Personalized medicine and proteomics: lessons from non-small cell lung cancer

DNA Polymerase Alpha Subunit B Is a Binding Protein for Erlotinib Resistance in Non-Small Cell Lung Cancer

Simple Summary

Non-small-cell lung carcinoma (NSCLC) covers for almost 85% of all lung cancers and a major contributor to the overall cancer death rate. Erlotinib is used to treat NSCLC via inhibition of epithelial growth factor receptor (EGFR) kinase activity. Despite its high efficacy, recurrence can occur in patients who become resistant to the drug. We performed DARTS LC-MS/MS with SWATH of DIA analysis and identified a novel binding protein of Erlotinib that may underlie NSCLC resistance. Our study indicated that Erlotinib binds POLA2 in addition to EGFR. This was confirmed by DARTS and CETSA results. Importantly, POLA2 expression levels in four NSCLC cell lines were positively correlated with anti-proliferative Erlotinib efficacy (Pearson correlation coefficient, R = 0.9886). These results suggest that POLA2 is a novel complementary target protein of Erlotinib, and could clinically provide validity as a surrogate marker for drug resistance in patients with NSCLC.

Abstract

Erlotinib inhibits epithelial growth factor receptor (EGFR) kinase activity and is used to treat non-small cell lung cancer (NSCLC). Despite its high efficacy, recurrence can occur in patients who become resistant to the drug. To address the underlying mechanism of Erlotinib resistance, we investigated additional mechanisms related to mode-of-drug-action, by multiple protein-binding interactions, besides EGFR by using drug affinity responsive target stability (DARTS) and liquid chromatography-mass spectrometry (LC-MS/MS) methods with non-labeled Erlotinib. DNA polymerase alpha subunit B (POLA2) was identified as a new Erlotinib binding protein that was validated by the DARTS platform, complemented with cellular thermal shift assays. Genetic knock-down of POLA2 promoted the anti-proliferative effect of the drug in the Erlotinib-resistant cell line H1299 with high POLA2 expression, whereas the overexpression of POLA2 restored anti-proliferative effects in the Erlotinib-sensitive cell line HCC827 with low POLA2 expression. Importantly, POLA2 expression levels in four NSCLC cell lines were positively correlated with anti-proliferative Erlotinib efficacy (Pearson correlation coefficient, R = 0.9886). These results suggest that POLA2 is a novel complementary target protein of Erlotinib, and could clinically provide validity as a surrogate marker for drug resistance in patients with NSCLC.

Clinical protein science in translational medicine targeting malignant melanoma

Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry-based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular functions underlying disease progression. In parallel to a streamlined, patient-centric, clinical proteomic pipeline, mass spectrometry-based imaging can aid in interrogating the spatial distribution of drugs and drug metabolites within tissues at single-cell resolution. These developments are an important advancement in studying drug action and efficacy in vivo and will aid in the development of more effective and safer strategies for the treatment of melanoma. A collaborative effort of gargantuan proportions between academia and healthcare professionals has led to the initiation, establishment and development of a cutting-edge cancer research centre with a specialisation in melanoma and lung cancer. The primary research focus of the European Cancer Moonshot Lund Center is to understand the impact that drugs have on cancer at an individualised and personalised level. Simultaneously, the centre increases awareness of the relentless battle against cancer and attracts global interest in the exceptional research performed at the centre.

Urothelial cancer proteomics provides both prognostic and functional information

Traditionally, bladder cancer has been classified based on histology features. Recently, some works have proposed a molecular classification of invasive bladder tumors. To determine whether proteomics can define molecular subtypes of  muscle invasive urothelial cancer (MIUC) and allow evaluating the status of biological processes and its clinical value. 58 MIUC patients who underwent curative surgical resection at our institution between 2006 and 2012 were included. Proteome was evaluated by high-throughput proteomics in routinely archive FFPE tumor tissue. New molecular subgroups were defined. Functional structure and individual proteins prognostic value were evaluated and correlated with clinicopathologic parameters. 1,453 proteins were quantified, leading to two MIUC molecular subgroups. A protein-based functional structure was defined, including several nodes with specific biological activity. The functional structure showed differences between subtypes in metabolism, focal adhesion, RNA and splicing nodes. Focal adhesion node has prognostic value in the whole population. A 6-protein prognostic signature, associated with higher risk of relapse (5 year DFS 70% versus 20%) was defined. Additionally, we identified two MIUC subtypes groups. Prognostic information provided by pathologic characteristics is not enough to understand MIUC behavior. Proteomics analysis may enhance our understanding of prognostic and classification. These findings can lead to improving diagnosis and treatment selection in these patients.

