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Analysis of PD-1 related immune transcriptional profile in different cancer types.

ABSTRACT: Background:Programmed cell death 1 (PD-1) functions as an immune checkpoint in the process of anti-tumor immune response. The PD-1 blockade is now becoming a fundamental part in cancer immunotherapy. So it's essential to elicit the PD-1 related immune process in different types of cancer. Methods:The Cancer Genome Atlas was used to collect the RNA-seq data of 33 cancer types. The microenvironment cell populations-counter was used to analyze the immune cell infiltrates. KEGG and GO analysis were performed to investigate PD-1 associated biological process. Kaplan-Meier survival curves and Cox's proportional hazards model were performed for prognostic value analysis. Results:We demonstrated that PD-1 expression varied in different cancer types. The uveal melanoma had a low PD-1 expression and poor infiltrated with immune cells. But it showed the strong correlation of PD-1 with the most types of immune cells. The PD-1 demonstrated a robust relationship with other immunomodulators and showed its involvement in critical functions correlated with anti-tumor immune pathways. Survival analysis indicated the PD-1 expression suggested different prognosis in different cancer types. Conclusions:Our investigations promote a better understanding of the PD-1 blockade and provide PD-1 related personized combined immunotherapy for different types of cancer patients.

SUBMITTER: Shang J

PROVIDER: S-EPMC6307327 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Analysis of PD-1 related immune transcriptional profile in different cancer types.

Shang Jun J Song Qian Q Yang Zuyi Z Sun Xiaoyan X Xue Meijuan M Chen Wenjie W Yang Jingcheng J Wang Sihua S

Cancer cell international 20181227

<h4>Background</h4>Programmed cell death 1 (PD-1) functions as an immune checkpoint in the process of anti-tumor immune response. The PD-1 blockade is now becoming a fundamental part in cancer immunotherapy. So it's essential to elicit the PD-1 related immune process in different types of cancer.<h4>Methods</h4>The Cancer Genome Atlas was used to collect the RNA-seq data of 33 cancer types. The microenvironment cell populations-counter was used to analyze the immune cell infiltrates. KEGG and GO ...[more]

PMID: 30607140

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Project description:Single-cell analysis has the potential to provide us with a host of new knowledge about biological systems, but it comes with the challenge of correctly interpreting the biological information. While emerging techniques have made it possible to measure inter-cellular variability at the transcriptome level, no consensus yet exists on the most appropriate method of data analysis of such single cell data. Methods for analysis of transcriptional data at the population level are well established but are not well suited to single cell analysis due to their dependence on population averages. In order to address this question, we have systematically tested combinations of methods for primary data analysis on single cell transcription data generated from two types of primary immune cells, neutrophils and T lymphocytes. Cells were obtained from healthy individuals, and single cell transcript expression data was obtained by a combination of single cell sorting and nanoscale quantitative real time PCR (qRT-PCR) for markers of cell type, intracellular signaling, and immune functionality. Gene expression analysis was focused on hierarchical clustering to determine the existence of cellular subgroups within the populations. Nine combinations of criteria for data exclusion and normalization were tested and evaluated. Bimodality in gene expression indicated the presence of cellular subgroups which were also revealed by data clustering. We observed evidence for two clearly defined cellular subtypes in the neutrophil populations and at least two in the T lymphocyte populations. When normalizing the data by different methods, we observed varying outcomes with corresponding interpretations of the biological characteristics of the cell populations. Normalization of the data by linear standardization taking into account technical effects such as plate effects, resulted in interpretations that most closely matched biological expectations. Single cell transcription profiling provides evidence of cellular subclasses in neutrophils and leukocytes that may be independent of traditional classifications based on cell surface markers. The choice of primary data analysis method had a substantial effect on the interpretation of the data. Adjustment for technical effects is critical to prevent misinterpretation of single cell transcript data.

Project description:IntroductionWe previously demonstrated that real-world progression (rwP) can be ascertained from unstructured electronic health record (EHR)-derived documents using a novel abstraction approach for patients with advanced non-small cell lung cancer (base case). The objective of this methodological study was to assess the reliability, clinical relevance, and the need for disease-specific adjustments of this abstraction approach in five additional solid tumor types.MethodsPatients with metastatic breast cancer (mBC), advanced melanoma (aMel), small cell lung cancer (SCLC), metastatic renal cell carcinoma (mRCC), and advanced gastric/esophageal cancer (aGEC) were selected from a real-world database. Disease-specific additions to the base case were implemented as needed. The resulting abstraction approach was applied to each disease cohort to capture rwP events and dates. To provide comprehensive clinical context, real-world progression-free survival (rwPFS) and time to progression (rwTTP) were compared to real-world overall survival (rwOS), time to next treatment (rwTTNT), and time to treatment discontinuation (rwTTD). Endpoint estimates were assessed using the Kaplan-Meier method. Correlations between real-world endpoints and rwOS were calculated using Spearman's ρ.ResultsAdditions to the base-case rwP abstraction approach were required for mBC, aMel, and SCLC. Inter-abstractor agreement for rwP occurrence, irrespective of date, ranged from 88% to 97%. Occurrence of clinically relevant downstream events (new antineoplastic systemic therapy start, antineoplastic systemic therapy end, or death relative to the rwP event) ranged from 59% (aMel) to 72% (mBC). Median rwPFS ranged from 3.7 (aMel) to 7.7 (mBC) months, and median rwTTP ranged from 4.6 (aMel) to 8.3 (mRCC) months. Correlations between rwOS and rwPFS ranged from 0.52 (aMel) to 0.82 (SCLC). The correlation between rwOS and rwTTD was often lower relative to other comparisons (range 0.40-0.62).ConclusionDerivation of a rwP variable from EHR documentation is feasible and reliable across the five solid tumors. Endpoint analyses show that rwP produces clinically meaningful information.

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Analysis of PD-1 related immune transcriptional profile in different cancer types.

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Analysis of PD-1 related immune transcriptional profile in different cancer types.

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