AMPK inhibition enhances apoptosis in MLL-rearranged pediatric B-acute lymphoblastic leukemia cells.

The serine/threonine kinase AMP-activated protein kinase (AMPK) and its downstream effectors, including endothelial nitric oxide synthase and BCL-2, are hyperactivated in B-cell precursor-acute lymphoblastic leukemia (BCP-ALL) cells with MLL gene rearrangements. We investigated the role of activated AMPK in supporting leukemic cell survival and evaluated AMPK as a potential drug target. Exposure of leukemic cells to the commercial AMPK inhibitor compound C resulted in massive apoptosis only in cells with MLL gene rearrangements. These results were confirmed by targeting AMPK with specific short hairpin RNAs. Compound C-induced apoptosis was associated with mitochondrial membrane depolarization, reactive oxygen species production, cytochrome c release and caspases cleavage, indicating intrinsic apoptosis pathway activation. Treatment with low concentrations of compound C resulted in a strong antileukemic activity, together with cytochrome c release and cleavage of caspases and poly(ADP-ribose) polymerase, also in MLL-rearranged primary BCP-ALL samples. Moreover, AMPK inhibition in MLL-rearranged cell lines synergistically enhanced the antiproliferative effects of vincristine, daunorubicin, cytarabine, dexamethasone and L-asparaginase in most of the evaluated conditions. Taken together, these results indicate that the activation of the AMPK pathway directly contributes to the survival of MLL-rearranged BCP-ALL cells and AMPK inhibitors could represent a new therapeutic strategy for this high-risk leukemia.

Nanoparticle technology: addressing the fundamental roadblocks to protein biomarker discovery.

Clinically relevant biomarkers exist in blood and body fluids in extremely low concentrations, are masked by high abundance high molecular weight proteins, and often undergo degradation during collection and transport due to endogenous and exogenous proteinases. Nanoparticles composed of a N-isopropylacrylamide hydrogel core shell functionalized with internal affinity baits are a new technology that can address all of these critical analytical challenges for disease biomarker discovery and measurement. Core-shell, bait containing, nanoparticles can perform four functions in one step, in solution, in complex biologic fluids (e.g. blood or urine): a) molecular size sieving, b) complete exclusion of high abundance unwanted proteins, c) target analyte affinity sequestration, and d) complete protection of captured analytes from degradation. Targeted classes of protein analytes sequestered by the particles can be concentrated in small volumes to effectively amplify (up to 100 fold or greater depending on the starting sample volume) the sensitivity of mass spectrometry, western blotting, and immunoassays. The materials utilized for the manufacture of the particles are economical, stable overtime, and remain fully soluble in body fluids to achieve virtually 100 percent capture of all solution phase target proteins within a few minutes.

Critical dependence of blood-borne biomarker concentrations on the half-lives of their carrier proteins.

Until recently, the low-abundance (LA) range of the serum proteome was an unexplored reservoir of diagnostic information. Today it is increasingly appreciated that a diagnostic goldmine of LA biomarkers resides in the blood stream in complexed association with more abundant higher molecular weight carrier proteins such as albumin and immunoglobulins. As we now look to the possibility of harvesting these LA biomarkers more efficiently through engineered nano-scale particles, mathematical approaches are needed in order to reveal the mechanisms by which blood carrier proteins act as molecular 'mops' for LA diagnostic cargo, and the functional relationships between bound LA biomarker concentrations and other variables of interest such as biomarker intravasation and clearance rates and protein half-lives in the bloodstream. Here we show, by simple mathematical modeling, how the relative abundance of large carrier proteins and their longer half-lives in the bloodstream work together to amplify the total blood concentration of these tiny biomarkers. The analysis further suggests that alterations in the production of biomarkers lead to gradual rather than immediate changes in biomarker levels in the blood circulation. The model analysis also points to the characteristics of artificial nano-particles that would render them more efficient harvesters of tumor biomarkers in the circulation, opening up possibilities for the early detection of curable disease, rather than simply better detection of advanced disease.

Signal pathway profiling of epithelial and stromal compartments of colonic carcinoma reveals epithelial-mesenchymal transition.

