In contrast to normal cells, cancer cells avidly take up glucose and metabolize it to lactate even when oxygen is abundant, a phenomenon referred to as the Warburg effect. This fundamental alteration in glucose metabolism in cancer cells enables their specific detection by positron emission tomography (PET) following i.v. injection of the glucose analogue 18F-fluorodeoxy-glucose (18FDG). However, this useful imaging technique is limited by the fact that not all cancers avidly take up FDG. To identify molecular determinants of 18FDG retention, we interrogated the transcriptomes of human-cancer cell lines and primary tumors for metabolic pathways associated with 18FDG radiotracer uptake. From ninety-five metabolic pathways that were interrogated, the glycolysis, and several glycolysis-related pathways (pentose phosphate, carbon fixation, aminoacyl-tRNA biosynthesis, one-carbon-pool by folate) showed the greatest transcriptional enrichment. This “FDG signature” predicted FDG uptake in breast cancer cell lines and overlapped with established gene expression signatures for the “basal-like” breast cancer subtype and MYC-induced tumorigenesis in mice. Human breast cancers with nuclear MYC staining and high RNA expression of MYC target genes showed high 18FDG-PET uptake (P < 0.005). Presence of the FDG signature was similarly associated with MYC gene copy gain, increased MYC transcript levels, and elevated expression of metabolic MYC target genes in a human breast cancer genomic dataset. Together, our findings link clinical observations of glucose uptake with a pathologic and molecular subtype of human breast cancer. Furthermore, they suggest related approaches to derive molecular determinants of radiotracer retention for other PET-imaging probes. Cancer Res; 71(15); 5164–74. ©2011 AACR.
Glycolysis and oxidative phosphorylation represent the main metabolic pathways that fuel energy-dependent processes in cells by generating ATP. Compared with normal differentiated cells, cancer cells show increased glycolytic rates and high lactate production in vitro even when oxygen levels are sufficient to support oxidative phosphorylation, a process called aerobic glycolysis or “Warburg effect” (1). Why cancer cells favor an energetically less efficient path of glucose utilization and how they avoid oxidative phosphorylation has remained unclear for decades. A number of recent observations have begun to shed light on the latter question. Knockdown of lactate dehydrogenase (LDH)-A in neu-initiated mammary epithelial tumor cell lines stimulated mitochondrial respiration, indicating that the glycolytic phenotype of cancer cells is not necessarily due to intrinsic mitochondrial defects in oxidative phosphorylation. Furthermore, activation of tyrosine kinase signaling, frequently caused by mutations in growth factor signaling pathways, has been found to inhibit the entry of pyruvate into the mitochondrial tricarboxylic acid cycle (TCA) cycle through hypoxia-inducible factor 1 (HIF1)-mediated induction of pyruvate dehydrogenase kinase 1 (PDK1) and posttranslational modification of the M2 splice isoform of pyruvate kinase (PK; Ref. 2).
The altered glucose metabolism of tumor cells can be observed in cancer patients by positron emission tomography (PET) following i.v. injection of the glucose analogue 18F-fluorodeoxy-glucose (18FDG; Ref. 3). Compared with normal surrounding tissue, tumors often show an increase in the FDG-PET signal which reflects their high rate of radiotracer uptake through membrane glucose transporters, phosphorylation by one of several hexokinase enzymes, and the resultant intracellular trapping of the radiotracer which is not further metabolized in the cell. Not all cancers, however, avidly take up FDG. Human breast cancers, for example, show up to 20-fold differences in their FDG-PET signal. This heterogeneity has been attributed, with substantial variation between studies, to differences in histopathologic subtypes, tumor size, microvasculature, tumor cell proliferation, hormone receptor status, and expression levels of hexokinase or glucose transporters (4).
Despite the widespread use of FDG-PET imaging in oncology, the relationship between FDG uptake of primary human tumors and their metabolic and genetic alterations is largely unknown. To address this question, we carried out genome wide transcriptomal analysis of cell lines and primary human tumors after determining their FDG uptake. We found that 18FDG retention is associated with coordinated transcriptional upregulation of multiple metabolic pathways, including the core glycolysis, pentose phosphate, and carbon fixation pathways. This “FDG signature” predicted radiotracer uptake in breast cancer cell lines and was closely linked to the “basal-like” intrinsic breast cancer subtype and activation of the MYC oncogene.
