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.

Cell lines

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).

Patients

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 analysis

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).

Figure 1.

Deriving an “FDG uptake” metabolic gene expression signature. A, schematic of our experimental approach. B, 18F-FDG retention in 16 cancer cell lines, including CaP, GBM, and MEL. Error bars indicate standard error. C, clinical datasets. Left, tumor FDG uptake in 18 breast cancer patients. Right, FDG-PET scan and Brain MRI in a patient with anaplastic astrocytoma. (Area 1: FDG-high, Area 2: FDG-low). Crosshairs are centered at sites of biopsy. D, “High FDG uptake” samples show transcriptional enrichment for glycolysis and glycolysis branch metabolic pathways. Shown are average rank-based GSEA results (see Supplementary Table S1 for the results of all KEGG metabolic pathways).

Figure 1.

Deriving an “FDG uptake” metabolic gene expression signature. A, schematic of our experimental approach. B, 18F-FDG retention in 16 cancer cell lines, including CaP, GBM, and MEL. Error bars indicate standard error. C, clinical datasets. Left, tumor FDG uptake in 18 breast cancer patients. Right, FDG-PET scan and Brain MRI in a patient with anaplastic astrocytoma. (Area 1: FDG-high, Area 2: FDG-low). Crosshairs are centered at sites of biopsy. D, “High FDG uptake” samples show transcriptional enrichment for glycolysis and glycolysis branch metabolic pathways. Shown are average rank-based GSEA results (see Supplementary Table S1 for the results of all KEGG metabolic pathways).

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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.

Table 1.

FDG-PET tumor uptake in 18 patients with locally advanced breast cancer

Patient IDFDG uptake (SUV max)AgeSexTumor size, cmHistologyHisto gradeNuc gradeLymph node invasionERPRHER2 DNA
BT06 22.1 48 8.5 ductal III III yes NEG NEG AMP (4.6) 
BT11a 18.5 46 3.5 ductal III III yes NEG NEG NEG 
BT03a 16.3 29 2.3 ductal II III yes NEG NEG not AMP (1.0) 
BT08a 15.7 57 4.0 ductal III III yes NEG NEG AMP (2.2) 
BT05a 11.9 34 2.1 ductal III III yes NEG NEG not AMP (1.1) 
BT02a 11.1 72 3.2 ductal III III N/A POS NEG not AMP (1.5) 
BT13 9.7 61 2.5 ductal III III yes NEG NEG AMP (5.5) 
BT14 6.5 28 3.2 ductal III III no POS NEG AMP (3.4) 
BT15 6.4 40 9.5 ductal III III yes POS NEG not AMP (1.6) 
BT16 5.3 64 2.0 ductal III III yes POS POS not AMP (1.0) 
BT07a 4.8 69 4.2 ductal III III yes POS POS not AMP (1.2) 
BT10a 4.3 59 2.1 ductal II II yes POS POS not AMP (1.3) 
BT12a 3.9 59 4.5 ductal III III yes NEG NEG not AMP (1.0) 
BT09a 3.8 62 2.5 ductal II II yes POS POS not AMP (1.1) 
BT17 3.5 42 7.0 lobular N/A N/A yes POS POS not AMP (1.0) 
BT04a 3.2 28 1.4 ductal III III yes POS POS not AMP (1.0) 
BT01a 2.6 46 2.5 ductal III III yes NEG NEG AMP (5.4) 
BT18 0.0 55 5.0 lobular N/A N/A yes POS NEG not AMP (1.0) 
Patient IDFDG uptake (SUV max)AgeSexTumor size, cmHistologyHisto gradeNuc gradeLymph node invasionERPRHER2 DNA
BT06 22.1 48 8.5 ductal III III yes NEG NEG AMP (4.6) 
BT11a 18.5 46 3.5 ductal III III yes NEG NEG NEG 
BT03a 16.3 29 2.3 ductal II III yes NEG NEG not AMP (1.0) 
BT08a 15.7 57 4.0 ductal III III yes NEG NEG AMP (2.2) 
BT05a 11.9 34 2.1 ductal III III yes NEG NEG not AMP (1.1) 
BT02a 11.1 72 3.2 ductal III III N/A POS NEG not AMP (1.5) 
BT13 9.7 61 2.5 ductal III III yes NEG NEG AMP (5.5) 
BT14 6.5 28 3.2 ductal III III no POS NEG AMP (3.4) 
BT15 6.4 40 9.5 ductal III III yes POS NEG not AMP (1.6) 
BT16 5.3 64 2.0 ductal III III yes POS POS not AMP (1.0) 
BT07a 4.8 69 4.2 ductal III III yes POS POS not AMP (1.2) 
BT10a 4.3 59 2.1 ductal II II yes POS POS not AMP (1.3) 
BT12a 3.9 59 4.5 ductal III III yes NEG NEG not AMP (1.0) 
BT09a 3.8 62 2.5 ductal II II yes POS POS not AMP (1.1) 
BT17 3.5 42 7.0 lobular N/A N/A yes POS POS not AMP (1.0) 
BT04a 3.2 28 1.4 ductal III III yes POS POS not AMP (1.0) 
BT01a 2.6 46 2.5 ductal III III yes NEG NEG AMP (5.4) 
BT18 0.0 55 5.0 lobular N/A N/A yes POS NEG not AMP (1.0) 

