ADME Genes) in Hepatocellular

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Deregulation of the Genes that Are Involved in Drug Absorption,

Distribution, Metabolism, and Excretion (ADME genes) in Hepatocellular

Carcinoma

Dong Gui Hu1, Shashikanth Marri2, Ross A. McKinnon1, Peter I. Mackenzie1, and Robyn

Meech1

1Department of Clinical Pharmacology and Flinders Centre for Innovation in Cancer, Flinders Downloaded from University College of Medicine and Public Health, Flinders Medical Centre, Bedford Park,

South Australia, Australia

2Department of Molecular Medicine and Pathology, Flinders University College of Medicine jpet.aspetjournals.org and Public Health, Flinders Medical Centre, Bedford Park, South Australia, Australia.

at ASPET Journals on October 2, 2021

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Running Title: Deregulation of ADME Genes In Hepatocellular Carcinoma

Address correspondence to:

Dr Dong Gui Hu,

Department of Clinical Pharmacology and Flinders Centre for Innovation in Cancer, Flinders

University College of Medicine and Public Health, Flinders Medical Centre, Bedford Park,

South Australia, Australia

Telephone: +61-8-82043085. Fax: +61-8-82045114. Downloaded from

Email: [email protected]

Number of text pages: 27 jpet.aspetjournals.org Number of tables: 2

Number of figures: 12

Number of references: 105 at ASPET Journals on October 2, 2021 Number of words in Abstract: 249

Number of words in Introduction: 707

Number of words in Discussion: 1610

Abbreviations

ABC, ATP-binding cassette; ADH: alcohol dehydrogenase; ALDH: aldehyde dehydrogenase;

ADME, Absorption, Distribution, Metabolism, and Excretion; BSEP, bile salt export pump;

BCRP, breast cancer resistance protein; CYP, cytochrome P450 enzyme; GST, glutathione S- transferase; SULT, sulfotransferase; HCC, hepatocellular carcinoma; MDR, multidrug resistance protein; MRP, multidrug resistance-associated protein; RNA-seq, RNA sequencing; TCGA , the Cancer Genome Atlas; UGT, UDP-glucuronosyltransferase;

Section assignment

Metabolism, Transport, and Pharmacogenomics

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Abstract

ADME genes are involved in drug Absorption, Distribution, Metabolism, and

Excretion. Currently, 298 genes that encode Phase I and II drug metabolizing enzymes, transporters, and modifiers are designated as ADME genes by the PharmaADME

Consortium. ADME genes are highly expressed in the liver and their levels can be influenced by liver diseases such as hepatocellular carcinoma (HCC). In this study, we obtained RNA-

seq and miRNA-seq data from 371 HCC patients via the TCGA hepatocellular carcinoma Downloaded from

(LIHC) project and performed ADME gene-targeted differential gene expression analysis and expression correlation analysis. 233 of the 298 ADME genes (78%) were expressed in HCC. jpet.aspetjournals.org Of these genes, almost one quarter (58 genes) were significantly downregulated, whilst only

6% (15) were upregulated in HCC relative to healthy liver. Moreover, half (14/28) of the

‘core ADME’ genes (CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2E1, at ASPET Journals on October 2, 2021 CYP3A4, NAT1, NAT2, UGT2B7, SLC22A1, SLCO1B1, SLCO1B3) were downregulated. In addition, about half of the core ADME genes were positively correlated with each other and were also positively (AHR, ARNT, HNF4A, PXR, CAR, PPARA, RXRA) or negatively

(PPARD, PPARG) correlated with transcription factors known as ADME modifiers. Finally, we show that most miRNAs known to regulate core ADME genes are upregulated in HCC.

Collectively, these data reveal 1) an extensive transcription factor-mediated ADME co- expression network in the liver that efficiently coordinates the metabolism and elimination of endogenous and exogenous compounds; 2) a widespread deregulation of this network in

HCC, most likely due to deregulation of both transcriptional and post-transcriptional

(miRNA) pathways.

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Introduction

The liver expresses a variety of Phase I and Phase II enzymes and transporters and thus it is the primary organ of drug and xenobiotic metabolism and clearance. Phase I enzymes mainly perform oxidative, reductive, or hydrolytic reactions that usually create or reveal a functional group (e.g. hydroxyl, carboxyl, amino) on a xenobiotic, whereas Phase II enzymes generally add a water-soluble group (e.g glutathione, sulfate, glucuronic acid) directly to a xenobiotic or to a metabolite of phase I metabolism. The influx and efflux transporters are responsible for the uptake and excretion of various compounds (e.g. drugs) in Downloaded from and out of cells. ADME genes are defined as a group of genes that are involved in drug

Absorption, Distribution, Metabolism, and Excretion, and consist mainly of genes encoding jpet.aspetjournals.org Phase I and II drug metabolizing enzymes, drug transporters, and modifiers; the latter are a group of factors that either modulate the expression of other ADME genes or influence the biochemistry of ADME enzymes (www.pharmaadme.org). Currently, 298 genes are at ASPET Journals on October 2, 2021 classified as ADME genes by the pharmaADME Consortium, including 32 core ADME genes and 266 extended ADME genes (www.pharmaadme.org). The combined activities of the ADME genes determine the capacity of the liver to metabolize and clear drugs and hence influence the efficacy of drug treatment.

The expression and activities of ADME genes are influenced by liver diseases, such as viral infection, alcohol liver disease, primary sclerosis cholangitis, non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) (Hardwick et al., 2013; Kurzawski et al., 2012). For example, NAFLD significantly alters the expression of ADME genes that encode a variety of Phase I and II drug metabolizing enzymes and drug transporters, including cytochrome P450 (CYP) enzymes (Fisher et al., 2009), glutathione S-transferases

(GSTs) (Hardwick et al., 2010), UDP-glucuronosyltransferases (UGTs) and sulfotransferases

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(SULTs) (Congiu et al., 2002; Hardwick et al., 2013), and ATP-binding cassette (ABC) transporters (Hardwick et al., 2011).

Hepatocellular carcinoma (HCC) is the fifth most common cancer and the third highest cause of cancer-related death globally (Lin et al., 2012). Common risk factors for

HCC are viral infection, chronic alcohol consumption, aflatoxin exposure and cirrhosis

(Farazi and DePinho, 2006). Surgical resection, liver transplantation, and ablation remain the only potentially curative treatments for early-stage HCC whereas two multi-kinase inhibitors, Downloaded from sorafenib and regorafenib, are the only approved systemic drugs for treating advanced HCC

(Bruix et al., 2017; Llovet et al., 2008). The sequential use of sorafenib (front-line) and

regorafenib (second-line) provides a survival benefit in advanced HCC (Bruix et al., 2017). jpet.aspetjournals.org

Several studies have investigated the differential expression of ADME genes in HCC relative to adjacent noncancerous liver tissue. Many of these studies reported altered

expression of a single ADME gene or gene superfamily, such as CYPs (Chen et al., 2014; at ASPET Journals on October 2, 2021

Yan et al., 2015a; Yan et al., 2015b), ABC transporters (Borel et al., 2012), or UGTs (Yan et al., 2015a). Some high-throughput studies using microarray-based gene expression profiling

(Jin et al., 2015a; Lee et al., 2004; Okabe et al., 2001; Park et al., 2006) or more recently

RNA deep sequencing (RNA-seq) (Ho et al., 2015; Woo et al., 2017) have revealed altered expression of ADME genes from a number of superfamilies (e.g. CYPs, UGTs, GSTs); however these untargeted studies did not draw specific conclusions about dysregulation of

ADME pathways. Based on the limitations of these previous studies, we sought to provide the first targeted, yet comprehensive, dysregulation analysis of all known ADME genes in

HCC.

The TCGA (The Cancer Genome Atlas) hepatocellular carcinoma (LIHC) project contains RNA-seq data from 371 HCC patients including 50 paired HCC and corresponding adjacent noncancerous liver tissues (https://portal.gdc.cancer.gov/). In the present study, we

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018 performed differential gene expression analysis of the 50 paired HCC and noncancerous tissues focussing on the defined ADME gene set. Key findings were that half of the core

ADME gene set and about one quarter of the extended ADME set were significantly downregulated in HCC, whilst few genes were upregulated. Furthermore, about half of the core ADME genes showed significant correlation with each other and were also significantly correlated with nine transcription factors (TFs) (AHR, ARNT, HNF4A, PXR, CAR, PPARA,

PPARD, PPARG, RXRA), indicating coordinated regulation at the transcriptional level. The study also identified specific miRNAs likely to be involved in ADME gene dysregulation in Downloaded from

HCC.

jpet.aspetjournals.org at ASPET Journals on October 2, 2021

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Materials and Methods

Liver Hepatocellular Carcinoma (LIHC) dataset of the Cancer Genome Atlas (TCGA)

(TCGA HCC)

The Cancer Genome Atlas (TCGA) is a community resource project that collects and analyses cancer specimens from a variety of human cancers (https://cancergenome.nih.gov).

Data generated from TCGA projects such as RNA-seq and miRNA-seq for various human cancers are made freely available for browser, download, and use for commercial, scientific Downloaded from and educational purposes. The results shown in the present study were mainly based upon

RNA-seq and miRNA-seq data generated by the TCGA Research Network of the Liver

Hepatocellular Carcinoma (LIHC) project (https://portal.gdc.cancer.gov/projects/TCGA- jpet.aspetjournals.org

LIHC). The TCGA LIHC project contains RNA-seq data of HCC tumours from 371 patients.

RNA-seq data from normal liver tissues adjacent to the corresponding tumours are also available for 50 of these patients. The TCGA LIHC project also contains miRNA-seq data for at ASPET Journals on October 2, 2021 these patients including paired normal and cancerous specimens from 49 patients. As described in detail below, the RNA-seq and miRNA-seq data from the paired normal and cancerous specimens were used for differential gene and miRNA expression analysis, respectively, and the RNA-seq data from the 371 tumours were used for gene expression correlation analysis.

The detailed clinical data for each of the 371 HCC patients are available from the cBioportal (http://www.cbioportal.org/study?id=lihc_tcga#clinical). This cohort includes 250 male and 121 female patients with the age at diagnosis ranging from 16 to 90 years. The ethnicities of the subjects are available for 361 patients, including Asians (158), Caucasians

(184), Black or African Americans (17), and American Indian or Alaska Native (2). The

American Joint Committee on Cancer (AJCC) proposes a “staging grouping” based on T, N, and M to define the three stages of tumours: the primary tumour (T), regional lymph nodes

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(N), and distant metastases (M) (Kamarajah et al, 2017). The progression stages of the tumours are available for 368 patients. According to this TNM staging system, all tumours were primary hepatocellular carcinomas at the Stage T, including 181, 94, 80, and 13 tumours at Stages T1, T2, T3, T4, respectively. Tumours at Stages N and M were not concluded in the

TCGA LIHC project. The TCGA LIHC clinical data does not provide the details of the pre/postoperative chemotherapeutic regimens of the patients.

ADME genes defined by the PharmaADME Consortium Downloaded from Thirty-two ‘core’ and 266 ‘extended’ ADME genes (298 total) are currently defined by the PharmaADME Consortium (http://www.pharmaadme.org). The core ADME gene set

represent the most important genes directly involved in drug metabolism/clearance; the jpet.aspetjournals.org extended ADME gene set represent other genes related to drug metabolism/clearance. Four extended ADME genes, either pseudogene (CYP2D7P1), anti-sense gene (SLC22A18AS), or

functionally unknown genes (LOC728667, LOC731931) were excluded from analysis in this at ASPET Journals on October 2, 2021 study. The remaining 294 (32 core; 262 extended) ADME genes, are listed in Supplemental

Table 1 (Table S1).

Download of the TCGA LIHC RNA-seq and miRNA-seq data

For differential gene expression analysis, TCGA LIHC RNA-seq data from the 50 paired HCC and corresponding adjacent noncancerous liver tissues that have been mapped to the human genome GRCh38 reference assembly, were downloaded as HT-Seq counts from the Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/). The HT-Seq counts contained the raw read counts of each gene generated using a HT-Seq tool

(http://htseq.readthedocs.io/en/release_0.9.1/) to measure gene expression level from the

GDC mRNA quantification analysis pipeline

(https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/).

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The HT-Seq counts of 609 genes that were included in the analysis in this study are given in

Supplemental Table 2 (Table S2).

For gene expression correlation analysis, the TCGA LIHC RNA-seq data from 371

HCC tumours were downloaded in the form of high-throughput sequencing counts from the

Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/). Genes (protein coding and noncoding) with a mean of less than 10 counts were discarded; the counts of the remaining genes were normalized using the upper quantile normalization method. The Downloaded from normalized read counts of the 609 genes that were included in the analysis of this study are given in Supplemental Table 3 (Table S3). jpet.aspetjournals.org For differential miRNA expression analysis, TCGA LIHC miRNA-seq data of the 49 paired HCC and corresponding adjacent non-cancerous liver tissues were downloaded from

The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/) and at ASPET Journals on October 2, 2021 normalized using the upper quantile normalization method. The normalized read counts of miRNAs that were included in the analysis of this study are given in Supplemental Table 4

(Table S4).

ADME genes and non-ADME genes analysed in this study

Most of the 298 ADME genes belong to gene superfamilies such as ABC transporters,

CYP enzymes, SLC transporters and UGT enzymes. However, many of these gene superfamilies also contain members that are not involved in drug Absorption, Distribution,

Metabolism, and Excretion. These genes are termed as non-ADME genes in this study. The

TCGA LIHC RNA-seq data showed HT-Seq counts for 743 genes, including 290 ADME genes and 453 non-ADME genes. For quality control, genes with HT-Seq counts of < 32

(equivalent to < 5 of log2-transformed counts) were excluded from the analysis. Based on this cut-off criterion, a total of 609 genes (Table S2), including 233 ADME genes and 376

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018 non-ADME genes were included in the final analysis. Inclusion of the non-ADME genes in our analysis allowed us to determine whether ADME genes were specifically dysregulated, rather than whole gene superfamilies.

Statistical analysis of the TCGA LIHC RNA-seq data and miRNA-seq data

The downloaded RNA-seq (HT-Seq counts) of the 50 paired HCC and corresponding adjacent normal liver tissues as described above were subjected to differential gene Downloaded from expression analysis using the DESeq2 program (Lover et al, 2014). Briefly, DESeq2 takes the

HT-Seq counts per gene and normalizes gene counts using a Generalized Linear Model

(GLM). After normalization, it estimates negative binomial distribution and performs jpet.aspetjournals.org variance shrinkage to determine the fold changes of genes between normal and cancerous tissues. DESeq2 uses a Wald test for statistical significant testing. Briefly, the shrunken

estimate of Log Fold Change (LFC) is divided by its standard error, resulting in a z-statistic, at ASPET Journals on October 2, 2021 which is compared to a standard normal distribution. The Wald test allows testing of individual coefficients, or contrasts of coefficients, without the need to fit a reduced model as with the likelihood ratio test (LRT). The Wald test P values from the subset of genes that pass the independent filtering step are adjusted for multiple testing using the test of Benjamini and

Hochberg. As detailed in Supplemental Table 5 (Table S5), the final report of DESeq2 analysis included a log2 fold change (log2Foldchange) in the expression level of each gene between the tumour and adjacent non-tumour liver tissues and the associated standard error estimate for the log2 fold change estimate (lfcSE) and the Wald test result (p value) and the

Benjamini-Hochberg test results (Adjusted p values). An adjust p value of <0.05 was considered statistically significant. Differentially expressed genes were defined by a log2foldchange of >1 (equivalent to an upregulation of >2 folds) or <-1 (equivalent to a

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018 downregulation of <2 folds). Table S5 gives the final results pertaining to the 609 genes that are included in analysis in this study.