Molecular crosstalk, including reciprocal stimulation, is theorized to take place between epithelial cancer cells and surrounding non-neoplastic stromal cells. This is the rationale for stromal therapy, which could eliminate support of a cancer by its genetically stable stroma. Epithelial-stromal crosstalk is so far poorly documented in vivo, and cell cultures and animal experiments may not provide accurate models. The current study details stromal-epithelial signalling pathways in 35 human colon cancers, and compares them with matched normal tissues using quantitative proteomic microarrays. Lysates prepared from separately microdissected epithelium and stroma were analysed using antibodies against 61 cell signalling proteins, most of which recognize activated phospho-isoforms. Analyses using unsupervised and supervised statistical methods suggest that cell signalling pathway profiles in stroma and epithelium appear more similar to each other in tumours than in normal colon. This supports the concept that coordinated crosstalk occurs between epithelium and stroma in cancer and suggests epithelial-mesenchymal transition. Furthermore, the data herein suggest that it is driven by cell proliferation pathways and that, specifically, several key molecules within the mitogen-activated protein kinase pathway may play an important role. Given recent findings of epithelial-mesenchymal transition in therapy-resistant tumour epithelium, these findings could have therapeutic implications for colon cancer.

Differentiation of tumour-stage mycosis fungoides, psoriasis vulgaris and normal controls in a pilot study using serum proteomic analysis.

BACKGROUND: Serum proteomic analysis is an analytical technique utilizing high-throughput mass spectrometry (MS) in order to assay thousands of serum proteins simultaneously. The resultant 'proteomic signature' has been used to differentiate benign and malignant diseases, enable disease prognosis, and monitor response to therapy. OBJECTIVES: This pilot study was designed to determine if serum protein patterns could be used to distinguish patients with tumour-stage mycosis fungoides (MF) from patients with a benign inflammatory skin condition (psoriasis) and/or subjects with healthy skin. METHODS: Serum was analysed from 45 patients with tumour-stage MF, 56 patients with psoriasis, and 47 controls using two MS platforms of differing resolution. An artificial intelligence-based classification model was constructed to predict the presence of the disease state based on the serum proteomic signature. RESULTS: Based on data from an independent testing set (14-16 subjects in each group), MF was distinguished from psoriasis with 78.6% (or 78.6%) sensitivity and 86.7% (or 93.8%) specificity, while sera from patients with psoriasis were distinguished from those of nonaffected controls with 86.7% (or 93.8%) sensitivity and 75.0% (or 76.9%) specificity (depending on the MS platform used). MF was distinguished from unaffected controls with 61.5% (or 71.4%) sensitivity and 91.7% (or 92.9%) specificity. In addition, a secondary survival analysis using 11 MS peaks identified significant survival differences between two MF groups (all P-values <0.05). CONCLUSIONS: Serum proteomics should be further investigated for its potential to identify patients with neoplastic skin disease and its ability to determine disease prognosis.

Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer.

The human proteome, due to the enormity of post-translational permutations that result in large numbers of isoforms, is much more complex than the genome and alterations in cancer can occur in ways that are not predictable by translational analysis alone. Proteomic analysis therefore represents a more direct way of investigating disease at the individual patient level. Furthermore, since most novel therapeutic targets are proteins, proteomic analysis potentially has a central role in patient care. At the same time, it is becoming clear that mapping entire networks rather than individual markers may be necessary for robust diagnostics as well as tailoring of therapy. Consequently, there is a need for high-throughput multiplexed proteomic techniques, with the capability of scanning multiple cases and analysing large numbers of endpoints. New types of protein arrays combined with advanced bioinformatics are currently being used to identify molecular signatures of individual tumours based on protein pathways and signalling cascades. It is envisaged that analysing the cellular 'circuitry' of ongoing molecular networks will become a powerful clinical tool in patient management.

Low molecular weight proteomic information distinguishes metastatic from benign pheochromocytoma.

