Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry

Multi-layered proteogenomic analysis unravels cancer metastasis directed by MMP-2 and focal adhesion kinase signaling

The role of matrix metalloproteinase-2 (MMP-2) in tumor cell migration has been widely studied, however, the characteristics and effects of MMP-2 in clinical sample of metastatic colorectal cancer (CRC) remain poorly understood. Here, in order to unveil the perturbed proteomic signal during MMP-2 induced cancer progression, we analyzed plasma proteome of CRC patients according to disease progression, HCT116 cancer secretome upon MMP-2 knockdown, and publicly available CRC tissue proteome data. Collectively, the integrative analysis of multi-layered proteomes revealed that a protein cluster containing EMT (Epithelial-to-Mesenchymal Transition)-associated proteins such as CD9-integrin as well as MMP-2. The proteins of the cluster were regulated by MMP-2 perturbation and exhibited significantly increased expressions in tissue and plasma as disease progressed from TNM (Tumor, Node, and Metastasis) stage I to II. Furthermore, we also identified a plausible association between MMP-2 up-regulation and activation of focal adhesion kinase signaling in the proteogenomic analysis of CRC patient tissues. Based on these comparative and integrative analyses, we suggest that the high invasiveness in the metastatic CRC resulted from increased secretion of MMP-2 and CD9-integrin complex mediated by FAK signaling activation.

Proteomic Interrogation in Cancer Biomarker

Biomarkers factor into the diagnosis and treatment of almost every patient with cancer. The innovation in proteomics follows improvement of mass spectrometry techniques and data processing strategy. Recently, proteomics and typical biological studies have been the answer for clinical applications. The clinical proteomics techniques are now actively adapted to protein identification in large patient cohort, biomarker development for more sensitive and specific screening based on quantitative data. And, it is important for clinical, translational researchers to be acutely aware of the issues surrounding appropriate biomarker development, in order to facilitate entry of clinically useful biomarkers into the clinic. Here, we discuss in detail include the case research for clinical proteomics. Furthermore, we give an overview on the current developments and novel findings in proteomics-based cancer biomarker research.

Proteomics of Colorectal Cancer: Tumors, Organoids, and Cell Cultures-A Minireview

Proteomics, the study of the complete protein composition of a sample, is an important field for cancer research. Changes in the proteome can serve as a biomarker of cancer or lead to the development of a targeted therapy. This minireview will focus on mass spectrometry-based proteomics studies applied specifically to colorectal cancer, particularly the variety of cancer model systems used, including tumor samples, two-dimensional (2D) and three-dimensional (3D) cell cultures such as spheroids and organoids. A thorough discussion of the application of these systems will accompany the review of the literature, as each provides distinct advantages and disadvantages for colorectal cancer research. Finally, we provide conclusions and future perspectives for the application of these model systems to cancer research as a whole.

Secretome Proteomic Approaches for Biomarker Discovery: An Update on Colorectal Cancer

Searching for new cancer-related biomarkers is a key priority for the early detection of solid tumors, such as colorectal cancer (CRC), in clinically relevant biological fluids. The cell line and/or tumor tissue secretome represents a valuable resource for discovering novel protein markers secreted by cancer cells. The advantage of a secretome analysis is the reduction of the large dynamic range characterizing human plasma/serum, and the simultaneous enrichment of low abundance cancer-secreted proteins, thereby overcoming the technical limitations underlying the direct search in blood samples. In this review, we provided a comprehensive overview of recent studies on the CRC secretome for biomarker discovery, focusing both on methodological and technical aspects of secretome proteomic approaches and on biomarker-independent validation in CRC patient samples (blood and tissues). Secretome proteomics are mainly based on LC-MS/MS analyses for which secretome samples are either in-gel or in-solution trypsin-digested. Adequate numbers of biological and technical replicates are required to ensure high reproducibility and robustness of the secretome studies. Moreover, another major challenge is the accuracy of proteomic quantitative analysis performed by label-free or labeling methods. The analysis of differentially expressed proteins in the CRC secretome by using bioinformatic tools allowed the identification of potential biomarkers for early CRC detection. In this scenario, this review may help to follow-up the recent secretome studies in order to select promising circulating biomarkers to be validated in larger screenings, thereby contributing toward a complete translation in clinical practice.

