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Cancer Genes and Genomics

Functional Genomics Reveals Diverse Cellular ProcessesThat Modulate Tumor Cell Response to Oxaliplatin

Kelly A. Harradine1, Michelle Kassner2, Donald Chow2, Meraj Aziz2, Daniel D. Von Hoff2, Joffre B. Baker1,Hongwei Yin2, and Robert J. Pelham1

AbstractOxaliplatin is widely used to treat colorectal cancer, as both adjuvant therapy for resected disease and palliative

treatment of metastatic disease. However, a significant number of patients experience serious side effects, includingprolonged neurotoxicity, from oxaliplatin treatment creating an urgent need for biomarkers of oxaliplatin responseor resistance to direct therapy to those most likely to benefit. As a first step to improve selection of patients foroxaliplatin-based chemotherapy, we have conducted an in vitro cell-based small interfering RNA (siRNA) screen of500 genes aimed at identifying genes whose loss of expression alters tumor cell response to oxaliplatin. The siRNAscreen identified twenty-seven genes, which when silenced, significantly altered colon tumor cell line sensitivity tooxaliplatin. Silencing of a group of putative resistance genes increased the extent of oxaliplatin-mediated DNAdamage and inhibited cell-cycle progression in oxaliplatin-treated cells. The activity of several signaling nodes,including AKT1 and MEK1, was also altered. We used cDNA transfection to overexpress two genes (LTBR andTMEM30A) that were identified in the siRNA screen as mediators of oxaliplatin sensitivity. In both instances,overexpression conferred resistance to oxaliplatin. In summary, this study identified numerous putative predictivebiomarkers of response to oxaliplatin that should be studied further in patient specimens for potential clinicalapplication. Diverse gene networks seem to influence tumor survival in response to DNA damage by oxaliplatin.Finally, those genes whose loss of expression (or function) is related to oxaliplatin sensitivity may be promisingtherapeutic targets to increase patient response to oxaliplatin. Mol Cancer Res; 9(2); 173–82. �2010 AACR.

Introduction

Oxaliplatin is a third-generation, platinum-based che-motherapeutic agent that has significant activity in color-ectal carcinoma (CRC). Adjuvant therapy with oxaliplatin,combined with fluoropyrimidine-based (5-FU) chemother-apy, results in significant increases in disease-free survivalrates in patients with stage II/III colon cancer (1). In themetastatic setting, combination therapy with 5-FU andoxaliplatin is the most commonly used frontline regimen,with superior response rates and longer survival than with 5-FU alone (2, 3). However, it is apparent that not all patientsbenefit from oxaliplatin treatment, and in the face ofsignificant side effects associated with oxaliplatin, mostnotably prolonged neurotoxicity, there is a great need for

clinical tools to guide the use of oxaliplatin in those patientswho are most likely to derive benefit from it.Oxaliplatin induces cytotoxicity through the formation of

platinum–DNA adducts, which, in turn, activate multiplesignaling pathways (4). Alterations in drug efflux anduptake, DNA repair, and inactivation of the apoptosispathways have been hypothesized to promote resistanceto platinum agents such as carboplatin and cisplatin (5, 6).None of these putative markers of oxaliplatin sensitivity andresistance have been clinically validated, and, at present,there are no markers established in clinical use for selectingCRC patients for oxaliplatin therapy.The current clinical practice used for making CRC treat-

ment decisions is determined by clinical and pathologicstaging. However, these prognostic tools do not predict drugresponse in an individual patient. Recent insights into thegenomics of cancers have enabled development of diagnostictests that inform clinical decisions for cancer patients (7–10).To further advance the personalization of CRC treatment,there is a need for a greater understanding of the geneticalterations in CRC tumors that are associated with patientsensitivity or resistance to oxaliplatin. One approach toidentifying genes responsible for drug sensitivity or resistanceis the use of in vitro loss-of-function (LOF) genetic screensvia RNA-mediated interference (RNAi). Genes that mod-ulate oxaliplatin sensitivity in vitro can be taken forward fortesting in clinical studies and, ultimately, may serve to be

Authors' Affiliations: 1Genomic Health, Inc., Redwood City, California;and 2Clinical Translational Research Division, Translational GenomicsResearch Institute, Scottsdale, Arizona

Note: Supplementary data for this article are available at Molecular CancerResearch Online (http://mcr.aacrjournals.org/).

