Molecular Predictors of Sensitivity to the Insulin-Like Growth Factor 1 Receptor Inhibitor Figitumumab (CP-751,871)

Published OnlineFirst October 9, 2013; DOI: 10.1158/1535-7163.MCT-13-0442-T

Molecular Cancer Companion Diagnostics and Cancer Biomarkers Therapeutics

Molecular Predictors of Sensitivity to the Insulin-like Growth Factor 1 Receptor Inhibitor Figitumumab (CP-751,871)

Adam Pavlicek1, Maruja E. Lira2, Nathan V. Lee2, Keith A. Ching1, Jingjing Ye3, Joan Cao2, Scott J. Garza2, Kenneth E. Hook2, Mark Ozeck2, Stephanie T. Shi2, Jing Yuan2, Xianxian Zheng2, Paul A. Rejto1, Julie L.C. Kan2, and James G. Christensen2

Abstract Figitumumab (CP-751,871), a potent and fully human monoclonal anti–insulin-like growth factor 1 receptor (IGF1R) antibody, has been investigated in clinical trials of several solid tumors. To identify biomarkers of sensitivity and resistance to figitumumab, its in vitro antiproliferative activity was analyzed in a panel of 93 cancer cell lines by combining in vitro screens with extensive molecular profiling of genomic aberrations. Overall response was bimodal and the majority of cell lines were resistant to figitumumab. Nine of 15 sensitive cell lines were derived from colon cancers. Correlations between genomic characteristics of cancer cell lines with figitumumab antiproliferative activity revealed that components of the IGF pathway, including IRS2 (insulin receptor substrate 2) and IGFBP5 (IGF-binding protein 5), played a pivotal role in determining the sensitivity of tumors to single-agent figitumumab. Tissue-specific differences among the top predictive genes highlight the need for tumor-specific patient selection strategies. For the first time, we report that alteration or expression of the MYB oncogene is associated with sensitivity to IGF1R inhibitors. MYB is dysregulated in hematologic and epithelial tumors, and IGF1R inhibition may represent a novel therapeutic opportunity. Although growth inhibitory activity with single-agent figitumumab was relatively rare, nine combinations comprising figitumumab plus chemotherapeutic agents or other targeted agents exhibited properties of synergy. Inhibitors of the ERBB family were frequently synergistic and potential biomarkers of drug synergy were identified. Several biomarkers of antiproliferative activity of figitumumab both alone and in combination with other therapies may inform the design of clinical trials evaluating IGF1R inhibitors. Mol Cancer Ther; 12(12); 2929–39. 2013 AACR.

Introduction ways: phosphoinositide 3-kinase/v-akt murine thymoma The insulin-like growth factor (IGF) pathway plays an viral oncogene homolog (PI3K/AKT) and RAS/RAF/ important role in normal growth and development. Bind- mitogen-activated protein kinase (1, 2). ing of IGF to the receptor, IGF1R, induces receptor trans- Aberration of IGF signaling can occur at various levels phosphorylation and the recruitment of insulin receptor involving overexpression of the ligands, the receptor, or substrates (IRS) and Src homology adaptor proteins. Sig- ligand binding proteins. As IGF signaling is antiapoptotic nal transduction is then mediated via two signaling path- and prosurvival, it is also key to the development and progression of cancer (1). High IGF1 serum concentrations have been associated with breast, prostate, and colorectal Authors' Afﬁliations: 1Computational Biology, Oncology Research Unit; cancers (3), whereas increased production of IGF2 is asso- 2Translational Research, Oncology Research Unit; and 3Global Preclinical Statistics, Pﬁzer Oncology, La Jolla, California ciated with several tumor types including lung and colorectal cancer (4, 5). Overexpression of IGF1R is frequently Note: Supplementary data for this article are available at Molecular Cancer Therapeutics Online (http://mct.aacrjournals.org/). observed in tumor cell lines and human cancers including non–small cell lung cancer (NSCLC) and mediates both Current address for A. Pavlicek: Regulus Therapeutics, San Diego, Cali- tumor growth and resistance to EGF receptor (EGFR/ fornia; current address for J. Ye, Division of Biostatistics, Center for Devices and Radiological Health; U.S. Food and Drug Administration, Silver Spring, ERBB) inhibitors (6). Compelling new data have shown Maryland; and current address for J.G. Christensen, Mirati Therapeutics, that both the a and b subunits of IGF1R can translocate to San Diego, California. the nucleus and directly regulate transcription (7). Nuclear A. Pavlicek and M.E. Lira contributed equally to this work. IGF1R has been detected in tumor cell lines and tumor Corresponding Author: James G. Christensen, Mirati Therapeutics, 9363 biopsies, and early data suggest an association with poor Towne Center Drive, Suite 200, San Diego, CA 92121. Phone: 858-699- prognosis (7). IGF1R has also been implicated in the ability 5021; Fax: 858-332-3411; E-mail: [email protected] of drug-resistant cancer cells to maintain viability (8). doi: 10.1158/1535-7163.MCT-13-0442-T IGF-binding proteins (IGFBP) modulate the activity of 2013 American Association for Cancer Research. IGFs, mainly by limiting IGF access to IGF1R. Growth