PBMC gene expression profiles of female Bangladeshi adults chronically exposed to arsenic-contaminated drinking water

Arsenic, a class I human carcinogen, is ubiquitously found throughout the environment and around the globe, posing a great public health concern. Notably, Bangladesh and regions of West Bengal have been found to have high levels (0.5-4600 μg/L) of arsenic drinking water contamination, and approximately 50 million of the world's 200 million people chronically exposed to arsenic in Bangladesh alone. This study was carried out to examine genome-wide gene expression changes in individuals chronically exposed to arsenic-contaminated drinking water. Our study population includes twenty-nine Bangladeshi female participants with urinary arsenic levels ranging from 22.32 to 1828.12 μg/g creatinine. RNA extracted from peripheral blood mononuclear cells (PBMCs) were evaluated using RNA-Sequencing analysis. Our results indicate that a total of 1,054 genes were significantly associated with increasing urinary arsenic levels (FDR p < 0.05), which include 418 down-regulated and 636 up-regulated genes. Further Ingenuity Pathway Analysis revealed potential target genes (DAPK1, EGR2, APP), microRNAs (miR-155, -338, -210) and pathways (NOTCH signaling pathway) related to arsenic carcinogenesis. The selection of female-only participants provides a homogenous study population since arsenic has significant sex dependent effects, and the wide exposure range provides new insight for key gene expression changes that correlate with increasing urinary arsenic levels.

An SDS-PAGE based proteomic approach for N-terminome profiling

Schematic diagram of the SDS-PAGE based N-termini enrichment (GelNrich) workflow.

Chemical composition and the potential for proteomic transformation in cancer, hypoxia, and hyperosmotic stress

The changes of protein expression that are monitored in proteomic experiments are a type of biological transformation that also involves changes in chemical composition. Accompanying the myriad molecular-level interactions that underlie any proteomic transformation, there is an overall thermodynamic potential that is sensitive to microenvironmental conditions, including local oxidation and hydration potential. Here, up- and down-expressed proteins identified in 71 comparative proteomics studies were analyzed using the average oxidation state of carbon (Z_C) and water demand per residue (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O), calculated using elemental abundances and stoichiometric reactions to form proteins from basis species. Experimental lowering of oxygen availability (hypoxia) or water activity (hyperosmotic stress) generally results in decreased Z_C or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O of up-expressed compared to down-expressed proteins. This correspondence of chemical composition with experimental conditions provides evidence for attraction of the proteomes to a low-energy state. An opposite compositional change, toward higher average oxidation or hydration state, is found for proteomic transformations in colorectal and pancreatic cancer, and in two experiments for adipose-derived stem cells. Calculations of chemical affinity were used to estimate the thermodynamic potentials for proteomic transformations as a function of fugacity of O₂ and activity of H₂O, which serve as scales of oxidation and hydration potential. Diagrams summarizing the relative potential for formation of up- and down-expressed proteins have predicted equipotential lines that cluster around particular values of oxygen fugacity and water activity for similar datasets. The changes in chemical composition of proteomes are likely linked with reactions among other cellular molecules. A redox balance calculation indicates that an increase in the lipid to protein ratio in cancer cells by 20% over hypoxic cells would generate a large enough electron sink for oxidation of the cancer proteomes. The datasets and computer code used here are made available in a new R package, canprot.

Biomarker discovery by proteomics-based approaches for early detection and personalized medicine in colorectal cancer

About one million people per year develop colorectal cancer (CRC) and approximately half of them die. The extent of the disease (i.e. local invasion at the time of diagnosis) is a key prognostic factor. The 5-year survival rate is almost 90% in the case of delimited CRC and 10% in the case of metastasized CRC. Hence, one of the great challenges in the battle against CRC is to improve early diagnosis strategies. Large-scale proteomic approaches are widely used in cancer research to search for novel biomarkers. Such biomarkers can help in improving the accuracy of the diagnosis and in the optimization of personalized therapy. Herein, we provide an overview of studies published in the last 5 years on CRC that led to the identification of protein biomarkers suitable for clinical application by using proteomic approaches. We discussed these findings according to biomarker application, including also the role of protein phosphorylation and cancer stem cells in biomarker discovery. Our review provides a cross section of scientific approaches and can furnish suggestions for future experimental strategies to be used as reference by scientists, clinicians and researchers interested in proteomics for biomarker discovery.

