Dataset Information

Immunogenomic Profiling and Classification of Prostate Cancer Based on HIF-1 Signaling Pathway.

ABSTRACT: The HIF-1 signaling pathway plays an important role in the pathogenesis of cancer. Many studies have explored the progression of prostate cancer (PCa) under hypoxic conditions based on transcriptome data; few have uncovered the immunogenomic profiling and prostate cancer classification based on the HIF-1 signaling pathway. This pathway may help to identify the optimal subset of PCa patients responsive to immunotherapy/chemotherapy. The immunogenomic PCa subsets were classified based on profiling of the HIF-1 signaling pathway, using four publicly available PCa datasets. Three PCa subtypes that named as HIF-1 High (HIF-1_H), HIF-1 Medium (HIF-1_M), and HIF-1 Low (HIF-1_L) were identified. Functional enrichment was analyzed in each subtype. Several cancer-associated and immune-related pathways were hyperactivated in the HIF-1_H subtypes. In contrast, HIF-1_L subtypes were enriched in cell cycle and cell repair. Compared with other subtypes, HIF-1_H subtypes have greater immune cell infiltration, anti-tumor immune activity, and better survival prognosis. The submap and TIDE algorithm were used to predict the clinical response to immune checkpoint blockade, and GDSC was employed to screen potential chemotherapeutic targets for the treatment of PCa. Several chemotherapy drugs were identified in the GDSC dataset, including ABT 888, Temsirolimus, and EHT 1864. Meanwhile, HIF-1_H was defined as an early PCa marker, which is more likely to respond to immunotherapy. The identification of immunogenomic PCa subtypes based on the HIF-1 signaling pathway has potential clinical implications for PCa treatment. Immunopositive PCa subtypes will help to explore the reasons for the poor response of PCa to immunotherapy, and it is expected that immunotherapy will guide the personalized treatment of PCa patients.

SUBMITTER: Song J

PROVIDER: S-EPMC7425731 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Immunogenomic Profiling and Classification of Prostate Cancer Based on HIF-1 Signaling Pathway.

Song Jukun J Chen Weiming W Zhu Guohua G Wang Wei W Sun Fa F Zhu Jianguo J

Frontiers in oncology 20200806

The HIF-1 signaling pathway plays an important role in the pathogenesis of cancer. Many studies have explored the progression of prostate cancer (PCa) under hypoxic conditions based on transcriptome data; few have uncovered the immunogenomic profiling and prostate cancer classification based on the HIF-1 signaling pathway. This pathway may help to identify the optimal subset of PCa patients responsive to immunotherapy/chemotherapy. The immunogenomic PCa subsets were classified based on profiling ...[more]

PMID: 32850440

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Project description:BACKGROUND:Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy. METHODS:Using four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification. RESULTS:We identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K-Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes (CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype. CONCLUSIONS:The identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment.

Project description:Background: The aim of this paper was to identify an immunotherapy-sensitive subtype for estrogen receptor-positive breast cancer (ER+ BC) patients by exploring the relationship between cancer genetic programs and antitumor immunity via multidimensional genome-scale analyses. Methods: Multidimensional ER+ BC high-throughput data (raw count data) including gene expression profiles, copy number variation (CNV) data, single-nucleotide polymorphism mutation data, and relevant clinical information were downloaded from The Cancer Genome Atlas to explore an immune subtype sensitive to immunotherapy using the Consensus Cluster Plus algorithm based on multidimensional genome-scale analyses. One ArrayExpress dataset and eight Gene Expression Omnibus (GEO) datasets (GEO-meta dataset) as well as the Molecular Taxonomy of Breast Cancer International Consortium dataset were used as validation sets to confirm the findings regarding the immune profiles, mutational features, and survival outcomes of the three identified immune subtypes. Moreover, the development trajectory of ER+ BC patients from the single-cell resolution level was also explored. Results: Through comprehensive bioinformatics analysis, three immune subtypes of ER+ BC (C1, C2, and C3, designated the immune suppressive, activation, and neutral subtypes, respectively) were identified. C2 was associated with up-regulated immune cell signatures and immune checkpoint genes. Additionally, five tumor-related pathways (transforming growth factor, epithelial-mesenchymal transition, extracellular matrix, interferon-γ, and WNT signaling) tended to be more activated in C2 than in C1 and C3. Moreover, C2 was associated with a lower tumor mutation burden, a decreased neoantigen load, and fewer CNVs. Drug sensitivity analysis further showed that C2 may be more sensitive to immunosuppressive agents. Conclusion: C2 (the immune activation subtype) may be sensitive to immunotherapy, which provides new insights into effective treatment approaches for ER+ BC.

Dataset Information

Immunogenomic Profiling and Classification of Prostate Cancer Based on HIF-1 Signaling Pathway.

Publications

Immunogenomic Profiling and Classification of Prostate Cancer Based on HIF-1 Signaling Pathway.

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