole-slide images has been shown to improve efficiency, accuracy, and consistency in histopathology evaluations. We aimed to assess whether immunohistochemistry (IHC) image quantitative features can reflect the immune status and provide prognostic information for colorectal cancer patients. A fully automated pipeline was designed to extract histogram features from IHC digital images in a training set (N = 243). A Hist-Immune signature was generated with selected features using the LASSO Cox model. The results were validated using internal (N = 147) and external (N = 76) validation sets. The five-feature-based Hist-Immune signature was significantly associated with overall survival in training (HR 2.72, 95% CI 1.68-4.41, P < .001), internal (2.86, 1.28-6.39, 0.010), and external (2.30, 1.02-6.16, 0.044) validation sets. The full model constructed by integrating the Hist-Immune signature and clinicopathological factors had good discrimination ability (C-index 0.727, 95% CI 0.678-0.776), confirmed using internal (0.703, 0.621-0.784) and external (0.756, 0.653-0.859) validation sets. Our findings indicate that the Hist-Immune signature constructed based on the quantitative features could reflect the immune status of patients with colorectal cancer, which might advocate change in risk stratification and consequent precision medicine.

Prognostic Impact of Tumor-Infiltrating Lymphocytes in Laryngeal Squamous Cell Carcinoma Patients

OBJECTIVES/HYPOTHESIS

Tumor-infiltrating lymphocytes (TILs) has been shown to be associated with the prognosis of many tumors, yet few studies have investigated their roles in laryngeal squamous cell carcinoma (LSCC). We aim to investigate the prognostic values of tumor-infiltrating CD3⁺ /CD4⁺ /CD8⁺ /Foxp3⁺ T-cells and neutrophils in LSCC patients that received total or partial laryngectomy.

STUDY DESIGN

Retrospective case series of LSCC patients who underwent total or partial laryngectomy from 2013 to 2014 at Eye, Ear, Nose, and Throat Hospital of Fudan University.

METHODS

In our study, 41 tumor tissues from patients with LSCC were retrospectively assessed using immunohistochemistry for CD3⁺ /CD4⁺ /CD8⁺ /Foxp3⁺ T-cells and CD66b⁺ neutrophils. Overall survival (OS) and disease-free survival (DFS) were recorded using Kaplan-Meier methods.

RESULTS

Generally, patients with high density of TILs (CD3, CD4, CD8) showed improved OS or DFS. Specifically, high density of CD3⁺ TILs were associated with better OS, yet poorer OS and DFS for CD66b⁺ neutrophils. Patients with an Immunoscore of 0-1 experienced the worst OS and DFS, compared with Immunoscore 2-4 (P = .0111 for OS, P = .0391 for DFS). In Cox proportional hazards analysis adjusted for N stage and T stage, only stroma CD66b⁺ neutrophils densities were able to predict OS, with odds ratios of 4.819 (95% confidence interval [CI] 1.149-20.206; P = .032*), and DFS 2.888 (95% CI 1.043-7.997; P = .041*).

CONCLUSIONS

The density of TILs and CD66b⁺ neutrophils may help predict the prognosis of patients with LSCC after surgery.

LEVEL OF EVIDENCE

3 Laryngoscope, 131:E1249-E1255, 2021.

Immunotherapy in prostate cancer: new horizon of hurdles and hopes

Purpose

Prostate cancer (PCa) is the most common malignancy in men and the cause for the second most common cancer-related death in the western world. Despite ongoing development of novel approaches such as second generation androgen receptor targeted therapies, metastatic disease is still fatal. In PCa, immunotherapy (IT) has not reached a therapeutic breakthrough as compared to several other solid tumors yet. We aimed at highlighting the underlying cellular mechanisms crucial for IT in PCa and giving an update of the most essential past and ongoing clinical trials in the field.

Methods

We searched for relevant publications on molecular and cellular mechanisms involved in the PCa tumor microenvironment and response to IT as well as completed and ongoing IT studies and screened appropriate abstracts of international congresses.

Results

Tumor progression and patient outcomes depend on complex cellular and molecular interactions of the tumor with the host immune system, driven rather dormant in case of PCa. Sipuleucel-T and pembrolizumab are the only registered immune-oncology drugs to treat this malignancy. A plethora of studies assess combination of immunotherapy with other agents or treatment modalities like radiation therapy which might increase its antineoplastic activity. No robust and clinically relevant prognostic or predictive biomarkers have been established yet.

Conclusion

Despite immunosuppressive functional status of PCa microenvironment, current evidence, based on cellular and molecular conditions, encourages further research in this field.

Comprehensive analysis of prognostic immune-related genes associated with the tumor microenvironment of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a specific tumor immune microenvironment (TIME). Therefore, investigating prognostic immune-related genes (IRGs) that are closely associated with TIME to predict PDAC clinical outcomes is necessary. In the present study, 459 samples of PDAC from the Genotype-Tissue Expression database, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) were included and a survival-associated module was identified using weighted gene co-expression network analysis. Based on the Cox regression analysis and least absolute shrinkage and selection operator analysis, four IRGs (2′-5′-oligoadenylate synthetase 1, MET proto-oncogene, receptor tyrosine kinase, interleukin 1 receptor type 2 and interleukin 20 receptor subunit β) were included in the prognostic model to calculate the risk score (RS), and patients with PDAC were divided into high- and low-RS groups. Kaplan-Meier survival and receiver operating characteristic curve analyses demonstrated that the low-RS group had significantly improved survival conditions compared with the high-RS group in TCGA training set. The prognostic function of the model was also validated using ICGC and GEO cohorts. To investigate the mechanism of different overall survival between the high- and low-RS groups, the present study included Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data and Cell Type Identification by Estimating Relative Subset of Known RNA Transcripts algorithms to investigate the state of the tumor microenvironment and immune infiltration inpatients in the cohort from TCGA. In summary, four genes associated with the TIME of PDAC were identified, which may provide a reference for clinical treatment.

A Seven Immune-Related lncRNAs Model to Increase the Predicted Value of Lung Adenocarcinoma

Background

Recent research has shown that immune-related lncRNA plays a crucial part in the tumor immune microenvironment. This study tried to identify immune-related lncRNAs and construct a robust prediction model to increase the predicted value of lung adenocarcinoma (LUAD).

Methods

RNA expression data of LUAD were download from the Cancer Genome Atlas (TCGA) database. Immune genes were acquired from the Molecular Signatures Database (MSigDB). The immune gene related lncRNAs were acquired by the “limma R” package and Cytoscape3.7.1. Cox regression analysis was applied to construct this forecast model. The prognostic model was validated by the testing cohort which was acquired by the bootstrap method.

Results

A total of 551 lncRNA expression profiles including 497 LUAD tissues and 54 non-LUAD tissues were obtained. A total of 331 immune genes were acquired. The result of the Cox regression analysis showed that seven lncRNAs (AC022784-1, NKILA, AC026355-1, AC068338-3, LINC01843, SYNPR-AS1, and AC123595-1) can be performed to construct the prediction model to forecast the prognosis of LUAD. Kaplan–Meier curves indicated that our prediction model can distribute LUAD patients into two different risk groups (high and low) with significant statistical significance (P = 1.484e-07). Cox analysis and independent analysis illustrated that the seven-lncRNAs prediction model was an isolated factor by comparing it with other clinical variables. We validated the accuracy of our model in the testing dataset. Furthermore, the prognostic model also showed higher predictive efficiency than three other published prognostic models. The two different survival groups represented diverse immune features according to principal components analysis. GSEA analysis (gene set enrichment analysis) indicated that seven-lncRNAs signatures may be involved in the progression of tumorigenesis.

Conclusions

We have established a seven immune-related lncRNAs prediction model. This prognostic model had significant clinical significance that increased the predicted value and guided the personalized treatment for LUAD patients.