Proteomic indicators of oxidation and hydration state in colorectal cancer

New integrative approaches are needed to harness the potential of rapidly growing datasets of protein expression and microbial community composition in colorectal cancer. Chemical and thermodynamic models offer theoretical tools to describe populations of biomacromolecules and their relative potential for formation in different microenvironmental conditions. The average oxidation state of carbon (Z_C) can be calculated as an elemental ratio from the chemical formulas of proteins, and water demand per residue (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O) is computed by writing the overall formation reactions of proteins from basis species. Using results reported in proteomic studies of clinical samples, many datasets exhibit higher mean Z_C or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O of proteins in carcinoma or adenoma compared to normal tissue. In contrast, average protein compositions in bacterial genomes often have lower Z_C for bacteria enriched in fecal samples from cancer patients compared to healthy donors. In thermodynamic calculations, the potential for formation of the cancer-related proteins is energetically favored by changes in the chemical activity of H₂O and fugacity of O₂ that reflect the compositional differences. The compositional analysis suggests that a systematic change in chemical composition is an essential feature of cancer proteomes, and the thermodynamic descriptions show that the observed proteomic transformations in host tissue could be promoted by relatively high microenvironmental oxidation and hydration states.

Applying Genomics and Proteomics in Translational Surgical Oncology Research

BACKGROUND

Translational surgical research can range from investigating clinically oriented basic pathophysiological research via the correlation of molecular findings with a patient's clinical outcome to conducting treatment response studies. Herein, the specialist's opinion as a 'surgeon scientist' plays a pivotal role, e.g. in planning experimental setups focusing on the clinically most relevant needs. SUMMARY AND KEY MESSAGES: This review provides a survey of genomic and proteomic technologies and gives an up-to-date overview of those studies applying these technologies exemplarily in colorectal cancer-based biomarker research. Although current results are promising, future validation studies within multicenter networks are required to promote the translation of 'omics' from bench to bedside. In this endeavor, departments of surgery and institutes of experimental surgery together should play a fundamental role. The program for 'clinician scientists' recently launched by the German Research Society (DFG) could be one tool to promote interdisciplinary networks and surgeons' impact on translational research.

Linking patient outcome to high throughput protein expression data identifies novel regulators of colorectal adenocarcinoma aggressiveness

A key question in cancer systems biology is how to use molecular data to predict the biological behavior of tumors from individual patients. While genomics data have been heavily used, protein signaling data are more directly connected to biological phenotype and might predict cancer phenotypes such as invasion, metastasis, and patient survival. In this study, we mined publicly available data for colorectal adenocarcinoma from the Cancer Genome Atlas and identified protein expression and signaling changes that are statistically associated with patient outcome. Our analysis identified a number of known and potentially new regulators of colorectal cancer. High levels of insulin growth factor binding protein 2 (IGFBP2) were associated with both recurrence and death, and this was validated by immunohistochemical staining of a tissue microarray for a secondary patient dataset. Interestingly, GATA binding protein 3 (GATA3) was the protein most frequently associated with death in our analysis, and GATA3 expression was significantly decreased in tumor samples from stage I-II deceased patients. Experimental studies using engineered colon cancer cell lines show that exogenous expression of GATA3 decreases three-dimensional colony growth and invasiveness of colon cancer cells but does not affect two-dimensional proliferation. These findings suggest that protein data are useful for biomarker discovery and identify GATA3 as a regulator of colorectal cancer  aggressiveness.

