Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: Practical biomarkers and robust multiplex models

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Recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact, but consequent recurrence or relapse still often occurs. Discovery of therapeutic response predictor to ddTCip therapy is eagerly awaited to improve current treatment strategy for disseminated ovarian cancer patients. Methods: Using datasets in 76 participants in this study and 189 patients published in The Cancer Genome Atlas, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping analyses with progression-free survival (PFS)and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm. Results: The association analyses revealed that SPINK1 expression could be a potent biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex prediction models for adverse events: CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes. Conclusion: Our proposed biomarkers and multiplex models composed here would work well in the response prediction of ddTCip therapy, which might contribute to realize optimal selection of the key therapy. Predictive biomarker of therapeutic response Personalized medicine Ovarian cancer Intraperitoneal carboplatin plus intravenous dose-dense paclitaxel (ddTCip) therapy First-line chemotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Metastatic ovarian cancer is still a challenging disease to treat [1-3]. The therapeutic impact of molecular and immune-targeting therapy is still limited in the disseminated cases. The mainstay of the current treatment remains debulking surgery with pre- and postoperative adjuvant platinum-based chemotherapy, largely combined with taxanes. The combination therapy is certainly effective as the first line chemotherapy. Furthermore, recent reports demonstrated the long-term survival benefit of its novel modality such as intraperitoneal chemotherapy with or without hyperthermia in patients undergoing interval cytoreductive surgery [4-6]. Even so, the response still varies among patients and consequent recurrence, or relapse often occurs. With an increase in the treatment options, the importance of optimal selection of the first-line chemotherapy is increasing in clinical practice. Despite of enormous effort, however, current chemotherapeutic algorithm is still far from the personalized medicine [7-23]. These lead us to conduct this theranostic study to identify truly active predictive biomarkers of the efficacy and toxicity, venturing to focus on the key first-line chemotherapy, intraperitoneal (IP) carboplatin in combination with dose-dense paclitaxel (ddTCip) [4, 5]. Here, we first examined previously reported biomarkers (41 single marker genes and 3 multiplex predictive models for therapeutic efficacy and 31 polymorphisms for toxicity) and then sought out more powerful markers and developed multiplex prediction models through a genome-wide screening study in 76 patients and integrated trans-OMICS data analyses using a large-scale public database. Materials And Methods Patients and tissues This theranostic study was performed in association with our phase II study on ddTCip therapy [4]. Between March 2009 and March 2012, a total of 117 patients with epithelial ovarian cancer or primary peritoneal cancer, FIGO stage II–IV, were considered eligible for the phase II study. Among 76 patients met the inclusion criteria and were enrolled. All patients had macroscopically residual peritoneal tumors after the initial debulking surgery and received weekly intravenous administration of paclitaxel (TXL) with every 3-week intraperitoneal bolus infusion of carboplatin (ddTCip therapy) as postoperative adjuvant chemotherapy after noncurative operations. For this theranostic study, fresh tumor specimens and peripheral blood samples were collected from all patients at surgery and stored respectively at -80˚C and -20°C (after separation to blood components) until use. Ethics Committees at Gunma University, Saitama Medical University, Tottori University School of Medicine, Iwate Medical University and Jichi Medical University approved this research protocol, which was registered at UMIN Clinical Trials Registry (ID: UMIN000001713) on Feb 16 th , 2009. All patients provided written informed consent, including donation of their blood and surgical specimens. Extraction and purification of total RNA Total RNA was extracted from ovarian cancer tissues using an RNA Nucleospin RNA II kit® (Macherey–Nagel, Düren, Germany) according to the manufacturer’s protocols. For microarray analysis, the quality of total RNA was examined using an RNA 6000 Nano Kit and Bioanalyzer 2100® (Agilent Technologies, Santa Clara, CA) to assign an RNA integrity number (RIN). Only RNA samples showing an RIN score>= 7.1 were used for further analyses. Real-time RT-PCR (reverse transcription polymerase chain reaction) One microgram of total RNA was converted to cDNA using ReverTra Ace® (Toyobo, Osaka, Japan) with random primers (9 mer) according to the manufacturer’s instructions. Primer and probe set for each gene other than ACTB (actin beta gene) were designed using Probe Finder software in the Universal Probe Library (UPL) Assay Design Center (Roche Applied Science, Mannheim, Germany) ( Supplementary Table S1) . For the ACTB , predeveloped TaqMan assay reagents were purchased from Applied Biosystems (Waltham, MA). Each reaction was conducted in triplicate using qPCR QuickGoldStar Mastermix Plus reagent® (Eurogentec, Liège, Belgium) and a LightCycler 480 II system® (Roche). The relative expression levels of each gene were calculated as a ratio to the geometric mean of the ACTB expression levels. DNA microarray analysis Quality-checked total RNAs (0.5 μg) successfully obtained from 55 patients were reverse transcribed to first-strand cDNA using Moloney Murine Leukemia Virus reverse transcriptase and T7 primer, and the Cy3-labeled cRNAs were generated by the Quick Amp Labeling Kit One-Color® (Agilent Technologies). The labelled cRNA was purified by RNeasy Kit® (Qiagen, Valencia, CA), and a total of 1.65 μg of cRNA was hybridized to Whole Human Genome Oligo 4×44K (Agilent Technologies) according to the manufacturer’s recommendations. The microarrays were scanned using an Agilent DNA Microarray Scanner® and analyzed with Agilent Feature Extraction software version 9.5® (Agilent Technologies). Expression levels were normalized to the 75th percentile expression value of the entire spot using GeneSpring GX® (Agilent Technologies). DNA extraction and genotyping Genomic DNA was extracted from a stored buffy coat from peripheral blood (7 mL) using a NucleoSpin Tissue kit® (Macherey-Nagel) following the manufacturer’s instructions. Single nucleotide polymorphisms (SNPs) in ABCB1 (ATP Binding Cassette Subfamily B Member 1 gene), ABCC1 (ATP Binding Cassette Subfamily C Member 1 gene), ABCC2 (ATP Binding Cassette Subfamily C Member 2 gene), CYP1B1 (Cytochrome P450 Family 1 Subfamily B Member 1 gene), CYP2C8 (Cytochrome P450 Family 2 Subfamily C Member 8 gene), CYP3A4 (Cytochrome P450 Family 3 Subfamily A Member 4 gene), CYP3A5 (Cytochrome P450 Family 3 Subfamily A Member 5 gene), ERCC1 (Excision Repair Cross-Complementation Group 1 gene), ERCC2 (Excision Repair Cross-Complementation Group 2 gene), GSTP1 (Glutathione S-Transferase Pi 1 gene), UGT1A1 (UDP Glucuronosyltransferase 1 family, polypeptide A1 gene), XRCC1 (X-ray Repair Cross Complementing 1 gene), and XRCC3 (X-Ray Repair Cross Complementing 3 gene) were determined using TaqMan Drug Metabolism Genotyping Assays® or TaqMan SNP Genotyping Assays® (Life Technologies Co., Carlsbad, CA,), LightCycler 480 Probes Master® Roche GSTM1 (glutathione S-transferase M1 gene) and GSTT1 (glutathione S-transferase theta 1 gene) null polymorphisms were determined by PCR amplification followed by 2% agarose gel electrophoresis as essentially described by Medeiros R. et al. [7, 19]. The primer sequences used are listed in Supplementary Table S2 . Statistical Analysis PFS (progression-free survival, day) was defined as the time interval between registration and progression or death, whichever occurred first, or the last follow-up for patients alive without progression, and used as an efficacy indicator (n=76). Linear regression analyses between z-scored gene expression value and logarithmic PFS were adopted to seek potent predictive markers for ddTCip-induced efficacy, using datasets in this prospective study (ddTCip cohort) and 189 cases published in The Cancer Genome Atlas (TCGA, https://gdc.cancer.gov/about-data/publications/ov_2011) (TCGA cohort) [13]. PFS curves were estimated using the Kaplan-Meier method, while the Cox proportional hazards model was used for the survival analysis. To construct a prediction formula, multiple regression analysis following the forward stepwise method was performed. Toxicities were analyzed in 76 patients who received at least one dose of chemotherapy. Pharmacogenomic analyses were performed in 73 patients excluding 3 patients (2 had acute hypersensitivity reactions and 1 was not assessable in genotyping). Adverse effects were determined using the National Cancer Institute Common Terminology Criteria for Adverse Effects, version 3.0, as with the clinical phase II study. Due to the limited number of cases, anorexia, nausea, vomiting, constipation, and diarrhea were combined into digestive symptoms. The association of a single genotype with toxicity was validated by using Fisher’s exact test. To construct the prediction model for toxicity, we applied the Classification and Regression Trees (CART) algorithm using the R packages r part (version 4.1.1) and party kit (version 1.2-16) and multiple logistic regression analysis through the following steps: 1) logistic regression analysis using data sets when the number of event occurrence cases was >= 10 and 2) evaluation by using the Akaike information criterion (AIC) with the addition of one variable at a time. When the AIC was improved, the variable was incorporated into the prediction model. 