Functional proteomics outlines the complexity of breast cancer molecular subtypes

Breast cancer is a heterogeneous disease comprising a variety of entities with various genetic backgrounds. Estrogen receptor-positive, human epidermal growth factor receptor 2-negative tumors typically have a favorable outcome; however, some patients eventually relapse, which suggests some heterogeneity within this category. In the present study, we used proteomics and miRNA profiling techniques to characterize a set of 102 either estrogen receptor-positive (ER+)/progesterone receptor-positive (PR+) or triple-negative formalin-fixed, paraffin-embedded breast tumors. Protein expression-based probabilistic graphical models and flux balance analyses revealed that some ER+/PR+ samples had a protein expression profile similar to that of triple-negative samples and had a clinical outcome similar to those with triple-negative disease. This probabilistic graphical model-based classification had prognostic value in patients with luminal A breast cancer. This prognostic information was independent of that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score.

A selected reaction monitoring mass spectrometric assessment of biomarker candidates diagnosing large-cell neuroendocrine lung carcinoma by the scaling method using endogenous references

Selected reaction monitoring mass spectrometry (SRM-MS) -based semi-quantitation was performed to assess the validity of 46 selected candidate proteins for specifically diagnosing large-cell neuroendocrine lung carcinoma (LCNEC) and differentiating it from other lung cancer subtypes. The scaling method was applied in this study using specific SRM peak areas (AUCs) derived from the endogenous reference protein that normalizes all SRM AUCs obtained for the candidate proteins. In a screening verification study, we found that seven out of the 46 candidate proteins were statistically significant for the LCNEC phenotype, including 4F2hc cell surface antigen heavy chain (4F2hc/CD98) (p-ANOVA ≤ 0.0012), retinal dehydrogenase 1 (p-ANOVA ≤ 0.0029), apolipoprotein A-I (p-ANOVA ≤ 0.0004), β-enolase (p-ANOVA ≤ 0.0043), creatine kinase B-type (p-ANOVA ≤ 0.0070), and galectin-3-binding protein (p-ANOVA = 0.0080), and phosphatidylethanolamine-binding protein 1 (p-ANOVA ≤ 0.0012). In addition, we also identified candidate proteins specific to the small-cell lung carcinoma (SCLC) subtype. These candidates include brain acid soluble protein 1 (p-ANOVA < 0.0001) and γ-enolase (p-ANOVA ≤ 0.0013). This new relative quantitation-based approach utilizing the scaling method can be applied to assess hundreds of protein candidates obtained from discovery proteomic studies as a first step of the verification phase in biomarker development processes.

Maternal vitamin D deficiency and fetal distress/birth asphyxia: a population-based nested case-control study

OBJECTIVE

Vitamin D deficiency causes not only skeletal problems but also muscle weakness, including heart muscle. If the fetal heart is also affected, it might be more susceptible to fetal distress and birth asphyxia. In this pilot study, we hypothesised that low maternal vitamin D levels are over-represented in pregnancies with fetal distress/birth asphyxia.

DESIGN AND SETTING

A population-based nested case-control study.

PATIENTS

Banked sera of 2496 women from the 12th week of pregnancy.

OUTCOME MEASURES

Vitamin D levels were analysed using a direct competitive chemiluminescence immunoassay. Vitamin D levels in early gestation in women delivered by emergency caesarean section due to suspected fetal distress were compared to those in controls. Birth asphyxia was defined as Apgar <7 at 5 min and/or umbilical cord pH≤7.15.

RESULTS

Vitamin D levels were significantly lower in mothers delivered by emergency caesarean section due to suspected fetal distress (n=53, 43.6±18 nmol/L) compared to controls (n=120, 48.6±19 nmol/L, p=0.04). Birth asphyxia was more common in women with vitamin D deficiency (n=95) in early pregnancy (OR 2.4, 95% CI 1.1 to 5.7).

CONCLUSIONS

Low vitamin D levels in early pregnancy may be associated with emergency caesarean section due to suspected fetal distress and birth asphyxia. If our findings are supported by further studies, preferably on severe birth asphyxia, vitamin D supplementation/sun exposure in pregnancy may lower the risk of subsequent birth asphyxia.

