Fertility outcomes following surgery and multiagent chemotherapy in malignant ovarian germ cell tumor survivors: a survey study.

OBJECTIVE: To assess fertility outcomes in long-term survivors of malignant ovarian germ cell tumors treated with fertility-sparing surgery with or without additional chemotherapy. METHODS: Women diagnosed and treated for malignant ovarian germ cell tumors at Charing Cross Hospital or Mount Vernon Cancer Centre between 1977 and 2015 were included. Questionnaires assessing fertility issues were sent to patients treated with fertility-sparing surgery. Fertility outcomes were evaluated according to the treatment received. The effect of the mean total dose of cyclophosphamide and cisplatin was assessed. RESULTS: A total of 146 patients were sent the questionnaire; 77 (56.5%) patients were included in the analysis. A total of 49 (64%) patients received platinum-based chemotherapy after surgery, 39 (79.6%) of these with cisplatin, vincristine, methotrexate, bleomycin, actinomycin D, cyclophosphamide, and etoposide, while 10 (20.4%) with bleomycin, etoposide, and cisplatin. After any treatment, 39/46 patients (85%) became pregnant: the conception rate was not different between those receiving surgery only and those receiving also chemotherapy (85.7% vs 84.4%, p=1.0). Live birth rate was 80.4% (37/46), with no statistically significant difference between the treatment groups (p=0.42). Median age of women achieving conception was 29 years (IQR 26-33). The probability of live birth at 5 years was 48% and 40% for patients in the surgery only and chemotherapy group, respectively (p=0.55). Infertility and miscarriage rates did not differ significantly between the two treatment groups (p=0.30 and p=0.32). The mean doses of cisplatin and cyclophosphamide received by patients failing and achieving conception were not different (p=0.10, p=0.47). CONCLUSIONS: Our results suggest that fertility may not be hampered in patients with malignant ovarian germ cell tumor treated with fertility-sparing surgery or receiving additional chemotherapy.

Broader implications of SILAC-based proteomics for dissecting signaling dynamics in cancer.

Large-scale transcriptome and epigenome analyses have been widely utilized to discover gene alterations implicated in cancer development at the genetic level. However, mapping of signaling dynamics at the protein level is likely to be more insightful and needed to complement massive genomic data. Stable isotope labeling with amino acids in cell culture (SILAC)-based proteomic analysis represents one of the most promising comparative quantitative methods that has been extensively employed in proteomic research. This technology allows for global, robust and confident identification and quantification of signal perturbations important for the progress of human diseases, particularly malignancies. The present review summarizes the latest applications of in vitro and in vivo SILAC-based proteomics in identifying global proteome/phosphoproteome and genome-wide protein-protein interactions that contribute to oncogenesis, highlighting the recent advances in dissecting signaling dynamics in cancer.

Advances in molecular, genetic and immune signatures of gastric cancer: Are we ready to apply them in our patients' decision making?

In the last few years we have witnessed a vast expansion of our knowledge regarding the molecular and genetic profile of gastric cancer. The molecular subtypes described have shed light on the pathogenesis of the disease, thus prompting the development of new therapeutic strategies and favoring a more individualized approach for treatment. Most of the clinical trials for so called targeted therapies could be considered, at best, partially successful. In addition, checkpoint inhibitors have recently been added to our armamentarium in later stages of the disease, and combinations with chemotherapy and targeted agents are currently under development. In view of the rapid advances of molecular oncology, a new challenge for the clinical oncologist arises: The appropriate patient selection for each new therapy, which can be made possible only through the implementation of predictive biomarkers in our therapy decision making.