3) Calculation of the VIF (Variance Inflation Factor) for collinearity of the variables by using the rms R package (version 6.2-0). When the VIF was more than 10, the variable was removed from the prediction model. 4) Evaluation of the odds ratio of the alternative allele to the reference allele for each variable, the AUC (area under the curve) by using the pROC R package (version 1.18.0), and positive discrimination rate (PDR) of the obtained model to estimate the predictivity of the adverse events. All statistical analyses were conducted using R software version 3.3.2. Functional enrichment analysis was performed by Ingenuity Pathway Analysis® (IPA version 2.3; QIAGEN, Germany). Results Previously suggested single markers in the prediction of therapeutic efficacy We first examined a total of 41 genes whose clinical and functional significance as drug sensitivity determinants of platinum and/or paclitaxel chemotherapy have been demonstrated in two or more reports on the National Library of Medicine’s PubMed (https://pubmed.ncbi.nlm.nih.gov/) and/or in our previous studies [8, 23]. The correlation analysis of the expression levels with fixed PFS in 64 non-censored ddTCip cohort and the validation study using 189 data sets published in TCGA indicated 4 highly correlated genes:serine peptidase inhibitor Kazal type 1 gene( SPINK1 ), troponin T3, fast skeletal type gene( TNNT3 ), interferon regulatory factor 9 gene ( IRF9 ) and ABCC2 ( Table 1, Fig. 1 ). Nevertheless , for IRF9 , the observed correlation slopes with PFS were directly opposite in the 2 cohort cases. Kaplan-Meier analyses in all 76 ddTCip patients including censored cases and 189 TCGA cases and demonstrated that the positive expression of SPINK1 alone signified worse PFS in both cohorts when tumors with greater than the mean value of the expression were defined as high expressors ( Fig. 2 ). Although the number of positive expression cases in the ddTCip cohort was very few (n=2), the suggested results were confirmed also when using TCGA datasets. Development of a multiplex prediction model for PFS Drug resistance is multifactorial. We, therefore, previously proposed 3 multiplex prediction models using the expression data of selected key marker genes (Formulae A, B, and C) with high fitness [7, 23], but their expected predictivities were not confirmed in this study: Formula C alone showed apparent high fitness, but the correlation slope observed in 76 ddTCip patients was opposite to that in 189 TCGA cases ( Supplementary Fig. S1 ). To develop a truly active prediction model, we sought to identify genes highly correlated with PFS genome-wide using DNA microarray expression data. The correlation analysis in the ddTCip cohort yielded a total of 61 correlative genes (p<0.05; Supplementary Table S3 ), which included ABCC2 and TNNT3 but did not contain SPINK1 (p=0.094). The functional enrichment analyses revealed that they were mostly classified into cancer-related genes ( Supplementary Table S4 ). Among 22 genes showed significant correlations of their expression levels with those in the protein (p<0.05, Pearson test) ( Supplementary Table S5 ). Multiple regression analysis on the 61 genes provided a novel prediction model (Formula-1) with the highest fitness (R 2 =0.76, p=7.5e-11), which was composed of expression data of 19 genes whose expression levels would most correctly explain the value of therapeutic efficacy, PFS ( Fig. 3 ). The predictivity was confirmed also when using TCGA datasets (R 2 =0.06, p=4.0e-4). Multiple regression analysis with the forward stepwise method using their expressions finally selected 5 genes as the most effective variables from the 22 genes: cullin 1 gene ( CUL1 ), solute carrier family 5 member 1 gene( SLC5A1 ),glycerol-3-phosphate dehydrogenase 1 gene ( GPD1 ), phosphodiesterase 3A gene ( PDE3A ) and VANGL planar cell polarity protein 1 gene( VANGL1 ). This led us to another potent prediction formula, Formula-2 (R 2 =0.54, p=46.5e-64) with showing higher predictive accuracy for PFS (R 2 =0.10, p=46.22e-9) than Formula-1 in the TCGA cohort. In the case of using expression data of the 22 genes, the random forest method with leave-one-out cross validation demonstrated that expression of Krev interaction trapped protein 1, ankyrin repeat containing gene ( KRIT1 ) was the most important variable [ Supplementary Fig. S2(A) ] in the constructed prediction model for PFS. Nevertheless, the expected predictivity of the model was inferior to those provided in the multiple regression analysis [ Supplementary Fig. S2(B), (C) ]. Previously suggested toxicity marker In the ddTCip cohort, 75 of 76 enrolled patients (98.7%) experienced grade 3/4 adverse events [4]. Hematological toxicities were the most common adverse events, and problematic grade 3 peripheral neuropathy was observed in 8 patients (10.5%). We investigated 31 polymorphisms of 15 genes known as potent toxicity markers for taxane and/or platinum therapy ( Supplementary Table S2 ). Genotype-toxicity association analysis revealed that ABCB1 rs1045642_A>G and ERCC1 rs11615_A>G could be potent single toxicity indicators of ddTCip therapy. ABCB1 rs1045642_A>G variant was significantly associated with grade 3/4 neutro- (p<0.0495) and thrombo-cytopenia (pG variant (p=0.0090) was closely related to peripheral sensory neuropathy in ddTCip cases [ Supplementary Fig. S3(A )]. A public resource of The Genotype-Tissue Expression project [GTEx (V8)] indicated that these 2 SNPs might cause a decrease in the expression of each gene in several tissues, such as testis and tibial nerve tissue (https://gtexportal.org/home/) [ Supplementary Fig. S3(B), (C )]. Multiplex prediction model for adverse events As with the efficacy, we approached the development of a multiplex prediction model for adverse events. The CART method indicated that the model using a total of 4 combinations of genotypes could predict the occurrence of 4 serious adverse events induced by ddTCip therapy. The genotypes used in the prediction model were, 1) ABCB1 (rs20232582_A>C/T and rs3213619_A>G) and XRCC1 rs25487_T>C for grade 3/4 white blood cells decreased (PDR, 69.803; AUC, 0.702), 2) ABCB1 rs1045642_A>G for grade 3/4 platelet count decreased (PDR, 83.562; AUC, 0.749), 3) GSTT1 null and ABCB1 rs203282_A>C/T for grade 3/4 anemia (PDR, 73.973; AUC, 0.711, and 4) XRCC1 rs25487_T>C, UGT1A1 rs4148323_G>A and ABCB1 rs2032582_A>C/T for grade 2-4 peripheral sensory neuropathy (PDR, 78.082; AUC, 0723) ( Fig. 4 ). Multiple logistic regression analysis also provided novel potent multiplex prediction models composed of the combinations of a total of 15 SNPs on 12 genes for 8 serious adverse events ( Table 2 ). These models demonstrated high predictivity for grade 3/4 neutrophile count decrease (PDR, 89.041; AUC, 0.803), grade 3/4 lymphocyte count decrease (PDR, 87.671; AUC, 0.762), grade 2-4 digestive symptoms (PDF, 68.493; AUC, 0.728), and grade 2-4 peripheral motor neuropathy (PDR, 82.192; AUC, 0.797), but the predictivity for grade 3/4 white blood cell count decrease was inferior to that of the CART models (PDR, 49.315; AUC, 0.586). Discussion The unmet medical needs ofadvanced or metastatic ovarian cancer patients remain high. This theranostic study suggests potent biomarkers and multiplex model in the prediction of individual response to a most active front-line chemotherapy, ddTCip. As a single predictive biomarker for efficacy, we first proposed high SPINK1 expression for efficacy (PFS). SPINK1 encodes pancreatic secretory trypsin inhibitor, which is secreted from pancreatic acinar cells into pancreatic juice and drives ovarian cancer cell proliferation through activation of EGFR or IL-6 signaling [24, 25]. Although its biological roles in cancers remain unclear [26], recent reports have suggested that SPINK1 promotes recurrence risk in prostate and breast cancers [27]. Current attention has also been focused on the role of SPINK1 as an attractive novel therapeutic target of cancer [28]. The suppression of a drug resistance determinant, SPINK1, may contribute not only to enhancing the therapeutic activity of the ddTCip regimen but also to preventing disease recurrence. Despite rapid progress in omics, genetic polymorphisms of drug metabolizing enzymes, drug transporters, and DNA repair enzymes are still the mainstay in toxicity prediction [29, 30]. Among we confirmed two genetic variations as a single toxicity predictor for ddTCip therapy: ABCB1 rs1045642_A>G for grade 3/4 bone marrow toxicity (neutro- and thrombo-cytopenia), and ERCC1 rs11615_A>G for the occurrence of peripheral sensory neuropathy. The relevance of ABCB1 rs1045642_A>Gand ERCC1 rs11615_A>G to the toxicity of platinum, taxane, and the combination has been reported in a variety of studies [22, 31, 32]. We also demonstrated that both SNPs might reduce the corresponding gene expression in normal tissues, which would cause prolonged drug retention in bone marrow and continuous inhibition of DNA repair in the nervous system. Particularly important in the current study is the successful construction of multiplex prediction models. A comprehensive molecular view is necessary for understanding the intricate drug response mechanisms. For the efficacy prediction, our efforts first selected 22 potent marker genes and finally yielded a putative multiplex model using expression data of 5 genes- CUL1 , SLC5A1 , GPD1 , PDE3A and VANGL1- . Dynamic proteins transcriptionally regulated in response to nutrient demand or