Clinical Pharmacogenetics Implementation Consortium Guideline for the Use of Aminoglycosides Based on MT-RNR1 Genotype.

Aminoglycosides are widely used antibiotics with notable side effects, such as nephrotoxicity, vestibulotoxicity, and sensorineural hearing loss (cochleotoxicity). MT-RNR1 is a gene that encodes the 12s rRNA subunit and is the mitochondrial homologue of the prokaryotic 16s rRNA. Some MT-RNR1 variants (i.e., m.1095T>C; m.1494C>T; m.1555A>G) more closely resemble the bacterial 16s rRNA subunit and result in increased risk of aminoglycoside-induced hearing loss. Use of aminoglycosides should be avoided in individuals with an MT-RNR1 variant associated with an increased risk of aminoglycoside-induced hearing loss unless the high risk of permanent hearing loss is outweighed by the severity of infection and safe or effective alternative therapies are not available. We summarize evidence from the literature supporting this association and provide therapeutic recommendations for the use of aminoglycosides based on MT-RNR1 genotype (updates at https://cpicpgx.org/guidelines/ and www.pharmgkb.org).

Description of the EuroTARGET cohort: A European collaborative project on TArgeted therapy in renal cell cancer-GEnetic- and tumor-related biomarkers for response and toxicity.

OBJECTIVE: For patients with metastatic renal cell cancer (mRCC), treatment choice is mainly based on clinical parameters. With many treatments available and the limited response to treatment and associated toxicities, there is much interest in identifying better biomarkers for personalized treatment. EuroTARGET aims to identify and characterize host- and tumor-related biomarkers for prediction of response to tyrosine kinase inhibitor therapy in mRCC. Here, we describe the EuroTARGET mRCC patient cohort. METHODS AND MATERIALS: EuroTARGET is a European collaborative project designed as an observational study for which patients with mRCC were recruited prospectively in 62 centers. In addition, 462 patients with mRCC from previous studies were included. Detailed clinical information (baseline and follow-up) from all patients was entered in web-based case record forms. Blood was collected for germline DNA and pharmacokinetic/pharmacodynamic analyses and, where available, fresh-frozen tumor material was collected to perform tumor DNA, RNA, kinome, and methylome analyses. RESULTS: In total, 1,210 patients with mRCC were included. Of these, 920 received a tyrosine kinase inhibitor as first-line targeted treatment (sunitinib [N = 713, 78%], sorafenib [N = 41, 4%], or pazopanib [N = 166, 18%]) and had at least 6 months of outcome assessment (median follow-up 15.3 months [interquartile range: 8.5-30.2 months]). Germline DNA samples were available from 824 of these patients, fresh-frozen tumor material from 142 patients, fresh-frozen normal kidney tissue from 95 patients, and tissue microarrays created from formalin-fixed paraffin-embedded tumor material from 247 patients. Of the 920 patients, germline DNA variant chip data were successfully generated for 811 patients (Illumina HumanOmniExpress BeadChip). For 80 patients, next-generation exome sequencing of germline and tumor DNA was performed, tumor RNA sequencing was performed for 124 patients, kinome activity measured and processed for 121 patients (PamChip), and methylome data (Illumina Infinium HumanMethylation450 BeadChip) were created for 116 RCC tissues (and 23 normal kidney tissues). For 73 out of the 920 patients, all platform data types were generated. In addition, 40 patients were included in a pharmacokinetic/pharmacodynamic phase IV substudy. CONCLUSIONS: Analysis of EuroTARGET cohort data will contribute to personalization of therapy for patients with mRCC. The extensive clinical data and multiplatform EuroTARGET data will be freely available.

Genetic polymorphisms of SCN9A are associated with oxaliplatin-induced neuropathy.

BACKGROUND: Oxaliplatin is a chemotherapy agent active against digestive tumors. Peripheral neuropathy is one of the most important dose-limiting toxicity of this drug. It occurs in around 60-80% of the patients, and 15% of them develop severe neuropathy. The pathophysiology of oxaliplatin neurotoxicity remains unclear. SCN9A is a gene codifying for a subtype sodium channel (type IX, subunit α) and mutations in this gene are involved in neuropathic perception. In this study we investigated whether SCN9A genetic variants were associated with risk of neurotoxicity in patients diagnosed of cancer on treatment with oxaliplatin. METHODS: Blood samples from 94 patients diagnosed of digestive cancer that had received oxaliplatin in adjuvant or metastatic setting were obtained from three hospitals in Madrid. These patients were classified into two groups: "cases" developed oxaliplatin-induced grade 3-4 neuropathy (n = 48), and "controls" (n = 46) had no neuropathy or grade 1. The neuropathy was evaluated by an expert neurologist and included a clinical examination and classification according to validated neurological scales: National Cancer Institute Common Toxicity Criteria (NCI-CTC), Oxaliplatin-Specific Neurotoxicity Scale (OSNS) and Total Neuropathy score (TNS). Genotyping was performed for 3 SCN9A missense polymorphisms: rs6746030 (R1150W), rs74401238 (R1110Q) and rs41268673 (P610T), and associations between genotypes and neuropathy were evaluated. RESULTS: We found that SCN9A rs6746030 was associated with protection for severe neuropathy (OR = 0.39, 95% CI = 0.16-0.96; p = 0.041). Multivariate analysis adjusting for diabetes provided similar results (p = 0.036). No significant differences in neuropathy risk were detected for rs74401238 and rs41268673. CONCLUSION: SCN9A rs6746030 was associated with protection for severe oxaliplatin-induced peripheral neuropathy. The validation of this exploratory study is ongoing in an independent series.

Molecular markers to predict response to therapy.

Currently approved treatments for metastatic renal cell carcinoma (RCC) include vascular endothelial growth factor (VEGF)-blocking agents, mammalian target of rapamycin (mTOR) inhibitors, and cytokine therapy. In the near future, we are likely to add immune checkpoint blocking agents to this list. As we develop treatment platforms around each therapeutic class, determining which drug is best for a particular patient becomes increasingly important. At this point, we do not have validated predictive biomarkers for patients with RCC. Here, we discuss the logistical challenges surrounding biomarker development, summarize the current crop of biomarker candidates, and explore potential avenues for the development of more effective predictive tools for patients with advanced RCC.