Immune system plays an important role in the success and failure of conventional cancer therapy

The paradox of radiation and T cells in tumors

In this review we consider what appears to be a paradox in immunotherapies based around radiation therapy. The paradox is based on three main points. 1. That T cells are needed for radiation's efficacy; 2. That tumor-specific T cells are enriched in the field of treatment; and 3. That radiation kills T cells in the treatment field. We discuss evidence of the effect of radiation on T cells in the field given their ongoing movement in and out of tissues and the tumor, and how the movement of T cells impacts the treated primary tumor and untreated distant metastases. Given this evidence, we revisit the paradox to understand how the extraordinary efficacy of radiation and immunity in preclinical models is dependent on this radiation sensitive cell.

Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system

Objectives

Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms.

Methods

The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model.

Results

Twenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis.

Conclusions

The current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/, which was valuable for individualized treatment decision before surgery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04657-3.

Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.

Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.

Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.

Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

Defining Immunogenic and Radioimmunogenic Tumors

In the cancer literature tumors are inconsistently labeled as ‘immunogenic’, and experimental results are occasionally dismissed since they are only tested in known ‘responsive’ tumor models. The definition of immunogenicity has moved from its classical definition based on the rejection of secondary tumors to a more nebulous definition based on immune infiltrates and response to immunotherapy interventions. This review discusses the basis behind tumor immunogenicity and the variation between tumor models, then moves to discuss how these principles apply to the response to radiation therapy. In this way we can identify radioimmunogenic tumor models that are particularly responsive to immunotherapy only when combined with radiation, and identify the interventions that can convert unresponsive tumors so that they can also respond to these treatments.

Investigating T Cell Immunity in Cancer: Achievements and Prospects

T cells play a key role in tumour surveillance, both identifying and eliminating transformed cells. However, as tumours become established they form their own suppressive microenvironments capable of shutting down T cell function, and allowing tumours to persist and grow. To further understand the tumour microenvironment, including the interplay between different immune cells and their role in anti-tumour immune responses, a number of studies from mouse models to clinical trials have been performed. In this review, we examine mechanisms utilized by tumour cells to reduce their visibility to CD8⁺ Cytotoxic T lymphocytes (CTL), as well as therapeutic strategies trialled to overcome these tumour-evasion mechanisms. Next, we summarize recent advances in approaches to enhance CAR T cell activity and persistence over the past 10 years, including bispecific CAR T cell design and early evidence of efficacy. Lastly, we examine mechanisms of T cell infiltration and tumour regression, and discuss the strengths and weaknesses of different strategies to investigate T cell function in murine tumour models.

Targeting MerTK Enhances Adaptive Immune Responses After Radiation Therapy

PURPOSE

The role of MerTK, a member of the Tyro3-Axl-MerTK family of receptor tyrosine kinase, in the immune response to radiation therapy (RT) is unclear. We investigated immune-mediated tumor control after RT in murine models of colorectal and pancreatic adenocarcinoma using MerTK wild-type and knock-out hosts and whether inhibition of MerTK signaling with warfarin could replicate MerTK knock-out phenotypes.

METHODS AND MATERIALS

Wild-type and MerTK^-/- BALB/c mice were grafted in the flanks with CT26 tumors and treated with computed tomography guided RT. The role of macrophages and CD8 T cells in the response to radiation were demonstrated with cell depletion studies. The role of MerTK in priming immune responses after RT alone and with agonist antibodies to the T cell costimulatory molecule OX40 was evaluated in a Panc02-SIY model antigen system. The effect of warfarin therapy on the in-field and abscopal response to RT was demonstrated in murine models of colorectal adenocarcinoma. The association between warfarin and progression-free survival for patients treated with SABR for early-stage non-small cell lung cancer was evaluated in a multi-institutional retrospective study.

