Accepted Manuscript Impact of Polymorphic Variations of Gemcitabine Metabolism, DNA Damage Repair, and Drug Resistance Genes on the Effect of High-Dose Chemotherapy for Relapsed or Refractory Lymphoid Malignancies Keiji Shinozuka, Hongwei Tang, Roy B. Jones, Donghui Li, Yago Nieto PII:
S1083-8791(15)01917-5
DOI:
10.1016/j.bbmt.2015.12.022
Reference:
YBBMT 54160
To appear in:
Biology of Blood and Marrow Transplantation
Received Date: 18 February 2015 Accepted Date: 22 December 2015
Please cite this article as: Shinozuka K, Tang H, Jones RB, Li D, Nieto Y, Impact of Polymorphic Variations of Gemcitabine Metabolism, DNA Damage Repair, and Drug Resistance Genes on the Effect of High-Dose Chemotherapy for Relapsed or Refractory Lymphoid Malignancies, Biology of Blood and Marrow Transplantation (2016), doi: 10.1016/j.bbmt.2015.12.022. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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IMPACT OF POLYMORPHIC VARIATIONS OF GEMCITABINE METABOLISM, DNA DAMAGE REPAIR, AND DRUG RESISTANCE GENES ON THE EFFECT OF HIGH-DOSE CHEMOTHERAPY FOR RELAPSED OR REFRACTORY LYMPHOID MALIGNANCIES
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Keiji Shinozuka1, Hongwei Tang1, Roy B Jones2, Donghui Li1, Yago Nieto2
AUTHORS’ AFFILIATIONS: Departments of Gastrointestinal Medical Oncology (1) and Stem Cell Transplantation and Cellular Therapy (2), The University of Texas M.D. Anderson Cancer
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Center, Houston, Texas, 77030, USA
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RUNNING TITLE: Pharmacogenomics of HDC for Lymphoid Malignancies.
KEYWORDS: Single nucleotide polymorphisms, gemcitabine metabolism, DNA damage repair, drug resistance gene, glutathione-S-transferases
FINANCIAL SUPPORT: Supported by a MD Anderson Cancer Center Institutional Research
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Grant (YN)
CORRESPONDING AUTHOR: Yago Nieto, MD, Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
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Phone: 713-792-2466, fax: 713-794-4902,
[email protected]
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POTENTIAL CONFLICTS OF INTEREST: None. WORD COUNT: 3426 words TOTAL NUMBER OF FIGURES AND TABLES: 6 tables (3 in the manuscript and 3 supplemental) and 2 figures
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ABSTRACT Purpose: The goal of this study was to determine whether single-nucleotide polymorphisms (SNPs) in genes involved in gemcitabine metabolism, DNA damage repair, multidrug resistance outcome of
patients with
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(MDR) and alkylator detoxification influence the clinical
refractory/relapsed lymphoid malignancies receiving high-dose gemcitabine/busulfan/melphalan (Gem/Bu/Mel) with autologous stem-cell support.
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Experimental Design: We evaluated 21 germline SNPs of the gemcitabine metabolism genes CDA, dCK, and hCNT3, DNA damage repair genes RECQL, XRCC1, RAD54L, ATM, ATR,
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MLH1, MSH2, MSH3, TREX1, EXO1, and TP73, and multidrug resistance genes MRP2 and MRP5, as well as glutathione-S-transferase GSTP1 in 153 patients with relapsed or refractory lymphoma or myeloma receiving Gem/Bu/Mel. We studied the association of genotypes with overall survival (OS), progression-free survival (PFS) and nonhematological grade 3-4 toxicity.
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Results: CDA C111T and TREX1 Ex14-460C>T genotypes had a significant effect on OS (P=0.007 and P=0.005, respectively), and CDA C111T, ATR C340T and EXO1 P757L genotypes were significant predictors for severe toxicity (P=0.037, P=0.024 and P=0.025,
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respectively) in multivariable models that adjusted for clinical variables. The multi-SNP risk score analysis identified the combined genotypes of TREX1 Ex14-460 TT and hCNT3 Ex5
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+25A>G AA as a significant predictor for OS and the combination of MRP2 Ex10 +40 GG/GA and MLH1 IVS12-169 TT as significant predictor for PFS. Conclusions: Polymorphic variants of certain genes involved in gemcitabine metabolism and DNA damage repair pathways may be potential biomarkers for clinical outcome in patients with refractory/relapsed lymphoid tumors receiving Gem/Bu/Mel.
