Development of an Integrated Genomic Classifier for a Novel Agent in Colorectal Cancer: Approach to Individualized Therapy in Early Development

Abstract

Background

A plethora of agents are in early stages of development for colorectal cancer, including those that target the insulin-like growth factor receptor (IGF1R) pathway. In the current environment of numerous cancer targets, it is imperative that patient selection strategies be developed with the intent of preliminary testing in the latter stages of phase I trials. The goal of this study was to develop and characterize predictive biomarkers for an IGF1R tyrosine kinase inhibitor, OSI-906, that could be applied in colorectal cancer (CRC)-specific studies of this agent.

Methods

Twenty-seven CRC cell lines were exposed to OSI-906 and classified according to IC₅₀ value as sensitive (< 1.5μM), or resistant (>5μM). Cell lines were subjected to immunoblotting and immunohistochemistry for effector proteins, IGFIR copy number by FISH, KRAS/BRAF/PI3K mutation status, and baseline gene array analysis. The most sensitive and resistant cell lines were utilized for gene array and pathway analyses, along with shRNA knockdown of highly ranked genes. The resulting integrated genomic classifier was then tested against 8 human CRC explants in vivo.

Results

Baseline gene array data from cell lines and xenografts was used to develop a k-Top Scoring Pair (k-TSP) classifier, which in combination with IGFIR FISH and KRAS mutational status, was able to predict with 100% accuracy a test set of patient-derived CRC xenografts.

Conclusions

These results indicate that an integrated approach to the development of individualized therapy is feasible and should be applied early in the development of novel agents, ideally in conjunction with late-stage phase I trials.

Introduction

Colorectal cancer (CRC) is the third most prevalent cancer type in both men and women in the United States, accounting for 10% of estimated new cases, and is the third leading cause of cancer deaths (1). Insulin-like growth factor I receptor and other components of the IGF system have been shown to be over-expressed in colon cancer and are associated with advanced stage of disease, metastasis, and reduced survival (2, 3). Interestingly, obesity, physical inactivity and diabetes are associated with an increased risk of colon cancer and this has been correlated with over-expression of the IGF system, whereas elevated plasma insulin or C-peptide concentrations and insulin resistance, can increase the risk of cancer recurrence and death, suggesting involvement of the insulin receptor (IR) signaling pathway as well (4, 5). Taken together, these data suggest that targeting the IGF1R (and potentially the IR) pathway in CRC may be attractive clinically. In preclinical systems, this has been confirmed in multiple in vitro and in vivo CRC models where blockade of the IGF1R inhibited proliferation and induced cell cycle arrest and apoptosis (6, 7).

Translational Relevance.

Insulin-like growth factor I receptor and other components of the IGF system have been shown to be over-expressed in colon cancer and are associated with advanced stage of disease, metastasis, and reduced survival. The objective of this study was to use preclinical models along with a systems biology approach to develop and characterize predictive biomarkers related to the small molecule IGF1R tyrosine kinase inhibitor, OSI-906, in CRC. The intent was to develop a predictive classifier that could subsequently be tested and refined in early clinical studies of OSI-906 in CRC. These data demonstrate that an integrated approach to the development of predictive biomarkers in the early clinical development of novel agents is feasible. By employing the same approach to the development of other anticancer drugs, individualized therapy will become a reality, hopefully leading to more efficient and successful drug development.

There are numerous strategies for targeting the IGF pathway, including targeting the ligands by using somatostatin, ligand-specific antibodies, or by targeting the receptor with receptor-specific antibodies or receptor tyrosine kinases. OSI-906 (cis-3-[8-amino-1-(2-phenyl-quinolin-7-yl)-imidazo[1,5-a]pyrazin-3-yl]-1-methyl-cyclobutanol) is a small molecule inhibitor of the IGF1R with IC₅₀'s of 0.018 and 0.054 in cell free fractions against the IGF1R and IR, respectively (8). In vitro, OSI-906 potently (EC₅₀ less than 400 nM) inhibited proliferation among a panel of 15 cell lines representative of colorectal, NSCLC, pancreatic, breast and pediatric cancers (8). In vivo, OSI-906 demonstrated robust anti-tumor activity in the GEO CRC xenograft, an IGF1R-dependent model, when administered orally once a day at doses of 30 and 60 mg/kg (9). These and other data led to the entry of OSI-906 into single-agent and combination phase I studies in cancer patients.

Patient selection is an emerging area in oncology drug development. A recent example is in colorectal cancer (CRC), where the initial development of epidermal growth factor receptor (EGFR)-targeted antibodies was dominated by controversies over the necessity of overexpression of EGFR by IHC to select patients for therapy (10). Interestingly, retrospective data obtained from numerous large randomized studies in CRC demonstrated that the presence of K-ras or B-raf mutations was, in fact, a robust negative predictor of patients deriving benefit from these agents, and has led to further studies indicating that we have only begun to understand the determinants of response to this class of drugs (10-13). In fact, issues over patient selection for most classes of signal transduction inhibitors continues to lag far behind the approval of these agents, which is generally based upon traditional randomized studies in unselected patients (14-16). Microarray data capable of monitoring genome-wide gene expression profiles have provided an exciting new technology for identifying classifiers that may be utilized to individualize treatments for patients, however, very few of these classifiers are used in clinical practice due to reliance on traditional preclinical models without proper validation in independent test samples, dependence on heterogeneous specimens in late-stage clinical trials, and the lack of a clinically feasible assay platform (17).

