Estrogen signaling crosstalk: Implications for endocrine resistance in ovarian cancer

Ribeiro, Jennifer; Freiman, Richard N.

doi:10.1016/j.jsbmb.2014.02.010

Cited by 47 publications

(34 citation statements)

References 175 publications

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“…We speculated that hormonal pathways might play a positive role in the development of ovarian cancer. High consumption of dietary fat could stimulate the secretion of extra ovarian estrogen [68, 69], which can exert tumor-promoting activity via mitogenic effects on ERα- positive [70, 71] or negative [72] tumor cells, therefore increasing the risk of ovarian cancer [66]. What's more, obese women may suffer from insulin resistance, and concurrent hyperinsulinemia with excess insulin-like growth factor-1 receptor (IGF-1) could additionally induce androgen steroidogenesis [73] and lead to tumor development [74, 75].…”

Section: Discussionmentioning

confidence: 99%

Dietary fat intake and ovarian cancer risk: a meta-analysis of epidemiological studies

Qiu

et al. 2016

Oncotarget

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Observational studies assessing the association of dietary fat and risk of ovarian cancer yield discrepant results. Pertinent prospective cohort studies were identified by a PubMed search from inception to December 2015. Sixteen independent case-control and nine cohort studies on dietary fat intake were included, with approximately 900,000 subjects in total. Relative risks (RRs) with 95% confidence intervals were pooled using a random effects model. Heterogeneity, sensitivity analysis and publication bias were assessed; subgroup analysis and analysis stratified by EOC histology were conducted. The reported studies showed a significant increase of ovarian cancer risk with high consumption of total-, saturated-, and trans-fats, while serous ovarian cancer was more susceptible to dietary fat consumption than other pathological subtypes. No evidence of positive association between dietary fat intake and ovarian cancer risk was provided by cohort studies. Menopausal status, hormone replacement therapy, body mass index (BMI), and pregnancy times, modified the objective associations. In conclusion, the meta-analysis findings indicate that high consumption of total, saturated and trans-fats increase ovarian cancer risk, and different histological subtypes have different susceptibility to dietary fat.

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Section: Discussionmentioning

confidence: 99%

Dietary fat intake and ovarian cancer risk: a meta-analysis of epidemiological studies

Qiu

et al. 2016

Oncotarget

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“…Furthermore, it is known that the disruption of the extra cellular matrix (ECM interaction) can lead to the overproduction of growth factors that promote abnormal proliferation [ 38 ]. Estrogen signaling is thought to be involved in the establishment of a pro-tumorigenic micro-environment, and the long-term use of estrogen-only hormone replacements is linked to ovarian cancer development [ 39 ]. Finally, cAMP signaling is involved in cell survival in ovarian cancer and the presence of mutant CREBBP proteins can lead to tumorigenesis [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration

et al. 2015

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The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method’s potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi)-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method’s ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad-outcome patient group could be related to ovarian tumor proliferation and survival.

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“…Approximately two thirds of breast cancers result positive for at least one of these two hormone receptors [69]. The analysis of hormone receptors in breast cancer cells and in some gynecologic cancers may be utilized for the diagnosis and to determine whether hormone therapy is suitable or not for the treatment of the malignancy [69,[79][80][81][82][83][84]. A typical example of hormone therapy is based on tamoxifen [69].…”

Section: Hormone Receptorsmentioning

confidence: 99%

Tumor markers currently utilized in cancer care

Romano¹

2015

MATER METHODS

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A review of tumor markers that are currently used in cancer care. Tumor marker Type of tumor/tumors Application 21-Gene signature (Oncotype DX). Breast cancer. Assess risk of tumor recurrence. 70-Gene signature (Mammaprint). Breast cancer. Assess risk of tumor recurrence. Anaplastic lymphoma kinase (ALK) (mutations). Non-small cell lung cancer and anaplastic large cell lymphoma. Diagnosis and determine type of treatment. BRAF (mutations). Melanoma, colorectal cancer and thyroid cancer. Diagnosis and determine type of treatment in patients with melanoma. Epidermal growth factor receptor (EGFR), or HER1 Cancers of the breast, head and neck, non-small cell lung, pancreas and colon. Predict outcomes and determine type of treatment. HER2, or HER2/neu, erbB-2, EGFR2 Breast cancer, stomach cancer and esophageal cancer. Predict outcomes and determine type of treatment. Hormone receptor (estrogen and progesterone) Breast cancer and gynecologic malignancies, such as endometrial stromal sarcomas and endometrial cancers. Predict outcomes and determine type of treatment. KIT Gastrointestinal stromal tumor and mucosal melanoma. Diagnosis and determine type of treatment. KRAS (mutations) Advanced colorectal cancer and lung cancer. Determine type of treatment. S-100 Melanoma. Diagnosis. Blood tests allow for the follow-up of the clinical course of the disease. Urokinase plasminogen activator (uPA) and plasminogen activator inhibitor (PAI-1). Breast cancer. To assess the aggressiveness of the malignancy and determine the type of treatment. Table 1. List of tumor markers that are detected in malignant tissues, other than the blood. Tumor markers detected in malignant tissues The detection of these tumor markers requires the removal of a biopsy from the patient [6-19]. In certain cases, biopsy-derived tumor markers are utilized just once, in order to confirm a diagnosis. The list of tumor markers that are analyzed in malignant tissues is summarized in Table 1. 21-Gene signature (Oncotype DX) This set of markers is utilized to assess the risk of the reappearance of the tumor in patients with breast cancer [9,26-28]. The 21-gene signature comprises genes that are related to the estrogen receptor (ESR1, PGR, BCL2, SCUBE2), HER2 (HER2 and GRB7), cell proliferation (Ki67, STK15, survivin, cyclin B1, MYBL2), cellular invasion (stromelysin3, cathepsin L2), macrophage marker CD68, anti-apoptotic genes (BAG1 and GSTM1) and five housekeeping genes that are used as reference (-actin, GAPDH, RPLPO, GUS and TFRC) [9, 26-28]. Oncotype DX is a gene-expression profiling that requires RNA samples derived from paraffinembedded tumor biopsies and was introduced for the first time in the U.S.A. market in 2004 (Genomic Health Inc., Redwood City, CA) [29]. 70-Gene signature (MammaPrint) This set of markers is utilized to assess the risk of the recurrence of the tumor in patients with breast cancer (for a review see references 31-35). In contrast to the 21-Gene signature (Oncotype DX), this assay needs a preparation of fresh tissues in a solution design...

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Estrogen signaling crosstalk: Implications for endocrine resistance in ovarian cancer

Cited by 47 publications

References 175 publications

Dietary fat intake and ovarian cancer risk: a meta-analysis of epidemiological studies

Dietary fat intake and ovarian cancer risk: a meta-analysis of epidemiological studies

Pathway Relevance Ranking for Tumor Samples through Network-Based Data Integration

Tumor markers currently utilized in cancer care

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