Discovery of genetic profiles impacting response to chemotherapy: application to gemcitabine

Potential role of CMPK1, SLC29A1, and TLE4 polymorphisms in gemcitabine-based chemotherapy in HER2-negative metastatic breast cancer patients: pharmacogenetic study results from the prospective randomized phase II study of eribulin plus gemcitabine versus paclitaxel plus gemcitabine (KCSG-BR-13-11)

Background

In this study, we evaluated the association between genetic polymorphisms of 23 genes associated with gemcitabine metabolism and the clinical efficacy of gemcitabine in breast cancer patients.

Patients and methods

This prospective, pharmacogenetic study was conducted in cooperation with a phase II clinical trial. A total of 103 genetic polymorphisms of the 23 genes involved in gemcitabine transport and metabolism were selected for genotyping. The associations of genetic polymorphisms with overall survival, progression-free survival (PFS), and 6-month PFS were analyzed.

Results

A total of 91 breast cancer patients were enrolled in this study. In terms of 6-month PFS, rs1044457 in CMPK1 was the most significant genetic polymorphism [55.9% for CT and TT and 78.9% for CC, P < 0.001, hazard ratio (HR): 4.444, 95% confidence interval (CI): 1.905-10.363]. For the rs693955 in SLC29A1, the median duration of PFS was 5.4 months for AA and 10.5 months for CA and CC (P = 0.002, HR: 3.704, 95% CI: 1.615-8.497). For the rs2807312 in TLE4, the median duration of PFS was 5.7 months for TT and 10.4 months for CT and CC (P = 0.005, HR: 4.948, 95% CI: 1.612-15.190). In survival analysis with a multi-gene model, the TT genotype of rs2807312 had the worst PFS regardless of other genetic polymorphisms, whereas the CA genotype of rs693955 or the CT genotype of rs2807312 without the AA genotype of rs693955 had the best PFS compared with those of other genetic groups (P < 0.001).

Conclusions

Genetic polymorphisms of rs1044457 in CMPK1, rs693955 in SLC29A1, and rs2807312 in TLE4 were significantly associated with the 6-month PFS rate and/or the duration of PFS. Further studies with a larger sample size and expression study would be helpful to validate the association of genetic polymorphisms and clinical efficacy of gemcitabine.

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This is the largest pharmacogenetic study of gemcitabine-based breast cancer treatment in a prospective clinical trial.

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Several genetic polymorphisms in CMPK1, SLC29A1, and TLE4 were associated with 6-month PFS rate and the duration of PFS.

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The result of this study may contribute to the personalized treatment of breast cancer.

Critical genomic regulation mediated by Enhancer of Polycomb

Enhancer of Polycomb (EPC) was first identified for its contributions to development in Drosophila and was soon-thereafter purified as a subunit of the NuA4/TIP60 acetyltransferase complex. Since then, EPC has often been left in the shadows as an essential, yet non-catalytic subunit of NuA4/TIP60; however, its deep conservation and disease association make clear that it warrants additional attention. In fact, recent studies in yeast demonstrated that its Enhancer of Polycomb, Epl1, was just as important for gene expression and acetylation as is the catalytic subunit of NuA4. Despite its conservation, studies of EPC have often remained siloed between organisms. Here, our goal is to provide a cohesive view of the current state of the EPC literature as it stands among the major model organisms in which it has been studied. EPC is involved in multiple processes, beginning with its cardinal role in regulating global and targeted histone acetylation. EPC also frequently serves as an important interaction partner in these basic cellular functions, as well as in multicellular development, such as in hematopoiesis and skeletal muscle differentiation, and in human disease. Taken together, a unifying theme from these studies highlights EPC as a critical genomic regulator.

Creating chemotherapeutic-resistant breast cancer cell lines: advances and future perspectives

The development of resistance remains the most significant impediment to generating effective treatments for cancer. In the modern age of personalized medicine, it is of critical importance to understand the principles of both innate and acquired resistance to achieve the most effective therapeutic outcomes. Significant differences exist between cancer cells that exhibit innate resistance verses those that acquire resistance over time. Studying the acquisition of resistance is essential to obtaining a complete understanding of how treatments contribute to disease recurrence and progression. This review will evaluate the current understanding of chemotherapeutic resistance and its role in personalized medicine. This review will also explore how generating resistant cells in culture is essential to the development of improved cancer therapeutics.