Cancer heterogeneity determined by functional proteomics

Current manuscript gives a synopsis of tumor heterogeneity related to patient samples analyzed by proteomics, protein expression analysis and imaging mass spectrometry. First, we discuss the pathophysiologocal background of cancer biology as a multifactorial and challenging diseases. Disease pathology forms the basis for protein target selection. Therefore, histopathological diagnostics and grading of tumors is highlighted. Pathology is the cornerstone of state-of-the-art diagnostics of tumors today both by establishing dignity and - when needed - describing molecular properties of the cancers. Drug development by the pharmaceutical industry utilizes proteomics studies to pinpoint the most relevant targets. Molecular studies profiling affinity-interactions of the protein(s) with targeted small drug molecules to reach efficacy and optimal patient safety are today requested by the FDA and other agencies for new drug development. An understading of basic mechanisms, controlling drug action and drug binding is central, as a new era of personalized medicine becomes an important milestone solution for the healthcare sector as well as the Pharma and Biotech industry. Development of further diagnostic, prognostic and predictive tests will aid current and future treatment of cancer patients. In the paper we present current status of Proteomics that we believe requires attention in order to collectively advance forward in the fight against cancer, addressing the burning opportunities and challenges.

Abnormal levels of heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) in tumour tissue and blood samples from patients diagnosed with lung cancer

Lung cancer is the second most common type of cancer in the world and is the most common cause of cancer-related death in both men and women. Research into causes, prevention and treatment of lung cancer is ongoing and much progress has been made recently in these areas, however survival rates have not significantly improved. Therefore, it is essential to develop biomarkers for early diagnosis of lung cancer, prediction of metastasis and evaluation of treatment efficiency, as well as using these molecules to provide some understanding about tumour biology and translate highly promising findings in basic science research to clinical application. In this investigation, two-dimensional difference gel electrophoresis and mass spectrometry were initially used to analyse conditioned media from a panel of lung cancer and normal bronchial epithelial cell lines. Significant proteins were identified with heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1), pyruvate kinase M2 isoform (PKM2), Hsc-70 interacting protein and lactate dehydrogenase A (LDHA) selected for analysis in serum from healthy individuals and lung cancer patients. hnRNPA2B1, PKM2 and LDHA were found to be statistically significant in all comparisons. Tissue analysis and knockdown of hnRNPA2B1 using siRNA subsequently demonstrated both the overexpression and potential role for this molecule in lung tumorigenesis. The data presented highlights a number of in vitro derived candidate biomarkers subsequently verified in patient samples and also provides some insight into their roles in the complex intracellular mechanisms associated with tumour progression.

Clinical initiatives linking Japanese and Swedish healthcare resources on cancer studies utilizing Biobank Repositories

The Tokyo Medical University Hospital in Japan and the Lund University hospital in Sweden have recently initiated a research program with the objective to impact on patient treatment by clinical disease stage characterization (phenotyping), utilizing proteomics sequencing platforms. By sharing clinical experiences, patient treatment principles, and biobank strategies, our respective clinical teams in Japan and Sweden will aid in the development of predictive and drug related protein biomarkers.

Data from joint lung cancer studies are presented where protein expression from Neuro- Endocrine lung cancer (LCNEC) phenotype patients can be separated from Small cell- (SCLC) and Large Cell lung cancer (LCC) patients by deep sequencing and spectral counting analysis. LCNEC, a subtype of large cell carcinoma (LCC), is characterized by neuroendocrine differentiation that small cell lung carcinoma (SCLC) shares. Pre-therapeutic histological distinction between LCNEC and SCLC has so far been problematic, leading to adverse clinical outcome. An establishment of protein targets characteristic of LCNEC is quite helpful for decision of optimal therapeutic strategy by diagnosing individual patients. Proteoform annotation and clinical biobanking is part of the HUPO initiative (http://www.hupo.org) within chromosome 10 and chromosome 19 consortia.

