Integrated Proteomic and Metabolic Analysis of Breast Cancer Progression

<div><p>One of the most persistent hallmarks of cancer biology is the preference of tumor cells to derive energy through glycolysis as opposed to the more efficient process of oxidative phosphorylation (OXPHOS). However, little is known about the molecular cascades by which oncogenic pathways bring about this metabolic switch. We carried out a quantitative proteomic and metabolic analysis of the MCF10A derived cell line model of breast cancer progression that includes parental cells and derivatives representing three different tumor grades of Ras-driven cancer with a common genetic background. A SILAC (Stable Isotope Labeling by Amino acids in Cell culture) labeling strategy was used to quantify protein expression in conjunction with subcellular fractionation to measure dynamic subcellular localization in the nucleus, cytosol and mitochondria. Protein expression and localization across cell lines were compared to cellular metabolic rates as a measure of oxidative phosphorylation (OXPHOS), glycolysis and cellular ATP. Investigation of the metabolic capacity of the four cell lines revealed that cellular OXPHOS decreased with breast cancer progression independently of mitochondrial copy number or electron transport chain protein expression. Furthermore, glycolytic lactate secretion did not increase in accordance with cancer progression and decreasing OXPHOS capacity. However, the relative expression and subcellular enrichment of enzymes critical to lactate and pyruvate metabolism supported the observed extracellular acidification profiles. This analysis of metabolic dysfunction in cancer progression integrated with global protein expression and subcellular localization is a novel and useful technique for determining organelle-specific roles of proteins in disease.</p> </div

Unbiased Discovery of Interactions at a Control Locus Driving Expression of the Cancer-Specific Therapeutic and Diagnostic Target, Mesothelin

Although significant effort is expended on identifying transcripts/proteins that are up-regulated in cancer, there are few reports on systematic elucidation of transcriptional mechanisms underlying such druggable cancer-specific targets. The mesothelin (MSLN) gene offers a promising subject, being expressed in a restricted pattern normally, yet highly overexpressed in almost one-third of human malignancies and a target of cancer immunotherapeutic trials. CanScript, a cis promoter element, appears to control MSLN cancer-specific expression; its related genomic sequences may up-regulate other cancer markers. CanScript is a 20-nt bipartite element consisting of an SP1-like motif and a consensus MCAT sequence. The latter recruits TEAD (TEA domain) family members, which are universally expressed. Exploration of the active CanScript element, especially the proteins binding to the SP1-like motif, thus could reveal cancer-specific features having diagnostic or therapeutic interest. The efficient identification of sequence-specific DNA-binding proteins at a given locus, however, has lagged in biomarker explorations. We used two orthogonal proteomics approachesunbiased SILAC (stable isotope labeling by amino acids in cell culture)/DNA affinity-capture/mass <u>s</u>pectrometry survey (SD-MS) and a large transcription factor protein microarray (TFM)and functional validation to explore systematically the CanScript interactome. SD-MS produced nine candidates, and TFM, 18. The screens agreed in confirming binding by TEAD proteins and by newly identified NAB1 and NFATc. Among other identified candidates, we found functional roles for ZNF24, NAB1 and RFX1 in MSLN expression by cancer cells. Combined interactome screens yield an efficient, reproducible, sensitive, and unbiased approach to identify sequence-specific DNA-binding proteins and other participants in disease-specific DNA elements

Pair-wise comparisons of gene expression profiles.

<p>Pair-wise comparisons of gene expression profiles.</p

Hierarchical clustering of potential signature genes.

<p>Gene expression levels of (A) PGC signature, (B) EGC and common PGC/EGC signature and (C) ESC, IPSC and ECC group genes are represented in a heat map. Lists of genes corresponding to each group are on the right hand side of the cluster tree. Order of the genes in the tables corresponds to their order in the heat map (high expression in red, log¹⁰ = >1.00; low expression in green; log¹⁰ = <–1.00).</p

Summary of experimental workflow and data normalization.