Significantly different immunological score in lung adenocarcinoma and squamous cell carcinoma and a proposal for a new immune staging system

TNM stage is not enough to accurately predict the prognosis of patients with non-small cell lung cancer (NSCLC). This study aimed to establish the immunological score (IS) in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), separately, and propose a new staging system in NSCLC. We used the multiplex fluorescent immunohistochemistry (mIHC) technology to detect 17 immune biomarkers of 304 patients with NSCLC. The LASSO-COX regression model was used to establish the IS_NSCLC in the training cohorts. The IS_NSCLC was then validated in the validation cohort. The constructed IS_LUAD contained three immune features: CD4⁺CD73⁺ _{core of tumor (CT)}, PD-L1⁺ _CT, and IDO⁺ _{invasive margin (IM)}. IS_LUSC also contained two immune features: CD8⁺CD39^-CD73^- _CT, CD8⁺Tim-3⁺ _IM. In the training cohort, significant prognostic differences were found upon comparing low-IS_NSCLC patients with high-IS_NSCLC patients. For LUAD, the 5-y disease-free survival (DFS) rates were 54.7% vs. 8.1% and the 5-y overall survival (OS) rates were 82.4% vs. 36% (all P< .0001). For LUSC, the 5-y DFS rates were 74.0% vs. 14.7% and the 5-y OS rates were 78.2% vs. 17.6% (all P< .0001). Multivariate analyses indicated that IS_NSCLC was an independent indicator for prognosis. Finally, we combined IS_NSCLC with clinicopathological factors to establish a TN-I staging system and two nomogram models for clinical use. The TN-I stage had better prediction accuracy than TNM stage. The newly established IS_LUAD and IS_LUSC were completely different, and both were excellent indicators for the prognostic prediction. The TN-I stage could effectively improve prognostic accuracy and facilitate clinical application. Abbreviations NSCLC, non-small cell lung cancer; IS, immunological score; mIHC, multiplex fluorescent immunohistochemistry; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; CT, core of tumor; IM, invasive margin; DFS, disease-free survival; OS, overall survival; SITC, the Society for Immunotherapy of Cancer; FFPE, formalin-fixed paraffin-embedded; MWT, microwave treatment; DCA, decision curve analysis; ROC, receiver operating characteristic; AUC, area under the curve; EGFR, epidermal growth factor receptor.

Immune contexture analysis in immuno-oncology: applications and challenges of multiplex fluorescent immunohistochemistry

The tumor microenvironment is an integral player in cancer initiation, tumor progression, response and resistance to anti-cancer therapy. Understanding the complex interactions of tumor immune architecture (referred to as 'immune contexture') has therefore become increasingly desirable to guide our approach to patient selection, clinical trial design, combination therapies, and patient management. Quantitative image analysis based on multiplexed fluorescence immunohistochemistry and deep learning technologies are rapidly developing to enable researchers to interrogate complex information from the tumor microenvironment and find predictive insights into treatment response. Herein, we discuss current developments in multiplexed fluorescence immunohistochemistry for immune contexture analysis, and their application in immuno-oncology, and discuss challenges to effectively use this technology in clinical settings. We also present a multiplexed image analysis workflow to analyse fluorescence multiplexed stained tumor sections using the Vectra Automated Digital Pathology System together with FCS express flow cytometry software. The benefit of this strategy is that the spectral unmixing accurately generates and analyses complex arrays of multiple biomarkers, which can be helpful for diagnosis, risk stratification, and guiding clinical management of oncology patients.

Prognosis Analysis and Validation of m6A Signature and Tumor Immune Microenvironment in Glioma

Glioma is one of the most typical intracranial tumors, comprising about 80% of all brain malignancies. Several key molecular signatures have emerged as prognostic biomarkers, which indicate room for improvement in the current approach to glioma classification. In order to construct a more veracious prediction model and identify the potential prognosis-biomarker, we explore the differential expressed m⁶A RNA methylation regulators in 665 gliomas from TCGA-GBM and TCGA-LGG. Consensus clustering was applied to the m6A RNA methylation regulators, and two glioma subgroups were identified with a poorer prognosis and a higher grade of WHO classification in cluster 1. The further chi-squared test indicated that the immune infiltration was significantly enriched in cluster 1, indicating a close relation between m⁶A regulators and immune infiltration. In order to explore the potential biomarkers, the weighted gene co-expression network analysis (WGCNA), along with Least absolute shrinkage and selection operator (LASSO), between high/low immune infiltration and m⁶A cluster 1/2 groups were utilized for the hub genes, and four genes (TAGLN2, PDPN, TIMP1, EMP3) were identified as prognostic biomarkers. Besides, a prognostic model was constructed based on the four genes with a good prediction and applicability for the overall survival (OS) of glioma patients (the area under the curve of ROC achieved 0.80 (0.76-0.83) and 0.72 (0.68-0.76) in TCGA and Chinese Glioma Genome Atlas (CGGA), respectively). Moreover, we also found PDPN and TIMP1 were highly expressed in high-grade glioma from The Human Protein Atlas database and both of them were correlated with m6A and immune cell marker in glioma tissue samples. In conclusion, we construct a novel prognostic model which provides new insights into glioma prognosis. The PDPN and TIMP1 may serve as potential biomarkers for prognosis of glioma.

Prognostic value of immune-related genes and immune cell infiltration analysis in the tumor microenvironment of head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is one of the few malignant tumors that respond well to immunotherapy. We aimed to investigate the immune-related genes and immune cell infiltration of HNSCC and construct a predictive model for its prognosis.

METHODS

We calculated the stromal/immune scores of patients with HNSCC from The Cancer Genome Atlas using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm and investigated the relationship between the scores and patients' prognosis. Three machine learning algorithms (LASSO, Random Forest, and Rbsurv) were performed to screen key immune-related genes and constructed a predictive model. The immune cell infiltrating was calculated by the Tumor Immune Estimation Resource algorithm.

RESULTS

The stromal and immune scores significantly correlated with prognosis. A 6-gene signature was selected and displayed a robust predictive effect. The expressions of key genes were associated with immune infiltrating. GSE65858 validated the results.

CONCLUSION

Our study comprehensively analyzed the tumor microenvironment of HNSCC and constructed a robust predictive model, providing a basis for further investigation of therapy.

A Molecular Subtype Model for Liver HBV-Related Hepatocellular Carcinoma Patients Based on Immune-Related Genes

Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with a very poor prognosis. Immunotyping is of great significance for predicting HCC outcomes and guiding immunotherapy. Therefore, we sought to establish a reliable prognostic model for HBV-related HCC based on immune scores. We identified immune-related modules of The Cancer Genome Atlas LIHC and GSE14520 data sets through weighted gene co-expression network analysis and evaluated HCC through a non-negative matrix factorization algorithm. Through further bioinformatics analyses, we identified causes for prognostic differences between subtypes. The results illustrate a significant difference in prognosis based on immunotypes, which may stem from metabolic disorders and increased tumor invasion associated with the high expression of genes related to stem cell characteristics. In conclusion, we identified a novel HBV-related HCC immune subtype and determined its immunological characteristics, which provides ideas for further individualized immunotherapy research.