Comprehensive exploration of novel chimeric transcripts in clear cell renal cell carcinomas using whole transcriptome analysis

The aim of this study was to clarify the participation of expression of chimeric transcripts in renal carcinogenesis. Whole transcriptome analysis (RNA sequencing) and exploration of candidate chimeric transcripts using the deFuse program were performed on 68 specimens of cancerous tissue (T) and 11 specimens of non-cancerous renal cortex tissue (N) obtained from 68 patients with clear cell renal cell carcinomas (RCCs) in an initial cohort. As positive controls, two RCCs associated with Xp11.2 translocation were analyzed. After verification by reverse transcription (RT)-PCR and Sanger sequencing, 26 novel chimeric transcripts were identified in 17 (25%) of the 68 clear cell RCCs. Genomic breakpoints were determined in five of the chimeric transcripts. Quantitative RT-PCR analysis revealed that the mRNA expression levels for the MMACHC, PTER, EPC2, ATXN7, FHIT, KIFAP3, CPEB1, MINPP1, TEX264, FAM107A, UPF3A, CDC16, MCCC1, CPSF3, and ASAP2 genes, being partner genes involved in the chimeric transcripts in the initial cohort, were significantly reduced in 26 T samples relative to the corresponding 26 N samples in the second cohort. Moreover, the mRNA expression levels for the above partner genes in T samples were significantly correlated with tumor aggressiveness and poorer patient outcome, indicating that reduced expression of these genes may participate in malignant progression of RCCs. As is the case when their levels of expression are reduced, these partner genes also may not fully function when involved in chimeric transcripts. These data suggest that generation of chimeric transcripts may participate in renal carcinogenesis by inducing dysfunction of tumor-related genes.

Pharmacogenomic characterization of gemcitabine response--a framework for data integration to enable personalized medicine

Supplemental Digital Content is available in the text.

Objectives

Response to the oncology drug gemcitabine may be variable in part due to genetic differences in the enzymes and transporters responsible for its metabolism and disposition. The aim of our in-silico study was to identify gene variants significantly associated with gemcitabine response that may help to personalize treatment in the clinic.

Methods

We analyzed two independent data sets: (a) genotype data from NCI-60 cell lines using the Affymetrix DMET 1.0 platform combined with gemcitabine cytotoxicity data in those cell lines, and (b) genome-wide association studies (GWAS) data from 351 pancreatic cancer patients treated on an NCI-sponsored phase III clinical trial. We also performed a subset analysis on the GWAS data set for 135 patients who were given gemcitabine+placebo. Statistical and systems biology analyses were performed on each individual data set to identify biomarkers significantly associated with gemcitabine response.

Results

Genetic variants in the ABC transporters (ABCC1, ABCC4) and the CYP4 family members CYP4F8 and CYP4F12, CHST3, and PPARD were found to be significant in both the NCI-60 and GWAS data sets. We report significant association between drug response and variants within members of the chondroitin sulfotransferase family (CHST) whose role in gemcitabine response is yet to be delineated.

Conclusion

Biomarkers identified in this integrative analysis may contribute insights into gemcitabine response variability. As genotype data become more readily available, similar studies can be conducted to gain insights into drug response mechanisms and to facilitate clinical trial design and regulatory reviews.

Effect of genetic polymorphisms on therapeutic response and clinical outcomes in pancreatic cancer patients treated with gemcitabine

AIM

Gemcitabine is the first chemotherapeutic agent to show clinical benefits in pancreatic cancer patients. While interindividual variability in chemoresponse is observed, genetic factors that affect drug metabolism have not been clearly defined. The purpose of this study is to evaluate the relationships between genetic polymorphisms and therapeutic efficacy in pancreatic cancer patients treated with gemcitabine.

PATIENTS & METHODS

The study population consisted of 102 pancreatic cancer patients who had been treated with a gemcitabine-based chemotherapeutic regimen. 102 genetic polymorphisms were selected from 23 genes involved in the metabolism and action sites of gemcitabine and screened for polymorphisms using the MassARRAY(®) system. The polymorphisms and haplotypes were analyzed in relation to overall survival (OS), time-to-progression (TTP) and disease progression.

