Predictive Gene Signatures Determine Tumor Sensitivity to ... · Translational Science Predictive Gene Signatures Determine Tumor Sensitivity to MDM2 Inhibition Jo Ishizawa1, Kenji

Translational Science

Predictive Gene Signatures Determine TumorSensitivity to MDM2 InhibitionJo Ishizawa1, Kenji Nakamaru2, Takahiko Seki2, Koichi Tazaki2, Kensuke Kojima1,3,Dhruv Chachad1, Ran Zhao1, Lauren Heese1,Wencai Ma4, Man Chun John Ma5,Courtney DiNardo6, Sherry Pierce6, Keyur P. Patel7, Archie Tse8, R. Eric Davis5,Arvind Rao4, and Michael Andreeff1

Abstract

Early clinical trials using murine double minute 2 (MDM2)inhibitors demonstrated proof-of-concept of p53-induced apo-ptosis by MDM2 inhibition in cancer cells; however, not allwild-type TP53 tumors are sensitive to MDM2 inhibition.Therefore, more potent inhibitors and biomarkers predictiveof tumor sensitivity are needed. The novel MDM2 inhibitor DS-3032b is 10-fold more potent than the first-generation inhib-itor nutlin-3a. TP53 mutations were predictive of resistance toDS-3032b, and allele frequencies of TP53 mutations werenegatively correlated with sensitivity to DS-3032b. However,sensitivity to DS-3032b of TP53 wild-type tumors varied great-ly. We thus used two methods to create predictive gene signa-tures. First, by comparing sensitivity to MDM2 inhibition withbasal mRNA expression profiles in 240 cancer cell lines, a 175-gene signature was defined and validated in patient-derivedtumor xenograft models and ex vivo human acute myeloid

leukemia (AML) cells. Second, an AML-specific 1,532-genesignature was defined by performing random forest analysiswith cross-validation using gene expression profiles of 41primary AML samples. The combination of TP53 mutationstatus with the two gene signatures provided the best positivepredictive values (81% and 82%, compared with 62% for TP53mutation status alone). In addition, the top-ranked 50 genesselected from the AML-specific 1,532-gene signature conservedhigh predictive performance, suggesting that a more feasiblesize of gene signature can be generated through this method forclinical implementation. Our model is being tested in ongoingclinical trials of MDM2 inhibitors.

Significance: This study demonstrates that gene expressionprofiling combined with TP53 mutational status predicts anti-tumor effects of MDM2 inhibitors in vitro and in vivo. Cancer Res;78(10); 2721–31. �2018 AACR.

IntroductionThe p53 protein plays a pivotal role in the tumor suppression

and maintenance of genome integrity, acting as a transcriptionfactor that can induce apoptosis, cell-cycle arrest, DNA repairand/or senescence in response to cellular stresses. Althoughapproximately half of all cancers harbor TP53 mutations, wild-

type p53 is also frequently inactivated by other mechanisms.The E3 ligase murine double minute 2 (MDM2, also known asHDM2) is frequently overexpressed in various cancer (1, 2),directly inactivating p53 function by binding to its transactivationdomain and also inducing its degradation through p53 ubiqui-tination. Pharmacologic inhibition of the p53–MDM2 interac-tion has been developed into a therapeutic approach for exertingp53-mediated antitumor effects.

Several preclinical (3–8) and early clinical (9–11) studies ofMDM2 inhibitors demonstrated the proof-of-concept of theirantitumor effects via p53 activation in both solid and hema-tologic tumors. There are multiple ongoing clinical studies(12, 13) of MDM2 inhibitors in several cancers, includingthe novel MDM2 inhibitor DS-3032b, still in an early phase(solid tumors and lymphoma, NCT01877382; myeloma,NCT02579824_DS3032b; leukemia NCT02319369). Clinicalresponses in these trials have been limited overall, but somepatients clearly achieve clinical benefit by monotherapy withMDM2 inhibitors (9–13). Therefore, there is a need forimproved MDM2 inhibitors, or efficacious combination ther-apies, as well as predictive biomarkers for monotherapy withcurrent MDM2 inhibitors.

We report here that DS-3032b exerts highly potent p53-depen-dent antitumor effects both in vitro and in vivo. In addition, to testthe hypothesis that gene signatures can predict sensitivity toMDM2 inhibition, wemeasured sensitivities toMDM2 inhibitionin a broad range of cancer cell lines, patient-derived xenograft

1Section of Molecular Hematology and Therapy, Department of Leukemia, TheUniversity of Texas MD Anderson Cancer Center, Houston, Texas. 2DaiichiSankyo Co., Ltd., Hiromachi, Shinagawa-ku, Tokyo, Japan. 3Hematology, Respi-ratory Medicine and Oncology, Department of Medicine, Saga University, Saga,Japan. 4Department of Bioinformatics and Computational Biology, The Univer-sity of Texas MD Anderson Cancer Center, Houston, Texas. 5Department ofLymphoma/Myeloma, The University of Texas MD Anderson Cancer Center,Houston, Texas. 6Department of Leukemia, The University of Texas MD Ander-son Cancer Center, Houston, Texas. 7Department of Hematopathology, TheUniversity of Texas MD Anderson Cancer Center, Houston, Texas. 8DaiichiSankyo, Inc., Edison, New Jersey.

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

Current address for A. Tse: Merck & Co. Inc., Rahway, NJ.

