A genome-wide association study identifies four genetic markers for hematological toxicities in cancer patients receiving gemcitabine therapy

The association of four genetic variants with myelosuppression in gemcitabine-treated Japanese is not evident in gemcitabine/carboplatin-treated Swedes

Gemcitabine/carboplatin‐induced myelosuppressive adverse drug reactions (ADRs) are clinical problems leading to patient suffering and dose alterations. There is a need for personalised medicine to improve treatment effects and patients' well‐being. We tested four genetic variants, rs11141915, rs1901440, rs12046844 and rs11719165, previously suggested as potential biomarkers for gemcitabine‐induced leukopenia/neutropenia in Japanese patients, in 213 Swedish gemcitabine/carboplatin‐treated non‐small cell lung cancer (NSCLC) patients. DNA was genotyped using TaqMan probes and real‐time PCR. The relationships between the risk alleles and low toxicity (non‐ADR: Common Terminology Criteria for Adverse Events [CTCAE] grades 0) or high toxicity (ADR: CTCAE grades 3–4) of platelets, leukocytes and neutrophils were evaluated using Fisher's exact test. The risk alleles did not correlate with myelosuppression, and the strongest borderline significance (not withstanding adjustment for multiple testing) was for rs1901440 (neutropenia, p = 0.043) and rs11719165 (leukopenia, p = 0.049) where the risk alleles trended towards lower toxicity, contrasting with previous study findings. Risk alleles and higher risk scores were more common among our patients. We conclude that the genetic variants do not apply to Swedish patients treated with gemcitabine/carboplatin. However, they can still be important in other populations and cohorts, especially in a gemcitabine monotherapy setting, where the causal genetic variation might influence myelosuppressive ADRs.

Gene-Gene Interactions of Gemcitabine Metabolizing-Enzyme Genes hCNT3 and WEE1 for Preventing Severe Gemcitabine-Induced Hematological Toxicity

Most patients experience severe hematological toxicity during treatment with gemcitabine; thus, preventing such toxicity would improve the treatment effects and patient quality of life. We analyzed 13 polymorphisms in the transporters, metabolizing enzymes, targets, and genes involved in DNA damage and the folate pathway among 132 patients treated with gemcitabine and studied their association with the severity of the hematological toxicities. Single-locus analysis showed that the single-nucleotide polymorphisms (SNPs) RRM1 rs12806698 and rs11031918 and DCTD rs7663494 were significantly associated with severe neutropenia, hENT1 rs760370 and hCNT3 rs7867504 and rs4877831 were associated with severe leukopenia, CDA rs2072671, DCTD rs7663494, and WEE1 rs3910384 were associated with severe anemia, and MTHFR rs1801133 was associated with severe thrombocytopenia after stringent Bonferroni correction (P < .0038). The gene-gene interaction analysis identified the overall best models, including a 2-way interaction model (hCNT3 rs7867504 and dCK rs12648166) for severe leukopenia (P = .0022) and a 3-locus model (CDA rs207671, DCTD rs7663494, and WEE1 rs3910384) for severe anemia with a strong synergistic effect (P = .0001). The association with hematological toxicity was further strengthened by the results of a haplotype analysis, in which the homozygous genotype combination of rs3910384 CC, rs2072671 AA, rs12648166 GG, rs7867504 CC, and rs7663494 TT conferred high genetic susceptibility to severe thrombocytopenia. Our results suggest that the gene-gene interaction of gemcitabine metabolic pathway genes and WEE1 contributes to susceptibility to gemcitabine-induced hematological toxicity. Moreover, we propose a promising data-mining analysis approach (generalized multifactor dimensionality reduction) to detect and characterize gene-gene interactions.

Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine

Researchers have long been presented with the challenge imposed by the role of genetic heterogeneity in drug response. For many years, Pharmacogenomics and pharmacomicrobiomics has been investigating the influence of an individual's genetic background to drug response and disposition. More recently, the human gut microbiome has proven to play a crucial role in the way patients respond to different therapeutic drugs and it has been shown that by understanding the composition of the human microbiome, we can improve the drug efficacy and effectively identify drug targets. However, our knowledge on the effect of host genetics on specific gut microbes related to variation in drug metabolizing enzymes, the drug remains limited and therefore limits the application of joint host-microbiome genome-wide association studies. In this paper, we provide a historical overview of the complex interactions between the host, human microbiome and drugs. While discussing applications, challenges and opportunities of these studies, we draw attention to the critical need for inclusion of diverse populations and the development of an innovative and combined pharmacogenomics and pharmacomicrobiomics approach, that may provide an important basis in personalized medicine.

Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients

Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10⁻³ for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.

