Dataset Information

Molecular and Clinical Characterization of PD-1 in Breast Cancer Using Large-Scale Transcriptome Data.

ABSTRACT: Despite the impressive impact of PD-1 (programmed cell death protein 1)-targeted cancer immunotherapy, a great part of patients with cancer fail to respond. PD-1 impact on immune cells in addition to T cells, and the synergistic role of PD-1 with other immune modulators remain largely unknown. To fill this gap, we systematically investigated PD-1-related transcriptome data and relevant clinical information derived from TCGA (The Cancer Genome Atlas) and METABRIC (Molecular Taxonomy of Breast Cancer International Consortium), which involved a total of 2,994 breast cancer patients. Our results revealed the relationship among PD-1 and major molecular and clinical characteristics in breast cancer. More importantly, we depicted the association landscape between PD-1 and other immune cell populations. Gene ontology analyses and gene set variation analysis (GSVA) of genes correlated with PD-1 revealed that PD-1 was mainly involved in immune responses and inflammatory activities. We also elucidated the association of PD-1 with other immune modulators in pan-cancer level, especially the potential synergistic relationship between PD-1 and other immune checkpoints members in breast cancer. In short, the expression level of PD-1 was bound up with breast cancer malignancy, which could be used as a potential biomarker; PD-1 might manipulate the anti-tumor immune response by impacting not just T cells, and this might vary among different tumor types. Furthermore, PD-1 might synergize with other immune checkpoint members to modulate the immune microenvironment in breast cancer.

SUBMITTER: Liu Q

PROVIDER: S-EPMC7718028 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Molecular and Clinical Characterization of PD-1 in Breast Cancer Using Large-Scale Transcriptome Data.

Liu Qiang Q Cheng Ran R Kong Xiangyi X Wang Zhongzhao Z Fang Yi Y Wang Jing J

Frontiers in immunology 20201117

Despite the impressive impact of PD-1 (programmed cell death protein 1)-targeted cancer immunotherapy, a great part of patients with cancer fail to respond. PD-1 impact on immune cells in addition to T cells, and the synergistic role of PD-1 with other immune modulators remain largely unknown. To fill this gap, we systematically investigated PD-1-related transcriptome data and relevant clinical information derived from TCGA (The Cancer Genome Atlas) and METABRIC (Molecular Taxonomy of Breast Can ...[more]

PMID: 33329517

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Project description:Large-scale transcriptome data, such as single-cell RNA-sequencing data, have provided unprecedented resources for studying biological processes at the systems level. Numerous dimensionality reduction methods have been developed to visualize and analyze these transcriptome data. In addition, several existing methods allow inference of functional variations among samples using gene sets with known biological functions. However, it remains challenging to analyze transcriptomes with reduced dimensions that are interpretable in terms of dimensions' directionalities, transferrable to new data, and directly expose the contribution or association of individual genes. In this study, we used gene set non-negative principal component analysis (gsPCA) and non-negative matrix factorization (gsNMF) to analyze large-scale transcriptome datasets. We found that these methods provide low-dimensional information about the progression of biological processes in a quantitative manner, and their performances are comparable to existing functional variation analysis methods in terms of distinguishing multiple cell states and samples from multiple conditions. Remarkably, upon training with a subset of data, these methods allow predictions of locations in the functional space using data from experimental conditions that are not exposed to the models. Specifically, our models predicted the extent of progression and reversion for cells in the epithelial-mesenchymal transition (EMT) continuum. These methods revealed conserved EMT program among multiple types of single cells and tumor samples. Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.

Project description:BACKGROUND: Retrotransposons are transposable elements that proliferate within eukaryotic genomes through a process involving reverse transcription. The numbers of retrotransposons within genomes and differences between closely related species may yield insight into the evolutionary history of the elements. Less is known about the ongoing dynamics of retrotransposons, as analysis of genome sequences will only reveal insertions of retrotransposons that are fixed--or near fixation--in the population or strain from which genetic material has been extracted for sequencing. One pre-requisite for retrotransposition is transcription of the elements. Given their intrinsic sequence redundancy, transcriptome-level analyses of transposable elements are scarce. We have used recently published transcriptome data from the fission yeast Schizosaccharomyces pombe to assess the ability to detect and describe transcriptional activity from Long Terminal Repeat (LTR) retrotransposons. LTR retrotransposons are normally flanked by two LTR sequences. However, the majority of LTR sequences in S. pombe exist as solitary LTRs, i.e. as single terminal repeat sequences not flanking a retrotransposon. Transcriptional activity was analysed for both full-length LTR retrotransposons and solitary LTRs. RESULTS: Two independent sets of transcriptome data reveal the presence of full-length, polyadenylated transcripts from LTR retrotransposons in S. pombe during growth phase in rich medium. The redundancy of retrotransposon sequences makes it difficult to assess which elements are transcriptionally active, but data strongly indicates that only a subset of the LTR retrotransposons contribute significantly to the detected transcription. A considerable level of reverse strand transcription is also detected. Equal levels of transcriptional activity are observed from both strands of solitary LTR sequences. Transcriptome data collected during meiosis suggests that transcription of solitary LTRs is correlated with the transcription of nearby protein-coding genes. CONCLUSIONS: Presumably, the host organism negatively regulates proliferation of LTR retrotransposons. The finding of considerable transcriptional activity of retrotransposons suggests that part of this regulation is likely to take place at a post-transcriptional level. Alternatively, the transcriptional activity may signify a hitherto unrecognized activity level of retrotransposon proliferation. Our findings underline the usefulness of transcriptome data in elucidating dynamics in retrotransposon transcription.

Dataset Information

Molecular and Clinical Characterization of PD-1 in Breast Cancer Using Large-Scale Transcriptome Data.

Publications

Molecular and Clinical Characterization of PD-1 in Breast Cancer Using Large-Scale Transcriptome Data.

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