Immune cell score in pancreatic cancer—comparison of hotspot and whole-section techniques

Tahkola, Kyösti; Leppänen, Joni; Ahtiainen, Maarit; Väyrynen, Juha P.; Haapasaari, Kirsi‐Maria; Karttunen, Tuomo J.; Kellokumpu, Ilmo; Helminen, Olli; Böhm, Jan

doi:10.1007/s00428-019-02549-1

Cited by 18 publications

(24 citation statements)

References 27 publications

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“…Although there was no statistical difference in high vs. low immune/stromal score groups, cases with higher immune/ stromal scores may actually obtain a longer OS or RFS. As recently reported, higher immune cell score was validated to be associated with better prognosis of resected PDAC patients (32,33). So that we utilized TIMER, an open-access web server for comprehensive analysis of tumor-infiltrating immune cells, to characterize the landscape of immune infiltrations, which was crucial for the investigation and validation of immune scores and looked for the distribution of each immune components (34).…”

Section: Discussionmentioning

confidence: 99%

Genetic landscape of prognostic value in pancreatic ductal adenocarcinoma microenvironment

Pu¹,

Chen²,

Gao³

et al. 2019

Ann. Transl. Med.

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Background: The prognosis of pancreatic ductal adenocarcinoma (PDAC) remains dismally poor and is widely considered as an intricate genetic disorder. The mutational landscape of PDAC may directly reflect cancer immunogenicity and dictate the extent and phenotype of immune cell infiltration. In adverse, the microenvironment may also effect the gene expression of cancer cells, which is associated with clinical prognosis. Thus, it is crucial to identify genomic alterations in PDAC microenvironment and its impacts on clinical prognosis. Methods:The gene expression profiles, mutation data and clinical information of 179 pancreatic cancer patients with an initial pathologic diagnosis ranging from 2001 to 2013 were retrieved from The Cancer Genome Atlas (TCGA) database. The MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm for calculating immune scores and stromal scores and Tumor IMmune Estimation Resource (TIMER) resource for cell infiltrations were applied in this study. Results:The average immune score or stromal score of PDAC subtype was significantly higher than that of other specific subtypes. KRAS mutant cases had significantly lower immune scores (P=0.001) and stromal scores (P=0.007), in concert with lower immune scores in TP53 mutant cases (P=0.030). However, no significant difference was found in SMAD4 and CDKN2A mutations. In the cohort OS/RFS, the infiltration levels of CD8+ T cells, B cells, Macrophages, Neutrophils and DCs in high stromal score group were higher than those in the low score group (all P<0.001), as well as in immune score groups except for Macrophages in the cohort RFS. In the cohort OS/RFS, 317/379 upregulated genes and 9/6 downregulated genes were observed in the high immune score group, while 227/205 upregulated genes and 17/6 downregulated genes in the high stromal score group. With the analysis for prognostic value of DEGs, 82 and 58 DEGs respectively in the high immune and stromal score group were verified to be significantly associated with better OS (P<0.05), while 53 and 17 DEGs respectively with longer RFS (P<0.05). Functional enrichment analysis showed genes of prognostic values were significantly related to immune response.Conclusions: A list of genes with prognostic value in PDAC microenvironment were obtained from functional enrichment analysis based on immune and stromal scores, which indicates a series of potential auxiliary prognostic biomarkers for PDAC are available. Further research on these genes may be valuable and Pu et al. Genetic landscape of prognostic value in PDAC microenvironment

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Section: Discussionmentioning

confidence: 99%

Genetic landscape of prognostic value in pancreatic ductal adenocarcinoma microenvironment

Pu¹,

Chen²,

Gao³

et al. 2019

Ann. Transl. Med.

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“…The upregulated CD4+ activated memory T cells, M2 macrophages, and neutrophils in the high stromal-score group may contribute to the poor outcome of PAAD [36]. In an investigation carried out by Tahkola K et al, immune scores were an independent prognostic factor for better disease-free survival and overall survival rates in PAAD patients [37], which may owe much to upregulated CD8 T lymphocytes, gamma delta T lymphocytes, resting NK cells, monocytes, and neutrophils in the high immune-score group [37]. Therefore, understanding the specific mechanism of how the portions of different types of immunocytes are regulated is essential for exploring novel therapeutic strategies to improve the immune response to PAAD.…”