Corresponding Author: Michael Andreeff, University of Texas MD AndersonCancer Center, 1515 Holecombe Boulevard, Unit 448, Houston, TX 77030-4009.Phone: 713-792-7261; Fax: 713-794-1903; E-mail: [email protected]

doi: 10.1158/0008-5472.CAN-17-0949

�2018 American Association for Cancer Research.

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models, andprimary leukemia specimens andused these toderivepredictive signatures from gene expression profiles determinedbefore treatment. We report two different bioinformaticapproaches to define predictive gene signatures: one used a panelof 240 molecularly annotated cancer cell lines to find a commonsignature among various tumor types and the other focusedprimarily on acute myeloid leukemia (AML) as an example todefine tumor-type–specific signatures. Considering that TP53mutation status is a predictive but not a sufficient biomarker, wealso combined the gene signatures with TP53 mutation status,aiming at improved prediction of response.

Materials and MethodsReagents

DS-5272 (8), DS-3032b, andDS-3032a (a salt-free form of DS-3032b) were developed and synthesized by Daiichi Sankyo Co.,Ltd. (Fig. 1A). (-)-Nutlin-3a was purchased from Cayman Chem-ical Company.

Cell lines and cell cultureSJSA-1, Saos-2,WM-115, andDLD-1 cells were purchased from

the ATCC.MOLM-14,OCI-AML3,DOHH-2, and Z-138 cells werepurchased from the Leibniz-Institut Deutsche Sammlung vonMikroorganismen und Zellkulturen. The authenticity of the celllines was confirmed byDNA fingerprinting with the short tandemrepeat method, using a PowerPlex 16 HS System (Promega)within 6 months before the experiments. Mycoplasma was testedby PCR method and confirmed the 30 negativity for all the celllines used (14). Cryopreserved cells, or freshpatient samples,werecultured as described previously (14) and used at passages 4 to 6for the in vitro and in vivo experiments.

Apoptosis assay and flow cytometric analysisFor 48-hour drug treatment experiments, cell lines were

harvested at log-phase growth, plated at a density of 1.5 �105/mL for MOLM-14, OCI-AML3, and Z-138, or 1.0 � 106/mLfor primary AML and MCL cells, and treated with DS-3032b(0, 25, 50, 100, 250, 500, 1,000 nmol/L). Annexin V andpropidium iodide (PI; purchased from Sigma-Aldrich) bindingassays were performed to assess apoptosis as described pre-viously (14). Annexin V- and PI-negative cells were counted aslive cells, and the percentage of live cells was calculated bynormalizing to untreated controls. Based on the live-cell countsmeasured at each concentration, IC50 was calculated by usingGraphPad Software (ver. 6). Calculations of specific apoptosisand area under the curve (AUC) based on the percentage of livecells are given in Supplementary Information.

Analysis of patient-derived tumor xenograft miceAn in vivo study in patient-derived tumor xenograft mice was

performed by Champions Oncology Inc. Thirteen tumor models,including melanoma, non–small cell lung carcinoma (NSCLC),colorectal and pancreatic, were selected from the ChampionsTumorGraft library. Gene expression data and TP53 genotypeinformation were provided by ChampionsOncology. Female nu/nu mice (Harlan) between 6 and 9 weeks of age were implantedbilaterally in the flank region with tumor fragments harvestedfrom host animals, each implanted from a specific passage lot.When tumors reached approximately 125 to 250 mm3, animalswere matched by tumor volume into treatment and control

groups and dosing initiated (day 0). Animals bearing tumorswere grouped into treatment and vehicle groups (n ¼ 10, respec-tively), and DS-3032b was administered at 10 mg/kg/day for10 days. Tumor dimensions were measured twice weekly bydigital caliper. Tumor volume (TV) was calculated using theformula (i): TV ¼ (width)2 � (length) � 0.52. Percent tumorgrowth inhibition (%TGI) values were calculated for each treat-ment group (T) versus control (C) using tumor measurementsof initial (i) and at the end of dosing or the closest tumormeasurement (f) by the formula (ii): %TGI ¼ 1 � Tf � Ti /Cf � C. All experimental procedures were performed in accor-dance with the in-house guideline of the Institutional AnimalCare and Use Committee of Champions Oncology, Inc.

Animal studies and experiments with human samplesFor all the animal studies in the present study, the study

protocols were approved by the Institutional Animal Care andUse Committee of Champions Oncology Inc. (patient-derivedxenograft mice) and Covance Laboratories Inc. (SJSA-1 andMOLM-13 xenograft mice). Studies with primary human leuke-mia samples were approved by the institutional review board atMDAnderson Cancer Center. Forty-one peripheral blood or bonemarrow samples from 38 patients with AML, newly diagnosed, orrelapsed/refractory after chemotherapy were collected.

Generation of the 175-gene signature and sensitivity scoreA differential expression analysis of themRNA datasets derived

from sensitive and resistant cell lines was performed. Genes wereranked by P value according to Student two class t test in each cellline in OncoPanel. Genes differentially upregulated in sensitivecell lines, defined by GI50 values of DS-5272 (<1.5 mmol/L),contributed to the sensitivity signature. The signature containedthe top 1% of ranked genes (n¼ 175). The robustness of the genesignatures was inspected in several ways. First, heat maps of theentire 1% gene lists were generated. Visual inspection of the heatmap was performed to characterize the extent and consistency ofthe differential expression across cell lines and down the gene list.Second, the Q value was calculated as (P value/P value rank) �number of genes measured. As a guideline, all signatures wererequired to have�10 genes withQ value� 0.1 to support furtheranalysis. Lastly, the sensitivity signature was compared againstother drug signatures by comparing the total number of genes attheQ value threshold 0.05 and theQ value of the least significantgene in the top 1% signature.

