Proteomic contributions to personalized cancer care

Galectin-3C inhibits tumor growth and increases the anticancer activity of bortezomib in a murine model of human multiple myeloma

Galectin-3 is a human lectin involved in many cellular processes including differentiation, apoptosis, angiogenesis, neoplastic transformation, and metastasis. We evaluated galectin-3C, an N-terminally truncated form of galectin-3 that is thought to act as a dominant negative inhibitor, as a potential treatment for multiple myeloma (MM). Galectin-3 was expressed at varying levels by all 9 human MM cell lines tested. In vitro galectin-3C exhibited modest anti-proliferative effects on MM cells and inhibited chemotaxis and invasion of U266 MM cells induced by stromal cell-derived factor (SDF)-1α. Galectin-3C facilitated the anticancer activity of bortezomib, a proteasome inhibitor approved by the FDA for MM treatment. Galectin-3C and bortezomib also synergistically inhibited MM-induced angiogenesis activity in vitro. Delivery of galectin-3C intravenously via an osmotic pump in a subcutaneous U266 cell NOD/SCID mouse model of MM significantly inhibited tumor growth. The average tumor volume of bortezomib-treated animals was 19.6% and of galectin-3C treated animals was 13.5% of the average volume of the untreated controls at day 35. The maximal effect was obtained with the combination of galectin-3C with bortezomib that afforded a reduction of 94% in the mean tumor volume compared to the untreated controls at day 35. In conclusion, this is the first study to show that inhibition of galectin-3 is efficacious in a murine model of human MM. Our results demonstrated that galectin-3C alone was efficacious in a xenograft mouse model of human MM, and that it enhanced the anti-tumor activity of bortezomib in vitro and in vivo. These data provide the rationale for continued testing of galectin-3C towards initiation of clinical trials for treatment of MM.

A database of reaction monitoring mass spectrometry assays for elucidating therapeutic response in cancer

PURPOSE

The Quantitative Assay Database (QuAD), http://proteome.moffitt.org/QUAD/, facilitates widespread implementation of quantitative mass spectrometry in cancer biology and clinical research through sharing of methods and reagents for monitoring protein expression and modification.

EXPERIMENTAL DESIGN

Liquid chromatography coupled to multiple reaction monitoring (LC-MRM) mass spectrometry assays are developed using SDS-PAGE fractionated lysates from cancer cell lines. Pathway maps created using GeneGO Metacore provide the biological relationships between proteins and illustrate concepts for multiplexed analysis; each protein can be selected to examine assay development at the protein and peptide levels.

RESULTS

The coupling of SDS-PAGE and multiple reaction monitoring mass spectrometry screening has been used to detect 876 peptides from 218 cancer-related proteins in model systems including colon, lung, melanoma, leukemias, and myeloma, which has led to the development of 95 quantitative assays including stable-isotope-labeled peptide standards. Methods are published online and peptide standards are made available to the research community. Protein expression measurements for heat shock proteins, including a comparison with ELISA and monitoring response to the HSP90 inhibitor, 17-(dimethylaminoethylamino)-17-demethoxygeldanamycin (17-DMAG), are used to illustrate the components of the QuAD and its potential utility.

CONCLUSIONS AND CLINICAL RELEVANCE

This resource enables quantitative assessment of protein components of signaling pathways and biological processes and holds promise for systematic investigation of treatment responses in cancer.

Human tissue biobanks as instruments for drug discovery and development: impact on personalized medicine

In recent years, biobanks of human tissues have evolved from small-scale collections of pathological materials into structured resource centers for acquisition, storage, processing and usage of high-quality biospecimens for research. This evolution goes hand in hand with the development of highly sensitive, high-throughput methods for biomarker discovery. The complexity of the molecular patterns of diseases such as cancer provides multiple opportunities for targeted therapeutic intervention, tailored to suit the particular characteristics of each patient. Developing and evaluating such novel therapies requires access to rigorously designed and well-structured collections of biospecimens. In turn, biobanking infrastructures have a critical impact on the discovery, development and implementation of new drugs for cancer treatment. Therefore, it is essential to harmonize biobanking procedures, and to develop innovative solutions supporting biobank interoperability and specimen sharing, ensuring that new drugs may effectively reach out to the largest possible number of patients.

Molecular biology: the key to personalised treatment in radiation oncology?

