Proteomic contributions to personalized cancer care

Image-guided patient-specific prediction of interstitial fluid flow and drug transport in solid tumors

Tumor fluid dynamics and drug delivery simulations in solid tumors are highly relevant topics in clinical oncology. The current study introduces a novel method combining computational fluid dynamics (CFD) modeling, quantitative magnetic resonance imaging (MRI; including dynamic contrast-enhanced (DCE) MRI and diffusion-weighted (DW) MRI), and a novel ex-vivo protocol to generate patient-specific models of solid tumors in four patients with peritoneal metastases. DCE-MRI data were analyzed using the extended Tofts model to estimate the spatial distribution of tumor capillary permeability using the Ktrans parameter. DW-MRI data analysis provided a 3D representation of drug diffusivity, and DW-MRI coupled to an ex-vivo measurement protocol informed the spatial heterogeneity of the hydraulic conductivity of tumor tissue. The patient-specific data were subsequently incorporated into a computational fluid dynamics (CFD) model to simulate individualized tumor perfusion and drug transport maps. The results on interstitial fluid flow demonstrated noticeable heterogeneity of interstitial fluid pressure and velocity within the tumor, along with heterogeneous drug penetration profiles among different tumors, even with a similar drug administration regimen.

Exploring Molecular Alterations in Breast Cancer Among Indian Women Using Label-Free Quantitative Serum Proteomics

The clinical data indicate that diverse parameters characterize breast cancer patients in India, including age at presentation, risk factors, outcomes, and behavior. Alarming incidence and mortality rates emphasize the crucial need for early screening measures to combat breast cancer-related deaths effectively. Quantitative proteomic approaches prove pivotal in predicting cancer prognosis, analyzing protein expression patterns tied to disease aggressiveness and metastatic potential, and facilitating conversant therapy selection. Thus, this study was envisioned with the goal of identifying protein markers associated with breast cancer in Indian women, which could potentially be developed as diagnostic tools and therapeutic targets in the future. Applying label-free proteomic quantitation method and statistical analysis, several differentially expressed proteins (DEPs) were identified in the serum of breast cancer patients compared to controls, including SBSN, ANG, PCOLCE, and WFDC3 (upregulated), and PFN1, FLNA, and DSG2 (downregulated). The expression of SBSN was also validated by western blotting. Statistical methods were employed to proteomic expression data, which highlighted the ability of DEPs to distinguish between breast cancer and control samples. Conclusively, this study recognizes prospective biomarkers for breast cancer among Indian women and highlights the requisite of in-depth functional studies to elucidate their precise roles in breast cancer development. We particularly emphasize on SBSN and PFN1, as these proteins were observed to be progressively overexpressed and under expressed, respectively, in breast cancer samples compared to control samples, ranging from early-stage to metastatic cases.

Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment

Breast cancer, a multi-networking heterogeneous disease, has emerged as a serious impediment to progress in clinical oncology. Although technological advancements and emerging cancer research studies have mitigated breast cancer lethality, a precision cancer-oriented solution has not been achieved. Thus, this review will persuade the acquiescence of proteomics-based diagnostic and therapeutic options in breast cancer management. Recently, the evidence of breast cancer health surveillance through imaging proteomics, single-cell proteomics, interactomics, and post-translational modification (PTM) tracking, to construct proteome maps and proteotyping for stage-specific and sample-specific cancer subtyping have outperformed conventional ways of dealing with breast cancer by increasing diagnostic efficiency, prognostic value, and predictive response. Additionally, the paradigm shift in applied proteomics for designing a chemotherapy regimen to identify novel drug targets with minor adverse effects has been elaborated. Finally, the potential of proteomics in alleviating the occurrence of chemoresistance and enhancing reprofiled drugs' effectiveness to combat therapeutic obstacles has been discussed. Owing to the enormous potential of proteomics techniques, the clinical recognition of proteomics in breast cancer management can be achievable and therapeutic intricacies can be surmountable.

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms

Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.

The role of clinical glyco(proteo)mics in precision medicine

Glycoproteomics reveals site-specific O- and N-glycosylation that may influence protein properties including binding, activity and half-life. The increasingly mature toolbox with glycomic- and glycoproteomic strategies is applied for the development of biopharmaceuticals and discovery and clinical evaluation of glycobiomarkers in various disease fields. Notwithstanding the contributions of glycoscience in identifying new drug targets, the current report is focused on the biomarker modality that is of interest for diagnostic and monitoring purposes. To this end it is noted that the identification of biomarkers has received more attention than corresponding quantification. Most analytical methods are very efficient in detecting large numbers of analytes but developments to accurately quantify these have so far been limited. In this perspective a parallel is made with earlier proposed tiers for protein quantification using mass spectrometry. Moreover, the foreseen reporting of multimarker readouts is discussed to describe an individual's health or disease state and their role in clinical decision-making. The potential of longitudinal sampling and monitoring of glycomic features for diagnosis and treatment monitoring is emphasized. Finally, different strategies that address quantification of a multimarker panel will be discussed.

Serum proteomic profiling reveals MTA2 and AGO2 as potential prognostic biomarkers associated with disease activity and adverse outcomes in multiple myeloma

Multiple myeloma (MM) is an incurable plasma cell malignancy accounting for approximately 10% of hematological malignancies. Identification of reliable biomarkers for better diagnosis and prognosis remains a major challenge. This study aimed to identify potential serum prognostic biomarkers corresponding to MM disease activity and evaluate their impact on patient outcomes. Serum proteomic profiles of patients with MM and age-matched controls were performed using LC-MS/MS. In the verification and validation phases, the concentration of the candidate biomarkers was measured using an ELISA technique. In addition, the association of the proposed biomarkers with clinical outcomes was assessed. We identified 23 upregulated and 15 downregulated proteins differentially expressed in newly diagnosed and relapsed/refractory MM patients compared with MM patients who achieved at least a very good partial response to treatment (≥VGPR). The top two candidate proteins, metastasis-associated protein-2 (MTA2) and argonaute-2 (AGO2), were selected for further verification and validation studies. Both MTA2 and AGO2 showed significantly higher levels in the disease-active states than in the remission states (p < 0.001). Regardless of the patient treatment profile, high MTA2 levels were associated with shorter progression-free survival (p = 0.044; HR = 2.48; 95% CI, 1.02 to 6.02). Conversely, high AGO2 levels were associated with IgG and kappa light-chains isotypes and an occurrence of bone involvement features (p < 0.05) and were associated with prolonged time to response (p = 0.045; HR = 3.00; 95% CI, 1.03 to 8.76). Moreover, the analytic results using a publicly available NCBI GEO dataset revealed that AGO2 overexpression was associated with shorter overall survival among patients with MM (p = 0.032, HR = 1.60, 95% CI, 1.04 to 2.46). In conclusion, MTA2 and AGO2 proteins were first identified as potential biomarkers that reflect disease activity, provide prognostic values and could serve as non-invasive indicators for disease monitoring and outcome predicting among patients with MM.