Digital image analysis in pathologist-selected regions of interest predicts survival more accurately than whole-slide analysis: a direct comparison study in 153 gastric carcinomas

Automatic quantification of biomarkers such as tumor‐infiltrating lymphocytes and PD‐L1 is one of the most studied topics in digital pathology image analysis (DIA). However, direct comparison between the DIA of a whole‐slide image (WSI) and that of regions of interest (ROIs) chosen by pathologists has not been performed. In this study, we aimed to compare the prognostic value of tumor microenvironment markers CD8 and PD‐L1, measured by DIA of WSIs and ROIs. We selected 153 primary gastric cancer tissues and stained them with CD8 and PD‐L1. All IHC slides were scanned at ×200 magnification and ratios of CD8 and PD‐L1 were measured in WSIs and ROIs from the invasive front, within the tumor, and the mucosa. Patients with high CD8 and PD‐L1 ratios showed more favorable outcomes compared to those with low ratios. Pathologist‐aided DIA predicted the survival of patients more accurately than WSI analysis (CD8, p = 0.025 versus p = 0.068; PD‐L1, p = 0.008 versus p = 0.2). Although a high density of CD8+ T cells at the invasive front correlated best with patient survival, CD8 ratio in the mucosa could also predict patient outcome. In conclusion, CD8 and PD‐L1 ratios measured by pathologist‐aided DIA predicted survival more accurately than WSI analyses and ROIs at the invasive front correlated best with patient outcome.

Identification of Key Genes of Prognostic Value in Clear Cell Renal Cell Carcinoma Microenvironment and a Risk Score Prognostic Model

Objective

We aimed at identifying the key genes of prognostic value in clear cell renal cell carcinoma (ccRCC) microenvironment and construct a risk score prognostic model.

Materials and Methods

Immune and stromal scores were calculated using the ESTIMATE algorithm. A total of 539 ccRCC cases were divided into high- and low-score groups. The differentially expressed genes in immune and stromal cells for the prognosis of ccRCC were screened. The relationship between survival outcome and gene expression was evaluated using univariate and multivariate Cox proportional hazard regression analyses. A risk score prognostic model was constructed based on the immune/stromal scores.

Results

The median survival time of the low immune score group was longer than that of the high immune score group (p = 0.044). Ten tumor microenvironment-related genes were selected by screening, and a predictive model was established, based on which patients were divided into high- and low-risk groups with markedly different overall survival (p < 0.0001). Multivariate Cox analyses showed that the risk score prognostic model was independently associated with overall survival, with a hazard ratio of 1.0437 (confidence interval: 1.0237-1.0641, p < 0.0001).

Conclusions

Low immune scores were associated with extended survival time compared to high immune scores. The novel risk predictive model based on tumor microenvironment-related genes may be an independent prognostic biomarker in ccRCC.

SIRT5 Contributes to Colorectal Cancer Growth by Regulating T Cell Activity

Over the past several years, SIRT5 has attracted considerable attention in metabolic regulation. However, the function of SIRT5 in tumorigenesis by regulating tumor microenvironment is poorly understood. In this work, we found that Sirt5 knockout mice were resistant to AOM and DSS-induced colitis-associated colorectal tumorigenesis and the level of IFN-γ in their tumor microenvironment was higher. Additionally, proteome and network analysis revealed that SIRT5 was important in the T cell receptor signaling pathway. Furthermore, we determined that a deficiency of Sirt5 induced stronger T cell activation and demonstrated that SIRT5 played a pivotal role in regulating the differentiation of CD4⁺ regulatory T (Treg) cells and T helper 1 (Th1) cells. An imbalance in the lineages of immunosuppressive Treg cells and the inflammatory Th1 subsets of helper T cells leads to the development of colon cancer. Our results revealed a regulatory role of SIRT5 in T cell activation and colorectal tumorigenesis.

Delivery of Personalized Care for Locally Advanced Rectal Cancer: Incorporating Pathological, Molecular Genetic, and Immunological Biomarkers Into the Multimodal Paradigm

Approximately one-third of all newly diagnosed colorectal cancer (CRC) is composed of rectal cancer, with the incidence rising in younger patients. The principal neoadjuvant treatments consist of neoadjuvant short-course radiotherapy and long-course chemoradiation. Locally advanced rectal cancer (LARC) is particularly challenging to manage given the anatomical constrictions of the pelvis and the risk for local recurrence. In appropriately treated patients, 5- and 10-year overall survival is estimated at 60 and 50%, respectively. The prognosis for LARC has improved in recent years with more access to screening, advances in surgical techniques, and perioperative care. Furthermore, the refinement of the multidisciplinary team with combined-modality management strategies has improved outcomes. These advancements have been augmented by significant improvements in the understanding of the underlying tumor biology. However, there are many instances where patient outcomes do not match those for their tumor stage and accurate prognostic information for individual patients can be difficult to estimate owing to the heterogeneous nature of LARC. Many new combinations of chemotherapy with radiotherapy, including total neoadjuvant therapy with targeted therapies that aim to diminish toxicity and increase survival, are being evaluated in clinical trials. Despite these advances, local recurrence and distant metastasis remain an issue, with one-third of LARC patients dying within 5 years of initial treatment. Although much of the new pathological, molecular genetics, and immunological biomarkers allow refinement in the classification and prognostication of CRC, the relative importance of each of these factors with regards to the development and progression of LARC remains incompletely understood. These factors are often insufficiently validated and seldom consider the individual characteristics of the host, the tumor and its location, the local available expertise, or the probable location of recurrence. Appreciating the mechanisms behind these differences will allow for a more comprehensive, personalized approach and more informed treatment options, leading to ultimately superior outcomes. This review aims to first outline the current multidisciplinary context in which LARC care should be delivered and then discuss how some key prognosticators, including novel histopathological, molecular genetics, and immunological biomarkers, might fit into the wider context of personalized LARC management in the coming years.

A Prognostic Nomogram Combining Immune-Related Gene Signature and Clinical Factors Predicts Survival in Patients With Lung Adenocarcinoma

The existence of tumor heterogeneity and complex carcinogenic mechanisms in lung adenocarcinoma (LUAD) make the most commonly used TNM staging system unable to well-interpret the prognosis of patients. Using transcriptome profiling and clinical data from The Cancer Genome Atlas (TCGA) database, we constructed an immune signature based on a multivariate Cox analysis (stepwise model). We estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients according to the pRRophetic algorithm. Gene-set variation analysis (GSVA) was used to reveal pathway enrichment between groups. Moreover, immune microenvironment landscape was described by single-sample gene-set enrichment analysis (ssGSEA) and CIBERSORT and systematically correlated with genomic of these patients. A prognostic nomogram combining the immune signature and TNM stage to predict the prognosis was developed by multivariate Cox regression. The novel signature with four immune-related genes (MAL, MS4A1, OAS1, and WFDC2) had good robustness, which can accurately distinguish between high- and low-risk patients. Compared with low-risk patients, high-risk patients with a worse prognosis (5-year OS: 46.5 vs. 59.4%, p = 0.002) could benefit more from immunotherapy and the application of common chemotherapeutic agents such as cisplatin and paclitaxel (Wilcoxon test, all p < 0.05). There were significant differences in tumor immune microenvironment and metabolic pathways between the two groups. Additionally, the constructed nomogram had reliable predictive performance with the C-index of 0.725 (95% CI = 0.668–0.781) in the development set (n = 500), 0.793 (95% CI = 0.728–0.858) in the internal validation set (n = 250) and 0.679 (95% CI = 0.644–0.714) in the external validation set (n = 442). The corresponding calibration curves also showed good consistency. To sum up, we developed an immune-related gene signature and comprehensively evaluated LUAD immune landscape and metabolic pathways. Effective differentiation of high- and low-risk patients and accurate construction of nomogram would be helpful to the development of individualized treatment strategies.

Organoids as Complex In Vitro Models for Studying Radiation-Induced Cell Recruitment

Patients with triple negative breast cancer (TNBC) typically receive chemotherapy, surgery, and radiation therapy. Although this treatment improves prognosis for most patients, some patients continue to experience recurrence within 5 years. Preclinical studies have shown that immune cell infiltration at the irradiated site may play a significant role in tumor cell recruitment; however, little is known about the mechanisms that govern this process. This lack of knowledge highlights the need to evaluate radiation-induced cell infiltration with models that have controllable variables and maintain biological integrity. Mammary organoids are multicellular three-dimensional (3D) in vitro models, and they have been used to examine many aspects of mammary development and tumorigenesis. Organoids are also emerging as a powerful tool to investigate normal tissue radiation damage. In this review, we evaluate recent advances in mammary organoid technology, consider the advantages of using organoids to study radiation response, and discuss future directions for the applications of this technique.

