An Immunophenotyping of Ovarian Cancer With Clinical and Immunological Significance

Immune checkpoint blockade (ICB), mainly anti-CTLA-4 and anti-PD-1/PD-L1 therapy, has showed promising clinical benefits in the treatment of some cancer types; however, its application in ovarian cancer is still in the primary stage. Immunophenotyping can help us understand the clinical characteristics and immune status of cancer, and thus benefit immunotherapy and personalized therapy. In this study, we clustered 907 ovarian cancer patients into three immune molecular subtypes (IMMSs) based on 48 genes. Expression data were downloaded from the Gene Expression Omnibus database. Unsupervised consensus clustering was used to identify IMMS. Clinical and immunological characteristics and gene expression patterns of different IMMS were compared, and associations between IMMS and tumor microenvironment immune types were explored. Three IMMSs with different clinical and immunological characteristics were identified, in which type I and II ovarian cancer patients were similar to each other. There were more serous and low-grade tumors in type I and II ovarian cancer. IMMS was associated with disease-free survival before and after adjusting for clinical characteristics and ICB-related genes. Among the differentially expressed genes identified in our study, about 90% (25/28) were highly expressed in type I and II ovarian cancer. Genes related to ICB (CTLA-4, PD-L1, and PD-L2) and cytotoxic lymphocytes (CD8A, GZMA, and PRF1) were all highly expressed in type I and II ovarian cancer. Patients with type I and II ovarian cancer may be more sensitive to anti-CTLA-4 therapy, anti-PD-1/PD-L1 therapy, and a combination of immunotherapies. In contrast, patients with type III ovarian cancer may be insensitive to these treatments and require new therapies.

[1]  Ying Sun,et al.  Genomic Analysis of Tumor Microenvironment Immune Types across 14 Solid Cancer Types: Immunotherapeutic Implications , 2017, Theranostics.

[2]  Asher Mullard FDA approvals for the first 6 months of 2017 , 2017, Nature Reviews Drug Discovery.

[3]  Asher Mullard New cancer vaccines show clinical promise , 2017, Nature Reviews Drug Discovery.

[4]  S. Khozin,et al.  U.S. Food and Drug Administration Approval Summary: Atezolizumab for Metastatic Non–Small Cell Lung Cancer , 2017, Clinical Cancer Research.

[5]  K. Goldberg,et al.  FDA Approval Summary: Pembrolizumab for the Treatment of Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma with Disease Progression on or After Platinum‐Containing Chemotherapy , 2017, The oncologist.

[6]  R. Voelker Immunotherapy for Rare Skin Cancer. , 2017, JAMA.

[7]  G. Freeman,et al.  Coinhibitory Pathways in the B7-CD28 Ligand-Receptor Family. , 2016, Immunity.

[8]  P. Keegan,et al.  FDA Approval Summary: Pembrolizumab for the Treatment of Patients With Metastatic Non-Small Cell Lung Cancer Whose Tumors Express Programmed Death-Ligand 1 , 2016, The oncologist.

[9]  Ju-Seog Lee,et al.  Pan-Cancer Immunogenomic Perspective on the Tumor Microenvironment Based on PD-L1 and CD8 T-Cell Infiltration , 2016, Clinical Cancer Research.

[10]  S. Gabriel,et al.  Genomic correlates of response to CTLA-4 blockade in metastatic melanoma , 2015, Science.

[11]  H. Shin,et al.  Replication of genome wide association studies on hepatocellular carcinoma susceptibility loci of STAT4 and HLA-DQ in a Korean population. , 2015, Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases.

[12]  Antoni Ribas,et al.  Classifying Cancers Based on T-cell Infiltration and PD-L1. , 2015, Cancer research.

[13]  C. Meyer,et al.  Ipilimumab-dependent cell-mediated cytotoxicity of regulatory T cells ex vivo by nonclassical monocytes in melanoma patients , 2015, Proceedings of the National Academy of Sciences.

[14]  C. Drake,et al.  Immune checkpoint blockade: a common denominator approach to cancer therapy. , 2015, Cancer cell.

[15]  N. Hacohen,et al.  Molecular and Genetic Properties of Tumors Associated with Local Immune Cytolytic Activity , 2015, Cell.