Therefore, the objective of this study was to use preclinical models along with a systems biology approach to develop and characterize predictive biomarkers related to the small molecule IGF1R tyrosine kinase inhibitor, OSI-906, in CRC. The intent was to develop a predictive classifier that could subsequently be tested and refined in early clinical studies of OSI-906 in CRC.

Materials and Methods

Cell Culture and Proliferation

Twenty-seven human colon cancer cell lines, were obtained from American Type Culture Collection (Manassas, Va). The GEO cells were a generous gift from Dr. Fortunato Ciardiello (Cattedra di Oncologia Medica, Dipartimento Medico-Chirurgico di Internistica Clinica e Sperimentale “F Magrassi e A Lanzara,” Seconda Università degli Studi di Napoli, Naples). All cells except GEO were grown in RPMI medium supplemented with 10% fetal bovine serum, 1% non-essential amino acids, 1% penicillin/streptomycin and were maintained at 37 °C in an incubator under an atmosphere containing 5% CO₂. GEO cells were grown in DMEM/F12 supplemented with 10% fetal bovine serum, 1% non-essential amino acids, and 1% penicillin/streptomycin. The cells were routinely screened for the presence of mycoplasma (MycoAlert, Cambrex Bio Science, Baltimore, MD) and were exposed to OSI-906 when they reached approximately 70% confluence. All cell lines were tested and authenticated in the University of Colorado Cancer Center DNA Sequencing and Analysis Core. CRC cell line DNA was tested using the Profiler Plus Kit (Applied Biosystems, Foster City, CA). The data obtained was compared with ATCC data to ensure the cell lines have not changed. The cell lines were last tested in September 2009. OSI-906 was provided by OSI Pharmaceuticals, (Boulder, CO) and prepared as a 10 mM stock solution in DMSO. Cytoxic/proliferation effects were determined using the sulforhodamine B (SRB) method (18). Briefly, cells in logarithmic growth phase were transferred to 96 well flat bottom plates with lids. One hundred μL cell suspensions containing 5000 viable cells were plated into each well and incubated overnight prior to exposure with different concentrations of OSI-906 for 72 hours. Post drug treatment, media was removed and cells were fixed with cold 10% trichloroacetic acid for 30 min. at 4 °C. Cells were then washed with water and stained with 0.4% SRB (Fisher Sci., Pittsburgh, PA) for 30 min at room temperature, washed again with 1% acetic acid, followed by stain solubilization with 10mM tris at room temperature. The plate was then read on a plate reader (Biotek Synergy 2, Winooski, VT) set at an absorbance wavelength of 565 nm. Cell proliferation curves were derived from the raw absorbance (OD) data.

Immunoblotting/ELISA

Cells were seeded into 6-well plates and allowed to grow (without drug) for 48-72 hours until 70% confluent. Cells were then scraped into RIPA buffer containing protease inhibitors, EDTA, NaF, and sodium orthovanadate. The total protein in samples was determined using the BioRad D_c Protein Assay (BioRad, Hercules, CA). Thirty micrograms of total protein was loaded onto a 10% gradient gel, electrophoresed, and then transferred to PVDF using the I-Blot (Invitrogen, Carlsbad, CA). The membranes were blocked for 1 hour at room temperature (RT) with 5% nonfat dry milk in TBS containing tween-20 (0.1%) prior to overnight incubation at 4°C with the following primary antibodies: pIGF1R, IGF1R, pSHC, SHC, pIRS-1, IRS-1, pAKT, AKT, pERK, ERK, pS6RP, S6RP, pPI3K, and PI3K (Cell Signaling, Beverly, MA). After the primary antibody, blots were washed 3 × 20 minutes in TBS-Tween (0.1%), incubated with the appropriate secondary anti-rabbit or anti-mouse IgG horseradish peroxidase-linked antibody at 1:20,000 (Jackson ImmunoResearch, West Grove, PA) for 1 hour at RT, washed 3 times and developed using the Immobilon Western Chemiluminescent HRP substrate (Millipore, Billerica, MA). Immunoblot experiments were performed in triplicate for each antibody. IGF-I and IGF-II in cell line supernatants were measured by ELISA and processed according to manufacturer's recommendations (R & D Systems, Minneapolis, MN).

Immunohistochemistry (IHC)

IGF1R and downstream effector protein expression was assessed by IHC using the following antibodies: IGF1R (Ventana medical systems, Tucson, AZ), IGF-2R (Santa Cruz, Santa Cruz, CA), phospho-ERK (Cell Signaling, Beverly, MA), survivin (Zymed, San Francisco, CA), and Ki67 (Dako, Glostrup, Denmark). The staining procedures were performed according to our previously published methods (19). For scoring of proteins, a staining index calculated as percent of stained tumor cells × average staining intensity graded from 0 to 4 was used, resulting in an index value between 0 and 400 (19). Consistent with previous reports, samples with a staining index of 200 or higher were predefined as protein-positive (19). The scoring was performed by pathologists who were blinded to the exposure data.