A Deep-Learning Proteomic-Scale Approach for Drug Design

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded "objective" signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

Absolute quantification of senescence mediators in cells using multiple reaction monitoring liquid chromatography-Tandem mass spectrometry

BACKGROUND

The identification of unique senescence markers remains challenging. Current hallmarks of senescent cells, including increased senescence-associated β-galactosidase activity, increased levels of cell cycle regulators such as p16^INK4a, p27, and p53, and altered levels of sirtuins and lamins, are detected commonly by Western blot and immunohistochemistry methods. Mass spectrometry outperforms these conventional quantification methods in terms of high throughput, specificity, and reproducibility.

OBJECTIVES

To develop multiple reaction monitoring-based tandem mass spectrometric senescence assay for simultaneous measuring of p16^INK4a, p27, p53, p53-β, the seven proteins of the sirtuins family and the four transcript variants of lamins proteins in aging cell model and cancerous cell lines.

METHODOLOGY

Multiple reaction monitoring-tandem mass transitions per protein were developed for each signature peptide(s) and stable isotope-labeled internal standard. The developed assay was validated in a matrix using breast cancer MCF7 cell lines according to the US-FDA guidelines for bioanalytical assays.

RESULTS

The analytes chromatographic peaks were baseline separated and showed linear behavior in a wide dynamic range with r² ≥ 0.98. The method for all proteins has passed the inter/intra-day precision and accuracy validation using three levels of quality control samples. The accuracy and the precision for most analytes were 80-120% and ≤20%, respectively. The method's sensitivity for the panels' signature peptides ranged from 1 ng μL^-1 to 1 μg mL^-1. Extraction recovery assessed in two quality control levels was >60% for most analytes. This LC-MS-MS validated senescence assay showed reduced lamin A, lamin A△10, lamin A△50, SIRT1, SIRT3, SIRT5, p53, and p16^INK4a, as well as p53-β induction, are implicated in replicative senescence. Meanwhile, increased lamin C: lamin A ratio was evident and can diagnose breast carcinogenesis. Moreover, in breast cancer metastasis, reduced SIRT2 and p27 and elevated levels of lamin A△50, SIRT5, SIRT7, and p53-β are evident.

CONCLUSION

LC-MS/MS is a potent alternative tool to the currently available assays. The high throughput method established can study senescence's role in different pathophysiological processes.

Integrated approaches for precision oncology in colorectal cancer: The more you know, the better

Colorectal cancer (CRC) is one of the most common human malignancies accounting for approximately 10 % of worldwide cancer incidence and mortality. While early-stage CRC is mainly a preventable and curable disease, metastatic colorectal cancer (mCRC) remains an unmet clinical need. Moreover, about 25 % of CRC cases are diagnosed only at the metastatic stage. Despite the extensive molecular and functional knowledge on this disease, systemic therapy for mCRC still relies on traditional 5-fluorouracil (5-FU)-based chemotherapy regimens. On the other hand, targeted therapies and immunotherapy have shown effectiveness only in a limited subset of patients. For these reasons, there is a growing need to define the molecular and biological landscape of individual patients to implement novel, rationally driven, tailored therapies. In this review, we explore current and emerging approaches for CRC management such as genomic, transcriptomic and metabolomic analysis, the use of liquid biopsies and the implementation of patients' preclinical avatars. In particular, we discuss the contribution of each of these tools in elucidating patient specific features, with the aim of improving our ability in advancing the diagnosis and treatment of colorectal tumors.

Global quantitative analysis of the human brain proteome and phosphoproteome in Alzheimer's disease

Alzheimer's disease (AD) is characterized by an early, asymptomatic phase (AsymAD) in which individuals exhibit amyloid-beta (Aβ) plaque accumulation in the absence of clinically detectable cognitive decline. Here we report an unbiased multiplex quantitative proteomic and phosphoproteomic analysis using tandem mass tag (TMT) isobaric labeling of human post-mortem cortex (n = 27) across pathology-free controls, AsymAD and symptomatic AD individuals. With off-line high-pH fractionation and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) on an Orbitrap Lumos mass spectrometer, we identified 11,378 protein groups across three TMT 11-plex batches. Immobilized metal affinity chromatography (IMAC) was used to enrich for phosphopeptides from the same TMT-labeled cases and 51,736 phosphopeptides were identified. Of these, 48,992 were quantified by TMT reporter ions representing 33,652 unique phosphosites. Two reference standards in each TMT 11-plex were included to assess intra- and inter-batch variance at the protein and peptide level. This comprehensive human brain proteome and phosphoproteome dataset will serve as a valuable resource for the identification of biochemical, cellular and signaling pathways altered during AD progression.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues

A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.

Methods for the evaluation of biomarkers in patients with kidney and liver diseases: multicentre research programme including ELUCIDATE RCT

Background

Protein biomarkers with associations with the activity and outcomes of diseases are being identified by modern proteomic technologies. They may be simple, accessible, cheap and safe tests that can inform diagnosis, prognosis, treatment selection, monitoring of disease activity and therapy and may substitute for complex, invasive and expensive tests. However, their potential is not yet being realised.

Design and methods

The study consisted of three workstreams to create a framework for research: workstream 1, methodology – to define current practice and explore methodology innovations for biomarkers for monitoring disease; workstream 2, clinical translation – to create a framework of research practice, high-quality samples and related clinical data to evaluate the validity and clinical utility of protein biomarkers; and workstream 3, the ELF to Uncover Cirrhosis as an Indication for Diagnosis and Action for Treatable Event (ELUCIDATE) randomised controlled trial (RCT) – an exemplar RCT of an established test, the ADVIA Centaur® Enhanced Liver Fibrosis (ELF) test (Siemens Healthcare Diagnostics Ltd, Camberley, UK) [consisting of a panel of three markers – (1) serum hyaluronic acid, (2) amino-terminal propeptide of type III procollagen and (3) tissue inhibitor of metalloproteinase 1], for liver cirrhosis to determine its impact on diagnostic timing and the management of cirrhosis and the process of care and improving outcomes.

Results

The methodology workstream evaluated the quality of recommendations for using prostate-specific antigen to monitor patients, systematically reviewed RCTs of monitoring strategies and reviewed the monitoring biomarker literature and how monitoring can have an impact on outcomes. Simulation studies were conducted to evaluate monitoring and improve the merits of health care. The monitoring biomarker literature is modest and robust conclusions are infrequent. We recommend improvements in research practice. Patients strongly endorsed the need for robust and conclusive research in this area. The clinical translation workstream focused on analytical and clinical validity. Cohorts were established for renal cell carcinoma (RCC) and renal transplantation (RT), with samples and patient data from multiple centres, as a rapid-access resource to evaluate the validity of biomarkers. Candidate biomarkers for RCC and RT were identified from the literature and their quality was evaluated and selected biomarkers were prioritised. The duration of follow-up was a limitation but biomarkers were identified that may be taken forward for clinical utility. In the third workstream, the ELUCIDATE trial registered 1303 patients and randomised 878 patients out of a target of 1000. The trial started late and recruited slowly initially but ultimately recruited with good statistical power to answer the key questions. ELF monitoring altered the patient process of care and may show benefits from the early introduction of interventions with further follow-up. The ELUCIDATE trial was an ‘exemplar’ trial that has demonstrated the challenges of evaluating biomarker strategies in ‘end-to-end’ RCTs and will inform future study designs.

