Premium
Multi‐omics in high‐grade serous ovarian cancer: Biomarkers from genome to the immunome
Author(s) -
Clifford Cole,
Vitkin Natasha,
Nersesian Sarah,
ReidSchachter Gillian,
Francis JulieAnn,
Koti Madhuri
Publication year - 2018
Publication title -
american journal of reproductive immunology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.071
H-Index - 97
eISSN - 1600-0897
pISSN - 1046-7408
DOI - 10.1111/aji.12975
Subject(s) - ovarian cancer , omics , disease , immunotherapy , biomarker , oncology , serous fluid , biomarker discovery , medicine , ovarian carcinoma , serous carcinoma , precision medicine , immune system , cancer , bioinformatics , biology , immunology , pathology , proteomics , biochemistry , gene
Epithelial ovarian cancer ( EOC ) is a lethal gynaecological disease that imposes significant burden on health care and patient quality of life. High‐grade serous carcinoma of the ovary ( HGSC ) is the most prevalent histological type of EOC . A vast majority of HGSC cases are diagnosed at late stages of the disease, limiting the opportunity for clinical intervention and resulting in a 10‐year survival rate of <20%. Recent innovations in high‐throughput molecular analysis of patient‐derived specimens may address these clinical challenges by providing an enhanced understanding of the molecular aetiology of ovarian cancer, in addition to offering several opportunities for rational biomarker and targeted therapy discovery. In this review, we highlight the most significant contributions of omics approaches and how the advent of immunomics can aid in personalized combination chemo‐immunotherapy in ovarian cancer treatment. We further provide insights into immunogenomic correlates of pre‐treatment tumour immune microenvironment and some of the potential interpretations of immunomic data that require further validation, based on stromal and immune contributions to biomarker signatures. We believe a comprehensive integrative approach via meta‐analysis of large ovarian cancer molecular profiling data sets is urgently needed to define robust prognostic and predictive classifiers of disease progression and treatment response. These investigations will inform rationalized biomarker‐driven combination chemo‐immunotherapy trials for improving response and survival of ovarian cancer patients.