Experimental models to study drug distributions in tissue using MALDI mass spectrometry imaging

Requirements for patient safety and improved efficacy are steadily increasing in modern healthcare and are key drivers in modern drug development. New drug characterization assays are central in providing evidence of the specificity and selectivity of drugs. Meeting this need, matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) is used to study drug localization within microenvironmental tissue compartments. Thin sections of human lung tumor and rat xenograft tissues were exposed to pharmaceutical drugs by either spotting or submerging. These drugs, the epidermal growth factor receptor antagonists, erlotinib (Tarceva) and gefitinib (Iressa), and the acetylcholine receptor antagonist, tiotropium, were characterized by microenvironment localization. Intact tissue blocks were also immersed in drug solution, followed by sectioning. MALDI-MSI was then performed using a Thermo MALDI LTQ Orbitrap XL instrument to localize drug-distribution patterns. We propose three MALDI-MSI models measuring drug disposition that have been used to map the selected compounds within tissue compartments of tumors isolated from lung cancer patients.

Identification of α1-antitrypsin as a potential prognostic biomarker for advanced nonsmall cell lung cancer treated with epidermal growth factor receptor tyrosine kinase inhibitors by proteomic analysis

OBJECTIVE

This retrospective study attempted to identify serum biomarkers that could help to indicate treatment response in advanced nonsmall-cell lung cancer (NSCLC) patients receiving epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) treatment.

METHODS

Two-dimensional fluorescence difference gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry were used to identify proteins expressed in serum samples from NSCLC patients with long (>6-month) progression-free survival (PFS) periods, following EGFR-TKI treatment.

RESULTS

Serum amyloid P component (APCS), α1-antitrypsin (AAT), fibrinogen-α (FGA), keratin type I cytoskeletal 10 (KRT10) and serotransferrin (TF) expression differed between samples taken from 18 patients before treatment (baseline) and when progressive disease (PD) was observed, during treatment. Changes in AAT, KRT10 and APCS levels were validated by Western blot analysis in the sample pool; findings were further validated by Western blot analysis in a random sample of four patients. These proteins were also present in serum samples obtained from the same patients at the partial response (PR) timepoint during EGFR-TKI treatment. AAT was upregulated at PD compared with baseline, but downregulated during the PR phase.

CONCLUSION

These observations suggest that AAT could be used as a serological biomarker for predicting the utility of EGFR-TKI treatment for advanced NSCLC.

Nanoproteomics enabling personalized nanomedicine

Nucleic Acid Programmable Protein Arrays utilize a complex mammalian cell free expression system to produce proteins in situ. In alternative to fluorescent-labeled approaches a new label free method, emerging from the combined utilization of three independent and complementary nanotechnological approaches, appears capable to analyze protein function and protein-protein interaction in studies promising for personalized medicine. Quartz Micro Circuit nanogravimetry, based on frequency and dissipation factor, mass spectrometry and anodic porous alumina overcomes indeed the limits of correlated fluorescence detection plagued by the background still present after extensive washes. This could be further optimized by a homogeneous and well defined bacterial cell free expression system capable to realize the ambitious objective to quantify the regulatory protein networks in humans. Implications for personalized medicine of the above label free protein array using different test genes proteins are reported.

Understanding drug uptake and binding within targeted disease micro-environments in patients: a new tool for translational medicine

BACKGROUND

For many common global diseases, such as cancer, diabetes, neurodegenerative and cardiovascular diseases there is an unmet need for diagnosing early indications of disease that could enable medical intervention and early treatment. The treatment of these diseases will require detailed knowledge of targeted pathways involved in disease pathogenesis but also the mode of drug actions at the biological location on these targets. Translational medicine is a new area of research where expert from different disciplines involved in basic science and clinical disciplines meet and join forces. Mode-of-drug-action mechanisms elucidation is key in the characterization of drugs that can relate to both efficacy and safety.

METHODS

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used providing evidence into the fate (destinations and distributions) of administered drugs within tumor regions of lung compartments.

RESULTS

We hereby present a pulmonary study in which we have isolated lung tissue after inhaled drug administration and then localized the drug within airway wall compartments. The histology also provides evidence of drug binding to smooth muscle cell microenvironments. We also identified lung tissue regions with tumor cell invasion in these COPD patients.

CONCLUSIONS

The ultimate goal is to identify bridging comprehension that forms a knowledge base that can be used by society to develop a better treatment and medicine for patients. Our results demonstrated that robust imaging data could be generated confirming drug localization in pulmonary regions of COPD patients with tumor pathology.

TRIAL REGISTRATION

Tallinn Medical Research Ethical Committee decision #1724, 18.06.2009.