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inhibitors including vitamin D, antiestrogens, retinoids, bated at 37 Cin5%CO2 humidified air. The following and TGF-b reduce IGF activity by increasing the secretion day, cells were treated with either single or double agents of IGFBPs. The wealth of evidence implicating the IGF (equimolar ratio for combination) at nine serially diluted system in tumorigenesis and cell survival has led to the concentration points ranging from 10 mmol/L to 152 development of targeted anticancer agents that inhibit pmol/L (1 mmol/L–15.2 pmol/L for figitumumab and IGF1R (2). trastuzumab). After 3 days of further incubation at 37C, Figitumumab (CP-751,871) is a highly potent and fully 0.1 mg/mL Resazurin salt dye (Sigma Aldrich) or one fifth human monoclonal antibody against IGF1R. Figitumu- of manufacturer’s recommended volume of CellTiter-Glo mab can prevent IGF1 from binding to IGF1R (IC50, 1.8 (CTG; Promega) was added to indirectly measure cell nmol/L), and as a result inhibits IGF1-induced autopho- viability/proliferation using an EnVision Multilabel sphorylation of IGF1R (IC50, 0.42 nmol/L) and indirectly Plate Reader (PerkinElmer). Readings from the Envision inhibits AKT activation in preclinical studies (9). Figitu- multilabel plate reader were obtained 6 hours after the mumab also inhibited xenograft tumor growth via inhi- addition of Resazurin, or 30 minutes after CTG was add- bition of IGF signaling, particularly when combined with ed. Relative fluorescence (Resazurin) or luminescence chemotherapy or endocrine therapy (9). (CTG) counts were first adjusted by subtracting the aver- In this article, molecular analyses of multiple tumor cell age counts taken from untreated cells assessed 1 day after lines were conducted to identify biomarkers potentially cell seeding. The cell growth inhibition (CGI) score for predictive of figitumumab antiproliferative activity, and each compound concentration was calculated as thereby identify patients most likely to respond to treat- well count baseline ment. In addition, the impact of figitumumab in combi- CGI ¼ 1 : nation with standard of care and several other targeted platecontrol mean baseline agents on the growth of cancer cell lines was assessed. The R dose–response curves package was used to fit the Materials and Methods adjusted CGI values versus the concentrations of the compounds (10). A four-parameter logistic model was Cell lines, cell cultures, and reagents used to fit the dose–response curves and generate estima- Figitumumab was tested in vitro in a panel of 93 cell tions and inferences of the IC50, slope, upper, and lower lines derived from human small-cell lung cancer (SCLC) limits. and NSCLC, breast, and colorectal tumors. Tumor cells Synergy was determined by calculating the improve- were obtained from the American Type Culture Collec- ment of the area under the curve (AUC) for the combi- tion (ATCC), the National Cancer Institute Cell Line nation compared with the AUC for the most potent single Repository (Bethesda, MD), or from internal Pfizer cell agent. Synergy is judged to have arisen if (i) the better banks. Cells lines acquired from ATCC were authenticat- of the single-agent curves is not flat, synergy is defined ed by short tandem repeat analysis by the vendor and when the percentage improvement of the AUC of the experiments were carried out within 6 months after combination is more than 15% of the better of the single receipt, except for CACO2, Calu6, DLD1, HCC1143, agents; or (ii) both single-agent curves are flat (i.e., no HCC1395, HCC1419, HCC1954, HCC2935, HCC70, response/resistant), synergy is defined when the inter- Ls174t, MX-1, NCI-H146, NCI-H1838, NCI-H187, NCI- cept of the linear fit of the combination is statistically H209, NCI-H23, NCI-H69, NCI-H82, NCI-N417, NCI- significantly different from the single agents and the N592, RKO, SW48, and SW948, which were not reauthen- linear slope of the combination is statistically significant. ticated. Cells were grown in appropriate culture media as recommended by the suppliers, including RPMI-1640, MEM (Minimum Essential Medium), DMEM (Dulbecco’s Baseline molecular profile of cell lines Modified Eagle Medium), and DMEM-F12 (all from Invi- The mutation status of the cell lines was determined trogen). Supplements included HEPES buffer, sodium from the Catalogue of Somatic Mutations in Cancer (COS- pyruvate, nonessential amino acids, penicillin–strepto- MIC) database (11) supplemented with custom Onco- mycin (Pen–Strep), ITS (insulin, transferrin, and seleni- Carta (Sequenom) profiling. Baseline whole-genome um), glutamine (all from Invitrogen), and FBS, 5% to 20% copy-number variation in selected cell lines was obtained (SAFC Biosciences). Some of the lung cancer cell lines from public sources and complemented with internal were grown in ACL-4 medium as described in the online profiling on Human Genome-U133 Plus 2.0 arrays (Affy- material of ATCC (supplements from Sigma-Aldrich). metrix) and single-nucleotide polymorphism (SNP) 6.0 arrays (Affymetrix) according to the manufacturer’s pro- Proliferation assays and sensitivity tests tocol. Publicly available reference profiles of cancer cell Cell viability assays were performed using cell lines lines on the same platforms were obtained from the copy- treated with either single agent or pair-wise combinations number analysis dataset of the Welcome Trust Sanger of agents: trastuzumab, dacomitinib, PF4691502, cetuxi- Institute (Cambridgeshire, UK; http://www.sanger.ac. mab, oxaliplatin, 5-FU, PD332991, erlotinib, and gemci- uk/genetics/CGP/CopyNumberMapping/Affy_SNP6. tabine. Three to five thousand cells per well were seeded shtml), GlaxoSmithKline (https://cabig.nci.nih.gov/ into 96-well tissue culture plates (Costar 3997) and incu- caArray_GSKdata/), and Genentech (GSE10843). All new

