Impact of p53, HIF1a, Ki-67, CA-9, and GLUT1 Expression on Treatment Outcomes in Locally Advanced Cervical Cancer Patients Treated With Definitive Chemoradiation Therapy.

PURPOSE/OBJECTIVE: The objective of this study was to assess the association between pretreatment p53, hypoxia inducible factor 1a (HIF1a), Ki-67, carbonic anhydrase-9 (CA-9), and glucose transporter 1 (GLUT1) expression in locally advanced cervical cancer patients treated definitively with concurrent chemoradiation therapy (CRT) and treatment outcomes including overall survival (OS), progression-free survival (PFS), local-regional control (LC), and distant metastases-free survival (DMFS). PATIENTS AND METHODS: Twenty-eight patients treated definitively and consecutively for cervical cancer with CRT had p53, HIF1a, Ki-67, CA-9, and GLUT1 protein expression assessed and scored semiquantitatively by 3 pathologists, blinded to the treatment outcomes. Outcomes were stratified by p53 (H-score: <15 vs. ≥15), HIF1a (H-score: <95 vs. ≥95), Ki-67 (labeling index <41% vs. ≥41%), CA-9 (H-score: <15 vs. ≥15), and GLUT1 (H-score: <175 vs. ≥175) expression. OS, PFS, LC, and DMFS rates were calculated using the Kaplan-Meier method, and differences between groups were evaluated by the log-rank test. RESULTS: Notable clinical characteristics of the cohort included median age of 51 years (range: 32 to 74 y), FIGO stage IIB disease (57.2%), clinical node-negative disease (64.3%), squamous cell carcinoma (89.3%), and adenocarcinoma (10.7%). Treatment outcomes included 5-year OS (57.2%), PFS (48.1%), LC (72.1%), and DMFS (62.9%). For HIF1a H-score <95 and ≥95, the 5-year OS (52.0% and 68.4%, P=0.58), PFS (53.0% and 40.9%, P=0.75), LC (71.6% and 68.2%, P=0.92), and DMFS (59.7% and 52.0%, P=0.91) were not significantly different. For Ki-67 labeling index <41% and ≥41%, the 5-year OS (44.9% and 66.6%, P=0.35), PFS (38.9% and 55.4%, P=0.53), LC (57.7% and 85.7%, P=0.22), and DMFS (67.3% and 61.0%, P=0.94) were not significantly different. For CA-9 H-score <15 and ≥15, the 5-year OS (54.4% and 66.7%, P=0.39), PFS (57.3% and 40.0%, P=0.87), LC (70.0% and 70.0%, P=0.95), and DMFS (70.0% and 46.7%, P=0.94) were not significantly different. For GLUT1 H-score <175 and ≥175, the 5-year OS (43.6% and 43.6%, P=0.32), PFS (55.6% and 49.5%, P=0.72), LC (72.9% and 71.5%, P=0.97), and DMFS (62.5% and 59.6%, P=0.76) were not significantly different. For p53, H-score <15 and ≥15, the 5-year OS (62% and 53%), PFS (63% and 30.3%), LC (87.5% and 52%), and DMFS (79.6% and 41.6%). CONCLUSIONS: In this study population, HIF1a, Ki-67, CA-9, and GLUT1 expression did not predict treatment response or outcomes in locally advanced cervical cancer patients treated definitively with CRT. There was a nonstatistically significant trend towards worse outcomes with p53 expression.

Markov model and cost-effectiveness analysis of bevacizumab in HER2-negative metastatic breast cancer.

PURPOSE: Metastatic breast cancer (MBC) remains an incurable disease despite advances in treatment modalities. In 2008, the FDA approved bevacizumab with paclitaxel for the initial treatment of HER2-negative MBC. The approval was then officially revoked by the FDA in November 2011. However, both the European Medicines Agency and NCCN still endorse bevacizumab for this indication. One of the greatest challenges facing health care worldwide is reconciling incremental clinical benefits with exponentially rising costs. This study aimed to assess the cost-effectiveness of bevacizumab with paclitaxel for HER2-negative MBC. METHODS: A Markov decision tree using Data 3.5 (TreeAge Software Inc.) was created for decision and cost-effectiveness analyses of using bevacizumab plus paclitaxel versus paclitaxel alone as first-line chemotherapy in HER2-negative MBC using efficacy and toxicity data from the E2100 study. The model was designed from the patient and payer perspectives and sensitivity analyses were run. RESULTS: The marginal cost between paclitaxel alone versus bevacizumab and paclitaxel was 86k with a marginal efficacy of 0.369 quality-adjusted life-years and marginal cost effectiveness of 232,720.72 USD. The expected outcome value was 1.86 for bevacizumab and paclitaxel and 1.67 for paclitaxel alone. The combination was not cost effective and only a marginal survival advantage was observed. CONCLUSIONS: This study demonstrates that, despite a significant progression-free survival advantage, the addition of bevacizumab to paclitaxel is not cost effective for the cohort of patients with HER2-negative MBC included in our analysis. Such data could be informative to policymakers who consider the health economics and incremental cost-effectiveness of medical therapies.