Metastatic lesions occur in up to 36% of patients with pheochromocytoma. Currently there is no way to reliably detect or predict which patients are at risk for metastatic pheochromocytoma. Thus, the discovery of biomarkers that could distinguish patients with benign disease from those with metastatic disease would be of great clinical value. Using surface-enhanced laser desorption ionization protein chips combined with high-resolution mass spectrometry, we tested the hypothesis that pheochromocytoma pathologic states can be reflected as biomarker information within the low molecular weight (LMW) region of the serum proteome. LMW protein profiles were generated from the serum of 67 pheochromocytoma patients from four institutions and analyzed by two different bioinformatics approaches employing pattern recognition algorithms to determine if the LMW component of the circulatory proteome contains potentially useful discriminatory information. Both approaches were able to identify combinations of LMW molecules which could distinguish all metastatic from all benign pheochromocytomas in a separate blinded validation set. In conclusion, for this study set low molecular mass biomarker information correlated with pheochromocytoma pathologic state using blinded validation. If confirmed in larger validation studies, efforts to identify the underlying diagnostic molecules by sequencing would be warranted. In the future, measurement of these biomarkers could be potentially used to improve the ability to identify patients with metastatic disease.

A mathematical model of combination therapy using the EGFR signaling network.

An increasing awareness of the significance of abnormal signal transduction in tumors and the concomitant development of target-based drugs to selectively modulate aberrantly-activated signaling pathways has given rise to a variety of promising new strategies in cancer treatment. This paper uses mathematical modeling to investigate a novel type of combination therapy in which multiple nodes in a signaling cascade are targeted simultaneously with selective inhibitors, pursuing the hypothesis that such an approach may induce the desired signal attenuation with lower doses of the necessary agents than when one node is targeted in isolation. A mathematical model is presented which builds upon previous theoretical work on EGFR signaling, simulating the effect of administering multiple kinase inhibitors in various combinations. The model demonstrates that attenuation of biochemical signals is significantly enhanced when multiple upstream processes are inhibited, in comparison with the inhibition of a single upstream process. Moreover, this enhanced attenuation is most pronounced in signals downstream of serially-connected target points. In addition, the inhibition of serially-connected processes appears to have a supra-additive (synergistic) effect on the attenuation of downstream signals, owing to the highly non-linear relationships between network parameters and signals.

Modeling of protein signaling networks in clinical proteomics.

Molecular interactions that underlie pathophysiological states are being elucidated using techniques that profile proteomic endpoints in cellular systems. Within the field of cancer research, protein interaction networks play pivotal roles in the establishment and maintenance of the hallmarks of malignancy, including cell division, invasion, and migration. Multiple complementary tools enable a multifaceted view of how signal protein pathway alterations contribute to pathophysiological states. One pivotal technique is signal pathway profiling of patient tissue specimens. This microanalysis technology provides a proteomic snapshot at one point in time of cells directly procured from the native context of a tumor microenvironment. To study the adaptive patterns of signal pathway events over time, before and after experimental therapy, it is necessary to obtain biopsies from patients before, during, and after therapy. A complementary approach is the profiling of cultured cell lines with and without treatment. Cultured cell models provide the opportunity to study short-term signal changes occurring over minutes to hours. Through this type of system, the effects of particular pharmacological agents may be used to test the effects of signal pathway inhibition or activation on multiple endpoints within a pathway. The complexity of the data generated has necessitated the development of mathematical models for optimal interpretation of interrelated signaling pathways. In combination, clinical proteomic biopsy profiling, tissue culture proteomic profiling, and mathematical modeling synergistically enable a deeper understanding of how protein associations lead to disease states and present new insights into the design of therapeutic regimens.

Proteomic analysis for the early detection and rational treatment of cancer--realistic hope?

Proteomics is an emerging field in medical science focused on the library of proteins specific to a given biosystem, the proteome, and understanding relationships therein. This field incorporates technologies that can be applied to serum and tissue in order to extract important biological information to aid clinicians and scientists in understanding the dynamic biology of their system of interest, such as a patient with cancer. These tools include laser capture microdissection, tissue lysate arrays and mass spectrometry approaches. These new technologies are more potent coupled with advanced bioinformatics analysis. They are used to characterize the content of, and changes in, the proteome induced by physiological changes, benign and pathologic. The application of these tools has assisted in the discovery of new biomarkers and may lead to new diagnostic tests and improvements in therapeutics. These tools additionally can provide a molecular characterization of cancers, which may allow for individualized molecular therapy. Understanding the basic concepts and tools used will illustrate how best to apply these technologies for patient benefit for the early detection of cancer and improved patient care.