other perturbations are more highly correlated with mRNA in the expression levels in ovarian cancer specimens [33]. In fact, the selected 22 genes are shown to be mostly involved in the pathways of “Carbohydrate Metabolism” and “Lipid metabolism”. Interestingly, all 5 genes finally selected have been proven to play a significant role in cancers: CUL1 and VANGL1 are known as oncogenes [34-36] while GPD1 acts as a tumor suppressor [37, 38]. Among PDE3A and SLC5A1 are well recognized as valuable therapeutic targets of cancers [39-43]. For toxicity, CART analyses demonstrated that 4 combinations of genetic variants in 4 genes could predict the occurrence of 4 serious adverse events: white blood cell count decrease, platelet count decrease, anemia, and peripheral sensory neuropathy. Multiple logistic regression analyses provided another potent multiplex prediction model composed of the combinations of a total of 15 genetic variations in 12 genes for 8 serious adverse events, including not only bone marrow suppression but also digestive symptoms and peripheral motor neuropathy. All prediction models demonstrated high PDRs and AUC values sufficient for toxicity prediction. Although the most active modality is still controversial, the fact remains that the paclitaxel plus carboplatin (TC) regimen is an essential treatment in patients with intractable advanced ovarian cancers [44, 45]. Our proposed prediction models might contribute to improve the therapeutic outcome through the selection of suitable patients for treatment. Even so, interaction of the suggested markers in the prediction models remains unknown. The prediction of the long-term overall survival is also our interest. For toxicity prediction, ethnic differences should be integrated into utility assessments [46]. We are now planning a larger-scale prospective clinical study to confirm the clinical significance of the predictive markers and prediction models along with continuing our search for the functional roles of the selected markers and their interactions in drug sensitivity and toxicity. Conclusion This theranostic study on a promising first-line chemotherapy, ddTCip therapy, demonstrated several potent single biomarkers: SPINK1 expression for efficacy and ABCB1 and ERCC1 polymorphisms for toxicity. We further successfully composed robust multiplex prediction models for individual responses. 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Linear regression analysis: Correlation of the expression with progression free survival (PFS) in 41 genes previously reported as potent efficacy markers of taxane and/or platinum therapy. Gene Description ddTCip cohort * non-censored 64 cases TCGA cohort ** non-censored 189 cases Adj_R 2*** slope p **** Adj_R2 slope p SPINK1 serine peptidase inhibitor Kazal type 1 0.104 -0.665 0.0055 0.026 -0.132 0.016 TNNT3 troponin T3, fast skeletal type 0.085 -23.527 0.011 0.021 -0.316 0.025 IL6 interleukin 6 0.080 -2.846 0.013 -0.004 -0.018 0.64 IRF9 interferon regulatory factor 9 0.074 -8.436 0.017 0.015 0.154 0.049 TUBB3 tubulin beta 3 class III 0.063 -7.383 0.026 -0.005 -0.025 0.73 ABCC2 ATP binding cassette subfamily C member 2 0.049 -74.998 0.044 0.048 -0.240 0.0014 FOXO1 forkhead box O1 0.035 -2.109 0.076 -0.002 -0.048 0.42 ERCC2 ERCC excision repair 2, TFIIH core complex helicase subunit 0.017 -6.609 0.153 -0.005 -0.009 0.93 NDE1 nudE neurodevelopment protein 1 0.016 -1.925 0.159 0.001 -0.114 0.27 ATP7B ATPase copper transporting beta 0.016 -66.085 0.162 -0.005 0.003 0.96 HS3ST4 heparan sulfate-glucosamine 3-sulfotransferase 4 0.015 -321.906 0.170 #N/A #N/A #N/A PRSS11 protease serine 11/HtrA serine peptidase 1 (HTRA1) 0.012 -0.455 0.192 0.008 0.083 0.11 VEGF vascular endothelial growth factor A 0.009 4.823 0.218 0.000 -0.069 0.32 JOSD2 Josephin domain containing 2 0.008 -30.042 0.228 #N/A #N/A #N/A XRCC1 X-ray repair cross complementing 1 0.000 -0.835 0.314 -0.003 0.053 0.55 ABCB1 ATP binding cassette subfamily B member 1 0.000 -75.564 0.322 0.005 0.113 0.17 TTC27 tetratricopeptide repeat domain 27 -0.001 -4.740 0.343 -0.002 -0.071 0.46 MYO5C myosin VC -0.003 12.686 0.363 -0.005 0.008 0.89 CSAG2 CSAG family member 2 -0.004 23.111 0.382 #N/A #N/A #N/A MTA1 metastasis associated 1 -0.006 -0.842 0.444 -0.005 0.007 0.94 PHF20 PHD finger protein 20 -0.007 -0.577 0.451 -0.004 -0.074 0.56 ERCC1 ERCC excision repair 1, endonuclease non-catalytic subunit -0.007 -0.159 0.464 -0.004 -0.046 0.60 DISP1 dispatched RND transporter family member 1 -0.007 -40.636 0.468 #N/A #N/A #N/A PLEK2 pleckstrin 2 -0.008 12.035 0.476 0.007 -0.110 0.13 IFIT3 interferon induced protein with tetratricopeptide repeats 3 -0.009 0.377 0.500 #N/A #N/A #N/A HSPC244 transmembrane protein 216 (TMEM216) -0.009 1.001 0.503 #N/A #N/A #N/A DDX58 DExD/H-box helicase 58 -0.010 -1.747 0.536 0.009 0.076 0.10 CISD1 CDGSH iron sulfur domain 1 -0.010 -0.311 0.555 -0.005 -0.022 0.79 SLC25A39 solute carrier family 25 member 39 -0.011 -3.054 0.589 #N/A #N/A #N/A TNFSF13B TNF superfamily member 13b -0.014 -1.663 0.690 #N/A #N/A #N/A ATP7A ATPase copper transporting alpha -0.014 3.351 0.710 0.000 0.076 0.31 BCL2 BCL2 apoptosis regulator -0.014 -1.016 0.724 0.000 0.074 0.31 NELL2 neural EGFL like 2 -0.014 4.332 0.743 0.004 0.054 0.18 HSPB9 heat shock protein family B (small) member 9 -0.014 5.224 0.746 #N/A #N/A #N/A PPFIA1 PTPRF interacting protein alpha 1 -0.015 0.748 0.791 -0.005 0.015 0.86 SCARB2 scavenger receptor class B member 2 -0.015 -0.046 0.840 -0.005 0.020 0.77 ARMCX3 armadillo repeat containing X-linked 3 -0.015 -1.392 0.843 0.011 0.122 0.083 BTN3A2 butyrophilin subfamily 3 member A2 -0.016 1.938 0.859 0.000 0.058 0.31 AURKA aurora kinase A -0.016 -2.136 0.901 -0.005 0.007 0.92 GBP1 guanylate binding protein 1 -0.016 -0.094 0.916 -0.002 0.036 0.42 ATP5G1 ATP synthase membrane subunit c locus 1 -0.016 0.046 0.928 -0.003 -0.046 0.48 *ddTCip cohort, ovarian cancer patients who were enrolled in this theranostic study and received intraperitoneal carboplatin plus intravenous dose-dense paclitaxel; **TCGA cohort, published data sets in The Cancer Genome Atlas (ovarian cancer patients who received taxane and/or platinum therapy); ***Adj_R2, adjusted R (regression coefficient)-squared; **** p, p-value for the slope; #N/A, not analyzed Table 2. Odds ratios of the adverse event occurrence in multiplex prediction models constructed by multiple logistic regression analysis using genetic polymorphism data Gene Polymorphism ID Genotype Odds ratio White blood cell decreased (grade 3-4) Neutrophil count decreased (grade 3-4) Lymphocyte count decreased (grade 3-4) Platelet count decreased (grade 3-4) Anemia (grade 3-4) Digestive symptom (grade 2-4) Peripheral motor neuropathy (grade 2-4) Peripheral sensory neuropathy (grade 2-4) ABCB1 rs1045642 A/G 0.006*** ABCB1 rs1045642 G/G 0.098** 0.006*** 4.817** ABCB1 rs1128503 A/G 0.253* ABCB1 rs2032582 A/T 18.972** 5.671** ABCB1 rs2032582 A/C 2.934 ABCB1 rs2032582 C/T 5.881** ABCC1 rs4148356 A/A 62.449* ABCC2 rs3740066 C/T 0.217* ABCC2 rs3740066 T/T 11.019 23.599* CYP1B1 rs1056836 G/C 3.883* CYP3A5 rs776746 T/C 3.02 0.157** CYP3A5 rs776746 C/C 0.134 3.91 ERCC1 rs3212986 C/A 0.112** 0.083** ERCC1 rs3212986 A/A 20.158** ERCC2 rs13181 T/G 5.547 GSTP1 rs1695 A/G 0.114* 0.273* 5.101** GSTP1 rs1695 G/G 0.054* GSTT1 Null Null 0.078*** UGT1A1 rs4148323 G/A 0.116*** XRCC1 rs1799782 G/A 0.225 XRCC1 rs25487 C/C 3.713** 6.653** XRCC3 rs861539 G/A 7.072** Sum of response 48 63 13 17 42 43 14 21 Positive discrimination rate (%) 49.315 89.041 87.671 89.041 72.603 68.493 82.192 78.082 Area under the curve 0.586 0.803 0.762 0.914 0.784 0.728 0.797 0.829 ***, p<0.01; **, 0.01<=p<0.05; *, 0.05<=p<0.10 Supplementary Files SupplementaryFig.S1.pptx SupplementaryFig.S2.pptx SupplementaryFig.S3.pptx SupplementaryTableS1.xlsx SupplementaryTableS2.xlsx SupplementaryTableS3.xlsx SupplementaryTableS4.xlsx SupplementaryTableS5.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 23 Jan, 2024 Reviewers agreed at journal 29 Dec, 2023 Reviewers invited by journal 29 Dec, 2023 Editor assigned by journal 29 Dec, 2023 First submitted to journal 27 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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models","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetastatic ovarian cancer is still a challenging disease to treat [1-3]. The therapeutic impact of molecular and immune-targeting therapy is still limited in the disseminated cases. The mainstay of the current treatment remains debulking surgery with pre- and\u0026nbsp;postoperative\u0026nbsp;adjuvant platinum-based chemotherapy, largely combined with taxanes. The combination therapy is certainly effective as the first line chemotherapy. Furthermore, recent reports demonstrated the long-term survival benefit of its novel modality such as intraperitoneal chemotherapy with or without hyperthermia in patients undergoing interval cytoreductive surgery [4-6]. Even so, the response still varies among patients and consequent recurrence, or relapse often occurs. With an increase in the treatment options, the importance of optimal selection of the first-line chemotherapy is increasing in clinical practice. Despite of enormous effort, however, current chemotherapeutic algorithm is still far from the personalized medicine [7-23].\u003c/p\u003e\n\u003cp\u003eThese lead us to conduct this theranostic study to identify truly active predictive biomarkers of the efficacy and toxicity, venturing to focus on the key first-line chemotherapy, intraperitoneal (IP) carboplatin in combination with dose-dense paclitaxel (ddTCip) [4, 5]. Here, we first examined previously reported biomarkers (41 single marker genes and 3 multiplex predictive models for therapeutic efficacy and 31 polymorphisms for toxicity) and then sought out more powerful markers and developed multiplex prediction models through a genome-wide screening study in 76 patients and integrated trans-OMICS data analyses using a large-scale public database.