Quantification Of Tumor-infiltrating Immune Cells Research Articles

Comprehensive analysis of the prognostic and role in immune cell infiltration of MSR1 expression in lower-grade gliomas.

BackgroundThe therapeutic effects of conventional treatment on gliomas are not promising. The tumor microenvironment (TME) has a close association with the invasiveness of multiple types of tumors, including low‐grade gliomas (LGG). This study aims to validate the prognostic and immune‐related role of macrophage scavenger receptor 1 (MSR1) in LGG patients.MethodsData in this study were obtained from public databases. The differential expression of MSR1 was analyzed in LGG patients with different clinicopathological characteristics. Kaplan–Meier survival analysis, a time‐dependent receiver operating characteristic (ROC) curve, and Cox regression analysis were used to assess the prognostic value of MSR1. Differentially expressed genes (DEGs) were screened between the high and low expression groups of MSR1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to annotate the function of these DEGs. Hallmark gene sets were identified based on MSR1 by Gene Set Enrichment Analysis (GSEA). Difference analysis and correlation analysis were used to study the relationship between MSR1 and TME‐related scores, tumor‐infiltrating immune cells (TIICs), immune‐related gene sets, and immune checkpoints (ICPs). The single‐cell sequencing data were processed to identify the cell types expressing MSR1. The quantification of TIICs in TME was calculated by single‐sample gene set enrichment analysis (ssGSEA). The differential expression of MSR1 in LGG and control brain tissues was verified by experiments.ResultsThere were significant differences in the expression level of MSR1 in different types of tissues and cells. MSR1 has a high prognostic value in LGG patients and can be used as an independent prognostic factor. MSR1 is closely related to TME and may play an important role in the immunotherapy of LGG patients.ConclusionsThe result of our study demonstrated that MSR1 is an independent prognostic biomarker in LGG patients and may play an important role in the TME of LGGs.

Absolute quantification of tumor-infiltrating immune cells in high-grade glioma identifies prognostic and radiomics values.

To understand the tumor immune microenvironment precisely, it is important to secure the quantified data of tumor-infiltrating immune cells, since the immune cells are true working unit. We analyzed unit immune cell number per unit volume of core tumor tissue of high-grade gliomas (HGG) to correlate their immune microenvironment characteristics with clinical prognosis and radiomic signatures. The number of tumor-infiltrating immune cells from 64 HGG core tissue were analyzed using flow cytometry and standardized. After sorting out patient groups according to diverse immune characteristics, the groups were tested if they have any clinical prognostic relevance and specific radiomic signature relationships. Sparse partial least square with discriminant analysis using multimodal magnetic resonance images was employed for all radiomic classifications. The median number of CD45 + cells per one gram of HGG core tissue counted 865,770 cells which was equivalent to 8.0% of total cells including tumor cells. There was heterogeneity in the distribution of immune cell subpopulations among patients. Overall survival was significantly better in T cell-deficient group than T cell-enriched group (p = 0.019), and T8 dominant group than T4 dominant group (p = 0.023). The number of tumor-associated macrophages (TAM) and M2-TAM was significantly decreased in isocitrate dehydrogenase mutated HGG. Radiomic signature classification showed good performance in predicting immune phenotypes especially with features extracted from apparent diffusion coefficient maps. Absolute quantification of tumor-infiltrating immune cells confirmed the heterogeneity of immune microenvironment in HGG which harbors prognostic impact. This immune microenvironment could be predicted by radiomic signatures non-invasively.

Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.

The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role in tumor immune escape, to determine patient prognosis and to predict response to immune checkpoint blockade. Current state-of-the-art methods that quantify immune cells from tumor biopsies using gene expression data apply computational deconvolution methods that present multicollinearity and estimation errors resulting in the overestimation or underestimation of the diversity of infiltrating immune cells and their quantity. To overcome such limitations, we developed MIXTURE, a new ν-support vector regression-based noise constrained recursive feature selection algorithm based on validated immune cell molecular signatures. MIXTURE provides increased robustness to cell type identification and proportion estimation, outperforms the current methods, and is available to the wider scientific community. We applied MIXTURE to transcriptomic data from tumor biopsies and found relevant novel associations between the components of the immune infiltrate and molecular subtypes, tumor driver biomarkers, tumor mutational burden, microsatellite instability, intratumor heterogeneity, cytolytic score, programmed cell death ligand 1 expression, patients' survival and response to anti-cytotoxic T-lymphocyte-associated antigen 4 and anti-programmed cell death protein 1 immunotherapy.

Abstract 4430: Quantification of tumor-infiltrating immune cell populations with an augmented transcriptome