The normalized gene expression levels (read counts ) of the 371 HCC tumours as described above were subjected to Spearman ranking correlation analysis (1) between core

ADME genes and (2) between core ADME genes and nine ADME modifier transcription factors and NRF2 and its coregulatory KEAP1 using GraphPad Prism (version 7.03). A p value of < 0.05 was considered statistically significant. Downloaded from The normalized expression levels of miRNAs of the 49 paired HCC and corresponding adjacent normal non-cancerous liver tissues as described above was analysed

using paired T-test via GraphPad Prism (version 7.03). A p value of < 0.05 was considered jpet.aspetjournals.org statistically significant.

Analysis of microarray-based gene expression profiles of hepatocellular carcinoma at ASPET Journals on October 2, 2021

Most of the results reported in this study were obtained from the analysis of the

TCGA LIHC RNA-seq data and miRNAseq data as described above. To support the findings from this dataset, we further analysed microarray-based whole genome gene expression profiles from four other studies (Chen et al., 2002; Roessler et al., 2010; Wurmbach et al.,

2007) via Oncomine, a publicly accessible database (Rhodes et al., 2007). As stated by

Oncomine (www.oncomine.org), Roessier S et al analysed 225 HCC and 220 adjacent non- tumour liver tissues using the Affymetrix human genome 96 HT HG-U133A 2.0 microarray platform (GSE14520) (Roessler et al., 2010). Using the same U133A 2.0 microarray,

Wurbach et al studied 35 HCC tumours and 10 heathy liver tissues (GSE6764) (Wurmbach et al., 2007). Chen et al analysed HCC tumour (71-76 specimens) and adjacent non-tumour (98-

104 specimens) liver tissues using custom-made cDNA microarrays containing 23,075 cDNA probes, representing 17,400 genes (Chen et al., 2002). Thurnherr et al studied 100 paired

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HCC tumour and adjacent non-tumour liver tissues using Agilent’s 60-mer oligo microarray

(Thurnherr et al., 2016) (GSE62043).

Downloaded from jpet.aspetjournals.org at ASPET Journals on October 2, 2021

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Results

Deregulation of ADME and non-ADME genes in HCC

This study examined the expression of 233 ADME genes (Table S6) in 50 paired

HCC and corresponding noncancerous liver tissues using the TCGA LIHC RNA-seq data set.

376 non-ADME genes from ADME gene superfamilies that are not involved in drug metabolism and clearance were also analysed for comparison. Overall, of the 609 genes analysed, 109 genes (18%) (Table S7) were downregulated and 55 genes (9%) (Table S8) were upregulated in HCC. Among the 233 ADME genes, 58 genes (24%) (Table S9) were Downloaded from downregulated and 15 genes (6%) (Table S10) were upregulated in HCC, whereas out of the

376 non-ADME genes, 51 genes (14%) (Table S11) were downregulated and 40 genes (11%) jpet.aspetjournals.org (Table S12) were upregulated in HCC. Chi-Square tests showed that ADME genes were significantly more likely to be downregulated than upregulated (p < 0.0001), and that ADME genes were significantly more likely to be downregulated than non-ADME genes (p < 0.001). at ASPET Journals on October 2, 2021

Deregulation of core ADME genes in HCC

The PharmaADME Consortium (www.pharmaadme.org) currently defines 32 core

ADME genes (Table S13), four (GSTT1, SLC15A2, SLC22A2, SLC22A6) of which were not expressed in the TCGA HCC specimens. Of the remaining 28 core ADME genes, 14 (50%) were significantly downregulated in HCC tissues as compared to paired adjacent noncancerous liver tissues, including eight phase I enzymes (CYP1A2, CYP2A6, CYP2B6,

CYP2C8, CYP2C9, CYP2C19, CYP2E1, CYP3A4), three phase II enzymes (NAT1, NAT2,

UGT2B7), and three transporters (SLC22A1, SLCO1B1, SLCO1B3) (Fig. 1, Table 1). None of the core ADME genes was upregulated. Chi-Square tests showed that core ADME genes were significantly more likely to be downregulated relative to extended ADME genes (p <

0.01) or non-ADME genes (p< 0.0001). Our analyses of four other microarray gene

2016; Wurmbach et al., 2007) showed that each of the 14 downregulated core ADME genes was reported to be downregulated in HCC in at least one of these other studies (Table 2).

Deregulation of ADME genes coding for phase I drug metabolism enzymes in HCC

One hundred and thirty of the 298 ADME genes code for Phase I drug metabolising enzymes (PharmaADME). Of these genes, 106 (82%) were expressed in the TCGA HCC specimens, including 34 downregulated genes (32%) (Table 1) and 5 upregulated genes (5%) Downloaded from

(ALDH3A1, CBR3, CYP17A1, NOS2A, CYP7A1). Among the downregulated genes, 27 encode members of three enzyme superfamilies, cytochromes P450 enzyme (CYPs), alcohol jpet.aspetjournals.org dehydrogenases (ADHs), and aldehyde dehydrogenases (ALDHs). The remaining seven genes were AOX1 (aldehyde oxidase 1), DHRS1 (dehydrogenase/reductase 1), EPHX2

(epoxide hydrolase 2), PLGLB1 (plasminogen-like B1), PON1 (paraoxonase 1), PON3 at ASPET Journals on October 2, 2021 (paraoxonase 3) and XDH (xanthine dehydrogenase).

There are 57 putatively functional CYP genes in the human genome that are grouped into 18 families and 44 subfamilies based on sequence similarity (Nelson et al., 2004). 47

CYP genes are classified as ADME genes (PhamaADME). Of these ADME CYP genes, 37 were expressed in the TCGA HCC specimens: 2 of these were upregulated (CYP7A1,

CYP17A1) and 17 were downregulated (Fig. 2A, Table 1). The downregulated CYP genes included the eight aforementioned downregulated core ADME CYP genes (Fig. 1) and 9 other CYP genes (CYP2C18, CYP3A7, CYP3A43, CYP4A11, CYP4F2, CYP4F12, CYP8B1,

CYP26A1, CYP39A1).

The human genome has seven functional ADH genes (1A, 1B, 1C, 4, 5, 6, 7) that are all classified as ADME genes based on their roles in drug clearance (Edenberg, 2007). Of these ADH genes, six (1A, 1B, 1C, 4, 5, 6) were expressed in the TCGA HCC specimens,

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018 five of which (1A, 1B, 1C, 4, 6) were significantly downregulated in HCC, consistent with the findings of recent reports (Shang et al., 2017; Wei et al., 2012).

There are 19 ALDH genes in humans (Marchitti et al., 2008), 15 of which are classified as ADME genes (www.pharmaadme.org). Of the ADME ALDH genes, 14 were expressed in the TCGA HCC specimens, including 5 genes that were downregulated

(ALDH1A3, 1B1, 2, 6A1, 8A1) and one that was upregulated (ALDH3A1) in HCC (Fig. 2B,

Table 1). The downregulation of ALDH1B1 (Yang et al., 2017) and ALDH2 (Jin et al., 2015b) in HCC was consistent with recent reports, whereas the downregulation of ALDH1A3, ALDH Downloaded from

6A1, ALDH 8A1 and the upregulation of ALDH3A1 in HCC were novel findings.

jpet.aspetjournals.org Deregulation of ADME genes coding for Phase II drug metabolism enzymes in HCC

Sixty-eight of the 298 ADME genes encode for Phase II drug metabolising enzymes

(PharmaADME). Of these ADME genes, 52 (76%) were expressed in the TCGA HCC at ASPET Journals on October 2, 2021 specimens, including 11 genes (23%) that were downregulated and 3 genes (6%) that were upregulated in HCC. Seven downregulated genes encode for members of three enzyme superfamilies, glutathione S-transferases (GSTs), sulfotransferases (SULTs) and UDP glucuronosyltransferases (UGTs) as described in detail below and the other four genes were carbohydrate (N-acetylglucosamine 6-O) sulfotransferase 4 (CHST4), nicotinamide N- methyltransferase (NNMT), and two arylamine N-acetyltransferases (NAT1 and NAT2). The upregulated genes were CHST10, SULT1C2, and UGT2B11.

The human genome contains 25 GST genes (www.genenames.org), 21 of which are classified as ADME genes (www.pharmaadme.org). Of these ADME GST genes, 17 genes from three GST subfamilies (cytosolic, mitochondrial, and microsomal) were expressed in the TCGA HCC specimens, including two GST genes (GSTM5, GSTZ1) that were significantly downregulated in HCC (Fig. 3A). GSTZ1 downregulation in HCC was recently

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018 reported (Jahn et al., 2016). The downregulation of GSTA1 (Li et al., 2008) and GSTP1 (Shen et al., 2012; Zhang et al., 2005; Zhong et al., 2002) and the upregulation of GST1A4 (Liu et al., 2017) in HCC were also previously reported. There was no overall significant deregulation of these three GST genes in HCC, but we observed reduced GSTA1 and GSTP1 mRNA levels and increased GST1A4 mRNA levels in HCC tumour tissues compared to corresponding adjacent normal liver tissues in nearly 50% of the patients (data not shown).

There are 13 SULT genes in the human genome, 10 of which are classified as ADME Downloaded from genes (Blanchard et al., 2004). Of these SULT genes, six (1A1, 1A2, 1B1, 1C2, 1E1, 2A1) were expressed in the TCGA HCC specimens, including 3 genes that were downregulated

(1A2, 1E1, 2A1) and one gene that was upregulated (1C2) in HCC (Fig. 3B). In agreement jpet.aspetjournals.org with these findings, the downregulation of SULT1E1 and SULT2A1 in HCC was recently reported (Xie et al., 2017). at ASPET Journals on October 2, 2021 There are 21 functional UGT genes in the human genome that are subdivided into 4 families (UGT1, UGT2, UGT3, UGT8) (Hu et al., 2014a). 18 UGT genes are classified as

ADME genes, including 4 core ADME genes (1A1, 2B15, 2B17, 2B7) and 14 extended

ADME genes (1A3-10, 2A1, 2B4, 2B10, 2B11, 2B28, UGT8) (www.pharmaadme.org). Of the

ADME UGT genes, 12 (1A1, 1A3, 1A4, 1A6, 1A9, 2A1, 2B4, 2B7, 2B10, 2B11, 2B15, 2B17) were expressed in the TCGA HCC specimens (Fig. 3C, Table 1). We show here for the first time, UGT2B10 downregulation and UGT2B11 upregulation in HCC (Fig. 3C). We also show UGT2B7 downregulation in HCC, a finding that was recently reported by others (Lu et al., 2015; Yan et al., 2015a). Previous studies have also reported downregulation of three other UGTs (1A1, 1A4, 1A9) in HCC (Lu et al., 2015; Yan et al., 2015a). There was no overall significant deregulation of these three UGT genes in the TCGA HCC specimens (Fig.

3C) but their mRNA levels were decreased in HCC as compared to paired adjacent noncancerous liver tissues in nearly 50% of the patients (Supplemental Fig. 1). As expected,

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Deregulation of ADME genes coding for drug transporters in HCC

Seventy-seven of the 298 ADME genes encode for drug transporters (PharmaADME) that belong to two transporter superfamilies: ATP-binding cassette transporters (ABCs) and

Solute carrier transporters (SLCs). Of these ADME genes, 54 (70%) were expressed in the

TCGA HCC specimens, including 9 genes (17%) that were downregulated and 7 genes (13%) Downloaded from that were upregulated in HCC. The details of the deregulation of ABC genes and SLC genes are described below.

The human genome contains 48 functional ABC genes (Dean et al., 2001; Wlcek and jpet.aspetjournals.org

Stieger, 2014), among which 26 genes are defined as ADME genes (PharmaADME). Of these ADME ABC genes, three (ABCA4, ABCC4, ABCC10) were upregulated and one

(ABCC9) was downregulated in HCC tumor (Fig. 4A). The expression of three core ADME at ASPET Journals on October 2, 2021 genes (ABCB1, ABCC2, ABCG2) showed high interindividual variabilities in HCC cancerous and non-cancerous liver tissues (Chou et al., 1997; Richart et al., 2002; Zollner et al., 2005) and the degregulation of these ABC genes in HCC has also been reported (Borel et al., 2012;

Moustafa et al., 2004; Zollner et al., 2005). Indeed, we observed similar high interindividual expression variabilities for these three genes in the TCGA HCC specimens with upregulation or downregulation in HCC relative to corresponding adjacent non-cancerous liver tissue in nearly 30% of the patients (data not shown).

The human genome contains 395 SLC genes (Hediger et al., 2013), among which 51 genes are defined as ADME genes (PharmaADME). Of these ADME SLC genes, 34 were expressed in the TCGA HCC specimens, including four genes that were upregulated

(SLC22A4, SLC22A11, SLC22A15, SLCO2A1) and eight genes that were downregulated

(SLC10A1, SLC22A1, SLC22A10, SLC7A8, SLCO1B1, SLCO1B3, SLCO2B1, SLCO4C1) in

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HCC (Fig. 4B, Table 1). The downregulation of three SLC genes (SLC22A1, SLCO1B1, and

SLCO1B3) in TCGA HCC was consistent with recent reports (Heise et al., 2012; Tsuboyama et al., 2010; Vander Borght et al., 2005; Vavricka et al., 2004). The downregulation of

SLC22A3 in HCC was also reported (Heise et al., 2012). This gene was downregulated in nearly 50% of the TCGA HCC specimens. Northern blot analysis showed that SLCO4C1 is only expressed in kidney and thus is considered as a kidney-specific SLC (Mikkaichi et al.,

2004). We showed here that this gene was abundantly expressed in non-cancerous liver tissues but was significantly downregulated in HCC (Fig. 4B). Downloaded from

Deregulation of ADME genes coding for modifiers in HCC jpet.aspetjournals.org Twenty-four of the 298 ADME genes encode for modifiers that either modulate the expression of ADME genes or affect the biochemistry of ADME enzymes (PharmaADME).

Of these ADME modifiers, 21 were expressed in TCGA HCC specimens, including nine at ASPET Journals on October 2, 2021 transcription factors (AHR, ARNT, HNF4A, PXR, CAR, PPARA, PPARD, PPARG, RXRA)

(Fig. 5, Table 1). Among these expressed modifier genes, four [catalase (CAT), cytidine deaminase (CDA), methionine adenosyltransferase I, alpha (MAT1A), pregnane X receptpr

(PXR)] were downregulated in HCC and one (potassium inwardly-rectifying channel, subfamily J, member 11, KCNJ11) was upregulated in HCC. The upregulation of KCNJ11

(Zhang et al., 2018) and the downregulation of CAT (Cho et al., 2014), PXR (Chen et al.,

2014), or CDA (Nwosu et al., 2017) in HCC have been recently reported. KCNJ11 is an HCC oncogene and its overexpression in HCC promotes cell proliferation and tumour progression

(Zhang et al., 2018). MAT1A upregulation in HCC was also recently reported (Nwosu et al.,

2017); however, the expression of this gene was significantly downregulated in the TCGA

HCC specimens. This discrepancy awaits further investigation.

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In addition to the nine transcription factors defined as ADME modifiers by the

PharmaADME Consortium, many other transcription factors are involved in transcription regulation of ADME genes (Hu et at., 2014a; Zanger et al., 2013). For example, a large number of ADME genes are regulated by the oxidative stress-responsive transcription factor

Nrf2 (the nuclear factor-erythroid 2 p45-related factor 2) (Hayes et al., 2014). Mutations of

NRF2 and its negative regulator KEAP1 (Kelch-like ECH-associated protein 1) in HCC have been reported (Guichard et al., 2012). There was no significant overall deregulation of NRF2 and KEAP1 in TCGA HCC specimens (Fig. 5); however, we show here that NRF2 had Downloaded from reduced mRNA levels in HCC in 42 of the 50 patients (Supplemental Fig. 2).