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003e\u003cstrong\u003ePatients and tissues\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis theranostic study was performed in association with our phase II study on ddTCip therapy [4]. Between March 2009 and March 2012, a total of 117 patients with epithelial ovarian cancer or primary peritoneal cancer, FIGO stage II\u0026ndash;IV, were considered eligible for the phase II study. Among 76 patients met the inclusion criteria and were enrolled. All patients had macroscopically residual peritoneal tumors after the initial debulking surgery and received weekly intravenous administration of paclitaxel (TXL) with every 3-week\u0026nbsp;intraperitoneal bolus infusion of carboplatin (ddTCip therapy) as postoperative adjuvant chemotherapy after noncurative operations.\u003c/p\u003e\n\u003cp\u003eFor this theranostic study, fresh tumor specimens and peripheral blood samples were collected from all patients at surgery and stored respectively at -80˚C and -20\u0026deg;C\u0026nbsp;(after separation to blood\u0026nbsp;components) until use. Ethics Committees at Gunma University, Saitama Medical University, Tottori University School of Medicine, Iwate Medical University and Jichi Medical University approved this research protocol, which was registered at UMIN Clinical Trials Registry (ID: UMIN000001713) on Feb 16\u003csup\u003eth\u003c/sup\u003e, 2009. All patients provided written informed consent,\u0026nbsp;including donation of their blood and surgical specimens.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eExtraction and purification of total RNA\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTotal RNA was extracted from ovarian cancer tissues using\u0026nbsp;an\u0026nbsp;RNA Nucleospin RNA II kit\u0026reg; (Macherey\u0026ndash;Nagel, D\u0026uuml;ren, Germany) according to the manufacturer\u0026rsquo;s protocols. For microarray analysis, the quality of total RNA was examined using\u0026nbsp;an\u0026nbsp;RNA 6000 Nano Kit and Bioanalyzer 2100\u0026reg; (Agilent Technologies, Santa Clara, CA) to assign an RNA integrity number (RIN). Only RNA samples showing\u0026nbsp;an\u0026nbsp;RIN score\u0026gt;= 7.1 were used for further analyses.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eReal-time RT-PCR (reverse transcription polymerase chain reaction)\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eOne\u0026nbsp;microgram\u0026nbsp;of total RNA was converted to cDNA using ReverTra Ace\u0026reg; (Toyobo, Osaka, Japan) with random\u0026nbsp;primers\u0026nbsp;(9 mer) according to the manufacturer\u0026rsquo;s instructions. Primer and probe\u0026nbsp;set\u0026nbsp;for each gene other than \u003cem\u003eACTB\u003c/em\u003e (actin beta gene) were designed using Probe Finder software in the Universal Probe Library (UPL) Assay Design Center (Roche Applied Science, Mannheim, Germany) (\u003cstrong\u003eSupplementary Table S1)\u003c/strong\u003e. For\u0026nbsp;the\u0026nbsp;\u003cem\u003eACTB\u003c/em\u003e,\u0026nbsp;predeveloped\u0026nbsp;TaqMan\u0026nbsp;assay reagents\u0026nbsp;were purchased from Applied Biosystems (Waltham, MA). Each reaction was conducted in triplicate using qPCR QuickGoldStar Mastermix Plus reagent\u0026reg; (Eurogentec, Li\u0026egrave;ge, Belgium) and\u0026nbsp;a\u0026nbsp;LightCycler 480 II system\u0026reg; (Roche). The relative expression levels of each gene were calculated as a ratio to\u0026nbsp;the\u0026nbsp;geometric mean of the \u003cem\u003eACTB\u003c/em\u003e expression levels.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDNA microarray analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eQuality-checked total RNAs (0.5 \u0026mu;g) successfully obtained from 55 patients were reverse transcribed to first-strand cDNA using Moloney Murine Leukemia Virus reverse transcriptase and T7 primer, and the Cy3-labeled\u0026nbsp;cRNAs were generated by the Quick Amp Labeling Kit One-Color\u0026reg;\u0026nbsp;(Agilent Technologies). The labelled cRNA was purified by\u0026nbsp;RNeasy Kit\u0026reg;\u0026nbsp;(Qiagen, Valencia, CA), and a total of 1.65 \u0026mu;g of cRNA was hybridized to Whole Human Genome Oligo 4\u0026times;44K (Agilent Technologies) according to the manufacturer\u0026rsquo;s\u0026nbsp;recommendations. The microarrays were scanned using an Agilent DNA Microarray Scanner\u0026reg;\u0026nbsp;and analyzed with Agilent Feature Extraction software version 9.5\u0026reg;\u0026nbsp;(Agilent Technologies). Expression levels were normalized to the 75th percentile expression value of the entire spot using GeneSpring GX\u0026reg;\u0026nbsp;(Agilent Technologies).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDNA extraction and genotyping\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eGenomic DNA was extracted from a stored buffy coat from peripheral blood (7 mL) using\u0026nbsp;a\u0026nbsp;NucleoSpin Tissue kit\u0026reg; (Macherey-Nagel) following the manufacturer\u0026rsquo;s\u0026nbsp;instructions. Single nucleotide polymorphisms (SNPs) in \u003cem\u003eABCB1\u0026nbsp;\u003c/em\u003e(ATP Binding Cassette Subfamily B Member 1 gene), \u003cem\u003eABCC1\u003c/em\u003e (ATP Binding Cassette Subfamily C Member 1 gene), \u003cem\u003eABCC2\u003c/em\u003e (ATP Binding Cassette Subfamily C Member 2 gene), \u003cem\u003eCYP1B1\u0026nbsp;\u003c/em\u003e(Cytochrome P450 Family 1 Subfamily B Member 1 gene), \u003cem\u003eCYP2C8\u0026nbsp;\u003c/em\u003e(Cytochrome P450 Family 2 Subfamily C Member 8 gene), \u003cem\u003eCYP3A4\u003c/em\u003e (Cytochrome P450 Family 3 Subfamily A Member 4 gene), \u003cem\u003eCYP3A5\u0026nbsp;\u003c/em\u003e(Cytochrome P450 Family 3 Subfamily A Member 5 gene), \u003cem\u003eERCC1\u0026nbsp;\u003c/em\u003e(Excision Repair Cross-Complementation Group 1\u0026nbsp;gene),\u0026nbsp;\u003cem\u003eERCC2\u003c/em\u003e (Excision\u0026nbsp;Repair Cross-Complementation Group 2\u0026nbsp;gene), \u003cem\u003eGSTP1\u003c/em\u003e (Glutathione S-Transferase Pi 1 gene), \u003cem\u003eUGT1A1\u003c/em\u003e (UDP Glucuronosyltransferase 1 family, polypeptide A1 gene), \u003cem\u003eXRCC1\u003c/em\u003e (X-ray\u0026nbsp;Repair Cross Complementing 1 gene), and \u003cem\u003eXRCC3\u003c/em\u003e (X-Ray Repair Cross Complementing 3 gene) were determined using TaqMan Drug Metabolism Genotyping Assays\u0026reg; or TaqMan SNP Genotyping Assays\u0026reg; (Life Technologies Co., Carlsbad, CA,), LightCycler 480 Probes Master\u0026reg; Roche\u0026nbsp;\u003cem\u003eGSTM1\u003c/em\u003e (glutathione\u0026nbsp;S-transferase M1 gene) and \u003cem\u003eGSTT1\u003c/em\u003e (glutathione S-transferase theta\u0026nbsp;1 gene) null polymorphisms were determined by PCR amplification followed by 2% agarose gel electrophoresis as essentially described by Medeiros R.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e [7, 19]. The primer sequences used are listed in \u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003ePFS (progression-free survival, day) was defined as the time interval between registration and progression or death, whichever occurred first, or the last follow-up for patients alive without progression, and used as an efficacy indicator (n=76). Linear regression analyses between z-scored gene expression value and logarithmic PFS were adopted to seek potent predictive markers for ddTCip-induced efficacy, using datasets in this prospective study (ddTCip cohort) and 189 cases published in The Cancer Genome Atlas (TCGA, https://gdc.cancer.gov/about-data/publications/ov_2011) (TCGA cohort) [13]. PFS curves were estimated using\u0026nbsp;the\u0026nbsp;Kaplan-Meier method, while\u0026nbsp;the\u0026nbsp;Cox proportional hazards model was used for the survival analysis. To construct a prediction formula, multiple regression analysis following\u0026nbsp;the\u0026nbsp;forward stepwise method was performed.\u003c/p\u003e\n\u003cp\u003eToxicities were analyzed in 76 patients who received at least one dose of chemotherapy. Pharmacogenomic analyses\u0026nbsp;were\u0026nbsp;performed in 73 patients excluding 3 patients (2 had acute hypersensitivity\u0026nbsp;reactions and 1 was\u0026nbsp;not assessable in\u0026nbsp;genotyping). Adverse effects were determined using\u0026nbsp;the\u0026nbsp;National Cancer Institute Common Terminology Criteria for Adverse Effects, version 3.0,\u0026nbsp;as with the clinical phase II study. Due to the limited number of cases, anorexia, nausea, vomiting, constipation, and diarrhea were combined into digestive\u0026nbsp;symptoms. The association of\u0026nbsp;a\u0026nbsp;single genotype with toxicity was validated by using Fisher\u0026rsquo;s exact test.\u0026nbsp;To construct the prediction model for toxicity, we applied the Classification and Regression Trees (CART) algorithm using the R packages r part (version 4.1.1) and party kit (version 1.2-16) and multiple logistic regression analysis\u0026nbsp;through the following steps:\u0026nbsp;1)\u0026nbsp;logistic\u0026nbsp;regression analysis using data sets when the number of event occurrence cases was \u0026gt;= 10\u0026nbsp;and\u0026nbsp;2)\u0026nbsp;evaluation\u0026nbsp;by using the Akaike information criterion (AIC) with\u0026nbsp;the addition of\u0026nbsp;one variable at a time. When the AIC was improved, the variable was incorporated into the prediction model. 3) Calculation of\u0026nbsp;the\u0026nbsp;VIF (Variance Inflation Factor) for collinearity of the variables by using\u0026nbsp;the\u0026nbsp;rms R package (version 6.2-0). When the VIF was more than 10, the variable was removed from the prediction model. 