Heterogeneity of immune microenvironment in ovarian cancer and its clinical significance: a retrospective study

ABSTRACT Treatment of ovarian cancer (OC) remains the biggest challenge among gynecological malignancies. Immune checkpoint blockade therapy is promising in many cancers but shows low response rates in OC because of its heterogeneity. Although the biological and molecular heterogeneity of OC has been extensively investigated, heterogeneity of immune microenvironment remains elusive. We have collected the expression profiles of 3071 OC patients from 22 publicly available datasets. CIBERSORT was applied to infer the infiltration fraction of 22 immune cells among 2086 patients with CIBERSORT P < .05. We then explored the heterogeneity landscape of immune microenvironment in OC at three levels (immune infiltration, prognostic relevance of immune infiltration, immune checkpoint expression patterns). Multivariable Cox regression model was used to investigate the associations between survival risk and immune infiltration. Constructed immune risk score stratified patients with significantly different survival risk (HR: 1.47, 95% CI: 1.31–1.66, P < .0001). The immune infiltration landscape, prognostic relevance of immune cells, and expression patterns of 79 immune checkpoints exhibited remarkable clinicopathological heterogeneity. For instance, M1 macrophages were significantly associated with better outcomes among patients with high-grade, late-stage, type-II OC (HR: 0.77–0.83), and worse outcomes among patients with type-I OC (HR: 1.78); M2 macrophages were significantly associated with worse outcomes among patients with high-grade, type-II OC (HR: 1.14–1.17); Neutrophils were significantly associated with worse outcomes among patients with high-grade, late-stage, type-I OC (HR: 1.14–1.73). The heterogeneous landscape of immune microenvironment presented in this study provided new insights into prognostic prediction and tailored immunotherapy of OC.

[1]  Jinwoo Hong,et al.  Regulatory T Cells Induce Metastasis by Activating Tgf-Β and Enhancing the Epithelial–Mesenchymal Transition , 2019, Cells.

[2]  James A. Eddy,et al.  The Immune Landscape of Cancer. , 2019, Immunity.

[3]  Xiaoyuan Chen,et al.  Recent advances in nanomaterial-based synergistic combination cancer immunotherapy. , 2019, Chemical Society reviews.

[4]  R. S. Conlan,et al.  Nanotechnology and Immunotherapy in Ovarian Cancer: Tracing New Landscapes , 2019, The Journal of Pharmacology and Experimental Therapeutics.

[5]  E. Lengyel,et al.  Neutrophils facilitate ovarian cancer premetastatic niche formation in the omentum , 2019, The Journal of experimental medicine.

[6]  Cuifeng Wang,et al.  Tunneling Nanotubular Expressways for Ultrafast and Accurate M1 Macrophage Delivery of Anticancer Drugs to Metastatic Ovarian Carcinoma. , 2019, ACS nano.

[7]  G. Zhu,et al.  Fibrinogen-like Protein 1 Is a Major Immune Inhibitory Ligand of LAG-3 , 2019, Cell.

[8]  Feng Chen,et al.  Development and validation of an immune gene-set based Prognostic signature in ovarian cancer , 2018, EBioMedicine.

[9]  T. Fujiwara,et al.  The epithelial-to-mesenchymal transition induced by tumor-associated macrophages confers chemoresistance in peritoneally disseminated pancreatic cancer , 2018, Journal of Experimental & Clinical Cancer Research.

[10]  P. Stiff,et al.  Regulation of Ovarian Cancer Prognosis by Immune Cells in the Tumor Microenvironment , 2018, Cancers.

[11]  Jun Zhu,et al.  EMT- and stroma-related gene expression and resistance to PD-1 blockade in urothelial cancer , 2018, Nature Communications.

[12]  Y. Song,et al.  Pro‐inflammatory M1 macrophage enhances metastatic potential of ovarian cancer cells through NF‐κB activation , 2018, Molecular carcinogenesis.

[13]  A. Whittemore,et al.  Dose-Response Association of CD8+ Tumor-Infiltrating Lymphocytes and Survival Time in High-Grade Serous Ovarian Cancer , 2017, JAMA oncology.

