Takeshi Ioroi

Serum lactate dehydrogenase levels as a predictive marker of oxaliplatin-induced hypersensitivity reactions in Japanese patients with advanced colorectal cancer.

Objective: Clinical laboratory test data obtained prior to treatments were previously analyzed from the standpoint of susceptibility to hypersensitivity reactions in patients treated with the platimun anticancer agent, oxaliplatin (L-OHP). In the present study, the time course from the first to last cycle of the treatment was additionally analyzed to determine a better predictor of these reactions. Methods: A total of 20 laboratory test data were obtained from 108 Japanese patients with advanced colorectal cancer who were treated with the L-OHP-containing regimens, FOLFOX4 and/or mFOLFOX6. The averages and variation coefficients (CV%) of the data until the last cycle of the treatment were compared between patients with hypersensitivity reactions and those without. Results: The average serum lactate dehydrogenase (LDH) level was lower in patients with grade 1/2 reactions (P=0.016), whereas its CV% value was higher in patients with grade 3/4 reactions (P=0.005) than in those without reactions. An increase in serum LDH levels was observed in some patients with grade 3/4 reactions as the cycle number increased, and thereafter hypersensitivity reactions occurred. This phenomenon was not always observed, but was never detected in patients with grade 1/2 reactions. Conclusions: Serum LDH levels may be a predictive marker of hypersensitivity reactions in patients treated with L-OHP. Further extensive examinations with a larger number of patients are needed to establish a patient management strategy.

Association of Single Nucleotide Polymorphisms in STAT3, ABCB1, and ABCG2 with Stomatitis in Patients with Metastatic Renal Cell Carcinoma Treated with Sunitinib: A Retrospective Analysis in Japanese Patients

Signal transducer and activator of transcription (STAT) 3 is a key factor in homeostasis of the oral mucosa by regulating the production of inflammatory cytokines. Sunitinib is a substrate of P-glycoprotein (multidrug resistance (MDR)-1/ABCB1) and breast-cancer resistance protein (BCRP/ABCG2). In this retrospective study, we evaluated the association between sunitinib-induced stomatitis and STAT3, ABCB1, and ABCG2 polymorphisms in patients with metastatic renal cell carcinoma (mRCC). Fifty-two Japanese patients with RCC treated with sunitinib were retrospectively genotyped to elucidate a potential association between STAT3, ABCB1, and ABCG2 polymorphisms and stomatitis development. Stomatitis occurred in 22 out of 52 patients. The TT+TC genotypes at STAT3 rs744166 had an odds ratio of 5.00 against CC genotype for the stomatitis development (95% confident interval, 0.97-25.8). In the Kaplan-Meier method for the cumulative incidence of stomatitis, a statistically significant difference was observed between the TT+TC and CC genotypes in STAT3 rs744166 (p=0.037). Both multiple logistic regression analysis and Cox proportional-hazards regression analysis show STAT3 rs744166 TT+TC genotypes and serum creatinine in each patient were significant independent factors for stomatitis development. In conclusion, STAT3 polymorphism may be a novel risk factor for sunitinib-induced stomatitis in patients with mRCC.

Data analysis methods for assessing palliative care interventions in one-group pre-post studies

Objectives: Studies of palliative care are often performed using single-arm pre–post study designs that lack causal inference. Thus, in this study, we propose a novel data analysis approach that incorporates risk factors from single-arm studies instead of using paired t-tests to assess intervention effects. Methods: Physical, psychological and social evaluations of eligible cancer inpatients were conducted by a hospital-based palliative care team. Quality of life was assessed at baseline and after 7 days of symptomatic treatment using the European Organization for Research and Treatment of Cancer QLQ-C15-PAL. Among 35 patients, 9 were discharged within 1 week and 26 were included in analyses. Structural equation models with observed measurements were applied to estimate direct and indirect intervention effects and simultaneously consider risk factors. Results: Parameters were estimated using full models that included associations among covariates and reduced models that excluded covariates with small effects. The total effect was calculated as the sum of intervention and covariate effects and was equal to the mean of the difference (0.513) between pre- and post-intervention quality of life (reduced model intervention effect, 14.749; 95% confidence intervals, −4.407 and 33.905; p = 0.131; covariate effect, −14.236; 95% confidence interval, −33.708 and 5.236; p = 0.152). Conclusion: Using the present analytical method for single-arm pre–post study designs, factors that modulate effects of interventions were modelled, and intervention and covariate effects were distinguished based on structural equation model.