Background Optimal management of oral cancer relies upon accurate and individualized risk prediction of relevant clinical outcomes. Individualized prognostic calculators have been developed to guide patient–physician communication and treatment-related decision-making. However it is critical to scrutinize their accuracy prior to integrating into clinical care. Aim To compare and evaluate oral cavity cancer prognostic calculators using an independent dataset. Methods: Five prognostic calculators incorporating patient and tumor characteristics were identified that evaluated five-year overall survival. A total of 505 patients with previously untreated oral cancer diagnosed between 2003 and 2014 were analyzed. Calculators were applied to each patient to generate individual predicted survival probabilities. Predictions were compared among prognostic tools and with observed outcomes using Kaplan-Meier plots, ROC curves and calibration plots. Results Correlation between the five calculators varied from 0.59 to 0.86. There were considerable differences between individual predictions from pairs of calculators, with as many as 64% of patients having predictions that differed by more than 10%. Four of five calculators were well calibrated. For all calculators the predictions were associated with survival outcomes. The area under the ROC curve ranged from 0.65 to 0.71, with C-indices ranging from 0.63 to 0.67. An average of the 5 predictions had slightly better performance than any individual calculator. Conclusion Five prognostic calculators designed to predict individual outcomes of oral cancer differed significantly in their assessments of risk. Most were well calibrated and had modest discriminatory ability. Given the increasing importance of individualized risk prediction, more robust models are needed.
OBJECTIVE: Ovarian cancer tumor cell estrogen receptor (ER) expression alone is a poor predictor of response to antiestrogen therapy and some patients with ER-negative cancers still demonstrate clinical response to therapy, suggesting modifiers of response that can potentially be targeted to improve outcomes. We have demonstrated that IL6 and LIF cytokine signaling from carcinoma-associated mesenchymal stem cells (CA-MSC) in the tumor microenvironment promotes tumorigenesis and hypothesize that this cytokine signaling also engages in crosstalk with estrogen signaling pathways. Furthermore, we propose that blocking both cytokine and estrogen signaling will improve anti-cancer effects. METHODS: High grade serous ovarian cancer (HGSOC) cell lines were treated with CA-MSC conditioned media or recombinant cytokines IL6 and/or LIF. Ovarian cancer cells were then assessed for (i) activation of estrogen response element (ERE)-driven luciferase reporter constructs (SABiosciences), (ii) changes in levels of reported ER target genes by qRT-PCR, and (iii) ER-alpha expression levels by immunoblotting. ER-alpha expression in CA-MSC was assessed by immunoblotting. We then assessed the effect of treating cells with ruxolitinib, the FDA-approved inhibitor of the Janus-associated kinase (JAK) protein that is downstream of IL6/LIF signaling. Tumor cells in the absence or presence of CA-MSC conditioned media were treated with ruxolitinib without or with anti-estrogen therapy. Anti-estrogens studied include the selective estrogen receptor modulator tamoxifen, the selective estrogen receptor downregulator fulvestrant, and the aromatase inhibitor letrozole. Following drug treatment, cell viability was assessed with the MTT assay and colony forming assays, and signaling cascade protein expression determined by immunoblotting. Synergy was calculated by the Chou-Talalay method using CompuSyn software. RESULTS: IL6 and LIF induce ERE reporter construct activation in HGSOC cell lines to a similar extent as control estradiol treatment and increase the expression of known ER target genes. Additionally, IL6 and LIF increase ER-alpha levels in HGSOC cell lines as detected by immunoblotting. The treatment of HGSOC cells with both ruxolitinib and antiestrogen therapy results in a synergistic decrease in cell viability. Variable effects are noted depending on the specific antiestrogen used and the cell line studied, indicating subtle mechanistic differences between cell lines. CA-MSC express ER-alpha, suggesting antiestrogens may exert their effects at least in part through changes in stromal signaling. Further studies are underway, including characterization of effects in both primary tumor cells and animal models. CONCLUSIONS: IL6 and LIF signaling from the tumor microenvironment promotes ovarian cancer cell estrogen signaling. The combination of inhibiting IL6/LIF signaling with ruxolitinib and antiestrogen therapy results in a synergistic decrease in ovarian cancer tumor cell viability. Given the clinical tolerability of antiestrogen therapy with a low side effect profile, strategies such as ruxolitinib treatment to sensitize tumor cells to antiestrogens are an exciting area that warrant further study. Citation Format: Lijun Tan, Victoria Prince, Jake Erba, Karen McLean. INHIBITION OF TUMOR MICROENVIRONMENT CYTOKINE SIGNALING SENSITIZES OVARIAN CANCER CELLS TO ANTIESTROGEN THERAPY [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr TMIM-079.
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