Fluorescence in situ Hybridization (FISH)

Dual-color FISH assays were performed on the prepared slides of the CRC cell lines using 120 ng of Spectrum Red-labeled IGF1R (University of Colorado Cancer Center Cytogenetics Lab) and 0.3 μl of Spectrum Green-labeled CEP15 (Abbott Molecular, Abbott Park, IL) per 113 mm² hybridization area according to our previously published procedures (20). The slides were first washed in 70% acetic acid for 20-30 seconds, then incubated in 0.008% pepsin/0.01 M HC1 at 37 °C for 3-5 minutes, in 1% formaldehyde for 10 minutes and dehydrated in a graded ethanol series. The probe mix was applied to the selected hybridization areas, which were covered with glass cover slips and sealed with rubber cement. DNA co-denaturation was performed for 9 minutes at 85°C and hybridization was allowed to occur at 37°C for 40-48 hours. Post-hybridization washes were performed with 2X SSC/0.3%NP-40 at 72°C and 2X SSC for 2 minutes at room temperature (RT) and dehydrated in a graded ethanol series. Chromatin was counterstained with DAPI (0.3 μg/ml in Vectashield Mounting Medium, Vector Laboratories, Burlingame, CA). Analysis was performed on epifluorescence microscope using single interference filter sets for green (FITC), red (Texas Red), and blue (DAPI) as well as dual (red/green) and triple (blue, red, green) band pass filters. Approximately 20 metaphase spreads and 100 interphase nuclei were analyzed in each cell line, and ploidy was assessed along with identification of the chromosomes harboring homologous sequences to the IGFIR/CEP15 probe set. To determine occurrence of genomic imbalances, IGF1R copy number per cell was compared with expected by the ploidy of the cell line (e.g., 2 copies in diploid lines, 3 copies in triploid lines). For documentation, images were captured using a CCD camera and merged using dedicated software (CytoVision, AI, San Jose, CA).

KRAS/BRAF/PI3K mutation analyses

For both CRC cell lines and human tumor explants, DNA was isolated using the Qiagen DNA extraction kit (Qiagen, Valencia, CA). KRAS mutations were analyzed by one of two methods. The human primary tumor explants were assayed by the University of Colorado Cancer Center Pathology Core with the DxS Scorpion method (DxS, Manchester, UK) using the manufactures instructions. Briefly, template DNA was analyzed for a set of seven known KRAS point mutations using the Therascreen KRAS Mutation Detection kit (DxS Ltd., Manchester, UK). Reactions and analysis were performed on a Lightcycler 480 real-time PCR instrument (LC480) that was calibrated using a dye calibration kit provided by the kit manufacturer. Reactions were performed on a 96-well plate in 20μl reactions using approximately 60 ng of each DNA template. Sample DNA was amplified with eight separate primer sets (one for the wild-type sequence and one for each of seven different point mutations) with an internal Scorpion reporter probe. Cycle cross point (CP) values were calculated using the LC480 Fit-point software suite, and the control Cp was subtracted from the Cp of each mutation specific primer set. Because there may be spurious low-level amplification in the absence of mutant template, amplification products are often visible at later cycle numbers for most of the primer sets. To avoid false-positive results due to background amplification, the assay was considered valid only if the control Cp value was < 35 cycles. ΔCp thresholds were calculated to compensate for this background amplification. Mutations were scored positive when the ΔCp was less than the statistically-set 5% confidence-value threshold (21).

The CRC cell lines were analyzed for KRAS mutations by the University of Colorado Cancer Center Pathology Core with a high resolution melting temperature method using custom primers and the Roche LC480 real time PCR machine (Mannheim, Germany). Briefly, template DNA was tested by High Resolution Melting (HRM) analysis using a Lightcycler 480 real-time PCR instrument (Roche Applied Science, Indianapolis, IN). Approximately 60 ng of tumor template DNA, wild type control DNA, and mutant control DNA were amplified on the Lightcycler 480 instrument using an HRM master mix (Roche Diagnostics, Indianapolis, IN), with the RASO1 and RASA2 primers and 1.75mM MgCl₂ in 10μl on a 96 well plate, using a 2-step cycling program (95 °C melting, 72 °C annealing and extension) for 45 cycles. PCR products were analyzed by HRM with 25 data acquisitions per degree of temperature increase, from 40 °C to 90 °C. Lightcycler 480 Gene Scanning software using the known wild-type control samples for baseline calculation was used for these analyses (21). BRAF and PI3K mutations were analyzed by PCR amplification and direct sequencing of the products as described previously (22). Primers used were: forward, AACACATTTCAAGCCCCAAA and reverse, GAAACTGGTTTCAAAATATTCGTT for amplification of exon 15 of BRAF; forward, GCTTTTTCTGTAAATCATCTGTG and reverse, CTGAGATCAGCCAAATTCAGT for exon 9 of PIK3CA; and forward, CATTTGCTCCAAACTGACCA and reverse, TACTCCAAAGCCTCTTGCTC for codon 1023 mutation of exon 20 of PI3KCA; forward, ACATTCGAAA-GACCCTAGCC and reverse, CAATTCCTATGCAATCGGTCT for codon 1047 mutation of exon 20 of PIK3CA.