MTRAQ-based quantitative analysis combined with peptide fractionation based on cysteinyl peptide enrichment

In the present study, the fractionation scheme for cysteinyl peptide enrichment (CPE) was combined with the mass differential tags for relative and absolute quantification (mTRAQ) method to reduce sample complexity and increase proteome coverage. Cysteine residues of the proteins were first alkylated using iodoacetyl PEG2-biotin instead of other conventional alkylating agents such as iodoacetamide. After trypsin digestion, amine groups were labeled with mTRAQ, and these labeled peptides were fractionated according to the presence or absence of cysteine residues using avidin-biotin affinity chromatography. With these approaches, we were able to divide the peptides into the two fractions with more than 90% fractionation efficiency for standard protein and MCF7 cell lysate. When the fractionation strategy was applied to colorectal cancer tissue samples, we were able to obtain quantitative information that was consistent with the previous study based on mTRAQ quantification, implying that the cysteine-based fractionation method does not affect mTRAQ quantification. We expect that the mTRAQ-based quantitative analysis combined with peptide fractionation through the CPE strategy would allow for deep-down analysis of proteome samples and ultimately for increasing proteome coverage with simultaneous quantification for biomarker discovery.

Impact of data-dependent exclusion list based mass spectrometry on label-free proteomic quantification

RATIONALE

Spectral count analysis via data-dependent acquisition (DDA) mode mass spectrometry is used as label-free protein quantification. However, combination of the DDA mode with exclusion list based DDA (DDA-EL) for the similar purpose has not yet been tested. Therefore, we have taken the initiative to check the protein abundance using DDA-EL and measured their suitability.

METHODS

To check the protein abundance correlation between different samples, multiple replicates of mass spectrometric analysis of peptides were conducted primarily in DDA mode. Subsequently, peptides were analyzed in multiple replicates in DDA-EL mode with an exclusion mass list prepared from the previous DDA analyses. The normalized spectral abundance factor (NSAF) for each identified protein was compared among replicated datasets of single DDA, DDA-EL, merged two DDAs, and merged DDA + DDA-EL or between different types of datasets.

RESULTS

A strong and linear NSAF correlation with an average correlation coefficient of 0.939 was observed in the comparison between each pair of DDA data. Similar connotation was also monitored in the comparison among DDA-EL data (r =0.928) or among merged DDA + DDA-EL data (r =0.960) while a reduced correlation coefficient (r =0.892) with increased deviation was marked between DDA and DDA-EL data.

CONCLUSIONS

Evaluation of protein abundance patterns from different cellular states can successfully be conducted by DDA-EL-based mass spectrometric analysis. Therefore, the new workflow, DDA-EL merged to DDA mode, is a potential alternative to protein identification and quantification method.

Discovery of melanotransferrin as a serological marker of colorectal cancer by secretome analysis and quantitative proteomics

To discover serological colorectal cancer (CRC) markers, we analyzed cell line secretome to gather proteins of higher potential to be secreted from tissues into circulation. A total of 898 human proteins were identified, of which 62.2% were predicted to be released or shed from cells. The identified proteins were compared with tissue proteomes to find candidate proteins whose expressions were elevated in tumor tissues compared with normal tissues as revealed by (i) quantitative proteomic analysis based on cICAT and mTRAQ or (ii) data mining of immunohistochemical images piled in Human Protein Atlas database. By applying various stringent criteria, 11 candidate proteins were selected. Among these, we validated an significant increase (p = 0.0018) of melanotransferrin (TRFM) at the plasma level of CRC patients through Western blotting, using 130 plasma samples containing 30 healthy controls, 80 CRC patients, and 20 patients of other diseases. Finally, we measured the expression level of TRFM in 325 plasma samples containing 77 healthy controls and 228 CRC patients (34.6 ± 4.2 ng/mL and 67.0 ± 6.4 ng/mL, p < 0.0001) through ELISA and demonstrated the area under the receiver operating characteristic curve of 0.723 (p < 0.0001) with a 92.5% specificity, 48.2% sensitivity, and 95.7% positive predictive value. Furthermore, unlike CEA and PAI-1, up-regulation of TRFM in pathological stages I & II groups compared with stages III & IV groups lead us to expect the use TRFM for early-stage diagnosis of CRC. In this study, we suggest TRFM as a potential serological marker for CRC and expect our discovery strategy to help identify highly cancer-specific and body-fluid-accessible biomarkers.