Integrative bioinformatics analysis of a prognostic index and immunotherapeutic targets in renal cell carcinoma

Renal cell carcinoma (RCC) is one of the most common malignancies. The immunogenomic landscape signature significantly correlates with the progression and prognosis of RCC. Novel therapeutic targets and prognostic indices in RCC are highly desirable. The TCGA database enables comprehensive immunogenomic landscape analysis. Differentially expressed immune-related genes (IRGs) were obtained from TCGA and GO analyses, and KEGG pathway analyses were performed to explore their functions and molecular mechanisms. Multivariable Cox analysis was utilized to calculate the risk score of each patient and locate survival-associated IRGs, thereby constructing a novel immune-related gene-based prognostic index (IRGPI). The correlation between IRGPI and immune cell infiltration was also investigated. A total of 41 differentially expressed IRGs were notably related to prognosis in RCC. GO functions and KEGG pathway analyses demonstrated that these genes were primarily associated with the tumour immune response and cytokine-cytokine receptor interaction pathway. An IRGPI based on seventeen survival-associated differentially expressed IRGs was constructed and exhibited a moderate predictive value in the prognosis of RCC patients and a powerful identification ability in refining the risk stratification of RCC patients. A close correlation was found between IRGPI and specific clinicopathological parameters, including age, gender, pathological stage, tumour stage, lymph node metastasis and distant metastasis. A positive correlation was found between IRGPI and the infiltration levels of neutrophils, dendritic cells, CD8+ T cells and B cells. Our results demonstrated the clinical significance and potential function of IRGs, providing additional data for prognostic risk prediction and immunotherapeutic target selection in RCC.

Baseline factors identified for the prediction of good responders in patients with end-stage diffuse coronary artery disease undergoing intracoronary CD34+ cell therapy

Background

Treating patients with end-stage diffuse coronary artery disease (EnD-CAD) unsuitable for coronary intervention remains a clinical challenge. They usually express refractory angina and have a high risk of mortality. Although growing data have indicated cell therapy is an alternative solution to medical or invasive therapy, there are still lacking useful markers to predict whether heart function will improve in the EnD-CAD patients who underwent circulatory-derived CD34+ cell therapy. By utilizing the baseline variables and results from our previous phase I/II clinical trials, the aim of this study tried to elucidate the variables predictive of the “good response” to CD34+ cell therapy.

Methods

This retrospective study included 38 patients in phase I clinical trial (2011–2014), and 30 patients in phase II clinical trial (2013–2017). These patients were categorized into “good responders” and “non-responders” according to their 1-year improvement of LVEF ≥ 7.0% or < 7.0% after intracoronary CD34+ cell therapy. Univariate and multivariate logistic regression models were performed to identify potential independent predictors of a good responder to cell therapy, followed by Hosmer–Lemeshow (H-L) test for goodness of fit and prediction power.

Results

Among baseline data, multivariate analysis demonstrated that the history of a former smoker was independently predictive of good responders (p = 0.006). On the other hand, male gender, the baseline Canadian Cardiovascular Society angina score ≥ 3, and grades of LV diastolic dysfunction ≥ 2 were significantly negative predictors of good responders (all p < 0.01). After administration of subcutaneous granulocyte-colony stimulating factor (G-CSF), a higher post-G-CSF neutrophil count in addition to the above four baseline variables also played crucial roles in early prediction of good response to CD34+ cell therapy for EnD-CAD (all p < 0.03). The H-L test displayed a good prediction power with sensitivity 83.3%, specificity 85.3%, and accuracy 84.4%.

Conclusions

Using the results of our phase I/II clinical trials, previous smoking habit, female sex, lower grades of angina score, and diastolic dysfunction were identified to be independently predictive of “good response” to CD34+ cell therapy in the patients with EnD-CAD.

Trial registration

This is a retrospective analysis based on phase I (ISRCTN72853206) and II (ISRCTN26002902) clinical trials.

Identification of prognosis-related genes and construction of multi-regulatory networks in pancreatic cancer microenvironment by bioinformatics analysis

Background

As one of the most lethal cancers, pancreatic cancer has been characterized by abundant supportive tumor-stromal cell microenvironment. Although the advent of tumor-targeted immune checkpoint blockers has brought light to patients with other cancers, its clinical efficacy in pancreatic cancer has been greatly limited due to the protective stroma. Thus, it is urgent to find potential new targets and establish multi-regulatory networks to predict patient prognosis and improve treatment.

Methods

We followed a strategy based on mining the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm to obtain the immune scores and stromal scores. Differentially expressed genes (DEGs) associated with poor overall survival of pancreatic cancer were screened from a TCGA cohort. By comparing global gene expression with high vs. low immune scores and subsequent Kaplan–Meier analysis, DEGs that significantly correlate with poor overall survival of pancreatic cancer in TCGA cohort were extracted. After constructing the protein–protein interaction network using STRING and limiting the genes within the above DEGs, we utilized RAID 2.0, TRRUST v2 database and degree and betweenness analysis to obtain non-coding RNA (ncRNA)-pivotal nodes and TF-pivotal nodes. Finally, multi-regulatory networks have been constructed and pivotal drugs with potential benefit for pancreatic cancer patients were obtained by screening in the DrugBank.

Results

In this study, we obtained 246 DEGs that significantly correlate with poor overall survival of pancreatic cancer in the TCGA cohort. With the advent of 38 ncRNA-pivotal nodes and 7 TF-pivotal nodes, the multi-factor regulatory networks were constructed based on the above pivotal nodes. Prognosis-related genes and factors such as HCAR3, PPY, RFWD2, WSPAR and Amcinonide were screened and investigated.

Conclusion

The multi-regulatory networks constructed in this study are not only beneficial to improve treatment and evaluate patient prognosis with pancreatic cancer, but also favorable for implementing early diagnosis and personalized treatment. It is suggested that these factors may play an essential role in the progression of pancreatic cancer.

Distant Metastasis in Colorectal Cancer Patients-Do We Have New Predicting Clinicopathological and Molecular Biomarkers? A Comprehensive Review

Colorectal cancer (CRC) remains a serious health problem worldwide. Approximately half of patients will develop distant metastasis after CRC resection, usually with very poor prognosis afterwards. Because patient performance after distant metastasis surgery remains very heterogeneous, ranging from death within 2 years to a long-term cure, there is a clinical need for a precise risk stratification of patients to aid pre- and post-operative decisions. Furthermore, around 20% of identified CRC cases are at IV stage disease, known as a metastatic CRC (mCRC). In this review, we overview possible molecular and clinicopathological biomarkers that may provide prognostic and predictive information for patients with distant metastasis. These may comprise sidedness of the tumor, molecular profile and epigenetic characteristics of the primary tumor and arising metastatic CRC, and early markers reflecting cancer cell resistance in mCRC and biomarkers identified from transcriptome. This review discusses current stage in employment of these biomarkers in clinical practice as well as summarizes current experience in identifying predictive biomarkers in mCRC treatment.

Earlier-Phased Cancer Immunity Cycle Strongly Influences Cancer Immunity in Operable Never-Smoker Lung Adenocarcinoma

Exome and transcriptome analyses of clinically homogeneous early-stage never-smoker female patients with lung adenocarcinoma were performed to understand tumor-T cell interactions and immune escape points. Using our novel gene panels of eight functional categories in the cancer-immunity cycle, three distinct subgroups were identified in this immune checkpoint blockade-refractory cohort by defective gene expression in two major domains, i.e., type I interferon production/signaling pathway and antigen-presenting machinery. Our approach could play a critical role in understanding immune evasion mechanisms, developing a method for effective selection of rare immune checkpoint blockade responders, and finding new treatment strategies.