asamples included in the biphenotypic GSEA comparison of “FDG-high”(SUV>10) versus “FDG-low”(SUV< 5) tumors.

Abbreviations: N/A, not available; NEG, negative; POS, positive; AMP, amplified; Histo, histology; Nuc, nuclear.

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).

Figure 2.

FDG signature score predicts in vitro FDG uptake. A, RNA levels of core glycolysis enzymes (red) versus all genes (gray) in high FDG-PET (n = 5, y-axis) versus low FDG-PET (n = 6, x-axis) breast carcinomas. Tumors included in the analysis are denoted with asterisks in Table 1. B, average rank of glycolysis/gluconeogenesis, carbon fixation, and pentose phosphate pathway enzyme members across all sample sets (see also Supplementary Fig. S1). Low-rank numbers represent high expression in the “FDG-high” samples. Enzymes in green promote glycolysis, whereas those in red promote gluconeogenesis. The core glycolysis pathway was included as a point of reference. Enzyme rankings for the primary breast tumor–based FDG signature alone are shown in Supplementary Fig. S1C. The schematic below shows the glycolysis/gluconeogenesis pathway (and related pathways) as annotated by KEGG. Enzyme names are in italics. C, correlation between the observed (x-axis) and predicted (y-axis) FDG uptake (see text and Supplementary Fig. S1C for details) in 7 breast cancer cell lines. The correlation (r = 0.92) was statistically significant (sample label permutation P = 0.03). Cell lines from low to high FDG uptake are: HCC1500, BT474, ZR7530, ZR751, HCC70, UACC812, and MCF7. D, correlations between observed FDG uptake and “FDG signature score” predictions, using either only genes from the 3 glycolysis-related metabolic pathways which comprise the “FDG signature” (as in panel C; gly + cf + pp), or “lists of the top n genes” with the highest differential expression between FDG-high and FDG-low breast cancers. Error bars: standard error.

Figure 2.

FDG signature score predicts in vitro FDG uptake. A, RNA levels of core glycolysis enzymes (red) versus all genes (gray) in high FDG-PET (n = 5, y-axis) versus low FDG-PET (n = 6, x-axis) breast carcinomas. Tumors included in the analysis are denoted with asterisks in Table 1. B, average rank of glycolysis/gluconeogenesis, carbon fixation, and pentose phosphate pathway enzyme members across all sample sets (see also Supplementary Fig. S1). Low-rank numbers represent high expression in the “FDG-high” samples. Enzymes in green promote glycolysis, whereas those in red promote gluconeogenesis. The core glycolysis pathway was included as a point of reference. Enzyme rankings for the primary breast tumor–based FDG signature alone are shown in Supplementary Fig. S1C. The schematic below shows the glycolysis/gluconeogenesis pathway (and related pathways) as annotated by KEGG. Enzyme names are in italics. C, correlation between the observed (x-axis) and predicted (y-axis) FDG uptake (see text and Supplementary Fig. S1C for details) in 7 breast cancer cell lines. The correlation (r = 0.92) was statistically significant (sample label permutation P = 0.03). Cell lines from low to high FDG uptake are: HCC1500, BT474, ZR7530, ZR751, HCC70, UACC812, and MCF7. D, correlations between observed FDG uptake and “FDG signature score” predictions, using either only genes from the 3 glycolysis-related metabolic pathways which comprise the “FDG signature” (as in panel C; gly + cf + pp), or “lists of the top n genes” with the highest differential expression between FDG-high and FDG-low breast cancers. Error bars: standard error.