We know considerably more about what makes cells and tissues resistant or sensitive to radiation than we did 20 years ago. Novel techniques in molecular biology have made a major contribution to our understanding at the level of signalling pathways. Before the "New Biology" era, radioresponsiveness was defined in terms of physiological parameters designated as the five Rs. These are: repair, repopulation, reassortment, reoxygenation and radiosensitivity. Of these, only the role of hypoxia proved to be a robust predictive and prognostic marker, but radiotherapy regimens were nonetheless modified in terms of dose per fraction, fraction size and overall time, in ways that persist in clinical practice today. The first molecular techniques were applied to radiobiology about two decades ago and soon revealed the existence of genes/proteins that respond to and influence the cellular outcome of irradiation. The subsequent development of screening techniques using microarray technology has since revealed that a very large number of genes fall into this category. We can now obtain an adequately robust molecular signature, predicting for a radioresponsive phenotype using gene expression and proteomic approaches. In parallel with these developments, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) can now detect specific biological molecules such as haemoglobin and glucose, so revealing a 3D map of tumour blood flow and metabolism. The key to personalised radiotherapy will be to extend this capability to the proteins of the molecular signature that determine radiosensitivity.

Database resources for proteomics-based analysis of cancer

Biological/bioinformatics databases are essential for medical and biological studies. They integrate and organize biologically related information in a structured format and provide researchers with easy access to a variety of relevant data. This review presents an overview of publicly available databases relevant to proteomics studies in cancer research. They include gene/protein expression databases, gene mutation and single nucleotide polymorphisms databases, tumor antigen databases, protein-protein interaction, and biological pathway databases. Automated information retrieval from these databases enables efficient large-scale proteomics data analysis.

Personalized medicine in oncology: tailoring the right drug to the right patient

Despite advances in the management of many human cancers over the past few decades, improvements in survival are marginal, and the overall diagnosis and prognosis for cancer patients remain poor. Tailoring therapy to the individual patient has become a promising approach for maximizing efficacy and minimizing drug toxicity. Aided by major technological advances, the field of personalized medicine has become extremely active in the identification of predictive biomarkers that can guide treatment decisions and, ultimately, improve treatment outcomes. Genomics and proteomics have provided a means for molecular profiling that allows tailoring of therapy. Although implementing genomic and proteomic testing into clinical practice is still in its infancy, the rapid development of newer technologies and platforms provides hope for personalized medicine.

Personalized cancer therapy using a patient-derived tumor tissue xenograft model: a translational field worthy of exploring further?

It has long been observed that interpatient variability in response to anticancer drugs is associated with different outcomes. Oncologists continually hold the desire of matching the right therapeutic regimen with the right cancer patient, which is termed ‘personalized cancer therapy’. Rapid advances in genetics, genomics and related technologies are promising a new era of personalized cancer therapy based on individual molecular biomarkers. However, these molecular predictors of tumor response are far from perfect. Because of the inherent limitations in the current approaches for anticancer drugs response prediction, the need for new techniques to predict tumor response to therapy is urgent. Using a patient-derived human tumor tissue (PDTT) xenograft model to predict tumor response to therapy might be an ideal candidate method to choose. This article provides an overview of the achievements and limitations of genetic, genomic and proteomic molecular markers for personalized cancer therapy, and further discusses the potentials of using a PDTT xenograft model as a candidate strategy for personalized cancer therapy.

Plasma proteomic analysis may identify new markers for radiation-induced lung toxicity in patients with non-small-cell lung cancer

PURPOSE

To study whether radiation induces differential changes in plasma proteomics in patients with and without radiation-induced lung toxicity (RILT) of Grade >/=2 (RILT2).

METHODS AND MATERIALS

A total of 57 patients with NSCLC received radiation therapy (RT) were eligible. Twenty patients, 6 with RILT2 with tumor stage matched to 14 without RILT2, were enrolled for this analysis. Platelet-poor plasma was obtained before RT, at 2, 4, 6 weeks during RT, and 1 and 3 months after RT. Plasma proteomes were compared using a multiplexed quantitative proteomics approach involving ExacTag labeling, reverse-phase high-performance liquid chromatography and nano-LC electrospray tandem mass spectrometry. Variance components models were used to identify the differential protein expression between patients with and without RILT2.

RESULTS

More than 100 proteins were identified and quantified. After excluding proteins for which there were not at least 2 subjects with data for at least two time points, 76 proteins remained for this preliminary analysis. C4b-binding protein alpha chain, Complement C3, and Vitronectin had significantly higher expression levels in patients with RILT2 compared with patients without RILT2, based on both the data sets of RT start to 3 months post-RT (p < 0.01) and RT start to the end of RT (p < 0.01). The expression ratios of patients with RILT2 vs. without RILT2 were 1.60, 1.36, 1.46, and 1.66, 1.34, 1.46, for the above three proteins, respectively.

CONCLUSIONS

This proteomic approach identified new plasma protein markers for future studies on RILT prediction.