Correlation analyses between core ADME genes and nine ADME modifiers coding for jpet.aspetjournals.org transcription factor genes in HCC

As mentioned above, nine ADME modifiers (AHR, ARNT, HNF4A, PXR, CAR,

PPARA, PPARD, PPARG, RXRA) are transcription factors (TFs) that are believed to be able at ASPET Journals on October 2, 2021 to alter the expression of other ADME genes. Indeed, three of these modifiers (PXR, CAR,

HNF4A) are involved in transcriptional regulation of some ADME genes in liver (Congiu et al., 2009; Xu et al., 2005; Zhong et al., 2017). Analysis of TCGA RNAseq data from 371

HCC tumour tissues revealed that most of the core ADME genes were significantly positively correlated to five TF modifiers (HNF4A, PXR, CAR, PPARA, RXRA), and that about half of the core ADME genes were significantly positively correlated to two other TF modifiers

(AHR, ARNT) (Table S14). In particular, PXR was significantly positively correlated with 25 core ADME genes (Fig. 6). By contrast, PPARD was only positively correlated with one core

ADME gene (DPYD) but was negatively correlated to 17 core ADME genes (Fig. 7).

Similarly, PPARG was positively correlated with only four core ADME genes (ABCC2,

CYP2E1, SLCO1B3, UGT1A1) but negatively correlated with 14 core ADME genes core

ADME genes (Table S14). Of note, GSTP1 was the only core ADME gene that was

We further examined whether these TFs were differentially expressed in HCC in individual patients relative to their paired adjacent noncancerous liver tissues. As expected from an overall downregulation of PXR in HCC (Fig. 5), it was downregulated in HCC in almost all patients (Supplemental Figure 2). There was no significant overall deregulation of AHR,

CAR, PPARD, and PPARG (Fig. 5), but we show here that AHR and CAR were downregulated in HCC in nearly 50% of the patients, and that there was upregulation of

PPARD and PPARG in HCC in nearly 50% of the patients (Supplemental Figure 2). Taken Downloaded from together, about half of core ADME genes are positively correlated to seven ADME TFs

(AHR, ARNT, HNF4A, PXR, CAR, PPARA, RXRA) and negatively correlated to two ADME jpet.aspetjournals.org TFs (PPARD, PPARG) in HCC. Furthermore, NRF2 was also positively correlated with more than 20 core ADME genes (Table S14). Given that 6 of these 11 transcription factors and their coregulators are deregulated in HCC, it is likely that they contribute to the widespread at ASPET Journals on October 2, 2021 downregulation of core ADME genes in HCC.

The expression levels of core ADME genes are correlated with each other in HCC

The correlation of the core ADME genes with ADME TFs described above suggested that core ADME genes are co-regulated. To examine this idea, we performed expression correlation analysis of all 28 core ADME genes. 22 core ADME genes (ABCB1, ABCC2,

ABCG2, CYP1A1, CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2D6, CYP3A4,

CYP3A5, NAT1, NAT2, SLC22A1, SLCO1B1, SULT1A1, TPMT, UGT1A1, UGT2B7,

UGT2B15, UGT2B17) were significantly positively correlated with at least 20 other core

ADME genes (Table S15); 5 core ADME genes (CYP2C19, CYP2E1, DPYD, GSTM1,

SLCO1B3) were significantly positively correlated with about half of the core ADME genes

(Table S15). For instance, UGT2B7 was significantly positively correlated with 25 core

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

ADME genes (Fig. 9). GSTP1 was the only core ADME gene that was significantly negatively correlated to more than half of the core ADME genes (Table S15), consistent with the aforementioned negative correlation of GSTP1 with seven ADME TFs. Taken together, the widespread expression correlation of core ADME genes with each other and with transcription factors as summarized in Table S16 support the hypothesis that ADME genes are co-ordinately regulated by transcription factors in the liver.

Deregulation of miRNAs that are known to regulate core ADME genes in HCC Downloaded from miRNAs generally repress gene expression through mRNA degradation or translational repression. Therefore, the upregulation of miRNAs that regulate ADME genes

might contribute to their downregulation in HCC. To test this hypothesis, we examined the jpet.aspetjournals.org expression levels of 12 miRNAs (miR-132, miR-128, miR-130b, miR-29a-3p, miR-27a, miR-107, miR-25, miR-629, miR-103-5p, miR-378a, miR-126, miR-142) (Table S4) that are

reported to target the downregulated core ADME genes (CYP1A2, CYP2C9, CYP2C9, at ASPET Journals on October 2, 2021

CYP2C19, CYP3A4, CYP2C8, CYP2B6, SLCO1B3, CYP2C8, CYP2E1, CYP2A6, and

UGT2B7, respectively) in HCC and paired adjacent noncancerous liver tissues (Chen et al.,

2017; Higuchi et al., 2016; Jin et al., 2016; Mohri et al., 2010; Nakano et al., 2015;

Papageorgiou and Court, 2017; Rieger et al., 2015; Shi et al., 2015; Yu et al., 2015a; Yu et al., 2015b; Zhang et al., 2012). Strikingly, most (8/12) of these miRNAs were upregulated in

HCC tumours (Fig. 10), suggesting that they may play an important role in core ADME gene dysregulation in HCC.

Deregulation of aldo-keto reductases (AKRs) in HCC

Aldo-keto reductases (AKRs) are involved in the phase I metabolism of numerous endogenous and xenobiotic compounds including steroids, carbohydrates, prostaglandins, aldehydes, ketones, and many therapeutic drugs such as the cytotoxic anthracyclines (e.g.

Skarka et al, 2011, Penning, 2015). There are 15 AKR genes in the human genome (Penning

TM, 2015) but none of these genes are classified as ADME genes by the PharmaADME consortium (pharmadame.org). We show here that 11 human AKR genes (e.g. AKR1A1,

AKR1B1, AKR1B10, AKR1C1, AKR1C2, AKR1C3, AKR1C4, AKR1D1, AKR1E2, AKR7A2, and AKR7A3) were expressed in TCGA HCC specimens (Fig. 11). Of these genes, AKR1B10 and AKR1C3 were significantly upregulated whereas AKR1D1 and AKR7A3 were significantly downregulated in HCC. AKR1B10 upregulation is consistent with the Downloaded from overexpression of AKR1B10 enzyme in HCC as reported recently in an immunohistochemistry study (Matkowskyj et al., 2014). AKR7A3 downregulation in HCC jpet.aspetjournals.org (Chow et al., 2017) and AKR1C3 upregulation in prostate and breast cancer (Fung et al.,

2006; Lin et al., 2004) has also been reported.

at ASPET Journals on October 2, 2021 Discussion

ADME genes are involved in drug Absorption, Distribution, Metabolism, and

Excretion and are abundantly expressed in the liver, the major organ of drug metabolism and clearance in humans. Although several studies have investigated the deregulation of subsets of ADME genes in HCC (Borel et al., 2012; Chen et al., 2014; Ho et al., 2015; Jin et al.,

2015a; Lee et al., 2004; Okabe et al., 2001; Park et al., 2006; Woo et al., 2017; Yan et al.,

2015a; Yan et al., 2015b), there has been no systematic, targeted analysis of the entire ADME gene set. In this study, our comprehensive analysis of paired cancerous and noncancerous specimens from the TCGA HCC dataset identified for the first time the complete list of

ADME genes that are deregulated in HCC. Overall, around one quarter of all ADME genes were downregulated whilst only 6% were upregulated in HCC. The deregulated genes included members of all four groups of ADME genes, namely Phase I (CYPs, ADHs, ALDHs)

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018 and II (GSTs, SULTs, UGTs) drug metabolizing enzymes, drug transporters (ABC and SLC transporters) and modifiers. Moreover, half of the core ADME genes were significantly downregulated in HCC. Among the ADME gene superfamilies, CYPs were the most significantly downregulated. Our analyses of four other microarray gene expression profiling reports of HCC observed similar downregulation of these core ADME genes in HCC (Chen et al., 2002; Roessler et al., 2010; Thurnherr et al., 2016; Wurmbach et al., 2007). The deregulation of many other ADME genes reported by us in the present study are consistent with the findings of previous studies as already discussed comprehensively. However, we Downloaded from report in the present study for the first time for the deregulation of a variety of ADME genes in HCC, such as AKR1C3, AKR1D1, ALDH1A3, ALDH3A1, ALDH6A1, ALDH8A1, jpet.aspetjournals.org UGT2B10, and UGT2B11.Most ADME genes belong to superfamilies that also contain non-

ADME members (for example, genes that mediate biosynthetic processes or transport endobiotics). To better understand the specificity of ADME deregulation, we compared the at ASPET Journals on October 2, 2021 ADME gene set to non-ADME genes from the same superfamilies. Our results showed that the ADME genes were far more likely to be downregulated than non-ADME genes in HCC.

We further performed unbiased pathway analysis (Reactome) using the entire ADME and non-ADME gene set (609 genes) and found that the downregulated gene set was significantly more enriched for drug metabolism pathways, than the unchanged or upregulated gene sets

(not shown). Together with the data on ADME TFs and miRNAs discussed below, this supports the idea that ADME-specific regulatory pathways are deregulated in HCC.

Decreased expression of genes encoding hepatocyte-specific gene products (e.g. albumin) and detoxification enzymes (CYPs), and associated loss of liver-specific functions, is a recognized phenomenon in HCC and is in part due to de-differentiation of cancer cells

(Andrisani et al., 2011; Obaidat et al., 2012; Okabe et al., 2001; Park et al., 2006). The widespread downregulation of ADME genes in HCC shown herein is likely part of this de-

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018 differentiation. The molecular mechanisms underlying de-differentiation are not completely understood but could be related to epigenetic (chromatin) modifications or deregulation of transcription factors and miRNAs that regulate liver-specific genes. The deregulation of TFs and miRNAs in HCC as we showed herein in this study supports this assumption. Briefly, we found that most of the miRNAs known to regulate the downregulated core ADME genes were upregulated in HCC. Furthermore, transcription factors (PXR, AHR, CAR) that are positively correlated with core ADME genes were downregulated whereas the transcription factors (PPARD and PPARG) that are negatively correlated with core ADME genes were Downloaded from upregulated in HCC. The recent reports of DNA hypermethylation at the promoters of three downregulated core ADME genes (CYP1A2, CYP2C9, and SLC22A1) in HCC links jpet.aspetjournals.org epigenetic modifications to their deregulation in this cancer (Schaeffeler et al., 2011; Shen et al., 2012).

There are no curative therapeutic options for advanced HCC (Llovet et al., 2008). at ASPET Journals on October 2, 2021

Most of the clinical trials evaluating anticancer agents in chemotherapy, hormone therapies, and immunotherapy have failed to show convincing survival benefits for patients with advanced HCC (Llovet and Hernandez-Gea, 2014; Llovet et al., 2008). The failure of these trials might be related to the expression of nearly 80% of ADME genes (233/298) in HCC as we found in the present study. Two multi-kinase inhibitors, sorafenib and regorafenib, are currently the only two drugs approved by FDA for treating advanced HCC (Bruix et al.,

2017; Llovet et al., 2008). The deregulation of the ADME genes involved in the metabolism and clearance of these two drugs may help to explain their relative efficacy. The key ADME genes involved in the metabolism and clearance of sorafenib and regorafenib in liver include the enzymes CYP3A4 (Ghassabian et al., 2012) and UGT1A9 (Miners et al., 2017; Peer et al.,

2012), influx transporters (SLCO1B1, SLCO1B3, and SLC22A1) (Ohya et al., 2015; Swift et al., 2013; Zimmerman et al., 2013), and efflux transporters (ABCB1, ABCC2, ABCG2) (Ohya

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018 et al., 2015; van Erp et al., 2009). Whilst the efflux transporters were not significantly altered in HCC, the expression of CYP3A4, UGT1A9, SLCO1B1, SLCO1B3, and SLC22A1 were all significantly downregulated in HCC, consistent with previous observations (Heise et al.,

2012; Tsuboyama et al., 2010; Vander Borght et al., 2005; Ye et al., 2014). The downregulation of CYP3A4 and UGT1A9 could reduce the metabolism of sorafenib and regorafenib and hence enhance therapeutic efficacy; however, the downregulation of influx transporters (SLCO1B1, SLCO1B3, and SLC22A1) may decrease the cellular uptake and reduce efficacy. Ultimately, the combined effects of deregulated uptake and metabolism will Downloaded from determine the therapeutic efficacy of sorafenib and regorafenib. The opposing influence of

ADME gene deregulation on the therapeutic efficacy of this effective HCC therapeutic jpet.aspetjournals.org regimen warrants further investigation.

Our observation of deregulated UGT expression may also provide some insights into cytotoxic drug efficacy in HCC. The chemotherapy regimen epirubicin, 5-fluoruracil, and at ASPET Journals on October 2, 2021 cisplatin (EFC) has been found to be poorly effective as a first line therapy for HCC; however it did provide survival benefits when used as second line therapy for advanced sorafenib- resistant HCC (Lee JE et al., 2014). UGT2B7 is the only UGT known to mediate epirubicin conjugation (Innocenti et al., 2001), and this metabolism is suggested to be responsible for its lower systemic toxicity relative to doxorubicin (Hu et al., 2014b). We observed downregulation of UGT2B7 in HCC as well as upregulation of UGT2B11. In recent studies performed in vitro, we found that UGT2B11 inhibits the ability of UGT2B7 to glucuronidate epirubicin (manuscript in preparation). Thus the combined downregulation of UGT2B7 and upregulation of UGT2B11 could reduce intratumoural epirubicin conjugation and clearance, and this may contribute to the modest efficacy of this regimen in advanced HCC.

CBR1 (carbonyl reductase 1) and multiple AKR subfamily 1 enzymes (AKR1A1,

AKR1B1, AKR1B10, AKR1C3 and AKR1C4) are involved in metabolism of doxorubicin, a

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018 common cytotoxic drug for treating HCC (Kassner et al., 2008, Novotna et al, 2008). We show in this study that all these enzymes were highly expressed in HCC and two of them

(AKR1B10, AKR1C3) were significantly upregulated in TCGA HCC specimens. The abundant expression of these enzymes and their deregulation in HCC may contribute to the development of cancer resistance to doxorubicin-containing HCC chemotherapeutic regimens

(Grazie et al, 2017).

It has been proposed that the process of absorption, distribution, metabolism, and excretion of xenobiotics (e.g. drugs, dietary constituents) is coordinated in the liver mainly Downloaded from through the co-regulation of ADME genes by transcription factors (Congiu et al., 2009) (Fig.

12). The direct evidence to support this hypothesis came from the discoveries that a large jpet.aspetjournals.org number of ADME genes (e.g. CYPs, UGTs, ABC and SLC transporters) are induced (rarely repressed) by ligand-activated transcription factors (e.g. PXR, FXR, CAR, PPAR, AhR,

Nrf2) upon exposure to xenobiotics (e.g. drugs) (Chen et al., 2013; Hu et al., 2014; Scotto, at ASPET Journals on October 2, 2021 2003; Zanger and Schwab, 2013). Further indirect evidence to support this hypothesis came from the studies that have shown positive correlation of the mRNA levels between ADME genes and transcription factors such as PXR, CAR and HNF4A in normal (Slatter et al., 2006;

Wortham et al., 2007) and non-HCC diseased (Congiu et al., 2002; Congiu et al., 2009;

Kurzawski et al., 2012) liver tissues. However, these correlation studies mainly relied on quantitative RT-PCR to measure gene expression levels and thus only analysed a limited number of ADME genes. In this study, a comprehensive expression correlation analysis of the TCGA HCC RNA-seq data showed for the first time that most core ADME genes were significantly positively correlated with each other. We further showed for the first time that the expression levels of about half of the core ADME genes were significantly positively

(AHR, ARNT, HNF4A, PXR, CAR, PPARA, RXRA) or negatively (PPARD, PPARG) correlated with those of the nine transcription factors that are classified as ADME modifiers.