Genetic association of gemcitabine/carboplatin-induced leukopenia and neutropenia in non-small cell lung cancer patients using whole-exome sequencing

OBJECTIVES

Gemcitabine/carboplatin treatment is known to cause severe adverse drug reactions which can lead to the need for reduction or cessation of chemotherapy. It would be beneficial to identify patients at risk of severe hematological toxicity in advance before treatment start. This study aims to identify genetic markers for gemcitabine/carboplatin-induced leukopenia and neutropenia in non-small cell lung cancer patients.

MATERIAL AND METHODS

Whole-exome sequencing was performed on 215 patients. Association analysis was performed on single-nucleotide variants (SNVs) and genes, and the validation was based on an independent genome-wide association study (GWAS). Based on the association and validation analyses the genetic variants were then selected for and used in weighted genetic risk score (wGRS) prediction models for leukopenia and neutropenia.

RESULTS

Association analysis identified 50 and 111 SNVs, and 12 and 20 genes, for leukopenia and neutropenia, respectively. Of these SNVS 20 and 19 were partially validated for leukopenia and neutropenia, respectively. The genes SVIL (p = 2.48E-06) and EFCAB2 (p = 4.63E-06) were significantly associated with leukopenia contain the partially validated SNVs rs3740003, rs10160013, rs1547169, rs10927386 and rs10927387. The wGRS prediction models showed significantly different risk scores for high and low toxicity patients.

CONCLUSION

We have identified and partially validated genetic biomarkers in SNVs and genes correlated to gemcitabine/carboplatin-induced leukopenia and neutropenia and created wGRS models for predicting the risk of chemotherapy-induced hematological toxicity. These results provide a strong foundation for further studies of chemotherapy-induced toxicity.

Exploring predictive biomarkers from clinical genome-wide association studies via multidimensional hierarchical mixture models

Although the detection of predictive biomarkers is of particular importance for the development of accurate molecular diagnostics, conventional statistical analyses based on gene-by-treatment interaction tests lack sufficient statistical power for this purpose, especially in large-scale clinical genome-wide studies that require an adjustment for multiplicity of a huge number of tests. Here we demonstrate an alternative efficient multi-subgroup screening method using multidimensional hierarchical mixture models developed to overcome this issue, with application to stroke and breast cancer randomized clinical trials with genomic data. We show that estimated effect size distributions of single nucleotide polymorphisms (SNPs) associated with outcomes, which could provide clues for exploring predictive biomarkers, optimizing individualized treatments, and understanding biological mechanisms of diseases. Furthermore, using this method we detected three new SNPs that are associated with blood homocysteine levels, which are strongly associated with the risk of stroke. We also detected six new SNPs that are associated with progression-free survival in breast cancer patients.

Clinical association between pharmacogenomics and adverse drug reactions

Adverse drug reactions (ADRs) are a major public health concern and cause significant patient morbidity and mortality. Pharmacogenomics is the study of how genetic polymorphisms affect an individual's response to pharmacotherapy at the level of a whole genome. This article updates our knowledge on how genetic polymorphisms of important genes alter the risk of ADR occurrence after an extensive literature search. To date, at least 244 pharmacogenes identified have been associated with ADRs of 176 clinically used drugs based on PharmGKB. At least 28 genes associated with the risk of ADRs have been listed by the Food and Drug Administration as pharmacogenomic biomarkers. With the availability of affordable and reliable testing tools, pharmacogenomics looks promising to predict, reduce, and minimize ADRs in selected populations.

Using Whole-Exome Sequencing to Identify Genetic Markers for Carboplatin and Gemcitabine-Induced Toxicities

PURPOSE

Chemotherapies are associated with significant interindividual variability in therapeutic effect and adverse drug reactions. In lung cancer, the use of gemcitabine and carboplatin induces grade 3 or 4 myelosuppression in about a quarter of the patients, while an equal fraction of patients is basically unaffected in terms of myelosuppressive side effects. We therefore set out to identify genetic markers for gemcitabine/carboplatin-induced myelosuppression.

EXPERIMENTAL DESIGN

We exome sequenced 32 patients that suffered extremely high neutropenia and thrombocytopenia (grade 3 or 4 after first chemotherapy cycle) or were virtually unaffected (grade 0 or 1). The genetic differences/polymorphism between the groups were compared using six different bioinformatics strategies: (i) whole-exome nonsynonymous single-nucleotide variants association analysis, (ii) deviation from Hardy-Weinberg equilibrium, (iii) analysis of genes selected by a priori biologic knowledge, (iv) analysis of genes selected from gene expression meta-analysis of toxicity datasets, (v) Ingenuity Pathway Analysis, and (vi) FunCoup network enrichment analysis.