RESULTS

CMPK1 360C>T was significantly associated with OS, TTP and disease progression (p = 0.042, 0.007 and 0.040, respectively, in a dominant genetic model). Additionally, CMPK1 240G>T was correlated with OS and TTP. The frequencies of the haplotypes for the CMPK1, SLC28A1, DCTD and TLE4 genes differed according to disease progression.

CONCLUSION

Genetic polymorphisms in genes related to metabolism and action sites of gemcitabine showed associations with the therapeutic efficacy, in terms of OS, TTP and disease progression in pancreatic cancer patients treated with gemcitabine-based chemotherapy. In particular, polymorphisms of the CMPK1 gene seem to provide important prognostic information.

Expression of microRNAs in the NCI-60 cancer cell-lines

The NCI-60 panel of 60 human cancer cell-lines of nine different tissues of origin has been extensively characterized in biological, molecular and pharmacological studies. Analyses of data from such studies have provided valuable information for understanding cellular processes and developing strategies for the diagnosis and treatment of cancer. Here, Affymetrix® GeneChip™ miRNA version 1 oligonucleotide microarrays were used to quantify 847 microRNAs to generate an expression dataset of 495 (58.4%) microRNAs that were identified as expressed in at least one cell-line of the NCI-60 panel. Accuracy of the microRNA measurements was partly confirmed by reverse transcription and polymerase chain reaction assays. Similar to that seen among the four existing NCI-60 microRNA datasets, the concordance of the new expression dataset with the other four was modest, with mean Pearson correlation coefficients of 0.37–0.54. In spite of this, comparable results with different datasets were noted in clustering of the cell-lines by their microRNA expression, differential expression of microRNAs by the lines’ tissue of origin, and correlation of specific microRNAs with the doubling-time of cells or their radiation sensitivity. Mutation status of the cell-lines for the TP53, PTEN and BRAF but not CDKN2A or KRAS cancer-related genes was found to be associated with changes in expression of specific microRNAs. The microRNA dataset generated here should be valuable to those working in the field of microRNAs as well as in integromic studies of the NCI-60 panel.

The use of genomic information to optimize cancer chemotherapy

The field of pharmacogenomics is focused on the characterization of genetic factors contributing to the response of patients to pharmacological interventions. Drug response and toxicity are complex traits; therefore the effects are likely influenced by multiple genes. The investigation of the genetic basis of drug response has evolved from a focus on single genes to relevant pathways to the entire genome. Preclinical (cell-based models) and clinical genome-wide association studies (GWAS) in oncology provide an unprecedented opportunity for a comprehensive and unbiased assessment of the heritable factors associated with drug response. The primary challenge with attempting to identify pharmacogenomic markers from clinical studies is that they require a homogeneous population of patients treated with the same dosage regimen and minimal confounding variables. Therefore, the development of cell-based models for pharmacogenomic marker identification has utility for the field since performing these types of studies in humans is difficult and costly. This review intends to provide a current report on the status of genomic studies in oncology, the methods for discovery, and implications for patient care. We present a perspective and summary of the challenges and opportunities in translating heritable genomic discoveries to patients.

NCI60 cancer cell line panel data and RNAi analysis help identify EAF2 as a modulator of simvastatin and lovastatin response in HCT-116 cells

Simvastatin and lovastatin are statins traditionally used for lowering serum cholesterol levels. However, there exists evidence indicating their potential chemotherapeutic characteristics in cancer. In this study, we used bioinformatic analysis of publicly available data in order to systematically identify the genes involved in resistance to cytotoxic effects of these two drugs in the NCI60 cell line panel. We used the pharmacological data available for all the NCI60 cell lines to classify simvastatin or lovastatin resistant and sensitive cell lines, respectively. Next, we performed whole-genome single marker case-control association tests for the lovastatin and simvastatin resistant and sensitive cells using their publicly available Affymetrix 125K SNP genomic data. The results were then evaluated using RNAi methodology. After correction of the p-values for multiple testing using False Discovery Rate, our results identified three genes (NRP1, COL13A1, MRPS31) and six genes (EAF2, ANK2, AKAP7, STEAP2, LPIN2, PARVB) associated with resistance to simvastatin and lovastatin, respectively. Functional validation using RNAi confirmed that silencing of EAF2 expression modulated the response of HCT-116 colon cancer cells to both statins. In summary, we have successfully utilized the publicly available data on the NCI60 cell lines to perform whole-genome association studies for simvastatin and lovastatin. Our results indicated genes involved in the cellular response to these statins and siRNA studies confirmed the role of the EAF2 in response to these drugs in HCT-116 colon cancer cells.