The expression values of the genes within the signature wereZ score normalized. Once normalized, the average Z score ofthe sensitivity signature genes was calculated on an individualcell line, murine or human sample basis to generate a sensitivityscore, respectively. The same calculation was used in GEP dataderived from Oncomine and primary AML samples. Data fromthe microarray studies have been submitted to the Gene Expres-sion Omnibus. The accession number is GSE110087.

Statistical analysesFor random forest methods with cross-validation in primary

AML samples, gene expression profiling of the 41 primary AMLsampleswas used. Eleven each among 33TP53wild-type samples,or 14 each among 41 (including 8 TP53-mutant samples) wereselected as sensitive or resistant to DS-3032 based on AUC basedon the percentage of live cells. Classification was performed usingthe random forest method to identify a predictive algorithm with

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a particular gene set. Forests were created with 10,000 decisiontrees. Performance was assessed by AUC of a receiver-operatingcharacteristic (ROC) curve, from internal out-of-bag (OOB) test-

ing results. Each tree uses a different random bootstrap sample oftwo thirds of cases from the original data. A test set classification isobtained for each case in about one third of the trees. By default,

Figure 1.

DS-3032b induces p53-dependent apoptosis in tumor cells by activating p53 via inhibition of MDM2–p53 interaction in culture and in mice. A, Chemical structuresof DS-3032b, DS-5272, and nutlin-3a. B, Immunoblot of p53, MDM2, p21, cleaved PARP1, and b-actin in tumor cells treated with DS-3032b for 24 hours. C,Apoptosis induced by DS-3032b treatment for 48 hours in TP53 knockdown or intact MOLM-14 and OCI-AML3 cells (n ¼ 3 experiments for each cell line). p53knockdown was confirmed. D, Time-course measurement of tumor xenograft volumes in mice inoculated with SJSA-1 cells treated with DS-3032b. Eachdata point and bar represent the mean and SE of the estimated tumor volume in each group, respectively (n ¼ 6). E, Time-course measurement of tumorxenograft volumes in mice inoculated with SJSA-1 cells treated with DS-3032b. Each data point and bar represent the mean and SE of the tumor volume ineach group, respectively (n ¼ 10). F, Immunoblot of p53, MDM2, and p21 in xenograft tumors at 6 and 24 hours after a single administration of DS-3032a to micebearing MOLM-13–derived tumors. b-Actin was used as an internal control.

Genetic Biomarkers for Tumor Sensitivity to MDM2 Inhibition

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the randomforestsmethodperforms abinary classification (in thepresent study, sensitive vs. resistant to DS-3032b). However, italso reports a probability for being sensitive to DS-3032b. Thethreshold for predicting sensitive or resistant was set as 0.5probability. Overall accuracy was defined as a ratio of the casescorrectly predicted as sensitive or resistant to the total cases.Positive predictive value was defined as a ratio of the casescorrectly predicted as sensitive to the total cases predicted assensitive. Negative predictive value was defined as a ratio of thecases correctly predicted as resistant to the cases predicted asresistant. Sensitivity was defined as a ratio of the cases correctlypredicted as sensitive to the total sensitive cases.

ResultsDS-3032b inhibits MDM2–p53 interaction and activates p53 atnanomolar concentrations

The chemical structure of DS-3032b is shown in Fig. 1A. Toexamine the in vitro inhibitory effects of DS-3032b on MDM2–p53 interaction, we utilized homogeneous time-resolved fluores-cence (HTRF). DS-3032b inhibited the MDM2–p53 interactionwith IC50 values of 5.57 nmol/L (Supplementary Fig. S1A).Consistently, nanomolar concentrations (30–1,000 nmol/L) ofDS-3032b increased p53 levels in a dose-dependent manner by24 hours, along with induction of the p53 targets MDM2 andp21, in various cancer cell lines (SJSA-1, Colo829, MOLM-13,DOHH-2, NCI-H929, and WM-115) harboring wild-type TP53(Fig. 1B). Treatment with DS-3032b also induced PARP cleav-age, indicating apoptosis induction (Fig. 1B). Dose-dependentincrease in expressions of the p53 target genesCDKN1A (p21) andBBC3 (PUMA)was also confirmed in SJSA-1 cells (SupplementaryFig. S1B). These results proved thatDS-3032b is a potent inhibitorof MDM2 and indeed activates p53 function.