Gene expression profiles

Cells were plated at 2 × 10⁶ in 6-well plates 24 hours prior to harvest. After 24-72 hours cells were rinsed twice with PBS, and RNA was prepared using a RNeasy Plus mini kit (Qiagen, Valencia, CA). RNA stabilization, isolation, and microarray sample labeling were carried out using standard methods for reverse transcription and one round of in vitro transcription. Total RNA isolated from CRC cell lines and tumor xenografts was hybridized on Affymetrix U133 Plus 2.0 gene arrays at least in duplicates. The sample preparation and processing procedure was performed as described in the Affymetrix GeneChip® Expression Analysis Manual (Affymetrix Inc., Santa Clara, CA). In addition, CRC cell line gene expression profiles were obtained from the GlaxoSmithKline (GSK) genomic profiling data via the NCI cancer Bioinformatics Grid (caBIG®) website (https://cabig.nci.nih.gov/). These data were also profiled using Affymetrix U133 Plus 2.0 gene arrays in triplicates. To integrate the data generated from our lab and GSK, absolute intensity signals from the microarray gene expression profiles were extracted and probe sets representing the same gene were collapsed based on maximum values. Next, the gene expression levels were converted to a rank-based matrix and standardized (mean = 0, standard deviation = 1) for each microarray. Using this preprocessing method, the same cell lines from different data sets were clustered based on their gene expression profiles. Data analyses were performed on this rank-based matrix.

shRNA knockdown

The pRS–shE2F6 gene-specific shRNA expression cassettes, along with control shRNA plasmids including the original pRS vector (TR20003, were purchased from OriGene (Rockville, MD). The sequence of the metallothionein 2A-specific 29mer shRNA is GTAAAGAACGCGACTTCCACA- AACCTGGA; caldesmon-GGCACACCAAATAAG-GAAACTGCTGGCTT, metallothionein 1E- ACCTCCGTCTATAAATAGAGCAG-CCAGTT, aldehyde dehydrogenase 1A1-CTGATGCCGATTGGACAATGCTGTTGAA, and mitogen-activated protein kinase kinase 6-TGCTGCATCGGTCAAGAGAAACTCCACTT. Stable clones were generated by transfecting the OSI-906 resistant (HCT116, SW480) and sensitive (HT29, LS513) cells in 6-well dishes with 1 μg of each of the shRNA plasmids using Fugene 6 (Roche, Basel Switzerland), according to the manufacturer's recommendations. Seventy-two hours after transfection, the cells were placed under selection with 2.0 μg/mL of puromycin, splitting 1:5 when the cells reached confluency. Multiple clones from the same transfection were pooled and grown under puromycin selection. Successful knockdown of specific genes and gene products was confirmed by semi-quantitative RT-PCR and immunoblotting with specific antibodies. Each experiment was conducted in triplicate.

Gene set enrichment analysis

Gene set analysis was performed using the GSEA software Version 2.0.1 obtained from the Broad Institute¹. Gene set permutations were performed 1000 times for each analysis. We used the nominal p-value and Normalized Enrichment Score (NES) to sort the pathways enriched in each phenotype. We used the pathways defined by BioCarta² and Kyoto Encyclopedia of Genes and Genomes (KEGG) database as the gene set in this study (23).

k-TSP classifier

We used the k-TSP algorithm (24) to construct a discriminative classifier in predicting tumors sensitive to OSI-906. In brief, the algorithm exploits the information contained in the rank-based matrix by focusing on “marker gene pairs” (i, j) for which there is a significant difference in the probability of the event (R_i < R_j) across the N samples from class Y=1 (OSI-906 sensitive) to Y=-1 (OSI-906 resistant), where the event (R_i < R_j) is equivalent to the rank of gene i is less than the rank of gene j if and only if gene i is expressed less then gene j (relative expression). Here, the quantities of interest are p_ij(m) = Prob(R_i < R_j | Y=m), m = (1, -1), i.e., the probabilities of observing R_i < R_j in each class. These probabilities are estimated by the relative frequencies of occurrences of R_i < R_j within profiles and over samples. Let Δ_ij denote the “score” of gene pair (i, j), where Δ_ij = | p_ij(1) – p_ij(-1)|. A score Δ_ij is computed for every pair of genes i, j ∈{1,...,P}, i ≠ j. Gene pairs with high scores are viewed as most informative for classification. Using an internal leave-one-out cross-validation, the final k-TSP classifier utilizes the k disjoint pairs of genes, which achieve the k best scores from the training set. In this study, maximum number of pairs (kmax) was fixed as 10.