<p>Lysates from 10A, T1K and CA1h cells were grown in light, medium or heavy SILAC medium, respectively, and equal numbers of cells were combined to obtain ratios of protein expression relative to parental cells. Light labeled CA1a cells were likewise combined with medium labeled T1K, and light to heavy ratios were normalized to the T1K fold change ratios in the 3-state experiment to calculate fold change of protein expression in CA1a cells relative to parental cells. After combining equal numbers of labeled cells, subcellular fractions of cytosolic, nuclear and mitochondrial proteins were obtained by centrifugation (500xg) over a sucrose gradient. Spectra from the peptide NPDDITQEEYGEFYK are shown to illustrate the fold change calculation for heat shock protein 90, which was identified in every cellular fraction and is disproportionately localized from the nucleus to the cytosol with breast cancer progression.</p

ATP generation relative to cell type and in response to the metabolic inhibitors, oligomycin alone, 2-DG alone, and the combination.

<p>Absolute ATP levels were quantified in each cell line. Each bar represents the mean of four independent measurements, and error bars are standard deviation of the mean. Significant differences were determined by comparing values for each cell line to 10A by Wilson’s T-Test (*p<0.05).</p

Identification of potential signature genes of PGCs, ECCs, EGCs, IPSCs, and ESCs.

<p>The vertical axis of each graph shows the log ratio of the expression data relative to the population mean. (A) Genes that are up-regulated in PGCs include (A) <i>SPO11, DMRT1, TEX13, and HBA1.</i> (B) Genes up-regulated in ECCs include <i>SALL4, GDF3, MYCN, and PIWIL2.</i> (C) Genes up-regulated in EGCs include <i>FN1, FKBP6, AXL, and SOX9</i>. (D) Genes up-regulated in IPSCs include <i>MAD2L1, PIK3R3, BAX and APC</i><b>.</b> (E) Genes up-regulated in ESCs include <i>DAZL, CCNG1, JARID2, and ZSCAN1.</i></p

Real-time qRT-PCR analysis of several key pluripotent and germ cell associated genes in primordial germ cells and pluripotent stem cells.

<p><i>OCT4, SOX2, NANOG, and DNMT3B</i> were normalized to the <i>beta-actin</i> gene using the comparative CT method, and plotted relative to the human foreskin fibroblast line, HFF1 (0 baseline) (N  = 3 biological samples with technical triplicates for each cell type, P<0.05). Asterisks denote statistical significant differences in cell lines compared to PGCs.</p

Graphical representation of biological relationships in known or suspected genes associated with controlling cell cycle, replication, DNA repair, recombination, and cell death.

<p>This network is specifically showing genes that are up-regulated in pluripotent stem cells compared to PGCs. Green color represents genes in this network that are highly up-regulated in the ESC, IPSC, and ECC group and gray color represents genes that are expressed in similar levels across all cell types. White signifies that the gene was not detected in the cell lines. Solid and dotted arrows represent direct and indirect interactions, respectively. Elevated levels of <i>KRAS</i> and <i>HSPD1</i> were also detected in EGCs.</p

Uniformity of cell ratios in SILAC experiments is confirmed by independent DNA copy number measurements for four genes, albumin (<i>ALB</i>) at chromosome locus 4q13.3; RNA-binding protein S1 (RNPS1) at chromosomal locus 16p13.3; citrate synthase (CS) at chromosome locus 12q13.2; mitochondrial D-loop (MT7S) which is the origin of replication for the mitochondrial genome.

<p>The exponential phase of the amplification curve was determined using the same parameters for each probe, and gene copy number is reflected as the threshold cycle (CT) at the optimal fluorescence point in the amplification curve determined for that probe. Box plots represent the upper and lower quartiles of the data, with black lines representing the median value and dashed error bars representing standard deviation from the median value. Statistical outliers are represented by open circles.</p