Corresponding Author: Robert J. Pelham, Genomic Health, Inc., 301Penobscot Drive, Redwood City, CA 94063. Phone: 650-569-2884; Fax:650-556-1132. E-mail: [email protected]

doi: 10.1158/1541-7786.MCR-10-0412

�2010 American Association for Cancer Research.

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clinically useful predictive biomarkers to guide patient selec-tion for therapy with oxaliplatin.We carried out a small interfering RNA (siRNA) screen

in human CRC cell lines to identify genes whose LOFmodulates in vitro tumor cell response to oxaliplatin. Ourprimary screen targeted 500 genes involved in DNArepair, drug transport, metabolism, apoptosis, and regula-tion of the cell cycle, and utilized 4 unique siRNAduplexes over 7 different oxaliplatin concentrations pergene. Here, we report 27 genes whose loss of expressioncan significantly alter the response (through eitherincreased sensitivity or increased resistance) of colontumor cell lines to oxaliplatin. These results provide afoundation for future work in clinical samples from CRCpatients treated with oxaliplatin and also reveal insightsinto the heterogeneity of cellular processes regulatingtumor cell response to DNA damage.

Materials and Methods

Cell linesColon cancer cell lines HCT 116 (ATCC# CCL-247)

and SW480 (ATCC# CCL-228) were obtained from theAmerican Type Culture Collection and were maintainedin McCoy's 5A medium supplemented with 10% FBS,1.5 mmol/L L-glutamine, and 1% Antibiotic-Antimycotic(Invitrogen).

siRNA screening and drug treatmentsFour siRNA sequences for each targeted gene were picked

from the Whole Human Genome siRNA Library (Qiagen)to create 6 custom 384-well assay plates. All assay platesincluded negative control siRNAs (Non-Silencing, All-StarNon-Silencing, and GFP), and 2 positive control siRNAsfor transfection (UBBs1 and All-Star Cell Death Control),all of which were purchased from Qiagen. Selected siRNAswere printed individually into white solid 384-well plates(1 mL of 0.667 mmol/L siRNA per well for a total of 9 ng ofsiRNA), using a Biomek FX (Beckman Coulter). Lipofec-tamine 2000 (Invitrogen) was diluted in serum-freeMcCoy's 5A media and 20 mL was transferred into eachwell of the 384-well plate containing siRNAs (final ratio of7.4 nL of lipid per ng of siRNA). After an incubation periodof 30 minutes at room temperature to allow the siRNA andlipid to form complexes, 20 mL of HCT 116 cells (2.5 �104 cells/mL) in antibiotic-free McCoy's 5A media wereadded into each well. Transfected cells were incubated for24 hours prior to the addition of 10 mL per well of differentconcentrations of oxaliplatin (35.0, 3.75, 3.0, 2.0, and 1.5mmol/L) and vehicle control (DMSO) for a total assayvolume of 50 mL. Oxaliplatin was obtained from Sigma.Cell viability was measured 72 hours post–drug treatment,using the CellTiter-Glo assay (Promega), measured on anAnalyst GT Multimode reader (Molecular Devices). Areplicate of the screen was also done, resulting in a totalof 56 data points per gene. Cell viability data were normal-ized to the median value of negative control siRNAs, andIC50 values were calculated using Prism 5.0 (GraphPad).

Hit identification and statistical analysisThe effect of siRNA treatment on the IC50 of oxaliplatin

was expressed as the log2 fold shift of the median IC50 ofsiRNA-treated cells relative to the median IC50 of non-silencing siRNA control-treated cells. Hits were identified asthose with a median IC50 shift of median IC50 � 3MAD(median absolute deviations) or greater (11, 12). To assignstatistical significance to siRNA hits identified from thesiRNA screen, we then modeled the collective activities ofthe 4 individual siRNAs used for each gene by usingredundant siRNA activity (RSA) analysis (13). Briefly,the normalized, log2 transformed IC50 shifts of all siRNAswere rank ordered and the rank distribution of all siRNAstargeting the same gene was examined and a P value wascalculated on the basis of an iterative hypergeometric dis-tribution formula (13). siRNAs with P < 0.05 values wereconsidered as significant. Subsequently, only genes with amedian IC50 shift of median IC50 �3 MAD or greater andan RSA P < 0.05 value were considered robust hits andanalyzed further. All other tests of significance were 2-sided,and P < 0.05 values were considered significant.