Profiling of normal human leg lymph proteins using the 2-D electrophoresis and SELDI-TOF mass spectrophotometry approach.

The parenchymatous cells are supplied by nutrients transported in fluid from blood across the capillary wall. This fluid, called tissue fluid (TF), contains proteins originating from plasma as well as those synthesized and secreted by tissue cells. The protein composition of TF remains largely unknown. The TF which has entered lymphatics is called lymph (L). Harvesting L and measuring its proteins concentrations and identifying them provide an insight into biochemical processes in the TF. Here we describe our initial evaluation of the normal human prenodal L protein profile of m.w. 2.5 to 12.5 kDa using the ProteinChip SELDI MS system and compare it with that of plasma (P) protein. This is the first study in the literature providing evidence for the presence of the so far non-identified proteins in L as well as proteins identified in L but absent from P and conversely present in P but not in L. Evident differences between paired L and P samples have been found, along with similarities. Thirteen proteins were detected in P and seven in L in the region of 2.5 to 12.5 kDa. Five identical proteins, although of different relative intensity, were found in L and P. The proteins specific for L but not P had 7070 and 8619 ion values. P proteins absent from L were of 3890, 3969, 4078, 6863, 7676, 7778, 7847 and 7937 ion values. In addition to detecting some so far unknown proteins in L, these preliminary findings throw a new light on our understanding of the mechanism of transcapillary transport of low m.w. proteins. They challenge the commonly accepted notion of unlimited free diffusion of peptides across the capillary membrane.

High-resolution serum proteomic features for ovarian cancer detection.

Serum proteomic pattern diagnostics is an emerging paradigm employing low-resolution mass spectrometry (MS) to generate a set of biomarker classifiers. In the present study, we utilized a well-controlled ovarian cancer serum study set to compare the sensitivity and specificity of serum proteomic diagnostic patterns acquired using a high-resolution versus a low-resolution MS platform. In blinded testing sets, the high-resolution mass spectral data contained multiple diagnostic signatures that were superior to the low-resolution spectra in terms of sensitivity and specificity (P<0.00001) throughout the range of modeling conditions. Four mass spectral feature set patterns acquired from data obtained exclusively with the high-resolution mass spectrometer were 100% specific and sensitive in their diagnosis of serum samples as being acquired from either unaffected patients or those suffering from ovarian cancer. Important to the future of proteomic pattern diagnostics is the ability to recognize inferior spectra statistically, so that those resulting from a specific process error are recognized prior to their potentially incorrect (and damaging) diagnosis. To meet this need, we have developed a series of quality-assurance and in-process control procedures to (a) globally evaluate sources of sample variability, (b) identify outlying mass spectra, and (c) develop quality-control release specifications. From these quality-assurance and control (QA/QC) specifications, we identified 32 mass spectra out of the total 248 that showed statistically significant differences from the norm. Hence, 216 of the initial 248 high-resolution mass spectra were determined to be of high quality and were remodeled by pattern-recognition analysis. Again, we obtained four mass spectral feature set patterns that also exhibited 100% sensitivity and specificity in blinded validation tests (68/68 cancer: including 18/18 stage I, and 43/43 healthy). We conclude that (a) the use of high-resolution MS yields superior classification patterns as compared with those obtained with lower resolution instrumentation; (b) although the process error that we discovered did not have a deleterious impact on the present results obtained from proteomic pattern analysis, the major source of spectral variability emanated from mass spectral acquisition, and not bias at the clinical collection site; (c) this variability can be reduced and monitored through the use of QA/QC statistical procedures; (d) multiple and distinct proteomic patterns, comprising low molecular weight biomarkers, detected by high-resolution MS achieve accuracies surpassing individual biomarkers, warranting validation in a large clinical study.

Proteomic analysis for early detection of ovarian cancer: a realistic approach?