Identification of 9-Core Immune-Related Genes in Bladder Urothelial Carcinoma Prognosis

Background: Immune microenvironment within tumors affects initiation, progression and clinical outcome of human cancers. Here we explored an immune-related gene signature associated with prognosis of patients with bladder urothelial carcinoma. Method: The Cancer Genome Atlas (TCGA) database was interrogated for expressions of immune-related genes in bladder urothelial carcinomas. Integrated bioinformatics analyses were performed to identify prognostic factors. Results: Twenty-seven immune-related genes were revealed significantly associated with patient's overall survival (OS) by univariate Cox proportional hazards regression analysis. Nine-core immune-related genes including MMP9, PDGFRA, AHNAK, OLR1, RAC3, IGF1, PGF, OAS1, and SH3BP2 were selected to construct a risk score model by multivariate Cox proportional hazards regression analysis. Bioinformatics analyses further validated that risk score could be used as an important independent factor in evaluating prognosis. Conclusion: We established a prognostic immune signature for patients with bladder urothelial carcinoma, which may provide novel targets for prediction and therapy of these patients.

Fatty Acid Mediators in the Tumor Microenvironment

Patients with cancer frequently overexpress inflammatory cytokines with an associated neutrophilia both of which may be downregulated by diets with high omega-3 polyunsaturated fatty acids (ω-3 PUFA). The anti-inflammatory activity of dietary ω-3 PUFA has been suggested to have anticancer properties and to improve survival of cancer patients. Currently, the majority of dietary research efforts do not differentiate between obesity and dietary fatty acid consumption as mediators of inflammatory cell expansion and tumor microenvironmental infiltration, initiation, and progression. In this chapter, we discuss the relationships between dietary lipids, inflammation, neoplasia and strategies to regulate these relationships. We posit that dietary composition, notably the ratio of ω-3 vs. ω-6 PUFA, regulates tumor initiation and progression and the frequency and sites of metastasis that, together, impact overall survival (OS). We focus on three broad topics: first, the role of dietary lipids in chronic inflammation and tumor initiation, progression, and regression; second, lipid mediators linking inflammation and cancer; and third, dietary lipid regulation of murine and human tumor initiation, progression, and metastasis.

Current challenges for assessing the long-term clinical benefit of cancer immunotherapy: a multi-stakeholder perspective

Immuno-oncologics (IOs) differ from chemotherapies as they prime the patient's immune system to attack the tumor, rather than directly destroying cancer cells. The IO mechanism of action leads to durable responses and prolonged survival in some patients. However, providing robust evidence of the long-term benefits of IOs at health technology assessment (HTA) submission presents several challenges for manufacturers. The aim of this article was to identify, analyze, categorize, and further explore the key challenges that regulators, HTA agencies, and payers commonly encounter when assessing the long-term benefits of IO therapies. Insights were obtained from an international, multi-stakeholder steering committee (SC) and expert panels comprising of payers, economists, and clinicians. The selected individuals were tasked with developing a summary of challenges specific to IOs in demonstrating their long-term benefits at HTA submission. The SC and expert panels agreed that standard methods used to assess the long-term benefit of anticancer drugs may have limitations for IO therapies. Three key areas of challenges were identified: (1) lack of a disease model that fully captures the mechanism of action and subsequent patient responses; (2) estimation of longer-term outcomes, including a lack of agreement on ideal methods of survival analyses and extrapolation of survival curves; and (3) data limitations at the time of HTA submission, for which surrogate survival end points and real-world evidence could prove useful. A summary of the key challenges facing manufacturers when submitting evidence at HTA submission was developed, along with further recommendations for manufacturers in what evidence to produce. Despite almost a decade of use, there remain significant challenges around how best to demonstrate the long-term benefit of checkpoint inhibitor-based IOs to HTA agencies, clinicians, and payers. Manufacturers can potentially meet or mitigate these challenges with a focus on strengthening survival analysis methodology. Approaches to doing this include identifying reliable biomarkers, intermediate and surrogate end points, and the use of real-world data to inform and validate long-term survival projections. Wider education across all stakeholders-manufacturers, payers, and clinicians-in considering the long-term survival benefit with IOs is also important.

Label-Free Infrared Spectral Histology of Skin Tissue Part II: Impact of a Lumican-Derived Peptide on Melanoma Growth

Melanoma is the most aggressive type of cutaneous malignancies. In addition to its role as a regulator of extracellular matrix (ECM) integrity, lumican, a small leucine-rich proteoglycan, also exhibits anti-tumor properties in melanoma. This work focuses on the use of infrared spectral imaging (IRSI) and histopathology (IRSH) to study the effect of lumican-derived peptide (L9Mc) on B16F1 melanoma primary tumor growth. Female C57BL/6 mice were injected with B16F1 cells treated with L9Mc (n = 10) or its scrambled peptide (n = 8), and without peptide (control, n = 9). The melanoma primary tumors were subjected to histological and IR imaging analysis. In addition, immunohistochemical staining was performed using anti-Ki-67 and anti-cleaved caspase-3 antibodies. The IR images were analyzed by common K-means clustering to obtain high-contrast IRSH that allowed identifying different ECM tissue regions from the epidermis to the tumor area, which correlated well with H&E staining. Furthermore, IRSH showed good correlation with immunostaining data obtained with anti-Ki-67 and anti-cleaved caspase-3 antibodies, whereby the L9Mc peptide inhibited cell proliferation and increased strongly apoptosis of B16F1 cells in this mouse model of melanoma primary tumors.

Computerised scoring protocol for identification and quantification of different immune cell populations in breast tumour regions by the use of QuPath software

AIMS

As important prognostic and predictive information can be obtained from the composition, functionality and spatial arrangement of different immune cell subtypes, this study aims at characterizing the immune infiltrate in breast tumours.

METHODS AND RESULTS

Tumour-infiltrating lymphocytes (TILs) in 62 patients with luminal B-like breast cancer were characterised by immunohistochemical staining with standard markers, and were subsequently classified and quantified by the use of QuPath software. In different delineated tumour regions, the proportion and density of CD3⁺ , CD4⁺ , CD5⁺ , CD8⁺ , CD20⁺ and FOXP3⁺ cells were assessed. The results of the software analysis were compared with those of manual counting for CD8 and CD20 staining. The QuPath scoring protocol slightly overestimated positive, negative and total lymphocyte counts and density, while minimally underestimating the proportion of positively stained lymphocytes. However, for density and proportion, no real differences from manual counting were observed. For all markers, the density of positively stained immune cells was higher in the invasive front than in the tumour centre, pointing to an accumulation of immune cells near the tumour boundaries. When we looked at the proportion of immunohistochemically positive immune cells, we observed enrichment of CD5 (P = 0.025) and CD20 (P < 0.001) at the periphery, and FOXP3 enrichment in the centre (P < 0.001).

CONCLUSION

The QuPath scoring protocol can adequately identify positively stained immune cells in breast tumours, and allows the evaluation of differences in immune cell proportion and density within different tumour regions. The entire tumour section can be quantitatively assessed quite rapidly, which is a major advantage over manual counting.

The prognostic value of Immunoscore in patients with cancer: A pooled analysis of 10,328 patients

OBJECTIVES

Over the past decade, some publications have reported that Immunoscore was associated with the prognosis of several cancers. To better understand this issue, we conducted this pooled analysis.

METHODS

We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library from their inceptions to 15 May 2019 to identify relevant articles. The pooled hazard ratio (HR) and 95% confidence interval (CI) was estimated for overall survival, disease-free survival, and disease-specific survival.