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Collectively, these observations provide compelling evidence supporting a coordinated hepatic regulation of the broader ADME transcriptome. Moreover, this study suggests that the coordinate regulation model of ADME genes may also extend to posttranscriptional regulation by miRNAs (Fig. 12); however further work will be required to define the ADME gene-miRNA regulatory network.

In conclusion, this study reveals a widespread deregulation of ADME genes in HCC and widespread expression correlations of ADME genes each other and with transcription Downloaded from factors. The deregulation of ADME genes represents part of the de-differentiation of liver function in HCC and is likely due to deregulation of epigenetics, transcription factors and

miRNAs. The co-regulation of ADME genes mediated by liver-enriched (e.g. HNF4A, jpet.aspetjournals.org

PPARA, PPARD, PPARG) and xenobiotic-activated (e.g. AHR, PXR, CAR, NRF2) transcription factors ensures that the process of absorption, distribution, metabolism and

excretion is coordinated efficiently upon exposure to endogenous and exogenous compounds at ASPET Journals on October 2, 2021 such as dietary constituents and therapeutic drugs (Fig. 12).

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Authorship Contributions

Participated in research design: Hu, Marri, McKinnon, Mackenzie and Meech

Conducted experiments: Hu, Marri

Performed data analysis: Hu, Marri, Meech

Wrote or contributed to the writing of the manuscript: Hu, Meech, Marri, Mackenzie,

McKinnon Downloaded from

jpet.aspetjournals.org at ASPET Journals on October 2, 2021

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References

Andrisani OM, Studach L and Merle P (2011) Gene signatures in hepatocellular carcinoma

(HCC). Semin Cancer Biol 21: 4-9.

Blanchard RL, Freimuth RR, Buck J, Weinshilboum RM and Coughtrie MW (2004) A

proposed nomenclature system for the cytosolic sulfotransferase (SULT) superfamily.

Pharmacogenetics 14: 199-211.

Borel F, Han R, Visser A, Petry H, van Deventer SJ, Jansen PL, Konstantinova P and Reseau

Centre de Ressources Biologiques Foie F (2012) Adenosine triphosphate-binding Downloaded from

cassette transporter genes up-regulation in untreated hepatocellular carcinoma is

mediated by cellular microRNAs. Hepatology 55: 821-832. jpet.aspetjournals.org Bruix J, Qin S, Merle P, Granito A, Huang YH, Bodoky G, Pracht M, Yokosuka O,

Rosmorduc O, Breder V, Gerolami R, Masi G, Ross PJ, Song T, Bronowicki JP,

Ollivier-Hourmand I, Kudo M, Cheng AL, Llovet JM, Finn RS, LeBerre MA, at ASPET Journals on October 2, 2021 Baumhauer A, Meinhardt G, Han G and Investigators R (2017) Regorafenib for

patients with hepatocellular carcinoma who progressed on sorafenib treatment

(RESORCE): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet

389: 56-66.

Chen H, Shen ZY, Xu W, Fan TY, Li J, Lu YF, Cheng ML and Liu J (2014) Expression of

P450 and nuclear receptors in normal and end-stage Chinese livers. World journal of

gastroenterology 20: 8681-8690.

Chen QX, Hu HH, Zhou Q, Yu AM and Zeng S (2013) An overview of ABC and SLC drug

transporter gene regulation. Curr Drug Metab 14: 253-264.

Chen X, Cheung ST, So S, Fan ST, Barry C, Higgins J, Lai KM, Ji J, Dudoit S, Ng IO, Van

De Rijn M, Botstein D and Brown PO (2002) Gene expression patterns in human liver

cancers. Mol Biol Cell 13: 1929-1939.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Chen Y, Zeng L, Wang Y, Tolleson WH, Knox B, Chen S, Ren Z, Guo L, Mei N, Qian F,

Huang K, Liu D, Tong W, Yu D and Ning B (2017) The expression, induction and

pharmacological activity of CYP1A2 are post-transcriptionally regulated by

microRNA hsa-miR-132-5p. Biochem Pharmacol 145: 178-191.

Cho MY, Cheong JY, Lim W, Jo S, Lee Y, Wang HJ, Han KH and Cho H (2014) Prognostic

significance of catalase expression and its regulatory effects on hepatitis B virus X

protein (HBx) in HBV-related advanced hepatocellular carcinomas. Oncotarget 5:

12233-12246. Downloaded from

Chou YY, Cheng AL and Hsu HC (1997) Expression of P-glycoprotein and p53 in advanced

hepatocellular carcinoma treated by single agent chemotherapy: clinical correlation. J jpet.aspetjournals.org Gastroenterol Hepatol 12: 569-575.

Chow RKK, Tsz-kwan Sin S, Liu M, Li Y, Man Chan TH, Song Y, Chen L, Lai-Wan Kwong

D and Guan XY (2017). AKR7A3 suppresses tumorigenicity and chemoresistance in at ASPET Journals on October 2, 2021 hepatocellular carcinoma through attenuation of ERK, c-Jun and NF-kB signalling

pathways. Oncotarget 8:83469-83479.

Congiu M, Mashford ML, Slavin JL and Desmond PV (2002) UDP glucuronosyltransferase

mRNA levels in human liver disease. Drug Metab Dispos 30: 129-134.

Congiu M, Mashford ML, Slavin JL and Desmond PV (2009) Coordinate regulation of

metabolic enzymes and transporters by nuclear transcription factors in human liver

disease. J Gastroenterol Hepatol 24: 1038-1044.

Dean M, Rzhetsky A and Allikmets R (2001) The human ATP-binding cassette (ABC)

transporter superfamily. Genome Res 11: 1156-1166.

Edenberg HJ (2007) The genetics of alcohol metabolism: role of alcohol dehydrogenase and

aldehyde dehydrogenase variants. Alcohol Res Health 30: 5-13.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Farazi PA and DePinho RA (2006) Hepatocellular carcinoma pathogenesis: from genes to

environment. Nat Rev Cancer 6: 674-687.

Fisher CD, Lickteig AJ, Augustine LM, Ranger-Moore J, Jackson JP, Ferguson SS and

Cherrington NJ (2009) Hepatic cytochrome P450 enzyme alterations in humans with

progressive stages of nonalcoholic fatty liver disease. Drug Metab Dispos 37: 2087-

2094.

Fung KM, Samara EN, Wong C, Metwalli A, Krlin R, Bane B, Liu CZ, Yang JT, Pitha JV,

Culkin DJ, Kropp BP, Penning TM and Lin HK (2006) Increased expression of type 2 Downloaded from

3alpha-hydroxysteroid dehydrogenase/type 5 17beta-hydroxysteroid dehydrogenase

(AKR1C3) and its relationship with androgen receptor in prostate carcinoma. Endocr jpet.aspetjournals.org Relat Cancer 13:169-180.

Ghassabian S, Rawling T, Zhou F, Doddareddy MR, Tattam BN, Hibbs DE, Edwards RJ, Cui

PH and Murray M (2012) Role of human CYP3A4 in the biotransformation of at ASPET Journals on October 2, 2021 sorafenib to its major oxidized metabolites. Biochem Pharmacol 84: 215-223.

Guichard C, Amaddeo G, Imbeaud S, Ladeiro Y, Pelletier L, Maad IB, Calderaro J,

Bioulac-Sage P, Letexier M, Degos F, Clément B, Balabaud C, Chevet E,

Laurent A, Couchy G, Letouzé E, Calvo F and Zucman-Rossi J (2012) Integrated

analysis of somatic mutations and focal copy-number changes identifies key genes

and pathways in hepatocellular carcinoma. Nat Genet 44:694-698.

Hardwick RN, Ferreira DW, More VR, Lake AD, Lu Z, Manautou JE, Slitt AL and

Cherrington NJ (2013) Altered UDP-glucuronosyltransferase and sulfotransferase

expression and function during progressive stages of human nonalcoholic fatty liver

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

disease. Drug Metab Dispos 41: 554-561.

Hardwick RN, Fisher CD, Canet MJ, Lake AD and Cherrington NJ (2010) Diversity in

antioxidant response enzymes in progressive stages of human nonalcoholic fatty liver

disease. Drug Metab Dispos 38: 2293-2301.

Hardwick RN, Fisher CD, Canet MJ, Scheffer GL and Cherrington NJ (2011) Variations in

ATP-binding cassette transporter regulation during the progression of human

nonalcoholic fatty liver disease. Drug Metab Dispos 39: 2395-2402. Downloaded from Hayes JD and Dinkova-kostova AT (2014) The Nrf2 regulatory network provides an

interface between redox and intermediary metabolism. Trends Biochem Sci 39:199-

218. jpet.aspetjournals.org

Hediger MA, Clemencon B, Burrier RE and Bruford EA (2013) The ABCs of membrane

transporters in health and disease (SLC series): introduction. Mol Aspects Med 34: 95-

107. at ASPET Journals on October 2, 2021

Heise M, Lautem A, Knapstein J, Schattenberg JM, Hoppe-Lotichius M, Foltys D, Weiler N,

Zimmermann A, Schad A, Grundemann D, Otto G, Galle PR, Schuchmann M and

Zimmermann T (2012) Downregulation of organic cation transporters OCT1

(SLC22A1) and OCT3 (SLC22A3) in human hepatocellular carcinoma and their

prognostic significance. BMC Cancer 12: 109.

Higuchi T, Todaka H, Sugiyama Y, Ono M, Tamaki N, Hatano E, Takezaki Y, Hanazaki K,

Miwa T, Lai S, Morisawa K, Tsuda M, Taniguchi T and Sakamoto S (2016)

Suppression of MicroRNA-7 (miR-7) Biogenesis by Nuclear Factor 90-Nuclear

Factor 45 Complex (NF90-NF45) Controls Cell Proliferation in Hepatocellular

Carcinoma. J Biol Chem 291: 21074-21084.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Ho DW, Kai AK and Ng IO (2015) TCGA whole-transcriptome sequencing data reveals

significantly dysregulated genes and signaling pathways in hepatocellular carcinoma.

Front Med 9: 322-330.

Hu DG, Meech R, McKinnon RA and Mackenzie PI (2014a) Transcriptional regulation of

human UDP-glucuronosyltransferase genes. Drug Metab Rev 46: 421-458.

Hu DG, Rogers A, Mackenzie PI (2014b) Epirubicin upregulates UDP

glucuronosyltransferase 2B7 expression in liver cancer cells via the p53 pathway. Mol

Pharmacol 85:887-897. Downloaded from

Innocenti F, Iyer L, Ramirez J, GReen MD, Ratain MJ (2001) Epirubicin glucruronidation is

catalyzed by human UDP-glucuronosyltransferase 2B7. Drug Metab Dispos 29:686- jpet.aspetjournals.org 692.

Jahn SC, Solayman MH, Lorenzo RJ, Langaee T, Stacpoole PW and James MO (2016)

GSTZ1 expression and chloride concentrations modulate sensitivity of cancer cells to at ASPET Journals on October 2, 2021 dichloroacetate. Biochim Biophys Acta 1860: 1202-1210.

Jin B, Wang W, Du G, Huang GZ, Han LT, Tang ZY, Fan DG, Li J and Zhang SZ (2015a)

Identifying hub genes and dysregulated pathways in hepatocellular carcinoma. Eur

Rev Med Pharmacol Sci 19: 592-601.

Jin S, Chen J, Chen L, Histen G, Lin Z, Gross S, Hixon J, Chen Y, Kung C, Chen Y, Fu Y,

Lu Y, Lin H, Cai X, Yang H, Cairns RA, Dorsch M, Su SM, Biller S, Mak TW and

Cang Y (2015b) ALDH2(E487K) mutation increases protein turnover and promotes

murine hepatocarcinogenesis. Proc Natl Acad Sci U S A 112: 9088-9093.

Jin Y, Yu D, Tolleson WH, Knox B, Wang Y, Chen S, Ren Z, Deng H, Guo Y and Ning B

(2016) MicroRNA hsa-miR-25-3p suppresses the expression and drug induction of

CYP2B6 in human hepatocytes. Biochem Pharmacol 113: 88-96.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Kamarajah SK, Frankel TL, Sonnenday C, Cho CS and Nathan H (2017) Critical eveluation

of the American Joint Commission on Cancer (AJCC) 8th edition staging system for

patients with hepatocellular carcinoma (HCC): a surveilance, epidemiology, end

results (SEER) analysis. J Surg Oncol 117; 644-650.

Kassner N, Huse K, Martin HJ, Gödtel-Armbrust U, Metzger A, Meineke I,

Brockmöller J, Klein K, Zanger UM, Maser E, and Wojnowski L (2008) Carbonyl

reductase 1 is a predominant doxorubicin reductase in the human liver. Drug Metab

Dispos 36:2113-2120. Downloaded from

Kurzawski M, Dziedziejko V, Post M, Wojcicki M, Urasinska E, Mietkiewski J and

Drozdzik M (2012) Expression of genes involved in xenobiotic metabolism and jpet.aspetjournals.org transport in end-stage liver disease: up-regulation of ABCC4 and CYP1B1.

Pharmacol Rep : 64: 927-939.

Le Grazie M, Biagini MR, Tarocchi M, Polvani S, and Galli A (2017) Chemotherapy for at ASPET Journals on October 2, 2021 hepatocellular carcinoma: The present and the future. World J Hepatol 9: 907-920.

Lee JS, Chu IS, Heo J, Calvisi DF, Sun Z, Roskams T, Durnez A, Demetris AJ and

Thorgeirsson SS (2004) Classification and prediction of survival in hepatocellular

carcinoma by gene expression profiling. Hepatology 40: 667-676.

Lee JE, Bae SH, Chol JY, Yoon SK, You YK, and Lee MA (2014) Epirubicin, cisplatin, 5-

FU combination chemoyterapy in sorafenib-refractory metastatic hepatocellular

carcinoma. World J Gastroenterol 20: 235-241.

Li Y, Wan D, Wei W, Su J, Cao J, Qiu X, Ou C, Ban K, Yang C and Yue H (2008) Candidate

genes responsible for human hepatocellular carcinoma identified from differentially

expressed genes in hepatocarcinogenesis of the tree shrew (Tupaia belangeri

chinesis). Hepatol Res 38: 85-95.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Lin S, Hoffmann K and Schemmer P (2012) Treatment of hepatocellular carcinoma: a

systematic review. Liver Cancer 1: 144-158.

Lin HK, Steckelbroeck S, Fung KM, Jones AN, and Penning TM (2004) Characterization of

a monoclonal antibody for human aldo-keto reductase AKR1C3 (type 2 3 alpha-

hydroxysteroid dehydrogenase/type 5 17beta-hydroxysteriod dehydrogenase):

immunohistochemical detection in breast and prostate. Steroid 69:795-801.

Liu CJ, Yang JH, Huang FZ, Nie WP, Liu CP, Mao XH, Yin XM, Shen XB, Peng C, Chen

MF, Jiang B, Liu XY and Wu JS (2017) Glutathione-s-transferase A 4 (GSTA4) Downloaded from

suppresses tumor growth and metastasis of human hepatocellular carcinoma by

targeting AKT pathway. Am J Transl Res 9: 301-315. jpet.aspetjournals.org Llovet JM and Hernandez-Gea V (2014) Hepatocellular carcinoma: reasons for phase III

failure and novel perspectives on trial design. Clin Cancer Res 20: 2072-2079.

Llovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc JF, de Oliveira AC, Santoro A, at ASPET Journals on October 2, 2021 Raoul JL, Forner A, Schwartz M, Porta C, Zeuzem S, Bolondi L, Greten TF, Galle

PR, Seitz JF, Borbath I, Haussinger D, Giannaris T, Shan M, Moscovici M, Voliotis

D, Bruix J and Group SIS (2008) Sorafenib in advanced hepatocellular carcinoma. N

Engl J Med 359: 378-390.

Love MI, Huber W and Anders S (2014) Moderated estimation of fold change and dispersion

for RNA-seq data with DESeq2. Genome Biol 15: 550.

Lu L, Zhou J, Shi J, Peng XJ, Qi XX, Wang Y, Li FY, Zhou FY, Liu L and Liu ZQ (2015)

Drug-Metabolizing Activity, Protein and Gene Expression of UDP-

Glucuronosyltransferases Are Significantly Altered in Hepatocellular Carcinoma

Patients. PloS one 10: e0127524.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Marchitti SA, Brocker C, Stagos D and Vasiliou V (2008) Non-P450 aldehyde oxidizing

enzymes: the aldehyde dehydrogenase superfamily. Expert Opin Drug Metab Toxicol

4: 697-720.

Matkowskyj KA, Bai H, Liao J, Zhang W, Li H, Rao S, Omary R and Yang GY (2014)

Aldoketoreductase family 1B10 (ARK1B10) as a biomarker to distinguish

hepatocellular carcinoma from benign liver lesions. Hum Pathol 45: 834-843.

Matsunaga T, Wada Y, Endo S, Soda M, EI-Kabbani O and Hara A (2012) Aldo-keto

reductase 1B10 and its role in proliferation capacity of drug-resistant cancers. Front Downloaded from

Pharmacol 3:5.

Mikkaichi T, Suzuki T, Onogawa T, Tanemoto M, Mizutamari H, Okada M, Chaki T, jpet.aspetjournals.org Masuda S, Tokui T, Eto N, Abe M, Satoh F, Unno M, Hishinuma T, Inui K, Ito S,

Goto J and Abe T (2004) Isolation and characterization of a digoxin transporter and

its rat homologue expressed in the kidney. Proc Natl Acad Sci U S A 101: 3569-3574. at ASPET Journals on October 2, 2021 Miners JO, Chau N, Rowland A, Burns K, McKinnon RA, Mackenzie PI, Tucker GT,

Knights KM and Kichenadasse G (2017) Inhibition of human UDP-

glucuronosyltransferase enzymes by lapatinib, pazopanib, regorafenib and sorafenib:

Implications for hyperbilirubinemia. Biochem Pharmacol 129: 85-95.

Mohri T, Nakajima M, Fukami T, Takamiya M, Aoki Y and Yokoi T (2010) Human

CYP2E1 is regulated by miR-378. Biochem Pharmacol 79: 1045-1052.

Moustafa MA, Ogino D, Nishimura M, Ueda N, Naito S, Furukawa M, Uchida T, Ikai I,

Sawada H and Fukumoto M (2004) Comparative analysis of ATP-binding cassette

(ABC) transporter gene expression levels in peripheral blood leukocytes and in liver

with hepatocellular carcinoma. Cancer Sci 95: 530-536.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Nakano M, Fukushima Y, Yokota S, Fukami T, Takamiya M, Aoki Y, Yokoi T and

Nakajima M (2015) CYP2A7 pseudogene transcript affects CYP2A6 expression in

human liver by acting as a decoy for miR-126. Drug Metab Dispos 43: 703-712.

Nelson DR, Zeldin DC, Hoffman SM, Maltais LJ, Wain HM and Nebert DW (2004)

Comparison of cytochrome P450 (CYP) genes from the mouse and human genomes,

including nomenclature recommendations for genes, pseudogenes and alternative-

splice variants. Pharmacogenetics 14: 1-18.

Novotna R, Wsol V, Xiong G and Maser E (2008). Inactivation of the antcancer drugs Downloaded from

doxorubicin and oracin by aldo-keto reductase (AKR) 1C3. Toxicol lett 181:1-6.

Nwosu ZC, Megger DA, Hammad S, Sitek B, Roessler S, Ebert MP, Meyer C and Dooley S jpet.aspetjournals.org (2017) Identification of the Consistently Altered Metabolic Targets in Human

Hepatocellular Carcinoma. Cell Mol Gastroenterol Hepatol 4: 303-323.e1.

Obaidat A, Roth M and Hagenbuch B (2012) The expression and function of organic anion at ASPET Journals on October 2, 2021 transporting polypeptides in normal tissues and in cancer. Annu Rev Pharmacol

Toxicol 52: 135-151.

Ohya H, Shibayama Y, Ogura J, Narumi K, Kobayashi M and Iseki K (2015) Regorafenib is

transported by the organic anion transporter 1B1 and the multidrug resistance protein

2. Biol Pharm Bull 38: 582-586.

Okabe H, Satoh S, Kato T, Kitahara O, Yanagawa R, Yamaoka Y, Tsunoda T, Furukawa Y

and Nakamura Y (2001) Genome-wide analysis of gene expression in human

hepatocellular carcinomas using cDNA microarray: identification of genes involved

in viral carcinogenesis and tumor progression. Cancer research 61: 2129-2137.

Papageorgiou I and Court MH (2017) Identification and validation of the microRNA

response elements in the 3'-untranslated region of the UDP glucuronosyltransferase

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

(UGT) 2B7 and 2B15 genes by a functional genomics approach. Biochem Pharmacol

146: 199-213.

Park T, Yi SG, Shin YK and Lee S (2006) Combining multiple microarrays in the presence of

controlling variables. Bioinformatics 22: 1682-1689.

Peer CJ, Sissung TM, Kim A, Jain L, Woo S, Gardner ER, Kirkland CT, Troutman SM,

English BC, Richardson ED, Federspiel J, Venzon D, Dahut W, Kohn E, Kummar S,

Yarchoan R, Giaccone G, Widemann B and Figg WD (2012) Sorafenib is an inhibitor

of UGT1A1 but is metabolized by UGT1A9: implications of genetic variants on Downloaded from

pharmacokinetics and hyperbilirubinemia. Clin Cancer Res : 18: 2099-2107.

Penning TM (2015) The aldo-keto reductases (ARKs): Overview. Chemo Biol Interact jpet.aspetjournals.org 234:236-246.

Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, Barrette

TR, Anstet MJ, Kincead-Beal C, Kulkarni P, Varambally S, Ghosh D and Chinnaiyan at ASPET Journals on October 2, 2021 AM (2007) Oncomine 3.0: genes, pathways, and networks in a collection of 18,000

cancer gene expression profiles. Neoplasia 9: 166-180.

Richart J, Brunt EM and Di Bisceglie AM (2002) Expression of P-glycoprotein and C-

MOAT in human hepatocellular carcinoma: detection by immunostaining. Dig Dis Sci

47: 2454-2458.

Rieger JK, Reutter S, Hofmann U, Schwab M and Zanger UM (2015) Inflammation-

associated microRNA-130b down-regulates cytochrome P450 activities and directly

targets CYP2C9. Drug Metab Dispos 43: 884-888.

Roessler S, Jia HL, Budhu A, Forgues M, Ye QH, Lee JS, Thorgeirsson SS, Sun Z, Tang ZY,

Qin LX and Wang XW (2010) A unique metastasis gene signature enables prediction

of tumor relapse in early-stage hepatocellular carcinoma patients. Cancer research

70: 10202-10212.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Schaeffeler E, Hellerbrand C, Nies AT, Winter S, Kruck S, Hofmann U, van der Kuip H,

Zanger UM, Koepsell H and Schwab M (2011) DNA methylation is associated with

downregulation of the organic cation transporter OCT1 (SLC22A1) in human

hepatocellular carcinoma. Genome Med 3: 82.

Scotto KW (2003) Transcriptional regulation of ABC drug transporters. Oncogene 22: 7496-

7511.

Shang L, Zhu G, Su H, Chen B, Ye X, Chen X, Xiao K, Li L, Peng M and Peng T (2017)

Identification of alcohol dehydrogenase as a potential prognostic marker in HBV- Downloaded from

related hepatocellular carcinoma. Int J Clin Exp Med 10: 4457-4472.

Shen J, Wang S, Zhang YJ, Kappil M, Wu HC, Kibriya MG, Wang Q, Jasmine F, Ahsan H, jpet.aspetjournals.org Lee PH, Yu MW, Chen CJ and Santella RM (2012) Genome-wide DNA methylation

profiles in hepatocellular carcinoma. Hepatology 55: 1799-1808.

Shi Y, Liu Y, Wei Z, Zhang Y, Zhang L, Jiang S, Xiong Y, Shen L, He L, Xing Q and Qin S at ASPET Journals on October 2, 2021 (2015) Hsa-miR-27a is involved in the regulation of CYP3A4 expression in human

livers from Chinese Han population. Pharmacogenomics 16: 1379-1386.

Skarka A, Skarydova L, Stambergova H and Wsol (2011) Anthracylines and their

metabolism in human liver microsomes and the participation of the new microsomal

carbonyl reductase. Chem Biol Interact 191:66-74

Slatter JG, Templeton IE, Castle JC, Kulkarni A, Rushmore TH, Richards K, He Y, Dai X,

Cheng OJ, Caguyong M and Ulrich RG (2006) Compendium of gene expression

profiles comprising a baseline model of the human liver drug metabolism

transcriptome. Xenobiotica 36: 938-962.

Swift B, Nebot N, Lee JK, Han T, Proctor WR, Thakker DR, Lang D, Radtke M, Gnoth MJ

and Brouwer KL (2013) Sorafenib hepatobiliary disposition: mechanisms of hepatic

uptake and disposition of generated metabolites. Drug Metab Dispos 41: 1179-1186.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Thurnherr T, Mah WC, Lei Z, Jin Y, Rozen SG and Lee CG (2016) Differentially Expressed

miRNAs in Hepatocellular Carcinoma Target Genes in the Genetic Information

Processing and Metabolism Pathways. Sci Rep 6: 20065.

Tsuboyama T, Onishi H, Kim T, Akita H, Hori M, Tatsumi M, Nakamoto A, Nagano H,

Matsuura N, Wakasa K and Tomoda K (2010) Hepatocellular carcinoma: hepatocyte-

selective enhancement at gadoxetic acid-enhanced MR imaging--correlation with

expression of sinusoidal and canalicular transporters and bile accumulation.

Radiology 255: 824-833. Downloaded from van Erp NP, Gelderblom H and Guchelaar HJ (2009) Clinical pharmacokinetics of tyrosine

kinase inhibitors. Cancer Treat Rev 35: 692-706. jpet.aspetjournals.org Vander Borght S, Libbrecht L, Blokzijl H, Faber KN, Moshage H, Aerts R, Van Steenbergen

W, Jansen PL, Desmet VJ and Roskams TA (2005) Diagnostic and pathogenetic

implications of the expression of hepatic transporters in focal lesions occurring in at ASPET Journals on October 2, 2021 normal liver. J Pathol 207: 471-482.

Vavricka SR, Jung D, Fried M, Grutzner U, Meier PJ and Kullak-Ublick GA (2004) The

human organic anion transporting polypeptide 8 (SLCO1B3) gene is transcriptionally

repressed by hepatocyte nuclear factor 3beta in hepatocellular carcinoma. J Hepatol

40: 212-218.

Wei RR, Zhang MY, Rao HL, Pu HY, Zhang HZ and Wang HY (2012) Identification of

ADH4 as a novel and potential prognostic marker in hepatocellular carcinoma. Med

Oncol 29: 2737-2743.

Wlcek K and Stieger B (2014) ATP-binding cassette transporters in liver. Biofactors 40: 188-

198.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Woo HG, Choi JH, Yoon S, Jee BA, Cho EJ, Lee JH, Yu SJ, Yoon JH, Yi NJ, Lee KW, Suh

KS and Kim YJ (2017) Integrative analysis of genomic and epigenomic regulation of

the transcriptome in liver cancer. Nat Commun 8: 839.

Wortham M, Czerwinski M, He L, Parkinson A and Wan YJ (2007) Expression of

constitutive androstane receptor, hepatic nuclear factor 4 alpha, and P450

oxidoreductase genes determines interindividual variability in basal expression and

activity of a broad scope of xenobiotic metabolism genes in the human liver. Drug

Metab Dispos 35: 1700-1710. Downloaded from

Wurmbach E, Chen YB, Khitrov G, Zhang W, Roayaie S, Schwartz M, Fiel I, Thung S,

Mazzaferro V, Bruix J, Bottinger E, Friedman S, Waxman S and Llovet JM (2007) jpet.aspetjournals.org Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular

carcinoma. Hepatology 45: 938-947.

Xie C, Yan TM, Chen JM, Li XY, Zou J, Zhu LJ, Lu LL, Wang Y, Zhou FY, Liu ZQ and Hu at ASPET Journals on October 2, 2021 M (2017) LC-MS/MS quantification of sulfotransferases is better than conventional

immunogenic methods in determining human liver SULT activities: implication in

precision medicine. Sci Rep 7: 3858.

Xu C, Li CY and Kong AN (2005) Induction of phase I, II and III drug metabolism/transport

by xenobiotics. Arch Pharm Res 28: 249-268.

Yan T, Gao S, Peng X, Shi J, Xie C, Li Q, Lu L, Wang Y, Zhou F, Liu Z and Hu M (2015a)

Significantly decreased and more variable expression of major CYPs and UGTs in

liver microsomes prepared from HBV-positive human hepatocellular carcinoma and

matched pericarcinomatous tissues determined using an isotope label-free UPLC-

MS/MS method. Pharm Res 32: 1141-1157.

Yan T, Lu L, Xie C, Chen J, Peng X, Zhu L, Wang Y, Li Q, Shi J, Zhou F, Hu M and Liu Z

(2015b) Severely Impaired and Dysregulated Cytochrome P450 Expression and

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Activities in Hepatocellular Carcinoma: Implications for Personalized Treatment in

Patients. Mol Cancer Ther 14: 2874-2886.

Yang CK, Wang XK, Liao XW, Han CY, Yu TD, Qin W, Zhu GZ, Su H, Yu L, Liu XG, Lu

SC, Chen ZW, Liu Z, Huang KT, Liu ZT, Liang Y, Huang JL, Xiao KY, Peng MH,

Winkle CA, O'Brien SJ and Peng T (2017) Aldehyde dehydrogenase 1 (ALDH1)

isoform expression and potential clinical implications in hepatocellular carcinoma.

PloS one 12: e0182208.

Ye L, Yang X, Guo E, Chen W, Lu L, Wang Y, Peng X, Yan T, Zhou F and Liu Z (2014) Downloaded from

Sorafenib metabolism is significantly altered in the liver tumor tissue of

hepatocellular carcinoma patient. PloS one 9: e96664. jpet.aspetjournals.org Yu D, Green B, Marrone A, Guo Y, Kadlubar S, Lin D, Fuscoe J, Pogribny I and Ning B

(2015a) Suppression of CYP2C9 by microRNA hsa-miR-128-3p in human liver cells

and association with hepatocellular carcinoma. Sci Rep 5: 8534. at ASPET Journals on October 2, 2021 Yu D, Green B, Tolleson WH, Jin Y, Mei N, Guo Y, Deng H, Pogribny I and Ning B (2015b)

MicroRNA hsa-miR-29a-3p modulates CYP2C19 in human liver cells. Biochem

Pharmacol 98: 215-223.

Zanger UM and Schwab M (2013) Cytochrome P450 enzymes in drug metabolism:

regulation of gene expression, enzyme activities, and impact of genetic variation.