DS-3032b causes p53-dependent antitumor effects both in vitroand in vivo

Wenext treated six human cancer cell lines derived fromvarioustumor types (MOLM-13, AML; DOHH-2, non-Hodgkin B-celllymphoma; SJSA-1, osteosarcoma; WM-115, melanoma; DLD-1, colon adenocarcinoma; Saos-2, osteosarcoma) with DS-3032bor nutlin-3a, and determined their GI50 values (SupplementaryTable S1). DLD-1 and Saos-2 cells with TP53 mutations wereresistant to either of the drugs, while the other four cell lines withwild-type TP53 were more sensitive with nanomolar values ofGI50. Compared with GI50 values of nutlin-3a, DS-3032b wasapproximately 10-fold more potent. We then used a TP53 wild-type AML and mantle cell lymphoma (MCL) cell lines (MOLM-14, OCI-AML3, and Z-138) and knocked down TP53 expressionusing short hairpin RNA to test for p53 dependency of DS-3032b.As expected, TP53 knockeddown cells are significantly resistant toDS-3032b– and nutlin-3a–induced apoptosis (Fig. 1C; Supple-mentary Fig. S1C), confirming the p53-dependency of apoptosisinduction of these two inhibitors. Of note, we also treatedcirculating MCL cells obtained from 5 patients. All samples hadwild-type TP53 as determined by Sanger sequencing. DS-3032binduced apoptosis in a dose-dependent manner in all of thesamples (Supplementary Fig. S1D).

We further tested the in vivo activity of DS-3032b using tumorxenograft models with TP53 wild-type MOLM-13 and SJSA-1cells. In both models, DS-3032b inhibited tumor growth in adose-dependent manner, especially with oral administration at

25 and 50mg/kg/day (Fig. 1D and E; Supplementary Fig. S1E andSupplementary Table S2), indicating the potential of clinical useof this agent. Daily and intermittent dosing (every 3 or 7 days; Fig.1E) both showed in vivo efficacy, suggesting relatively long-lastingefficacy of DS-3032b in vivo. We also confirmed induction of p53,MDM2, and p21 protein in tumor cells 6 and 24 hours after in vivotreatment with DS-3032b (Fig. 1F).

We also treated a panel of 240 cell lines from various cancers(OncoPanel, Eurofins Panlabs)withDS-3032a (a free formofDS-3032b), DS-5272 (another prototypic MDM2 inhibitor devel-oped previously; ref. 8), andnutlin-3a. As expected, GI50 profile ofDS-3032b is highly correlated with that of DS-5272 (r¼ 0.98, P¼2.2e�16; Supplementary Fig. S2A; Supplementary File S1).

TP53 mutation status alone is not a sufficient biomarker todetermine antitumor effects of DS-3032b

We have shown in clinical studies of the MDM2 inhibitorRG7112 that therapeutic response to RG7112 as a single drugvaries from patient to patient in cancer patients harboring wild-type TP53 tumors (10). We established first that sensitivity toMDM2 inhibition is in part dependent on baseline MDM2 levels(4) and confirmed thisfinding in theAML clinical trial of RG7112,where both baseline and induced MDM2 levels were correlatedwith response (10). The heterogeneous response in p53wild-typecell lines had also been observed in cell lines from a single diseaseentity (5, 15).

Indeed, the data from OncoPanel cell lines treated withthe three MDM2 inhibitors showed that TP53 wild-type celllines have a large range of GI50 values (e.g., varying from <0.1to >10 mmol/L for both DS-5272 and DS-3032b; Supplemen-tary Fig. S2B; Supplementary Table S3 and SupplementaryFile S1). Cell lines that were TP53 wild-type but were resistantto all the three inhibitors included HeLa, C4I, and C32 cells,which may be related to human papilloma virus infection(HeLa and C4I) and ARF gene deletion (C32) that inactivatep53. However, the 24% of TP53 wild-type cell lines that weredeemed resistant did not all have known causes of resistance(Fig. 2A). Also, our in vitro data using cell lines or primary MCLcells with wild-type TP53 (Supplementary Table S1 and Sup-plementary Fig. S1D) showed that sensitivity varied over acertain range (GI50 < 100 to > 250 nmol/L).

To confirm the variance of sensitivities to DS-3032b of wild-typeTP53 tumor cells through ex vivo experiments, we collected 41primary AML specimens obtained from 38 patients, which wereall clinically annotated (Supplementary Table S4). Eight (19.5%)had TP53 point mutations, detected by next-generation sequenc-ing and Sanger sequencing. Samples with TP53 wild-type weresignificantly more sensitive to DS-3032b than those with TP53mutations (Fig. 2B). Because exact GI50 values could not becalculated in highly sensitive (<25 nmol/L) or highly resistant(>1,000 nmol/L) samples, we also used the AUC based onpercentages of live cell number (normalized to untreated con-trols) at each concentration (Figs. 2C; Supplementary Fig. S2CandSupplementary File S2).We then focused on the outer one third ofsensitive or resistant samples, and assessed how efficiently TP53mutation status alone could predict the sensitivity to DS-3032b(Fig. 2C; Supplementary Table S5). Reflecting that some of thewild-type TP53 samples were as resistant or even more resistantthan TP53-mutated samples, the positive predictive value (PPV;62%) and overall accuracy (68%) were relatively low, comparedwith high sensitivity of 93% (Table 1). This indicates that

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predictive biomarkerswithhigher PPV than that ofTP53mutationstatus alone are desired for more effectively predicting tumorsensitivity to MDM2 inhibition.

TP53 mutation status can also be a continuous variable,based on the allele frequency of mutant TP53. Of note, AUCvalues based on live-cell numbers (Fig. 2D) and apoptosis (Fig.2E) are both correlated with allele frequencies of TP53 muta-tions in a positive and negative manner, respectively. Thisindicates that in cases with mutated TP53, the allele frequencyof TP53 mutations could potentially serve to predict sensitivityto MDM2 inhibition.