Cancer Phenotype Diagnosis and Drug Efficacy within Japanese Health Care

An overview on targeted personalized medicine is given describing the developments in Japan of lung cancer patients. These new targeted therapies with novel personalized medicine drugs require new implementations, in order to follow and monitor drug efficacy and outcome. Examples from IRESSA (Gefitinib) and TARCEVA (Erlotinib) treatments used in medication of lung cancer patients are presented. Lung cancer is one of the most common causes of cancer mortality in the world. The importance of both the quantification of disease progression, where diagnostic-related biomarkers are being implemented, in addition to the actual measurement of disease-specific mechanisms relating to pathway signalling activation of disease-progressive protein targets is summarised. An outline is also presented, describing changes and adaptations in Japan, meeting the rising costs and challenges. Today, urgent implementation of programs to address these needs has led to a rebuilding of the entire approach of medical evaluation and clinical care.

PTRF/cavin-1 and MIF proteins are identified as non-small cell lung cancer biomarkers by label-free proteomics

With the completion of the human genome sequence, biomedical sciences have entered in the “omics” era, mainly due to high-throughput genomics techniques and the recent application of mass spectrometry to proteomics analyses. However, there is still a time lag between these technological advances and their application in the clinical setting. Our work is designed to build bridges between high-performance proteomics and clinical routine. Protein extracts were obtained from fresh frozen normal lung and non-small cell lung cancer samples. We applied a phosphopeptide enrichment followed by LC-MS/MS. Subsequent label-free quantification and bioinformatics analyses were performed. We assessed protein patterns on these samples, showing dozens of differential markers between normal and tumor tissue. Gene ontology and interactome analyses identified signaling pathways altered on tumor tissue. We have identified two proteins, PTRF/cavin-1 and MIF, which are differentially expressed between normal lung and non-small cell lung cancer. These potential biomarkers were validated using western blot and immunohistochemistry. The application of discovery-based proteomics analyses in clinical samples allowed us to identify new potential biomarkers and therapeutic targets in non-small cell lung cancer.

Personalized medicine: changing the paradigm of drug development

Despite an increased investment in research and development, there has been a steady decline in the number of drugs brought to market over the past 40 years. The tools of personalized medicine are refining diseases into molecular categories, and future therapeutics may be dictated by a patient's molecular profile relative to these categories. The adoption of a personalized medicine approach to drug development may improve the success rate by minimizing variability during each phase of the drug development process. This chapter describes the current paradigm of drug development and then discusses how molecular profiling/personalized medicine might be used to improve upon this paradigm.

State-of-the-art MS technology applications in lung disease

Two frontline MS technologies, which have recently gained much attention, are discussed within the scope of this review. Besides a brief summary on the contemporary state of lung cancer and chronic obstructive pulmonary disease, the principles of multiple reaction monitoring and matrix assisted laser desorption ionization (MALDI) MS imaging are presented. A comprehensive overview of quantitative mass spectrometry applications is provided, covering multiple reaction monitoring assay developments for analysis of proteins (biomarkers) and low-molecular-weight compounds (drugs) with a special focus on the disease areas of lung cancer and chronic obstructive pulmonary disease. The MALDI-MS imaging applications are discussed similarly, providing references to studies conducted on lung tissues in order to localize drug compounds and protein biomarkers.

Biobank resources for future patient care: developments, principles and concepts

The aim of the overview is to give a perspective of global biobank development is given in a view of positioning biobanking as a key resource for healthcare to identify new potential markers that can be used in patient diagnosis and complement the targeted personalized drug treatment. The fast progression of biobanks around the world is becoming an important resource for society where the patient benefit is in the focus, with a high degree of personal integrity and ethical standard. Biobanks are providing patient benefits by large scale screening studies, generating large database repositories. It is envisioned by all participating stakeholders that the biobank initiatives will become the future gateway to discover new frontiers within life science and patient care. There is a great importance of biobank establishment globally, as biobanks has been identified as a key area for development in order to speed up the discovery and development of new drugs and protein biomarker diagnostics. One of the major objectives in Europe is to establish concerted actions, where biobank networks are being developed in order to combine and have the opportunity to share and build new science and understanding from complex disease biology. These networks are currently building bridges to facilitate the establishments of best practice and standardizations.

Identification by differential tissue proteome analysis of talin-1 as a novel molecular marker of progression of hepatocellular carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) is characterized by a multistage process of tumor progression. This study addressed its molecular features to identify novel protein candidates involved in HCC progression.

