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.

STAT3 polymorphism rs4796793 may be a predictive factor of tumor response to multiple tyrosine kinase inhibitors in metastatic renal cell carcinoma in Japanese population.

Signal transducer and activator of transcription (STAT) 3 is a key factor in multiple tyrosine kinase inhibitor (mTKI)-induced growth inhibition and apoptosis of renal cell carcinoma (RCC) cells. This study aimed to identify associations between single-nucleotide polymorphisms (SNPs) in the STAT3 gene and tumor response to mTKIs in patients with metastatic RCC (mRCC). Seventy-one patients with clear cell RCC treated with any mTKI were retrospectively genotyped to elucidate a potential association between STAT3 SNPs and overall best response to drugs. Of 50 patients included for analysis, a partial or complete response was observed in 17. A significant association was found between rs4796793 alleles and tumor response [G vs. C, odds ratio (OR) 3.25, 95 % confidence interval (CI) 1.30-8.07]. There were a higher percentage of responders with the C/C genotype at rs4796793 than with the G/C + G/G genotypes (OR 4.46, 95 % CI 1.31-15.28). Time-to-event analysis demonstrated a statistically significant difference between patients with the CC genotype and those with G/C + G/G genotypes in time-to-treatment response, but not in progression-free survival or time-to-treatment failure. The rs4796793 genotype is a novel predictive factor of the response to mTKIs in patients with mRCC. However, prospective translational trials with larger patient cohorts are required to confirm these results.

Visualizing the human connectome.

Innovations in data visualization punctuate the landmark advances in human connectome research since its beginnings. From tensor glyphs for diffusion-weighted imaging, to advanced rendering of anatomical tracts, to more recent graph-based representations of functional connectivity data, many of the ways we have come to understand the human connectome are through the intuitive insight these visualizations enable. Nonetheless, several unresolved problems persist. For example, probabilistic tractography lacks the visual appeal of its deterministic equivalent, multimodal representations require extreme levels of data reduction, and rendering the full connectome within an anatomical space makes the contents cluttered and unreadable. In part, these challenges require compromises between several tensions that determine connectome visualization practice, such as prioritizing anatomic or connectomic information, aesthetic appeal or information content, and thoroughness or readability. To illustrate the ongoing negotiation between these priorities, we provide an overview of various visualization methods that have evolved for anatomical and functional connectivity data. We then describe interactive visualization tools currently available for use in research, and we conclude with concerns and developments in the presentation of connectivity results.