A cell line–based sensitivity gene signature better predicts thesensitivity of tumor cells to MDM2 inhibition than TP53mutation status alone

Among the 240 OncoPanel cell lines, we defined 62-sensitiveand 164-resistant cell lines, based on GI50 values of DS-5272(cutoff values of <1.5 and >10 mmol/L, respectively; Supplemen-tary Fig. S2B and Supplementary Table S6). This categorizationwas almost identical to that for DS-3032b. Specifically, only three(786-o, HS294T, and RPMI8226) among 240 cell lines on thepanel were categorized discordantly. As to 786-o and HS294T,the dose–response curves are essentially similar (Supplementary

Figure 2.

p53 mutation status and cell susceptibility to MDM2 inhibition. A, Pie charts showing the proportion of cancer cell lines sensitive or resistant to DS-5272and TP53 mutation status. The following cell lines were excluded due to unclear annotation on TP53: A172, DOHH-2, HT-1197, SW-48, MALME3M,A673, CAOV3, CAPAN-2, EB3, HS-766T, K-562, L428, SKNAS, and DLD1. B, GI50 values of DS-3032b in 41 primary AML cells and TP53 mutationstatus (P ¼ 0.0053, Mann–Whitney test). C, Sensitivity of 41 primary AML cells to DS-3032b and TP53 mutation status. Y-axis represents the AUCcalculated according to %live cell number at each concentration of DS-3032b in each sample. D and E, Correlation between allele frequenciesof TP53 mutations and sensitivities to DS-3032b in 8 primary AML samples. Y-axes represent AUC calculated according to %live cell number (D) or%apoptosis (E) at each concentration of DS-3032b in each sample.






Fig. S2D), and we therefore concluded that the sensitivities ofthese lines to DS-3032b and DS-5272 are basically similar. Thereason for discordant data for RPMI8226 cells remains unclear.

Using publicly available gene expression profiling data for allthe cell lines, we then selected 175 genes ranked at the top 1% ofthe genes of higher expression level with lowest P value in the62 sensitive cell lines than resistant cell lines (SupplementaryFile S3). We thus defined the average of Z-scores of the 175 genesas a "sensitivity score." The sensitivity score similarly predictedresponses to three different MDM2 inhibitors (DS-5272,nutlin3a, and DS-3032a; free form of DS-3032b; SupplementaryFig. S2E), indicating its robustness as predictive gene signature fordifferent MDM2 inhibitors. Of note, we also defined a "resistancescore" by selecting the top 1% of genes of higher expression levelwith lowest P value in resistant lines (Supplementary File S3);however, it did not predict the sensitivity/resistance well (worsethan predictive performance by TP53 mutation status alone);thus, we decided to further develop the sensitivity score.

A combination of TP53 genotype and gene signature showedfurther enrichment of sensitive cell lines compared with usingeither of them alone (Supplementary Fig. S3A). Specifically, byapplying the sensitivity signature, 41 of 55 p53wild-type cell lineshad high-sensitivity scores and the rest of 14 lines had low scores.Thirty-nine of 41 (95%) cell lines with high scores were sensitive,while 11 of 14 (79%) cell lines with low scores are resistant to DS-5272, suggesting potentially high predictive performance of thissignature.

Cell line–based 175-gene signature is validated in differentdatasets in tumors ex vivo and in vivo

To validate the 175-gene signature, we used patient-derivedtumor xenografts. In vivo antitumor activities of DS-3032b wereevaluated in 13 total tumor models, derived from melanoma,NSCLC, colorectal, and pancreatic cancers (Supplementary TableS7A). A positive correlation was observed between the sensitivityscores and TGI values (Fig. 3A, Spearman r¼ 0.67, P¼0.031). Theprediction accuracy, sensitivity, and PPV were 85%, 88%, and88%, respectively (Supplementary Table S7B).

Performing gene expression profiling in addition to ex vivo cellviability assays of AML samples described above (SupplementaryTable S4), we tested whether the sensitivity score of each samplepredicts the sensitivity to DS-3032b. First, we focused only on the33 cases with wild-type TP53. As the outer third of sensitive orresistant samples, eleven p53 wild-type samples each were select-ed as sensitive or resistant to DS-3032 based on AUC of %live cellnumber, and the 175-gene signature was tested. The predictionaccuracy, sensitivity and PPV were 73%, 91% and 67%, respec-tively (Fig. 3B; Supplementary Fig. S3B). Next, we also tested the175-gene signature in all 41 samples including additional 8 caseswith TP53mutations. Focusing on the outer third (fourteen each)

sensitive or resistant samples in the 41 samples, the predictionaccuracy, sensitivity and positive predictive values (PPV) were79%, 93% and 72% (Fig. 3C; Supplementary Fig. S3C) respec-tively. Compared with results for TP53 mutation status alonedescribed above (68%, 93%, and 62%, respectively; Supplemen-tary Table S5), the 175-gene signature improved PPV (Table 1).Using the same dataset of our primary AML samples, we alsotested a recently reported 4-gene predictive signature (16), whichshowed relatively low predictive performance (prediction accu-racy, sensitivity, and PPV were 68%, 86%, and 63%, respectively;Supplementary Table S8 and Table 1), compared with the 175-gene signature (Supplementary Fig. S3D).