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array data were deposited into the National Society for tion from the median value in less than 20% of the cell Biotechnology Information gene expression omnibus lines, and those probe sets with more than 50% of their (GSE34211). For gene expression, we first excluded all data missing. Probe sets with a univariate misclassifica- arrays with a normalized unscaled SE score of more than tion rate less than 0.1 were used for class prediction using 1.05 and then normalized by the GeneChip robust multi- the diagonal linear discriminant analysis (DLDA) model array averaging (GC-RMA; ref. 12) using the GC-RMA (Supplementary Material: DLDA expression signature). Bioconductor package. When multiple arrays were avail- The leave-one-out cross-validation method was used to able, we used the median of normalized values. SNP 6.0 estimate the misclassification rate. arrays were processed using the R-based aroma.affymetrix To satisfy the requirements of independence in statis- methods (ref. 13; http://www.aroma-project.org/). Rela- tical tests, we kept only one cell line derived from a tive copy-number was inferred by computing the ratio particular tumor and excluded duplicates using genotyp- of the arrays versus a baseline profile of the average of ing information from the Sanger Institute (http://www. the 128 females from the International HapMap Project sanger.ac.uk/genetics/CGP/Genotyping/synlinestable. (http://www.affymetrix.com/estore/support/technical/ shtml). HCT-15 was removed and DLD1 kept, SW620 sample_data/genomewide_snp6_data.affx). removed and SW480 kept. NICH69 was kept in place of Expression levels of MYB/c-Myb and IGF family mRNA NCIH249 and NCIH592. were confirmed using TaqMan real-time PCR (RT-PCR; Applied Biosystems). The manufacturer’s assay codes are Classification of 300 cell lines with an IGF pathway listed in Supplementary Table S1. The ABI Prism 7900 response signature (Applied Biosystems) was used according to the manu- To develop the IGF pathway signature, we started with facturer’s protocol. The mRNA expression levels were probe sets from the Affymetrix HG-U133 Plus 2.0 arrays first normalized to GAPDH (glyceraldehyde-3-phosphate that correspond to 14 IGF pathway genes (without MYB). dehydrogenase), and the relative gene expression was We used these probe sets to develop an IGF pathway DC calculated by subtracting the average t of triplicate classifier in our panel of 93 lines using DLDA. This new DC reactions from the median of the average t across each classifier contained 14 probe sets from 7 distinct IGF tumor type. Final values used in the analysis are provided pathway genes (full model available in the Supplemen- in Supplementary Table S2. tary Material: DLDA-classifier_IGF-pathway_score). The performance of this IGF pathway–restricted (70% correct) Data analysis and statistical methods classifier was lower compared with the whole-genome Any DNA variation leading to change at the amino acid signature (85% correct). This new pathway signature was level was classified as a mutation; silent mutations were used to score IGF pathway activity in 300 cell lines, excluded from the analysis. Gene amplifications from previously profiled by GlaxoSmithKline (https://cabig. whole-genome SNP 6.0 arrays were defined as members nci.nih.gov/caArray_GSKdata/). This pathway score of focally amplified chromosomal segments 10 Mb con- was compared with the relative copy-number of all taining at least five probes each with a mean log2 1. human genes using the Pearson linear correlation coeffi- There was no size constraint on the length of the chro- cient. The copy-number data in the 300 cell lines were mosomal deletion: segments were required to contain a obtained from genome-wide human arrays processed as minimum of five probes each with a mean log2 less than described above. 2. As part of the TaqMan RT-PCR analysis, a given gene was defined as overexpressed if its relative expression Results was more than 1 SD above the median of all lines. Molecular predictors of sensitivity to inhibition with In vitro growth inhibition was correlated with categor- single-agent figitumumab in vitro ical predictors such as the presence of genetic changes and A panel of 93 cell lines was used to assess the pattern of tissue of origin using the nonparametric Kruskal–Wallis response to figitumumab and the relationship of rank test as well as Fisher exact binomial test as imple- response to selected molecular biomarkers. The sensitiv- mented in R. Spearman rank correlation was used for ity profile of figitumumab in the panel of cell lines was continuous RNA expression profiles. The contributions of bimodal with 15 cell lines highly sensitive to figitumu- tissue origin to the observed correlations were tested mab at IC50 values 100 nmol/L (Fig. 1A). The remain- using analysis of covariance and analysis of variance tests ing cell lines were insensitive to this agent and the IC50 from the linear model function in R after factoring the values exceeded 1 mmol/L. The distribution of cell lines tissue effect. reflecting different tumor types of origin was nonrandom The presence of a predictive signature was tested using among sensitive lines (P ¼ 0.0017; Kruskal–Wallis rank BRB-ArrayTools v4.1 (14). We split the samples into two test); colon cancer cell lines were more sensitive than categories: sensitive, defined as IC50 less than 100 nmol/L others (Fig. 1B). m and resistant, with IC50 more than 1 mol/L, and removed We evaluated the relationship of the figitumumab sen- the cell lines with intermediate values of IC50. We filtered sitivity profile with 19 established cancer driver mutations out control probe sets, noninformative probe sets whose and found that APC and CTNNB1/b-catenin mutations expression values deviated at least 1.5-fold in either direc- were associated with cell-line sensitivity across different

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A 10 Color keys A: IC50 B: Tissue C and D: Alteration E: Expression 9 <100 nmol/L Breast Wild-type High 8 100–500 nmol/L Colon Mutation

50 7 500–2,500 nmol/L NSCLC Amplification >_ 2,500 nmol/L SCLC Mutation/deletion Low 6 5 −Log10 IC [nmol/L] 4 T84 MX1 RKO A549 BT20 T47D DLD1 SNU1 MCF7 SW48 LS123 184A1 LS513 BT474 BT549 ZR751 CALU1 CALU6 MCF10 HCC38 HCC70 SKBR3 SW480 SW948 SW403 SNUC1 SKCO1 CACO2 CAMA1 LS1034 LS174T NCIH69 NCIH82 NCIH23 ZR7530 HS578T HCT116 HCC827 MCF12A SKMES1 NCIH345 NCIH446 NCIH520 NCIH647 NCIH716 NCIH747 NCIH841 NCIH889 NCIN417 NCIN592 NCIH146 NCIH187 NCIH196 NCIH209 NCIH226 NCIH441 NCIH526 NCIH524 NCIH378 NCIH508 HCC1143 HCC1187 HCC1419 HCC1569 HCC1806 HCC1954 HCC1395 HCC1937 HCC2935 COLO205 NCIH2347 NCIH2405 NCIH1184 NCIH1355 NCIH1437 NCIH1573 NCIH1650 NCIH1651 NCIH1666 NCIH1703 NCIH1755 NCIH1838 NCIH1963 NCIH1975 NCIH2087 NCIH2107 MDAMB157 MDAMB231 MDAMB415 MDAMB436 MDAMB453 MDAMB468 MDAMB361 Kruskal B p = 0.0017