Section: Aging Discussionmentioning

confidence: 98%

Using ESTIMATE algorithm to establish an 8-mRNA signature prognosis prediction system and identify immunocyte infiltration-related genes in Pancreatic adenocarcinoma

Meng

Ren

Zhang

et al. 2020

Aging

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Objective: The tumour microenvironment is one of the significant factors driving the carcinogenesis of Pancreatic adenocarcinoma (PAAD). However, the underlying mechanism of how the tumour microenvironment impacts the prognosis of PAAD is not completely clear. Results: The transcriptome and clinical data of 182 PAAD program cases were downloaded from the TCGA database. Three hundred thirty-three differentially expressed genes (DEGs) between high and low stromal groups and 314 DEGs between high and low immune score groups were identified using ESTIMATE score. Based on the 203 genes differentially expressed simultaneously in two score-related comparisons, we established an 8-mRNA signature to evaluate the prognosis of PAAD patients. Kaplan-Meier curves showed significantly worse survival for patients with high-risk scores in both the training and validation groups. The risk score was an independent prognostic factor and had a high predictive value for the prognosis of patients with PAAD. By searching the TCGA database, we showed that CA9, CXCL9, and GIMAP7 from the 8-mRNA signature were associated with the infiltration levels of immunocytes by regulating FOXO1 expression in PAAD. Conclusions: Unlike traditional methods of screening for differential genes in cancer and healthy tissues, we constructed a novel 8-mRNA signature to predict the prognosis of PAAD patients by applying ESTIMATE scoring to RNA-seq-based transcriptome data. Most importantly, we identified CA9, CXCL9, and GIMAP7 from the above eight genes as regulators of immunocyte infiltration by adjusting the expression of FOXO1 in PAAD. Thus, CA9, CXCL9, and GIMAP7 might be the ideal targets of immune therapy of PAAD. Methods: ESTIMATE scoring was used to determine the stromal and immune scores of transcriptome datasets downloaded from the TCGA database. An mRNA-based prognostic signature was built for the training cohort via the LASSO Cox regression model. The signature was verified using a validation cohort. Kaplan-Meier curves

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“…Previous studies have noted the strong relationship between the host immune response and survival of patients with cancer [ 19 – 21 ]. Researchers frequently focus on using well-established immune-related gene sets to screen prognostic genes.…”

Section: Discussionmentioning

confidence: 99%

Gene coexpression network approach to develop an immune prognostic model for pancreatic adenocarcinoma

Zhang

et al. 2021

World J Surg Onc

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Background Pancreatic adenocarcinoma (PAAD) is a nonimmunogenic tumor, and very little is known about the relationship between the host immune response and patient survival. We aimed to develop an immune prognostic model (IPM) and analyze its relevance to the tumor immune profiles of patients with PAAD. Methods We investigated differentially expressed genes between tumor and normal tissues in the TCGA PAAD cohort. Immune-related genes were screened from highly variably expressed genes with weighted gene correlation network analysis (WGCNA) to construct an IPM. Then, the influence of IPM on the PAAD immune profile was comprehensively analyzed. Results A total of 4902 genes highly variably expressed among primary tumors were used to construct a weighted gene coexpression network. One hundred seventy-five hub genes in the immune-related module were used for machine learning. Then, we established an IPM with four core genes (FCGR2B, IL10RA, and HLA-DRA) to evaluate the prognosis. The risk score predicted by IPM was an independent prognostic factor and had a high predictive value for the prognosis of patients with PAAD. Moreover, we found that the patients in the low-risk group had higher cytolytic activity and lower innate anti-PD-1 resistance (IPRES) signatures than patients in the high-risk group. Conclusions Unlike the traditional methods that use immune-related genes listed in public databases to screen prognostic genes, we constructed an IPM through WGCNA to predict the prognosis of PAAD patients. In addition, an IPM prediction of low risk indicated enhanced immune activity and a decreased anti-PD-1 therapeutic response.

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Immune cell score in pancreatic cancer—comparison of hotspot and whole-section techniques

Cited by 18 publications

References 27 publications

Genetic landscape of prognostic value in pancreatic ductal adenocarcinoma microenvironment

Genetic landscape of prognostic value in pancreatic ductal adenocarcinoma microenvironment

Using ESTIMATE algorithm to establish an 8-mRNA signature prognosis prediction system and identify immunocyte infiltration-related genes in Pancreatic adenocarcinoma

Gene coexpression network approach to develop an immune prognostic model for pancreatic adenocarcinoma

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