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INTRODUCTION Gemcitabine is a pyrimidine nucleoside analogue with broad antitumor activity and wide clinical use. This prodrug requires cellular uptake and intracellular phosphorylation before incorporation
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into DNA, which is believed to be its mechanism of cytotoxicity.1, 2 The profile of gemcitabine, with dose-dependent cytotoxicity and few nonhematological side effects, has prompted its study at high doses with autologous stem-cell support. 3 Since gemcitabine inhibits DNA damage
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repair,4 combinations of this agent with alkylators should have synergistic or additive antitumor
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activity and a favorable therapeutic index.
We have developed a new high-dose combination of infusional gemcitabine with busulfan and melphalan (Gem/Bu/Mel) for lymphoid tumors, with promising results in relapsed Hodgkin’s and diffuse large B-cell lymphoma (DLBCL). 5,6 The extramedullary toxicity profile of Gem/Bu/Mel includes mucositis, skin rash and transaminase elevation. Since these nonhematological side
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effects can be severe, it would be helpful to predict their occurrence for a given patient. Unfortunately, no patient or clinical features have been associated with toxicity.5
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The cellular pharmacodynamic effect of gemcitabine depends on multiple enzymes, such as those involved in its intracellular metabolism, DNA damage repair and multidrug resistance
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mechanisms, whose activity may depend on their genetic polymorphic variants. We have previously identified single nucleotide polymorphisms (SNPs) of key enzymes in these pathways with a major impact on clinical outcome or toxicity on patients with pancreatic cancer undergoing gemcitabine-based chemoradiation7,8,9,10,11,12,13,14 In contrast, the pharmacogenomics of high-dose gemcitabine have not been adequately studied. Since the effect of gemcitabine on normal and tumor cells is greater at higher doses, it is conceivable that the impact of polymorphic genetic variation of relevant enzymes may be greater in the transplant setting.
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The electrophilic alkylators busulfan and melphalan are detoxified inside the cell by reduced glutathione (GSH). GSH conjugation of alkylating agents is mediated by glutathione Stransferase (GST), whose activity also depends on polymorphic variations. 15 , 16 , 17 GST pi 1
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(GSTP1) is the most abundant GST class found in many normal cell and malignant tissues.18,19 The GSTP1 Ile105Val polymorphism has been associated with improved outcomes in patients
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with myeloma receiving high-dose melphalan.20
We hypothesized that polymorphic variations of genes involved in gemcitabine metabolism,
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DNA damage repair, multidrug resistance and glutathione detoxification correlate with the toxicity and outcome of patients with relapsed/refractory lymphoid tumors receiving Gem/Bu/Mel.
Materials and Methods
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Patient recruitment and data collection
This prospective study involved patients with relapsed/refractory lymphoid malignancies, including Hodgkin’s lymphoma or DLBCL and myeloma, with refractory or poor-risk features that made them eligible for clinical trials of Gem/Bu/Mel with autologous stem-cell transplantation at
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our institution.5,6 This laboratory study was approved by the Institutional Review Board and all patients provided informed consent prior to enrollment. All patients received the same treatment
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doses and schema of Gem/Bu/Mel, as previously described.5 Overall survival (OS) and progression-free survival (PFS) were calculated from the date of diagnosis to date of death and progression/death, respectively. Living patients and patients without progression at the last follow up time were censored. Nonhematological toxicities, including mucositis, skin rash and transaminase elevation, were graded according to the Common Terminology Criteria for Adverse Events (CTCAE 3.0).21
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DNA extraction and genotyping
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We selected 21 SNPs of the deoxycytidine deaminase (CDA), deoxycytidine kinase (dCK), human concentrative nucleotide transporter (hCNT3), RECQL, X-ray repair complementing (XRCC)1, RAD54L, ATM, ATM and Rad3-related (ATR), mutL homolog (MLH)1, mutS homolog (MSH)2, MSH3, three prime repair exonuclease (TREX)1, exonuclease I (EXO1), tumor protein
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(TP)73, multidrug resistance-associated protein (MRP)2, MRP5, and GSTP1 genes according to the following criteria: 1) Minor allele frequency of the SNP >15% among Caucasians, 2)
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coding SNPs including nonsynonymous or synonymous SNPs, and 3) association with cancer risk or clinical outcome in previous studies. The genes, chromosome locations, nucleotide substitutions, function (such as encoding amino acid changes), reference SNP identification numbers, and minor allele frequencies of the 21 SNPs evaluated in this study are summarized
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in Table 1.