APC 0.00519 C BRAF n.s. CDKN2A n.s. CDKN2B n.s. CTNNB1 0.00087 EGFR n.s. ERBB2 n.s. ERBB3 n.s. ERBB4 n.s. HRAS n.s. KRAS n.s. MYC n.s. NRAS n.s. PIK3CA n.s. PTEN n.s. RB1 n.s. SMAD4 n.s. STK11 n.s. D TP53 n.s. 13q33.3-34 IRS2 0.00034 17q11.2 DHRS13 0.00049 11q24.2 PKNOX2 0.00057 6q23.3 AHI1 0.00096 6q23.3 MYB 0.01441 17q22-24.3 GH1 0.0058 12q12 ARID2 0.00584 Rank R P value IGF1R -0.17 0.1027 E IGF2R -0.08 0.4360 INSR -0.25 0.0338 IGF1 -0.12 0.2493 IGF2 0.12 0.2514 IRS1 0.04 0.7554 IRS2 -0.10 0.1194 IGFBP1 -0.03 0.8023 IGFBP2 -0.01 0.9046 IGFBP3 0.18 0.0823 IGFBP4 -0.08 0.5300 IGFBP5 0.37 0.0012 IGFBP6 0.12 0.2519 IGF2BP3 0.15 0.2005 MYB -0.44 0.0001

Figure 1. Figitumumab activity in a cell panel correlated with the status of pathway genes and major cancer drivers. A, ordered figitumumab in vitro antiproliferative activity measured as IC50 in a panel of breast, colon, lung small cell, and non–small cell cancer lines. B, comparison of IC50 values with the tissue of origin. C, comparison with the genetic status of major cancer drivers. The red color shows protein-coding mutations as obtained from COSMIC and Oncocarta profiling; green indicates amplification (see Material and Methods); blue represents mutations or deletions in tumor suppressors; and yellow indicates likely wild-type status. On the right are the P values from the Kruskal–Wallis test. D, representative genes from six major copy-number loci in Table 1 associated with figitumumab sensitivity at FDR less than 0.25. E, comparison with PCR-measured RNA expression of IGF pathway genes and MYB. Significant correlations are highlighted in colors; red means positive correlation between high expression and IC50 (and thus sensitivity); blue signifies correlation between high expression and resistance (Spearman rank correlation).

cancer types (Fig. 1C). However, no such correlation was 1D; see Material and Methods), a total of 52 genes from found within colon cancer cell lines, indicating that the six distinct genomic loci were found to be associated observed pattern is related to the prevalence of these with sensitivity at a false discovery rate (FDR) of less mutations in colon cancer models. than 0.25 (Table 1; Supplementary Table S3). The most Whentheactivityprofileoffigitumumabwascorre- significantly amplified locus at 13q33.3 contains MYO16 lated with whole-genome copy-number alterations (Fig. and the IGF1R-associated adaptor protein, IRS2.The

Table 1. Six copy-number loci associated with ﬁgitumumab sensitivity at an FDR of less than 0.25

Nominal P value Cancer driver IGF pathway Locus Best hit (best hit) FDR (distance) gene 13q33.3-34 IRS2, MYO16 0.0003 0.16 IRS2 17q11.2 6 genes (DHRS13, FLOT2, 0.0005 0.16 SUZ12 (6Mb apart), PHF12, PIPOX, SEZ6, TIAF1) NF1 (2Mb) 11q24.2 PKNOX2 0.0006 0.17 FLI1 (4Mb) 6q23.3 AHI1 0.0010 0.23 MYB (170kb) 17q22-24.3 42 genes 0.0058 0.23 PRKAR1A GH1, GH2 12q12 ARID2, LOC400027 0.0058 0.23

NOTE: The activity proﬁle of ﬁgitumumab was correlated with whole-genome copy-number alterations and six distinct genomic loci were found to be associated with sensitivity at an FDR of less than 0.25.

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17q22-24 amplicon harbors 42 significant genes includ- model, including the probe sets and weights is available in ing two loci encoding the human growth hormones, the Supplementary Material (DLDA-classifier_IGF- GH1 and GH2, which are known regulators of the IGF pathway_score). pathway (15). Four other significantly amplified loci Table 2 shows correlations between IGF pathway 17q11.2, 11q24.2, 6p23.3, and 12q12 did not contain any genes and figitumumab activity in breast and colon well-established IGF pathway genes. Interestingly, the cancer cell lines. High MYB and IGF2 expression pre- AHI1 gene on 6p23.3 is adjacent to a mitochondrial RNA dicted sensitivity in breast cancer cell lines, whereas high gene, HSA-MIR-548A-2 and a well-known oncogene, IGF1R, IGFBP4 and low IGF2BP3, and IGFBP6 expression MYB/c-Myb. was significant in colon cancer cell lines. The lack of The correlation between figitumumab activity and gene overlap between the predictors suggests potential tissue- expression was investigated using two approaches: one specific predictors of response to figitumumab. Only biased toward known IGF pathway genes and another IGF1R itself and MYB mRNA expression were either unbiased that incorporated whole-genome data. The significant or close to significant in both panels. Normal- hypothesis-driven–biased approach concentrated on ized expression values are listed in Supplementary Table mRNA expression of key IGF pathway members mea- S2. No significant correlations were found in the SCLC sured by TaqMan RT-PCR. Figure 1E shows the heatmap and NSCLC panels, likely due to the small number of of expression values sorted by figitumumab sensitivity. In sensitive models. the combined panel, high insulin receptor (INSR; Spear- In summary, our analysis of the baseline molecular man rank R ¼0.25; P ¼ 0.03) and low IGFBP5 (Spearman profile of figitumumab-sensitive cell lines uncovered rank R ¼ 0.37; P ¼ 0.001) mRNA levels were associated several potential predictors of in vitro response to fig- with response. There was a strong association between itumumab at both the DNA and RNA levels. Figure 2 high MYB mRNA, as determined by RT-PCR, and figitu- shows P values comparing sensitive and resistant mod- mumab sensitivity (Spearman rank R ¼0.44; P ¼ 0.0001). els plotted against ORs. From the various predictors, the An attempt was made to determine an unbiased figitu- DLDA multigene signature, colon cancer origin, MYB mumab response signature from whole-genome Affyme- overexpression, and low expression of IGFBP5,repre- trix HG-U133 Plus 2.0 arrays. Twenty-five probe sets with sent the best biomarkers of response. However, one univariate misclassification rate less than 0.1 were used caveat of multigene signatures is the strong possibility for class prediction using the DLDA. However, this of "over-fitting" the data, and indeed our error estima- approach resulted in rather poor performance in cross- tion by cross-validation indicate that the discovery of a validation: sensitive models were identified with sensi- more robust signature would require profiling of addi- tivity equal to 0.533 and specificity to 0.91. The complete tional samples.