Validation siRNA knockdownFor validation of siRNA hits, ON-TARGETplus siRNAs

(Thermo Scientific), containing pools of 4 siRNAs per gene,were utilized (Supplementary Table S3). Seventy microlitersof HCT 116 or SW480 cells (1.0� 105 cells/mL) was platedin black, clear-bottomed 96-well plates in antibiotic-freeMcCoy's 5Amedium and allowed to adhere overnight. Cellswere then transfected with 25 nmol/L of siRNA by usingDharmaFECT transfection reagent (Thermo Scientific).Following a 4-hour incubation period, 10 mL per well ofan 11-point, 2-fold serial dilution of oxaliplatin (50 mmol/Lmaximum concentration) was then added, for a total assayvolume of 100 mL. Assays were done in triplicate, with ON-TARGETplusNon-Targeting siRNA (Thermo Scientific) asa negative control, with biological replicates. Cell viabilitywasmeasured 72 hours later using theCellTiter 96AQueousOne Solution Cell Proliferation Assay (Promega), and IC50values calculated using Prism 5.0 (GraphPad). siRNAknockdown after 72 hours was validated by qRT-PCR,using the Roche LightCycler480 (Supplementary Fig. S3A).

Overexpression experimentsFull-length LTBR or TMEM30AORFs were cloned into

pCMV-XL4 or pCMV-XL5, respectively (Origene), andvalidated by sequencing. Transfection was carried out usingTurbofectin 8.0 (Origene) in 96-well format as per man-ufacturer's instructions, using 100 ng of cDNA per well.Following a 4-hour incubation period, 10 mL per well of an11-point, 2-fold serial dilution of oxaliplatin (50 mmol/Lmaximum concentration) was then added. Assays were donein triplicate, using the empty pCMV-XL4 vector or emptypCMV-XL5 vector as a negative control, with biologicalreplicates. Cell viability was measured 0, 24, 48, and 72hours later (Fig. 2C and Supplementary Fig. S3C) by usingthe CellTiter 96 AQueous One Solution Cell ProliferationAssay (Promega), and IC50 values calculated using Prism 5.0

Harradine et al.

Mol Cancer Res; 9(2) February 2011 Molecular Cancer Research174




(GraphPad). Transcript levels following cDNA overexpres-sion was measured by qRT-PCR at 0, 24, 48, and 72 hours,using the Roche LightCycler480, with transcript levelsnormalized to the housekeeping gene, b-actin, and resultswere expressed as normalized expression relative to emptypCMV-XL4 or empty pCMV-XL5 vector-transfected cells(2�DDCp; Supplementary Fig. S3B).

DNA damage, phosphoryation, and caspase-3 cleavageassaysDNA damage was assessed by quantification of apurinic/

apyrimidinic (AP) sites (BioVision) following man-ufacturer's instruction.Phosphorylation status of AKT1 (Ser473), MAP/ERK

kinase (MEK) 1 (Ser217/221), p38 mitogen activated pro-tein kinase (MAPK; Thr180/Tyr182), STAT3 (Tyr705),and NF-kB p65 (Ser536) was determined using the PathS-can Signaling Nodes Multi-Target Sandwich ELISA (CellSignaling Technology) as per manufacturer's instructions.The phosphorylation status of p53 (Ser15), Bad (Ser112),PARP (Asp214), and cleavage status of caspase-3 wasdetermined using the PathScan Apoptosis Multi-TargetSandwich ELISA (Cell Signaling Technology), followingmanufacturer's instructions. Raw signal intensity was nor-malized to either total Akt or Bad protein levels. Assays weredone in duplicate, and the log2 fold change (OD450 siRNA-treated cells/OD450 non-silencing siRNA-treated cells), fol-lowing median normalization, was converted into a heatmapusing Java TreeView.

Cell-cycle analysisTransfections were carried out as described earlier in the

validation experiments, using 6-well plates (5 � 105 cellsper well). Cells were collected by gentle trypsinization,followed by centrifugation at 500 rpm for 5 minutes, fixedwith 70% ethanol at �20�C, washed with PBS, and resus-pended in 0.5 mL of PBS containing propidium iodide(10 mg/mL). After a final incubation at 37�C for 30minutes,cells were analyzed by flow cytometry with a FACSCaliburflow cytometer (Becton Dickinson). Data were analyzedusing FlowJo software (Tree Star).

Functional annotation of siRNA hitsFunctional annotation of gene hits and networks of

interacting proteins was determined through the use ofIngenuity Pathways Analysis (Ingenuity Systems; www.ingenuity.com).