RESULTS

A total of 26 cohort studies with 10,328 patients involving eight cancer specialties were evaluated mainly by the consensus Immunoscore. The pooled analysis indicated that a lower Immunoscore was associated with a poor overall survival (HR 2.23, 95% CI 1.58, 2.70), disease-free survival (HR 2.40, 95% CI 1.96, 2.49), and disease-specific survival (HR 2.81, 95% CI 2.10, 3.77) for all cancers. The same convincing results were found in colorectal cancer, gastric cancer, and non-small cell lung cancer (especially the consensus Immunoscore for colon cancer). In five other types of cancer the results were similar, but the sample sizes were limited.

CONCLUSIONS

These findings support that Immunoscore is significantly associated with the prognosis of patients with cancer. It provides a reliable estimate of the risk of recurrence in patients with colon cancer. However, more high-quality studies are necessary to assess the prognostic value of Immunoscore in non-colon cancers.

Prevalence of CD8+ cytotoxic lymphocytes in human neoplasms

PURPOSE

Immune checkpoint inhibitors have recently been approved by the US FDA as first and/or second line therapy in a subset of cancer types. Recent evidence suggests that the quantity of tumor infiltrating lymphocytes (TILs) influences the likelihood of response to immune checkpoint inhibitors. Here, we set out to assess the density of CD8+ lymphocytes in a wide range of different cancer types and subtypes.

METHODS

The density of CD8+ lymphocytes was compared across different cancer types using tissue microarrays (TMAs) composed of up to 50 tumor samples each from 84 different cancer types and subtypes. In total 2652 cancers and 608 normal tissues were successfully analyzed by CD8 immunohistochemistry followed by automated image analysis of digitized slides.

RESULTS

We found that the median CD8+ lymphocyte counts ranged from 6 cells/mm2 in pleomorphic adenoma up to 1573 cells/mm2 in Hodgkin's lymphoma. The CD8 counts were generally lower in normal tissues compared to cancer tissues. Blood vessels of the spleen were the only non-lymphatic tissue staining positive for CD8. Tumor types approved for checkpoint inhibitor therapy, including malignant melanoma (81), muscle invasive urothelial carcinoma (119), small cell lung cancer (120), clear cell renal cell cancer (153), squamous cell carcinoma (189) and adenocarcinoma of the lung (328) as well as Hodgkin's lymphoma (1573) were all ranking among the upper half of our list. Comparably high CD8 densities (median cells/mm2) were also found in several rare and aggressive cancer types including Merkel cell carcinoma (70), angiosarcoma (95), anaplastic thyroid cancer (156) and embryonal carcinoma of the testis (186). In 73 of the 84 analyzed cancer types, the highly variable CD8 counts occasionally exceeded the average CD8 count of tumors for which checkpoint inhibitors have been approved.

CONCLUSION

These data support the concept that among most tumor types at least some individual cancers may benefit from treatment with immune checkpoint inhibitors.

Integration of Digital Pathologic and Transcriptomic Analyses Connects Tumor-Infiltrating Lymphocyte Spatial Density With Clinical Response to BRAF Inhibitors

Metastatic melanoma is one of the most immunogenic malignancies due to its high rate of mutations and neoantigen formation. Response to BRAF inhibitors (BRAFi) may be determined by intratumoral immune activation within melanoma metastases. To evaluate whether CD8+ T cell infiltration and distribution within melanoma metastases can predict clinical response to BRAFi, we developed a methodology to integrate immunohistochemistry with automated image analysis of CD8+ T cell position. CD8+ distribution patterns were correlated with gene expression data to identify and quantify “hot” areas within a tumor. Furthermore, the relative activation of CD8+cells, based on transcriptomic analysis, and their relationship to other CD8+ T cells and non-CD8+ cells within the tumor suggested a less crowded distribution of cells around activated CD8+ T cells. Furthermore, the relative activation of these CD8+ T cells was associated with improved clinical outcomes and decreased tumor cell proliferation. This study demonstrates the potential of digital pathomics to incorporate immune cell spatial distribution within metastases and RNAseq analysis to predict clinical response to BRAF inhibition in metastatic melanoma.

Systematic Multiomic Analysis of Ly75 Gene Expression and Its Prognostic Value Through the Infiltration of Natural Killer (NK) Cells in Skin Cutaneous Melanoma

Ly75 (also known as DEC-205 or CD205) is expressed in immune cells and cancers and involved in tumor immunity. However, clinical relevance of Ly75 expression in skin cutaneous melanoma (SKCM) have not been comprehensively studied. This study analyzed the correlation between Ly75 mRNA expression and patient survival using systematic multiomic analysis tools. Ly75 mRNA expression level was significantly lower in SKCM tissues than in normal tissues. Survival analysis showed that Ly75 expression significantly correlated with good patient survival. To determine possible mechanisms, the association between Ly75 expression and immune cell infiltration was analyzed. Ly75 expression was positively correlated with various infiltrated immune cells, particularly with natural killer (NK) cell infiltration and activation in SKCM. Moreover, analysis of Ly75-co-altered gene expression revealed that Ptprc (CD45) was most significantly correlated with Ly75. Gene ontology analysis of Ly75-co-altered genes indicated the relation to lymphocyte activation, including NK cell activation. Overall, our study provides the first clinical evidence that Ly75 expression is significantly associated with melanoma patient survival and NK cell infiltration, suggesting that Ly75 could be a useful prognostic factor.

An individualized immune prognostic signature in lung adenocarcinoma

Background

Tumor immune infiltration is closely associated with clinical outcome in lung cancer. We aimed to develop an immune signature to improve the prognostic predictions of lung adenocarcinoma (LUAD).

Methods

We applied “Cell type Identification by Estimating Relative Subsets of RNA Transcripts” method to quantify the fraction of 22 leukocyte cells from six public microarray datasets. Four datasets from GPL570 were treated as the training cohort and two datasets from GPL96 and GPL10379 as the validation cohorts. An immune risk score (IRS) based on leukocyte cell fraction was established by least absolute shrinkage and selection operator cox regression model.

Results

IRS consisting of 6 types of leukocytes was constructed in the training dataset. In the training cohort (520 patients), the IRS stratified patients into high-IRS group (215 patients) and low-IRS group (305 patients) with significant differences in overall survival (OS) (HR: 2.77, 95% CI 2.08–3.06). Multivariate analysis including age, gender, stage, IRS and tumor purity revealed the IRS to be an independent prognostic factor in all datasets (training: HR: 10.71, 95% CI 5.72–20.07; validation-1: HR 2.68, 95% CI 1.15–6.27; validation-2: HR 3.71, 95% CI 1.33–10.33); all p < 0.05). IRS was significantly positively correlated to the expression levels of PD1, PDL1, CTLA and LAG3 (all p < 0.001). When integrated with clinical characteristics including stage and age, the composite immune and clinical signature presented with improved prognostic accuracy than IRS (mean C-index 0.66 vs. 0.60).

Conclusions

The proposed immune-clinical signature could predict OS in patients with LUAD effectively.

Biomarkers for immunotherapy for treatment of glioblastoma

Immunotherapy is a promising new therapeutic field that has demonstrated significant benefits in many solid-tumor malignancies, such as metastatic melanoma and non-small cell lung cancer. However, only a subset of these patients responds to treatment. Glioblastoma (GBM) is the most common malignant primary brain tumor with a poor prognosis of 14.6 months and few treatment advancements over the last 10 years. There are many clinical trials testing immune therapies in GBM, but patient responses in these studies have been highly variable and a definitive benefit has yet to be identified. Biomarkers are used to quantify normal physiology and physiological response to therapies. When extensively characterized and vigorously validated, they have the potential to delineate responders from non-responders for patients treated with immunotherapy in malignancies outside of the central nervous system (CNS) as well as GBM. Due to the challenges of current modalities of radiographic diagnosis and disease monitoring, identification of new predictive and prognostic biomarkers to gauge response to immune therapy for patients with GBM will be critical in the precise treatment of this highly heterogenous disease. This review will explore the current and future strategies for the identification of potential biomarkers in the field of immunotherapy for GBM, as well as highlight major challenges of adapting immune therapy for CNS malignancies.