DNA was extracted from peripheral-blood lymphocytes in a single 10-cc blood sample of patients using Qiagen DNA isolation kits (Valencia, CA). Genotyping was performed using the
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Taqman 5’ nuclease assay. Primers and TaqMan MGB probes were provided by TaqMan SNP Genotyping Assay Services (Applied Biosystems, Foster City, Calif). The probes were labeled
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with the fluorescent dye VIC or FAM for each allele at the 5’ end. Polymerase chain reaction (PCR) was performed in a 5-µL total volume consisting of TaqMan Universal PCR Master Mix, 20 ng of genomic DNA (diluted with dH2O), and TaqMan SNP Genotyping Assay Mix. Allele discrimination was accomplished by running endpoint detection using the ABI Prism 7900HT Sequence Detection System and SDS 2.3 software (Applied Biosystems). Twenty percent of the samples were analyzed in duplicate, with 100% concordance in genotype calling.
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Statistical analysis The distribution of genotypes was tested for Hardy-Weinberg equilibrium with the goodness-offit χ2 test. The association of clinical factors and genotypes with OS and PFS was evaluated
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using log-rank test and Kaplan-Meier methods. Hazard rations (HRs) and 95% confidence intervals (CIs) were estimated using univariable or multivariate Cox proportional hazard models. The association of genotypes and severe toxicity was estimated with odds ratios (OR) using
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univariable or multivariate logistic regression. Multivariate analyses of OS adjusted for age, number of prior chemotherapy lines, progression, and severe toxicity in this study. The effect of
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genotype on severe toxicity was adjusted for age, number of prior chemotherapy lines, and progression.
We estimated the false-positive report probability (FPRP) for the observed statistically significant associations using the Wacholder method. 22 FPRP is the probability of no true
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association between a genetic variant and a phenotype given a statistically significant finding. It depends on the observed P value, on the prior probability that the association between the genetic variant and the phenotype is real, as well as on the statistical power of the test. In the current study, we set the HR and OR values of 2.0-4.0 as a likely threshold value. The prior
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probability used was 0.25 for all SNPs. The FPRP value for noteworthiness was set at 0.2.
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To better assess combined genotype effects, we also conducted the risk score analysis as previously described.23,24 Briefly, we selected SNPs with P ≤0.15 in likelihood ratio test derived from Cox or logistic regression models. We generated a risk score for these SNPs with deleterious genotype(s) as “1” and the reference genotype(s) as “0”. We explored all possible combinations of multiple SNPs to find the best-fitting models with consideration of the model likelihood and C statistic (Cox regression) or Area Under the Curve (AUC) (logistic regression). 25 In parallel, each selected SNP went through stepwise selection (α = 0.05) in
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1,000 bootstraps. The SNPs surviving >50% bootstraps were most likely to be selected to the best-fitting model. A final multi-SNP risk score was developed based on the best-fitting SNPs through summation of risk scores. We compared the performance of the clinical factor based
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model, the SNP only based model and the clinical factor and SNP combined model using Cstatistics or AUC. All statistical testing used SPSS Statistics, v17.0 (SPSS Inc., Chicago, IL) and
RESULTS
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Patient Characteristics and genotype frequencies
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R v.3.1.0 software packages. Statistical significance was defined as PT and MRP2 G40A) showed significant associations (PG)
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showed nonsignificant associations with OS (Table 2). The association of CDA C111T and TREX1 Ex14-460C>T genotypes with OS remained statistically significant after adjusting for age, number of prior chemo lines, progression and severe toxicity (P=0.007 and P=0.005,
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respectively). The FPRP was 0.086 for CDA and 0.026 for TREX1, respectively, given a prior probability of 25%. Both are below the threshold of 0.20 indicating noteworthiness.