Results and Discussion

A siRNA screen identifies genes that control tumor cellsensitivity to oxaliplatinWe screened a custom siRNA library targeting 500

genes with putative roles in DNA damage repair, apop-tosis, cell-cycle regulation, drug metabolism, and trans-port, using the colorectal cancer tumor cell line HCT 116(Supplementary Table S1). The siRNA library contained4 siRNAs targeting each gene, with each siRNA trans-fected individually. The screen was conducted in dupli-cate, with a non-silencing siRNA negative control. siRNAswere used at 17 nmol/L to reduce off-target effects.Twenty-four hours after transfection, 5 different con-centrations of oxaliplatin (35.0, 3.75, 3.0, 2.0, and1.5 mmol/L) and vehicle control (DMSO) were addedand cell viability was measured 72 hours after the additionof drug. The mean Z0 factor for the screen was 0.78,suggesting that our assay had a robust signal-to-noise ratio(Supplementary Fig. S1B).Two stringent criteria were used to limit the discovery of

false-positive genes. First, we identified all genes whosesilencing shifted the IC50 of oxaliplatin to �3 MAD orgreater from the median IC50 of oxaliplatin in control cells,an approach (median � kMAD) that has been shown to berobust to outliers and effective in controlling the false-positive rate in siRNA screens (11, 12). Second, we modeledthe collective activities of the 4 individual siRNAs usedfor each gene by the RSA analysis (13). siRNAs with P< 0.05 values were considered significant (SupplementaryTable S2). Twenty-seven high confidence hits that satisfiedboth of these criteria were identified (Fig. 1; Table 1) andanalyzed further.

Figure 1. Identification of genesmodulating HCT 116 tumor cellsensitivity to oxaliplatin. Results of500-gene siRNA screen for genesthat modulate sensitivity tooxaliplatin. The median log2 foldshift in the IC50 of oxaliplatinfollowing siRNA treatment isplotted for each gene in thescreen. Genes with a median IC50

shift of median IC50 �3 MAD andgreater and an RSA P < 0.05 valueare indicated in red (increasedresistance to oxaliplatin) or blue(increased sensitivity tooxaliplatin).

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Validation of selected hits from siRNA screenOn the basis of functional categorization, we selected 12

hits from our primary screen and tested their validity withadditional siRNAs. Using independently targeted pools of 4siRNAs (ON-TARGETplus siRNAs) to reduce off-targeteffects and increase specificity of silencing, we categorizedretested genes as validated if the resulting IC50 of oxaliplatinwas shifted greater than 50% from the IC50 of oxaliplatin incells treated with non-silencing siRNAs. All 12 of the hitsselected for validation exceeded this 50% threshold(Fig. 2A).We then examined the impact of 9 of these genes in the

oxaliplatin-resistant SW480 colorectal tumor cell line (14).Silencing of each of these 9 genes (CUL4B, LTBR, MBD4,MCM3, NHEJ1, PRDX4, SFHM1, and TMEM30A), all ofwhich conferred increased sensitivity to the HCT 116tumor cell line, also increased sensitivity of the SW480tumor cell line to oxaliplatin (Fig. 2B).To independently test whether the expression of the

identified genes relates to tumor cell sensitivity to oxalipla-tin, we assayed the effects of overexpression of 2 genes,LTBR or TMEM30A, on tumor cell response to oxaliplatin.Transient overexpression of full-length LTBR orTMEM30A (validated by qRT-PCR; SupplementaryFig. S3B) increased the IC50 of oxaliplatin by greater than

2-fold (Fig. 2C), significantly increasing the resistance of theHCT 116 cell line to oxaliplatin, as predicted by the resultswith siRNA silencing.

Alterations in DNA damage and activity of signalingnodes in tumor cells with increased or decreasedsensitivity to oxaliplatinTo begin to address the cellular mechanisms responsible

for modulated cell sensitivity to oxaliplatin, we askedwhether siRNA silencing of the identified screening hitsaltered the amount of DNA damage in tumor cells treatedwith oxaliplatin. Platinum–DNA adducts formed uponexposure to platinum-based chemotherapies are thoughtto be primarily removed through the nucleotide excisionrepair pathway (NER). Using an in vitro assay that measuresthe number of AP sites on the DNA of oxaliplatin-treatedcells, we found that siRNA silencing of CUL4B andNHEJ1,both with known roles in the repair of DNA damage via theNER (15, 16), significantly increased the amount of DNAdamage relative to oxaliplatin-treated control cells (Fig. 3A).siRNA silencing of 2 other hits with known roles in DNAreplication and repair, MBD4 and MCM3 (17, 18), alsoincreased the amount of DNA damage accumulated ontreatment with oxaliplatin (Fig. 3A), although the increasedid not reach statistical significance (P < 0.05).

