Objectives: Docetaxel and Cabazitaxel are taxane chemotherapy approved in men with mCRPC after they demonstrated improved survival in first and second line respectively. If recent data suggested similar efficacy, these two taxanes have different safety profile and unit price, raising the question of their administration sequence. A cost-utility analysis comparing two sequences of treatment (Cabazitaxel followed by Docetaxel versus Docetaxel followed by Cabazitaxel) for first-line chemotherapy in metastatic prostate cancer was performed in the French context, using data from the CABADOC randomized trial. MethOds: The CABADOC study is a randomized trial with a cross-over design. Patients were randomized to receive either Docetaxel 75mg/m²/q3w x 4 followed by Cabazitaxel 25mg/m²/q3w x 4, or the reverse sequence. The economic analysis included a prospective collection of resources consumed (chemotherapy, hospitalizations, transportation, nurses and consultations) and utility data (using the EQ-5D questionnaire administered before cycle 1, cycle 5 and at the end of chemotherapy) alongside the trial. Costs were evaluated from the French collective perspective and horizon time was limited from the randomization date to the end of 2nd sequence chemotherapy. The ICER was calculated and sensitivity analyses were conducted. Results: From June 2014 to October 2016, 195 patients (median age of 70 years) were randomized in 17 centers. Patients received 3.8 ± 0.7 and 3.2 ± 1.5 cycles of chemotherapy during the first and the second period, respectively. The sequence Docetaxel-Cabazitaxel appears to be more effective (mean QALY per patient of 0.353 ± 0.025 versus 0.328 ± 0.063) and less expensive (mean cost per patient of 17 350 € ± 2955 versus 17 862 € ± 2320) as compared to the sequence Cabazitaxel-Docetaxel. cOnclusiOns: The sequence of treatment with Docetaxel followed by Cabazitaxel appears the optimal one for first line chemotherapy in metastatic prostate cancer from a cost-utility standpoint. NCT: NCT02044354.
#2026 To determine the prognosis of breast cancer patients, clinical and pathological factors are currently employed. Gene expression micro-arrays offer new opportunities to determine individual prognosis. Publications have raised concerns about micro-arrays studies who have the potential to preclude their use in clinical routine. To improve the understanding of gene-expression classifiers we addressed the following issues: 1) Is the performance similar between independent classifiers? 2) Is proliferation a common biological theme that represents various signatures? 3) Are there other enriched pathways among signatures with prognostic ability?
 Methods:
 On 6 public datasets we applied the 76-gene signature; the Molecular subtypes; the Chromosomal Instability Signature; the Wound Signature; the Invasiveness Gene Signature; the Molecular Prognosis Index; and the Genomic Grade Index. Survival, predictive accuracy and overlap analyses were performed. We created enlarged signatures by including all probes with significant correlation to at least one of the genes in the original signatures. We gathered a collection of gene sets from four databases (GO, KEGG, Reactome, MSDB). For each signature, we evaluated whether specific gene sets (modules) are overrepresented. We tested the prognosis ability of each of them.
 Results:
 The survival and predictive accuracy analyses gave similar results for each of the 9 signatures. They all added significant information to a multivariate model including standard pathological and clinical criteria. Nevertheless, we showed that none of these signatures were able to identify good and poor prognosis patients when applied to samples with intrinsically poor prognosis features (Positive Lymph Node, Negative Estrogen Receptor, High Grade). Conversely they identified good and poor prognosis patients when applied to samples with intrinsically good prognosis features (Negative LN, Positive ER Low Grade). The overlap analysis showed a low agreement between the signatures. 50% of the samples had almost one discordant classification result out of the 9 classifiers tested. The intersection of the signatures revealed a set of proliferation genes. The signatures were build on 10 different gene ontology modules with prognostic ability.
 Conclusion:
 This study underlines the need of large prospective validation studies of gene expression signatures. Further computational intelligence and system biology studies would be held to determine the best way to use these classifiers in clinical routine. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2026.
Background: Many different treatment regimens are currently available for Multiple Myeloma (MM) patients. There is ongoing research to identify subgroups of patients who may benefit from specific treatments. Recently, it was found that patients with low-risk MM (SKY92 standard-risk, ISS stage I, and no del(17p)) had a better overall survival after receiving Bortezomib, Melphalan and Prednisone (VMP) combination therapy compared to High-dose Melphalan therapy followed by an autologous stem cell transplantation (HDM+ASCT)(Hofste op Bruinink et al. 2018, The American Society of Hematology, # 3186). Clinicians may consider a risk-stratified treatment approach based on SKY92, ISS, and del(17p) status for transplant-eligible MM patients instead of treating all transplant-eligible patients with HDM+ASCT. Aims: We evaluated the cost-effectiveness of a risk-stratified treatment approach for transplant-eligible MM patients in which low-risk individuals are directly treated with combination therapy instead of HDM+ASCT. All others patients will receive HDM+ASCT as initial treatment. Methods: The cost-effectiveness analysis was restricted to the low-risk patient group because risk stratification only led to a therapy change for this subgroup. A decision model was developed consisting of three health states: progression-free survival, progressive disease and death. A partitioned survival model, based on Kaplan-Meier curves reported by Hofste op Bruinink et al. (2018), was used to estimate the proportion of patients in each health state over time. Quality of life utilities and costs were derived from literature. Costs consisted of additional testing costs, initial treatment costs, maintenance treatment costs and costs of progressive disease. It was assumed that all patients received maintenance treatment until disease progression and that second and further-line treatments were identical for both VMP and HDM+ASCT. The main outcome of the analysis was the incremental costs per life year or quality-adjusted life year (QALY) gained. The analysis was performed from a Dutch health care perspective (in 2018 Euros) with a lifetime horizon. Results: 26% of all transplant-eligible patients were classified as lowrisk. In these patients, VMP resulted in better health outcomes compared to HDM+ASCT. The difference in life years was 9.6 years (VMP: 21.3 years, HDM+ASCT: 11.7 years). The QALY gain was 6.4 QALY (VMP: 14.8, HDM+ASCT: 8.4 QALYs). Initial treatment costs were almost similar for the two treatments, but the costs of maintenance treatment and progressive disease were substantially larger for VMP due to a longer life expectancy. The total difference in costs was €345,000 (VMP: €876,245, HDM+ASCT: €531,245). The incremental cost-effectiveness ratio was €35,677 per life year gained and €54,005 per QALY gained. Summary/Conclusion: The proposed risk-stratified treatment approach resulted in substantial survival and QALY gain. However, this health gain is also associated with higher costs due to continuous maintenance treatment until prog...
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