Table 2. Correlation between ﬁgitumumab sensitivity and mRNA expression of key genes in breast and colorectal cancer lines

Breast cancer Colorectal cancer

Gene Spearman RP FDR Spearman RP FDR IGF1 0.112 0.550 0.687 0.067 0.784 0.840 IGF1R 0.334 0.066a 0.331 0.542 0.017c 0.093b IGF2 0.369 0.041c 0.307 0.228 0.349 0.638 IGF2R 0.036 0.848 0.848 0.256 0.290 0.621 IGF2BP3 0.182 0.337 0.569 0.487 0.035c 0.130 IGFBP1 0.167 0.386 0.569 0.078 0.758 0.840 IGFBP2 0.311 0.094a 0.353 0.070 0.775 0.840 IGFBP3 0.179 0.336 0.569 0.022 0.929 0.929 IGFBP4 0.086 0.652 0.752 0.545 0.016c 0.093b IGFBP5 0.157 0.408 0.569 0.289 0.230 0.574 IGFBP6 0.226 0.221 0.553 0.534 0.019c 0.093b INSR 0.154 0.417 0.569 0.110 0.654 0.840 IRS1 0.256 0.172 0.516 0.212 0.383 0.638 IRS2 0.055 0.777 0.833 0.175 0.472 0.709 MYB 0.400 0.029c 0.307 0.444 0.057a 0.171

aP < 0.1. bSigniﬁcant at FDR less than 0.1. cP < 0.05.

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more than 300 additional cell lines, and then correlated 1. 6q23.3/AHI1 amp 12 2. 6q23.3/MYB amp IGF pathway activity with copy number in these cell 14 3. 11q24.2 amp 4. 13q33.3.34/IRS2 amp lines(seeMaterialsandMethods).Figure3showsthe 5. 17q22.24.3/GH1-2 amp 11 6.17q11.2 amp correlation between copy number of all human genes 15 7. IGF1R Overexpressed 8. INSR Overexpressed (rank-ordered) and the predictive pathway signature. 1 9. IRS2 Overexpressed Circles represent rank-ordered human genes; red circles 10. IGFBP3 Underexpressed 13 4 5 6 11. IGFBP5 Underexpressed are genes signiﬁcantly correlated with the IGF pathway −Log10( P ) 2 12. MYB Overexpressed 9 3 13. IGF pathway classifier (PCR) score at FDR of less than 0.25. In total, seven genomic 107 14. DLDA Classifier

012345 8 15. Colon origin regions were significantly correlated with IGF pathway − 8 8 1/20 1/10 1 10 20 score in the 300 cell lines: 4q, 6q, 10q23, 13q, 15q, 17q, OR and 18q. BCL6 was the only gene on 4q that showed a significant correlation. Another gene with significant FNDC1 Figure 2. Molecular predictors of sensitivity to figitumumab. OR and correlation, ,thistimeon6q,isimmediately P value comparison for significant predictors of sensitivity. For each adjacent to the insulin-dependent diabetes locus, predictor, the genomic information was condensed into a binary TAGAP,andclosetoIGF2R. The 10q23 locus harbors absent–present status in each line. Then we divided cell lines into many significant genes including the well-known IGF < m sensitive (IC50 100 nmol/L) and resistant (IC50 3 mol/L) and PTEN pathway regulator (16, 17). The next significant obtained a 2 2 table with frequencies. From the frequency tables, IRS2 ORs and Fisher binomial test P values were calculated. The dashed region on 13q contains the key predictive gene as vertical line corresponds to OR 1 (equal frequency in resistant and well as other IGF pathway regulators including FOXO1 sensitive lines). ORs more than 1 indicate alterations positively (18). Another significant region on 15q contains many correlated with response; ratios less than 1 characterize changes genes close to IGF1R. The 17q region contains several found in resistant lines. Changes found only in resistant or only in BRCA1 sensitive cell lines are plotted outside the OR scale (grey vertical lines) significant genes including the tumor suppres- on the left and right, respectively. sor. The last significant region 18q, contains several pathway regulators including GRP, SALL3,andMBP; the SMAD4 locus is close but not significant. Whole-genome analysis of copy-number changes associated with IGF pathway expression Combination screens Given the importance of the IGF pathway, we looked Because of the low response rate observed for single- for DNA aberrations associated with expression pat- agent figitumumab in both nonclinical and clinical stud- terns typical for sensitive models. We used the signature ies, figitumumab was tested in combination with several comprising 7 IGF pathway genes (14 probe sets) that standard chemotherapeutic agents as well as other novel predict response to figitumumab in our panel of 93 cell compounds in clinical development. Supplementary Fig. lines described above to score IGF pathway activity in S1 depicts combined activity of selected agents with

IRS2

PTEN 0.2 FNDC1 FOXO1 CSK BRC A 1 TAGAP BRC A 2 MYC IGF1R IGF2R 0.1 IGF2 IGFBP 4

MYB INS R IGFBP 6 GH1 GH2 IRS 1 0.0 IGFBP 2 IGF1 IGFBP5

IGF2BP3 IGFBP1 −0.1 IGFBP3

COBL SMAD4 BCL6 SALL3 GRB10 GRPMBP Pearson correlation with IGF pathway score −0.2 chr1 chr2 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chrX chrY chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19 chr20 chr21 chr22

Figure 3. Genomic copy-number changes associated with changes in IGF pathway predictive signature in 300 cancer cell lines. The plot shows correlation between the predictive IGF pathway signature and gene copy number changes. The signature was developed as follows: we started with probe sets from Affymetrix U133 Plus 2 arrays that correspond to 14 IGF pathway genes in Fig. 1E (without MYB). We used these probe sets to develop an IGF pathway classifier in our panel of 93 lines using DLDA. This new classifier contains 14 probe sets from 7 IGF pathway genes (full model is available in the Supplementary Data). The performance of this IGF pathway–restricted (70% correct) classifier was lower compared with the whole-genome signature (85% correct). This new pathway signature was used to score IGF pathway activity in 300 cell lines and compared with copy number status of all human genes (see Materials and Methods). The plots show the correlation between copy-number statuses of all human genes (rank-ordered) correlated with the IGF pathway signature. Circles represent rank-ordered human genes; red circles are genes significantly correlated with the IGF pathway score at a FDR less than 0.15. Key predictors and pathway genes are labeled; green circles correspond to genes significant at FDR less than 0.15; blue are genes not significantly associated with IGF pathway modulation.

