When undergoing treatment for breast cancer, many women experience severe side effects, some of which result in treatment-related death and some that can persist for years. Little is understood regarding factors that may predict drug toxicities. Pharmacogenetics, the investigation of variants in candidate genes in drug metabolism pathways, has been used to determine susceptibility to treatment-related toxicities, as well as to cancer recurrence. Although there have been some strong and important findings using this approach, such as identification of single nucleotide polymorphisms (SNPs) that predispose to side effects associated with thiopurines and irinotecan, there has been less progress in assessment of genetic variants that predispose to toxicities resulting from the multi-drug regimen commonly used to treat breast cancer, anthracyclines (A), cyclophosphamide (C), and taxanes (T). These difficulties in identification of key gene variants may be due to the complex metabolic pathways of these drugs, the lack of rate limiting enzymes in the processes, or the limitations of single SNP analysis, rather than capturing all of the variability across the genes. In addition to drug metabolism pathways, however, there may be a number of constitutional factors or other processes that affect damage to normal tissues in the course of chemotherapy, some known or hypothesized, such as DNA repair and oxidative stress pathways, and others not yet discovered. However, there have been no clear candidate genes that account for a large part of the variation in drug or treatment response, and there are likely important genes that influence sensitivity of cells to chemotherapy through unknown pathways which have not yet been identified or hypothesized. The present technological capabilities to screen the entire genome for variants that discriminate populations allows the opportunity to identify these as yet unknown pathways, and open the doors to exciting new avenues of research into mechanisms that had not been previously considered. We conducted a genome-wide scan (GWAS) in a clinical trial (S0221), in which women with high risk breast cancer were treated with different dosing schedules of C, A and T. Blood specimens were collected and banked, and DNA extracted for genotyping on the Illumina OMNI 1M platform. The GWAS data were used to examine genetic variants significantly associated with grades 3 and 4 toxicities, including peripheral neuropathy recorded during the T segment. In S0221, toxicities were graded according to the NCI Common Toxicity Criteria for Adverse Events (CTCAE). The neurotoxicity is predominantly a distal sensory neuropathy, which is characterized by pain, numbness, tingling, and reduced functional capacity in the extremities. Other symptoms include parasthesias, ataxia, impaired vibration and joint position sense, and loss of tendon reflexes. By using a GWAS approach, it is likely that important pathways not previously considered can be revealed as important in susceptibility to treatment-related toxicities, identifying those at greatest risk for alternate drugs or dose reduction, and opening new areas of research for prevention of taxanes-related neuropathy among patients receiving chemotherapy for breast cancer.
ICGC MMML-seq Data Freeze November 2012 transcriptome sequencing
Data access for raw and processed H3K27ac ChIP-seq and ATAC-seq data from "Effect of ETS2 modulation on chromatin accessibility and enhancer activity in human macrophages".
A KNIH009 mRNA-seq paired end data for preadipocytes
A SMC07_smRNA-Seq single end data for skeletal muscle cells
A SMC03_smRNA-Seq single end data for skeletal muscle cells
A SMC08_smRNA-Seq single end data for skeletal muscle cells
A SMC09_smRNA-Seq single end data for skeletal muscle cells
A KNIH007 mRNA-seq paired end data for adipocytes
SMC09 h3k9me3 ChIP-Seq paired end data
A KNIH009 miRNA-seq single end data for preadipocytes
ADMSC06 h3k4me3 ChIP-Seq paired end data
A SMC01_smRNA-Seq single end data for skeletal muscle cells
SMC04 h3k4me1 ChIP-Seq paired end data
SMC01 h3k9me3 ChIP-Seq paired end data
SMC02 h3k36me3 ChIP-Seq paired end data
ADMSC01 input ChIP-Seq paired end data
ADMSC02 h3k36me3 ChIP-Seq paired end data
SMC03 h3k9me3 ChIP-Seq paired end data
SMC04 h3k36me3 ChIP-Seq paired end data
SMC04 h3k4me3 ChIP-Seq paired end data
SMC04 h3k9me3 ChIP-Seq paired end data
SMC08 h3k27ac ChIP-Seq paired end data
ADMSC01 h3k27ac ChIP-Seq paired end data
ADMSC08 h3k4me1 ChIP-Seq paired end data