RESULTS

A total of 53 genetic variants that differed among these groups were validated in an additional 291 patients and were correlated to the patients' myelosuppression. In the validation, we identified rs1453542 in OR4D6 (P = 0.0008; OR, 5.2; 95% CI, 1.8-18) as a marker for gemcitabine/carboplatin-induced neutropenia and rs5925720 in DDX53 (P = 0.0015; OR, 0.36; 95% CI, 0.17-0.71) as a marker for thrombocytopenia. Patients homozygous for the minor allele of rs1453542 had a higher risk of neutropenia, and for rs5925720 the minor allele was associated with a lower risk for thrombocytopenia.

CONCLUSIONS

We have identified two new genetic markers with the potential to predict myelosuppression induced by gemcitabine/carboplatin chemotherapy.

Genome-wide association study: a useful tool to identify common genetic variants associated with drug toxicity and efficacy in cancer pharmacogenomics

In recent years, the utilization of genome-wide association study (GWAS) has proved to be a beneficial method to identify novel common genetic variations not only for disease susceptibility but also for drug efficacy and drug-induced toxicity, creating a field of pharmacogenomics studies. In addition, the findings from GWAS also generate new biologic hypotheses that could improve the understanding of pathophysiology for disease or the mechanism of drug-induced toxicity. This review highlights the implications of GWAS that have been published to date and discusses the successes as well as challenges of using GWAS in cancer pharmacogenomics. The aim of pharmacogenomics is to realize the vision of personalized medicine; it is hoped that through GWAS, novel common genetic variations could be identified to predict clinical outcome and/or toxicity in cancer therapies that subsequently could be implemented to improve the quality of lives of patients with cancer. Nevertheless, given the complexity of cancer therapies, underpowered studies, and large heterogeneity of study designs, collaborative efforts are needed to validate these findings and overcome the limitations of GWA studies before clinical implementation. See all articles in this ccr focus section, "Progress in pharmacodynamic endpoints."

'Toxgnostics': an unmet need in cancer medicine

If we were to summarize the rationale that underpins medical oncology in a Latin aphorism, it might be 'veneno ergo sum'; that is, I poison, therefore I am. The burden of chemotherapy-associated toxicity is well recognized, but we have relatively few tools that increase the precision of anticancer drug prescribing. We propose a shift in emphasis from the focussed study of polymorphisms in drug metabolic pathways in small sets of patients to broader agnostic analyses to systematically correlate germline genetic variants with adverse events in large, well-defined cancer populations. Thus, we propose the new science of 'toxgnostics' (that is, the systematic, agnostic study of genetic predictors of toxicity from anticancer therapy).

Role of solute carrier transporters in pancreatic cancer: a review

Nucleoside analogs such as gemcitabine and 5-fluorouracil are currently the cornerstone of chemotherapy in patients with pancreatic ductal adenocarcinoma (PDAC). Decreased drug transport into tumor cells that may be caused by low expression of membrane proteins, such as solute carrier transporters, represents one of the principal mechanisms of chemotherapy resistance. Individual diversity of multidrug resistance is the major challenge limiting the success of anticancer treatment. Novel biomarkers and pharmacogenomic approaches could further optimize treatment algorithms leading to better survival and lower treatment toxicity in PDAC patients. In this review, the most promising predictive biomarkers from the solute carrier transporter family of membrane transporters and the potential applications for PDAC therapy with nucleoside analogues are summarized.

Genetic polymorphisms of SLC28A3, SLC29A1 and RRM1 predict clinical outcome in patients with metastatic breast cancer receiving gemcitabine plus paclitaxel chemotherapy

BACKGROUND

Paclitaxel and gemcitabine (PG) combination chemotherapy is effective as a maintenance chemotherapeutic regimen in metastatic breast cancer (MBC) patients because it increases progression-free survival (PFS), which increases overall survival (OS). The primary purpose of our study was to investigate the association between genetic polymorphisms in the genes involved in PG pathways and clinical outcomes in MBC patients treated with PG chemotherapy.

METHODS

A total of 324 MBC patients were enrolled in this prospective multicenter trial of PG as the first-line chemotherapy. Eighty-five of the 324 patients from two institutes were available for analysis of single nucleotide polymorphisms (SNPs). Germline DNA was extracted from peripheral blood mononuclear cells. Thirty-eight SNPs in 15 candidate genes selected from pathways that may influence the metabolism and transport of, or sensitivity, to PG were analysed.