Materials and Methods
LAPC4 and HCT116 PTEN knockout cells were derived and provided by Dr. Sawyers and Dr. Waldmann, respectively. All other cell lines were obtained from American Type Culture Collection (ATCC) and the National Cancer Institute (NCI).
Eighteen breast cancer patients who presented for operative management of primary breast carcinoma were imaged with FDG-PET within 4 weeks prior to surgery; excluding patients with secondary breast cancers and recurrent disease. None of the patients received systemic therapy or radiation prior to imaging. All breast tumor samples were collected surgically. Our study included 1 patient with anaplastic astrocytoma. This study was approved by the Institutional Review Board (IRB) of Memorial Sloan-Kettering Cancer Center, and all participating patients signed the informed consent.
Gene expression signature–based predictions of FDG uptake were made using weighted gene voting (WGV; Ref. 5). The rank–rank hypergeometric overlap (RRHO) algorithm (6) was used to examine the statistical significance of similarity between our FDG signature and other gene expression signatures. Details of the bioinformatic approaches are described under Supplementary Methods.
Upregulation of glycolysis and glycolysis branch pathways in 18F-fluorodeoxy-glucose–avid cancer cells
Our strategy to identify determinants of FDG retention consisted of measurements of 18FDG retention in cancer cell lines and primary human tumors, RNA expression profiling of these samples, comparison of 18FDG “high” versus “low” samples using gene set enrichment analysis (GSEA; Ref. 7), and mining of genomic datasets for this “FDG signature” (Fig. 1A).
We first measured 18FDG radiotracer uptake of 16 cancer cell lines representing prostate cancer (CaP), glioblastoma (GBM), and melanoma (MEL). We observed up to 5-fold differences in FDG uptake between cell lines of the same histologic type (Fig. 1B). The clinical sample set included tumors from 18 breast cancer patients. 18FDG-tumor uptake was quantified as standardized uptake values (SUV) and showed the expected wide dynamic range (0–22.1; Fig. 1C, top). Breast cancers with the highest 18FDG-PET SUVs frequently lacked expression of the estrogen receptor (ER) and the progesterone receptor (PR), but hormone receptor-negative tumors were also represented amongst the tumors with the lowest FDG uptake (Table 1). Our clinical sample set also included a patient with anaplastic astrocytoma whose tumor showed areas of distinct FDG uptake. Both, an FDG-high and FDG-low region were amenable to stereotactic biopsy (Fig. 1C, bottom) and represented viable tumor.
For each tumor type represented in our panel, we selected samples with particularly high and low FDG uptake for GSEA (7) using 95 metabolic pathways annotated by the Kyoto Encyclopedia of Genes and Genomes (KEGG; Ref. 8). We hypothesized that a comparison of samples at the extremes of the FDG uptake spectrum would facilitate the identification of FDG uptake–associated metabolic pathways. The following FDG-high versus FDG-low sample sets were used: (i) LNCaP versus LAPC4 cells (CaP), (ii) U87 versus SF268 (GBM), (iii) SKMel28 versus CHL cells (MEL; marked with asterisk in Fig. 1B), (iv) FDG-high versus FDG-low region of the anaplastic astrocytoma, and (v) breast cancers with SUVs above 10 versus breast cancers with SUVs below 5 (marked with asterisk in Table 1). We initially excluded lobular breast carcinomas, because they have been shown to take up less FDG than ductal carcinomas (9). We initially also excluded large breast carcinomas (>5-cm) and breast carcinomas with multifocal FDG uptake because our protocol did not include tissue autoradiography to direct the molecular tissue analysis to areas of distinct radiotracer retention. There was no significant difference in patient age, tumor volume, and lymph node involvement between the group of FDG-high and FDG-low breast cancers.
For our combined GSEA analysis using the 5 FDG-high versus FDG-low sample sets and the 95 KEGG metabolic pathways, we first defined a rank-based gene expression signature for each histology type (breast and astrocytoma tumors; and prostate, glioblastoma, and MEL cell lines). Then the average rank for each gene was determined to define an average-rank signature that was interrogated using GSEA. The glycolysis/gluconeogenesis pathway scored as the most highly enriched metabolic pathway. The related carbon fixation and pentose phosphate pathways also showed significant enrichment in the FDG-high samples, as well as the pathways for aminoacyl-tRNA biosynthesis and one-carbon-pool by folate (Fig. 1D). Results were consistent between this average-rank approach and enrichment analysis of the individual signatures (Supplementary Table S1).