Pharmacol Ther 138: 103-141.

Zhang K, Mu L, Ding MC, Xu R, Ding ZJ and Liang J (2018) NFkappaB mediated elevation

of KCNJ11 promotes tumor progression of hepatocellular carcinoma through

interaction of lactate dehydrogenase A. Biochem Biophys Res Commun 495: 246-253.

Zhang SY, Surapureddi S, Coulter S, Ferguson SS and Goldstein JA (2012) Human CYP2C8

is post-transcriptionally regulated by microRNAs 103 and 107 in human liver. Mol

Pharmacol 82: 529-540.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Zhang YJ, Chen Y, Ahsan H, Lunn RM, Chen SY, Lee PH, Chen CJ and Santella RM (2005)

Silencing of glutathione S-transferase P1 by promoter hypermethylation and its

relationship to environmental chemical carcinogens in hepatocellular carcinoma.

Cancer letters 221: 135-143.

Zhong S, Han W, Hou C, Liu J, Wu L, Liu M, Liang Z, Lin H, Zhou L, Liu S and Tang L

(2017) Relation of Transcriptional Factors to the Expression and Activity of

Cytochrome P450 and UDP-Glucuronosyltransferases 1A in Human Liver: Co-

Expression Network Analysis. AAPS J 19: 203-214. Downloaded from

Zhong S, Tang MW, Yeo W, Liu C, Lo YM and Johnson PJ (2002) Silencing of GSTP1 gene

by CpG island DNA hypermethylation in HBV-associated hepatocellular carcinomas. jpet.aspetjournals.org Clin Cancer Res : 8: 1087-1092.

Zimmerman EI, Hu S, Roberts JL, Gibson AA, Orwick SJ, Li L, Sparreboom A and Baker

SD (2013) Contribution of OATP1B1 and OATP1B3 to the disposition of sorafenib at ASPET Journals on October 2, 2021 and sorafenib-glucuronide. Clin Cancer Res : 19: 1458-1466.

Zollner G, Wagner M, Fickert P, Silbert D, Fuchsbichler A, Zatloukal K, Denk H and

Trauner M (2005) Hepatobiliary transporter expression in human hepatocellular

carcinoma. Liver Int 25: 367-379.

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Footnotes

This study was supported by the National Health and Medical Research Council

(NHMRC) of Australia [Grant ID 1143175 (R.M, R.A.M, P.I.M, D.G.H), Grants ID1020931

(P.I.M.) and ID1085410 (P.I.M., R.A.M., and R.M.)]. The project was also supported by funding from the Flinders Medical Centre Foundation. R.A.M. is a Cancer Council/SA

Health Beat Cancer Professorial Chair. During the preparation period, P.I.M. was an

NHMRC Senior Principal Research Fellow; R.M. was an Australian Research Council Future

Fellow. Downloaded from

jpet.aspetjournals.org at ASPET Journals on October 2, 2021

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Legends

Fig. 1: Deregulation of core ADME genes in Hepatocellular Carcinoma (HCC). The

TCGA LIHC RNA-seq data set representing 50 paired HCC cancerous and corresponding adjacent noncancerous liver tissues was downloaded from the Genomic Data Commons

(GDC) Data Portal (https://portal.gdc.cancer.gov/) and subjected to differential gene expression analysis using DESeq2 program as described in Materials and Methods. The box- and-whisker plot shows the log2-transformed expression levels (minimum, first quartile, Downloaded from median, third quartile, and maximum) of 28 core ADME genes in 50 HCC cancer tissues and their corresponding adjacent noncancerous liver tissues. * p < 0.05, *** p < 0.001. N:

noncancerous liver tissues, T: HCC cancerous liver tissues jpet.aspetjournals.org

Fig. 2: Deregulation of phase I drug metabolizing enzymes in Hepatocellular Carcinoma

(HCC). The TCGA LIHC RNA-seq data set representing 50 paired HCC cancerous and at ASPET Journals on October 2, 2021 corresponding adjacent noncancerous liver tissues was downloaded from the Genomic Data

Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/) and subjected to differential gene expression analysis using DESeq2 program as described in Materials and Methods. The box-and-whisker plot shows the log2-transformed expression levels (minimum, first quartile, median, third quartile, and maximum) of 29 ADME CYP genes (A) and 6 ADH and 14 ALDH genes (B) in 50 HCC cancer tissues and their corresponding adjacent noncancerous liver tissues. * p < 0.05, *** p < 0.01, *** p < 0.001. CYP: cytochrome P450; ADH: alcohol dehydrogenase; ALDH: aldehyde dehydrogenase. N: noncancerous liver tissues, T: HCC cancerous liver tissues.

Fig. 3: Deregulation of phase II drug metabolizing enzymes in Hepatocellular

Carcinoma (HCC). The TCGA LIHC RNA-seq data set representing 50 paired HCC cancerous and corresponding adjacent noncancerous liver tissues was downloaded from the

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/) and subjected to differential gene expression analysis using DESeq2 program as described in Materials and

Methods. The box-and-whisker plot shows the distribution (minimum, first quartile, median, third quartile, and maximum) of the log2-transformed expression levels of (A) 17 GST genes of three subfamilies (cytosolic, mitochondrial, and microsomal), (B) 6 SULT genes, and (C)

12 UGT genes in 50 HCC cancer tissues and their corresponding adjacent noncancerous liver tissues. *** p < 0.001. GST: glutathione S-transferase; SULT: sulfotransferase; UGT: UDP glucuronosyltransferase. N: noncancerous liver tissues, T: HCC cancerous liver tissues. Downloaded from

Fig. 4: Deregulation of drug transporter genes in Hepatocellular Carcinoma (HCC). The

TCGA LIHC RNA-seq data set representing 50 paired HCC cancerous and corresponding jpet.aspetjournals.org adjacent noncancerous liver tissues was downloaded from the Genomic Data Commons

(GDC) Data Portal (https://portal.gdc.cancer.gov/) and subjected to differential gene

expression analysis using DESeq2 program as described in Materials and Methods. The box- at ASPET Journals on October 2, 2021 and-whisker plot shows the distribution (minimum, first quartile, median, third quartile, and maximum) of the log2-transformed expression levels of (A) 20 ABC transporter genes and

(B) 37 SLC transporter genes in 50 HCC cancer tissues and their corresponding adjacent noncancerous liver tissues. *** p < 0.001. ABC: ATP-binding cassette transporter; SLC:

Solute carrier transporter. N: noncancerous liver tissues, T: HCC cancerous liver tissues.

Fig. 5: Deregulation of genes coding for nine ADME modifier transcription factors and

NRF2 and its negative regulator KEAP1 in Hepatocellular Carcinoma (HCC). The

TCGA LIHC RNA-seq data set representing 50 paired HCC cancerous and corresponding adjacent noncancerous liver tissues was downloaded from the Genomic Data Commons

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018 maximum) of the log2-transformed expression levels of nine transcription factors that are classified as ADME modifiers. *** p < 0.001. N: noncancerous liver tissues, T: HCC cancerous liver tissues.

Fig. 6: Correlation analysis between PXR and core ADME genes in Hepatocellular

Carcinoma (HCC). The TCGA LIHC RNA-seq data set representing 371 HCC tumors was downloaded from the Genomic Data Commons (GDC) Data Portal

(https://portal.gdc.cancer.gov/) and subjected to Spearman’s ranking correlation analysis Downloaded from using GraphPad Prism 7.03. The graphs shown are the correlation analyses of the expression

(mRNA) levels between PXR and 28 core ADME genes. A p value of < 0.05 was considered

statistically significant. r, correlation coefficient. jpet.aspetjournals.org

Fig. 7: Correlation analysis between PPARD and core ADME genes in Hepatocellular

Carcinoma (HCC). The TCGA LIHC RNA-seq data set representing 371 HCC tumors was at ASPET Journals on October 2, 2021 downloaded from the Genomic Data Commons (GDC) Data Portal

(https://portal.gdc.cancer.gov/) and subjected to Spearman’s ranking correlation analysis using GraphPad Prism 7.03. The graphs shown are the correlation analyses of the expression

(mRNA) levels between PPARD and 28 core ADME genes. A p value of < 0.05 was considered statistically significant. r, correlation coefficient.

Fig. 8: Correlation analysis between GSP1 and nine transcription factors that are classified as ADME modifiers in Hepatocellular Carcinoma (HCC). The TCGA LIHC

RNA-seq data set representing 371 HCC tumors was downloaded from the Genomic Data

Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/) and subjected to Spearman’s ranking correlation analysis using GraphPad Prism 7.03. The graphs shown are the correlation analyses of the expression (mRNA) levels between GSP1 and nine TFs that are

Fig. 9: Correlation analysis of the expression levels between UGT2B7 and 27 other core

ADME genes in Hepatocellular Carcinoma (HCC). The TCGA LIHC RNA-seq data set representing 371 HCC tumors was downloaded from the Genomic Data Commons (GDC)

Data Portal (https://portal.gdc.cancer.gov/) and subjected to Spearman’s ranking correlation analysis using GraphPad Prism 7.03. The graphs shown are the correlation analyses of the Downloaded from expression (mRNA) levels between UGT2B7 and 27 other core ADME genes. A p value of <

0.05 was considered statistically significant. r, correlation coefficient.

Fig. 10: Deregulation of miRNAs in Hepatocellular Carcinoma (HCC). The TCGA LIHC jpet.aspetjournals.org miRNA-seq data set representingt 50 paired HCC and corresponding adjacent non-cancerous liver tissues was downloaded from The Cancer Genome Atlas (TCGA) data portal at ASPET Journals on October 2, 2021 (https://gdc-portal.nci.nih.gov/) and normalized using the upper quantile normalization method. The difference in the expression levels of 12 miRNAs between HCC cancerous and corresponding adjacent normal non-cancerous liver tissues were statistically assessed using

GraphPad Prism (version 7.03) (T-test). A p value of < 0.05 was considered statistically significant.

Fig. 11: Deregulation of Aldo-keto reductases (AKRs) in Hepatocellular Carcinoma

(HCC). The TCGA LIHC RNA-seq data set representing 50 paired HCC cancerous and corresponding adjacent noncancerous liver tissues was downloaded from the Genomic Data

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018 cancer tissues and their corresponding adjacent noncancerous liver tissues. *** p < 0.001. N: noncancerous liver tissues, T: HCC cancerous liver tissues.

Fig. 12: Coordinated regulation of ADME genes in the liver (hepatocytes) and its deregulation in hepatocellular carcinoma (HCC). A: It is believed that the process of absorption, distribution, metabolism and excretion of xenobiotics (e.g. drugs, dietary constituents) is coordinated efficiently in the liver upon exposure to endogenous and Downloaded from exogenous compounds such as dietary constituents and therapeutic drugs. B: This coordinated process is mainly mediated through the coregulation of ADME genes by ADME

modifiers, including liver-enriched (e.g. HNF4A, PPARA, PPARD, PPARG) and xenobiotic- jpet.aspetjournals.org activated (e.g. AHR, PXR, CAR) transcription factors. The deregulation of these transcription factors, miRNAs, and epigenetics (e.g. hypermethylation) lead to a wide spread deregulation

of ADME genes in HCC. at ASPET Journals on October 2, 2021

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Table 1. 28 core ADME genes and 205 extended ADME genes that are expressed in TCGA HCC (Hepatocellular carcinoma) cohort and their deregulation in HCC tumour tissues as compared to corresponding adjacent non-cancerous liver tissues. Phase I enzymes Phase II enzymes Transporters Modifiers Core CYP1A1 GSTM1 ABCB1 ADME CYP1A2 GSTP1 ABCC2 CYP2A6 NAT1 ABCG2 Genes CYP2B6 NAT2 SLC22A1 CYP2C19 SULT1A1 SLCO1B1 CYP2C8 TPMT SLCO1B3 CYP2C9 UGT1A1 CYP2D6 UGT2B15 CYP2E1 UGT2B17 CYP3A4 UGT2B7 CYP3A5 DPYD Extended ADH1A CYP51A1 CHST1 ABCA1 AHR ADME ADH1B CYP7A1* CHST10* ABCA4* ARNT ADH1C CYP7B1 CHST11 ABCB11 ARSA genes ADH4 CYP8B1 CHST12 ABCB4 ATP7A Downloaded from ADH5 DDO CHST13 ABCB6 ATP7B ADH6 DHRS1 CHST2 ABCB7 CAT ADHFE1 DHRS12 CHST3 ABCB8 CDA ALDH1A1 DHRS13 CHST4 ABCC1 HNF4A ALDH1A2 DHRS2 CHST7 ABCC10* KCNJ11* ALDH1A3 DHRS3 CHST9 ABCC11 MAT1A ALDH1B1 DHRS4 GSTA1 ABCC3 NR1I2 ALDH2 DHRS4L1 GSTA2 ABCC4* NR1I3 jpet.aspetjournals.org ALDH3A1* DHRS4L2 GSTA4 ABCC6 POR ALDH3A2 DHRS7 GSTCD ABCC9 PPARA ALDH3B1 DHRS7C GSTK1 ABCG1 PPARD ALDH4A1 DHRS9 GSTM2 SLC10A1 PPARG ALDH5A1 DHRSX GSTM3 SLC13A2 RXRA ALDH6A1 EPHX1 GSTM4 SLC13A3 SERPINA7 ALDH7A1 EPHX2 GSTM5 SLC15A1 SOD1 ALDH8A1 FMO1 GSTO1 SLC16A1 SOD2 ALDH9A1 FMO3 GSTO2 SLC19A1 SOD3 at ASPET Journals on October 2, 2021 AOX1 FMO4 GSTZ1 SLC22A10 CBR1 FMO5 HNMT SLC22A11* CBR3* GPX1 MGST1 SLC22A15* CES1 GPX2 MGST2 SLC22A17 CES2 GPX3 MGST3 SLC22A18 CYB5R3 GPX4 NNMT SLC22A3 CYP11A1 GPX7 SULF1 SLC22A4* CYP17A1* GSR SULT1A2 SLC22A5 CYP1B1 GSS SULT1B1 SLC22A7 CYP20A1 HAGH SULT1C1 SLC22A9 CYP21A2 HSD11B1 SULT1C2* SLC27A1 CYP26A1 HSD17B11 SULT1E1 SLC28A1 CYP27A1 HSD17B14 SULT2A1 SLC29A1 CYP2A13 METAP1 UGT1A3 SLC29A2 CYP2A7 NOS1 UGT1A4 SLC2A4 CYP2C18 NOS2A* UGT1A6 SLC5A6 CYP2J2 NOS3 UGT1A9 SLC7A5 CYP2R1 PDE3A UGT2A1 SLC7A7 CYP2S1 PDE3B UGT2B10 SLC7A8 CYP39A1 PLGLB1 UGT2B11* SLCO1A2 CYP3A43 PON1 UGT2B4 SLCO2A1* CYP3A7 PON2 SLCO2B1 CYP46A1 PON3 SLCO3A1 CYP4A11 XDH SLCO4A1 CYP4F11 SLCO4C1 CYP4F12 TAP1 (ABCB2) CYP4F2 TAP2 (ABCB3) CYP4F3

Downregulated genes (Bold) and upregulated genes (Bold and arrowed)

JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. JPET # 255018

Table 2. Downregulation of 14 core ADME genes in hepatocellular carcinoma

TCGA HCC (this study) Thurnherr T et al (2016) Roessler S et al (2010) Wurmbach E et al (2007) Chen X et al (2002) T (50)/N (50) T (100)/N (100) T (225)/N (220) T (35)/N (10) T (98-104)/N (71-76) GSE62043 GSE14520 GSE6764

Genes Fold p Fold P Fold p Fold p Fold p Change value change value change value change value change value