Table 1. Genes conferring sensitivity or resistance to oxaliplatin

Symbol Entrez ID Full name

BCL10 8915 B-cell CLL/lymphoma 10BCL2L10 10017 BCL2-like 10 (apoptosis facilitator)BFAR 51283 Bifunctional apoptosis regulatorBRIP1 83990 BRCA1 interacting protein C-terminal helicase 1CHAF1A 10036 Chromatin assembly factor 1, subunit A (p150)CUL4B 8450 Cullin 4BDFFA 1676 DNA fragmentation factor, 45-kDa, a-polypeptideIL8 3576 Interleukin 8LTBR 4055 Lymphotoxin b receptor (TNFR superfamily, member 3)MBD2 8932 Methyl-CpG binding domain protein 2MBD4 8930 Methyl-CpG binding domain protein 4MCM3 4172 Minichromosome maintenance complex component 3MCM4 4173 Minichromosome maintenance complex component 4MCM6 4175 Minichromosome maintenance complex component 6MPG 4350 N-Methylpurine-DNA glycosylaseMSH4 4438 mutS homologue 4 (E. coli)NHEJ1 79840 Nonhomologous end-joining factor 1PRDX4 10549 Peroxiredoxin 4PTTG1 9232 Pituitary tumor-transforming 1RAD51L1 5890 RAD51-like 1 (S. cerevisiae)RRM1 6240 Ribonucleotide reductase M1SHFM1 7979 Split hand/foot malformation (ectrodactyly) type 1TMEM30A 55754 Transmembrane protein 30ACDKN1A 1026 Cyclin-dependent kinase inhibitor 1A (p21, Cip1)KPNA2 3838 Karyopherin a 2 (RAG cohort 1, importin a 1)SUMO1 7341 SMT3 suppressor of mif two 3 homologue 1 (S. cerevisiae)TP53 7157 Tumor protein p53

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Silencing of the identified genes that modulate tumor cellsensitivity to oxaliplatin resulted in altered phosphorylationof several pathway signaling nodes, including mitogen-activated protein kinase cascade, JAK (Janus activatedkinase)/STAT, and NF-kB pathways—pathways whoseactivity may be contributing significantly to changes in cellproliferation (19, 20). We carried out quantitative analysesto determine the activity of p-Akt1, p-MEK1, p-p38MAPK, p-Stat3, and p-NF-kB p65 (Fig. 3B). Hierarchicalclustering of phosphorylation levels (relative to control cells)revealed diverse and nonoverlapping clusters of pathwaysignaling following siRNA silencing of our hits, with thenoticeable exception of p-NF-kB p65, suggesting thatmultiple distinct cellular mechanisms for each hit are likelyresponsible for altered cell survival. Similarly, when weprobed the activities of several gene regulators of apoptosis,including of p-p53, p-Bad, cleaved caspase-3, and cleavedPARP, distinct clusters of pathway activity were observed,suggesting that, upon siRNA silencing of our hits, bothcaspase-dependent and caspase-independent pathways reg-ulating changes in apoptosis and/or cell death are modulatedin response to DNA damage upon treatment with oxali-platin (Fig 3C). These results again suggest that a diversityof alterations in cell signaling, including the modulation ofapoptosis and necrosis, are responsible for increased tumorcell sensitivity to oxaliplatin (5).

Cell-cycle alterations in tumor cells with alteredsensitivities to oxaliplatinWe also evaluated the effects that siRNA silencing of

genes modulating oxaliplatin sensitivity would have on thecell cycle. Cell-cycle analysis indicates that upon treatmentwith oxaliplatin, all siRNA-treated cells, including thosewith increased siRNA-mediated resistance to oxaliplatin(CDKN1A and p53) exhibited a significant decrease inthe percentage of cells in G1 with a concomitant increase inthe percentage of cells in G2/M as compared with controlcells (Fig 4). This is consistent with previous observationsthat G2/M arrest facilitates platinum-mediated cell death(21, 22), although it is interesting to note that there were nogross differences between oxaliplatin-sensitive and -resistantcells.