To exclude the possibility that our selection of breast cancer samples had introduced experimental bias, we repeated the GSEA analysis with all 18 primary breast cancer samples using a continuum SUV correlation-based ranking approach. Enrichment of the glycolysis, pentose phosphate, and carbon fixation remained significantly associated with the 18FDG-PET signal (Supplementary Table S1, column “Breast Cancer, SUV Continuum”).
Gene-expression-based “F-Fluorodeoxy-glucose Signature” predicts F-fluorodeoxy-glucose uptake in vitro
We next examined whether pathway enrichment was driven by modest differences in the levels of many pathway members or more dramatic effects on only 1 to 2 key enzymes. As illustrated in Fig. 2A, for the glycolysis core pathway in the breast cancer samples, pathway enrichment was due to moderate (< 2-fold), but highly concordant differences in the transcript levels of many pathway members. Similarly modest differences in transcript levels of functionally related genes have been shown to regulate metabolic flow in other biological and disease systems (10–12).
We also determined the contribution of individual enzymes to overall pathway enrichment based on their rank in each separate GSEA analysis (Fig. 2B and Supplementary Fig. S1). Although there were differences between tissue types (Supplementary Fig. S1B), enzymes that direct metabolic flow toward glycolysis (e.g., phosphofructokinase, hexokinase, and PK) showed the most consistent enrichment within the glycolysis/gluconeogenesis KEGG pathway (Fig. 2B and Supplementary Fig. S1B and C). The mean rank in the combined analysis for members of the top 3 enriched metabolic pathways (glycolysis, pentose phosphate, and carbon fixation) is shown in Fig. 2B. The “carbon fixation” KEGG pathway is functionally complete only in plants and scored in our analysis due to the overlap of its enzymes with the glycolysis/gluconeogenesis and pentose-phosphate pathways.
We next tested whether transcript levels of the most highly-ranked members of the top 3 enriched metabolic pathways could serve as an “FDG signature” and predict FDG uptake. We explored this question in a panel of 7 human breast cancer cell lines, which were not included in our initial FDG uptake studies. We selected breast-cancer cell lines for the validation of our FDG signature because the majority of human tumor samples used for the derivation of this signature were breast carcinomas. Predictions were made using the WGV approach (13) and using the primary breast tumors as the FDG signature training set (Supplementary Table S2, Supplementary Fig. S1C). FDG-uptake assays were conducted, blinded to our computational analysis, and showed a wide range of FDG uptake, as has been reported for breast-cancer cell lines (14). We found a strong correlation between measured FDG uptake and predicted FDG uptake (r = 0.92, permutation P = 0.03; Fig. 2C).
We next tested WGV predictions using individual genes with the greatest differential expression between FDG-high and FDG-low breast cancers (top 100–2,000 genes). Predictions of FDG uptake using the top differentially expressed individual genes showed less correlation with the measured FDG uptake (Fig. 2D), suggesting that our metabolism-oriented bioinformatic approach uncovered a shared metabolic state in FDG-high samples that would be more difficult to detect using “gene-centric” data analysis approaches.
F-Fluorodeoxy-glucose signature is associated with the “basal-like” breast-cancer subtype
The derivation of a gene expression–based FDG signature enabled us to search published genomic datasets for the presence of this signature with the goal to identify tumor types, genetic lesions, or signaling pathways that might be associated with FDG uptake. We focused this analysis on human breast cancer because of the wealth of validated RNA expression signatures in this disease (15). We first developed a method for quantitating the degree of overlap between 2 signatures defined by differential gene expression (6; Supplementary Fig. S2). We then applied this RRHO method to a dataset of 295 primary human breast cancers (16) to determine the overlap between the FDG signature and the main intrinsic breast-cancer subtypes, i.e., “basal-like,” “luminal,” “HER2/ErbB2,” and “normal-like.” We found significant overlap with the signature for the “basal-like” subtype, an inverse relationship with the signature for the luminal and normal subtypes (all with P-values < 10−4) and no overlap with the ErbB2 subtype (Fig. 3A).
Our results indicated that the expression of genes in basal-like breast cancers resembled FDG-high cancer cells more closely than any other breast-cancer subtype. If true, a direct GSEA comparison between “basal-like” and “other” breast cancers should identify similar metabolic pathways as our previous GSEA comparison between “FDG-PET high versus FDG-PET low” breast cancers. We tested this hypothesis in a second dataset of 286 primary human breast cancers (17). Only 2 metabolic pathways were significantly enriched in this analysis (glycolysis/gluconeogenesis and aminoacyl-tRNA biosynthesis; Supplementary Table S3), both of which had shown strong enrichment in our prior GSEA comparison of FDG-high versus FDG-low human primary breast cancers (Supplementary Table S1).