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To find the best multi-SNP predictor for OS and PFS, we conducted a risk score analysis. We first compared clinical factor-based, SNP-based and clinical factor and SNP combined model to find the best-fitting model (Table S1). SNP alone model had the highest predicting power compared to the other models. (Table S2). We found that the best multi-SNP risk score for OS is the TREX1 Ex14-460 TT genotype which had C-statistics as high as 0.91 in SNP alone model
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and 0.84 in combined model (Table S2). Patients carrying at-risk genotypes had HR=2.77 (95% CI, 1.26-6.11) (P=0.011) (Figure 1A). Furthermore, the best SNP combination of MRP2 Ex10 +40 GG/GA and MLH1 IVS12-169 TT remained a significant predictor of PFS after adjusting
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age, number of chemotherapy lines, and severe toxicity, with HR=2.06 (95% CI, 1.35-3.16) for patients with one or more variant alleles compared with those with no variant allele (PG had non-
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significant associations with severe toxicity (Table 4). The CDA C111T, ATR C340T and EXO1 P757L genotypes remained as significant predictors after adjusting for age, number of prior chemo lines, and progression (P=0.037, P=0.024 and P=0.025, respectively). The FPRP was
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0.207 for CDA, 0.133 for ATR, and 0.137 for EXO1.
Multi-SNP risk score analysis showed the clinical factor and SNP combined model moderately
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increased the power compared to clinical factor or SNP alone model (Table S2). The combined genotypes of EXO1 rs9350 (Ex15 +59C>T, P757L) and CDA rs1048977 (Ex4 +111C>T, T145T) had a HR of 2.47 (95% CI: 1.40-4.35, P = 0.0018) (Table 5).
DISCUSSION
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In this study, we evaluated the effect of polymorphic variants of genes involved in gemcitabine metabolism, DNA damage repair, drug resistance and glutathione detoxification on patient outcomes. The study was conducted in patients enrolled in clinical trials of our new HDC
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regimen Gem/Bu/Mel. Out of the 21 SNPs evaluated, CDA C111T and TREX1 Ex14-460C>T genotypes were independently associated with OS. The CDA C111T, ATR C340T and EXO1
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P757L genotypes were independent predictors for severe toxicity. Furthermore, risk score analysis showed that the TREX1 Ex14-460 TT genotype and the combined genotypes of MRP2 Ex10 +40 GG/GA and MLH1 IVS12-169 TT were significant predictors for OS and PFS, respectively. These findings suggest that genetic variations in drug metabolism and DNA damage repair have value as prognostic biomarkers.
The selection of SNPs in this study was based on our previous observations in patients with pancreatic cancer receiving gemcitabine-based chemoradiation.1-8 CDA, an enzyme involved in
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the salvage pathway of pyrimidine, is the major gemcitabine inactivation enzyme. Three potentially functional SNPs of the CDA gene, i.e. C111T (T145T), A-76C (K27Q), and G208A (A70T) have been clinically investigated in patients receiving gemcitabine. 11,13, 26,27 The CDA
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C111T was significantly associated with severe toxicity in pancreatic cancer patients,5 as well as fewer tumor responses and worse outcomes in advanced non-small cell lung cancer. 28 , 29 Although CDA exon 4 C111T is a synonymous SNP that does not result in amino acid change, bioinformatics analysis predicted possible changes in splicing regulation and transcriptional
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regulation.30 It is possible that the variant allele confers a reduced enzyme activity, making the
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variant allele carriers more susceptible to drug toxicity. Further functional studies are required to elucidate the mechanisms underlying the observed associations.
ATR, TREX1 and EXO1 are all DNA damage response genes that were significantly associated with either outcome or toxicity in the current study. Gemcitabine incorporation causes DNA
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replication arrest, and ATR/Chk1 signaling pathway plays a crucial role in the cellular response to the stalled DNA replication fork.31 Cells lacking ATR or Chek1 genes have been shown to be more sensitive to gemcitabine. The ATR gene encodes a protein kinase that is critically
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important in maintaining the integrity of the replication apparatus following damage that arrests the progression of the complex.32 ATR C340T (rs2227928) is a nonsynonymous SNP, and the
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replacement of threonine to methionine could have an impact on transcriptional regulation and post-translation consequence as predicted by bioinformatic models. 33 A lower level of expression or activity of ATR could explain the increased toxicity in patients with the variant allele observed in the current study. TREX1 is a major 3 prime exonuclease in mammalian cells. Loss of TREX1 leads to reduce the phosphorylation of the Chk1 gene in cells exposed to hydroxyurea,34 which suggests a compromised ATR signaling pathway function. The TREX1 SNP (rs17971) investigated in the current study is an expression quantitative trait locus (eQTL).34 As in or previous study, we saw a significant association of TREX1 Ex14-460C>T
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genotype with outcome. Thus, TREX1 is a critical determinant of efficacy of gemcitabineinduced DNA damage. EXO1 is a 5’-3’ exonuclease involved in the DNA mismatch repair and other DNA metabolic pathways affecting genomic stability, including homologous recombination
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and DNA damage repair.35,36,37 EXO1 stability is dependent on ATR signaling. 38 The current study found a significant association of EXO1 P757L genotype with drug toxicity. The EXO1 P757L is a nonsynonymous SNP that result in replacement of amino acids, possibly affecting
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the protein functions.