Bioinformatic analyses identifies novel protein-coding pharmacogenomic markers associated with paclitaxel sensitivity in NCI60 cancer cell lines

BACKGROUND

Paclitaxel is a microtubule-stabilizing drug that has been commonly used in treating cancer. Due to genetic heterogeneity within patient populations, therapeutic response rates often vary. Here we used the NCI60 panel to identify SNPs associated with paclitaxel sensitivity. Using the panel's GI50 response data available from Developmental Therapeutics Program, cell lines were categorized as either sensitive or resistant. PLINK software was used to perform a genome-wide association analysis of the cellular response to paclitaxel with the panel's SNP-genotype data on the Affymetrix 125 k SNP array. FastSNP software helped predict each SNP's potential impact on their gene product. mRNA expression differences between sensitive and resistant cell lines was examined using data from BioGPS. Using Haploview software, we investigated for haplotypes that were more strongly associated with the cellular response to paclitaxel. Ingenuity Pathway Analysis software helped us understand how our identified genes may alter the cellular response to paclitaxel.

RESULTS

43 SNPs were found significantly associated (FDR<0.005) with paclitaxel response, with 10 belonging to protein-coding genes (CFTR, ROBO1, PTPRD, BTBD12, DCT, SNTG1, SGCD, LPHN2, GRIK1, ZNF607). SNPs in GRIK1, DCT, SGCD and CFTR were predicted to be intronic enhancers, altering gene expression, while SNPs in ZNF607 and BTBD12 cause conservative missense mutations. mRNA expression analysis supported these findings as GRIK1, DCT, SNTG1, SGCD and CFTR showed significantly (p<0.05) increased expression among sensitive cell lines. Haplotypes found in GRIK1, SGCD, ROBO1, LPHN2, and PTPRD were more strongly associated with response than their individual SNPs.

CONCLUSIONS

Our study has taken advantage of available genotypic data and its integration with drug response data obtained from the NCI60 panel. We identified 10 SNPs located within protein-coding genes that were not previously shown to be associated with paclitaxel response. As only five genes showed differential mRNA expression, the remainder would not have been detected solely based on expression data. The identified haplotypes highlight the role of utilizing SNP combinations within genomic loci of interest to improve the risk determination associated with drug response. These genetic variants represent promising biomarkers for predicting paclitaxel response and may play a significant role in the cellular response to paclitaxel.

A whole-genome SNP association study of NCI60 cell line panel indicates a role of Ca2+ signaling in selenium resistance

Epidemiological studies have suggested an association between selenium intake and protection from a variety of cancer. Considering this clinical importance of selenium, we aimed to identify the genes associated with resistance to selenium treatment. We have applied a previous methodology developed by our group, which is based on the genetic and pharmacological data publicly available for the NCI60 cancer cell line panel. In short, we have categorized the NCI60 cell lines as selenium resistant and sensitive based on their growth inhibition (GI50) data. Then, we have utilized the Affymetrix 125K SNP chip data available and carried out a genome-wide case-control association study for the selenium sensitive and resistant NCI60 cell lines. Our results showed statistically significant association of four SNPs in 5q33–34, 10q11.2, 10q22.3 and 14q13.1 with selenium resistance. These SNPs were located in introns of the genes encoding for a kinase-scaffolding protein (AKAP6), a membrane protein (SGCD), a channel protein (KCNMA1), and a protein kinase (PRKG1). The knock-down of KCNMA1 by siRNA showed increased sensitivity to selenium in both LNCaP and PC3 cell lines. Furthermore, SNP-SNP interaction (epistasis) analysis indicated the interactions of the SNPs in AKAP6 with SGCD as well as SNPs in AKAP6 with KCNMA1 with each other, assuming additive genetic model. These genes were also all involved in the Ca²⁺ signaling, which has a direct role in induction of apoptosis and induction of apoptosis in tumor cells is consistent with the chemopreventive action of selenium. Once our findings are further validated, this knowledge can be translated into clinics where individuals who can benefit from the chemopreventive characteristics of the selenium supplementation will be easily identified using a simple DNA analysis.