4) Evaluation of the odds ratio of the alternative allele to the reference allele for each variable, the AUC (area under the curve)\u0026nbsp;by using\u0026nbsp;the\u0026nbsp;pROC R package (version 1.18.0), and positive discrimination rate (PDR) of the obtained model to estimate the predictivity of the adverse events.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using R software version 3.3.2. Functional enrichment analysis was performed by Ingenuity Pathway Analysis\u0026reg; (IPA version 2.3; QIAGEN, Germany).\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003ePreviously suggested single markers in the prediction of therapeutic efficacy\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe first examined a total of 41 genes whose clinical and functional significance as\u0026nbsp;drug sensitivity determinants\u0026nbsp;of platinum and/or paclitaxel chemotherapy\u0026nbsp;have\u0026nbsp;been demonstrated in two or more reports on the National Library of Medicine\u0026rsquo;s PubMed (https://pubmed.ncbi.nlm.nih.gov/) and/or in our previous studies [8, 23]. The correlation analysis of the expression levels with fixed PFS in 64 non-censored ddTCip cohort and\u0026nbsp;the validation study using 189 data sets published in TCGA\u0026nbsp;indicated 4 highly\u0026nbsp;correlated\u0026nbsp;genes:serine peptidase inhibitor Kazal type\u0026nbsp;1 gene(\u003cem\u003eSPINK1\u003c/em\u003e),\u0026nbsp;troponin\u0026nbsp;T3,\u0026nbsp;fast skeletal type\u0026nbsp;gene(\u003cem\u003eTNNT3\u003c/em\u003e),\u0026nbsp;interferon regulatory factor\u0026nbsp;9 gene (\u003cem\u003eIRF9\u003c/em\u003e) and \u003cem\u003eABCC2\u003c/em\u003e (\u003cstrong\u003eTable 1, Fig. 1\u003c/strong\u003e). Nevertheless\u003cstrong\u003e,\u003c/strong\u003e for \u003cem\u003eIRF9\u003c/em\u003e, the observed correlation slopes with PFS were directly opposite in the 2 cohort cases.\u003c/p\u003e\n\u003cp\u003eKaplan-Meier analyses in all 76 ddTCip patients including censored\u0026nbsp;cases\u0026nbsp;and 189 TCGA cases and demonstrated that the positive expression of \u003cem\u003eSPINK1\u003c/em\u003e alone signified worse PFS in both cohorts when\u0026nbsp;tumors with greater than the mean value of the expression were defined as high expressors (\u003cstrong\u003eFig. 2\u003c/strong\u003e). Although the number of positive expression cases in\u0026nbsp;the\u0026nbsp;ddTCip cohort was very few (n=2), the suggested results were confirmed also when using TCGA datasets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;a\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;multiplex prediction model for PFS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrug resistance is multifactorial.\u0026nbsp;We, therefore, previously proposed 3 multiplex prediction models using\u0026nbsp;the\u0026nbsp;expression data of selected key marker genes (Formulae A, B, and C) with high fitness\u0026nbsp;[7, 23], but their expected predictivities were not confirmed in this study: Formula C alone showed apparent high fitness, but the correlation slope observed in\u0026nbsp;76 ddTCip patients was opposite to that in\u0026nbsp;189 TCGA cases (\u003cstrong\u003eSupplementary Fig. S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTo develop a truly active prediction model, we sought\u0026nbsp;to identify genes\u0026nbsp;highly\u0026nbsp;correlated\u0026nbsp;with PFS genome-wide using DNA microarray expression data. The correlation analysis\u0026nbsp;in\u0026nbsp;the\u0026nbsp;ddTCip cohort\u0026nbsp;yielded a total of 61 correlative genes (p\u0026lt;0.05; \u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eTable S3\u003c/strong\u003e), which included \u003cem\u003eABCC2\u003c/em\u003e and \u003cem\u003eTNNT3\u003c/em\u003e but did not contain\u0026nbsp;\u003cem\u003eSPINK1\u003c/em\u003e (p=0.094). The functional enrichment analyses revealed that they were mostly classified into cancer-related genes (\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eTable S4\u003c/strong\u003e). Among 22 genes showed significant correlations of their expression levels with those in the protein (p\u0026lt;0.05, Pearson test) (\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eTable S5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eMultiple regression analysis on the 61 genes provided a novel prediction model (Formula-1) with the highest fitness (R\u003csup\u003e2\u003c/sup\u003e=0.76, p=7.5e-11), which was composed of expression data of 19 genes whose expression levels would most correctly explain the value of therapeutic efficacy,\u0026nbsp;PFS\u0026nbsp;(\u003cstrong\u003eFig. 3\u003c/strong\u003e). The predictivity was confirmed also when using TCGA datasets (R\u003csup\u003e2\u003c/sup\u003e=0.06, p=4.0e-4). Multiple regression analysis with\u0026nbsp;the\u0026nbsp;forward\u0026nbsp;stepwise method using their expressions finally selected 5 genes as the most effective variables from the 22 genes:\u0026nbsp;cullin 1 gene (\u003cem\u003eCUL1\u003c/em\u003e), solute carrier family 5 member 1 gene(\u003cem\u003eSLC5A1\u003c/em\u003e),glycerol-3-phosphate dehydrogenase 1 gene (\u003cem\u003eGPD1\u003c/em\u003e), phosphodiesterase 3A gene (\u003cem\u003ePDE3A\u003c/em\u003e) and VANGL planar cell polarity protein 1 gene(\u003cem\u003eVANGL1\u003c/em\u003e). This led us to another potent prediction formula, Formula-2 (R\u003csup\u003e2\u003c/sup\u003e=0.54, p=46.5e-64) with showing higher predictive accuracy\u0026nbsp;for\u0026nbsp;PFS (R\u003csup\u003e2\u003c/sup\u003e=0.10, p=46.22e-9) than\u0026nbsp;Formula-1 in\u0026nbsp;the\u0026nbsp;TCGA cohort.\u003c/p\u003e\n\u003cp\u003eIn the case of using expression data of the 22 genes,\u0026nbsp;the\u0026nbsp;random forest method with leave-one-out cross validation demonstrated that expression of Krev interaction trapped protein 1, ankyrin repeat containing gene (\u003cem\u003eKRIT1\u003c/em\u003e) was the most important variable [\u003cstrong\u003eSupplementary Fig. S2(A)\u003c/strong\u003e] in the constructed prediction model for PFS. Nevertheless,\u0026nbsp;the expected predictivity of the model was inferior to those provided in the multiple regression analysis [\u003cstrong\u003eSupplementary Fig. S2(B), (C)\u003c/strong\u003e].\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003ePreviously suggested toxicity marker\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eIn\u0026nbsp;the\u0026nbsp;ddTCip cohort, 75 of 76 enrolled patients (98.7%) experienced grade 3/4 adverse events [4].\u0026nbsp;Hematological\u0026nbsp;toxicities were the most common adverse events, and problematic grade 3 peripheral neuropathy was observed in 8 patients (10.5%). We investigated 31\u0026nbsp;polymorphisms of 15 genes known\u0026nbsp;as potent toxicity\u0026nbsp;markers\u0026nbsp;for taxane and/or platinum therapy (\u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e). Genotype-toxicity association analysis revealed that \u003cem\u003eABCB1\u0026nbsp;\u003c/em\u003ers1045642_A\u0026gt;G\u0026nbsp;and \u003cem\u003eERCC1\u003c/em\u003e rs11615_A\u0026gt;G\u0026nbsp;could be potent single toxicity\u0026nbsp;indicators\u0026nbsp;of ddTCip therapy.\u003cem\u003eABCB1\u0026nbsp;\u003c/em\u003ers1045642_A\u0026gt;G variant was significantly associated with grade 3/4 neutro- (p\u0026lt;0.0495) and thrombo-cytopenia (p\u0026lt;0.0001), while\u0026nbsp;the\u0026nbsp;\u003cem\u003eERCC1\u003c/em\u003e rs11615_A\u0026gt;G variant (p=0.0090) was closely\u0026nbsp;related to\u0026nbsp;peripheral sensory neuropathy in ddTCip cases [\u003cstrong\u003eSupplementary Fig. S3(A\u003c/strong\u003e)]. A public resource of The Genotype-Tissue Expression project [GTEx (V8)] indicated\u0026nbsp;that these 2 SNPs might cause a decrease\u0026nbsp;in the expression\u0026nbsp;of each gene in several\u0026nbsp;tissues,\u0026nbsp;such as testis and tibial nerve tissue (https://gtexportal.org/home/)\u0026nbsp;[\u003cstrong\u003eSupplementary Fig. S3(B), (C\u003c/strong\u003e)].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eMultiplex prediction model for adverse events\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAs with the efficacy, we\u0026nbsp;approached\u0026nbsp;the development of\u0026nbsp;a\u0026nbsp;multiplex prediction model for adverse events.\u0026nbsp;The\u0026nbsp;CART method indicated that\u0026nbsp;the\u0026nbsp;model using a total of 4 combinations of genotypes could predict the occurrence of 4 serious adverse events induced by ddTCip therapy. The genotypes used in the prediction model were,\u0026nbsp;1) \u003cem\u003eABCB1\u003c/em\u003e (rs20232582_A\u0026gt;C/T and rs3213619_A\u0026gt;G) and \u003cem\u003eXRCC1\u0026nbsp;\u003c/em\u003ers25487_T\u0026gt;C for\u0026nbsp;grade\u0026nbsp;3/4 white blood\u0026nbsp;cells\u0026nbsp;decreased (PDR, 69.803; AUC, 0.702), 2) \u003cem\u003eABCB1\u003c/em\u003e rs1045642_A\u0026gt;G for\u0026nbsp;grade\u0026nbsp;3/4 platelet count decreased (PDR, 83.562; AUC, 0.749), 3) \u003cem\u003eGSTT1\u003c/em\u003e null and \u003cem\u003eABCB1\u003c/em\u003e rs203282_A\u0026gt;C/T for\u0026nbsp;grade\u0026nbsp;3/4 anemia (PDR, 73.973; AUC, 0.711, and 4) \u003cem\u003eXRCC1\u003c/em\u003e rs25487_T\u0026gt;C, \u003cem\u003eUGT1A1\u003c/em\u003e rs4148323_G\u0026gt;A and \u003cem\u003eABCB1\u003c/em\u003e rs2032582_A\u0026gt;C/T for grade 2-4 peripheral sensory neuropathy (PDR, 78.082; AUC, 0723) (\u003cstrong\u003eFig. 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eMultiple logistic regression analysis also provided novel potent multiplex prediction models composed of the combinations of a total of 15 SNPs on 12 genes for 8 serious adverse events (\u003cstrong\u003eTable 2\u003c/strong\u003e). These models demonstrated high predictivity for grade 3/4 neutrophile count\u0026nbsp;decrease\u0026nbsp;(PDR, 89.041; AUC, 0.803), grade 3/4 lymphocyte count\u0026nbsp;decrease\u0026nbsp;(PDR, 87.671; AUC, 0.762), grade 2-4 digestive\u0026nbsp;symptoms\u0026nbsp;(PDF, 68.493; AUC, 0.728), and grade 2-4 peripheral motor neuropathy (PDR, 82.192; AUC, 0.797), but the predictivity for grade 3/4 white blood cell\u0026nbsp;count decrease\u0026nbsp;was inferior to\u0026nbsp;that of the\u0026nbsp;CART models (PDR, 49.315; AUC, 0.586).