Network of biological processes identified as potentialmodulators of tumor cell sensitivity to oxaliplatinTo further understand the functional relationships

between those genes whose loss of expression altered thesensitivity of tumor cells to oxaliplatin, we conducted anextensive bioinformatic analysis, using the genes identifiedin the initial screen (Table 1), to identify relevant networksof interacting proteins (Fig. 5).The largest interaction network is heavily populated with

genes that have roles in DNA replication, recombination,

Figure 2. Validation of genesidentified in an siRNA screen forgenes regulating sensitivity orresistance to oxaliplatin. A, siRNAsilencing of gene hits identified inthe primary screenwas repeated inthe HCT 116 tumor cell line withON-TARGETplus siRNAs, eachcontaining pools of 4 siRNAs pertarget gene. Cell viability wasassayedand IC50of oxaliplatinwascalculated 72 hours after siRNAtransfection and addition of an 11-point, 2-fold serial dilution ofoxaliplatin (50 mmol/L maximal). B,siRNA silencing of selected hitswas done using the SW480 tumorcell lineasdescribed in (A).C,effectof cDNA overexpression of full-length LTBR or TMEM30A on theIC50 of oxaliplatin. The effect ofsiRNA silencing or cDNAoverexpression on the IC50 ofoxaliplatin was expressed as thelog2 fold shift of the mean IC50 ofsiRNA-treated (or cDNA-overexpressing)cells relative to themean IC50 of nonsilencing siRNAcontrol-treated (or vector-alone)cells.Cell viabilitywasassayedandIC50 of oxaliplatin was calculated72 hours after cDNA transfectionand addition of an 11-point, 2-foldserial dilution of oxaliplatin (50mmol/L maximal). Data representmean � SEM (n ¼ 3).






repair, and cell-cycle progression (Fig. 5). It is, however, ofinterest that this interaction network contains nodes pre-viously not associated with response to oxaliplatin, whichlink it to proteins from the canonical (BCL10, TRAF6) andnoncanonical (LTBR, TRAF3, PRDX4) NF-kB pathways(23), as well as the estrogen signaling (ESR1, MPG,MDB2), apoptosis (BCL2, BCL2L10, BCL10, DFFA,CASP3, and BIRC2), and BRCA1/2 signaling pathways(BRCA1, BRCA2, SHFM1, and BRIP1). The diversity ofinteracting proteins in this network suggests that theresponse of tumor cells to DNA damage by oxaliplatin,as well as intrinsic and/or acquired resistance to this drug,may be influenced by a larger network of biological pro-cesses than previously appreciated (24).

Conclusion

This study is an attempt to identify putative biomarkersfor response to oxaliplatin, as well as potential therapeutictargets for enhancing tumor cell response to oxaliplatin. Wehave identified 27 genes, as well as associated cellularnetworks, which may serve to modulate tumor cell responseto oxaliplatin.Of the genes identified in our screen whose loss of

expression results in altered tumor cell sensitivity to oxali-platin, only 3 have been previously shown to be associatedwith tumor cell sensitivity to other platinum-containingchemotherapies, including cisplatin. In our study, the HCT116 TP53þ/þ colon cancer cell line becomes resistant tooxaliplatin upon siRNA-mediated loss of TP53 expression(Figs. 1 and 2A), whereas previous work has shown that anHCT 116 TP53�/� clone is resistant to oxaliplatin, with aresulting G2/M arrest (25). That our data provides similarconclusions, using an orthogonal approach (e.g., siRNAknockdown), regarding the role of TP53 in increased tumorcell resistance to oxaliplatin and the resulting cell-cyclearrest, only serves to validate our experimental designand screening endpoint, as well as the biological role ofTP53 in modulating response to oxaliplatin.Bartz and colleagues, using a genome-wide siRNA screen

in a TP53� HeLa cell line, also previously showed that theloss of SHFM1 or BRIP1 expression enhanced cisplatincytotoxicity (26). Our study showed that the loss of eitherSHFM1 or BRIP1 expression increased oxaliplatin cytotoxi-city in an siRNA screen by using the HCT 116 TP53þ cellline. It is interesting to note that despite the similarmechanism of action for both cisplatin and oxaliplatin(4), only these genes (SHFM1 and BRIP1) overlappedbetween our study and Bartz and colleagues (SupplementaryFig. S2). Likely explanations for these differences includeboth the underlying mutational status of TP53 in the celllines used and the more fundamental genomic variationbetween HeLa and HCT 116 tumor cell lines (derived froma human cervical adenocarcinoma and CRC, respectively),resulting in a differential sensitization upon the loss ofadditional genes in the presence of DNA-damaging agentssuch as cisplatin and oxaliplatin (26). Indeed, 11 genes thatwere previously identified as enhancers of cisplatin cyto-