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ﬁgitumumab along with synergy calculations as outlined between ﬁgitumumab and ERBB family inhibitors includ- in Materials and Methods. Altogether, 14 combinations ing synergy with trastuzumab in more than 50% of breast with 9 different compounds in NSCLC, colon and breast cancer cell lines, synergy with cetuximab in 67% of colon cancer cell lines are shown together with a brief summary cancer cell lines, and with the pan-ERBB inhibitor, daco- of the results (Table 3). We noted frequent synergy mitinib (PF299804), in 25% of NSCLC cell lines. Other

Table 3. Synergies between ﬁgitumumab and other anticancer agents

Figitumumab Mechanism Synergy Biomarkers Supplementary Tissue combination of action count of synergya ﬁgure Breast Trastuzumab ERBB2 (HER2) Inhibitor 13/25 (52%) RB1 Wild-typeb S1A ERBB3 mRNA Highb ERBB4 mRNA Highb NRAS mRNA Lowc IGF2R mRNA Lowb IRS2 mRNA Lowb IGFBP6 mRNA Lowb IGF2BP3 mRNA Lowc Breast Dacomitinib pan-ERBB Inhibitor 5/27 (19%) ERBB3 mRNA Highb S1B IGFBP3 mRNA Lowb Breast PF4691502 PI3K/mTOR Inhibitor 4/27 (15%) TP53 Wild-typeb S1C TP53 mRNA Lowb IGF2R mRNA Highb INSR mRNA Highb IRS1 mRNA Highb IGF2BP3 mRNA Highb Colon PF4691502 PI3K/mTOR Inhibitor 7/19 (37%) BRAF mRNA Lowc S1D EGFR mRNA Lowb PTEN mRNA Lowb SMAD4 mRNA Lowb Colon Cetuximab EGFR Inhibitor 12/18 (67%) IGFBP3 mRNA Lowb S1E Colon Dacomitinib pan-ERBB Inhibitor 7/19 (37%) ERBB3 mRNA Highb S1F NRAS mRNA Lowb IGFBP1 mRNA Highb Colon Oxaliplatin Alkylating agent 14/18 (78%) EGFR mRNA Highc S1G HRAS mRNA Lowb Colon 5-FU TYMS (thymidylate synthase) 4/19 (21%) IGF1R mRNA Lowb S1H Inhibitor Colon PD332991 CDK4/CDK6 Inhibitor 13/19 (68%) STK11 mRNA Highb S1I NSCLC PD332991 CDK4/CDK6 Inhibitor 6/24 (25%) SMAD4 mRNA Highc S1J IGF1R mRNA Highb IRS2 mRNA Highb NSCLC Dacomitinib pan-ERBB Inhibitor 6/24 (25%) NRAS mRNA Lowb S1K TP53 mRNA Highc IGF1 mRNA Highb NSCLC Erlotinib EGFR Inhibitor 4/24 (17%) – S1L NSCLC Gemcitabine Antimetabolite 2/24 (8%) IGF2BP3 mRNA Lowb S1M NSCLC PF4691502 PI3K/mTOR Inhibitor 4/24 (17%) KRAS mRNA Lowb S1N SMAD4 mRNA Highb IRS2 mRNA Highb

amRNA expression of IGF pathway genes and Myb were obtained from TaqMan RT-PCR, expression of other genes (main cancer drivers) is from Affymetrix whole-genome expression arrays. The statistical significance was calculated using the Fisher exact binomial test for mutation data, and by Kruskal–Wallis test for mRNA data. bSignificant at P < 0.05. cSignificant at P < 0.01.