We further tested the predictive performance when the 175-gene signature was applied after TP53mutation status was incor-porated as a first predictive factor for resistance. Specifically, wepredicted samples with TP53 mutations as resistant, and sensi-tivities to DS-3032b of the rest of samples with wild-type TP53was predicted using the 175-gene signature (Fig. 3D, top). Theaccuracy, sensitivity, and PPV for predicting the top 14 sensitive orresistant samples were 82%, 86%, 81%, respectively (Fig. 3D,bottom, and Table 1), showing potentially more balanced per-formance with higher PPV compared with using TP53 mutationstatus or the gene signature alone.

Of note, the top-ranked genes in the signaturewerewell-knownp53-inducible genes (Supplementary Table S9), suggesting thatthe gene signature may reflect high basal activity of p53, whichmay lower the threshold for inducing p53-mediated apoptosis byMDM2 inhibition.With this notion, supporting the superiority ofour gene signature to TP53 mutation status alone, the 175-genesignature applied to genomic data of more than 15,000 clinicalsamples obtained from theOncoMinedatabase revealed that onlya subset of tumors relatively enriched with wild-type TP53 (mel-anoma, lymphoma, myeloma, renal cell cancer, and leukemia)contains high-sensitivity scores (Fig. 3E; Supplementary TableS10). On the other hand, cervical cancer has low p53 mutationfrequency and a low sensitivity score, probably owing to p53inactivation via HPV infection in cervical cancer.

Generating a tumor-type–specific gene signature for predictingdrug sensitivity of MDM2 inhibitors

Whereas a common gene signature regardless of tumor types isuseful in terms of its broad application, tumor-type–specific genesignatures perhaps need to be explored to achieve a higher per-formance for predicting drug sensitivity in a particular tumor type.Thus, we examined another bioinformatics approach by utilizingonly the ex vivo dataset of primary AML samples described above.

We first hypothesized that genes with high expression variancehave the highest predictive value for determining drug sensitivity.Using a fairly routine bioinformatics method for classifying genesignatures (17), wefirst selected the 1,500 top-ranked genes based

Table 1. Predictive performance of 14 sensitive/resistant samples among 41 primary AML samples using different algorithms

Overallaccuracy (%)

Positive predictivevalue (%)

Negative predictivevalue (%) Sensitivity

A TP53 mutation status alone 68 62 86 93B 4-gene signature 68 63 78 86C 175-gene signature alone 79 72 90 93D Combination of A&C 82 81 85 86E Random forest (1,532 genes) 68 67 69 72F Combination of A&E 75 82 71 68

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on expression variance among 33 primary AML samples withwild-type TP53 (Supplementary File S2). We used these genes'expression values to predict the results of ex vivo treatment with

DS-3032b with the random forest method (18), ranking everygene based on the significance for the prediction (referred to as"variable importance"; Fig. 4A, top). Cross-validation was

Figure 3.

Validation of the 175-gene signature for predicting tumor sensitivity to MDM2 inhibition. A, Correlation between sensitivity scores based on the 175-gene signatureand tumor growth inhibition (%TGI, see also Materials and Methods) by DS-3032b in patient-derived xenograft mice. Spearman r ¼ 0.67, P ¼ 0.031; see alsoSupplementary Table S7. B and C, Validation of the sensitivity score based on the 175-gene signature for predicting sensitivity to DS-3032b of primary AMLsamples with wild-type TP53 (B, left), or irrespective of TP53 mutation status (C, left). Confusion matrices (B and C, right) correspond to the point with bestsensitivity/specificity tradeoff on each ROC curve. D, Schema of predicting sensitivity of primary AML samples to DS-3032b by combining TP53 mutationstatus and sensitivity scores of 175-gene signature. E, Correlation between the frequency of specimens with high-sensitivity scores and that of specimens with TP53mutations in various tumor types. Sensitivity scores > 0 were counted as high scores.






Figure 4.

Generating a predictive gene signature using random forest methods with cross-validation. A, Unbiased approach. Fifty genes ranked with highest significance inthe random forest method are listed as examples from 1,500 genes, which showed high expression variances in 33 primary AML samples with wild-type TP53(top). B, Referenced approach. The selected 32 genes are shown in order according to the significance for prediction (top). C, Combined approach. The1,500 and 32 genes used in A and B were used, and the 50 genes assessed with highest significance in the random forest method are listed (top). Confusionmatrices were generated at the point of the ROC curve, which provided highest predictive performance of random forest methods using each gene set to predict11 of most sensitive or resistant specimens among 33 wild-type TP53 AML samples to DS-3032b (A–C, bottom). D, The 1,500 genes that showed highexpression variances in all the 41 primary AML samples were selected and the 32 genes used in Bwere used, and the 50 genes assessed with highest significance inthe random forest method are listed (top). E, Schema of predicting sensitivity of primary AML samples to DS-3032b by combining TP53 mutation status andrandom forest method with the same gene set as in C. F, Schema of predicting sensitivity of primary AML samples to DS-3032b by combining TP53 mutationstatus and random forest method with the 50 top-ranked genes in C. Confusion matrices were generated at the point of the ROC curve, which providedhighest predictive performance of random forest methods, using each gene set to predict 14 of most sensitive or resistant specimens among 41 wild-type TP53AML samples to DS-3032b (D–F, bottom).