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Figure 3. Functional analyses of genes modulating sensitivity tooxaliplatin. A, increased levels of DNA damage, as determined byquantification of AP sites (as % of nonsilencing siRNA-treated cells), inCUL4B- andNHEJ1-silenced HCT 116 tumor cells. Cells were transfected,treated with 1.56 mmol/L of oxaliplatin, and DNA damage was measuredafter 72 hours. Dashed line, 100% of control. Data represent mean� SEM(n ¼ 3); *, P < 0.05; NS, nonsilencing. B, hierarchical clustering of relativeactivities of pathway signaling nodes in cells with altered sensitivity tooxaliplatin. Heat map, the normalized log2 ratio of the phosphorylationlevels of AKT1 (Ser473), MEK1 (Ser217/221), p38 MAPK (Thr180/Tyr182),STAT3 (Tyr705), and NF-kBp65 (Ser536) in test siRNA-treated cells (þ1.56mmol/L oxaliplatin) relative to nonsilencing siRNA-treated cells (þ1.56mmol/L oxaliplatin), as assessed by quantitative analysis using a sandwichELISA with epitope-specific antibodies 72 hours posttransfection andaddition of oxaliplatin. C, hierarchical clustering of relative activities of keyapoptotic regulators in cells with altered sensitivity to oxaliplatin. Heatmap, the normalized log2 ratio of the phosphorylation levels of p53 (Ser15)and Bad (Ser112) as well as the cleavage status of PARP and caspase-3 intest siRNA-treated cells (þ1.56 mmol/L oxaliplatin) relative to nonsilencingsiRNA-treated cells (þ1.56 mmol/L oxaliplatin), as assessed by quantitativeanalysis using a sandwich ELISAwith epitope-specific antibodies 72 hoursposttransfection and addition of oxaliplatin. Color bar, log2 of relativeactivity (phosphorylation or cleavage).

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toxicity in the study of Bartz and colleagues (26) wereamong the 500 genes assayed in our primary siRNA screen(Supplementary Table S1) yet were not found to alter thecytotoxicity of oxaliplatin (Supplementary Table S2).Furthermore, Bartz and colleagues showed that the loss

of BRCA1 and BRCA2 selectively enhanced the cytotoxi-city of cisplatin in TP53-deficient cells as compared withisogenic wild-type TP53 cells (26). These observed differ-ences in the modulation of cytotoxicity to oxaliplatin andcisplatin underscore difficulties in identifying generalized

Figure 4. Alterations in cell-cycle distribution in cells with altered sensitivity to oxaliplatin; x-axis, DNA content (as determined by propidium iodidestaining); y-axis, cell count. Color coding, G1 (green), S (yellow), or G2/M (blues) phases of the cell cycle. Percentages of each stage are indicated. Cellswere transfected, treated with 1.56 mmol/L oxaliplatin, and processed for FACScan after 72 hours. NS, nonsilencing.






predictive biomarkers for drug response, as well as the needto utilize tumors and cell lines of diverse genetic lineages forthe identification of robust biomarkers for predicting drugresponse.Many genes that regulate diverse aspects of DNA repair,

recombination, and replication, including components ofthe NER, nonhomologous end-joining pathway, and mis-match repair (MMR) pathways, were represented amongour hits. The NER portion of this module includes CUL4B,an E3 ubiquitin ligase that binds to damaged DNA andubiquitinates histone H2A, modifying the structure ofchromatin and facilitating NER (15, 27). NHEJ1 encodesa DNA repair protein that is essential for the nonhomolo-gous end-joining pathway, which preferentially mediatesrepair of double-stranded breaks in DNA (16, 28).The defects in the MMR has long been appreciated as a

possible mechanism of resistance to chemotherapy bydirectly impairing the ability of the tumor cell to detectDNA damage, faithfully replicate DNA, and ultimately theactivation of apoptosis (29). Our data identify the MMRpathway genes MSH4, MBD2, and MBD4 as drivers ofoxaliplatin resistance (30).Alterations in DNA replication due to defects in the

recruitment of minichromosome maintenance (MCM)proteins onto chromatin during the assembly of the pre-replication complex at origins of replication (18, 31, 32),while not previously implicated in drug resistance, may