CYP1A2 -5.79 8.05E-07 -7.25 1.06E-33 -18.87 1.50E-131 -23.814 1.69E-9 CYP2A6 -2.08 0.021 -6.07 3.35E-19 -11.33 7.21E-72 -6.53 6.56E-15 CYP2B6 -5.68 1.26E-15 -1.52 4.85E-21 -10.80 3.59E-100 -2.95 9.51E-27 CYP2C8 -5.40 6.67E-17 -7.88 5.10E-34 -3.561 2.69E-5 -5.76 2.32E-20 CYP2C9 -4.09 1.75E-13 -5.11 1.48E-14 -4.87 2.89E-95 -5.60 1.33E-19 CYP2C19 -6.24 6.06E-08 -4.87 1.15E-67 CYP2E1 -2.03 0.028 -4.34 4.43E-17 -8.69 3.46E-38 -3.825 2.48E-5 CYP3A4 -3.32 0.00027 -4.00 7.87E-14 -7.09 3.85E-71 -2.53 3.17E-10 NAT1 -2.37 2.10E-15 -1.59 1.11E-13 -2.67 6.34E-62 -2.56 6.33E-6 -3.04 4.67E-24 NAT2 -7.64 3.23E-18 -5.62 2.58E29 -13.99 7.61E-107 -36.89 4.54E-16 -8.02 2.07E-34 SLC22A1 -5.29 9.86E-11 -6.2 1.56E-25 -16.90 9.61E-65 -15.41 1.23E-8 -11.38 1.94E-21 SLCO1B1 -2.16 7.08E-05 -3.63 3.50E-13 -2.88 8.83E-33 SLCO1B3 -4.16 4.37E-05 -8.29 1.89E-18 -11.48 2.97E-57 -16.97 2.04E-9 UGT2B7 -3.61 1.94E-12 -3.64 5.98E-20 -3.35 7.99E-21

Downloaded from

The fold change of gene expression and related significant p values from four studies (Themherr T etal, Roessler S et al, Wurbach E et al, and Chen X et al) were obtained from the publicly accessible database Oncomine (www.oncomine.org). Of note, data of several genes were not available in one or two studies. GSE62043, GSE14520, and GSE6764 are the Gene Expression Omnibus accession numbers for related studies. T, Tumour; N, normal.

jpet.aspetjournals.org at ASPET Journals on October 2, 2021

Figure 1

25 * *** 20 * *** *** *** *** *** *** *** ***

15 *** *** *** 10

-5 NTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNT

Log2-transformed read counts of core ADME genes Log2-transformed read counts of core 2 1 7 5 7 A B 1 1 PYD AT1 AT2 PMT B B BCB1 BCC2 BCG D STM1 STP1 N N T A A A YP1A1YP1A2YP2A6YP2B6YP2C8YP2C9 YP2D6YP2E1YP3A4YP3A5 G G LT1A1 GT1 GT2 C C C C C C YP2C19C C C C LC22A1LCO1B1LCO1B3U U U GT2 GT2 C S S S S U U Figure 2 JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version.

25 (CYP4A11) (CYP8B1) (CYP17A1)

20 (CYP3A7) (CYP4F2) *** *** (CYP7A1) ** *** (CYP2C18) (CYP26A1) (CYP39A1 (CYP4F12) *** *** *** *** 15 *** *** (CYP3A43) ***

Log2-transformed read counts of CYP genes Log2-transformed read counts of CYP 0 Downloaded from -5

N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T

3 8 1 3 1 1 1 1 2 1 1 1 1 1 1A1 1B1 2A7 2D6 2J2 2R1 2S 3A5 3A7 4F2 4F3 7A1 7B1 8B1 P P P 2A1 2C1 P P P P P P 3A4 4A1 P P 4F11 4F12 P P P 11A 17A 20A 21A 26A 27A 39A 46A 51A P P P P P P P P P P P P P P P CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY CY jpet.aspetjournals.org

B at ASPET Journals on October 2, 2021

25 (ADH1B) (ADH4) (ALDH3A1) (ADH1C) (ALDH2) (ADH1A) *** *** (ALDH6A1) (ADH6) *** *** (ALDH1B1) *** 20 *** (ALDH8A1) *** *** *** ***

15 (ALDH1A3) ***

0 Log2-transformed read counts of ADH and ALDH genes ADH and Log2-transformed read counts of

-5 N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T

ADH1A ADH4 ADH5 ADH6 ADH1B ADH1C ALDH2 ALD3A2 ALDH1A1ALDH1A2ALDH1A3ALDH1B1 ALDH3A1 ALDH3B1ALDH4A1ALDH5A1ALDH6A1ALDH7A1ALDH8A1ALDH4A1 JPET Fast Forward.Figure Published 3 on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version.

20 (GSTZ1) (GSTM5) ***

15 ***

Log2-transformed read counts of GST genes Log2-transformed read counts of GST -5

N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T Downloaded from

GSTA1GSTA2GSTA4GSTCDGSTM1GSTM2GSTM3GSTM4GSTM5 GSTO1GSTO2GSTP1 GSTZ1 GSTK1 MGST1MGST2MGST3 Cytosolic GSTs Mitochondrial GST Microsomal GSTs

B C jpet.aspetjournals.org (SULT2A1) (UGT2B7)

20 20 (UGT2B10) *** (UGT2B11) *** (SULT1A2) *** (SULT1C2) *** (SULT1E1) *** *** 15 15 *** at ASPET Journals on October 2, 2021

10 10

5 5

0 0

Log2-transformed read counts of SULT genes Log2-transformed read counts of SULT -5 -5 N T N T N T N T N T N T genes Log2-transformed read counts of UGT N T N T N T N T N T N T N T N T N T N T N T N T

UGT1A1UGT1A3 UGT1A4 UGT1A6 UGT1A9 UGT2A1 UGT2B4 UGT2B7 SULT1A1SULT1A2SULT1B1SULT1C2SULT1E1SULT2A1 UGT2B10UGT2B11 UGT2B15UGT2B17 JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version.

Figure 4

20 (ABCC9) (ABCC4) (ABCA4) *** 15 (ABCC10) *** *** ***

10 Downloaded from 5

0 jpet.aspetjournals.org -5

Log2-transformed read counts of ABC genes Log2-transformed read counts of N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T N T

BCA1 BCA4 BCB1 BCB2 BCB3 BCB4 BCB6 BCB7 BCB8 BCC1 BCC2 BCC3 BCC4 BCC6 BCC9 BCG1 BCG2 A A BCB11 BCC10 BCC11 A A A A A A A A A A A A A A A A A A at ASPET Journals on October 2, 2021

20 (SLC22A1) (SLCO1B1) (SLC10A1) (SLCO2B1) *** (SLC22A10) *** *** *** (SLC22A11) (SLCO1B3) (SLC7A8) *** (SLC22A4) (SLCO2A1) (SLCO4C1) *** 15 *** (SLC22A15) *** *** *** *** ***

Log2-transformed read counts of SLC genes -5 NTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNTNT

2 27A1 LC10A1LC13A2LC13A3LC15A1LC16A1LC19A1LC22A1LC22A3LC22A4LC22A5LC22A7 LC LC 28A1 LC 2A4LC 5A6LC 7A5LC 7A7LC 7A8 LCO2A1 LCO3A1LCO4A1 S S S S S S S S LC22A9 S LC 29A1LC 29A2S S S S S SLCO1B1LCO1B3 LCO2B1 LCO4C1 S S S S LC22A10LC 22A11LC 22A15LC 22A17LC 22A18 S S S LC O1A S S S S S S S S S S S S JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from jpet.aspetjournals.org at ASPET Journals on October 2, 2021 Figure 5

20 (PXR) 15 ***

5 Log2-transformed read counts of TF genes Log2-transformed read counts of

N T N T N T N T N T N T N T N T N T N T N T

AHR ARNTHNF4A PPARAPPARDPPARGRXRA KEAP1

NR1I2(PXR)NR1I3 (CAR) NFE2L2 (NrF2) JPET Fast Forward. Published on DecemberFigure 6 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version.

r = 0.35; p < 0.0001; n = 357 10000 r = 0.29; p < 0.0001; n = 362 r = 0.46; p < 0.0001; n = 362 r = 0.34; p < 0.0001; n = 348 25000 8000 8000 10000 20000 6000 6000 15000 4000 BC G2 YP 1A1

ABCB 1 4000 5000 A C ABCC 2 10000 2000 2000 5000

0 0 0 0 0 2000 4000 6000 8000 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR)

15000 r = 0.39; p < 0.0001; n = 342 200000 r = 0.68; p < 0.0001; n = 352 50000 r = 0.53; p < 0.0001; n = 369 80000 r = 0.58; p < 0.0001; n = 357

40000 150000 60000 10000 30000 100000 40000 YP 2A6 YP 1A2

20000 CYP2C8 CYP2B6 C C 5000 20000 50000 10000

0 0 0 0 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR)

r = 0.26; p < 0.0001; n = 357 800000 r = 0.0006; p = 0.99; n = 358 80000 r = 0.49; p < 0.0001; n = 356 200 r = 0.29; p < 0.0001; n = 359 60000 600000

60000 150 Downloaded from 40000 400000

40000 100 YP 2E1 C CYP2D6 CYP2C9

CYP2C19 20000 200000 20000 50

0 0 0 0 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR)

r = 0.53; p < 0.0001; n = 356 r = 0.25; p < 0.0001; n = 359 r = 0.13; p < 0.05; n = 352 jpet.aspetjournals.org 500000 80000 r = 0.33; p < 0.0001; n = 358 6000 20000

400000 60000 15000 4000 300000 DPYD CYP3A4 40000 CYP3A5 10000 GSTM1 200000 2000 20000 5000 100000

0 0 0 0 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 NR1I2 (PXR) at ASPET Journals on October 2, 2021 NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR)

6000 r = -021; p < 0.0001; n = 348 800 r = 0.49; p < 0.0001; n = 362 2000 r = 0.56; p < 0.0001; n = 350; 80000 r = 0.61; p < 0.0001; n = 351

600 1500 60000 4000

400 NAT2 1000 40000 SLC22A1 NAT1 GSTP1 2000 200 500 20000

0 0 0 0 0 1000 2000 3000 4000 5000 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR)

r = 0.46 ; p < 0.0001; n = 364 r = - 0.03 ; p = 0.56; n = 360 r = 0.33; p < 0.0001; n = 361 r = 0.47; p < 0.0001; n = 365 25000 8000 20000 8000 20000 6000 15000 6000 15000

SLCO1B1 4000 10000 4000 TPMT SULT1A1

10000 SLCO1B3

5000 2000 5000 2000

0 0 0 0 2000 4000 6000 0 2000 4000 6000 0 0 2000 4000 6000 0 2000 4000 6000 NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR)

r = 0.52 ; p < 0.0001; n = 359 20000 r = 0.29 ; p < 0.0001; n = 361 80000 r = 0.57 ; p < 0.0001; n = 361 50000 8000 r = 0.31 ; p < 0.0001; n = 354

40000 15000 60000 6000 30000 10000 40000 4000 UGT1A1

UGT2B7 20000 UGT2B15 UGT2B17 2000 5000 20000 10000

0 0 0 0 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 8000 0 2000 4000 6000 NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR) NR1I2 (PXR) JPET Fast Forward. Published on DecemberFigure 21, 7 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version.

r = - 0.12; p < 0.05; n=352 r = - 0.16; p < 0.01; n=354 r = - 0.36; p < 0.0001; n=357; 15000 r = - 0.35; p < 0.0001; n=350 8000 25000 8000

6000 20000 6000 10000 15000 4000 4000 YP 1A1 BC G2 C A ABCB 1 ABCC 2 10000 5000 2000 2000 5000

0 0 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 PPARD PPARD PPARD PPARD

50000 r = - 0.16; p < 0.01; n=361 100000 r = - 0.09; p = 0.06; n=356 20000 r = - 0.14; p < 0.01; n=363 400000 r = - 0.08; p = 0.10; n=363 40000 80000 15000 300000 30000 60000 10000 200000 YP 2A6 YP 1A2 40000 20000 CYP2C8 CYP2B6 C C 100000 5000 10000 20000

0 0 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 5000 0 1000 2000 3000 4000 PPARD PPARD PPARD PPARD

r = - 0.08; p = 0.11; n=351 1000000 r = - 0.29; p < 0.001; n=360 100000 r = - 0.20; p < 0.001; n=358 200 r = - 0.05; p = 0.39; n=347 50000 80000 800000 150 40000 600000 60000 30000 Downloaded from 100 YP 2E1 400000 40000 20000 C CYP2C9 CYP2D6 CYP2C19 50 20000 10000 200000

0 0 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 PPARD PPARD PPARD PPARD

25000 ;r = - 0.04; p = 0.47; n=351 600000 r = - 0.24; p < 0.0001; n=349 60000 r = - 0.13; p < 0.05; n=350 8000 r = 0.13; p < 0.01; n=359 jpet.aspetjournals.org 20000 6000 400000 40000 15000

PY D 4000 YP 3A4 GSTM1 D

YP 3A5 10000 C 200000 C 20000 2000 5000

0 0 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 PPARD PPARD PPARD PPARD at ASPET Journals on October 2, 2021

r = - 0.03; p = 0.52; n=348 r = - 0.16; p < 0.01; n=363 8000 800 1500 r = - 0.18; p < 0.001; n=344; 80000 r = - 0.04; p = 0.93; n=358

6000 600 60000 1000

4000 400 22 A1 40000 NA T1 NA T2 GSTP1

500 SLC 2000 200 20000

0 0 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 PPARD PPARD PPARD PPARD

25000 r = - 0.14; p < 0.01; n=359 10000 r = - 0.07; p = 0.13; n=357; 15000 r = - 0.17; p < 0.001; n=351 8000 r = - 0.06; p = 0.22; n=357

20000 8000 6000 10000 15000 6000 4000 LT 1A1 10000 4000 TPMT SU SLCO1B1 SLCO1B3 5000 2000 5000 2000

0 0 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 PPARD PPARD PPARD PPARD

r = - 0.10; p = 0.05; n=343 20000 r = - 0.15; p < 0.01; n=351 80000 r = - 0.23; p < 0.001; n=356 50000 r = - 0.17; p < 0.001; n=355 5000

40000 4000 15000 60000 30000 3000 10000 40000 20000 2000 UGT2B17 UGT1A1 UGT2B7 UGT2B15

5000 20000 10000 1000

0 0 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 1000 2000 3000 4000 PPARD PPARD PPARD PPARD JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. Figure 8

15000 r = - 0.19; p < 0.0001; n = 351 r = - 0.33; p < 0.0001; n = 352 40000 r = - 0.42; p < 0.0001; n = 346

10000 30000 10000 ARNT HNF4A AHR 20000 5000 5000 10000

0 0 0 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 GSTP1 GSTP1 GSTP1

6000 r = - 0.21; p < 0.0001; n = 365 15000 r = - 0.22; p < 0.0001; n = 347 15000 r = - 0.36; p < 0.0001; n = 343 Downloaded from

4000 10000 10000 PPARA jpet.aspetjournals.org CAR (NR1I3) PXR (NR1I2) 2000 5000 5000

0 0 0 0 2000 4000 6000 0 2000 4000 6000 0 2000 4000 6000 GSTP1 GSTP1 GSTP1 at ASPET Journals on October 2, 2021

5000 r = 0.007; p = 0.81; n = 331 3000 r = 0.08; p = 0.11; n = 328 30000 r = - 0.18; p < 0.001; n = 350

4000

2000 20000 3000 RXRA PPARG PPARD 2000 1000 10000 1000

0 0 0 0 1000 2000 3000 4000 0 1000 2000 3000 4000 0 2000 4000 6000 GSTP1 GSTP1 GSTP1 JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted.Figure The 9 final version may differ from this version.