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frequent synergies were observed with the PI3K/mTOR (15). Interestingly, a recent study by Guevara-Aguirre and inhibitor, PF4691502 (19), CDK4/CDK6 inhibitor, colleagues identified growth hormone receptor deficiency PD332991, and several chemotherapeutic agents, most in cancer- and diabetes-free Ecuadorian families with notably with oxaliplatin in 78% of colon cancer cell lines. severe short stature that, on the molecular level, was We also attempted to identify molecular predictors of manifested by severe IGF1 deficiency, further corroborat- synergy between different drug combinations with figitu- ing the interplay between growth hormone signaling, the mumab (Table 3). Given thelimitednumber of models from IGF pathway, and cancer (23). individual cell line panels, we restricted the analysis to 19 Of note, 6p23.3, perhaps the most interesting peak of key cancer drivers and IGF pathway genes to limit potential association, is next to a well-known oncogene, MYB/c- false discoveries from unbiased whole-genome analyses. Myb. Analysis of MYB mRNA expression revealed a Several potential predictors were found, most for combina- strong correlation between MYB levels and figitumumab MYB tions comprising figitumumab plus other targeted agents, IC50 values. Although expression has never been and there were several common synergies across cell lines. linked to response to IGF1R inhibitors, several reports Low NRAS mRNA was associated with synergy with indicate that MYB has a role in IGF pathway regulation. trastuzumab in breast cancer cell lines and dacomitinib in MYB has been shown to increase both IGF1 and IGF1R colon cancer as well as NSCLC cell lines. High ERBB3 mRNA levels (24). MYB stimulates cell growth by regu- expression predicted synergies with trastuzumab in breast lation of IGF and IGFBP3 in K562 leukemia cells (25). The cancer cell lines and dacomitinib in both breast and colon authors also found that MYB overexpression induced cell cancer cell lines. Low IGF2BP3 mRNA was associated with proliferation compared with control, and MYB-induced synergies with trastuzumab in breast cancer cell lines, and cell growth was inhibited by anti-IGF1R antibodies, indi- also with gemcitabine in NSCLC cell lines. Intriguingly, cating the therapeutic potential of IGF1R inhibitors in breast cancer cell lines with wild-type and/or low TP53 MYB-driven malignancies. mRNA levels were often synergistic for the figitumumab MYB encodes a transcription factor essential for hema- plus PF4691502 combination. topoiesis and is frequently deregulated in human cancer (26). Recurrent MYB translocations were reported in ade- Discussion noid cystic carcinomas (27). MYB translocations and There is growing interest in identifying predictors of amplification were also found in acute T-cell leukemia response to anti-IGF1R therapy (20–22). The aim of this (28, 29), pancreatic tumors (30), BRCA1-linked hereditary study was to identify biomarkers predictive of clinical breast cancer (31), as well as other tumors. Notably, MYB efficacy with the IGF1R inhibitor, figitumumab, to help overexpression has been reported in colorectal cancer (32) identify patient subgroups that may benefit from this type further supporting the link between figitumumab activity of targeted treatment. To do this, we combined in vitro and MYB expression in colorectal cancer models. Finally, screens with extensive molecular profiling of genomic MYB is an essential regulator of hematopoietic stem cell aberrations in a broad panel of 93 cell lines. The overall proliferation and differentiation (33), suggesting that response was bimodal and the majority of cell lines were IGF1R inhibition could be considered for the treatment resistant to figitumumab. A small fraction of sensitive of general hematopoietic malignancies beyond acute models was identified: 9 of 15 were from colon cancers. T-cell leukemia. To evaluate the correlation between results obtained in Apart from MYB expression, IGF pathway members broad in vitro screening assays and in vivo tumor growth were among the strongest predictors identified in this inhibition, the antitumor activity of figitumumab was study. In the whole panel of 93 lines, high INSR and low assessed in eight of the cell lines (four sensitive and four IGFBP5 were significantly associated with figitumumab resistant) implanted as subcutaneous xenografts in immu- activity. Our findings also reveal that tissue-specific pre- nocompromised mice in vivo. The results from these dictors of figitumumab sensitivity exist. The molecular experiments are captured in Supplementary Table S4 and predictors of sensitivity to figitumumab depend on the indicate that all four sensitive lines responded to figitu- tumor type: IGF2 and possibly also IGF1R mRNA expres- mumab in vivo, whereas figitumumab did not demon- sion predicted sensitivity to figitumumab in breast cancer strate significant tumor growth inhibition in all four cell lines, whereas IGF1R, IGF2BP3, IGFBP4, and IGFBP6 resistant lines. mRNA expression were significant predictors of activity Figitumumab activity was correlated with copy num- in colon cancer cell lines. ber alteration in six distinct genomic loci, three of which Other studies have also demonstrated associations contain known IGF pathway genes and regulators. The between components of the IGF pathway and adminis- most significant amplified locus harbors IRS2. High IRS2 tration of anti-IGF1R agents. High levels of total IGF1R mRNA expression has been previously shown to correlate were associated with moderate sensitivity to R1507, a fully with response to an IGF1R monoclonal antibody in breast humanized anti-IGF1R monoclonal antibody, in NSCLC and colon cancer cell lines (21). The 17q22-24 amplification models (20). Zha and colleagues suggested that colon and harbors 42 significant genes including those that encode breast cancer models with low expression of IGF1R were the human growth hormones, GH1 and GH2. Growth unlikely to show dependence on the IGF1R pathway and hormones are recognized regulators of the IGF pathway were, therefore, unlikely to respond to an anti-IGF1R