RESULTS

MerTK^-/- hosts had better tumor control after RT compared with wild-type mice in a macrophage and CD8 T cell-dependent manner. MerTK^-/- mice showed increased counts of tumor antigen-specific CD8 T cells in the peripheral blood after tumor-directed RT alone and in combination with agonist anti-OX40. Warfarin therapy phenocopied MerTK^-/- for single-flank tumors treated with RT and improved abscopal responses for RT combined with anti-CTLA4. Patients on warfarin therapy when treated with SABR for non-small cell lung cancer had higher progression-free survival rates compared with non-warfarin users.

CONCLUSIONS

MerTK inhibits adaptive immune responses after SABR. Because warfarin inhibits MerTK signaling and phenocopies genetic deletion of MerTK in mice, warfarin therapy may have beneficial effects in combination with SABR and immune therapy in patients with cancer.

Using Preclinical Data to Design Combination Clinical Trials of Radiation Therapy and Immunotherapy

Immunotherapies are rapidly entering the clinic as approved treatments for diverse cancer pathologies. Radiation therapy is an integral partner in cancer therapy, commonly as part of complicated multimodality approaches that optimize patient outcomes. Preclinical studies have demonstrated that the success of radiation therapy in tumor control is due in part to immune mechanisms, and that outcomes following radiation therapy can be improved through combination with a range of immunotherapies. However, preclinical models of cancer are very different from patient tumors, and the way these preclinical tumors are treated is often very different from standard of care treatment of patients. This review examines the preclinical and clinical data for the role of the immune system in radiation therapy outcomes, and how to integrate preclinical findings into clinical trials, using ongoing studies as examples.

Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms

An increasing body of evidence supports the association of immune genes with tumorigenesis and prognosis of breast cancer (BC). This research aims at exploring potential regulatory mechanisms and identifying immunogenic prognostic markers for BC, which were used to construct a prognostic signature for disease-free survival (DFS) of BC based on artificial intelligence algorithms. Differentially expressed immune genes were identified between normal tissues and tumor tissues. Univariate Cox regression identified potential prognostic immune genes. Thirty-four transcription factors and 34 immune genes were used to develop an immune regulatory network. The artificial intelligence survival prediction system was developed based on three artificial intelligence algorithms. Multivariate Cox analyses determined 17 immune genes (ADAMTS8, IFNG, XG, APOA5, SIAH2, C2CD2, STAR, CAMP, CDH19, NTSR1, PCDHA1, AMELX, FREM1, CLEC10A, CD1B, CD6, and LTA) as prognostic biomarkers for BC. A prognostic nomogram was constructed on these prognostic genes. Concordance indexes were 0.782, 0.734, and 0.735 for 1-, 3-, and 5- year DFS. The DFS in high-risk group was significantly worse than that in low-risk group. Artificial intelligence survival prediction system provided three individual mortality risk predictive curves based on three artificial intelligence algorithms. In conclusion, comprehensive bioinformatics identified 17 immune genes as potential prognostic biomarkers, which might be potential candidates of immunotherapy targets in BC patients. The current study depicted regulatory network between transcription factors and immune genes, which was helpful to deepen the understanding of immune regulatory mechanisms for BC cancer. Two artificial intelligence survival predictive systems are available at https://zhangzhiqiao7.shinyapps.io/Smart_Cancer_Survival_Predictive_System_16_BC_C1005/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_16_BC_C1005/. These novel artificial intelligence survival predictive systems will be helpful to improve individualized treatment decision-making.

Prognostic nomogram based on immune scores for breast cancer patients

Background

Increased attention has been focused on cancer immunity gene signature. However, the threshold of immune scores to predict disease‐free survival (DFS) and overall survival (OS) in breast cancer has not yet been defined. This study aimed to explore the association of immune scores with prognosis and build a clinical nomogram to predict the survival of patients with breast cancer.

Methods

A total of 986 subjects were analyzed, and clinicopathological characteristics and immune scores were obtained from the TCGA database. Cox proportional hazards regression model was used to estimate the adjusted hazard ratios (HRs). Based on results of multivariate analysis, nomograms were built. The models were subjected to bootstrap internal validation. The predictive accuracy and discriminative ability were measured by concordance index (C‐index) and the calibration curve.