Conclusions

The limitations in the programme were principally that, during the collection and curation of the cohorts of patients with RCC and RT, the pace of discovery of new biomarkers in commercial and non-commercial research was slower than anticipated and so conclusive evaluations using the cohorts are few; however, access to the cohorts will be sustained for future new biomarkers. The ELUCIDATE trial was slow to start and recruit to, with a late surge of recruitment, and so final conclusions about the impact of the ELF test on long-term outcomes await further follow-up. The findings from the three workstreams were used to synthesise a strategy and framework for future biomarker evaluations incorporating innovations in study design, health economics and health informatics.

Trial registration

Current Controlled Trials ISRCTN74815110, UKCRN ID 9954 and UKCRN ID 11930.

Funding

This project was funded by the NIHR Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 6, No. 3. See the NIHR Journals Library website for further project information.

Comprehensive Analysis of Cancer-Proteogenome to Identify Biomarkers for the Early Diagnosis and Prognosis of Cancer

During the past century, our understanding of cancer diagnosis and treatment has been based on a monogenic approach, and as a consequence our knowledge of the clinical genetic underpinnings of cancer is incomplete. Since the completion of the human genome in 2003, it has steered us into therapeutic target discovery, enabling us to mine the genome using cutting edge proteogenomics tools. A number of novel and promising cancer targets have emerged from the genome project for diagnostics, therapeutics, and prognostic markers, which are being used to monitor response to cancer treatment. The heterogeneous nature of cancer has hindered progress in understanding the underlying mechanisms that lead to abnormal cellular growth. Since, the start of The Cancer Genome Atlas (TCGA), and the International Genome consortium projects, there has been tremendous progress in genome sequencing and immense numbers of cancer genomes have been completed, and this approach has transformed our understanding of the diagnosis and treatment of different types of cancers. By employing Genomics and proteomics technologies, an immense amount of genomic data is being generated on clinical tumors, which has transformed the cancer landscape and has the potential to transform cancer diagnosis and prognosis. A complete molecular view of the cancer landscape is necessary for understanding the underlying mechanisms of cancer initiation to improve diagnosis and prognosis, which ultimately will lead to personalized treatment. Interestingly, cancer proteome analysis has also allowed us to identify biomarkers to monitor drug and radiation resistance in patients undergoing cancer treatment. Further, TCGA-funded studies have allowed for the genomic and transcriptomic characterization of targeted cancers, this analysis aiding the development of targeted therapies for highly lethal malignancy. High-throughput technologies, such as complete proteome, epigenome, protein-protein interaction, and pharmacogenomics data, are indispensable to glean into the cancer genome and proteome and these approaches have generated multidimensional universal studies of genes and proteins (OMICS) data which has the potential to facilitate precision medicine. However, due to slow progress in computational technologies, the translation of big omics data into their clinical aspects have been slow. In this review, attempts have been made to describe the role of high-throughput genomic and proteomic technologies in identifying a panel of biomarkers which could be used for the early diagnosis and prognosis of cancer.

Relative protein quantification and accessible biology in lung tumor proteomes from four LC-MS/MS discovery platforms

Discovery proteomics experiments include many options for sample preparation and MS data acquisition, which are capable of creating datasets for quantifying thousands of proteins. To define a strategy that would produce a dataset with sufficient content while optimizing required resources, we compared (1) single-sample LC-MS/MS with data-dependent acquisition to single-sample LC-MS/MS with data-independent acquisition and (2) peptide fractionation with label-free (LF) quantification to peptide fractionation with relative quantification of chemically labeled peptides (sixplex tandem mass tags (TMT)). These strategies were applied to the same set of four frozen lung squamous cell carcinomas and four adjacent tissues, and the overall outcomes of each experiment were assessed. We identified 6656 unique protein groups with LF, 5535 using TMT, 3409 proteins from single-sample analysis with data-independent acquisition, and 2219 proteins from single-sample analysis with data-dependent acquisition. Pathway analysis indicated the number of proteins per pathway was proportional to the total protein identifications from each method, suggesting limited biological bias between experiments. The results suggest the use of single-sample experiments as a rapid tissue assessment tool and digestion quality control or as a technique to maximize output from limited samples and use of TMT or LF quantification as methods for larger amounts of tumor tissue with the selection being driven mainly by instrument time limitations. Data are available via ProteomeXchange with identifiers PXD004682, PXD004683, PXD004684, and PXD005733.

Increased AAA-TOB3 correlates with lymph node metastasis and advanced stage of lung adenocarcinoma

BACKGROUND

This study was to investigate the differential mitochondrial protein expressions in human lung adenocarcinoma and provide preliminary data for further exploration of the carcinogenic mechanism.

METHODS

Total proteins of A549 and 16HBE mitochondria were extracted through 2D polyacrylamide gel electrophoresis (2-DE). The differential mitochondria proteins were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and were further confirmed by Western blot, immunoelectron microscopy and immunohistochemistry (IHC) in A549 cells as well as lung adenocarcinoma tissues.

RESULTS

A total of 41 differentially expressed protein spots were found in A549 mitochondria. Of them, 15 proteins were highly expressed and 26 proteins were lowly expressed in the mitochondria of A549 (by more than 1.5 times). Among the 15 more highly expressed proteins, AAA-TOB3 (by more than 3 times) was highly expressed in the mitochondria of A549 compared with the 16HBE, by LC-MS/MS identification. High electron density and clear circular colloidal gold-marked AAA-TOB3 particles were observed in the A549 cells via immunoelectron microscopy. Besides, AAA-TOB3 was confirmed to be elevated in lung adenocarcinoma by Western blot and IHC. Moreover, increased AAA-TOB3 correlated with lymph node metastasis and advanced stage of lung adenocarcinoma (p<0.05).

CONCLUSIONS

AAA-TOB3 was highly expressed in lung adenocarcinoma, and the up-regulation of AAA-TOB3 correlated with lymph node metastasis and advanced stage of lung adenocarcinoma, which suggested that it could serve as a potential molecular marker for lung adenocarcinoma.

An optimized procedure for on-tissue localized protein digestion and quantification using hydrogel discs and isobaric mass tags: analysis of cardiac myxoma

An optimized workflow for multiplexed and spatially localized on-tissue quantitative protein analysis is here presented. The method is based on the use of an enzyme delivery platform, a polymeric hydrogel disc, allowing for a localized digestion directly onto the tissue surface coupled with an isobaric mass tag strategy for peptide labeling and relative quantification. The digestion occurs within such hydrogels, followed by peptide solvent extraction and identification by liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-MS/MS). Since this is a histology-directed on-tissue analysis, multiple hydrogels were placed onto morphologically and spatially different regions of interest (ROIs) within the tissue surface, e.g., cardiac myxoma tumor vascularized region and the adjacent hypocellular area. After a microwave digestion step (2 min), enzymatically cleaved peptides were labeled using TMT reagents with isobaric mass tags, enabling analysis of multiple samples per experiment. Thus, N = 8 hydrogel-digested samples from cardiac myxoma serial tissue sections (N = 4 from the vascularized ROIs and N = 4 from the adjacent hypocellular areas) were processed and then combined before a single LC-MS/MS analysis. Regulated proteins from both cardiac myxoma regions were assayed in a single experiment. Graphical abstract The workflow for histology-guided on-tissue localized protein digestion followed by isobaric mass tagging and LC-MS/MS analysis for proteins quantification is here summarized.