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performed by holding out one third of the total samples fortesting, and the learners (decision tree) ensembles were trainedon only the remaining two thirds of the data. The overallaccuracies, sensitivity, and PPV of the identified algorithm withthe 1,500 genes for predicting the top 11 sensitive or resistantsamples among 33 were 68%, 73%, and 67%, respectively (theconfusion matrix is shown in Fig. 4A, bottom), showingsimilar accuracy and PPV to the 175-gene signature describedabove. Of note, only 8 of 175 genes in our cell line–basedsignature (Supplementary File S1) and none of 13 genes (19)or 4 genes (16), previously reported gene signatures, wereranked in these 1,500 genes. These results indicate that genesexpressed with high variance in a particular tumor type,regardless of the relationship to p53, also participate in deter-mining the drug sensitivity to a certain extent, perhaps indifferent way(s) from the other gene sets enriched with p53transcriptional targets. In contrast to the unbiased approachbased on gene expression variance above, we next selected 32genes (Supplementary Table S11) based on previous reportssuggesting the potential predictive values of the genes. Thoseincluded 13 genes previously published in predictive genesignatures for the sensitivity to MDM2 inhibitors (16, 19),22 genes that were upregulated or downregulated by an MDM2inhibitor (RG7112) determined in a phase I clinical trial (10),TRIAP1 and E2A (TCF3), which are reported to be transcrip-tional cofactors of p53 (20), and Drosha, which is reported tobe downstream of MDM2 (21). Various cell-intrinsic mechan-isms of p53 inactivation are known (22), and most of them (e.g., MDM2 overexpression, ARF deletion, NPM1 mutation,FLT3-ITD, PML-RARalpha, activation of PI3K or MAPK path-ways) result in hyperactivation of MDM2, which inhibits p53-mediated signaling. XPO1, a nuclear exporter of p53, contri-butes another mechanism of p53 inactivation. Therefore, weincluded these two key genes in the list. We then applied these32 genes in the random forest method, which showed theaccuracy of 59.1%, sensitivity of 73%, and PPV of 57% (Fig.4B). Next, we combined the 1,500 genes selected by theunbiased approach above and the referenced 32 genes. Therandom forest method created the algorithm with accuracy of77%, sensitivity of 82%, and PPV of 75% (Fig. 4C; Table 1),indicating that combining the two gene selection methodsimproved the predictive performance.

We then tested the algorithm of combined genes on all 41samples including ones with TP53 mutations. Specifically,another set of 1,500 genes was selected according to expressionvariance among all the 41 AML samples, combined with thesame 32 referenced genes, then the random forest method wasperformed. The accuracy, sensitivity, and PPV for predicting thetop 14 sensitive or resistant samples were 68%, 72%, 67%,respectively (Fig. 4D and Table 1), showing reduced predictiveperformance than when focusing on only TP53 wild-typesamples.

Finally, we combined this algorithm with TP53 mutationstatus. Specifically, we predicted samples with TP53 muta-tions as resistant, and then applied the random forest methodusing 1,532 genes to the remaining wild-type TP53 samples(Fig. 4E, upper). The accuracy, sensitivity and PPV for pre-dicting the top 14 sensitive or resistant samples were 75%,68%, 82%, respectively (Fig. 4E and Table 1), showing betteraccuracy and PPV than the random forest method alone. Thisindicated that combining the random forest method with the

prediction by TP53 mutation status provides a better algo-rithm compared with each alone. Of note, when we followedthe same flow chart and ran the random forest method withthe 50 top-ranked genes in the 1,532 genes (Fig. 4F), the highaccuracy, sensitivity and PPV were conserved (96%, 93%,100%; Fig. 4F). Although re-selecting the 50 genes definedby the first learning iteration has a possibility of overfitting, itsuggests that narrowing down gene numbers based on therank defined by the random forest method could be an optionfor defining a more feasible scale of gene set for furtherclinical implementation.

DiscussionOur study demonstrated that DS-3032b is a highly potent

MDM2 inhibitor and induces p53-mediated apoptogeniceffects in various cancer cells. In addition, to overcome insuf-ficient prediction of tumor cell sensitivities to MDM2 inhibi-tion by TP53 mutation status alone, two different approachesfor defining genetic biomarkers were developed: (i) a 175-genesignature defined by a cell line–based approach; and (ii) anAML-specific 1,532-gene signature defined by the random for-est method. We propose that a combination of the classicalfactor TP53 mutation status and the gene signatures providesthe best algorithms for predicting tumor sensitivities to MDM2inhibitors (Fig. 5A).

Our 175-gene signature showed a robust predictive perfor-mance of the sensitivities to DS-3032b in validation datasets ofhuman ex vivo specimens and patient-derived xenograft murinemodels. Importantly, although the 175 genes in the present studyincluded all of the 4 genes, which were previously reported aspredictive gene signature for other MDM2 inhibitors (16), oursignature of 175 genes had higher predictive power. Of note,althoughanother 13-gene signature has alsobeen reported (19), itwas found later not to be predictive due to the revised annotationofTP53mutations in the cell lines (23). Thesefindings collectivelysuggest that incorporating a broader spectrum of p53 transcrip-tional targets into the gene signature is superior to focusing onfewer genes. A concern for further developing this signature is thatit is derived from cell lines of various cancers, not a single diseaseentity. Considering that a variety of p53 target genes are differentlyregulated depending on tissue types (24, 25), the cell line panel–based identification of key predictive genes may not be the bestapproach.