Pharmacogenomic discovery using cell-based models

Quantitative variation in response to drugs in human populations is multifactorial; genetic factors probably contribute to a significant extent. Identification of the genetic contribution to drug response typically comes from clinical observations and use of classic genetic tools. These clinical studies are limited by our inability to control environmental factors in vivo and the difficulty of manipulating the in vivo system to evaluate biological changes. Recent progress in dissecting genetic contribution to natural variation in drug response through the use of cell lines has been made and is the focus of this review. A general overview of current cell-based models used in pharmacogenomic discovery and validation is included. Discussion includes the current approach to translate findings generated from these cell-based models into the clinical arena and the use of cell lines for functional studies. Specific emphasis is given to recent advances emerging from cell line panels, including the International HapMap Project and the NCI60 cell panel. These panels provide a key resource of publicly available genotypic, expression, and phenotypic data while allowing researchers to generate their own data related to drug treatment to identify genetic variation of interest. Interindividual and interpopulation differences can be evaluated because human lymphoblastoid cell lines are available from major world populations of European, African, Chinese, and Japanese ancestry. The primary focus is recent progress in the pharmacogenomic discovery area through ex vivo models.

Clinical pharmacology and pharmacogenetics of gemcitabine

Gemcitabine is a cytotoxic nucleoside analog, which is widely used in the treatment of malignancies. Interindividual differences in gemcitabine pharmacokinetics and pharmacodynamics have been demonstrated. Pharmacogenetic factors may account for a significant proportion of these differences. This review provides an update on the pharmacogenetics of gemcitabine and its influence on gemcitabine efficacy and toxicity.

Generating genome-scale candidate gene lists for pharmacogenomics

A critical task in pharmacogenomics is identifying genes that may be important modulators of drug response. High-throughput experimental methods are often plagued by false positives and do not take advantage of existing knowledge. Candidate gene lists can usefully summarize existing knowledge, but they are expensive to generate manually and may therefore have incomplete coverage. We have developed a method that ranks 12,460 genes in the human genome on the basis of their potential relevance to a specific query drug and its putative indications. Our method uses known gene-drug interactions, networks of gene-gene interactions, and available measures of drug-drug similarity. It ranks genes by building a local network of known interactions and assessing the similarity of the query drug (by both structure and indication) with drugs that interact with gene products in the local network. In a comprehensive benchmark, our method achieves an overall area under the curve of 0.82. To showcase our method, we found novel gene candidates for warfarin, gefitinib, carboplatin, and gemcitabine, and we provide the molecular hypotheses for these predictions.

Integrating Epigenomics into Pharmacogenomic Studies

The goal of personalized medicine is to recommend drug treatment based on an individual's genetic makeup. Pharmacogenomic studies utilize two main approaches: candidate gene and whole-genome. Both approaches analyze genetic variants such as single nucleotide polymorphisms (SNPs) to identify associations with drug response. In addition to DNA sequence variations, non-genetic but heritable epigenetic systems have also been implicated in regulating gene expression that could influence drug response. The International HapMap Project lymphoblastoid cell lines (LCLs) have been used to study genetic determinants responsible for expression variation and drug response. Recent studies have demonstrated that common genetic variants, including both SNPs and copy number variants (CNVs) account for a substantial fraction of natural variation in gene expression. Given the critical role played by DNA methylation in gene regulation and the fact that DNA methylation is currently the most studied epigenetic system, we suggest that profiling the variation in DNA methylation in the HapMap samples will provide new insights into the regulation of gene expression as well as the mechanisms of individual drug response at a new level of complexity. Epigenomics will substantially add to our knowledge of how genetics explains gene expression and pharmacogenomics.