We further tested whether our “FDG signature score,” which had predicted FDG uptake in breast-cancer cell lines (Fig. 2C), would preferentially identify basal-like breast cancers within a sample set representing all breast-cancer subtypes. To test this hypothesis, we used metabolic pathway–based WGV to predict the FDG uptake of tumors in a third independent dataset of 80 locally advanced primary human breast cancers (18). Consistent with our prior analysis, 14 of 18 (77.8%) tumors with the highest FDG signature score were of the “basal-like” subtype, compared with 0 of 18 (0%) tumors with negative FDG signature score (multivariate hypergeometric P = 10−8). A Kolmogorov–Smirnov sliding threshold-based analysis using all 80 tumors also yielded statistical significance with a permutation P-value of 10−7 (Fig. 3B; Supplementary Table S4).
Basal-like breast cancer has been shown to harbor a greater number of low-level gene copy number alterations than other breast-cancer subtypes (19), and breast carcinomas with highest FDG signature score harbored a significantly greater number of gene copy number alterations than tumors lacking the FDG signature (Fig. 3C, top). Based on these results, we conducted array-cGH profiling of breast cancer samples for which we had residual frozen tissue (11 of the original 18). We found significantly more gene copy number alterations in the group of FDG-high tumors (SUV > 10) compared with the group of FDG-low tumors (SUV<5; Fig. 3C, bottom), providing further evidence that FDG-avid breast carcinomas exhibit genetic properties of the “basal-like” breast-cancer subtype.
Activation of MYC in F-Fluorodeoxy-glucose–avid breast cancers
Mutant alleles of ras and activation of the phosphoinositide 3-kinase (PI3K) pathway have been shown to increase glucose uptake in experimental models (20). None of the breast tumors in our collection harbored an activating mutation in a ras-family member (data not shown). We found activating mutations in the catalytic subunit of PI3K in 3 breast carcinomas (BT07: C420R; BT09: H1047R; and BT04: H1047R), all 3 ER-positive tumors, consistent with the reported association between PIK3CA mutations and ER positivity in breast cancer (21). We next mined our breast carcinomas for transcriptional evidence of increased PI3K pathway activity, based on similarity with signatures derived from an Akt-driven murine tumor model (22) and human breast-cancer samples lacking expression of the PTEN tumor suppressor (23). As with our FDG uptake signature (Fig. 1D), these PI3K activation signatures showed enrichment of the glycolysis, pentose phosphate, and carbon-fixation pathways (Supplementary Fig. S3A), and this enrichment was driven by similar glycolysis-promoting enzymes (Supplementary Fig. S3B).
We next examined protein levels of PTEN by immunohistochemistry (Supplementary Fig. S3C), as diminished protein levels of this tumor suppressor have been reported for 15% to 25% of all breast carcinomas and more commonly in basal-like breast cancer (24). Compared with adjacent nonneoplastic cells, we observed reduced PTEN staining of tumor cells in 6 of 16 (37.5%) tumors; five of the PTEN-deficient breast carcinomas were in the group of tumors with highest FDG-PET uptake. We next examined the effects of PTEN inactivation on cellular FDG uptake in cancer cell lines. In HCT116 colon cancer cells (25), which harbor mutations in PIK3CA and KRAS, PTEN knockout raised the FDG uptake by about 50% (P < 0.05; Supplementary Fig. S3D). PTEN knockdown in 3 other cancer cell lines (A431, HCC827, and SKBR3), on the other hand, only raised FDG uptake in 1 of the lines (SKBR3) and this increase was not statistically significant (Supplementary Fig. S3E). These results suggest that the effects of PTEN on glucose metabolism are cell context-specific.
