Improving ovarian cancer treatment decision using a novel risk predictive tool

Background: As a major component of the tumor tissue, the tumor microenvironment (TME) has been proven to associate with tumor progression and immunotherapy. Ovarian cancer accounts for the highest mortality rate among gynecologic malignancies. Its clinical treatment decision is highly correlated with the prognosis, underscoring the need to evaluate the prognosis and choose the proper clinical treatment through TME information. Method: This study constructs a score with TME information obtained by the CIBERSORT algorithm, which classifies the patients into high and low TMEscore groups with quantified TME infiltration patterns through the PCA algorithm. TMEscore was constructed by TCGA cohort and validated in GEO cohort. Univariate and multivariate Cox proportional hazards model analyses were used to demonstrate prognostic value of TMEscore in overall and stratified analysis. Result: TMEscore is highly correlated with survival and high TMEscore group has a better prognosis. In order to improve treatment decision, the expression of immune checkpoints, immunophenoscore (IPS) and ESTIMATE score showed a high TMEscore have a better immune microenvironment and respond better to immune checkpoint inhibitors (ICIs). Meanwhile, the mutation landscape between TMEscore groups was profiled, and 13 genes were found mutated differently between the two groups. Among them, BRCA1 has more mutations in the high TMEscore group and speculated that high TMEscore patients might be a beneficiary population of PARP inhibitors combined with immunotherapy. Conclusion: TMEscore based on TME with prognostic value and clinical value is proposed for the identification of targets treatment and immunotherapy strategies for ovarian cancer.

[1]  L. Kuroki,et al.  Treatment of epithelial ovarian cancer , 2020, BMJ.

[2]  Zemin Zhang,et al.  The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications , 2020, Cellular & Molecular Immunology.

[3]  Brian Craft,et al.  Visualizing and interpreting cancer genomics data via the Xena platform , 2020, Nature Biotechnology.

[4]  K. Tennekoon,et al.  Role of the PI3K/AKT/mTOR signaling pathway in ovarian cancer: Biological and therapeutic significance. , 2019, Seminars in cancer biology.

[5]  Cesar M. Castro,et al.  Assessment of Combined Nivolumab and Bevacizumab in Relapsed Ovarian Cancer: A Phase 2 Clinical Trial. , 2019, JAMA oncology.

[6]  P. Konstantinopoulos,et al.  Combined PARP and Immune Checkpoint Inhibition in Ovarian Cancer. , 2019, Trends in cancer.

[7]  L. Shevde,et al.  The Tumor Microenvironment Innately Modulates Cancer Progression. , 2019, Cancer research.

[8]  F. Feng,et al.  Impact of Immune and Stromal Infiltration on Outcomes Following Bladder-Sparing Trimodality Therapy for Muscle-Invasive Bladder Cancer. , 2019, European urology.

[9]  T. Yoshikawa,et al.  Exploratory subgroup analysis of patients with prior trastuzumab use in the ATTRACTION-2 trial: a randomized phase III clinical trial investigating the efficacy and safety of nivolumab in patients with advanced gastric/gastroesophageal junction cancer , 2019, Gastric Cancer.

[10]  D. Trafalis,et al.  Targeting Programmed Cell Death ‐1 (PD‐1) and Ligand (PD‐L1): A new era in cancer active immunotherapy , 2019, Pharmacology & therapeutics.

[11]  A. Jemal,et al.  Cancer statistics, 2019 , 2019, CA: a cancer journal for clinicians.

[12]  G. Freeman,et al.  PARP Inhibition Elicits STING-Dependent Antitumor Immunity in Brca1-Deficient Ovarian Cancer , 2018, Cell reports.

[13]  I. Melero,et al.  Cytokines in clinical cancer immunotherapy , 2018, British Journal of Cancer.

[14]  A. Jemal,et al.  Ovarian cancer statistics, 2018 , 2018, CA: a cancer journal for clinicians.

[15]  F. Finkernagel,et al.  Prognosis of ovarian cancer is associated with effector memory CD8+ T cell accumulation in ascites, CXCL9 levels and activation-triggered signal transduction in T cells , 2018, Oncoimmunology.

[16]  K. Odunsi.  Immunotherapy in ovarian cancer. , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.

[17]  S. Pignata,et al.  Immunotherapy in ovarian, endometrial and cervical cancer: State of the art and future perspectives. , 2017, Cancer treatment reviews.

[18]  L. Senter,et al.  BRCA mutation in ovarian cancer: testing, implications and treatment considerations , 2017, Therapeutic advances in medical oncology.

[19]  S. Kaye,et al.  Delivering widespread BRCA testing and PARP inhibition to patients with ovarian cancer , 2017, Nature Reviews Clinical Oncology.

[20]  Tasuku Honjo,et al.  Cancer immunotherapies targeting the PD-1 signaling pathway , 2017, Journal of Biomedical Science.

[21]  Ting Wu,et al.  Tumor microenvironment and therapeutic response. , 2017, Cancer letters.

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

[23]  Pornpimol Charoentong,et al.  Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade , 2016, bioRxiv.

[24]  L. Zitvogel,et al.  Targeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy. , 2016, Annals of oncology : official journal of the European Society for Medical Oncology.

[25]  Ye Chen,et al.  Tumor microenvironment: Sanctuary of the devil. , 2015, Cancer letters.

[26]  K. Yamaguchi,et al.  Safety and Antitumor Activity of Anti-PD-1 Antibody, Nivolumab, in Patients With Platinum-Resistant Ovarian Cancer. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

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

[28]  Mithat Gonen,et al.  Nomograms in oncology: more than meets the eye. , 2015, The Lancet. Oncology.

[29]  J. Wolchok,et al.  Immune Checkpoint Blockade in Cancer Therapy. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[30]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[31]  E. Kohn,et al.  The MAPK pathway across different malignancies: A new perspective , 2014, Cancer.

[32]  G. Getz,et al.  Inferring tumour purity and stromal and immune cell admixture from expression data , 2013, Nature Communications.

[33]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[34]  Guangchuang Yu,et al.  clusterProfiler: an R package for comparing biological themes among gene clusters. , 2012, Omics : a journal of integrative biology.

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

[36]  J. Abbruzzese,et al.  F1000 highlights , 2009, JAMA.

[37]  T. Whiteside.  The tumor microenvironment and its role in promoting tumor growth , 2008, Oncogene.

[38]  M. J. van de Vijver,et al.  Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. , 2006, Journal of the National Cancer Institute.

[39]  Pablo Tamayo,et al.  Metagenes and molecular pattern discovery using matrix factorization , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[40]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[41]  W. Mcguire,et al.  The treatment of epithelial ovarian cancer , 1995, CA: a cancer journal for clinicians.

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

[43]  J. Zucman‐Rossi,et al.  Immune Contexture, Immunoscore, and Malignant Cell Molecular Subgroups for Prognostic and Theranostic Classifications of Cancers. , 2016, Advances in immunology.

[44]  Alexander D. Diehl,et al.  BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btm029 Databases and ontologies Ontology development for biological systems: immunology , 2006 .

[45]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[46]  N. Dubrawsky.  Cancer statistics , 1989, CA: a cancer journal for clinicians.

[47]  John D. Storey,et al.  Bioinformatics Applications Note Gene Expression the Sva Package for Removing Batch Effects and Other Unwanted Variation in High-throughput Experiments , 2022 .