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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).

Figure 3.

FDG signature overlaps with “basal-like” breast cancer subtype. A, overlap between the FDG uptake signature and signatures for intrinsic breast cancer subtypes (16). Using the RRHO approach (Supplementary Fig. S2), genes were ranked by their degree of correlation with FDG-PET SUV values across the tumors (n = 18) or their degree of differential expression between the indicated subclasses to define the rank-based signature. The Spearman rank correlation coefficient (ρ) between signatures was calculated. All cases, except the ErbB2 case, had significant correlation based on sample permutation–based statistical analysis (P < 0.0001). B, FDG signature score preferentially identifies basal-like breast tumors. Left, schematic of experimental approach. Right, rank-ordered distribution of intrinsic human breast cancer subtypes relative to their predicted FDG signature score. See also Supplementary Table S4. C, elevated genomic instability of human breast cancers with high “FDG signature score” (top, n = 18 high, 18 low) or high FDG-PET uptake [bottom, FDG-PET SUV>10 (n = 5) compared with SUV<5 (n = 5)]. Shown are cGH profiles with regions of copy-number gain (loss) shown as shades of red (blue). The graphs on the right show a quantification of gene copy number alterations. The higher absolute number of transitions per chromosome in the bottom plot (vs. top plot) is due to the higher resolution of the cGH platform.

Figure 3.

FDG signature overlaps with “basal-like” breast cancer subtype. A, overlap between the FDG uptake signature and signatures for intrinsic breast cancer subtypes (16). Using the RRHO approach (Supplementary Fig. S2), genes were ranked by their degree of correlation with FDG-PET SUV values across the tumors (n = 18) or their degree of differential expression between the indicated subclasses to define the rank-based signature. The Spearman rank correlation coefficient (ρ) between signatures was calculated. All cases, except the ErbB2 case, had significant correlation based on sample permutation–based statistical analysis (P < 0.0001). B, FDG signature score preferentially identifies basal-like breast tumors. Left, schematic of experimental approach. Right, rank-ordered distribution of intrinsic human breast cancer subtypes relative to their predicted FDG signature score. See also Supplementary Table S4. C, elevated genomic instability of human breast cancers with high “FDG signature score” (top, n = 18 high, 18 low) or high FDG-PET uptake [bottom, FDG-PET SUV>10 (n = 5) compared with SUV<5 (n = 5)]. Shown are cGH profiles with regions of copy-number gain (loss) shown as shades of red (blue). The graphs on the right show a quantification of gene copy number alterations. The higher absolute number of transitions per chromosome in the bottom plot (vs. top plot) is due to the higher resolution of the cGH platform.

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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).

Figure 4.

MYC activation in FDG-high primary human breast cancers. A, rank of MSigDB gene sets when analyzed for enrichment in the FDG uptake signature. NES: normalized enrichment score. Enrichment results for all 1,822 MSigDB C2 gene sets are listed in Supplementary Table S5. B, MYC IHC-positive breast cancers have higher FDG-PET SUV values. Left, representative MYC-IHC images (40×). Sample numbers and FDG-PET SUVs (in parenthesis) are shown above each panel. Right, distribution of FDG-PET SUVs in MYC IHC-positive versus negative breast carcinomas. C, RNA levels of MYC and MYC target genes in tumors with negative (left) versus positive (right) nuclear MYC staining by IHC. Red = high; P values represent t-test analysis of the MYC IHC-negative versus positive samples. D, breast cancers with high FDG signature score are more likely to have elevated c-myc gene dosage (MYC DNA), MYC transcript level (MYC RNA), and MYC target gene expression. Tumors are ordered by FDG signature score (low to high); P values represent a t-test analysis of the top 40 versus bottom 40 scoring FDG signature samples. Similar P values were obtained for top/bottom 18 samples or for the correlation of copy number or expression values with the FDG signature score.