To identify additional signaling pathways that are associated with FDG-PET uptake in breast cancer, we searched a Molecular Signatures Database (MSigDB) for our FDG signature. This database is composed of 1,822 gene sets representing canonical signaling pathways, cellular processes, chemical and genetic perturbations, and human disease states (7). One hundred and ten of the 1,822 gene sets, extracted directly from MSigDB without modifying their contents, were positively enriched in the transcriptome of FDG-high samples (Supplementary Table S5). The top gene sets included gene sets related to poor prognosis (rank 6 and 9) and high-tumor grade (rank 25) in breast carcinoma. The top gene sets also included multiple gene sets both directly and indirectly related to the transcription factor MYC (Fig. 4A; Supplementary Table S5). The direct MYC group was composed of gene signatures that are upregulated in transgenic mouse models of c-myc–induced cancer (rank 12, 28, and 49; Refs. 26–28); MYC-related gene signatures included the serum fibroblast response/wound healing signature (rank 2, 17, and 39) linked to MYC activation in breast cancer (29–31), and a signature linked to MYC activation in lymphoma (rank 38; Ref. 32). We found an inverse relationship (i.e., negative enrichment score) between our FDG signature and genes repressed by MYC (rank 1,628, 1,643, and 1,751; Refs. 26, 33, 34). The overall association between our FDG signature and gene sets directly linked to MYC was statistically significant (P = 0.002; Fig. 4A).
We next stained all breast carcinomas for which we had remaining tissue (16/18) with an antibody against the MYC protein. Eight of 16 (50%) tumors showed nuclear staining of tumor cells, similar to the reported frequency of MYC immunoreactivity (40%–50%) in human breast cancer (35, 36; Fig. 4B, left). MYC immunopositivity was significantly associated with high 18FDG-PET SUV values (P = 0.002; Fig. 4B, right). Nuclear localization of MYC was associated with increased MYC transcriptional activity based on the overexpression of genes under direct transcriptional control of MYC in the MYC IHC-positive group, including the glutamine transporter SCL7A5 (37; P < 0.001), serine hydroxymethyl-transferase (SHMT; Ref. 38; P < 0.05), LDH-A (39; P < 0.05), and transferrin receptor 1 (TFRC1; Ref. 40; P < 0.01; Fig. 4C).
We also examined the relationship between the FDG signature score, MYC levels, and MYC target gene expression in the breast-cancer dataset (18) used in our prior analysis (Fig. 3B). The frequency of c-myc copy gain (log2 ratio ≥ 0.4) in the top half of the FDG signature score–ranked tumors (19/40 = 47.5%) significantly exceeded the frequency of c-myc copy gain in the bottom half (6/40 = 15%) and in the entire cohort of patients (25/80 = 31.3%; hypergeometric P = 0.002; Fig. 4D), showing that the FDG signature significantly enriches for tumors harboring this molecular alteration. When we focused on the subgroup of tumors with the highest versus lowest FDG signature scores, we found elevated c-myc gene dosage in 10 of 18 (55.5%) breast cancers with high FDG-signature score, but none (0/18) of the breast cancers with low FDG signature score (t-test P = 0.0002). MYC transcript levels and the expression of MYC target genes were similarly statistically associated with the FDG signature score (Fig. 4D).
As genes induced by hypoxia also emerged from our pathway analysis (rank 8 and 79; Fig. 4A), we also stained all breast tumors with an antibody against HIF-1. HIF1α is overexpressed in human cancers as a result of intratumoral hypoxia and genetic alterations in tumor cells (41). Twelve of 16 (75%) tumors showed intense nuclear staining for HIF1α (Supplementary Fig. S4A), including all breast carcinomas with nuclear MYC staining and highest FDG-PET signal. Seven of 12 HIF-1–positive tumors, but none of the HIF-1–negative tumors, also showed cytoplasmic staining with an antibody against phosphorylated proline-rich AKT1 substrate 1 (PRAS40; Supplementary Fig. S4B and C), a target of Akt and readout for PI3K pathway activity (42).
FDG-PET is widely used in the clinic for the detection of cancer. Despite a wealth of data linking glucose uptake to mutations in oncogenes and tumor suppressor genes in vitro (20), most studies of primary human tumors have focused on expression levels of hexokinase and glucose transporters. Our study sought to define the broader context of metabolic and genetic alterations in FDG-avid cancers. We show that FDG-avid tumors share a transcriptional program that involves not only members of the core glycolysis pathway, but also several glycolysis branch pathways critical for nucleotide and amino acid synthesis. These findings support the model that cancer cells favor aerobic glycolysis, despite the “penalty” of inefficient ATP production, because its metabolic intermediates can be used by the proliferating cancer cell for the replenishment of NADPH and the synthesis of highly needed macromolecules (43).