cause tumor cells to develop resistance to chemotherapies.Indeed, MCM3, MCM4, and MCM6, all components ofthe MCM complex, were identified as modulators of tumorcell sensitivity to oxaliplatin.The mechanisms through which tumor cells may acquire

altered sensitivity to oxaliplatin have been thought to belinked to the DNA-damaging activity of oxaliplatin (e.g.,NER, MMR; ref. 4). However, in addition to DNAreplication, several gene hits from our siRNA screen havefunctional annotations not previously implicated in themodulation of tumor cell sensitivity to oxaliplatin—severalof which may have heretofore unappreciated roles in DNAdamage response. LTBR, lymphotoxin-b receptor, is amember of the TNF receptor (TNFR) superfamily thatstimulates the noncanonical NF-kB signaling and cell deathvia the recruitment of TNFR-associated factor 3 (TRAF3)to the LTBR cytoplasmic domain (19, 33) and plays animportant role in the pathobiology of hepatocellular carci-noma (34). PRDX4, an additional hit from our screen, isthought to activate NF-kB by modulating IkB-a phos-phorylation (35).It is noteworthy that siRNA knockdown of the gene

ERCC1 did not alter tumor cell sensitivity to oxaliplatin.Multiple prior studies have shown that in vitro and in vivo,mRNA levels of ERCC1 are modest predictors of tumorcell sensitivity and patient response to cisplatin (36–39).However, the utility of ERCC1 in predicting patient response

CASP3

TMEM30A DFFAShapes

Lines

Colors

Cytokine/growth factor

Enzyme

Ligand-dependent nuclear receptor

Peptidase

Transcription regulator

Transmembrane receptor

Transporter

Other

Complex

Direct interactionDirect bindingDirect inhibitionIndirect interactionIndirect binding

Increased resistance to oxaliplatinIncreased sensitivity to oxaliplatin

BIRC2

SUM01

TRAF6

MCM4MCM3

MCM6

PRDX4 MBD2

ESR1

MPG

CDKN1A

PTTG1

SHFM1

HNF4A

KPNA2

BRIP1 BRCA1

BRCA2

RRM1RRM2

TNFCHAF1A

TRAF3

LTBR

NF-κB(complex)

TP53

BCL2

BCL10

BCL2L10

IL2

IL8

Figure 5. Network modeling of screening hits identifies networks of multiple cellular processes linked to oxaliplatin sensitivity. Networks of interactingproteins identified using Ingenuity Pathways Analysis. Color indicates genes identified in the siRNA screen whose loss of expression increased tumor cellresistance to oxaliplatin (red) or increased tumor cell sensitivity to oxaliplatin (blue).

Harradine et al.





to oxaliplatin is limited to combination 5-FU/oxaliplatinchemotherapies in the clinical setting (40, 41). Our data,while not specifically identifying ERCC1 as a candidate hit,did identify numerous genes involved in the repair of DNAdamage, includingmembers of theNER (of which ERCC1 isa key molecule) and DNA MMR pathways (Table 1). It ispossible that ERCC1 mRNA levels may predict resistanceto oxaliplatin only in the clinical setting of combinationtherapy with 5-FU (42). In our screen, we treated cells withoxaliplatin alone to reduce the identification of false-positivehits and to simplify the screen design. It is also possible thatour assay endpoint, by design, resulted in a particularlystringent cutoff for hit selection that yielded only very robustmodulators of tumor cell sensitivity to oxaliplatin.Our results indicate that diverse, and possibly, redundant

processes may influence cell proliferation and survival upontreatment with oxaliplatin. That multiple processes may

function redundantly to drive cell resistance to DNA-damaging agents might make it difficult to find robustbiomarkers to predict patient response to oxaliplatin.Finally, protein products of the identified genes suggesttherapeutic strategies to sensitize tumor cells to oxaliplatinand other DNA-damaging chemotherapeutic agents.

Disclosure of Potential Conflicts of Interest

K. Harradine, J. Baker, and R. Pelham are employees of Genomic Health, Inc.

Grant Support

This study was sponsored by Genomic Health, Inc.

Received September 8, 2010; revised November 2, 2010; accepted December 7,2010; published OnlineFirst December 17, 2010.

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2011;9:173-182. Published OnlineFirst December 17, 2010.Mol Cancer Res Kelly A. Harradine, Michelle Kassner, Donald Chow, et al. Modulate Tumor Cell Response to OxaliplatinFunctional Genomics Reveals Diverse Cellular Processes That

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