Figure 4.

MYC activation in FDG-high primary human breast cancers. A, rank of MSigDB gene sets when analyzed for enrichment in the FDG uptake signature. NES: normalized enrichment score. Enrichment results for all 1,822 MSigDB C2 gene sets are listed in Supplementary Table S5. B, MYC IHC-positive breast cancers have higher FDG-PET SUV values. Left, representative MYC-IHC images (40×). Sample numbers and FDG-PET SUVs (in parenthesis) are shown above each panel. Right, distribution of FDG-PET SUVs in MYC IHC-positive versus negative breast carcinomas. C, RNA levels of MYC and MYC target genes in tumors with negative (left) versus positive (right) nuclear MYC staining by IHC. Red = high; P values represent t-test analysis of the MYC IHC-negative versus positive samples. D, breast cancers with high FDG signature score are more likely to have elevated c-myc gene dosage (MYC DNA), MYC transcript level (MYC RNA), and MYC target gene expression. Tumors are ordered by FDG signature score (low to high); P values represent a t-test analysis of the top 40 versus bottom 40 scoring FDG signature samples. Similar P values were obtained for top/bottom 18 samples or for the correlation of copy number or expression values with the FDG signature score.

Close modal

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).

No potential conflicts of interest were disclosed.

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).

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.

1.
Warburg
O
. 
On the origin of cancer cells
.
Science
1956
;
123
:
309
14
.
2.
Kaelin
WG
 Jr
,
Thompson
CB
. 
Q&A: Cancer: clues from cell metabolism
.
Nature
2010
;
465
:
562
4
.
3.
Fletcher
JW
,
Djulbegovic
B
,
Soares
HP
,
Siegel
BA
,
Lowe
VJ
,
Lyman
GH
, et al
Recommendations on the use of 18F-FDG PET in oncology
.
J Nucl Med
2008
;
49
:
480
508
.
4.
Jadvar
H
,
Alavi
A
,
Gambhir
SS
. 
18F-FDG uptake in lung, breast, and colon cancers: molecular biology correlates and disease characterization
.
J Nucl Med
2009
;
50
:
1820
7
.
5.
Golub
TR
,
Slonim
DK
,
Tamayo
P
,
Huard
C
,
Gaasenbeek
M
,
Mesirov
JP
, et al
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring
.
Science
1999
;
286
:
531
7
.
6.
Plaisier
SB
,
Taschereau
R
,
Wong
JA
,
Graeber
TG
. 
Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures
.
Nucleic Acids Res
2010
;
38
:
e169
.
7.
Subramanian
A
,
Tamayo
P
,
Mootha
VK
,
Mukherjee
S
,
Ebert
BL
,
Gillette
MA
, et al
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles
.
Proc Natl Acad Sci U S A
2005
;
102
:
15545
50
.
8.
Kanehisa
M
,
Araki
M
,
Goto
S
,
Hattori
M
,
Hirakawa
M
,
Itoh
M
, et al
KEGG for linking genomes to life and the environment
.
Nucleic Acids Res
2008
;
36
:
D480
4
.
9.
Avril
N
,
Menzel
M
,
Dose
J
,
Schelling
M
,
Weber
W
,
Janicke
F
, et al
Glucose metabolism of breast cancer assessed by 18F-FDG PET: histologic and immunohistochemical tissue analysis
.
J Nucl Med
2001
;
42
:
9
16
.
10.
Zaslaver
A
,