Multiplexed Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry Quantification of Cancer Signaling Proteins

Quantitative evaluation of protein expression across multiple cancer-related signaling pathways (e.g., Wnt/β-catenin, TGF-β, receptor tyrosine kinases (RTK), MAP kinases, NF-κB, and apoptosis) in tumor tissues may enable the development of a molecular profile for each individual tumor that can aid in the selection of appropriate targeted cancer therapies. Here, we describe the development of a broadly applicable protocol to develop and implement quantitative mass spectrometry assays using cell line models and frozen tissue specimens from colon cancer patients. Cell lines are used to develop peptide-based assays for protein quantification, which are incorporated into a method based on SDS-PAGE protein fractionation, in-gel digestion, and liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM/MS). This analytical platform is then applied to frozen tumor tissues. This protocol can be broadly applied to the study of human disease using multiplexed LC-MRM assays.

Versatile, sensitive liquid chromatography mass spectrometry - Implementation of 10 μm OT columns suitable for small molecules, peptides and proteins

We have designed a versatile and sensitive liquid chromatographic (LC) system, featuring a monolithic trap column and a very narrow (10 μm ID) fused silica open tubular liquid chromatography (OTLC) separation column functionalized with C18-groups, for separating a wide range of molecules (from small metabolites to intact proteins). Compared to today's capillary/nanoLC approaches, our system provides significantly enhanced sensitivity (up to several orders) with matching or improved separation efficiency, and highly repeatable chromatographic performance. The chemical properties of the trap column and the analytical column were fine-tuned to obtain practical sample loading capacities (above 2 μg), an earlier bottleneck of OTLC. Using the OTLC system (combined with Orbitrap mass spectrometry), we could perform targeted metabolomics of sub-μg amounts of exosomes with 25 attogram detection limit of a breast cancer-related hydroxylated cholesterol. With the same set-up, sensitive bottom-up proteomics (targeted and untargeted) was possible, and high-resolving intact protein analysis. In contrast to state-of-the-art packed columns, our platform performs chromatography with very little dilution and is "fit-for-all", well suited for comprehensive analysis of limited samples, and has potential as a tool for challenges in diagnostics.

Deciphering lymphoma pathogenesis via state-of-the-art mass spectrometry-based quantitative proteomics

Mass spectrometry-based quantitative proteomics specifically applied to comprehend the pathogenesis of lymphoma has incremental value in deciphering the heterogeneity in complex deregulated molecular mechanisms/pathways of the lymphoma entities, implementing the current diagnostic and therapeutic strategies. Essential global, targeted and functional differential proteomics analyses although still evolving, have been successfully implemented to shed light on lymphoma pathogenesis to discover and explore the role of potential lymphoma biomarkers and drug targets. This review aims to outline and appraise the present status of MS-based quantitative proteomic approaches in lymphoma research, introducing the current state-of-the-art MS-based proteomic technologies, the opportunities they offer in biological discovery in human lymphomas and the related limitation issues arising from sample preparation to data evaluation. It is a synopsis containing information obtained from recent research articles, reviews and public proteomics repositories (PRIDE). We hope that this review article will aid, assimilate and assess all the information aiming to accelerate the development and validation of diagnostic, prognostic or therapeutic targets for an improved and empowered clinical proteomics application in lymphomas in the nearby future.

A Pathway Proteomic Profile of Ischemic Stroke Survivors Reveals Innate Immune Dysfunction in Association with Mild Symptoms of Depression - A Pilot Study

Depression after stroke is a common occurrence, raising questions as to whether depression could be a long-term biological and immunological sequela of stroke. Early explanations for post-stroke depression (PSD) focused on the neuropsychological/psychosocial effects of stroke on mobility and quality of life. However, recent investigations have revealed imbalances of inflammatory cytokine levels in association with PSD, though to date, there is only one published proteomic pathway analysis testing this hypothesis. Thus, we examined the serum proteome of stroke patients (n = 44, mean age = 63.62 years) and correlated these with the Montgomery–Åsberg Depression Rating Scale (MADRS) scores at 3 months post-stroke. Overall, the patients presented with mild depression symptoms on the MADRS, M = 6.40 (SD = 7.42). A discovery approach utilizing label-free relative quantification was employed utilizing an LC-ESI–MS/MS coupled to a LTQ-Orbitrap Elite (Thermo-Scientific). Identified peptides were analyzed using the gene set enrichment approach on several different genomic databases that all indicated significant downregulation of the complement and coagulation systems with increasing MADRS scores. Complement and coagulation systems are traditionally thought to play a key role in the innate immune system and are established precursors to the adaptive immune system through pro-inflammatory cytokine signaling. Both systems are known to be globally affected after ischemic or hemorrhagic stroke. Thus, our results suggest that lowered complement expression in the periphery in conjunction with depressive symptoms post-stroke may be a biomarker for incomplete recovery of brain metabolic needs, homeostasis, and inflammation following ischemic stroke damage. Further proteomic investigations are now required to construct the temporal profile, leading from acute lesion damage to manifestation of depressive symptoms. Overall, the findings provide support for the involvement of inflammatory and immune mechanisms in PSD symptoms and further demonstrate the value and feasibility of the proteomic approach in stroke research.

Identification and verification of PRDX1 as an inflammation marker for colorectal cancer progression

Chronic inflammation contributes to high risk of colorectal cancer (CRC) development. Thus, discovering inflammation biomarkers for monitoring of CRC progression is necessary. In this study, we performed isobaric tags for relative and absolute quantitation-based proteomic assay on CRC tissues and paired normal mucosal tissues to identify key components in CRC pathogenesis. A total of 115 altered protein expressions were found with over twofold difference as compared with normal controls, which were associated with various molecular functions and biological processes. Here, we found that peroxiredoxin 1 (PRDX1) expression was higher in CRC tissues than that of matched controls and was determined as a tumor biomarker by receiver operating characteristic curve. PRDX1 expression was significantly upregulated in NCM460 cells challenged by H2O2 in a dose-dependent manner. PRDX1 depletion in SW480 cells enhanced reactive oxygen species (ROS), NO, and ONOO(-) production and increased the mRNA and protein expressions of pro-inflammatory cytokines [tumor necrosis factor-α, interleukin (IL)-1β, and IL-6] and chemokines (IL-8 and CXCL1), and partly activated nuclear factor-κB p65. Overall, our findings provide data on global alteration in the proteome of CRC tissues and reveal the potential of PRDX1 as an inflammation marker in CRC development, suggesting a novel therapy against inflammation-associated CRC.