In vivo xenograft studies

Five to six-week-old female athymic nude mice (Harlan Sprague Dawley) were used. Mice were caged in groups of 5 and kept on a 12-hour light/dark cycle and provided with sterilized food and water ad libitum. Animals were allowed to acclimate for at least 7 days before any handling. All CRC cells were harvested in an exponential phase growth and resuspended in a 1:1 mixture of serum-free RPMI 1640 and Matrigel (BD Biosciences). Five to 10 million cells per mouse were injected s.c. into the flank using a 23-gauge needle. Mice were monitored daily for signs of toxicity and were weighed twice weekly. Tumor size was evaluated twice per week by caliper measurements using the following formula: tumor volume = [length × width2] / 0.52. When tumors reached 150-300 mm³ mice were randomized into 2 groups with at least 10 tumors per group. Mice were then treated for 14 days with either vehicle control (25mM tartaric acid), or OSI-906 (40 mg/kg) once daily by oral gavage. The shorter duration of treatment of the cell line xenografts was used only to establish that the tumors exhibited similar in vitro and in vivo responsiveness to OSI-906 (sensitive or resistant).

The human CRC explant xenografts were generated according to previously published methods (25). Briefly, surgical specimens of patients undergoing either removal of a primary CRC or metastatic tumor at the University of Colorado Hospital were reimplanted s.c. into 5 mice for each patient. CU-CRC-006, 7, 10, 21 and 27 were obtained from primary tumor sites, whereas CU-CRC-001, 12, and 26 originated from peritoneal, pelvic, or omental metastatic sites, respectively. Tumors were allowed to grow to a size of 1000-1500 mm³ (F1) at which point they were harvested, divided, and transplanted to another 5 mice (F2) to maintain the tumor bank. After a subsequent growth passage, tumors were excised and expanded into cohorts of ≥25 mice for treatment. All experiments were conducted on F3-5 generations. Tumors from this cohort were allowed to grow until reaching □150-300 mm³, at which time they were equally distributed by size into the two treatment groups (control and OSI-906 treated). Mice with tumors from this treatment stage were treated for 28 days with either vehicle control (25mM tartaric acid), or OSI-906 (40 mg/kg) once daily by oral gavage. Monitoring of mice and measurements of tumors was conducted as described above. The relative tumor growth index was calculated by taking the relative tumor growth of treated mice divided by the relative tumor growth of control mice since the initiation of therapy (T/C) as described previously (25). Tumors with a T/C of < 50% were considered sensitive.

All of the xenograft studies were conducted in accordance with the NIH guidelines for the care and use of laboratory animals, were conducted in a facility accredited by the American Association for Accreditation of Laboratory Animal Care, and received approval from University of Colorado Animal Care and Use Committee prior to initiation. Obtaining tissue from CRC patients at the time of removal of a primary tumor or metastectomy was conducted under a Colorado Multi-Institutional Review Board (COMIRB) approved protocol.

Statistical methods

To determine the statistical significance of mutational status and FISH analysis a contingency table was constructed and the Fisher's Exact test was performed using GraphPad Prism Software (La Jolla, CA). The bioinformatics approach and relevant references are stated above. Differences were considered significant at P < 0.05.

Results

Assessment of responsiveness of a panel of CRC cell lines to OSI-906

To evaluate the sensitivity of CRC cell lines to OSI-906, a panel of 27 CRC cell lines were exposed to increasing concentrations and assessed for proliferation using an SRB assay (18). As depicted in Figure 1 there was a broad range of sensitivity of the CRC cell lines to OSI-906. For categorization, a sensitive cell line was classified as one with an IC₅₀ < 1.5 μmol/L, whereas resistant cell lines had IC₅₀ values >5 μmol/L; six cell lines met the criteria as being sensitive, and the remaining 21 were resistant.

Figure 1.

Cell proliferation assay on the panel of 27 CRC cell lines. Cell proliferation was evaluated by SRB following exposure to OSI-906 for 72 hours. Cells were plated at an optimized density in 96-well plates, incubated overnight at 37°C, and then exposed to a serial dilution of OSI-906. After 72 hours incubation, cells were fixed with trichloroacetic acid and an SRB was performed as described in Materials and Methods. Red lines indicate sensitive cell lines, yellow lines , intermediate, and red lines resistant.

IGF1R pathway analysis by immunoblotting and IHC

The use of protein biomarkers as predictive tools has been fraught with controversy for both EGFR1 and EGFR2 (HER2/NEU)-directed therapies (26, 27), nonetheless, we thought it important to analyze upstream and downstream effectors of the IGF1R pathway by immunoblotting and IHC at baseline. As depicted in Supplemental figure 1, none of the major components of the IGF1R pathway appeared to predict sensitivity to OSI-906. For example, activated (phosphorylated) IGF1R, ERK, AKT, IRS-1, PI3K, Shc, and S6 kinase were variably present at baseline in both sensitive and resistant cell lines. Similarly, baseline expression of p-IGF1R, IGF1R, IGF-2R, IGF-2, pERK, survivin, and Ki67 by IHC did not correspond to sensitivity to OSI-906 (Supplemental Table 1). Only two cell lines, GEO and LS1034 (both sensitive to OSI-906), demonstrated IGF-2 in the cellular supernatant, whereas none of the cell lines had detectable IGF-1 secretion (data not shown).

