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Prognostic Landscape of Tumor-Infiltrating T and B Cells in Human Cancer.

ABSTRACT: Recently, immunotherapy targeting tumor-infiltrating lymphocytes (TILs) has emerged as a critical and promising treatment in several types of cancer. However, not all cancer types have been tested in immunotherapeutic trials, and different patients and cancer types may have unpredictable clinical outcomes. This situation has created a particular exigency for analyzing the prognostic significance of tumor-infiltrating T cells (TIL-T) and B cells (TIL-B) across different cancer types. To address the critical role of TILs, the abundances of TIL-T and TIL-B cells, as determined by the protein levels of LCK and CD20, were analyzed across heterogeneous human malignancies. TIL-T and TIL-B cells showed varying prognostic significances across heterogeneous cancer types. Additionally, distinct distributions of TIL-T and TIL-B cells were observed in different cancer and tumor microenvironment (TME) subtypes. Next, we analyzed the cellular context for the TME communication network involving the well-acknowledgeable chemokine receptors of TIL-T and TIL-B cells, implying the functional interactions with TME. Additionally, these chemokine receptors, expressed by TIL-T and TIL-B cells, were remarkably correlated with the levels of TIL-T or TIL-B cell infiltrations across nearly all the cancer types, indicating these chemokine receptors as universal targets for up- and down-regulating the TIL-T and TIL-B cells. Lastly, we provide the prognostic landscape of TIL-T and TIL-B cells across 30 cancer types and the subgroups defined by gender, histopathology, histological grade, therapeutic approach, drug, and TME subtype, which are intended to be a resource to fuel the investigations of TILs, with important implications for cancer immunotherapy.

SUBMITTER: Zheng M

PROVIDER: S-EPMC8771864 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGastric cancer (GC) is the fourth most frequently diagnosed malignancy and the second leading cause of cancer-associated mortality worldwide. The tumor microenvironment, especially tumor-infiltrating immune cells (TIICs), exhibits crucial roles both in promoting and inhibiting cancer growth. The aim of the present study was to evaluate the landscape of TIICs and develop a prognostic nomogram in GC.Materials and methodsA gene expression profile obtained from a dataset from The Cancer Genome Atlas (TCGA) was used to quantify the proportion of 22 TIICs in GC by the CIBERSORT algorithm. LASSO regression analysis and multivariate Cox regression were applied to select the best survival-related TIICs and develop an immunoscore formula. Based on the immunoscore and clinical information, a prognostic nomogram was built, and the predictive accuracy of it was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROC) and the calibration plot. Furthermore, the nomogram was validated by data from the International Cancer Genome Consortium (ICGC) dataset.ResultsIn the GC samples, macrophages (25.3%), resting memory CD4 T cells (16.2%) and CD8 T cells (9.7%) were the most abundant among 22 TIICs. Seven TIICs were filtered out and used to develop an immunoscore formula. The AUC of the prognostic nomogram in the TCGA set was 0.772, similar to that in the ICGC set (0.730) and whole set (0.748), and significantly superior to that of TNM staging alone (0.591). The calibration plot demonstrated an outstanding consistency between the prediction and actual observation. Survival analysis revealed that patients with GC in the high-immunoscore group exhibited a poor clinical outcome. The result of multivariate analysis revealed that the immunoscore was an independent prognostic factor.DiscussionThe immunoscore could be used to reinforce the clinical outcome prediction ability of the TNM staging system and provide a convenient tool for risk assessment and treatment selection for patients with GC.

Project description:BackgroundThis study was to explore the infiltration pattern of immune cells in the prostate cancer (PCa) microenvironment and evaluate the possibility of specific infiltrating immune cells as potential prognostic biomarkers in PCa.MethodsInfiltrating percentage of 22 immune cells were extracted from 27 normalized datasets by CIBERSORT algorithm. Samples with CIBERSORT p-value < 0.05 were subsequently merged and divided into normal or tumor groups. The differences of 22 immune cells between normal and tumor tissues were analyzed along with potential infiltrating correlations among 22 immune cells and Gleason grades. SNV data from TCGA was used to calculate the TMB score. A univariate and multivariate regression were used to evaluate the prognostic effects of immune cells in PCa.ResultsTen immune cells with significant differences were identified, including seven increased and three decreased infiltrating immune cells from 190 normal prostate tissues and 537 PCa tissues. Among them, the percentage of infiltration of resting NK cells increased the most, whereas the percentage of infiltration of resting mast cells decreased the most. In normal tissues, CD8+ T cells had the strongest infiltrating correlation with monocytes, while activated NK cells and naive B cells were the highest in PCa tissues. Moreover, the infiltration of five immune cells was significantly associated with TMB score and mutations of immune gene change the infiltration of immune cells. The Area Under Curve (AUC) of the multivariate regression model for the five- and 10-year survival prediction of PCa reached 0.796 and 0.862. The validation cohort proved that the model was reproducible.ConclusionsThis study demonstrated that different infiltrating immune cells in prostate cancer, especially higher infiltrating M1 macrophages and neutrophils in PCa tissue, are associated with patients' prognosis, suggesting that these two immune cells might be potential targets for PCa diagnosis and prognosis of treatment.

Project description:Urothelial bladder cancer (BCa) is the ninth most commonly diagnosed cancer worldwide and accounts for approximately 3% of global cancer diagnoses. We are interested in prognostic markers that may characterize tumor cells (TCs) and immune cells (ICs) and their relationship in BCa. A potential candidate marker that meets these criteria is progranulin (GP88), which is expressed separately in TCs and ICs. We analyzed GP88 expression by immunohistochemistry (IHC) in 196 muscle-invasive BCa samples using a tissue microarray. The immunoreactive score for GP88 staining in TCs and the percentage of GP88-positive ICs was determined. An easy cutoff for the staining status of TCs (positive vs. negative) and ICs (0% vs. >0%) and, more generally, negative vs. positive GP88 staining could be applied. We detected 93 patients (47.4%) and 92 patients (46.9%) with GP88-positive TCs or ICs, respectively. The IHC results were correlated with clinicopathological and survival data. Positive GP88 staining in TCs appeared to be an independent poor prognostic factor for disease-specific survival (DSS) (RR (relative risk) = 1.74; p = 0.009) and recurrence-free survival (RFS) (RR = 1.92; p = 0.002). In contrast, negative GP88 staining in ICs was an independent negative predictor for overall survival (OS) (RR = 2.18; p < 0.001), DSS (RR = 2.84; p < 0.001) and RFS (RR = 2.91; p < 0.001) in multivariate Cox's regression analysis. When combining GP88 staining in TCs and ICs, a specific combination of GP88-positive TCs and GP88-negative ICs was associated with a 2.54-fold increased risk of death, a 4.21-fold increased risk of disease-specific death and a 4.81-fold increased risk of recurrence compared to GP88-negative TCs and GP88-positive ICs. In summary, GP88 positivity in TCs is a negative prognostic factor for DSS and RFS. In addition, GP88 positivity can mark ICs that are associated with a good prognosis (OS, DSS and RFS). The combination of GP88 staining in TCs and ICs appears to be a significant independent prognostic biomarker in muscle-invasive BCa.

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Prognostic Landscape of Tumor-Infiltrating T and B Cells in Human Cancer.

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