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In addition to the individual SNP effects, we have observed significant associations of the combined at-risk alleles of the TREX1, hCNT3 (involved in gemcitabine intracellular uptake), MRP2 (involved in exporting bilirubin and glucuronides of certain anticancer drugs) and MLH1 (DNA mismatch repair enzyme) genes with outcomes and toxicity. Although many of the at-risk alleles showed non-significant mild effect individually, the combined genotype had a strong
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effect on the clinical outcome, even within the disease subgroups. These observations support the concept that genes act in concert, and that the combined action of many genes exerts a greater influence on phenotype than individual SNPs. For future clinical applications, a battery
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of several genes/SNPs involved in the same pathway may have a better predicting power than
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relying on single gene/SNP.
Limitations to the present study include its moderate sample size and the heterogeneity of diagnoses. While the effect of the relevant SNPs was similar across patient diagnoses, our findings should be confirmed in disease-specific studies. Although our sample size is moderate and some observations might have occurred by chance, the consistency with previously reported associations, the functional basis of the observed associations, and the good performance of the risk scores argue for their potential importance.
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In conclusion, we observed an important effect of polymorphic variants of genes involved in gemcitabine metabolism, DNA repair and multidrug resistance in a population of patients with lymphoid tumors receiving homogeneous HDC with Gem/Bu/Mel. The ultimate goal of this
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research is to identify genetic profiles that can be used in the clinic as predictors for therapy response or prognosis. If these findings are replicated in additional patient populations, such information may be helpful in stratifying patients for a more individualized high-dose cancer
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therapy.
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Acknowledgements
KS is the recipient of a research fellowship from the Uehara Memorial Foundation, Tokyo,
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Japan.
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RS No.
CDA
1p36.12b
dCK
4q13.3b
hCNT3
9q21.32c
RECQL XRCC1 RAD54L ATM
12p12 19q13.2 1p32 11q22-q23
ATR MLH1 MSH2 MSH3 TREX1 EXO1 TP73 MRP2 MRP5 GSTP1
3q22-q24 3q21.3 2p22-p21 5q11-q12 3p21 1q42-q43 1p36.3 10q24.2c 3q27.1b 11q13
Ex4 +111C>T, T145T Ex2 -76A>C, K27Q IVS6 -1205C>T IVS2 +9846A>G Ex14 -69C>T, L461L Ex5 +25A>G, T89T Ex15 +159A>C Ex6 -22C>T, R194W Ex18 +157C>T, A730A IVS22 -77C>T Ex38+61A>G, D1853N Ex4 +340C>T, T211M IVS12 -169C>T IVS12 -6T>C Ex4 -100G>A, P231P Ex14 -460C>T Ex15 +59C>T, P757L Ex2 +4G>A Ex10 +40G>A, V417I Ex10 -2A>G, Q382Q Ex5 -24A>G, I105V
1048977 2072671 4694362 12648166 7853758 7867504 13035 1799782 1048771 664677 1801516 2227928 2286940 2303428 1805355 11797 9350 2273953 2273697 7636910 1695
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Minor Allele Frequency a Observed 0.31 0.30 0.41 0.43 0.18 0.44 0.40 0.09 0.10 0.43 0.07 0.47 0.37 0.10 0.14 0.38 0.20 0.23 0.19 0.36 0.35
Minor Allele Frequency b Reported 0.28 0.44 0.45 0.43 0.15 0.39 0.28 0.15 0.15 0.29 0.16 0.38 0.41 0.15 0.15 0.43 0.18 0.26 0.25 0.35 0.46
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SNP
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Chromosome
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Abbreviation: SNP, single-nucleotide polymorphism; RS No., reference SNP identification number. a The data observed in current study. b
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The reported minor allele frequency was from SNP500 cancer database.