Ovarian cancer is a multifaceted disease wherein most women are diagnosed with advanced stage disease. One of the most imperative issues in ovarian cancer is early detection. Biomarkers that allow cancer detection at stage I, a time when the disease is amenable to surgical and chemotherapeutic cure in over 90% of patients, can dramatically alter the horizon for women with this disease. Recent developments in mass spectroscopy and protein chip technology coupled with bioinformatics have been applied to biomarker discovery. The complexity of the proteome is a rich resource from which the patterns can be gleaned; the pattern rather than its component parts is the diagnostic. Serum is a key source of putative protein biomarkers, and, by its nature, can reflect organ-confined events. Pioneering use of mass spectroscopy coupled with bioinformatics has been demonstrated as being capable of distinguishing serum protein pattern signatures of ovarian cancer in patients with early- and late-stage disease. This is a sensitive, precise, and promising tool for which further validation is needed to confirm that ovarian cancer serum protein signature patterns can be a robust biomarker approach for ovarian cancer diagnosis, yielding improved patient outcome and reducing the death and suffering from ovarian cancer.

Proteomic profiling of the cancer microenvironment by antibody arrays.

Critical changes in protein expression that enable tumors to initiate and progress originate in the local tissue microenvironment, and there are increasing indications that these microenvironmental alterations in protein expression play critical roles in shaping and directing this process. As a model to better understand how patterns of protein expression shape the tissue microenvironment, we analyzed protein expression in tissue derived from squamous cell carcinoma of the oral cavity through an antibody microarray approach for high-throughput proteomic analysis. Utilizing laser capture microdissection to procure total protein from specific microscopic cellular populations, we demonstrate that quantitative, and potentially qualitative, differences in expression patterns of multiple proteins within epithelial cells reproducibly correlate with oral cavity tumor progression. Furthermore, differential expression of multiple proteins was also found in stromal cells surrounding and adjacent to regions of diseased epithelium that directly correlated with tumor progression of the epithelium. Most of the proteins identified in both cell types are involved in signal transduction pathways, thus we hypothesize that extensive molecular communication involving complex cellular signaling between epithelium and stroma play a key role in driving oral cavity cancer progression.

Cancer proteomics: the state of the art.

Now that the human genome has been determined, the field of proteomics is ramping up to tackle the vast protein networks that both control and are controlled by the information encoded by the genome. The study of proteomics should yield an unparalleled understanding of cancer as well as an invaluable new target for therapeutic intervention and markers for early detection. This rapidly expanding field attempts to track the protein interactions responsible for all cellular processes. By careful analysis of these systems, a detailed understanding of the molecular causes and consequences of cancer should emerge. A brief overview of some of the cutting edge technologies employed by this rapidly expanding field is given, along with specific examples of how these technologies are employed. Soon cellular protein networks will be understood at a level that will permit a totally new paradigm of diagnosis and will allow therapy tailored to individual patients and situations.

Autotaxin (NPP-2), a metastasis-enhancing motogen, is an angiogenic factor.

Autotaxin [ATX (NPP-2)], originally isolated as a tumor motility-stimulating protein, has recently been shown to augment tumor aggressiveness. Specifically, atx-transfected, ras-transformed NIH3T3 cell lines have been shown to be more invasive, tumorigenic, and metastatic than mock-transfected ras-transformed control cells. In addition, the atx-transfected ras-transformed cell lines appeared to produce tumors that were much more hyperemic than those formed by appropriate control cells. This observation led to the present study, in which we demonstrate that ATX modulates angiogenesis both directly and indirectly. We have used a murine in vivo angiogenesis model in which treated Matrigel plugs are injected s.c. into athymic nude BALB/c mice. Using the same transfected cell lines as before, we found that mixing atx-transfected ras-transformed NIH3T3 cells into the Matrigel resulted in greater new blood vessel formation than control cells. Similarly, mixing purified ATX into the Matrigel resulted in new blood vessel formation within the plug, similar to that produced by vascular endothelial growth factor. Mechanistically, ATX is not a strong chemoattractant for human endothelial cells (HUVECs); however, it strongly stimulates motility in human coronary artery smooth muscle cells. In addition, ATX stimulates HUVECs grown on Matrigel to form tubules, much like vascular endothelial growth factor. Both of these normal cell types are shown to express and secrete ATX. In HUVECs, ATX expression is up-regulated by basic fibroblast growth factor in a time-dependent manner. This up-regulation also extends to secretion of enzymatically active protein, as demonstrated by Western blot analysis and quantification of type-1 phosphodiesterase activity. These results establish the presence of ATX in HUVECs and coronary artery smooth muscle cells and specify ATX as a novel angiogenic factor, suggesting that ATX could contribute to the metastatic cascade through multiple mechanisms, perhaps by supporting an invasive microenvironment for both normal and tumor cells.