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe unmet medical needs ofadvanced or metastatic ovarian cancer patients remain high. This theranostic study suggests potent biomarkers and multiplex model in the prediction of individual response to a most active front-line chemotherapy, ddTCip.\u003c/p\u003e\n\u003cp\u003eAs a single predictive biomarker for efficacy, we first proposed high \u003cem\u003eSPINK1\u003c/em\u003e expression for efficacy (PFS). \u003cem\u003eSPINK1\u0026nbsp;\u003c/em\u003eencodes pancreatic secretory trypsin inhibitor, which is secreted from pancreatic acinar cells into pancreatic juice and drives ovarian cancer cell proliferation through activation of EGFR or IL-6 signaling [24, 25]. Although\u0026nbsp;its\u0026nbsp;biological roles in cancers remain unclear [26], recent reports have suggested that SPINK1\u0026nbsp;promotes\u0026nbsp;recurrence risk in prostate and breast cancers [27]. Current attention has\u0026nbsp;also\u0026nbsp;been focused on the role of SPINK1 as an attractive novel therapeutic target of cancer [28]. The suppression of a drug\u0026nbsp;resistance\u0026nbsp;determinant, SPINK1, may contribute not only to\u0026nbsp;enhancing\u0026nbsp;the therapeutic activity of\u0026nbsp;the\u0026nbsp;ddTCip regimen but also to\u0026nbsp;preventing\u0026nbsp;disease recurrence.\u003c/p\u003e\n\u003cp\u003eDespite rapid progress in omics, genetic polymorphisms of drug metabolizing enzymes, drug transporters, and DNA repair enzymes are still\u0026nbsp;the\u0026nbsp;mainstay in toxicity prediction [29, 30]. Among we confirmed two genetic variations as a single toxicity predictor for ddTCip therapy:\u0026nbsp;\u003cem\u003eABCB1\u003c/em\u003e rs1045642_A\u0026gt;G for grade 3/4 bone marrow toxicity (neutro- and thrombo-cytopenia), and \u003cem\u003eERCC1\u003c/em\u003e rs11615_A\u0026gt;G for the occurrence of peripheral sensory neuropathy. The relevance of \u003cem\u003eABCB1\u003c/em\u003e rs1045642_A\u0026gt;Gand \u003cem\u003eERCC1\u003c/em\u003e rs11615_A\u0026gt;G to the toxicity of platinum, taxane, and the combination\u0026nbsp;has\u0026nbsp;been reported in a variety of\u0026nbsp;studies\u0026nbsp;[22, 31, 32]. We also demonstrated that both\u0026nbsp;SNPs\u0026nbsp;might reduce the corresponding gene expression in normal tissues, which would cause prolonged drug retention in bone marrow and continuous inhibition of DNA repair in\u0026nbsp;the nervous\u0026nbsp;system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticularly important in the current study is the successful construction of multiplex prediction models.\u0026nbsp;A comprehensive molecular view is necessary for understanding the intricate drug response mechanisms.\u0026nbsp;For the efficacy prediction,\u0026nbsp;our efforts first selected 22 potent marker genes and finally yielded a putative multiplex model using expression data of 5 genes-\u003cem\u003eCUL1\u003c/em\u003e,\u0026nbsp;\u003cem\u003eSLC5A1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;GPD1\u003c/em\u003e, \u003cem\u003ePDE3A\u003c/em\u003e and \u003cem\u003eVANGL1-\u003c/em\u003e.\u0026nbsp;Dynamic\u0026nbsp;proteins transcriptionally regulated in response to nutrient demand or other perturbations are more highly correlated with mRNA in the expression levels in ovarian cancer specimens [33].\u0026nbsp;In fact, the selected 22 genes are shown to be\u0026nbsp;mostly\u0026nbsp;involved in the pathways of \u0026ldquo;Carbohydrate Metabolism\u0026rdquo; and \u0026ldquo;Lipid metabolism\u0026rdquo;. Interestingly, all\u0026nbsp;5 genes finally\u0026nbsp;selected have been\u0026nbsp;proven\u0026nbsp;to play a significant role in cancers:\u003cem\u003e\u0026nbsp;CUL1\u0026nbsp;\u003c/em\u003eand \u003cem\u003eVANGL1\u0026nbsp;\u003c/em\u003eare known as\u0026nbsp;oncogenes\u0026nbsp;[34-36] while \u003cem\u003eGPD1\u003c/em\u003e acts as a tumor suppressor [37, 38].\u0026nbsp;Among\u0026nbsp;\u003cem\u003ePDE3A\u003c/em\u003e and\u0026nbsp;\u003cem\u003eSLC5A1\u0026nbsp;\u003c/em\u003eare well recognized as valuable therapeutic\u0026nbsp;targets\u0026nbsp;of cancers [39-43].\u003c/p\u003e\n\u003cp\u003eFor\u0026nbsp;toxicity,\u0026nbsp;CART analyses demonstrated that 4 combinations of genetic variants\u0026nbsp;in\u0026nbsp;4 genes could predict the occurrence of 4 serious adverse events: white blood cell\u0026nbsp;count decrease,\u0026nbsp;platelet count\u0026nbsp;decrease,\u0026nbsp;anemia,\u0026nbsp;and\u0026nbsp;peripheral sensory neuropathy. Multiple logistic regression analyses provided another potent multiplex prediction\u0026nbsp;model\u0026nbsp;composed of the combinations of a total of 15 genetic variations\u0026nbsp;in\u0026nbsp;12 genes for 8 serious adverse events,\u0026nbsp;including not only bone marrow suppression but also digestive symptoms and peripheral motor neuropathy. All prediction models demonstrated high\u0026nbsp;PDRs\u0026nbsp;and AUC values sufficient\u0026nbsp;for\u0026nbsp;toxicity prediction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough\u0026nbsp;the most active modality is still\u0026nbsp;controversial, the fact remains that the paclitaxel plus carboplatin (TC) regimen is an essential treatment in patients with intractable advanced ovarian cancers [44, 45]. Our proposed prediction models might contribute to improve the therapeutic outcome through the\u0026nbsp;selection\u0026nbsp;of suitable patients for treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEven so, interaction of the suggested markers in the prediction models remains unknown. The prediction of the long-term overall survival is also our interest. For toxicity prediction, ethnic differences should be integrated into utility assessments [46]. We are now planning a larger-scale prospective clinical study to confirm the clinical significance of the predictive markers and prediction models along with continuing our search for the functional roles of the selected markers and their interactions in drug sensitivity and toxicity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis theranostic study on\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ea promising first-line chemotherapy, ddTCip therapy, demonstrated several potent single biomarkers: \u003cem\u003eSPINK1\u003c/em\u003e expression for efficacy and \u003cem\u003eABCB1\u003c/em\u003e and \u003cem\u003eERCC1\u003c/em\u003e polymorphisms for toxicity. We further successfully composed robust multiplex prediction models for individual responses. These may raise the potential to realize precision medicine in essential treatment and address an unmet medical need in ovarian cancer treatment.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTorre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics, CA Cancer J Clin. 2018;68(4):284-296. doi: 10.3322/caac.21456\u003c/li\u003e\n\u003cli\u003eJayson GC, Kohn EC, Kitchener HC, Ledermann JA. Ovarian cancer. Lancet 2014; 384(9951): 1376\u0026ndash;1388. doi: 10.1016/S0140-6736(13)62146-7\u003c/li\u003e\n\u003cli\u003eSambasivan S. Epithelial ovarian cancer: Review article. Cancer Treat Res Commun. 2022;100629. doi: 10.1016/j.ctarc.2022.100629.\u003c/li\u003e\n\u003cli\u003eHasegawa K, Shimada M, Takeuchi S, et al. A phase II study of intraperitoneal carboplatin plus intravenous dose-dense paclitaxel in front-line treatment of suboptimal residual ovarian cancer. 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Mol Cancer Ther. 2020;19(3):868-81. doi: 10.1158/1535-7163.MCT-18-1233.\u003c/li\u003e\n\u003cli\u003ede Waal L, Lewis TA, Rees MG, et al. Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics. Nat Chem Biol. 2016;12(2):102-8. doi: 10.1038/nchembio.1984.\u003c/li\u003e\n\u003cli\u003eKoepsell H. The Na+-D-glucose cotransporters SGLT1 and SGLT2 are targets for the treatment of diabetes and cancer. Pharmacol Ther. 2017;170:148-165. doi: 10.1016/j.pharmthera.2016.10.017.\u003c/li\u003e\n\u003cli\u003eLai B, Xiao Y, Pu H, Cao Q, Jing H, Liu X. Overexpression of SGLT1 is correlated with tumor development and poor prognosis of ovarian carcinoma. Arch Gynecol Obstet. 2012;285(5):1455-61. doi: 10.1007/s00404-011-2166-5.\u003c/li\u003e\n\u003cli\u003eWalker JL, Brady MF, Wenzel L, et al. Randomized Trial of Intravenous Versus Intraperitoneal Chemotherapy Plus Bevacizumab in Advanced Ovarian Carcinoma: An NRG Oncology/Gynecologic Oncology Group Study. J Clin Oncol. 2019;37(16):1380-90. doi: 10.1200/JCO.18.01568.\u003c/li\u003e\n\u003cli\u003eGandara DR, Kawaguchi T, Crowley J, et al. Japanese-US common-arm analysis of paclitaxel plus carboplatin in advanced non-small-cell lung cancer: a model for assessing population-related pharmacogenomics. J Clin Oncol. 2009;27(21):3540-6. doi: 10.1200/JCO.2008.20.8793.\u003c/li\u003e\n\u003cli\u003eKomatsu M, Wheeler HE, Chung S, et al. Pharmacoethnicity in Paclitaxel-Induced Sensory Peripheral Neuropathy. Clin Cancer Res. 2015;21(19):4337-46. doi: 10.1158/1078-0432.CCR-15-0133.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Linear regression analysis: Correlation of the expression with progression free survival (PFS) in 41 genes previously reported as potent efficacy markers of taxane and/or platinum therapy.