METHODS

Using liquid chromatography-tandem mass spectrometry, proteomes of 4 early HCCs and 4 non-HCC tissues derived from 2 cases of liver transplant surgery were compared with respect to the separation profiles of their tryptic peptides. Immunohistochemistry was performed on 106 HCC nodules to confirm the results of the proteomic analysis.

RESULTS

Statistical analysis of the profiles selected the peptide peaks differentiating HCC from non-HCC. A database search of the tandem mass spectrometry data from those peptide peaks identified 61 proteins, including a cytoskeletal protein, talin-1, as upregulated in HCC. Talin-1 expression levels in HCC nodules were significantly associated with the dedifferentiation of HCC (p = 0.001). A follow-up survey of the examined clinical cases revealed a correlation between talin-1 upregulation and a shorter time to recurrence after resection (p = 0.039), which may be related to the higher rate of portal vein invasion in HCCs with talin-1 up-regulation (p = 0.029).

CONCLUSIONS

Proteomic analysis led to identification of talin-1 as a promising HCC marker. Talin-1 upregulation is associated with HCC progression and may serve as a prognostic marker.

Proteomic biomarkers for acute interstitial lung disease in gefitinib-treated Japanese lung cancer patients

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10(-25)), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.

Moving towards high density clinical signature studies with a human proteome catalogue developing multiplexing mass spectrometry assay panels

A perspective overview is given describing the current development of multiplex mass spectrometry assay technology platforms utilized for high throughput clinical sample analysis. The development of targeted therapies with novel personalized medicine drugs will require new tools for monitoring efficacy and outcome that will rely on both the quantification of disease progression related biomarkers as well as the measurement of disease specific pathway/signaling proteins.The bioinformatics developments play a key central role in the area of clinical proteomics where targeted peptide expressions in health and disease are investigated in small-, medium- and large-scaled clinical studies.An outline is presented describing applications of the selected reaction monitoring (SRM) mass spectrometry assay principle. This assay form enables the simultaneous description of multiple protein biomarkers and is an area under a fast and progressive development throughout the community. The Human Proteome Organization, HUPO, recently launched the Human Proteome Project (HPP) that will map the organization of proteins on specific chromosomes, on a chromosome-by-chromosome basis utilizing the SRM technology platform. Specific examples of an SRM-multiplex quantitative assay platform dedicated to the cardiovascular disease area, screening Apo A1, Apo A4, Apo B, Apo CI, Apo CII, Apo CIII, Apo D, Apo E, Apo H, and CRP biomarkers used in daily diagnosis routines in clinical hospitals globally, are presented. We also provide data on prostate cancer studies that have identified a variety of PSA isoforms characterized by high-resolution separation interfaced to mass spectrometry.

Developments for a growing Japanese patient population: facilitating new technologies for future health care

Lung cancer, COPD and cardiovascular diseases are highlighted as some of the most common disease that cause mortality, and for that reason are the most active areas for drug development. This perspective paper overviews the urgent need to develop a health care system for a rapidly growing patient population in Japan, including forthcoming demands on clinical care, expecting outcomes, and economics. There is an increasing requirement to build on the strengths of the current health care system, thereby delivering urgent solutions for the future. There is also a declaration from the Ministry of Health, Labour and Welfare (MHLW), to develop new biomarker diagnostics, which is intended for patient stratification, aiding in diagnostic phenotype selection for responders to drug treatment of Japanese patients. This perspective was written by the panel in order to introduce novel technologies and diagnostic capabilities with successful implementation. The next generation of personalized drugs for targeted and stratified patient treatment will soon be available in major disease areas such as, lifestyle-related cancers, especially lung cancers with the highest mortality including a predisposing disorder chronic obstructive pulmonary disease, cardiovascular disease, and other diseases. Mass spectrometric technologies can provide the "phenotypic fingerprint" required for the concept of Personalized Medicine. Mass spectrometry-driven target biomarker diagnoses in combination with high resolution computed tomography can provide a critical pathway initiative facilitated by a fully integrated e-Health infrastructure system. We strongly recommend integrating validated biomarkers based on clinical proteomics, medical imaging with clinical care supported by e-Health model to support personalized treatment paradigms to reduce mortality and healthcare costs of chronic and co-morbid diseases in the elderly population of Japan.