Significance of amyloid A immunoexpression in the prognosis of renal cell carcinoma

The study investigated immunoexpression of amyloid A (AA) in clear cell renal cell carcinoma (CCRCC) and evaluated its clinicopathologic correlation, particularly in disease progression. Expression of AA protein was evaluated in patients with CCRCC by immunohistochemistry. 146 cancerous tissue samples from 86 male and 60 female patients were studied. The relationship between AA protein expression and TNM stage, nuclear grade, renal capsule invasion, perirenal invasion, and survival of the patients were assessed. Thirty four percent of CCRCC cases were AA positive. The positive AA immunoexpression was related to higher Fuhrman nuclear grade, presence of perirenal invasion of the tumor, and poor survival of patients with CCRCC. There was not any statistically significant difference between patients' gender, status of capsule invasion, and stages of the tumor in terms of AA immunoexpression. Tumor stage (Hazard ratio (HR) = 7.76 (95% CI: 2.43-24.8) for stage 3 and HR = 29.9 (95% CI: 6.97-128.32) for stage 4) and AA immunoexpression (HR = 2.16 (95% CI: 1.01-4.64) were found to be associated with survival of the patients with CCRCC in Cox regression analysis. Immunoexpression of AA was increased in high grade CCRCCs. Immunoexpression of AA was associated with poor survival in patients with CCRCC. Thus, AA staining might be used as a useful immunohistological marker for the prediction of poor prognosis in renal cell cancer.

Biomarkers for Bone Tumors: Discovery from Genomics and Proteomics Studies and Their Challenges

Diagnosis of bone tumor currently relies on imaging and biopsy, and hence, the need to find less invasive ways for its accurate detection. More recently, numerous promising deoxyribonucleic acid (DNA) and protein biomarkers with significant prognostic, diagnostic and/or predictive abilities for various types of bone tumors have been identified from genomics and proteomics studies. This article reviewed the putative biomarkers for the more common types of bone tumors (that is, osteosarcoma, Ewing sarcoma, chondrosarcoma [malignant] and giant cell tumor [benign]) that were unveiled from the studies. The benefits and drawbacks of these biomarkers, as well as the technology platforms involved in the research, were also discussed. Challenges faced in the biomarker discovery studies and the problems in their translation from the bench to the clinical settings were also addressed.

Real-time monitoring efficiency and toxicity of chemotherapy in patients with advanced lung cancer

Background

The Response Evaluation Criteria in Solid Tumors (RECIST) guideline and Common Terminology Criteria for Adverse Events (CTCAE) criteria are used to assess chemotherapy efficiency and toxicity in patients with advanced lung cancer. However, no real-time, synchronous indicators that can evaluate chemotherapy outcomes are available. We wanted to evaluate tumor response and toxicity in advanced lung cancer chemotherapy by using a novel synchronous strategy.

Results

We enrolled 316 patients with advanced lung cancer who were treated with cisplatin-based therapy and followed up them for 3 years. Plasma was obtained before and after every chemotherapy cycle. We quantitative assayed total plasma DNA and methylation of the APC/RASSF1A genes. Four parameters were assessed: methylation level before chemotherapy (meth_0 h), methylation level 24 h after chemotherapy (meth_24 h), total plasma DNA concentration before chemotherapy (DNA_0 h), and total plasma DNA concentration 24 h after chemotherapy (DNA_24 h). When meth_24 h > meth_0 h of at least one gene was used to predict tumor response, the correct prediction rate was 82.4 %. Additionally, patients for whom DNA_24 h/DNA_0 h ≤ 2 had mild toxicities. Therefore, meth_24 h > meth_0 h and DNA_24 h/DNA_0 h ≤ 2 were defined as criteria for better tumor response and fewer adverse events with a high correct prediction rate (84.7 %).

Conclusions

Quantitative analysis of total plasma DNA and plasma APC/RASSF1A methylation provide a real-time synchronous rapid monitoring indicator for therapeutic outcomes of advanced lung cancer, which could be a reference or supplementary guidelines in evaluating chemotherapy effects.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-015-0150-9) contains supplementary material, which is available to authorized users.

Mass spectrometry-based proteomics: from cancer biology to protein biomarkers, drug targets, and clinical applications

Proteomics is optimally suited to bridge the gap between genomic information on the one hand and biologic functions and disease phenotypes at the other, since it studies the expression and/or post-translational modification (especially phosphorylation) of proteins--the major cellular players bringing about cellular functions--at a global level in biologic specimens. Mass spectrometry technology and (bio)informatic tools have matured to the extent that they can provide high-throughput, comprehensive, and quantitative protein inventories of cells, tissues, and biofluids in clinical samples at low level. In this article, we focus on next-generation proteomics employing nanoliquid chromatography coupled to high-resolution tandem mass spectrometry for in-depth (phospho)protein profiling of tumor tissues and (proximal) biofluids, with a focus on studies employing clinical material. In addition, we highlight emerging proteogenomic approaches for the identification of tumor-specific protein variants, and targeted multiplex mass spectrometry strategies for large-scale biomarker validation. Below we provide a discussion of recent progress, some research highlights, and challenges that remain for clinical translation of proteomic discoveries.

Prognostic Fifteen-Gene Signature for Early Stage Pancreatic Ductal Adenocarcinoma

The outcomes of patients treated with surgery for early stage pancreatic ductal adenocarcinoma (PDAC) are variable with median survival ranging from 6 months to more than 5 years. This challenge underscores an unmet need for developing personalized medicine strategies to refine the current treatment decision-making process. To derive a prognostic gene signature for patients with early stage PDAC, a PDAC cohort from Moffitt Cancer Center (n = 63) was used with overall survival (OS) as the primary endpoint. This was further evaluated using an independent microarray cohort dataset (Stratford et al: n = 102). Technical validation was performed by NanoString platform. A prognostic 15-gene signature was developed and showed a statistically significant association with OS in the Moffitt cohort (hazard ratio [HR] = 3.26; p<0.001) and Stratford et al cohort (HR = 2.07; p = 0.02), and was independent of other prognostic variables. Moreover, integration of the signature with the TNM staging system improved risk prediction (p<0.01 in both cohorts). In addition, NanoString validation showed that the signature was robust with a high degree of reproducibility and the association with OS remained significant in the two cohorts. The gene signature could be a potential prognostic tool to allow risk-adapted stratification of PDAC patients into personalized treatment protocols; possibly improving the currently poor clinical outcomes of these patients.

Identification of plasma protein markers common to patients with malignant tumour and Abnormal Savda in Uighur medicine: a prospective clinical study

Background

Traditional Uighur medicine shares an origin with Greco-Arab medicine. It describes the health of a human body as the dynamic homeostasis of four normal Hilits (humours), known as Kan, Phlegm, Safra, and Savda. An abnormal change in one Hilit may cause imbalance among the Hilits, leading to the development of a syndrome. Abnormal Savda is a major syndrome of complex diseases that are associated with common biological changes during disease development. Here, we studied the protein expression profile common to tumour patients with Abnormal Savda to elucidate the biological basis of this syndrome and identify potential biomarkers associated with Abnormal Savda.