Mayo
AE
,
Rosenberg
R
,
Bashkin
P
,
Sberro
H
,
Tsalyuk
M
, et al
Just-in-time transcription program in metabolic pathways
.
Nat Genet
2004
;
36
:
486
91
.
11.
Ihmels
J
,
Levy
R
,
Barkai
N
. 
Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae
.
Nat Biotechnol
2004
;
22
:
86
92
.
12.
Mootha
VK
,
Lindgren
CM
,
Eriksson
KF
,
Subramanian
A
,
Sihag
S
,
Lehar
J
, et al
PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes
.
Nat Genet
2003
;
34
:
267
73
.
13.
Matei
D
,
Graeber
TG
,
Baldwin
RL
,
Karlan
BY
,
Rao
J
,
Chang
DD
. 
Gene expression in epithelial ovarian carcinoma
.
Oncogene
2002
;
21
:
6289
98
.
14.
Robey
IF
,
Stephen
RM
,
Brown
KS
,
Baggett
BK
,
Gatenby
RA
,
Gillies
RJ
. 
Regulation of the Warburg effect in early-passage breast cancer cells
.
Neoplasia
2008
;
10
:
745
56
.
15.
Fan
C
,
Oh
DS
,
Wessels
L
,
Weigelt
B
,
Nuyten
DS
,
Nobel
AB
, et al
Concordance among gene-expression-based predictors for breast cancer
.
N Engl J Med
2006
;
355
:
560
9
.
16.
Chang
HY
,
Nuyten
DS
,
Sneddon
JB
,
Hastie
T
,
Tibshirani
R
,
Sorlie
T
, et al
Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival
.
Proc Natl Acad Sci U S A
2005
;
102
:
3738
43
.
17.
Wang
Y
,
Klijn
JG
,
Zhang
Y
,
Sieuwerts
AM
,
Look
MP
,
Yang
F
, et al
Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer
.
Lancet
2005
;
365
:
671
9
.
18.
Chin
K
,
DeVries
S
,
Fridlyand
J
,
Spellman
PT
,
Roydasgupta
R
,
Kuo
WL
, et al
Genomic and transcriptional aberrations linked to breast cancer pathophysiologies
.
Cancer Cell
2006
;
10
:
529
41
.
19.
Bergamaschi
A
,
Kim
YH
,
Wang
P
,
Sorlie
T
,
Hernandez-Boussard
T
,
Lonning
PE
, et al
Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer
.
Genes Chromosomes Cancer
2006
;
45
:
1033
40
.
20.
Kroemer
G
,
Pouyssegur
J
. 
Tumor cell metabolism: cancer's Achilles' heel
.
Cancer Cell
2008
;
13
:
472
82
.
21.
Saal
LH
,
Holm
K
,
Maurer
M
,
Memeo
L
,
Su
T
,
Wang
X
, et al
PIK3CA mutations correlate with hormone receptors, node metastasis, and ERBB2, and are mutually exclusive with PTEN loss in human breast carcinoma
.
Cancer Res
2005
;
65
:
2554
9
.
22.
Majumder
PK
,
Febbo
PG
,
Bikoff
R
,
Berger
R
,
Xue
Q
,
McMahon
LM
, et al
mTOR inhibition reverses Akt-dependent prostate intraepithelial neoplasia through regulation of apoptotic and HIF-1-dependent pathways
.
Nat Med
2004
;
10
:
594
601
.
23.
Saal
LH
,
Johansson
P
,
Holm
K
,
Gruvberger-Saal
SK
,
She
QB
,
Maurer
M
, et al
Poor prognosis in carcinoma is associated with a gene expression signature of aberrant PTEN tumor suppressor pathway activity
.
Proc Natl Acad Sci U S A
2007
;
104
:
7564
9
.
24.
Saal
LH
,
Gruvberger-Saal
SK
,
Persson
C
,
Lovgren
K
,
Jumppanen
M
,
Staaf
J
, et al
Recurrent gross mutations of the PTEN tumor suppressor gene in breast cancers with deficient DSB repair
.
Nat Genet
2008
;
40
:
102
7
.
25.
Lee
C
,
Kim
JS
,
Waldman
T
. 
PTEN gene targeting reveals a radiation-induced size checkpoint in human cancer cells
.
Cancer Res
2004