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.

A reevaluation of the clinical significance of histological subtyping of non--small-cell lung carcinoma: diagnostic algorithms in the era of personalized treatments

The classification of lung cancer has always been primarily based on the morphologic assessment of routinely stained histological sections, but this approach may be difficult or even unfeasible in cytological preparations or small biopsies. Moreover, the simplistic dichotomization between small-cell carcinoma and non-small cell carcinoma (NSCLC) should be overcome, as new drugs have been discovered that are effective in specific subtypes of lung cancer. A more accurate characterization of NSCLC, however, may be hard in carcinomas lacking clear-cut signs of differentiation. The incorporation into the diagnostic algorithm of poorly differentiated carcinomas of an immunohistochemical panel including markers of squamous (high-molecular-weight cytokeratins, p63) and glandular (TTF-1, cytokeratin 7) cell differentiation seems the most promising approach. The evaluation of lung cancer for gene mutations, gene amplification, tumor-related angiogenesis, expression levels of DNA repair genes and genomic or proteomic profiles represents an exciting challenge for the pathologist in the near future.

Quantitative mass spectrometry-based techniques for clinical use: biomarker identification and quantification

The potential for development of personalised medicine through the characterisation of novel biomarkers is an exciting prospect for improved patient care. Recent advances in mass spectrometric (MS) techniques, liquid phase analyte separation and bioinformatic tools for high throughput now mean that this goal may soon become a reality. However, there are challenges to be overcome for the identification and validation of robust biomarkers. Bio-fluids such as plasma and serum are a rich source of protein, many of which may reflect disease status, and due to the ease of sampling and handling, novel blood borne biomarkers are very much sought after. MS-based methods for high throughput protein identification and quantification are now available such that the issues arising from the huge dynamic range of proteins present in plasma may be overcome, allowing deep mining of the blood proteome to reveal novel biomarker signatures for clinical use. In addition, the development of sensitive MS-based methods for biomarker validation may bypass the bottleneck created by the need for generation and usage of reliable antibodies prior to large scale screening. In this review, we discuss the MS-based methods that are available for clinical proteomic analysis and highlight the progress made and future challenges faced in this cutting edge area of research.

Utilizing the molecular gateway: the path to personalized cancer management

BACKGROUND

Personalized medicine is the provision of focused prevention, detection, prognostic, and therapeutic efforts according to an individual's genetic composition. The actualization of personalized medicine will require combining a patient's conventional clinical data with bioinformatics-based molecular-assessment profiles. This synergistic approach offers tangible benefits, such as heightened specificity in the molecular classification of cancer subtypes, improved prognostic accuracy, targeted development of new therapies, novel applications for old therapies, and tailored selection and delivery of chemotherapeutics.

CONTENT

Our ability to personalize cancer management is rapidly expanding through biotechnological advances in the postgenomic era. The platforms of genomics, proteomics, single-nucleotide polymorphism profiling and haplotype mapping, high-throughput genomic sequencing, and pharmacogenomics constitute the mechanisms for the molecular assessment of a patient's tumor. The complementary data derived during these assessments is processed through bioinformatics analysis to offer unique insights for linking expression profiles to disease detection, tumor response to chemotherapy, and patient survival. Together, these approaches permit improved physician capacity to assess risk, target therapies, and tailor a chemotherapeutic treatment course.

SUMMARY

Personalized medicine is poised for rapid growth as the insights provided by new bioinformatics models are integrated with current procedures for assessing and treating cancer patients. Integration of these biological platforms will require refinement of tissue-processing and analysis techniques, particularly in clinical pathology, to overcome obstacles in customizing our ability to treat cancer.

Comparative analysis of the liver and plasma proteomes as a novel and powerful strategy for hepatocellular carcinoma biomarker discovery

The extraordinary developments made in the past decade in proteomic technologies, in particular in mass spectrometry, have enabled investigators to consider designing studies to search for diagnostic and therapeutic biomarkers by scanning complex proteome samples. We developed a method based on extensive fractionation of intact proteins, to comprehensively and quantitatively profile the liver and plasma proteomes in health and disease. We have applied this method to samples collected from patients with early hepatocellular carcinoma (HCC) and from patients with liver cirrhosis as well as to samples collected from three mouse models of HCC. This method allowed for the identification of proteins that differ in expression levels in liver tissue or in plasma with disease progression from liver fibrosis, cirrhosis or steatohepatitis to HCC. The comparative analysis of the liver and plasma proteomes generated from human and mouse specimens, constitutes a novel and powerful strategy for HCC biomarker discovery.