Methods

Patients with malignant tumours were classified by the diagnosis of Uighur medicine into two groups: Abnormal Savda type tumour (ASt) and non-Abnormal Savda type tumour (nASt), which includes other syndromes. The profile of proteins that were differentially expressed in ASt compared with nASt and normal controls (NC) was analysed by iTRAQ proteomics and evaluated by bioinformatics using MetaCore™ software and an online database. The expression of candidate proteins was verified in all plasma samples by enzyme-linked immunosorbent assay (ELISA).

Results

We identified 31 plasma proteins that were differentially expressed in ASt compared with nASt, of which only 10 showed quantitatively different expression between ASt and NC. Bioinformatics analysis indicated that most of these proteins are known biomarkers for neoplasms of the stomach, breast, and lung. ELISA detection showed significant upregulation of plasma SAA1 and SPP24 and downregulation of PIGR and FASN in ASt compared with nASt and NC (p < 0.05).

Conclusions

Abnormal Savda may be causally associated with changes in the whole regulation network of protein expression during carcinogenesis. The expression of potential biomarkers might be used to distinguish Abnormal Savda from other syndromes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12906-015-0526-6) contains supplementary material, which is available to authorized users.

Label-free quantitative mass spectrometry reveals a panel of differentially expressed proteins in colorectal cancer

To identify potential biomarkers involved in CRC, a shotgun proteomic method was applied to identify soluble proteins in three CRCs and matched normal mucosal tissues using high-performance liquid chromatography and mass spectrometry. Label-free protein profiling of three CRCs and matched normal mucosal tissues were then conducted to quantify and compare proteins. Results showed that 67 of the 784 identified proteins were linked to CRC (28 upregulated and 39 downregulated). Gene Ontology and DAVID databases were searched to identify the location and function of differential proteins that were related to the biological processes of binding, cell structure, signal transduction, cell adhesion, and so on. Among the differentially expressed proteins, tropomyosin-3 (TPM3), endoplasmic reticulum resident protein 29 (ERp29), 18 kDa cationic antimicrobial protein (CAMP), and heat shock 70 kDa protein 8 (HSPA8) were verified to be upregulated in CRC tissue and seven cell lines through western blot analysis. Furthermore, the upregulation of TPM3, ERp29, CAMP, and HSPA8 was validated in 69 CRCs byimmunohistochemistry (IHC) analysis. Combination of TPM3, ERp29, CAMP, and HSPA8 can identify CRC from matched normal mucosal achieving an accuracy of 73.2% using IHC score. These results suggest that TPM3, ERp29, CAMP, and HSPA8 are great potential IHC diagnostic biomarkers for CRC.

Quantification of peptides from immunoglobulin constant and variable regions by LC-MRM MS for assessment of multiple myeloma patients

PURPOSE

Quantitative MS assays for Igs are compared with existing clinical methods in samples from patients with plasma cell dyscrasias, for example, multiple myeloma (MM).

EXPERIMENTAL DESIGN

Using LC-MS/MS data, Ig constant region peptides, and transitions were selected for LC-MRM MS. Quantitative assays were used to assess Igs in serum from 83 patients. RNA sequencing and peptide-based LC-MRM are used to define peptides for quantification of the disease-specific Ig.

RESULTS

LC-MRM assays quantify serum levels of Igs and their isoforms (IgG1-4, IgA1-2, IgM, IgD, and IgE, as well as kappa (κ) and lambda (λ) light chains). LC-MRM quantification has been applied to single samples from a patient cohort and a longitudinal study of an IgE patient undergoing treatment, to enable comparison with existing clinical methods. Proof-of-concept data for defining and monitoring variable region peptides are provided using the H929 MM cell line and two MM patients.

CONCLUSIONS AND CLINICAL RELEVANCE

LC-MRM assays targeting constant region peptides determine the type and isoform of the involved Ig and quantify its expression; the LC-MRM approach has improved sensitivity compared with the current clinical method, but slightly higher inter-assay variability. Detection of variable region peptides is a promising way to improve Ig quantification, which could produce a dramatic increase in sensitivity over existing methods, and could further complement current clinical techniques.

Open tubular lab-on-column/mass spectrometry for targeted proteomics of nanogram sample amounts

A novel open tubular nanoproteomic platform featuring accelerated on-line protein digestion and high-resolution nano liquid chromatography mass spectrometry (LC-MS) has been developed. The platform features very narrow open tubular columns, and is hence particularly suited for limited sample amounts. For enzymatic digestion of proteins, samples are passed through a 20 µm inner diameter (ID) trypsin + endoproteinase Lys-C immobilized open tubular enzyme reactor (OTER). Resulting peptides are subsequently trapped on a monolithic pre-column and transferred on-line to a 10 µm ID porous layer open tubular (PLOT) liquid chromatography LC separation column. Wnt/ß-catenein signaling pathway (Wnt-pathway) proteins of potentially diagnostic value were digested+detected in targeted-MS/MS mode in small cell samples and tumor tissues within 120 minutes. For example, a potential biomarker Axin1 was identifiable in just 10 ng of sample (protein extract of ∼1,000 HCT15 colon cancer cells). In comprehensive mode, the current OTER-PLOT set-up could be used to identify approximately 1500 proteins in HCT15 cells using a relatively short digestion+detection cycle (240 minutes), outperforming previously reported on-line digestion/separation systems. The platform is fully automated utilizing common commercial instrumentation and parts, while the reactor and columns are simple to produce and have low carry-over. These initial results point to automated solutions for fast and very sensitive MS based proteomics, especially for samples of limited size.

Estimation of treatment policies based on functional predictors

Biosignatures such as brain scans, mass spectrometry, or gene expression profiles might one day be used to guide treatment selection and improve outcomes. This article develops a way of estimating optimal treatment policies based on data from randomized clinical trials by interpreting patient biosignatures as functional predictors. A flexible functional regression model is used to represent the treatment effect and construct the estimated policy. The effectiveness of the estimated policy is assessed by furnishing prediction intervals for the mean outcome when all patients follow the policy. The validity of these prediction intervals is established under mild regularity conditions on the functional regression model. The performance of the proposed approach is evaluated in numerical studies.

Differential expression analysis of Golgi apparatus proteomes in hepatocellular carcinomas and the surrounding liver tissues

AIM

Hepatocellular carcinoma (HCC) is the sixth most common malignancy worldwide. Liver is the largest human digestive gland with abundant Golgi apparatus involved in cell division, migration and apoptosis and others.

METHODS

In the present study, Golgi apparatus of HCC and the surrounding liver tissues were isolated by sucrose density gradient centrifugation and identified by electron microscopy and enzymology methods. Using 2-D gel electrophoresis and mass spectrometry, 17 differentially expressed protein of Golgi apparatus in HCC and the surrounding liver tissue were screened and identified in the Mascot database.