Evaluation of IGFIR gene copy number by FISH

Based upon prior studies suggesting that increased EGFR copy number may be predictive for EGFR-directed agents (28) we assessed IGF1R gene copy number in the panel of cell lines using a specific probe set. Although gene amplification was not observed in any of the CRC cell lines, several of them displayed an unbalanced IGF1R copy number gain based upon ploidy, which demonstrated a statistically significant relationship (p= 0.0152) between the presence of unbalanced gain and sensitivity to OSI-906 (Supplemental Table 2). Representative spreads/interphase nuclei are depicted in Figure 2 for one sensitive (Colo205) (3A) and one resistant (SW1463) (3B) cell line.

Figure 2.

Metaphase spreads and interphase nuclei from two triploid colorectal cell lines hybridized with the IGF1R (red)/CEP15 (green) probe set. A. Cell line COLO 205 showing 4 copies of IGF1R (copy number gain relative to ploidy). B. Cell line SW1417 showing 3 copies of IGF1R (balanced with ploidy).

Assessment of KRAS/BRAF/PI3K gene mutation status by sequencing

Since tumors with mutant KRAS/BRAF or PI3K demonstrate resistance to EGFR-based therapies, we characterized the KRAS/BRAF/PI3K mutational status of the CRC cell lines (29-32). Although no significant correlation was observed between KRAS status and OSI-906 sensitivity, there was a trend towards KRAS wild-type tumors being more sensitive. There was no relationship between either BRAF or PI3K mutation status and responsiveness to OSI-906 (Supplemental Table 3).

Identification of differentially expressed genes between CRC cell lines sensitive or resistant to OSI 906

To initially identify genes that correlated with sensitivity to OSI-906, we analyzed the basal gene expression profiles of the four sensitive and the four most resistant CRC cell lines. Using the two-sample t-test, 139 genes were identified as differentially expressed in OSI-906 sensitive and OSI-906 resistant CRC cell lines. Of these, there were 61 top scoring (p< 0.002) genes (Supplemental Table 4). Strikingly, genes that encode metallothioneins (MT), a family of ubiquitous, low molecular weight intracellular proteins that bind and detoxify heavy metal ions, were increased in the resistant cells and represented the highest ranked group (9-36-fold increase) of differentially expressed genes. MT can be induced by a variety of stimuli, are involved in other cellular functions such development, differentiation, proliferation, and carcinogenesis, and have been associated with a poor prognosis and metastasis in cancer as well as drug resistance (33-35). Genes that were up-regulated in the sensitive lines included aldehyde dehydrogenease (ALDH1A1, 83-fold) an enzyme involved in the metabolism of alcohol and the oxidation of all-trans retinal to all-trans retinoic acid, and the mitogen-activated protein kinase kinase 6 (MAP2K6, 11-fold), a protein that activates p38 MAP kinase and mediates stress-induced cell cycle arrest, transcriptional activation, invasion/migration and apoptosis (36, 37).

shRNA knockdown of potential predictive biomarker genes

In order to determine whether any of the highly ranked genes had a functional role in mediating responsiveness to OSI-906 we performed knockdown experiments with shRNA. OSI-906 resistant (HCT116, SW480) CRC cell lines were transfected with shRNA for caldesmon, metallothionein 2A (MT2A), or MT1E. OSI-906 sensitive (HT29, LS513) CRC cell lines were transfected with shRNA for ALDH1A1, or MAP2K6. The phenotype was analyzed by exposing the CRC cell lines to increasing concentrations of OSI-906. As depicted in Figure 3, shRNA knockdown of MT2A resulted in a robust decrease in MT2A RNA and protein in the resistant HCT116 and SW480 cells which was associated with a statistically significant increase in the antiproliferative effects of OSI-906 in the HCT116 but not the SW480 cells. Similar shRNA knockdown of the other genes noted above, did not demonstrate a functional role in mediating sensitivity (ALDH1A1, MAP2K6) or resistance (CALD1, MT1E) to OSI-906 (data not shown).

Figure 3.

Proliferation effects of MT2A knockdown on HCT116 and SW480 cells exposed to OSI906. HCT116 (A) and SW480 (B) cells were stably transfected with MT2A shRNA. The cells were then exposed to varying concentrations of OSI-906. Proliferation by SRB (at 5μM OSI-906), RT-PCR, and immunoblotting was performed as described in Materials and Methods.