Predicting the future for people with lung cancer

A large multicenter study shows that lung adenocarcinomas have messenger RNA expression signatures that greatly add to the use of clinical data in predicting an individual’s survival (pages 822–827).

Mining the archival formalin-fixed paraffin-embedded tissue proteome: opportunities and challenges

The significant potential of tissue-based proteomic biomarker studies can be restricted by difficulties in accessing samples in optimal fresh-frozen form. While archival formalin-fixed tissue collections with attached clinical and outcome data represent a valuable alternate resource, the use of formalin as a fixative which induces protein cross-linking, has generally been assumed to render them unsuitable for proteomic studies. However, this view has been challenged recently with the publication of several papers accomplishing variable degrees of heat-induced reversal of cross-links. Although still in its infancy and requiring the quantitative optimisation of several critical parameters, formalin-fixed tissue proteomics holds promise as a powerful tool for biomarker-driven translational research. Here, we critically review the current status of research in the field, highlighting challenges which need to be addressed for robust quantitative application of protocols to ensure confident high impact inferences can be made.

Multidimensional proteomics analysis of amniotic fluid to provide insight into the mechanisms of idiopathic preterm birth

Background

Though recent advancement in proteomics has provided a novel perspective on several distinct pathogenetic mechanisms leading to preterm birth (inflammation, bleeding), the etiology of most preterm births still remains elusive. We conducted a multidimensional proteomic analysis of the amniotic fluid to identify pathways related to preterm birth in the absence of inflammation or bleeding.

Methodology/Principal Findings

A proteomic fingerprint was generated from fresh amniotic fluid using surface-enhanced laser desorbtion ionization time of flight (SELDI-TOF) mass spectrometry in a total of 286 consecutive samples retrieved from women who presented with signs or symptoms of preterm labor or preterm premature rupture of the membranes. Inflammation and/or bleeding proteomic patterns were detected in 32% (92/286) of the SELDI tracings. In the remaining tracings, a hierarchical algorithm was applied based on descriptors quantifying similarity/dissimilarity among proteomic fingerprints. This allowed identification of a novel profile (Q-profile) based on the presence of 5 SELDI peaks in the 10–12.5 kDa mass area. Women displaying the Q-profile (mean±SD, gestational age: 25±4 weeks, n = 40) were more likely to deliver preterm despite expectant management in the context of intact membranes and normal amniotic fluid clinical results. Utilizing identification-centered proteomics techniques (fluorescence two-dimensional differential gel electrophoresis, robotic tryptic digestion and mass spectrometry) coupled with Protein ANalysis THrough Evolutionary Relationships (PANTHER) ontological classifications, we determined that in amniotic fluids with Q-profile the differentially expressed proteins are primarily involved in non-inflammatory biological processes such as protein metabolism, signal transduction and transport.

Conclusion/Significance

Proteomic profiling of amniotic fluid coupled with non-hierarchical bioinformatics algorithms identified a subgroup of patients at risk for preterm birth in the absence of intra-amniotic inflammation or bleeding, suggesting a novel pathogenetic pathway leading to preterm birth. The altered proteins may offer opportunities for therapeutical intervention and future drug development to prevent prematurity.

Proteomics of the human placenta: promises and realities

Proteomics is an area of study that sets as its ultimate goal the global analysis of all of the proteins expressed in a biological system of interest. However, technical limitations currently hamper proteome-wide analyses of complex systems. In a more practical sense, a desired outcome of proteomics research is the translation of large protein data sets into formats that provide meaningful information regarding clinical conditions (e.g., biomarkers to serve as diagnostic and/or prognostic indicators of disease). Herein, we discuss placental proteomics by describing existing studies, pointing out their strengths and weaknesses. In so doing, we strive to inform investigators interested in this area of research about the current gap between hyperbolic promises and realities. Additionally, we discuss the utility of proteomics in discovery-based research, particularly as regards the capacity to unearth novel insights into placental biology. Importantly, when considering under studied systems such as the human placenta and diseases associated with abnormalities in placental function, proteomics can serve as a robust 'shortcut' to obtaining information unlikely to be garnered using traditional approaches.