RESULTS

Of those differentially expressed proteins, six were upregulated and 11 were downregulated, some of them were related to the biological processes such as protein sorting, glycosylation, cell cycle regulation, transcription regulation and Golgi integrity. One protein, annexin A5, was verified to be upregulated in HCC by western blot.

CONCLUSION

The differentially expressed proteins may provide new insight into HCC biology and potential diagnostic and therapeutic biomarkers.

Angiogenic markers: molecular targets for personalized medicine in pituitary adenoma

AIM

Pituitary adenomas are typically slow-growing and histologically benign tumors that can occasionally behave in a malignant-like manner, invading adjacent structures or recurring after treatment. Using protein analysis methods and multiplex xMAP assays, we aimed to find out if these particular types of tumors express angiogenic markers VEGF and basic FGF (bFGF), which are associated with tumor growth and invasiveness, and quantify them in order to establish their usefulness as biomarkers.

MATERIALS & METHODS

We have analysed the expression of angiogenic markers VEGF and bFGF in serum and tissue specimens from 66 pituitary adenomas (43 invasive and 23 noninvasive). For serum analysis, we used xMAP and ELISA, and for tissue analysis, we performed histopathology and immunohistochemistry.

RESULTS & CONCLUSION

We measured the serum angiogenic factors in pituitary adenomas. The quantification methods revealed significant differences between pituitary adenoma patients and controls, for both VEGF (212.4 vs 112.5 pg/ml in controls) and bFGF (mean value of 12.6 vs 10.8 pg/ml in controls), and also differentiated between invasive and noninvasive adenomas (p < 0.05). The tissue expression of VEGF and bFGF strongly correlated with their serum level increase. Our findings can be further developed into methods for selection of patients suitable for personalized, antiangiogenic therapy.

Phosphoproteomics-based network medicine

One of the major tasks of phosphoproteomics is providing potential biomarkers for either diagnosis or drug targets in medical applications. Because most complex diseases are due to the actions of multiple genes/proteins, the identification of complex phospho-signatures containing multiple phosphorylation events within phosphoproteomics-based networks generates more efficient and robust biomarkers than a single, differentially phosphorylated substrate or site. Here, we briefly summarize the current efforts and progress in this newly emerging field of phosphoproteomics-based network medicine by reviewing the computational (re)construction of phosphorylation-mediated signaling networks from unannotated phosphoproteomic data, the discovery of robust network phospho-signatures and the application of these signatures for classifying cancers and predicting drug responses. The challenges as well as the potential advantages are evaluated and discussed. Although the current techniques are at present far from mature, we believe that such a systematic approach as we describe can generate more useful and robust biomarkers for biomedical usage, even at the current stage of development.

Prediction of cancer outcome using DNA microarray technology: past, present and future

BACKGROUND

The use of DNA microarray technology to predict cancer outcome already has a history of almost a decade. Although many breakthroughs have been made, the promise of individualized therapy is still not fulfilled. In addition, new technologies are emerging that also show promise in outcome prediction of cancer patients.

OBJECTIVE

The impact of DNA microarray and other 'omics' technologies on the outcome prediction of cancer patients was investigated. Whether integration of omics data results in better predictions was also examined.

METHODS

DNA microarray technology was focused on as a starting point because this technology is considered to be the most mature technology from all omics technologies. Next, emerging technologies that may accomplish the same goals but have been less extensively studied are described.

CONCLUSION

Besides DNA microarray technology, other omics technologies have shown promise in predicting the cancer outcome or have potential to replace microarray technology in the near future. Moreover, it is shown that integration of multiple omics data can result in better predictions of cancer outcome; but, owing to the lack of comprehensive studies, validation studies are required to verify which omics has the most information and whether a combination of multiple omics data improves predictive performance.

Implementing personalized medicine in a cancer center

In 2006, the Moffitt Cancer Center partnered with patients, community clinicians, industry, academia, and 17 hospitals in the United States to begin a personalized cancer care initiative called Total Cancer Care. Total Cancer Care was designed to collect tumor specimens and clinical data throughout a patient's lifetime, with the goal of finding "the right treatment, for the right patient, at the right time." Because Total Cancer Care is a partnership with the patient and involves collection of clinical data and tumor specimens for research purposes, a formal protocol and patient consent process was developed, and an information technology platform was constructed to provide a robust "warehouse" for clinical and molecular profiling data. To date, more than 76,000 cancer patients from Moffitt and consortium medical centers have been enrolled in the protocol. The Total Cancer Care initiative has developed many of the capabilities and resources that are building the foundation of personalized medicine.

Identification of the up-regulation of TP-alpha, collagen alpha-1(VI) chain, and S100A9 in esophageal squamous cell carcinoma by a proteomic method

Esophageal squamous cell carcinoma (ESCC) is one of the most common primary malignant tumor of digestive tract. However, the early diagnosis and molecular mechanisms that underlie tumor formation and progression have been progressed less. To identify new biomarkers for ESCC, we performed a comparative proteomic research. Isobaric tags for relative and absolute quantitation-based proteomic method was used to screen biomarkers between ESCC and normal. 802 non-redundant proteins were identified, 39 of which were differentially expressed with 1.5-fold difference (29 up-regulated and 10 down-regulated). Through Swiss-Prot and GO database, the location and function of differential proteins were analyzed, which are related to the biological processes of binding, cell structure, signal transduction, cell adhesion, etc. Among the differentially expressed proteins, TP-alpha, collagen alpha-1(VI) chain and S100A9 were verified to be upregulated in 77.19%, 75.44% and 59.65% of ESCC by immunohistochemistry and western-blot. Diagnostic value of these three proteins was validated. These results provide new insights into ESCC biology and potential diagnostic and therapeutic biomarkers, which suggest that TP-alpha, collagen alpha-1(VI) chain and S100A9 are potential biomarkers of ESCC, and may play an important role in tumorigenesis and development of ESCC.

From modules to medicine: How modular domains and their associated networks can enable personalized medicine

Unveiling of cancer genomes is unleashing new therapeutic strategies for cancer. With cancer parts lists in hand, new approaches to personalized medicine can be developed by understanding the assembly of cancer machines using modular domains in proteins and their associated networks. Using the Src-homology-2 (SH2) domain as an example, new profiling approaches can discern global patterns of tyrosine phosphorylation in cancer cells that can enable molecular cancer medicine. Identifying and quantifying protein-protein interactions also has the potential to subtype tumors and guide clinical decision making. These approaches should extend the impact of genomics through viewing the architecture of cancer systems and improve predictions of patient outcome and therapeutic response, as well as guide combination therapy approaches that attack cancer systems.