[16]  T. Walker FDA approves Keytruda for advanced melanoma , 2014 .

[17]  Y-H Wu,et al.  COL11A1 promotes tumor progression and predicts poor clinical outcome in ovarian cancer , 2014, Oncogene.

[18]  D. Wraith,et al.  New inhibitory signaling by CTLA-4 , 2014, Nature Immunology.

[19]  R. Weichselbaum,et al.  Irradiation and anti-PD-L1 treatment synergistically promote antitumor immunity in mice. , 2014, The Journal of clinical investigation.

[20]  G. Freeman,et al.  Therapeutic PD-1 pathway blockade augments with other modalities of immunotherapy T-cell function to prevent immune decline in ovarian cancer. , 2013, Cancer research.

[21]  Jason B. Williams,et al.  Up-Regulation of PD-L1, IDO, and Tregs in the Melanoma Tumor Microenvironment Is Driven by CD8+ T Cells , 2013, Science Translational Medicine.

[22]  Benjamin Haibe-Kains,et al.  curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome , 2013, Database J. Biol. Databases Curation.

[23]  Alison P. Klein,et al.  Colocalization of Inflammatory Response with B7-H1 Expression in Human Melanocytic Lesions Supports an Adaptive Resistance Mechanism of Immune Escape , 2012, Science Translational Medicine.

[24]  Jae K. Lee,et al.  Multi-Gene Expression Predictors of Single Drug Responses to Adjuvant Chemotherapy in Ovarian Carcinoma: Predicting Platinum Resistance , 2012, PloS one.

[25]  O. Mariani,et al.  miR-141 and miR-200a act on ovarian tumorigenesis by controlling oxidative stress response , 2011, Nature Medicine.

[26]  D. Armstrong,et al.  Recent progress in the diagnosis and treatment of ovarian cancer , 2011, CA: a cancer journal for clinicians.

[27]  B. Karlan,et al.  Gene expression profile of BRCAness that correlates with responsiveness to chemotherapy and with outcome in patients with epithelial ovarian cancer. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[28]  J. Allison,et al.  PD-1 and CTLA-4 combination blockade expands infiltrating T cells and reduces regulatory T and myeloid cells within B16 melanoma tumors , 2010, Proceedings of the National Academy of Sciences.

[29]  W. Wong,et al.  A gene signature predictive for outcome in advanced ovarian cancer identifies a survival factor: microfibril-associated glycoprotein 2. , 2009, Cancer cell.

[30]  R. Tothill,et al.  Novel Molecular Subtypes of Serous and Endometrioid Ovarian Cancer Linked to Clinical Outcome , 2008, Clinical Cancer Research.

[31]  G. Freeman,et al.  PD-1 and its ligands in tolerance and immunity. , 2008, Annual review of immunology.

[32]  Yoshimasa Tanaka,et al.  Programmed cell death 1 ligand 1 and tumor-infiltrating CD8+ T lymphocytes are prognostic factors of human ovarian cancer , 2007, Proceedings of the National Academy of Sciences.

[33]  S. Quezada,et al.  Principles and use of anti-CTLA4 antibody in human cancer immunotherapy. , 2006, Current opinion in immunology.

[34]  J. Mesirov,et al.  Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data , 2003, Machine Learning.

[35]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[36]  G. Freeman,et al.  The B7–CD28 superfamily , 2002, Nature Reviews Immunology.

[37]  J. Bonnefoy,et al.  A soluble form of CTLA‐4 generated by alternative splicing is expressed by nonstimulated human T cells , 1999, European journal of immunology.

[38]  S. Orsulic,et al.  Ovarian Cancer , 1993, British Journal of Cancer.

[39]  M. Atkins,et al.  Therapeutic uses of anti-PD-1 and anti-PD-L1 antibodies. , 2015, International immunology.

[40]  L. Terracciano,et al.  HLA class II antigen expression in colorectal carcinoma tumors as a favorable prognostic marker. , 2014, Neoplasia.

[41]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[42]  Jill P. Mesirov,et al.  A resampling-based method for class discovery and visualization of gene expression microarray data , 2003 .

[43]  J. Bluestone,et al.  Complexities of CD28/B7: CTLA-4 costimulatory pathways in autoimmunity and transplantation. , 2001, Annual review of immunology.