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"898\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" rowspan=\"2\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" rowspan=\"2\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.363737486095662%\" colspan=\"3\"\u003e\n \u003cp\u003eddTCip cohort\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003enon-censored 64 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.363737486095662%\" colspan=\"3\"\u003e\n \u003cp\u003eTCGA cohort\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003enon-censored 189 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.5849056603773586%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.471698113207546%\"\u003e\n \u003cp\u003eAdj_R\u003csup\u003e2***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.471698113207546%\"\u003e\n \u003cp\u003eslope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.471698113207546%\"\u003e\n \u003cp\u003ep\u003csup\u003e****\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.5849056603773586%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.471698113207546%\" valign=\"top\"\u003e\n \u003cp\u003eAdj_R2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.471698113207546%\" valign=\"top\"\u003e\n \u003cp\u003eslope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.471698113207546%\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSPINK1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eserine peptidase inhibitor Kazal type 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0055\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTNNT3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003etroponin T3, fast skeletal type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-23.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIL6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003einterleukin 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-2.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIRF9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003einterferon regulatory factor 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-8.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTUBB3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003etubulin beta 3 class III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-7.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eABCC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eATP binding cassette subfamily C member 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-74.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFOXO1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eforkhead box O1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-2.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eERCC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eERCC excision repair 2, TFIIH core complex helicase subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-6.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eNDE1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003enudE neurodevelopment protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-1.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eATP7B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eATPase copper transporting beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-66.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHS3ST4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eheparan sulfate-glucosamine 3-sulfotransferase 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-321.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePRSS11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eprotease serine 11/HtrA serine peptidase 1 (HTRA1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eVEGF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003evascular endothelial growth factor A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e4.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eJOSD2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eJosephin domain containing 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-30.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eXRCC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eX-ray repair cross complementing 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eABCB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eATP binding cassette subfamily B member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-75.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTTC27\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003etetratricopeptide repeat domain 27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-4.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMYO5C\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003emyosin VC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e12.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCSAG2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eCSAG family member 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e23.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMTA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003emetastasis associated 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePHF20\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003ePHD finger protein 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eERCC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eERCC excision repair 1, endonuclease non-catalytic subunit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eDISP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003edispatched RND transporter family member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-40.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePLEK2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003epleckstrin 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e12.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIFIT3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003einterferon induced protein with tetratricopeptide repeats 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHSPC244\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003etransmembrane protein 216 (TMEM216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eDDX58\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eDExD/H-box helicase 58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-1.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCISD1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eCDGSH iron sulfur domain 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC25A39\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003esolute carrier family 25 member 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-3.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTNFSF13B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eTNF superfamily member 13b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-1.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eATP7A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eATPase copper transporting alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e3.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eBCL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eBCL2 apoptosis regulator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eNELL2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eneural EGFL like 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e4.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHSPB9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eheat shock protein family B (small) member 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e5.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e#N/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePPFIA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003ePTPRF interacting protein alpha 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSCARB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003escavenger receptor class B member 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eARMCX3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003earmadillo repeat containing X-linked 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-1.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eBTN3A2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003ebutyrophilin subfamily 3 member A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e1.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAURKA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eaurora kinase A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-2.