We identified overexpression of the transcription factor MYC as the molecular alteration most highly associated with FDG uptake in human breast cancer. MYC is a plausible candidate to orchestrate the metabolic program of FDG-avid cancers. Nuclear magnetic resonance (NMR) studies have shown that MYC regulates the flux of glucose carbon not only through the core-glycolysis pathway, but also through glycolysis branch pathways, which were consistently upregulated in our analysis (i.e., pentose-phosphate pathway, amino-acid metabolism, and C1/folate metabolism; Ref. 44). Furthermore, MYC directly regulates RNA levels of several members of the glycolysis and glutamine pathway which showed increased transcript levels in FDG-avid (Fig. 4C) and FDG signature–positive tumors (Fig. 4D). These include PDK1 and LDH-A, which attenuate entry of pyruvate into the TCA cycle, the glutamine transporters SLC7A5 and SLC1A5, and with some differences between published experimental models (45, 46), glutaminase (GLS).
FDG uptake in breast cancer did not correlate with Akt activation, a finding previously reported in short-term human breast-cancer cultures (14). Our further examination of PI3K “pathway output” showed that the PI3K pathway is nonetheless activated in the majority of FDG-avid breast cancers, perhaps through alterations parallel or downstream of Akt (47). In peripheral nerve sheath tumors, induced by monoallelic PTEN inactivation and mutant K-ras in mice, loss of the second PTEN allele coincides with a marked increase in tumor FDG uptake (48), suggesting that the strength of PI3K pathway activation may be an important determinant of the glycolytic state. PI3K pathway activation may also cooperate with other oncogenic events, such as MYC, to induce a maximally glycolytic state. Several genes that were significantly upregulated in FDG-avid breast cancers in our study have previously been shown to be regulated by both MYC and HIF-1, including LDH-A, PDK1, and TFRC1. Studies in a larger panel of primary human tumors are warranted to define the relationship between FDG uptake, the PI3K-HIF1 axis, and other cancer genes.
The role of FDG-PET imaging in the management of human breast-cancer remains to be defined (49). One of the challenges is the detection of small tumors (< 2.0 cm) as partial volume effects (PVE) can result in underestimation of true radiotracer retention (50). This may have affected our estimation of the SUVs and should be addressed in future studies using different PVE correction schemes (51). Our study connects the clinical observation of altered glucose metabolism with a molecular subtype of human breast cancer, namely, basal-like breast cancer with MYC activation. This conclusion, reached through a genome-wide approach, links prior observations that (i) the basal-like breast-cancer subtype is enriched for tumors with MYC copy gain (52) and a MYC gene-expression signature (30, 31) and that (ii) breast cancers which lack expression of ERs, PRs, and HER2 gene amplification (triple-negative), as is true for the majority of basal-like breast cancers, have shown increased FDG uptake in larger clinical studies (53, 54). However, basal-like breast cancers are defined by their gene-expression profile, express ER, or overexpress HER2 in up to 20% of cases and represent a disease subgroup that is distinct from triple-negative breast cancer (55). Our findings suggest that FDG-PET may be particularly useful as biomarker for therapies that target the basal-like breast cancer subtype or the “addiction” of MYC-induced tumors to the glycolysis and glutamine pathway (45, 46).
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
This work was supported through grants 5P50 CA086306-07 (T.G. Graeber and I.K. Mellinghoff), U54 CA143798 (I.K. Mellinghoff), R21 CA137896 (I.K. Mellinghoff), 5 R25 CA 098010 (N. Palaskas), P50 CA086438-10 (S.M. Larson), and 2PO1 CA094060 (S.M. Larson) from the NCI. Further support was provided by the Leon Levy Foundation (J.T. Huse, I.K. Mellinghoff), the Sontag Foundation (I.K. Mellinghoff), and the Doris Duke Charitable Foundation (I.K. Mellinghoff).
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
The authors thank members of the Mellinghoff and Graeber Laboratories for helpful discussions and Drs. Jim Fagin, Neal Rosen, and Charles Sawyers for reviewing the manuscript. T.G. Graeber is an Alfred P. Sloan Research Fellow. I.K. Mellinghoff is the recipient of an Advanced Clinical Research Award from the American Society of Clinical Oncology and a Forbeck Scholar. This work is dedicated to the memory of William Gerald (MSKCC).
Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).
- Received December 28, 2010.
- Revision received May 20, 2011.
- Accepted June 2, 2011.
- ©2011 American Association for Cancer Research.