Pathway analysis of OSI-906-sensitive and -resistant CRC cell lines

To determine whether any particular pathway was associated with responsiveness to OSI-906, pathway enrichment analysis was performed on the baseline CRC cell lines. In the sensitive cell lines, p53, insulin and IGF1R signaling pathways were among the top 25 pathways up-regulated according to BioCarta annotations. Among the core genes in the insulin and IGF1R signaling pathways were IRS-1, PIK3CB, RASA1, and FOS. Individually, these core genes were not in the differentially expressed gene list in Supplemental Table 4, however, coordinate over expression of these core genes in the insulin and IGF1R signaling pathways correlated with OSI-906 sensitivity (Figure 4). In contrast, the EGFR, MAPK, VEGF, Wnt and multiple cell adhesion/motility signaling pathways were prominently represented in the top 25 up-regulated pathways of resistant cell lines. Similar results were also observed when using KEGG gene set annotations in the pathway analysis.

Figure 4.

IGF1R and Insulin pathway analysis of OSI-906 sensitive and resistant cell lines. The genes that are differentially expressed are depicted in red.

Training and validation of a k-TSP classifier for predicting OSI-906 sensitivity

The primary goal of this project was to develop a classifier to predict sensitivity to OSI-906. To accomplish this, we used the baseline gene arrays from the 4 sensitive and 4 most resistant cell lines grown in vitro and in vivo as xenografts. In this study we used the previously described k-TSP algorithm to as a discriminative classifier (24). Using an internal leave-one-out cross-validation, the final k-TSP classifier utilizes the k disjoint pairs of genes, which achieve the k best scores from the training set. In this study, the maximum number of pairs (kmax) was fixed at 10. The k-TSP classifier identified three gene pairs as the final classifier: (PROM1>MT1E), (LY75> OXCT1) and (HSD17B2>CALD1). Interestingly two of these genes, MT1E and CALD1, also appeared in the gene analysis above. From the training data, the k-TSP classifier achieved an estimated leave-one-out cross-validation of 85.8%. To potentially improve the predictive power of the k-TSP classifier, we integrated KRAS mutational (WT) status and IGF1R FISH (unbalanced gain). Figure 5 depicts diagrammatically how this integrated classifier is used. In order for a tumor to be predicted as sensitive it must meet 4 of the 5 classifiers. In the validation set of 18 additional CRC cell lines (independent from the training set), the classifier correctly predicted responsiveness to OSI-906 in 89% (17/19) of cell lines.

Figure 5.

Diagram depicting the predictive classifier of responsiveness to OSI-906. Arrows indicate where the classifier was in error.

Validation of the integrated genomic classifier against human CRC explants in vivo

To further validate the classifier we assessed the baseline k-TSP from above, IGF1R by FISH, and KRAS status of 8 human CRC explants (primary tumors or metastectomy specimens) grown in vivo as xenografts, prior to treatment with OSI-906. The classifier predicted 6 explants as resistant and 2 as sensitive. Following treatment with OSI-906, the 2 that were predicted to be sensitive were indeed sensitive (TGI = 21-28%) and the 6 that were predicted to be resistant were resistant (TGI = 69-178%), leading to an overall accuracy of prediction against the human CRC explants of 100% (Figure 6).

Figure 6.

Antitumor activity of OSI-906 (40 mg/kg) against mouse models implanted with human tumor explants. Relative TGI was calculated by relative tumor growth of treated mice divided by the relative tumor growth of control mice × 100. (A) Antitumor effect of OSI-906 on the tumor growth of 8 explants. (B) Growth curves representative of tumors resistant to OSI-906. (C) Growth curves representative of tumors sensitive to OSI-906.

Discussion

IGF1R inhibitors as therapeutic agents in cancer constitute a promising class of targeted cancer therapies (6, 38). Initial phase I studies of these agents (antibodies and small molecules) have demonstrated mild but reversible toxicities including hyperglycemia and fatigue. Early signs of activity have been observed in tumors where IGF1R is thought to play a role including breast, liver, and colorectal cancer as well as soft tissue sarcoma (39). We chose to study OSI-906, a potent, oral, tyrosine kinase inhibitor (TKI) that is currently in phase I clinical trials as a single agent and in combination. Whereas previously, there was thought to be a selectivity advantage of antibodies against the IGF1R, recent data suggests that small molecule TKI's may confer the biological benefit of inhibiting signaling through the insulin receptor in cancer cells, which is now thought to be a cooperative partner in mediating cellular processes associated with the malignant phenotype (40, 41). Hyperglycemia, initially thought to be associated only with TKIs, is a mechanism-based toxicity associated with this class of agents (antibodies and TKIs), and is manageable (38). Nonetheless, despite promising hints of efficacy, there has been limited evidence that receptor activation or over-expression can adequately select patients for treatment with IGF1R inhibitors (42, 43). Therefore, the goal of this study was to utilize an unbiased genomic approach in developing a predictive classifier that could be validated in human CRC explants prior to clinical testing.