ACCEPTED MANUSCRIPT Table 2. Characteristics of the study No. of Patients
Age, years ≦50 51-60 61-70 Diagnosis Hodgkin’s lymphoma Diffuse large B-cell lymphoma Myeloma
No. of Deaths (%)
87 37 29
19 (21.8) 5 (13.5) 12 (41.4)
54 64 35
7 (13.0) 18 (28.1) 11 (31.4)
Progression Yes (Progression) No (No Progression)
81 72
36 (44.4) 0 (0)
Number of prior chemo lines 1 2 3 4 5 6 7 10
5 73 41 17 8 4 4 1
0 (0) 12 (16.4) 10 (24.4) 5 (29.4) 4 (50.0) 2 (50.0) 2 (50.0) 1 (100)
47 106
12 (25.5) 24 (22.6)
SC
-
81 (100) 72 (100)
M AN U
0.002
0.006
2 (40.0) 31 (42.5) 24 (58.5) 10 (58.8) 7 (87.5) 3 (75.0) 3 (75.0) 1 (100)
0.591
TE D
0.744
27 (50.0) 32 (50.0) 22 (62.9)
T CC CT TT CC/CT vs TT Drug resistance gene MRP2 G40A GG AG AA GG/AG vs AA GSTP1 Ex5-24A>G AA AG GG AA/GG vs AG
1.0 0.54 (0.26-1.10) 0.88 (0.33-2.37) 0.56 (0.28-1.10)
0.090 0.799 0.091
AC C
EP
TE D
Abbreviations: P(LR), log-rank P; HR, hazard ratio; CI, confidence interval. *HR was from multivariate Cox regression model with adjustment for age, number of prior chemo lines, progression and severe toxicity.
ACCEPTED MANUSCRIPT
P (X2)
Univariate OR (95% CI)
0.086 0.186 0.052
P
1.0 0.49 (0.23-1.06) 0.34 (0.80-1.40) 0.46 (0.22-0.95)
0.070 0.134 0.037 (0.043)
1.0 0.70 (0.34-1.47) 0.63 (0.19-2.14) 0.69 (0.34-1.37)
0.350 0.459 0.288
1.0 0.60 (0.28-1.31) 0.56 (0.16-2.01) 0.60 (0.29-1.24)
0.207 0.380 0.169
0.726 0.271 0.283
1.0 1.03 (0.45-2.40) 2.04 (0.68-5.47) 2.01 (0.84-4.82)
0.897 0.155 0.119
0.326 0.666 0.202
1.0 0.66 (0.30-1.45) 1.24 (0.41-3.69) 0.61 (0.30-1.26)
0.296 0.706 0.186
1.0 0.92 (0.33-2.56) 4.62 (0.41-52.4) 4.67 (0.41-52.8)
0.878 0.217 0.213
1.0 0.97 (0.34-2.83) 6.24 (0.42-92.6) 6.27 (0.42-92.5)
0.960 0.183 0.182
1.0 0.69 (0.32-1.50) 0.31 (0.11-0.90) 0.39 (0.15-1.01)
0.348 0.031 0.053
0.60 (0.27-1.35) 0.28 (0.10-0.84) 0.39 (0.15-1.03)
0.219 0.024 0.057
1.0 0.38 (0.16-0.90)
0.028
1.0 0.38 (0.16-0.92)
0.032
0.528
M AN U
SC
RI PT
1.0 0.53 (0.25-1.10) 0.40 (0.10-1.55) 0.50 (0.25-1.01) 0.559
1.0 1.16 (0.52-2.59) 1.68 (0.67-4.23) 1.55 (0.70-2.44)
0.398
1.0 0.33 (0.32-1.46) 1.25 (0.45-3.44) 0.64 (0.32-1.27)
0.306
EP
AC C
P
Multivariate OR (95% CI)*
0.139
TE D
Table 4. Extramedullary toxicity and genotype Grade 3-4 Toxicity, n (%) Genotype No Yes Gemcitabine metabolic gene CDA C111T (T145T) CC 45 (61.6) 28 (38.4) CT 49 (75.4) 16 (24.5) TT 12 (80.0) 3 (20.0) CC vs CT/TT CDA A-76C (K27Q) AA 51 (65.4) 27 (34.6) AC 43 (72.9) 16 (27.1) CC 12 (75.0) 4 (25.0) AA vs AC/CC hCNT3 A25G (T89T) TT 38 (73.1) 14 (26.9) CT 47 (70.1) 20 (29.9) CC 21 (61.8) 13 (38.2) TT/CT vs CC DNA repair gene RECQL A159C AA 35 (66.0) 18 (34.0) AC 57 (74.0) 20 (26.0) CC 14 (60.9) 9 (39.1) AA/CC vs AC XRCC1 194 CC 90 (69.8) 39 (30.2) CT 15 (71.4) 6 (28.6) TT 1 (33.3) 2 (66.7) CC/CT vs TT ATR C340T CC 27 (60.0) 18 (40.0) CT 50 (68.5) 23 (31.5) TT 29 (82.9) 6 (17.1) CC/CT vs TT EXO1 P757L CC 63 (62.4) 38 (37.6) CT 35 (81.4) 8 (18.6)