Preliminary proteomic-based identification of a novel protein for Down's syndrome in maternal serum

Prenatal screening for Down's syndrome (DS) is in need of improvement. As a powerful platform, proteomics techniques could also be used for identification of new biomarkers for DS screening. In this case-control proteome study, pregnant women were diagnosed prenatally by karyotype analysis from amniotic fluid (AF). Maternal serum samples were collected from six pregnancies with fetuses affected by DS and six pregnancies with normal fetuses. First, we used two-dimensional electrophoresis and mass spectrometry to identify the different levels of expression of proteins in maternal serum between the DS and control groups in the second trimester. Second, we used bioinformatics to analyze the proteins by DAVID. Then, the interesting candidates were further tested by enzyme-linked immunosorbent assay (ELISA). Twenty-nine proteins were successfully identified in maternal serum obtained from pregnancies with fetuses affected by DS. The top five proteins up-regulated were serotransferrin (TF), alpha-1b-glycoprotein (A1BG), desmin (DES), alpha-1-antitrypsin (SERPINA1) and ceruloplasmin (CP), while serum amyloid P-component (APCS) was the most down-regulated protein. These 29 proteins were categorized based on binding, catalytic activity and enzyme regulator activity. The biological roles were involved in biological regulation, metabolic processes, cellular processes and response to a stimulus. Based on ELISA, the median concentrations of CP and complement factor B (CFB) were 332.3 and 412.3 ng/mL, respectively. The concentrations of CP and CFB were significantly higher in the DS group than in the control group ( P < 0.05). In conclusion, proteomic approaches offer the possibility of further improving the performance of DS screening and our identification of up- and down-regulated proteins may lead to new candidates for DS screening.

The proteome of normal pancreatic juice

OBJECTIVES

The aims of this study were to characterize the proteome of normal pancreatic juice, to analyze the effect of secretin on the normal proteome, and to compare these results with published data from patients with pancreatic cancer.

METHODS

Paired pancreatic fluid specimens (before and after intravenous secretin stimulation) were obtained during endoscopic pancreatography from 3 patients without significant pancreatic pathology. Proteins were identified and quantified by mass spectrometry-based protein quantification technology. The human RefSeq (NCBI) database was used to compare the data in samples from patients without pancreatic disease with published data from 3 patients with pancreatic cancer.

RESULTS

A total of 285 proteins were identified in normal pancreatic juice. Ninety had sufficient amino acid sequences identified to characterize the protein with a high level of confidence. All 90 proteins were present before and after secretin administration but with altered relative concentrations, usually by 1 to 2 folds, after stimulation. Comparison with 170 published pancreatic cancer proteins yielded an overlap of only 42 proteins.

CONCLUSIONS

Normal pancreatic juice contains multiple proteins related to many biological processes. Secretin alters the concentration but not the spectrum of these proteins. The pancreatic juice proteome of patients without pancreatic disease and that of patients with pancreatic cancer differ markedly.

Prognostic and predictive value of a malignancy-risk gene signature in early-stage non-small cell lung cancer

BACKGROUND

The malignancy-risk gene signature is composed of numerous proliferative genes and has been applied to predict breast cancer risk. We hypothesized that the malignancy-risk gene signature has prognostic and predictive value for early-stage non-small cell lung cancer (NSCLC) patients.

METHODS

The ability of the malignancy-risk gene signature to predict overall survival (OS) of early-stage NSCLC patients was tested using a large NSCLC microarray dataset from the Director's Challenge Consortium (n = 442) and two independent NSCLC microarray datasets (n = 117 and 133, for the GSE13213 and GSE14814 datasets, respectively). An overall malignancy-risk score was generated by principal component analysis to determine the prognostic and predictive value of the signature. An interaction model was used to investigate a statistically significant interaction between adjuvant chemotherapy (ACT) and the gene signature. All statistical tests were two-sided.

RESULTS

The malignancy-risk gene signature was statistically significantly associated with OS (P < .001) of NSCLC patients. Validation with the two independent datasets demonstrated that the malignancy-risk score had prognostic and predictive values: Of patients who did not receive ACT, those with a low malignancy-risk score had increased OS compared with a high malignancy-risk score (P = .007 and .01 for the GSE13212 and GSE14814 datasets, respectively), indicating a prognostic value; and in the GSE14814 dataset, patients receiving ACT survived longer in the high malignancy-risk score group (P = .03), and a statistically significant interaction between ACT and the signature was observed (P = .02).

CONCLUSIONS

The malignancy-risk gene signature was associated with OS and was a prognostic and predictive indicator. The malignancy-risk gene signature could be useful to improve prediction of OS and to identify those NSCLC patients who will benefit from ACT.

Baseline plasma proteomic analysis to identify biomarkers that predict radiation-induced lung toxicity in patients receiving radiation for non-small cell lung cancer

PURPOSE

To identify new plasma proteomic markers before radiotherapy start to predict later grade ≥2 radiation-induced lung toxicity (RILT2).

METHODS

Fifty-seven patients with non-small cell lung cancer received radiotherapy (RT) were eligible. Forty-eight patients with minimum follow-up of 1 year, nine with RILT2 with tumor stage matched to 39 without RILT2, were enrolled for this analysis. Platelet-poor plasma was obtained within 2 weeks before radiotherapy. The plasma proteomes were compared using a multiplexed quantitative proteomics approach involving ExacTag labeling, reverse-phase high-performance liquid chromatography, and nano liquid chromatography electrospray ionization tandem mass spectrometry. Z scores and Bonferroni-adjusted p values for the two-sample mean comparison were used to identify the differential protein expression between patients with and without RILT2.

RESULTS

More than 200 proteins were identified and quantified. After excluding proteins that were not detected in at least 40% of the 48 patient samples, C4b-binding protein alpha chain and vitronectin had significantly higher (p < 0.001 and p = 0.02) expression levels in patients with RILT2 compared with patients without RILT2. These two proteins were validated by Western blot. Ingenuity pathway analysis revealed that they both play important roles in the inflammatory response and are associated with the known pathways of radiation-induced lung damage.

CONCLUSIONS

This proteomic approach demonstrates new plasma protein biomarkers before treatment for future studies on RILT2 prediction.

Tracking human multiple myeloma xenografts in NOD-Rag-1/IL-2 receptor gamma chain-null mice with the novel biomarker AKAP-4

BACKGROUND

Multiple myeloma (MM) is a fatal malignancy ranking second in prevalence among hematological tumors. Continuous efforts are being made to develop innovative and more effective treatments. The preclinical evaluation of new therapies relies on the use of murine models of the disease.

METHODS

Here we describe a new MM animal model in NOD-Rag1null IL2rgnull (NRG) mice that supports the engraftment of cell lines and primary MM cells that can be tracked with the tumor antigen, AKAP-4.

RESULTS

Human MM cell lines, U266 and H929, and primary MM cells were successfully engrafted in NRG mice after intravenous administration, and were found in the bone marrow, blood and spleen of tumor-challenged animals. The AKAP-4 expression pattern was similar to that of known MM markers, such as paraproteins, CD38 and CD45.

CONCLUSIONS

We developed for the first time a murine model allowing for the growth of both MM cell lines and primary cells in multifocal sites, thus mimicking the disease seen in patients. Additionally, we validated the use of AKAP-4 antigen to track tumor growth in vivo and to specifically identify MM cells in mouse tissues. We expect that our model will significantly improve the pre-clinical evaluation of new anti-myeloma therapies.