RESULTS

The median PFS and OS of all 324 patients were 8.7 months (95% confidence interval [CI]: 7.5-9.6 months) and 26.9 months (95% CI: 23.6-30.1 months), respectively. An SNP in SLC28A3 (rs7867504, C/T) was associated with OS (CC or CT versus TT: 37 versus 21 months, p = 0.027, hazard ratio [HR] 2.6, 95% CI: 1.1-6.3). SLC29A1 GA haplotype had a significantly shorter OS (p = 0.030, HR 3.391, 95% CI: 1.13-10.19). RRM1 (ribonucleotide reductase large subunit M1) SNP (rs9937), and haplotypes ATAA and ATGA were significantly associated with neurotoxicity.

CONCLUSION

Genetic polymorphisms in SLC28A3, SLC29A1 and RRM1 can influence the clinical outcome of MBC patients treated with PG chemotherapy. Further studies on the functional mechanisms relating to these germline polymorphisms in these genes are warranted.

Implications of genome-wide association studies in cancer therapeutics

Genome wide association studies (GWAS) provide an agnostic approach to identifying potential genetic variants associated with disease susceptibility, prognosis of survival and/or predictive of drug response. Although these techniques are costly and interpretation of study results is challenging, they do allow for a more unbiased interrogation of the entire genome, resulting in the discovery of novel genes and understanding of novel biological associations. This review will focus on the implications of GWAS in cancer therapy, in particular germ-line mutations, including findings from major GWAS which have identified predictive genetic loci for clinical outcome and/or toxicity. Lessons and challenges in cancer GWAS are also discussed, including the need for functional analysis and replication, as well as future perspectives for biological and clinical utility. Given the large heterogeneity in response to cancer therapeutics, novel methods of identifying mechanisms and biology of variable drug response and ultimately treatment individualization will be indispensable.

Genome-wide association study of chemotherapeutic agent-induced severe neutropenia/leucopenia for patients in Biobank Japan

Chemotherapeutic agents are notoriously known to have a narrow therapeutic range that often results in life-threatening toxicity. Hence, it is clinically important to identify the patients who are at high risk for severe toxicity to certain chemotherapy through a pharmacogenomics approach. In this study, we carried out multiple genome-wide association studies (GWAS) of 13 122 cancer patients who received different chemotherapy regimens, including cyclophosphamide- and platinum-based (cisplatin and carboplatin), anthracycline-based (doxorubicin and epirubicin), and antimetabolite-based (5-fluorouracil and gemcitabine) treatment, antimicrotubule agents (paclitaxel and docetaxel), and topoisomerase inhibitors (camptothecin and etoposide), as well as combination therapy with paclitaxel and carboplatin, to identify genetic variants that are associated with the risk of severe neutropenia/leucopenia in the Japanese population. In addition, we used a weighted genetic risk scoring system to evaluate the cumulative effects of the suggestive genetic variants identified from GWAS in order to predict the risk levels of individuals who carry multiple risk alleles. Although we failed to identify genetic variants that surpassed the genome-wide significance level (P < 5.0 × 10(-8) ) through GWAS, probably due to insufficient statistical power and complex clinical features, we were able to shortlist some of the suggestive associated loci. The current study is at the relatively preliminary stage, but does highlight the complexity and problematic issues associated with retrospective pharmacogenomics studies. However, we hope that verification of these genetic variants through local and international collaborations could improve the clinical outcome for cancer patients.

Pharmacogenetic markers of toxicity for chemotherapy in colorectal cancer patients

Chemotherapeutic agents used in colorectal cancer are frequently associated with severe adverse reactions that compromise the efficacy of treatment. Predicting toxicity could enable therapy to be tailored. Genetic variations have been associated with toxicity in patients treated with fluoropyrimidines (5-fluorouracil, capecitabine and tegafur), oxaliplatin, irinotecan and cetuximab. Complexity of treatment and variability in toxicity classifications make it difficult to compare studies. This article analyzes the association between toxicity and polymorphisms in DPYD, TYMS, MTHFR, ABCB1, UGT1A1, ERCC1, ERCC2, XRCC1, GSTT1 and GSTM1. In addition, the state-of-the-art and future perspectives are discussed.

The business value and cost–effectiveness of genomic medicine

Genomic medicine offers the promise of more effective diagnosis and treatment of human diseases. Genome sequencing early in the course of disease may enable more timely and informed intervention, with reduced healthcare costs and improved long-term outcomes. However, genomic medicine strains current models for demonstrating value, challenging efforts to achieve fair payment for services delivered, both for laboratory diagnostics and for use of molecular information in clinical management. Current models of healthcare reform stipulate that care must be delivered at equal or lower cost, with better patient and population outcomes. To achieve demonstrated value, genomic medicine must overcome many uncertainties: the clinical relevance of genomic variation; potential variation in technical performance and/or computational analysis; management of massive information sets; and must have available clinical interventions that can be informed by genomic analysis, so as to attain more favorable cost management of healthcare delivery and demonstrate improvements in cost–effectiveness.