Association of serum amyloid A protein and peptide fragments with prognosis in renal cancer

BACKGROUND

In renal cell carcinoma (RCC), the discovery of biomarkers for clinical use is a priority. This study aimed to identify and validate diagnostic and prognostic serum markers using proteomic profiling.

METHODS

Pre-operative sera from 119 patients with clear cell RCC and 69 healthy controls was analysed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry with stringent in-house quality control and analysis routines. Following identification of one prognostic peak as a fragment of serum amyloid A (SAA), total serum SAA and CRP were also determined by immunoassay for further validation.

RESULTS

Several peptides were identified as having independent prognostic but not diagnostic significance on multivariable analysis. One was subsequently identified as a 1525 Da fragment of SAA (hazard ratio (HR)=0.26, 95% CI 0.08-0.85, P=0.026). This was weakly negatively correlated with total SAA, which was also of independent prognostic significance (HR=2.46, 95% CI 1.17-5.15, P=0.017). Both potentially strengthened prognostic models based solely on pre-operative variables.

CONCLUSIONS

This is the first description of the prognostic value of this peptide in RCC and demonstrates proof of principle of the approach. The subsequent examination of SAA protein considerably extends previous studies, being the first study to focus solely on pre-operative samples and describing potential clinical utility in pre-operative prognostic models.

Comparative proteomic analysis of plasma membrane proteins between human osteosarcoma and normal osteoblastic cell lines

BACKGROUND

Osteosarcoma (OS) is the most common primary malignant tumor of bone in children and adolescents. However, the knowledge in diagnostic modalities has progressed less. To identify new biomarkers for the early diagnosis of OS as well as for potential novel therapeutic candidates, we performed a sub-cellular comparative proteomic research.

METHODS

An osteosarcoma cell line (MG-63) and human osteoblastic cells (hFOB1.19) were used as our comparative model. Plasma membrane (PM) was obtained by aqueous two-phase partition. Proteins were analyzed through iTRAQ-based quantitative differential LC/MS/MS. The location and function of differential proteins were analyzed through GO database. Protein-protein interaction was examined through String software. One of differentially expressed proteins was verified by immunohistochemistry.

RESULTS

342 non-redundant proteins were identified, 68 of which were differentially expressed with 1.5-fold difference, with 25 up-regulated and 43 down-regulated. Among those differential proteins, 69% ware plasma membrane, which are related to the biological processes of binding, cell structure, signal transduction, cell adhesion, etc., and interaction with each other. One protein--CD151 located in net nodes was verified to be over-expressed in osteosarcoma tissue by immunohistochemistry.

CONCLUSION

It is the first time to use plasma membrane proteomics for studying the OS membrane proteins according to our knowledge. We generated preliminary but comprehensive data about membrane protein of osteosarcoma. Among these, CD151 was further validated in patient samples, and this small molecule membrane might be a new target for OS research. The plasma membrane proteins identified in this study may provide new insight into osteosarcoma biology and potential diagnostic and therapeutic biomarkers.

The cancer cell secretome: a good source for discovering biomarkers?

Cancer is a leading cause of death. Early detection is usually associated with better clinical outcomes. Recent advances in genomics and proteomics raised hopes that new biomarkers for diagnosis, prognosis or monitoring therapeutic response will soon be discovered. Proteins secreted by cancer cells, referred also as "the cancer cell secretome", is a promising source for biomarker discovery. In this review we will summarize recent advances in cancer cell secretome analysis, focusing on the five most fatal cancers (lung, breast, prostate, colorectal, and pancreatic). For each cancer type we will describe the proteomic approaches utilized for the identification of novel biomarkers. Despite progress, identification of markers that are superior to those currently used has proven to be a difficult task and very few, if any, newly discovered biomarker has entered the clinic the last 10 years.

Applying mass spectrometry based proteomic technology to advance the understanding of multiple myeloma

Multiple myeloma (MM) is the second most common hematological malignancy in adults. It is characterized by clonal proliferation of terminally differentiated B lymphocytes and over-production of monoclonal immunoglobulins. Recurrent genomic aberrations have been identified to contribute to the aggressiveness of this cancer. Despite a wealth of knowledge describing the molecular biology of MM as well as significant advances in therapeutics, this disease remains fatal. The identification of biomarkers, especially through the use of mass spectrometry, however, holds great promise to increasing our understanding of this disease. In particular, novel biomarkers will help in the diagnosis, prognosis and therapeutic stratification of MM. To date, results from mass spectrometry studies of MM have provided valuable information with regards to MM diagnosis and response to therapy. In addition, mass spectrometry was employed to study relevant signaling pathways activated in MM. This review will focus on how mass spectrometry has been applied to increase our understanding of MM.