A novel immune-related genes prognosis biomarker for melanoma: associated with tumor microenvironment

BACKGROUND

Melanoma is a cancer of the skin with potential to spread to other organs and is responsible for most deaths due to skin cancer. It is imperative to identify immune biomarkers for early melanoma diagnosis and treatment.

RESULTS

63 immune-related genes of the total 1039 unique IRGs retrieved were associated with overall survival of melanoma. A multi-IRGs classifier constructed using eight IRGs showed a powerful predictive ability. The classifier had better predictive power compared with the current clinical data. GSEA analysis showed multiple signaling differences between high and low risk score group. Furthermore, biomarker was associated with multiple immune cells and immune infiltration in tumor microenvironment.

CONCLUSIONS

The immune-related genes prognosis biomarker is an effective potential prognostic classifier in the immunotherapies and surveillance of melanoma.

METHODS

Melanoma samples of genes were retrieved from TCGA and GEO databases while the immune-related genes (IRGs) were retrieved from the ImmPort database. WGCNA, Cox regression analysis and LASSO analysis were used to classify melanoma prognosis. ESTIMATE and CIBERSORT algorithms were used to explore the relationship between risk score and tumor immune microenvironment. GSEA analysis was performed to explore the biological signaling pathway.

Immunotherapy in Metastatic Colorectal Cancer: Could the Latest Developments Hold the Key to Improving Patient Survival?

Immunotherapy has considerably increased the number of anticancer agents in many tumor types including metastatic colorectal cancer (mCRC). Anti-PD-1 (programmed death 1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) immune checkpoint inhibitors (ICI) have been shown to benefit the mCRC patients with mismatch repair deficiency (dMMR) or high microsatellite instability (MSI-H). However, ICI is not effective in mismatch repair proficient (pMMR) colorectal tumors, which constitute a large population of patients. Several clinical trials evaluating the efficacy of immunotherapy combined with chemotherapy, radiation therapy, or other agents are currently ongoing to extend the benefit of immunotherapy to pMMR mCRC cases. In dMMR patients, MSI testing through immunohistochemistry and/or polymerase chain reaction can be used to identify patients that will benefit from immunotherapy. Next-generation sequencing has the ability to detect MSI-H using a low amount of nucleic acids and its application in clinical practice is currently being explored. Preliminary data suggest that radiomics is capable of discriminating MSI from microsatellite stable mCRC and may play a role as an imaging biomarker in the future. Tumor mutational burden, neoantigen burden, tumor-infiltrating lymphocytes, immunoscore, and gastrointestinal microbiome are promising biomarkers that require further investigation and validation.

Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study

OBJECTIVE

Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.

DESIGN

We have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (TC) and invasive margin (IM). Using a LASSO algorithm, DGMate (DiGital tuMor pArameTErs), we detected digital parameters within the tumour cells related to patient outcomes.

RESULTS

Within the dataset of 1018 patients, we observed that a poorer relapse-free survival (RFS) was associated with high IM stromal area (HR 5.65; 95% CI 2.34 to 13.67; p<0.0001) and high DGMate (HR 2.72; 95% CI 1.92 to 3.85; p<0.001). Higher CD3+ TC, CD3+ IM and CD8+ TC densities were significantly associated with a longer RFS. Analysis of variance showed that CD3+ TC yielded a similar prognostic value to the classical CD3/CD8 Immunoscore (p=0.44). A combination of the IM stromal area, DGMate and CD3, designated 'DGMuneS', outperformed Immunoscore when used in estimating patients' prognosis (C-index=0.601 vs 0.578, p=0.04) and was independently associated with patient outcomes following Cox multivariate analysis. A predictive nomogram based on DGMuneS and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk.

CONCLUSION

These findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients' prognosis.

The Biology of Immune-Active Cancers and Their Regulatory Mechanisms

The development of cancer results from the evolutionary balance between the proliferating aptitude of cancer cells and the response of the host's tissues. Some cancers are characterized by genetic instability dependent upon impaired DNA repair mechanisms that lead to the chaotic disruption of multiple cellular functions often in excess of the cancer survival needs and may exert broad effects on surrounding tissues, some beneficial and some detrimental to cancer growth. Among them, inflammatory processes that accompany wound healing may initiate a reaction of the host against the neo-formation. This is possibly triggered by the release by dying cancer cells of molecules known as Damage-Associated Molecular Patterns (DAMPs) following a process termed Immunogenic Cell Death (ICD) that initiates an immune response through innate and adaptive mechanisms. Indeed, analysis of large cancer data sets has shown that ICD is strictly associated with the activation of other immune effector or immune-regulatory pathways. Here, we will describe how immune activation and compensatory immune-regulatory mechanisms balance anti-cancer immune surveillance and the roles that innate and adaptive immunity play including the weight that neo-epitopes may exert as initiators and sculptors of high-affinity memory and effector immune responses against cancer. We will discuss the evolutionary basis for the existence of immune checkpoints and how several theories raised to explain cancer resistance to immunotherapy represent a facet of a similar evolutionary phenomenon that we described in the Theory of Everything. We will show how the biology of immunogenicity and counterbalancing immune regulation is widespread across cancers independent of their ontogenesis while subtle idiosyncratic differences are discernible among them. Finally, we will suggest that overcoming immune resistance implies distinct approaches relevant to the immune context of individual cancers.

Prognostic value of immune-related genes in the tumor microenvironment of lung adenocarcinoma and lung squamous cell carcinoma

Non-small cell lung cancer (NSCLC), which consists mainly of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), are the leading cause of cancer deaths worldwide. In this study, we performed a comprehensive analysis of the tumor microenvironmental and genetic factors to identify prognostic biomarkers for NSCLC. We evaluated the immune and stromal scores of patients with LUAD and LUSC using data from The Cancer Genome Atlas database with the ESTIMATE algorithm. Based on these scores, the differentially expressed genes were obtained and immune-related prognostic genes were identified. Functional analysis and protein-protein interaction network further revealed the immune-related biological processes in which these genes participated. Additionally, 22 subsets of tumor-infiltrating immune cells (TIICs) in the tumor microenvironment were analyzed with the CIBERSORT algorithm. Finally, we validated these valuable genes using an independent cohort from the Gene Expression Omnibus database. The associations of the immune and stromal scores with patients’ clinical characteristics and prognosis were positive in LUAD but negative in LUSC and the correlations of TIICs with clinical characteristics were clarified. Several differentially expressed genes were identified to be potential immune-related prognostic genes. This study comprehensively analyzed the tumor microenvironment and presented immune-related prognostic biomarkers for NSCLC.

Repertoire of peripheral T cells in patients with oral squamous cell carcinoma

BACKGROUND

The establishment of adaptive immune responses to neoplasms involves not only the tumour tissue, but also the peripheral blood. We aimed to conduct a preliminary exploration to understand the immune response of T lymphocytes of peripheral blood mononuclear cells (PBMC-Ts) in oral squamous cell carcinoma (OSCC).

METHODS

A total of 103 blood samples from OSCC patients and 18 blood samples from healthy donors (HD) were analysed by flow cytometry.

RESULTS

Compared to those in HD, a series of unique features of PBMC-Ts were observed in OSCC patients including a significant increase in CD4+ T cells, a shift from naïve to memory/effector phenotype, an increased frequency of exhausted phenotypes (programmed death-1 [PD-1], T cell Ig and mucin protein-3 [Tim-3] and Tregs), an abundance of Th17s and Tc17s and an imbalance in Th17/Tc17 and Th17/Treg ratios. Furthermore, in OSCC patients, we also found that CD4+ T cells were significantly increased in patients with larger tumours than smaller tumours, memory/effector phenotype and exhausted phenotypes were significantly associated with advanced clinical stage and lymph node metastasis, and the Th17/Treg ratio was associated with early clinical stage and no lymph node metastasis.