r = 0.48; p < 0.0001; n = 360 r = 0.33; p < 0.0001; n = 360 r = 0.49; p < 0.0001; n = 357 r = 0.38; p < 0.0001; n = 351 10000 25000 8000 15000

8000 20000 6000 10000 6000 15000 4000 BC G2 YP 1A1 A C ABCC 2 ABCB 1 4000 10000 5000 2000 2000 5000

0 0 0 0 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 UGT2B7 UGT2B7 UGT2B7 UGT2B7

15000 r = 0.41; p < 0.0001; n = 325 200000 r = 0.59; p < 0.0001; n = 351 25000 r = 0.47; p < 0.0001; n = 347 80000 r = 0.57; p < 0.0001; n = 362

20000 150000 60000 10000 15000 100000 40000 YP 2A6 YP 1A2 10000 CYP2C8 CYP2B6 C C 5000 20000 50000 5000

0 0 0 0 0 20000 40000 60000 0 20000 40000 60000 0 10000 20000 30000 40000 0 20000 40000 60000 UGT2B7 UGT2B7 UGT2B7 UGT2B7

100000 r = 0.49; p < 0.0001; n = 362 80 r = 0.14; p < 0.0001; n = 329 40000 r = 0.21; p < 0.0001; n = 341 1000000 r = 0.14 p < 0.01; n = 359

800000 80000 60 30000 Downloaded from 60000 600000 40 20000 YP 2E1 400000 C

40000 CYP2D6 CYP2C9 CYP2C19 20 10000 200000 20000

0 0 0 0 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 UGT2B7 UGT2B7 UGT2B7 UGT2B7 jpet.aspetjournals.org 500000 r = 0.50; p < 0.0001; n = 350 60000 r = 0.25; p < 0.0001; n = 350 8000 r = 0.18; p < 0.001; n = 358 8000 r = 0.19; p < 0.001; n = 343 400000 6000 6000 40000 300000 PY D 4000 PY D 4000 YP 3A4 YP 3A5 D 200000 D C C 20000 2000 100000 2000

0 0 0 0 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 UGT2B7 UGT2B7 UGT2B7 UGT2B7 at ASPET Journals on October 2, 2021

r = -0.03 p = 0.48; n = 336 r = 0.37; p < 0.0001; n = 359 4000 600 1500 r = 0.56; p < 0.0001; n = 347 80000 r = 0.62; p < 0.0001; n = 351

3000 60000 400 1000

2000 22 A1 40000 NA T2 NA T1 GSTP1 200 500 SLC 1000 20000

0 0 0 0 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 UGT2B7 UGT2B7 UGT2B7 UGT2B7

r = 0.42 ; p < 0.0001; n = 362 25000 8000 r = 0.03 ; p = 0.45; n = 357 20000 r = 0.33 ; p < 0.0001; n = 354 8000 r = 0.44 ; p < 0.0001; n = 362

20000 6000 15000 6000 15000 4000 10000 4000 LT 1A1

10000 TPMT SU SLCO1B3 SLCO1B1 2000 5000 2000 5000

0 0 0 0 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 0 20000 40000 60000 UGT2B7 UGT2B7 UGT2B7 UGT2B7

r = 0.35 ; p < 0.0001; n = 354 20000 r = 1 ; p < 0.0001; n = 366 r = 0.65 ; p < 0.0001; n = 352 r = 0.40 ; p < 0.0001; n = 347 80000 50000 5000

40000 15000 60000 4000 30000 3000 10000 40000 20000 2000 UGT1A1 UGT2B7 UGT2B17 UGT2B15 5000 20000 10000 1000

0 0 0 0 0 20000 40000 60000 0 20000 40000 60000 80000 0 20000 40000 60000 0 20000 40000 60000 UGT2B7 UGT2B7 UGT2B7 UGT2B7 Figure. 10 JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and Aformatted. The final version may differ from this version.

1500 p < 0.0001 2500 p < 0.0001 1500 p = 0.0013

2000 1000 1000 1500

1000 miR-132 levels miR-130b levels 500 miR-128-3p levels 500

500

0 0 0 Non-cancer HCC Non-cancer HCC Non-cancer HCC CYP1A2 CYP2C9 CYP2C9

200000 p < 0.0001 40000 p = 0.0083 200000 p = 0.09

150000 150000 30000 Downloaded from

100000 100000 20000 miR-25 levels 50000 miR-27a levels 10000 miR-29a-3p levels 50000 jpet.aspetjournals.org 0 0 0 Non-cancer HCC Non-cancer HCC Non-cancer HCC CYP2C19 CYP2B6 CYP3A4

1500 p < 0.001 p < 0.0001 300000 p < 0.0001 5000 at ASPET Journals on October 2, 2021

4000 1000 200000 3000

2000 500 miR-629 levels 100000 miR-107 levels miR-103-5p levels 1000

0 0 0 Non-cancer HCC Non-cancer HCC Non-cancer HCC CYP2C8 CYP2C8 SLCO1B3

80000 p = 0.44 50000 p < 0.0001 40000 p = 0.0018

60000 40000 30000

30000 40000 20000 miR-126 levels 20000 miR-378a levels 20000 miR-142 levels 10000 10000

0 0 0 Non-cancer HCC Non-cancer HCC Non-cancer HCC CYP2A6 UGT2B7 CYP2E1 JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from jpet.aspetjournals.org at ASPET Journals on October 2, 2021 Figure 11 ARK1B10 ARK1C3 ***

20 ARK1D1 ARK7A3 *** *** *** 15

5 of ARK genes

0 Log2-transformed read counts -5 N T N T N T N T N T N T N T N T N T N T N T

AKR1A1 AKR1B1 AKR1C1AKR1C2 AKR1C3AKR1C4 AKR1D1AKR1E2AKR7A2AKR7A3 AKR1B10 JPET Fast Forward. Published on December 21, 2018 as DOI: 10.1124/jpet.118.255018 This article has not been copyedited and formatted. The final version may differ from this version. Downloaded from

Fig. 12

Xenobiotics (e.g. Drugs) Metabolitesjpet.aspetjournals.org

A Roles of ADME genes at ASPET Journals on October 2, 2021 Absorption Phase I Metab Influx Efflux Distribution Metabolites Metabolism transporters transporters Phase II Metab Excretion

B Activation Modifiers Up/downregulation AHR, ARNT, Xenobiotic response motif Coordinated HNF4A, PXR Phase I regulation CAR, PPARA Phase II of PPARD, PPARG promoter CDS 3’UTR Transporters ADME genes RXRA ADME genes Repression

Xenobiotic Binding response motif motif

promoter microRNAs

Hepatocyte Journal: The Journal of Pharmacology and Experimental Therapeutics Titile: Deregulation of the Genes that are involved in Drug Absorption, Distribution, Metabolism, Excretion (ADME genes) in Hepatocellular Carcinoma Authors: Dong Gui Hu, Shashikanth Marri, Ross A Mckinnon, Peter I Mackenzie, and Robyn Meech

Supplemental Figure 1

adjacent non-tumour liver tissue HCC tumour

50000 UGT1A1 40000

30000

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

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8000 Normalized reads of target genes (log2-transformed) Normalized reads of target 6000

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Supplemental Fig. 1: Deregulation of UGT genes In Hepatocellular Carcinoma (HCC). The TCGA LIHC RNA-seq data set representing 50 paired HCC and corresponding adjacent noncancerous liver tissues was downloaded from the Genome Data Commons (GDC) Data Portal (https://portal/gdc.cancer.gov). Data shown are the expression levels of three UGT genes (UGT1A1, UGT1A4, UGT1A9) in HCC as compared to corresponding adjacent non-cancerous liver tissues of 50 HCC patients. Journal: The Journal of Pharmacology and Experimental Therapeutics Titile: Deregulation of the Genes that are involved in Drug Absorption, Distribution, Metabolism, Excretion (ADME genes) in Hepatocellular Carcinoma Authors: Dong Gui Hu, Shashikanth Marri, Ross A Mckinnon, Peter I Mackenzie, and Robyn Meech

Supplemental Figure 2

adjacent non-tumour liver tissue HCC tumour

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0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647484950

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0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132333435363738394041424344454647484950

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Supplemental Fig. 2: Deregulation of Transcription Factors In Hepatocellular Carcinoma (HCC). The TCGA LIHC RNA-seq data set representing 50 paired HCC and corresponding adjacent noncancerous liver tissues was downloaded from the Genome Data Commons (GDC) Data Portal (https://portal/gdc.cancer.gov). Data shown are the expression levels of transcription factors including five ADME modifiers (PXR, AHR, CAR, PPARD, PPARG) and NFR2 in HCC as compared to corresponding adjacent non-cancerous liver tissues of 50 HCC patients. Journal: The Journal of Pharmacology and Experimental Therapeutics Titile: Deregulation of the Genes that are involved in Drug Absorption, Distribution, Metabolism, Excretion (ADME genes) in Hepatocellular Carcinoma Authors: Dong Gui Hu, Shashikanth Marri, Ross A Mckinnon, Peter I Mackenzie, and Robyn Meech

Legends of Supplementary Figures

Supplemental Figs. 1 and 2: Deregulation of ADME genes in Hepatocellular Carcinoma

(HCC). The TCGA LIHC RNA-seq data set representing 50 paired HCC cancerous and corresponding adjacent noncancerous liver tissues was downloaded from the Genomic Data

Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/) as described in Materials and

Methods. Data shown are the expression levels of three UGT genes (UGT1A1, UGT1A4,

UGT1A9) (Supplemental Fig. 1) and six transcription factors genes coding for ADME modifier TFs (PXR, AHR, CAR, PPARD, PPARG, NRF2) (Supplemental Fig. 2) in HCC tumors as compared to their corresponding adjacent non-cancerous liver tissues in 50 HCC patients.

Legends of Supplementary Tables

Supplemental Table 1 (Table S1): 294 ADME genes are defined by the PharmaADME consortium, including 32 core ADME gene and 262 extended ADME genes.

Supplemental Table 2 (Table S2): The HT-Seq counts of 609 genes included in the differential gene expression analysis of this study. For differential gene expression analysis, the TCGA (the Cancer Genome Atlas) hepatocellular carcinoma (LIHC) RNA-seq data of the 50 paired HCC cancerous and corresponding adjacent noncancerous liver tissues that has been mapped to the human genome GRCh38 reference assembly were downloaded as HT-Seq counts from the Genomic Data Commons (GDC) Data Portal

(https://portal.gdc.cancer.gov/). The HT-Seq counts contained the raw read counts of each gene generated using a HT-Seq tool (http://htseq.readthedocs.io/en/release_0.9.1/) to measure gene expression level from the GDC mRNA quantification analysis pipeline.

Supplemental Table 3 (Table S3): The expression levels of 609 genes that are included in the gene expression correlation analysis of this study. The RNA-seq data of 371 HCC cancerous tissues were downloaded in the form of high-throughput sequencing counts from the Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/). Genes with a mean of less than 10 counts were discarded; the counts of the remaining genes were normalized using the upper quantile normalization method.

Supplemental Table 4 (Table S4): The expression levels of miRNAs that are included in the differential miRNA expression analysis of this study. The miRNA-seq data of TCGA

HCC 49 paired HCC and corresponding adjacent non-cancerous liver tissues were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://gdc- portal.nci.nih.gov/) and normalized using the upper quantile normalization method.

Supplemental Table 5 (Table S5): The DESeq2 results of 609 genes that are included in the differential gene expression analysis of this study. The DESeq2 analysis reports a log2 fold change (log2Foldchange) in the expression level of each gene between the tumor and adjacent non-tomour liver tissues and the associated standard error estimate for the log2 fold change estimate (lfcSE) and the Wald test result (p value). The Wald test p values were further adjusted (adjusted p values) using the Benjamini-Hochberg (BH) method. An adjust p value of <0.05 was considered statistically significant. Differentially expressed genes were defined by a log2foldchange of >1 (equivalent to an upregulation of >2 folds) or <-1

(equivalent to a downregulation of <2 folds).

Supplemental Tables 6-12 (Table S6-S12): Deregulation of ADME and non-ADME genes in the TCGA LIHC specimens. For quality control, genes with HT-Seq counts of <32

(equivalent to < 5 of log2-transformed counts) were excluded from analysis. Based on this cutoff criterion, a total of 609 genes, including 233 ADME genes (Table S6) and 376 non-

ADME genes were included in the final analysis. Of the 609 genes analysed by DESeq2, 109

(Table S7) and 55 (Table S8) were found to be downregulated and upregulated in HCC, respectively. Of the 109 downregulated genes, 58 (Table S9) and 51 (Table S11) are ADME genes and non-ADME genes, respectively. Of the 55 upregulated genes, 15 (Table 10) and

40 (Table 12) are ADME genes and non-ADME genes, respectively.

Supplemental Table 13 (Table S13): 32 core ADME genes defined by PharmaADME consortium.

Supplemental Table 14 (Table S14): Spearman’s ranking correlation analysis of the expression (mRNA) levels between 28 core ADME genes and the nine ADME modifier transcription factors and NRF2 using GraphPad Prism 7.03 as described in Materials and

Methods. Shown are the correlation coefficient (r) and the p value for each of the correlation analysis. *: p < 0.05; **: p < 0.01; ***: p < 0.001; ns: p ≥ 0.05

Supplemental Table 15 (Table S15): Spearman’s ranking correlation analysis of the expression (mRNA) levels between 28 core ADME genes using GraphPad Prism 7.03. RNAseq data of TCGA LIHC cohort of 371 patients with hepatocellular carcinoma were downloaded and normalized as described in Materials and Methods. Spearman's ranking correlation of the expression levels between 28 core ADME genes was conducted using the GraphPad Prism

7.03. Shown are the correlation coefficient, 95% confidence interval, and the p value of the significance analysis of each of the correlation analysis. *: p < 0.05; **: p < 0.01; ***: p <

0.001; ns: p ≥ 0.05

Supplemental Table 16 (Table S16): Summary of the results of expression correlation analyses of 28 core ADME genes with each other and with transcription factors and their coregulators in hepatocellular carcinoma. Data shown in the table are the numbers of core ADME genes that are correlated with individual core ADME genes and transcription factors. Supplemental Table 16. Expression (mRNA) correlation analyses of 28 core ADME genes with each other and with transcription factors and their coregulators in hepatocellular carcinoma The number of core ADME genes that are correlated with individual genes and transcription factors positively negatively Not correlated

Core ADME genes

ABCB1 22 1 4 ABCC2 22 1 4 ABCG2 24 1 2 CYP1A1 23 2 2 CYP1A2 24 2 1 CYP2A6 24 1 2 CYP2B6 25 1 1 CYP2C8 26 1 0 CYP2C9 25 1 1 CYP2C19 16 0 11 CYP2D6 21 2 4 CYP2E1 18 0 9 CYP3A4 24 1 2 CYP3A5 20 1 6 DPYD 13 5 9 GSTM1 13 1 13 GSTP1 1 18 8 NAT1 24 1 3 NAT2 25 1 1 SLC22A1 24 0 3 SLCO1B1 25 1 1 SLCO1B3 15 1 11 SULT1A1 22 1 4 TPMT 26 1 0 UGT1A1 23 1 3 UGT2B7 25 0 2 UGT2B15 23 0 4 UGT2B17 23 2 2

Transcription factors and their co-regulators

AHR 17 3 8 ARNT 15 3 10 HNF4A 26 1 1 PXR 25 1 2 CAR 25 1 2 PPARA 27 1 0 PPARD 1 17 10 PPARG 4 14 10 RXRA 23 1 4 NRF2 22 1 5