Alterations of p14ARF, p53, and p73 genes involved in the E2F-1-mediated apoptotic pathways in non-small cell lung carcinoma.

Overexpression of E2F-1 induces apoptosis by both a p14ARF-p53- and a p73-mediated pathway. p14ARF is the alternate tumor suppressor product of the INK4a/ARF locus that is inactivated frequently in lung carcinogenesis. Because p14ARF stabilizes p53, it has been proposed that the loss of p14ARF is functionally equivalent to a p53 mutation. We have tested this hypothesis by examining the genomic status of the unique exon 1beta of p14ARF in 53 human cell lines and 86 primary non-small cell lung carcinomas and correlated this with previously characterized alterations of p53. Homozygous deletions of p14ARF were detected in 12 of 53 (23%) cell lines and 16 of 86 (19%) primary tumors. A single cell line, but no primary tumors, harbored an intragenic mutation. The deletion of p14ARF was inversely correlated with the loss of p53 in the majority of cell lines (P = 0.02), but this relationship was not maintained among primary tumors (P = 0.5). E2F-1 can also induce p73 via a p53-independent apoptotic pathway. Although we did not observe inactivation of p73 by either mutation or DNA methylation, haploinsufficiency of p73 correlated positively with either p14ARF or p53 mutation or both (P = 0.01) in primary non-small cell lung carcinomas. These data are consistent with the current model of p14ARF and p53 interaction as a complex network rather than a simple linear pathway and indicate a possible role for an E2F-1-mediated failsafe, p53-independent, apoptotic pathway involving p73 in human lung carcinogenesis.

A phase I trial of carboxyamido-triazole and paclitaxel for relapsed solid tumors: potential efficacy of the combination and demonstration of pharmacokinetic interaction.

PURPOSE: Preclinical and clinical investigation of the combination of the antiangiogenesis/anti-invasion agent carboxyamido-triazole (CAI) administered with the cytotoxic agent paclitaxel (PAX). EXPERIMENTAL DESIGN: Colony-forming assays were used to test the activity of CAI plus PAX on A2780 human ovarian cancer. The sequence of CAI followed by PAX (CAI>Pax) was modeled in nude mice to test for potential additive toxicity. The Phase I clinical dose escalation schema tested p.o. administered CAI in PEG-400 (50-100 mg/m(2)) or micronized CAI (250 mg/m(2)) for 8 days followed by a 3-h infusion of PAX (110-250 mg/m(2)) every 21 days. Patients were assessed for toxicity, pharmacokinetics of CAI and PAX, and disease outcome. RESULTS: In preclinical studies, CAI>Pax was additive in A2780 human ovarian cancer cell lines when CAI (1 or 5 microM) preceded subtherapeutic doses of PAX. CAI did not reverse PAX resistance and collateral resistance to CAI was documented in PAX-resistant cells. CAI>PAX administration had no overt additive toxicity in nude mice. Thirty-nine patients were treated on a dose-escalation Phase I trial using daily oral CAI for 8 days followed by the PAX infusion. Pharmacokinetic analysis revealed that PAX caused an acute increase in circulating CAI concentrations in a dose-dependent fashion. No additive or cumulative toxicity was observed, and grade 3 nonhematological toxicity was rare. Three partial responses and two minor responses were observed. CONCLUSIONS: The sequential combination of CAI and PAX is well tolerated, and the activity observed suggests that further study of the combination is warranted.

The microenvironment of the tumour-host interface.

Throughout the entire process of cancer aetiology, progression and metastasis, the microenvironment of the local host tissue can be an active participant. Invasion occurs within a tumour-host microecology, where stroma and tumour cells exchange enzymes and cytokines that modify the local extracellular matrix, stimulate migration, and promote proliferation and survival. A new class of cancer therapies that targets this pathological communication interface between tumour cells and host cells is currently under development.