Abstract Comprehensive characterization of the tumor and tumor microenvironment (TME) can improve our understanding of tumor progression and treatment outcomes. For example, quantification of the immune infiltrate can inform mechanisms of immune escape and predict response to checkpoint blockade. Standard experimental approaches exist to enumerate tumor-infiltrating immune cells, but they can have practical limitations of throughput, number of markers, or sample requirements. RNA sequencing can be used as a scalable solution to comprehensively profile the immune cell composition of the TME. However, care must be taken to ensure that the computational analysis accurately reflects the underlying immune cell composition. To address these challenges, we developed ImmunoID NeXT, an augmented, immuno-oncology-optimized exome/transcriptome platform designed to provide comprehensive information regarding the tumor and TME from a single FFPE tumor sample. This includes the quantification of tumor-infiltrating immune cells using RNA-seq analysis, which we compare to quantification by orthogonal methods. To generate our reference data, we profiled the transcriptomes of eight purified immune cell types using ImmunoID NeXT. Then, we analyzed multiple sample types and orthogonally quantified immune cells in each. These include profiling of healthy donor PBMCs with cytometry by time of flight (CyTOF), and immunofluorescence (IF) characterization of FFPE tumor samples. We also used ImmunoID NeXT to profile the immune infiltrate of over 500 tumor samples across 13 cancer types. Finally, we created a set of in vitro cell mixtures and profiled them by flow cytometry. We utilized the transcriptome profiles of eight purified immune cell types to develop reference expression signatures specific for each cell type. Then, we compared ImmunoID NeXT's transcriptome-based approach to CyTOF results of healthy donor PBMCs, showing accuracy in real samples with diverse immune populations. We also compared to FFPE tumor samples with IF, ensuring that our approach is able to profile the immune composition in tumor samples. Next, we highlight the diversity of immune populations across cancer types by applying ImmunoID NeXT to over 500 tumor samples. Finally, to demonstrate concordance across the eight cell types, we compared to flow cytometry results of in vitro cell mixtures. Analysis of the immune infiltrate of tumor samples can add to our understanding of the tumor-immune interaction, with potential applications including studies of response to immunotherapy. RNA sequencing can be utilized as a scalable approach for such analysis. Here, we test the accuracy of our approach using multiple sample sets with orthogonal profiling. We demonstrate that ImmunoID NeXT can accurately evaluate the composition of infiltrating immune cells in tumor samples. Citation Format: Eric Levy, Pamela Milani, Charles W. Abbott, Manxia Lee, Robert Power, John West, Richard Chen, Sean M. Boyle. Quantification of tumor-infiltrating immune cell populations with an augmented transcriptome [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4430.

Bioinformatic profiling of tumor immunity from patient biopsies to predict survival and response to immunotherapy.

e15198 Background: Immunotherapies have revolutionized cancer treatment, but responses are not universal and patients who initially respond to therapy develop resistance. The accurate quantification of tumor-infiltrating immune cells holds the promise to reveal the role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. We present MIXTURE, a new algorithm for tumor immune cell-type proportions deconvolution from transcriptomic data that overcomes competitive methods and revealed novel associations of immune cell types with patient survival and immunotherapy response. Methods: We applied MIXTURE to transcriptomic data from BRCA (n = 1095), LUAD (n = 506), SKCM (n = 472), HNSC (n = 499) and COAD (n = 521) cohorts from TCGA and five published datasets of melanoma patients treated with anti-PD-1/anti-CLTA-4. Results: The analysis of TCGA breast cancer biopsies showed high proportions of M2-macrophages associated with poor patient survival (p < 0.001). In contrast, we found that proportions of follicular T helper cells were associated with better outcome (p < 0.001). We observed a differential immune composition in biopsies of lung adenocarcinoma patients with mutations in TP53 (WT; n = 247; Mut; n = 259) and EGFR (WT, n = 438; Mut, n = 68) related with their different response to immunotherapy (p < 0.05). We found correlations between immune cells proportions and current biomarkers of response to immunotherapy such as TMB, intratumoral heterogeneity, MSI and PD-L1 expression in the TCGA cohorts (p < 0.05). The meta-analysis of melanoma patients treated with immunotherapy showed a distinct immune infiltrate in patients who responded to anti-PD-1 with an increase of immune effector cells such as CD8, CD4 memory activated and gamma-delta T cells, and a decrease in immunosuppressive M2-macrophages (p < 0.05; R = 81; NR = 107). According with the latest findings, we observed higher presence of B cells in responders to anti-PD-1 on-treatment (p = 0.033; R = 31; NR = 23) and in baseline of responders to anti-CTLA-4 (p = 0.028; R = 14; NR = 26). Interestingly, patients that previously progressed to anti-CTLA-4 showed a differential immune profile that was associated with response to anti-PD-1 (p < 0.05; Ipi-Prog = 59; Ipi-Naïve = 102). Conclusions: We demonstrated the potential of MIXTURE to understand the tumor immune microenvironment and its relationship with patient survival and response to immunotherapies. MIXTURE is available for the wider scientific community as web application and as packages for R and Python.

NeoANT-HILL: an integrated tool for identification of potential neoantigens

BackgroundCancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives.ResultsWe have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity.ConclusionneoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens. The software is available through github at https://github.com/neoanthill/neoANT-HILL.

CD163+ immune cell infiltrates and presence of CD54+ microvessels are prognostic markers for patients with embryonal rhabdomyosarcoma

Rhabdomyosarcomas (RMS) are rare and often lethal diseases. It is assumed that the tumor microenvironment (TME) of RMS exerts an immunosuppressive function, but there is currently no systematic analysis of the immune cells infiltrating sarcoma tissue. Focusing on two common types of RMS (alveolar [RMA] and embryonal [RME]), we performed a comprehensive immunohistochemical analysis of tumor-infiltrating immune cells in the TME. We performed a qualitative estimation of infiltrating immune cells in the tumor microenvironment by an experienced pathologist as well as a quantitative digital pathology analysis. We found that (1) manual and automatic quantification of tumor-infiltrating immune cells were consistent; (2) RME tumors showed a higher degree of immune cell infiltration than RMA tumors but (3) the number of tumor infiltrating lymphocytes was low compared to other solid tumor types; (4) microvascular density correlated with immune cell infiltration and (5) CD163 positive macrophages as well as CD54 positive microvessels were more often detected in RME than in RMA and correlated with patient overall and event free survival. Our systematic analysis provides a comprehensive view of the immune landscape of RMS which needs to be taken into account for developing immunotherapies for this rare type of cancer.

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples

Background: Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. Methods: Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. Results: In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92–0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87–0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (κ = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (κ = 0.78). Conclusions: Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists’ visual assessment.