Optimizing Molecular-Targeted Therapies in Ovarian Cancer: The Renewed Surge of Interest in Ovarian Cancer Biomarkers and Cell Signaling Pathways

Abstract

The hallmarks of ovarian cancer encompass the development of resistance, disease recurrence and poor prognosis. Ovarian cancer cells express gene signatures which pose significant challenges for cancer drug development, therapeutics, prevention and management. Despite enhancements in contemporary tumor debulking surgery, tentative combination regimens and abdominal radiation which can achieve beneficial response rates, the majority of ovarian cancer patients not only experience adverse effects, but also eventually relapse. Therefore, additional therapeutic possibilities need to be explored to minimize adverse events and prolong progression-free and overall response rates in ovarian cancer patients. Currently, a revival in cancer drug discovery is devoted to identifying diagnostic and prognostic ovarian cancer biomarkers. However, the sensitivity and reliability of such biomarkers may be complicated by mutations in the BRCA1 or BRCA2 genes, diverse genetic risk factors, unidentified initiation and progression elements, molecular tumor heterogeneity and disease staging. There is thus a dire need to expand existing ovarian cancer therapies with broad-spectrum and individualized molecular targeted approaches. The aim of this review is to profile recent developments in our understanding of the interrelationships among selected ovarian tumor biomarkers, heterogeneous expression signatures and related molecular signal transduction pathways, and their translation into more efficacious targeted treatment rationales.

Mechanistic Analysis of Taxol-induced Multidrug Resistance in an Ovarian Cancer Cell Line

“…In addition, cellular responses toward cytotoxic drugs are also modulated by crosstalk between oncogenic signaling cascades and resistance mechanisms. Recently, growing evidence suggests activation of PKC-α (Zhao et al, 2012), MAPK cascade and PI3K/Akt signaling (Hiss, 2012) play important roles in chemo-resistance of ovarian cancer. The MAPK family has been classified into 3 distinct subfamilies: the extracellular signal-regulated protein kinases (ERKs) including ERK1/2, the stress-activated c-Jun N-terminal protein kinase (JNKs) and p38 kinase (Hoshino et al, 1999;Doddareddy et al, 2012).…”

“…Some common mechanisms include overexpression of some drugresistant associated proteins including P-glycoprotein (Pgp) (Kim et al, 2013), protein kinase C-α (PKC-α) (Zhe et al, 2012), and activation of some signaling pathway including mitogen-activated protein kinase (MAPK) cascade and phosphatidylinositol-3-kinase (PI3K)/Akt signaling (Mackay et al, 2003;Hiss et al, 2012). Drug resistant mechanisms need further understand for solving some clinical problems.…”

Identification of Prognostic Groups in High-Grade Serous Ovarian Cancer Treated with Platinum–Taxane Chemotherapy

“…The standard treatment for HGS-OvCa is surgery and platinum-taxane combination therapy, and while most patients with HGS-OvCa at the advanced stage of the disease initially respond to it, and some patients experience extended and repetitive responses, the treatment is very seldom curative and the majority suffers relapse within 18 months (2). This is mostly due to the fact that despite being a single morphologic and clinical entity, genomic alterations in HGS-OvCa are complex (1,3), and there are no known targetable mutations that could serve as uniform therapeutic targets. Accordingly, HGS-OvCa cannot yet be managed optimally, often leading to adverse side effects and health costs without reasonable benefit.…”

Analysis of Chemotherapeutic Response in Ovarian Cancers Using Publicly Available High-Throughput Data

“…Several mechanisms have been described to contribute to chemoresponse, including drug efflux, increased cellular glutathione levels, increased DNA repair, and drug tolerance, but the exact mechanisms are not fully defined (5)(6)(7), and there are no valid clinical biomarkers or molecular signatures that effectively predict response to chemotherapy (8,9). Understanding the underlying processes could lead to the identification of prognostic signatures, which in turn, could be used to stratify patients who are likely to develop resistance to standard chemotherapy and, thus, could benefit from alternative strategies (9).…”