;
64
:
6906
14
.
26.
Yu
D
,
Cozma
D
,
Park
A
,
Thomas-Tikhonenko
A
. 
Functional validation of genes implicated in lymphomagenesis: an in vivo selection assay using a Myc-induced B-cell tumor
.
Ann N Y Acad Sci
2005
;
1059
:
145
59
.
27.
Schuhmacher
M
,
Kohlhuber
F
,
Holzel
M
,
Kaiser
C
,
Burtscher
H
,
Jarsch
M
, et al
The transcriptional program of a human B cell line in response to Myc
.
Nucleic Acids Res
2001
;
29
:
397
406
.
28.
Coller
HA
,
Grandori
C
,
Tamayo
P
,
Colbert
T
,
Lander
ES
,
Eisenman
RN
, et al
Expression analysis with oligonucleotide microarrays reveals that MYC regulates genes involved in growth, cell cycle, signaling, and adhesion
.
Proc Natl Acad Sci U S A
2000
;
97
:
3260
5
.
29.
Adler
AS
,
Lin
M
,
Horlings
H
,
Nuyten
DS
,
van de Vijver
MJ
,
Chang
HY
. 
Genetic regulators of large-scale transcriptional signatures in cancer
.
Nat Genet
2006
;
38
:
421
30
.
30.
Alles
MC
,
Gardiner-Garden
M
,
Nott
DJ
,
Wang
Y
,
Foekens
JA
,
Sutherland
RL
, et al
Meta-analysis and gene set enrichment relative to er status reveal elevated activity of MYC and E2F in the “basal” breast cancer subgroup
.
PLoS One
2009
;
4
:
e4710
.
31.
Chandriani
S
,
Frengen
E
,
Cowling
VH
,
Pendergrass
SA
,
Perou
CM
,
Whitfield
ML
, et al
A core MYC gene expression signature is prominent in basal-like breast cancer but only partially overlaps the core serum response
.
PLoS One
2009
;
4
:
e6693
.
32.
Shipp
MA
,
Ross
KN
,
Tamayo
P
,
Weng
AP
,
Kutok
JL
,
Aguiar
RC
, et al
Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning
.
Nat Med
2002
;
8
:
68
74
.
33.
Chang
HY
,
Sneddon
JB
,
Alizadeh
AA
,
Sood
R
,
West
RB
,
Montgomery
K
, et al
Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds
.
PLoS Biol
2004
;
2
:
E7
.
34.
Ellwood-Yen
K
,
Graeber
TG
,
Wongvipat
J
,
Iruela-Arispe
ML
,
Zhang
J
,
Matusik
R
, et al
Myc-driven murine prostate cancer shares molecular features with human prostate tumors
.
Cancer Cell
2003
;
4
:
223
38
.
35.
Chrzan
P
,
Skokowski
J
,
Karmolinski
A
,
Pawelczyk
T
. 
Amplification of c-myc gene and overexpression of c-Myc protein in breast cancer and adjacent non-neoplastic tissue
.
Clin Biochem
2001
;
34
:
557
62
.
36.
Naidu
R
,
Wahab
NA
,
Yadav
M
,
Kutty
MK
. 
Protein expression and molecular analysis of c-myc gene in primary breast carcinomas using immunohistochemistry and differential polymerase chain reaction
.
Int J Mol Med
2002
;
9
:
189
96
.
37.
Gao
P
,
Tchernyshyov
I
,
Chang
TC
,
Lee
YS
,
Kita
K
,
Ochi
T
, et al
c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism
.
Nature
2009
;
458
:
762
5
.
38.
Nikiforov
MA
,
Chandriani
S
,
O'Connell
B
,
Petrenko
O
,
Kotenko
I
,
Beavis
A
, et al
A functional screen for Myc-responsive genes reveals serine hydroxymethyltransferase, a major source of the one-carbon unit for cell metabolism
.
Mol Cell Biol
2002
;
22
:
5793
800
.
39.
Shim
H
,
Dolde
C
,
Lewis
BC
,
Wu
CS
,
Dang
G
,
Jungmann
RA
, et al
c-Myc transactivation of LDH-A: implications for tumor metabolism and growth