0.087
0.032
ACCEPTED MANUSCRIPT
8 (88.9)
1 (11.1)
65 (69.9) 33 (64.7) 8 (88.9)
28 (30.1) 18 (35.3) 1 (21.1)
0.21 (0.03-1.72) 0.35 (0.15-0.79)
0.145 0.012
0.24 (0.03-2.10) 0.38 (0.16-0.88)
0.195 0.0258)
1.0 1.27 (0.61-2.62) 0.29 (0.04-2.43) 0.27 (0.03-2.19)
0.524 0.254 0.219
1.0 1.32 (0.63-2.77) 0.27 (0.03-2.16) 0.22 (0.03-1.94)
0.467 0.206 0.174
1.0 1.78 (0.86-3.68) 1.87 (0.30-11.8) 1.79 (0.88-3.62)
0.121 0.505 0.108
1.0 1.72 (0.81-3.63) 2.06 (0.28-15.3) 1.74 (0.84-3.63)
0.158 0.479 0.139
1.0 1.73 (0.80-3.73) 0.67 (0.17-2.56) 1.87 (0.90-3.88)
0.165 0.554 0.092
26 (26.3) 19 (38.8) 2 (40.0)
46 (74.2) 47 (63.5) 13 (76.5)
16 (25.8) 27 (36.5) 4 (23.5)
1.0 1.65 (0.79-3.46) 0.89 (0.25-3.11) 1.70 (0.85-3.39)
EP
TE D
0.321
M AN U
73 (73.7) 30 (61.2) 3 (60.0)
SC
0.083
RI PT
0.342
AC C
TT CC vs CT/TT TP73 Ex2+4G>A GG GA AA GG/GA vs AA Drug resistance gene MRP2 G40A GG AG AA GG vs AG/AA GSTP1 Ex5-24A>G AA AG GG AA/GG vs AG
0.184 0.848 0.136
ACCEPTED MANUSCRIPT
Table 5. Risk estimates and 95% confidence intervals for the best multi-SNP score models Outcome Regression Model type SNPs for Score HR/OR (95% CI) a
P
Cox Cox
1-SNP 2-SNP
TREX1 rs11797 MRP2 rs2273697, MLH1 rs2286940
2.77 (1.26-6.11) 2.06 (1.35-3.16)b
0.011 0.0009
Toxicity
Logistic
2-SNP
EXO1 rs9350, CDA rs1048977
2.47 (1.4-4.35)c
0.0018
a
OS (overall survival) adjusted for progression free survival. PFS (progression free survival) adjusted for age and number of chemotherapy lines. c Toxicity adjusted for PFS and number of chemotherapy lines.
AC C
EP
TE D
M AN U
SC
b
RI PT
OS PFS
ACCEPTED MANUSCRIPT
FIGURE LEGEND
Figure 1. Kaplan Meier plot of overall survival (panel A) and progression free survival (panel B)
RI PT
by risk score in the entire study population. The number 0 or 1 in panel A indicates the absence or presence of the at-risk TT genotype of TREX1 Ex14-460 (rs11797), respectively.
The
number 0-2 in panel B indicates the number of at-risk genotypes of MRP2 Ex10 +40 GG/GA
AC C
EP
TE D
M AN U
curve and their 95% confidence intervals.
SC
and MLH1 IVS12-169 TT genotypes. The three lines for each genotype represent the survival
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
HIGHLIGHTS: •
This prospective study shows that polymorphic variants of enzymes involved in gemcitabine metabolism, DNA damage repair and multidrug resistance pathways are
lymphoma
or
myeloma
receiving
gemcitabine/busulfan/melphalan.
high-dose
chemotherapy
with
EP
TE D
M AN U
individualized high-dose cancer therapy.
SC
These findings suggest the value of pharmacogenomics in stratifying patients for
AC C
•
RI PT
associated with outcome and severe toxicity in patients with refractory/relapsed