Proteomics for discovery of candidate colorectal cancer biomarkers

Colorectal cancer (CRC) is the second most common cause of cancer-related deaths in Europe and other Western countries, mainly due to the lack of well-validated clinically useful biomarkers with enough sensitivity and specificity to detect this disease at early stages. Although it is well known that the pathogenesis of CRC is a progressive accumulation of mutations in multiple genes, much less is known at the proteome level. Therefore, in the last years many proteomic studies have been conducted to find new candidate protein biomarkers for diagnosis, prognosis and as therapeutic targets for this malignancy, as well as to elucidate the molecular mechanisms of colorectal carcinogenesis. An important advantage of the proteomic approaches is the capacity to look for multiple differentially expressed proteins in a single study. This review provides an overview of the recent reports describing the different proteomic tools used for the discovery of new protein markers for CRC such as two-dimensional electrophoresis methods, quantitative mass spectrometry-based techniques or protein microarrays. Additionally, we will also focus on the diverse biological samples used for CRC biomarker discovery such as tissue, serum and faeces, besides cell lines and murine models, discussing their advantages and disadvantages, and summarize the most frequently identified candidate CRC markers.

Quantitative mass spectrometry for colorectal cancer proteomics

This review documents the uses of quantitative MS applied to colorectal cancer (CRC) proteomics for biomarker discovery and molecular pathway profiling. Investigators are adopting various labeling and label-free MS approaches to quantitate differential protein levels in cells, tumors, and plasma/serum. We comprehensively review recent uses of this technology to examine mouse models of CRC, CRC cell lines, their secretomes and subcellular fractions, CRC tumors, CRC patient plasma/serum, and stool samples. For biomarker discovery these approaches are uncovering proteins with potential diagnostic and prognostic utility, while in vitro cell culture experiments are characterizing proteomic and phosphoproteomic responses to disrupted signaling pathways due to mutations or to inhibition of drugable enzymes.

Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.

High expression of AT-rich interactive domain 3A (ARID3A) is associated with good prognosis in colorectal carcinoma

BACKGROUND

AT-rich interactive domain 3A (ARID3A) is a member of the ARID family of DNA-binding proteins. Previous reports have shown that ARID3A controls cell growth in a p53-dependent manner. Recently, it has been reported that expression of the ARID3A protein was markedly increased in colon cancer tissue compared with matched normal colonic mucosa. However, little is currently known about the role of ARID3A in colorectal cancer (CRC). The aim of this study was to investigate the clinical significance of ARID3A expression in CRC.

METHODS

We examined ARID3A expression in 690 CRC patients using tissue microarray and immunohistochemistry. Kaplan-Meier analysis and Cox proportional hazard models were used to estimate the impact of ARID3A expression on overall survival.

RESULTS

Of the 690 cases, 195 tumors were strongly positive for ARID3A, 187 were weakly positive, and 308 were negative. ARID3A expression in CRC was significantly correlated with age, degree of differentiation, depth of invasion, lymph node metastasis, distant metastasis, tumor-node-metastasis stage, status of microsatellite instability, and carcinoembryonic antigen levels. The overall survival of CRC patients with strong ARID3A expression was significantly longer than that of patients with weak or negative ARID3A expression. We also performed an additional survival analysis on 388 colon cancer patients and 302 rectal cancer patients. In doing so, a favorable prognostic effect of ARID3A expression was revealed only in the colon cancer group (p = 0.002), not in rectal cancer. Moreover, we showed that the effect of ARID3A on the survival was correlated with p53 status. Using multivariate analysis, we found that strong expression of ARID3A was an independent predictor for better prognosis in CRC.

CONCLUSIONS

Our data suggested that strong expression of ARID3A may predict a good prognosis in patients with CRC.