[14]  Yongping Song,et al.  Recent development in clinical applications of PD-1 and PD-L1 antibodies for cancer immunotherapy , 2017, Journal of Hematology & Oncology.

[15]  C. Genestie,et al.  Ovarian Cancer: A Heterogeneous Disease , 2017, Pathobiology.

[16]  A. Iyer,et al.  PD-1 and PD-L1 Checkpoint Signaling Inhibition for Cancer Immunotherapy: Mechanism, Combinations, and Clinical Outcome , 2017, Front. Pharmacol..

[17]  Ann L Oberg,et al.  Pooled Clustering of High-Grade Serous Ovarian Cancer Gene Expression Leads to Novel Consensus Subtypes Associated with Survival and Surgical Outcomes , 2017, Clinical Cancer Research.

[18]  Yang Bai,et al.  The prognostic value of tumor-infiltrating T lymphocytes in ovarian cancer , 2017, Oncotarget.

[19]  Carlos Caldas,et al.  Patterns of Immune Infiltration in Breast Cancer and Their Clinical Implications: A Gene-Expression-Based Retrospective Study , 2016, PLoS medicine.

[20]  A. Talhouk,et al.  Neoadjuvant Chemotherapy of Ovarian Cancer Results in Three Patterns of Tumor-Infiltrating Lymphocyte Response with Distinct Implications for Immunotherapy , 2016, Clinical Cancer Research.

[21]  G. Giornelli.  Management of relapsed ovarian cancer: a review , 2016, SpringerPlus.

[22]  I. Shih,et al.  The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. , 2016, The American journal of pathology.

[23]  Bin Shang,et al.  Prognostic value of tumor-infiltrating FoxP3+ regulatory T cells in cancers: a systematic review and meta-analysis , 2015, Scientific Reports.

[24]  Jaime Rodriguez-Canales,et al.  A Patient-Derived, Pan-Cancer EMT Signature Identifies Global Molecular Alterations and Immune Target Enrichment Following Epithelial-to-Mesenchymal Transition , 2015, Clinical Cancer Research.

[25]  H. Kitchener,et al.  Primary chemotherapy versus primary surgery for newly diagnosed advanced ovarian cancer (CHORUS): an open-label, randomised, controlled, non-inferiority trial , 2015, The Lancet.

[26]  C. Mills.  Anatomy of a Discovery: M1 and M2 Macrophages , 2015, Front. Immunol..

[27]  A. Santin,et al.  Past, present and future targets for immunotherapy in ovarian cancer. , 2014, Immunotherapy.

[28]  E. Latz,et al.  Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation , 2014, Immunity.

[29]  G. Smyth,et al.  voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.

[30]  Michael Peyton,et al.  An Epithelial–Mesenchymal Transition Gene Signature Predicts Resistance to EGFR and PI3K Inhibitors and Identifies Axl as a Therapeutic Target for Overcoming EGFR Inhibitor Resistance , 2012, Clinical Cancer Research.

[31]  C. Sautès-Fridman,et al.  The immune contexture in human tumours: impact on clinical outcome , 2012, Nature Reviews Cancer.

[32]  S. Maiella,et al.  Single-cell analysis techniques reveal a striking heterogeneity of human CD4+ T cell subsets , 2010, Journal of Translational Medicine.

[33]  Y. Wan,et al.  Transforming growth factor-beta regulation of immune responses. , 2006, Annual review of immunology.

[34]  A. Berner,et al.  NK- and B-cell infiltration correlates with worse outcome in metastatic ovarian carcinoma. , 2006, American journal of clinical pathology.

[35]  Gerd Ritter,et al.  Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[36]  George Coukos,et al.  Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival , 2004, Nature Medicine.

[37]  R. van Furth,et al.  Characteristics of human mononuclear phagocytes. , 1979, Blood.

[38]  Ash A. Alizadeh,et al.  SUPPLEMENTARY NOTE , 1879, Botanical Gazette.

[39]  K. Fujio,et al.  Overview of LAG-3-Expressing, IL-10-Producing Regulatory T Cells. , 2017, Current topics in microbiology and immunology.