Results

The patients were divided into three subgroups according to their immune scores. We found that compared with patients with low immune scores, those with intermediate and high immune scores had significantly improved DFS (HR and 95% confidence interval [CI]: 0.439 [0.242‐0.799], 0.541 [0.343‐0.855], respectively), whereas only intermediate immune scores significantly indicated better OS (HR and 95% CI: 0.385 [0.163‐0.910]). The C‐index for DFS and OS prediction was 0.723 (95% CI, 0.661‐0.785) and 0.800 (95% CI, 0.724‐0.877), respectively. The calibration curves for probability of 3‐ and 5‐year DFS showed significant agreement between nomogram predictions and the actual observations.

Conclusions

High and/or intermediate immune scores are significantly correlated with better DFS and OS in patients with breast cancer. Moreover, the nomograms for predicting prognosis may help to estimate the survival of patients.

Role of the immunosuppressive microenvironment in immunotherapy

Immunotherapy is reshaping cancer treatment paradigms; however, response rates to immune therapies are low and depend on the host's pre-existing antitumor immunity. The tumor microenvironment is comprised of malignant cells, stroma, and extracellular molecules and can hinder immune control of tumors. Herein, we review how anti-tumor immune responses are formed and how tumors avoid immune destruction. We also outline potential therapeutic targets in the immunosuppressive tumor microenvironment to promote immune control of tumors.

Optimizing Timing of Immunotherapy Improves Control of Tumors by Hypofractionated Radiation Therapy

The anecdotal reports of promising results seen with immunotherapy and radiation in advanced malignancies have prompted several trials combining immunotherapy and radiation. However, the ideal timing of immunotherapy with radiation has not been clarified. Tumor bearing mice were treated with 20Gy radiation delivered only to the tumor combined with either anti-CTLA4 antibody or anti-OX40 agonist antibody. Immunotherapy was delivered at a single timepoint around radiation. Surprisingly, the optimal timing of these therapies varied. Anti-CTLA4 was most effective when given prior to radiation therapy, in part due to regulatory T cell depletion. Administration of anti-OX40 agonist antibody was optimal when delivered one day following radiation during the post-radiation window of increased antigen presentation. Combination treatment of anti-CTLA4, radiation, and anti-OX40 using the ideal timing in a transplanted spontaneous mammary tumor model demonstrated tumor cures. These data demonstrate that the combination of immunotherapy and radiation results in improved therapeutic efficacy, and that the ideal timing of administration with radiation is dependent on the mechanism of action of the immunotherapy utilized.

The impact of the myeloid response to radiation therapy

Radiation therapy is showing potential as a partner for immunotherapies in preclinical cancer models and early clinical studies. As has been discussed elsewhere, radiation provides debulking, antigen and adjuvant release, and inflammatory targeting of effector cells to the treatment site, thereby assisting multiple critical checkpoints in antitumor adaptive immunity. Adaptive immunity is terminated by inflammatory resolution, an active process which ensures that inflammatory damage is repaired and tissue function is restored. We discuss how radiation therapy similarly triggers inflammation followed by repair, the consequences to adaptive immune responses in the treatment site, and how the myeloid response to radiation may impact immunotherapies designed to improve control of residual cancer cells.

Genetically engineered mouse models of cancer reveal new insights about the antitumor immune response

Cancer is a complex disease that can originate in virtually all the tissues of the body, and tumors progress through many different stages during their development. While genetic mutations in the emerging cancer cells drive this disease, it has become increasingly clear that cancer development is strongly influenced by the surrounding microenvironment. Cells of the immune system are critical components of this extrinsic network of cancer regulators, contributing significantly to the microenvironment of most cancers and either promoting or inhibiting the initiation and progression of this disease. Genetically engineered mouse (GEM) mouse models of spontaneous cancer are starting to shape our understanding of how antitumor T cells may act to prevent or inhibit cancer progression in some settings and not others. Lessons learned from investigating spontaneous mouse cancer models have important implications for directing clinical efforts that attempt to direct a cancer patient's immune system to eradicate their disease.