Multiple reaction monitoring of multiple low-abundance transcription factors in whole lung cancer cell lysates

Lung cancer-related transcription factors (TFs) were identified by integrating previously reported genomic, transcriptomic, and proteomic data and were quantified by multiple reaction monitoring (MRM) in various cell lines. All experiments were performed without affinity depletion or subfractionation of cell lysates. Since the target proteins were expected to be present in low abundance, we experimentally optimized MRM transition parameters with chemically synthesized peptides. Quantitation was based on stable isotope-labeled standard peptides (SIS peptides). Out of 288 MRM measurements (36 peptides representing 28 TFs × 8 cell lines), 241 were successfully obtained within a quantitation limit of 15 amol, 221 measurements (91.7%) showed coefficients of variation (CVs) of ≤ 20%, and 149 (61.8%) showed CVs of ≤ 10%, quantifying as low as 19.4 amol/μg protein for STAT2 with a CV of 6.3% in an A549 cell. Comparisons between MRM measurements and levels of the corresponding mRNAs revealed linear, nonlinear, or no relationship between protein and mRNA levels, indicating the need for an MRM assay. An integrative analysis of MRM and gene expression profiles from doxorubicin-resistant H69AR and sensitive H69 cells further showed that 14 differentially expressed TFs, such as STAT1 and SMAD4, regulated genes associated with drug resistance and cell differentiation-related processes. Thus, the analytical performance of MRM for the quantitation of low abundance TFs suggests its usefulness for biological application.

Quantitative analysis of the human AKR family members in cancer cell lines using the mTRAQ/MRM approach

Members of human aldo-keto reductase (AKR) superfamily have been reported to be involved in cancer progression, whereas the final conclusion is not generally accepted. Herein, we propose a quantitative method to measure human AKR proteins in cells using mTRAQ-based multiple reaction monitoring (MRM). AKR peptides with multiple transitions were carefully selected upon tryptic digestion of the recombinant AKR proteins, while AKR proteins were identified by SDS-PAGE fractionation coupled with LC-MS/MS. Utilizing mTRAQ triplex labeling to produce the derivative peptides, calibration curves were generated using the mixed lysate as background, and no significantly different quantification of AKRs was elicited from the two sets of calibration curves under the mixed and single lysate as background. We employed this approach to quantitatively determine the 6 AKR proteins, AKR1A1, AKR1B1, AKR1B10, AKR1C1/C2, AKR1C3, and AKR1C4, in 7 different cancer cell lines and for the first time to obtain the absolute quantities of all the AKR proteins in each cell. The cluster plot revealed that AKR1A and AKR1B were widely distributed in most cancer cells with relatively stable abundances, whereas AKR1Cs were unevenly detected among these cells with diverse dynamic abundances. The AKR quantitative distribution in different cancer cells, therefore, may assist further exploration toward how the AKR proteins are involved in tumorigenesis.

Cancer cell growth and survival as a system-level property sustained by enhanced glycolysis and mitochondrial metabolic remodeling

Systems Biology holds that complex cellular functions are generated as system-level properties endowed with robustness, each involving large networks of molecular determinants, generally identified by “omics” analyses. In this paper we describe four basic cancer cell properties that can easily be investigated in vitro: enhanced proliferation, evasion from apoptosis, genomic instability, and inability to undergo oncogene-induced senescence. Focusing our analysis on a K-ras dependent transformation system, we show that enhanced proliferation and evasion from apoptosis are closely linked, and present findings that indicate how a large metabolic remodeling sustains the enhanced growth ability. Network analysis of transcriptional profiling gives the first indication on this remodeling, further supported by biochemical investigations and metabolic flux analysis (MFA). Enhanced glycolysis, down-regulation of TCA cycle, decoupling of glucose and glutamine utilization, with increased reductive carboxylation of glutamine, so to yield a sustained production of growth building blocks and glutathione, are the hallmarks of enhanced proliferation. Low glucose availability specifically induces cell death in K-ras transformed cells, while PKA activation reverts this effect, possibly through at least two mitochondrial targets. The central role of mitochondria in determining the two investigated cancer cell properties is finally discussed. Taken together the findings reported herein indicate that a system-level property is sustained by a cascade of interconnected biochemical pathways that behave differently in normal and in transformed cells.