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antibody (21). Furthermore, these investigators showed "escape" mechanisms, hence the interest in administration that other components of the pathway, such as the adaptor of therapeutic regimens with multiple molecular targets. proteins, IRS1 and IRS2, and the ligand, IGF2, have pre- Sharma and colleagues demonstrated that the exposure of dictive value in these tumor models. A similar study, cancer models to several different inhibitors including which aimed to develop and characterize predictive bio- anti-EGFR, -ERBB2, -RAF, and -MET agents selected markers for the IGF1R tyrosine kinase inhibitor, OSI-906, drug-tolerant subpopulations with activated IGF1 signal- in colorectal cancer, also noted that the IGF1R/EGFR/ ing (8). This chromatin-mediated reversible state was insulin pathways were dominant in sensitive cells (22). efficiently ablated by treatment with IGF1R inhibitors. We also looked for genetic changes associated with IGF Also, acquired resistance to BRAF inhibitors via enhanced pathway activity with DNA aberrations in a broader panel IGF1R/PI3K signaling was observed in BRAFV600E mel- of more than 300 cell lines. Altogether, seven genomic anoma cells, rendering them sensitive to concomitant regions correlated with the predictive IGF pathway score; MEK and IGF1R inhibition (39). Flanigan and colleagues these genomic loci contain several known IGF or insulin reported synergies between PQIP, a dual IGF1R/insulin pathway regulators such IRS2, PTEN, FOXO1, and receptor tyrosine kinase inhibitor, and standard che- TAGAP. Amplifications of BCL6 on 4q were also signifi- motherapies (5-fluorouracil, oxaliplatin, or SN38) in four cant with the predictive IGF pathway score. BCL6 mRNA colorectal cancers in vitro models (40). is strongly decreased after GH treatment (34). BCL6 down- It is as yet unclear which pathway provides the optimal regulates expression of IGFBP4 and the IGFBP4 protein combination strategy with inhibition of IGF1R (41). A may play a role downstream of the BCL6 signaling path- recent phase I combination study of figitumumab and way during B-lymphoid differentiation (35). We speculate the mTOR inhibitor, everolimus, in patients with that some of these genes may also affect response to IGF1R advanced sarcomas and other solid tumors indicated that inhibitors due to their correlation with the predictive IGF the combination was well tolerated with no unexpected pathway score. toxicities. Stable disease was observed in the majority of The genetic complexity of most human malignancies patients and the combination also yielded a partial indicates that ablation of a single target is unlikely to response in one patient with malignant solitary fibrous produce sustained growth inhibition and combination tumor (42). Furthermore, a phase I trial evaluating the approaches are needed (36). In the tumor cell lines exam- humanized anti-IGF1R monoclonal antibody, dalotuzu- ined in this study, we demonstrated figitumumab activity mab, combined with the mTOR inhibitor, ridaforolimus, in combination with several standard chemotherapeutic demonstrated antitumor activity in patients with estrogen agents as well as novel compounds in clinical develop- receptor–positive metastatic breast cancer (43). However, ment. We found several synergistic combinations identi- the anti-IGF1R monoclonal antibody, IMC-A12, com- fied in NSCLC, colon, and breast cancer cell lines. The bined with cetuximab in patients with cetuximab- or most striking was figitumumab plus oxaliplatin that was panitumumab-refractory metastatic colorectal cancer was synergistic in three-quarters of colon cancer cell lines. inactive in all but 1 of 41 patients treated (44). It is clear that ERBB family inhibitors were also frequently synergistic the identification of predictive biomarkers to select with figitumumab. Synergy with trastuzumab was patients for combination treatment is required. In this observed in more than 50% of breast cancer cell lines, work, we applied our predictive biomarker analysis to with cetuximab in 67% of colon cancer cell lines, and with synergies and several potential predictors of synergy were the pan-ERBB inhibitor, dacomitinib (PF299804), in 25% of identified including high baseline ERBB3 expression that NSCLC cell lines. predicted synergies with trastuzumab in breast cancer cell Other IGF1R inhibitors have also demonstrated syner- lines and dacomitinib in both breast and colon cell lines. If gy in combination with targeted agents. In a study similar validated in independent sets of models, these markers to ours, Carboni and colleagues showed that BMS-754807, could potentially be used to stratify likely responders in a small-molecule inhibitor of IGF1R and the insulin recep- future combination trials. tor, was synergistic with cytotoxic, hormonal, and tar- In this study, we analyzed the in vitro antitumor activity geted agents in a variety of preclinical models including of figitumumab in a broad panel of cancer cell lines. cetuximab (colon cancer), trastuzumab (breast cancer), Although antitumor activity with single-agent figitumu- lapatinib (lung cancer), bicalutamide (prostate cancer), mab was relatively rare, numerous combinations and dasatinib (sarcoma and colon cancer; ref. 37). Fur- comprising figitumumab plus standard of care or other thermore, dual inhibition of EGFR (gefitinib) and IGF1R targeted agents were found to be synergistic. We also by the small-molecule inhibitor, AZ12253801, was effec- investigated correlations between genetic and genomic þ tive in reducing intestinal adenomas in the Apc / mouse characteristics of the cancer cell lines with figitumumab (38). antiproliferative activity to identify predictive biomarkers The synergistic associations observed in our study of response. We confirmed that components of the IGF could be related to the cross talk that exists between pathway, including IGFBPs, play a pivotal role in deter- different signaling pathways that control tumor develop- mining the sensitivity of tumor cells to IGF1R inhibitors. ment. Tumor cells may counteract inhibition of a single For the first time, we report that amplifications of IRS2, signaling pathway by exploiting this cross-talk to invoke growth factor genes, and the MYB oncogene greatly

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sensitized cancer models to IGF1R inhibitors. MYB is Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A. Pavlicek, N.V. Lee, J. Ye, J. Cao, S.J. Garza, activated in both hematologic and epithelial tumors and M. Ozeck, S.T. Shi, X. Zheng, J.L.C. Kan, J.G. Christensen IGF1R inhibition may represent a novel therapeutic Writing, review, and/or revision of the manuscript: A. Pavlicek, M.E. Lira, N.V. Lee, J. Ye, J. Cao, K.E. Hook, S.T. Shi, P.A. Rejto, J.L.C. Kan, J.G. opportunity to treat these neoplasms. To conclude, we Christensen have identified several biomarkers of antiproliferative Administrative, technical, or material support (i.e., reporting or orga- activity following administration of figitumumab both nizing data, constructing databases): K.A. Ching, J. Cao, S.J. Garza, M. Ozeck as a single agent and in combination with other agents. Study supervision: J.L.C. Kan Although clinical development of figitumumab has now been discontinued, our findings add to the growing body of knowledge related to the identification of bio- Acknowledgments markers of response and may assist in the improved The authors thank John Lamb (Pfizer Inc.) for scientific discussions and design of clinical trials of IGF1R inhibitors. suggestions. Predictive signatures analyses were performed using BRB ArrayTools developed by Dr. Richard Simon and the BRB-ArrayTools Development Team. Disclosure of Potential Conflicts of Interest M.E. Lira, P.A Rejto, and J.G. Christensen have ownership interest (including patents) in Pfizer Inc. No potential conflicts of interest were disclosed by the other authors. Grant support This study was sponsored by Pfizer Inc. Medical writing support was provided by Nicola Crofts at ACUMED (Tytherington, UK), and was Authors' Contributions funded by Pfizer Inc. Conception and design: A. Pavlicek, M.E. Lira, S.T. Shi, P.A. Rejto, J.G. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked Christensen advertisement Development of methodology: M.E. Lira, N.V. Lee, J. Ye, J. Cao, S.T. Shi, in accordance with 18 U.S.C. Section 1734 solely to indicate X. Zheng, P.A. Rejto this fact. Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): N.V. Lee, J. Cao, K.E. Hook, M. Ozeck, Received June 11, 2013; revised September 19, 2013; accepted September S.T. Shi, J. Yuan, X. Zheng 25, 2013; published OnlineFirst October 9, 2013.

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www.aacrjournals.org Mol Cancer Ther; 12(12) December 2013 2939

Molecular Predictors of Sensitivity to the Insulin-like Growth Factor 1 Receptor Inhibitor Figitumumab (CP-751,871)

Adam Pavlicek, Maruja E. Lira, Nathan V. Lee, et al.

Mol Cancer Ther 2013;12:2929-2939. Published OnlineFirst October 9, 2013.

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