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGBP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eguanylate binding protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.454949944382648%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eATP5G1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.590656284760847%\" valign=\"top\"\u003e\n \u003cp\u003eATP synthase membrane subunit c locus 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.1134593993325916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.121245828698553%\" valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*ddTCip cohort, ovarian cancer patients who were enrolled in this theranostic study and received intraperitoneal carboplatin plus intravenous dose-dense paclitaxel; **TCGA cohort, published data sets in The Cancer Genome Atlas (ovarian cancer patients who received taxane and/or platinum therapy); ***Adj_R2, adjusted R (regression coefficient)-squared; **** p, p-value for the slope; #N/A, not analyzed\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"11\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Odds ratios of the adverse event occurrence in multiplex prediction models constructed by multiple logistic regression analysis using genetic polymorphism data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.668711656441718%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.656441717791411%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolymorphism ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.4642126789366054%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"73.21063394683027%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhite blood cell decreased (grade 3-4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophil count decreased\u003cbr\u003e\u0026nbsp; (grade 3-4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocyte count\u0026nbsp;\u003cbr\u003e\u0026nbsp;decreased\u0026nbsp;\u003cbr\u003e\u0026nbsp;(grade 3-4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ecount\u0026nbsp;\u003cbr\u003e\u0026nbsp;decreased\u0026nbsp;\u003cbr\u003e\u0026nbsp;(grade 3-4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnemia\u0026nbsp;\u003cbr\u003e\u0026nbsp;(grade 3-4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigestive symptom (grade 2-4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeripheral motor\u0026nbsp;\u003cbr\u003e\u0026nbsp;neuropathy\u0026nbsp;\u003cbr\u003e\u0026nbsp;(grade 2-4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeripheral sensory\u0026nbsp;\u003cbr\u003e\u0026nbsp;neuropathy\u0026nbsp;\u003cbr\u003e\u0026nbsp;(grade 2-4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eABCB1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers1045642\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.006***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eABCB1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers1045642\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.098**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.006***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e4.817**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eABCB1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers1128503\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.253*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eABCB1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers2032582\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eA/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e18.972**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e5.671**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eABCB1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers2032582\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eA/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e2.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eABCB1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers2032582\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e5.881**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eABCC1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers4148356\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eA/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e62.449*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eABCC2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers3740066\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.217*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eABCC2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers3740066\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eT/T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e11.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e23.599*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCYP1B1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers1056836\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eG/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eERCC1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers3212986\u003c/strong\u003e\u003c/p\u003e\n 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valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eERCC2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers13181\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eT/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e5.547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGSTP1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers1695\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.114*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.273*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e5.101**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGSTP1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n 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width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGSTT1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNull\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eNull\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.078***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eUGT1A1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers4148323\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.116***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eXRCC1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers1799782\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eXRCC1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers25487\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e3.713**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e6.653**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.700205338809035%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eXRCC3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.704312114989733%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ers861539\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.494866529774128%\"\u003e\n \u003cp\u003eG/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e7.072**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.899383983572896%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of response\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.899383983572896%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive discrimination rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e49.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e89.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e87.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e89.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e72.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e68.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e82.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e78.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.899383983572896%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea under the curve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.137577002053389%\" valign=\"top\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"11\" valign=\"top\"\u003e\n \u003cp\u003e***, p\u0026lt;0.01; **, 0.01\u0026lt;=p\u0026lt;0.05; *, 0.05\u0026lt;=p\u0026lt;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Predictive biomarker of therapeutic response, Personalized medicine, Ovarian cancer, Intraperitoneal carboplatin plus intravenous dose-dense paclitaxel (ddTCip) therapy, First-line chemotherapy","lastPublishedDoi":"10.21203/rs.3.rs-3814637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3814637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Platinum/taxane chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis. Recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact, but consequent recurrence or relapse still often occurs. Discovery of therapeutic response predictor to ddTCip therapy is eagerly awaited to improve current treatment strategy for disseminated ovarian cancer patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eUsing datasets in 76 participants in this study and 189 patients published in The Cancer Genome Atlas, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping analyses\u003cstrong\u003e \u003c/strong\u003ewith progression-free survival (PFS)and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The association analyses\u003cstrong\u003e \u003c/strong\u003erevealed that \u003cem\u003eSPINK1 \u003c/em\u003eexpression could be a potent biomarker of ddTCip efficacy, while \u003cem\u003eABCB1\u003c/em\u003e rs1045642 and \u003cem\u003eERCC1\u003c/em\u003ers11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex prediction models for adverse events: CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur proposed biomarkers and multiplex models composed here would work well in the response prediction of ddTCip therapy, which might contribute to realize optimal selection of the key therapy.\u003c/p\u003e","manuscriptTitle":"Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: Practical biomarkers and robust multiplex models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 18:02:52","doi":"10.21203/rs.3.rs-3814637/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-01-23T22:00:57+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2023-12-30T00:19:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-29T22:52:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-29T21:11:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Clinical Oncology","date":"2023-12-27T18:01:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijco","sideBox":"Learn more about [International Journal of Clinical Oncology](http://link.springer.com/journal/10147)","snPcode":"10147","submissionUrl":"https://www.editorialmanager.com/ijco/default2.aspx","title":"International Journal of Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"db4ba663-da01-46da-9475-b39e770cd06d","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-14T09:21:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-03 18:02:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3814637","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3814637","identity":"rs-3814637","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}