Therefore, an AML-specific gene signature was developedfor correcting the potential weakness of the 175-gene signa-ture. However, this approach is also challenging due to thelimited availability of independent datasets for validation. Asa partial solution, we utilized the random forest method (18),an ensemble classifier that leverages the predictive power ofmultiple learners through an ensemble approach, for tworeasons: (i) it is capable of handling a large number offeatures (i.e., >1,500 genes analyzed in an algorithm with10,000 trees in the present study), (ii) it provides intrinsiccross-validation during the testing and assessment procedure.Thus, the process of 3-fold cross-validation, intrinsic in therandom forest procedure, maximized the generalizability ofthe defined signature. However, we recognize the limitation ofthe present study because we lack another independent AMLdataset for complete validation of the developed gene signa-ture. A matched clinical cohort from recent MDM2 inhibitor






trials would be optimal, but only one such database exists(from the completed phase I study of RG7112; ref. 10), whichis not publicly available. However, the ongoing clinical trial ofDS-3032b in AML (NCT02319369) will definitely providesuch a dataset in near future. One could also argue thefeasibility of 1,532-gene expression profiling for clinicalimplementation. In this regard, we could narrow down thenumber of selected genes because every gene is ranked accord-ing to its predictive significance. For example, the 50 top-ranked genes used in Fig. 4F will be also tested in the trialdataset (Fig. 5A).

The cell line–based 175-gene signature and the AML-specific1,532-gene signature share only a limited number of genes (26genes), for at least two reasons: (i) the signatures were devel-oped through entirely different algorithms (one selected high-ly expressed genes in sensitive cell lines, while the other usedrandom forest machine learning), (ii) cell types used for geneexpression profiling were different (AML specific or not).

Considering that only about 60 genes were found to becommon p53 target genes in different tissue types in themeta-analysis of 16 genome-wide datasets (24, 25), the 26-gene overlap in our signatures is not surprising, and may ratherwell reflect AML-specific characteristics. Therefore, eventhough validation is more advanced for the 175-gene signa-ture, we believe that it will be important to develop these twosignatures in parallel. Specifically, we plan to validate the twosignatures in the ongoing clinical study of DS-3032b in AMLpatients (NCT02319369; Fig. 5A). Furthermore, because thetumor-type–specific approach can be applied to relativelysmall datasets (e.g., 33 samples as in the present study),ongoing clinical studies of DS-3032b and other MDM2 inhi-bitors for any cancers could additionally define improvedtumor-type–specific gene signatures (Fig. 5B). Moreover, asimilar tumor-type–specific approach may be potentiallyapplied to other molecularly targeted agents, if a referencedgene set related to the agent's mechanism of action (corre-sponding to the 32 referenced genes for MDM2 inhibition) canbe identified.

Disclosure of Potential Conflicts of InterestJ. Ishizawa has ownership interest (including patents) in Daiichi Sankyo

Co., Ltd. No potential conflicts of interest were disclosed by the otherauthors.

Authors' ContributionsConception and design: J. Ishizawa, K.Nakamaru, T. Seki, K. Kojima, K.P. Patel,A. Tse, R.E. Davis, A. Rao, M. AndreeffDevelopment of methodology: J. Ishizawa, K. Nakamaru, T. Seki, K. Tazaki,K. Kojima, A. RaoAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): J. Ishizawa, T. Seki, K. Kojima, D. Chachad, R. Zhao,L. Heese, C. DiNardoAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): J. Ishizawa, K. Nakamaru, T. Seki, K. Tazaki,K. Kojima, R. Zhao, L. Heese, W. Ma, M.C.J. Ma, C. DiNardo, K.P. Patel,A. Tse, R.E. Davis, A. Rao, M. AndreeffWriting, review, and/or revision of the manuscript: J. Ishizawa, K. Nakamaru,K. Tazaki, K. Kojima, K.P. Patel, A. Tse, R.E. Davis, A. Rao, M. AndreeffAdministrative, technical, or material support (i.e., reporting or orga-nizing data, constructing databases): D. Chachad, C. DiNardo, S. Pierce,A. RaoStudy supervision: K. Nakamaru, T. Seki, M. Andreeff

AcknowledgmentsThis work was supported in part by grants from the Japan Society for the

Promotion of Science Postdoctoral Fellowship for Research Abroad Award(to J. Ishizawa); the Ministry of Education, Culture, Sports, Science andTechnology in Japan (26461425), the Princess Takamatsu Cancer ResearchFund (14-24610), the Osaka Cancer Research Foundation (to K. Kojima);and the National Institutes of Health USA (CA49639, 100632, CA136411,and CA16672) and the Paul and Mary Haas Chair in Genetics (toM. Andreeff). We thank Jairo A. Matthews for clinical data collection. Wethank Dr. N. Hail and Mr. D.R. Norwood for editorial assistance. We alsothank Kenji Watanabe for technical support.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received April 3, 2017; revised December 5, 2017; accepted February 22,2018; published first February 28, 2018.

Figure 5.

Proposal of further investigation of predictive gene signatures. A, A schematicfigure of generating predictive gene signatures in the present study and furthervalidation planned in future. B, A proposal of utilizing similar algorithmsanalyzed in the present study for future clinical trials.


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2018;78:2721-2731. Published OnlineFirst February 28, 2018.Cancer Res Jo Ishizawa, Kenji Nakamaru, Takahiko Seki, et al. InhibitionPredictive Gene Signatures Determine Tumor Sensitivity to MDM2

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Predictive Gene Signatures Determine Tumor Sensitivity to ... · Translational Science Predictive Gene Signatures Determine Tumor Sensitivity to MDM2 Inhibition Jo Ishizawa1, Kenji