As expected, we observed a broad range of sensitivity to OSI-906 among the 27 CRC cell lines in vitro that was recapitulated by the 4 sensitive and 4 most resistant cell lines in vivo. Similar to EGFR-based inhibitors, there did not appear to be a relationship between baseline quantity or activation status of relevant effector proteins and sensitivity to OSI-906, nor was there a pattern of post-treatment changes of selected proteins that was consistently related to responsiveness to OSI-906. Interestingly, when the IGF1R gene copy number (based upon ploidy) was assessed by FISH, there was a trend towards a gain in copy number and sensitivity to OSI-906 that did not meet statistical significance. Previous studies have demonstrated that CRC cell lines and tumors are infrequently amplified for IGF1R, but little has been reported with regards to unbalanced gains of IGF1R (20). Of note, since OSI-906 targets IR as well as IGF1R, and these can form hybrid receptors, the role of IR amplification is also worth investigating and is included in the ongoing clinical trial of OSI-906 in patients with colorectal cancer (5).Among the IGF1R therapeutic antibodies and small molecule tyrosine kinase inhibitors, the most extensive predictive biomarker investigations have been in sarcomas, where elevated expression of ligands and IGF1R have been associated with a sensitive phenotype, while increased expression of the binding proteins IGFBP-3 and -6 have been associated with resistance (42, 43). Interestingly, although a prior study of basal-like breast cancers in murine models suggested that IGF1R inhibition was effective in KRAS mutant models, amongst our CRC cell lines, there was a non-statistically significant trend towards cells with wild-type KRAS being more sensitive to OSI-906 (44). Indeed, one could make the case that mutations downstream of the IGF1R (KRAS or PI3K), would confer resistance to IGF1R-directed therapies, in the same manner as has been described with EGFR-directed antibodies in CRC (22).

The single gene lists and pathway analysis between CRC cells sensitive or resistant to OSI-906 yielded some important observations. Highly overexpressed in the resistant cells were caldesmon and metallothioneins. Caldesmon is an actin-binding protein that has recently been shown to play a critical role in regulating the formation and dynamics of podosomes and invadopodia, cell adhesion structures that protrude from the plasma membrane and degrade the extracellular matrix (ECM) (45). Although caldesmon is generally thought to be a negative regulator of podosome formation by sequestering actin, phosphorylation of caldesmon by either cdc2 kinase or Erk1/2 MAPK reverses its inhibitory function, a situation that may dominate in cancer cells (46). Also highly represented in the resistant cells were the metallothioneins, a family of cysteine-rich low molecular weight proteins with affinity for heavy metal ions that have not only been associated with drug resistance to a variety of anti-cancer compounds, but also with a more invasive cancer phenotype (33-35). Reduction of MT2A expression in HCT-116 cells using siRNA did lead to enhancement of sensitivity to OSI-906, indicating a functional role in these cells, consistent with other reports where reduced MT2A expression by antisense or siRNA was associated with a reduction in proliferation and viability in breast cancer cell lines (47). Interestingly, metallothioneins were also very prominent on the list of genes associated with resistance to the small molecule IGF1R/IR inhibitor, BMS-536924, amongst a panel of 28 sarcoma and neuroblastoma cell lines, indicating this may represent a disease-independent mechanism of resistance to these agents (43). Interesting trends noted in the pathway analysis indicate that cell cycle and cell adhesion/communication pathways were prominent among resistant cells, whereas IGF1R/EGFR/insulin pathways including downstream effectors, were dominant in sensitive cells. Similar dependence on IGF1R signaling has been described for breast and CRC cell lines when exposed to the IGF1R antibody, h10H5 (42). Taken together, these results indicate potential mechanisms to overcome inherent resistance to OSI-906, including modulation of cell cycle, MAP kinase, and cell adhesion/invasion pathways, perhaps by rational combinations with inhibitors of cyclin dependent kinases, MEK, focal adhesion kinase (FAK), or src- all of which are clinically available. Based upon data indicating that inhibition of mTOR leads to IGF1R-dependent activation of AKT, combinations of agents that inhibit these pathways also appear warranted (48).

To develop the k-TSP classifier, we utilized both in vitro and in vivo baseline gene array for a given cell line in the training set so that differences relating purely to the tumor microenvironment would be minimized. We acknowledge that there are limitations to the k-TSP classifier, as it is dependent on gene expression and may not reflect the complex biology that mediates inherent drug resistance, therefore to increase accuracy, we developed an algorithm that combines gene expression, copy number, and mutation. Our integrative genomic classifier differs from other published drug-response gene-expression signatures in two important aspects, first, as opposed to other studies where the gene signatures remain unvalidated, we validated our integrative genomic signature on a set of independent direct-human CRC explants as an intermediate step before moving into clinic, and second, the classifier is comprised of the relative expression of 3 gene pairs, KRAS mutation status, and IGF1R FISH analyses, all of which can be readily applied to clinical settings. (42, 49). Interestingly, the rate of responsiveness (and presence of a “sensitive” biomarker pattern) in this small sample was 25%, which is similar to the rate of HER2 gene amplification in breast cancer, used for selecting patients for treatment with trastuzumab (50). Clearly, these results require validation in a clinical trial, which has recently been initiated in CRC patients.

In summary, these data demonstrate that an integrated approach to the development of predictive biomarkers in the early clinical development of novel agents is feasible. By employing the same approach to the development of other anticancer drugs, individualized therapy will become a reality, hopefully leading to more efficient and successful drug development.