CONCLUSION

PBMC-Ts may be involved in the development and progression of OSCC, which suggested to be a manifestation of an immune response between host and tumour neoantigens.

Pathways of spread in rectal cancer: a reappraisal of the true routes to distant metastatic disease

Rectal cancer can spread in a number of ways which have been previously recognised and validated as prognostic markers. These routes of spread are not adequately recognised in the stage grouping of the tumour-node-metastasis system, which focuses predominantly on the depth of invasion and nodal status, thus limiting its prognostic accuracy. Tumour spread involving veins occurs in 40% of patients. Venous channels have greater direct access to distant sites by means of a vascular 'anatomical highway'. This rapid spread of tumour cells to distant metastatic sites by veins cannot occur by means of lymph node pathways. Thus, lymph nodes have been overestimated in their importance. Distinction between local tumour spread (lymph node metastases, perineural and lymphatic invasion) and tumour spread mediated by a direct vascular pathway to distant dissemination (extramural venous invasion and tumour deposits) must be made as the implications for prognosis and choice of treatment are not likely to be equal. Improved precision of radiological and pathological assessment is needed to scrutinise and carefully document each route of tumour spread. Only with this accurate information will it be possible to correctly weight each feature and develop a more prognostically accurate staging method that would allow separation of true high- and low-risk groups and subsequent improvements in patient care.

Screening TCGA database for prognostic genes in lower grade glioma microenvironment

Background

To identify prognostic hub genes which associated with tumor microenvironment (TME) in lower grade glioma (LGG) of central nervous system.

Methods

We downloaded LGG patients gene transcriptome profiles of the central nervous system in The Cancer Genome Atlas (TCGA) database. Clinical characteristics and survival data through the Genomic Data Commons (GDC) tool were extracted. We used limma package for normalization processing. Scores of immune, stromal and ESTIMATE were calculated using ESTIMATE algorithm. Then, box plots were applied to explore the association between immune scores, stromal scores, ESTIMATE scores and histological type, tumor grade. Kaplan-Meier (K-M) analysis was utilized to explore the prognostic value of scores. Furthermore, heatmaps and volcano plots were applied for visualizing expression of differential expressed-gene screening and cluster analysis. Venn plots were constructed to screen the intersected differentially expressed genes (DEGs). In addition, enrichment of functions and signaling pathways and Gene Set Enrichment Analysis (GESA) of the DEGs were performed. Then we used protein-protein interaction (PPI) network and Cytoscape software to identify hub genes. We evaluated the prognostic value of hub genes and risk score (RS) calculated based on multivariate cox regression analysis. Finally, relationships of hub genes with the TME of LGG patients were evaluated based on tumor immune estimation resource (TIMER) database.

Results

Gene expression profiles and clinical data of 514 LGG samples were extracted and the results revealed that higher scores were significantly related with histological types and higher tumor grade (P<0.0001, respectively). Besides, higher scores were associated with worse survival outcomes in immune scores (P=0.0167), stromal scores (P=0.0035) and ESTIMATE scores (P=0.0190). Then, 785 up-regulated intersected genes and 357 down-regulated intersected genes were revealed. Functional enrichment analysis revealed that intersected genes were associated with immune response, inflammatory response, plasma membrane and receptor activity. After PPI network construction and cytoHubba analysis, 25 tumor immune-related hub genes were identified and enriched pathways were identified by GSEA. Besides, receiver operating characteristic (ROC) curves showed significantly predictive accuracy [area under curve (AUC) =0.771] of RS. Furthermore, significant prognostic values of hub genes were observed, and the relationships between hub genes and LGG TME were demonstrated.

Conclusions

We identified 25 TME-related genes which significantly associated with overall survival in patients with central nervous system LGG from TCGA database.

Neoadjuvant chemotherapy is associated with a transient increase of intratumoral T-cell density in microsatellite stable colorectal liver metastases

Patients with colorectal liver metastases (CLM) commonly receive neoadjuvant chemotherapy (NACT) prior to surgical resection. NACT may induce immunogenic cell death with subsequent recruitment of T-cells to the tumor microenvironment, which could be exploited by immune checkpoint inhibition (ICI). In theory, this could expand the use of ICI to obtain responses also in microsatellite stable colorectal cancer, but evidence to suggest optimal treatment schedules are lacking. In this study, densities of total-, cytotoxic-, helper- and regulatory T-cells were quantified by immunohistochemistry in resected CLM from 92 patients included in the OSLO-COMET trial (NCT01516710). All but one patient had microsatellite stable tumors (91/92). Associations between T-cell densities and clinicopathological parameters were analyzed. Fluoropyrimidine-based NACT (in most cases with addition of oxaliplatin or irinotecan) was administered to 45 patients completed median 8 weeks prior to surgical resection. No overall association was found between NACT administration and intratumoral T-cell densities. However, within the NACT group, a short time interval (<9.5 weeks) between NACT completion and CLM resection was strongly associated with high intratumoral T-cell densities compared to the long-interval and no NACT groups (medians 491, 236, and 292 cells/mm², respectively; P < .0001). The results from this study suggest that the observed increase in intratumoral T-cells after NACT administration may be transient. The significance of this finding should be further explored to ensure that optimal treatment schedules are chosen for studies combining cytotoxic chemotherapy and ICI.

Identification of prognostic immune-related genes in the tumor microenvironment of endometrial cancer

Endometrial cancer (EC) is one of the most common gynecologic malignancies. To identify potential prognostic biomarkers for EC, we analyzed the relationship between the EC tumor microenvironment and gene expression profiles. Using the ESTIMATE R tool, we found that immune and stromal scores correlated with clinical data and the prognosis of EC patients. Based on the immune and stromal scores, 387 intersection differentially expressed genes were identified. Eight immune-related genes were then identified using two machine learning algorithms. Functional enrichment analysis revealed that these genes were mainly associated with T cell activation and response. Kaplan-Meier survival analysis showed that expression of TMEM150B, CACNA2D2, TRPM5, NOL4, CTSW, and SIGLEC1 significantly correlated with overall survival times of EC patients. In addition, using the TIMER algorithm, we found that expression of TMEM150B, SIGLEC1, and CTSW correlated positively with the tumor infiltration levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, and dendritic cells. These findings indicate that the composition of the tumor microenvironment affects the clinical outcomes of EC patients, and suggests that it may provide a basis for development of novel prognostic biomarkers and immunotherapies for EC patients.

Tumor microenvironment: Challenges and opportunities in targeting metastasis of triple negative breast cancer

Triple negative breast cancer (TNBC) is most aggressive subtype of breast cancers with high probability of metastasis as well as lack of specific targets and targeted therapeutics. TNBC is characterized with unique tumor microenvironment (TME), which differs from other subtypes. TME is associated with induction of proliferation, angiogenesis, inhibition of apoptosis and immune system suppression, and drug resistance. Exosomes are promising nanovesicles, which orchestrate the TME by communicating with different cells within TME. The components of TME including transformed ECM, soluble factors, immune suppressive cells, epigenetic modifications and re-programmed fibroblasts together hamper antitumor response and helps progression and metastasis of TNBCs. Therefore, TME could be a therapeutic target of TNBC. The current review presents latest updates on the role of exosomes in modulation of TME, approaches for targeting TME and combination of immune checkpoint inhibitors and target chemotherapeutics. Finally, we also discussed various phytochemicals that alter genetic, transcriptomic and proteomic profiles of TME along with current challenges and future implications. Thus, as TME is associated with the hallmarks of TNBC, the understanding of the impact of different components can improve the clinical benefits of TNBC patients.