.
Proc Natl Acad Sci U S A
1997
;
94
:
6658
63
.
40.
O'Donnell
KA
,
Yu
D
,
Zeller
KI
,
Kim
JW
,
Racke
F
,
Thomas-Tikhonenko
A
, et al
Activation of transferrin receptor 1 by c-Myc enhances cellular proliferation and tumorigenesis
.
Mol Cell Biol
2006
;
26
:
2373
86
.
41.
Semenza
GL
. 
Targeting HIF-1 for cancer therapy
.
Nat Rev Cancer
2003
;
3
:
721
32
.
42.
Solit
DB
,
Mellinghoff
IK
. 
Tracing cancer networks with phosphoproteomics
.
Nat Biotechnol
2010
;
28
:
1028
9
.
43.
Vander Heiden
MG
,
Cantley
LC
,
Thompson
CB
. 
Understanding the Warburg effect: the metabolic requirements of cell proliferation
.
Science
2009
;
324
:
1029
33
.
44.
Morrish
F
,
Isern
N
,
Sadilek
M
,
Jeffrey
M
,
Hockenbery
DM
. 
c-Myc activates multiple metabolic networks to generate substrates for cell-cycle entry
.
Oncogene
2009
;
28
:
2485
91
.
45.
Wise
DR
,
DeBerardinis
RJ
,
Mancuso
A
,
Sayed
N
,
Zhang
XY
,
Pfeiffer
HK
, et al
Myc regulates a transcriptional program that stimulates mitochondrial glutaminolysis and leads to glutamine addiction
.
Proc Natl Acad Sci U S A
2008
;
105
:
18782
7
.
46.
Dang
CV
,
Le
A
,
Gao
P
. 
MYC-induced cancer cell energy metabolism and therapeutic opportunities
.
Clin Cancer Res
2009
;
15
:
6479
83
.
47.
Shaw
RJ
,
Cantley
LC
. 
Ras, PI(3)K and mTOR signalling controls tumour cell growth
.
Nature
2006
;
441
:
424
30
.
48.
Gregorian
C
,
Nakashima
J
,
Dry
SM
,
Nghiemphu
PL
,
Smith
KB
,
Ao
Y
, et al
PTEN dosage is essential for neurofibroma development and malignant transformation
.
Proc Natl Acad Sci U S A
2009
;
106
:
19479
84
.
49.
Hodgson
NC
,
Gulenchyn
KY
. 
Is there a role for positron emission tomography in breast cancer staging?
J Clin Oncol
2008
;
26
:
712
20
.
50.
Avril
N
,
Rose
CA
,
Schelling
M
,
Dose
J
,
Kuhn
W
,
Bense
S
, et al
Breast imaging with positron emission tomography and fluorine-18 fluorodeoxyglucose: use and limitations
.
J Clin Oncol
2000
;
18
:
3495
502
.
51.
Soret
M
,
Bacharach
SL
,
Buvat
I
. 
Partial-volume effect in PET tumor imaging
.
J Nucl Med
2007
;
48
:
932
45
.
52.
Hynes
NE
,
Stoelzle
T
. 
Key signalling nodes in mammary gland development and cancer: Myc
.
Breast Cancer Res
2009
;
11
:
210
.
53.
Basu
S
,
Chen
W
,
Tchou
J
,
Mavi
A
,
Cermik
T
,
Czerniecki
B
, et al
Comparison of triple-negative and estrogen receptor-positive/progesterone receptor-positive/HER2-negative breast carcinoma using quantitative fluorine-18 fluorodeoxyglucose/positron emission tomography imaging parameters: a potentially useful method for disease characterization
.
Cancer
2008
;
112
:
995
1000
.
54.
Specht
JM
,
Kurland
BF
,
Montgomery
SK
,
Dunnwald
LK
,
Doot
RK
,
Gralow
JR
, et al
Tumor metabolism and blood flow as assessed by positron emission tomography varies by tumor subtype in locally advanced breast cancer
.
Clin Cancer Res
2010
;
16
:
2803
10
.
55.
Foulkes
WD
,
Smith
IE
,
Reis-Filho
JS
. 
Triple-negative breast cancer
.
N Engl J Med
2010
;
363
:
1938
48
.