Super-SILAC mix for quantitative proteomics of human tumor tissue

We describe a method to accurately quantify human tumor proteomes by combining a mixture of five stable-isotope labeling by amino acids in cell culture (SILAC)-labeled cell lines with human carcinoma tissue. This generated hundreds of thousands of isotopically labeled peptides in appropriate amounts to serve as internal standards for mass spectrometry-based analysis. By decoupling the labeling from the measurement, this super-SILAC method broadens the scope of SILAC-based proteomics.

Differential proteomics identification of HSP90 as potential serum biomarker in hepatocellular carcinoma by two-dimensional electrophoresis and mass spectrometry

The aim of the current study is to identify the potential biomarkers involved in Hepatocellular carcinoma (HCC) carcinogenesis. A comparative proteomics approach was utilized to identify the differentially expressed proteins in the serum of 10 HCC patients and 10 controls. A total of 12 significantly altered proteins were identified by mass spectrometry. Of the 12 proteins identified, HSP90 was one of the most significantly altered proteins and its over-expression in the serum of 20 HCC patients was confirmed using ELISA analysis. The observations suggest that HSP90 might be a potential biomarker for early diagnosis, prognosis, and monitoring in the therapy of HCC. This work demonstrates that a comprehensive strategy of proteomic identification combined with further validation should be adopted in the field of cancer biomarker discovery.

Urine collection and processing for protein biomarker discovery and quantification

BACKGROUND

Urine is a useful source of protein for biomarker discovery and assessment because it is readily available, can be obtained by noninvasive collection methods, and enables monitoring of a wide range of physiologic processes and diseases. Urine aliquots provide enough protein for multiple analyses, combining current protocols with new techniques.

CONCLUSIONS

Standardized collection and processing protocols are now being established and new methods for protein detection and quantification are emerging to complement traditional immunoassays. The current state of urine collection, specimen processing, and storage is reviewed with regard to discovery and quantification of protein biomarkers for cancer.

Dual-color proteomic profiling of complex samples with a microarray of 810 cancer-related antibodies

Antibody microarrays have the potential to enable comprehensive proteomic analysis of small amounts of sample material. Here, protocols are presented for the production, quality assessment, and reproducible application of antibody microarrays in a two-color mode with an array of 1,800 features, representing 810 antibodies that were directed at 741 cancer-related proteins. In addition to measures of array quality, we implemented indicators for the accuracy and significance of dual-color detection. Dual-color measurements outperform a single-color approach concerning assay reproducibility and discriminative power. In the analysis of serum samples, depletion of high-abundance proteins did not improve technical assay quality. On the contrary, depletion introduced a strong bias in protein representation. In an initial study, we demonstrated the applicability of the protocols to proteins derived from urine samples. We identified differences between urine samples from pancreatic cancer patients and healthy subjects and between sexes. This study demonstrates that biomedically relevant data can be produced. As demonstrated by the thorough quality analysis, the dual-color antibody array approach proved to be competitive with other proteomic techniques and comparable in performance to transcriptional microarray analyses.

What does the future hold for Top Down mass spectrometry?

Mass spectrometry (MS) research has revolutionized modern biological and biomedical fields. At the heart of the majority of mass spectrometry experiments is the use of Bottom Up mass spectrometry methods where proteins are first proteolyzed into smaller fragments before MS interrogation. The advent of electron capture dissociation and, more recently, electron-transfer dissociation, however, has allowed Top Down (analysis of intact proteins) or middle down (analysis of large polypeptides) mass spectrometry to both experience large increases in development, growth, and overall usage. Nevertheless, for high-throughput large-scale proteomic studies, Bottom Up mass spectrometry has easily dominated the field. As Top Down mass spectrometry methodology and technology continue to develop, will it genuinely be able to compete with Bottom Up mass spectrometry for whole proteome analysis? Discussed here are the current approaches, applications, issues, and future view of high-throughput Top Down mass spectrometry.

Current advances in tumor proteomics and candidate biomarkers for hepatic cancer

Tumor proteomics apply proteomics techniques to tumor biological research, mainly by screening candidate biomarkers for early tumor diagnosis, prognosis and treatment. Hepatocellular carcinoma (HCC) is a type of malignant tumor with one of the highest death rates in the world. With the advent of the post-genomic age, tumor biological research developing the technology of proteomics has become a major focus of researchers. The discovery of novel candidate biomarkers is one of crucial problems for the early diagnosis of HCC. In general, there are three distinct types of candidate biomarkers for HCC based on different areas: biochemical biomarkers, antigenic biomarkers and epigenetic biomarkers. This review mainly discusses current advances in the problems and prospects of candidate biomarker for the early diagnosis of HCC, discovered by technologies of tumor proteomics.

Pre-operative urinary cathepsin D is associated with survival in patients with renal cell carcinoma

Background:

No circulating markers are routinely used for renal cancer. The objective of this pilot study was to investigate whether conditioned media (CM) from renal cancer cell lines contains potential biomarkers that, when measured in clinical fluids, have diagnostic or prognostic utility.

Methods:

Comparative 2D PAGE profiling of CM from renal cell carcinoma (RCC) and normal renal cultures identified cathepsin D that was subsequently validated in urine samples from 239 patients and healthy and benign disease subjects.

Results:

Urinary cathepsin D was found to be significantly associated with overall (OS) (hazard ratio, HR, 1.33, 95%CI [1.09–1.63], P=0.005) and cancer-specific survival (HR 1.36, 95%CI [1.07–1.74], P=0.013) in RCC patients on univariate analysis. An optimal cut point (211 ng ml⁻¹ μmolCr⁻¹) around which to stratify patients by OS was determined. Five-year OS equal to/above and below this value was 47.0% (95%CI 35.4%, 62.4%) and 60.9% (48.8%, 76.0%), respectively. On multivariable analysis using pre-operative variables, cathepsin D showed some evidence of independent prognostic value for OS (likelihood ratio test P-value=0.056) although requiring further validation in larger patient numbers with sufficient statistical power to determine independent significance.

Conclusion:

These data establish an important proof of principle and show the potential of proteomics-based studies. Cathepsin D may be of value as a pre-operative urinary biomarker for RCC, alone or in combination.

Proteomic analysis of serum in lung cancer induced by 3-methylcholanthrene

Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of lung cancer is problematic due to the lack of a marker with high diagnosis sensitivity and specificity. To determine the differently expressed proteins in the serum of lung cancer and figure out the function of the proteins, two-dimensional electrophoresis (2DE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) were used to screen the serum proteins of lung cancer model induced by 3-methylcholanthrene (MCA). From optimized 2DE image, 455 spots in the normal sera and 716 spots in the lung cancers sera were detected. Among them, 141 protein spots were differentially expressed when comparing the serum from normal rat and serum from lung cancer model, including 82 overexpressed proteins and 59 underexpressed proteins. Changes of haptoglobin, transthyretin, and TNF superfamily member 8 (TNFRS8) were confirmed in sera from lung cancer by MALDI-TOF-MS. Proteomics technology leads to identify changes of haptoglobin, transthyretin, and TNFRS8 in serum of rat lung cancer model and represents a powerful tool in searching for candidate proteins as biomarkers.

A single lysis solution for the analysis of tissue samples by different proteomic technologies

Cancer, being a major healthcare concern worldwide, is one of the main targets for the application of emerging proteomic technologies and these tools promise to revolutionize the way cancer will be diagnosed and treated in the near future. Today, as a result of the unprecedented advances that have taken place in molecular biology, cell biology and genomics there is a pressing need to accelerate the translation of basic discoveries into clinical applications. This need, compounded by mounting evidence that cellular model systems are unable to fully recapitulate all biological aspects of human dissease, is driving scientists to increasingly use clinically relevant samples for biomarker and target discovery. Tissues are heterogeneous and as a result optimization of sample preparation is critical for generating accurate, representative, and highly reproducible quantitative data. Although a large number of protocols for preparation of tissue lysates has been published, so far no single recipe is able to provide a "one-size fits all" solubilization procedure that can be used to analyse the same lysate using different proteomics technologies. Here we present evidence showing that cell lysis buffer 1 (CLB1), a lysis solution commercialized by Zeptosens [a division of Bayer (Schweiz) AG], provides excellent sample solubilization and very high 2D PAGE protein resolution both when using carrier ampholytes and immobilized pH gradient strips. Moreover, this buffer can also be used for array-based proteomics (reverse-phase lysate arrays or direct antibody arrays), allowing the direct comparison of qualitative and quantitative data yielded by these technologies when applied to the same samples. The usefulness of the CLB1 solution for gel-based proteomics was further established by 2D PAGE analysis of a number of technically demanding specimens such as breast carcinoma core needle biopsies and problematic tissues such as brain cortex, cerebellum, skeletal muscle, kidney cortex and tongue. This solution when combined with a specific sample preparation technique - cryostat sectioning of frozen specimens - simplifies tissue sample preparation and solves most of the difficulties associated with the integration of data generated by different proteomic technologies.