Unveiling the potential of proteomic and genetic signatures for precision therapeutics in lung cancer management

Lung cancer's enduring global significance necessitates ongoing advancements in diagnostics and therapeutics. Recent spotlight on proteomic and genetic biomarker research offers a promising avenue for understanding lung cancer biology and guiding treatments. This review elucidates genetic and proteomic lung cancer biomarker progress and their treatment implications. Technological strides in mass spectrometry-based proteomics and next-generation sequencing enable pinpointing of genetic abnormalities and abnormal protein expressions, furnishing vital data for precise diagnosis, patient classification, and customized treatments. Biomarker-driven personalized medicine yields substantial treatment improvements, elevating survival rates and minimizing adverse effects. Integrating omics data (genomics, proteomics, etc.) enhances understanding of lung cancer's intricate biological milieu, identifying novel treatment targets and biomarkers, fostering precision medicine. Liquid biopsies, non-invasive tools for real-time treatment monitoring and early resistance detection, gain popularity, promising enhanced management and personalized therapy. Despite advancements, biomarker repeatability and validation challenges persist, necessitating interdisciplinary efforts and large-scale clinical trials. Integrating artificial intelligence and machine learning aids analyzing vast omics datasets and predicting treatment responses. Single-cell omics reveal cellular connections and intratumoral heterogeneity, valuable for combination treatments. Biomarkers enable accurate diagnosis, tailored medicines, and treatment response tracking, significantly impacting personalized lung cancer care. This approach spurs patient-centered trials, empowering active patient engagement. Lung cancer proteomic and genetic biomarkers illuminate disease biology and treatment prospects. Progressing towards individualized efficient therapies is imminent, alleviating lung cancer's burden through ongoing research, omics integration, and technological strides.

Colorimetric and photothermal immunosensor for sensitive detection of cancer biomarkers based on enzyme-mediated growth of gold nanostars on polydopamine

BACKGROUND

Detecting cancer biomarker levels in body fluids is essential for medical diagnosis. Enzyme-linked immunosorbent assay (ELISA) has been broadly used to detect cancer biomarkers. However, colorimetric ELISA based solely on nanoparticles (NPs) are susceptible to environmental influences, which often results in the detection inaccuracy, being limited in clinical applications. In this regard, the dual-mode approach would add signal diversity to the detection, making the results more reliable.

RESULTS

We present colorimetric and photothermal immunosensor that enables direct reading of the color and temperature of the solution. A core-satellite nanoprobe constructed by polydopamine (PDA) as the core and gold seeds as satellites is rationally designed as the signal reporter. When ascorbic acid is present in the solution, PDA can cooperate with ascorbic acid to reduce chloroauric acid and mediate the growth of gold seeds on the PDA surface, inducing a redshift of the localized surface plasmon resonance peak of the nanosensor and the change in photothermal conversion efficiency. The method is further combined with the sandwiched immunoassay to construct an alkaline phosphatase based colorimetric and photothermal ELISA for the highly sensitive and accurate evaluation and detection of prostate-specific antigen (PSA). The linear range was from 0.05 to 100 ng mL-1 with a detection limit of 6.71 pg mL-1 for the colorimetric detection, while the linear range was from 0.5 to 90 ng mL-1 with a detection limit of 0.13 ng mL-1 in the photothermal analysis. The accurate detection of PSA levels in serum samples was well demonstrated with the dual-mode approach.

SIGNIFICANCE

The presented immunoassay allows straightforward, sensitive, and selective readout by color and temperature without advanced instrumentation. Particularly, the LOD was much lower than the threshold in clinical trials for PSA. Therefore, this method has a great prospect in the early diagnosis of cancer biomarkers based on a dual-mode multifunctional platform.

Nucleases as molecular targets for cancer diagnosis

Early cancer diagnosis is a crucial element to improved treatment options and survival. Great research efforts have been made in the search for better performing cancer diagnostic biomarkers. However, the quest continues as novel biomarkers with high accuracy for an early diagnosis remain an unmet clinical need. Nucleases, which are enzymes capable of cleaving nucleic acids, have been long considered as potential cancer biomarkers. The implications of nucleases are key for biological functions, their presence in different cellular counterparts and catalytic activity led the enthusiasm towards investigating the role of nucleases as promising cancer biomarkers. However, the most essential feature of these proteins, which is their enzymatic activity, has not been fully exploited. This review discusses nucleases interrogated as cancer biomarkers, providing a glimpse of their physiological roles. Moreover, it highlights the potential of harnessing the enzymatic activity of cancer-associated nucleases as a novel diagnostic biomarker using nucleic acid probes as substrates.

Prognostic and Predictive Clinical and Biological Factors in HPV Malignancies

Human papillomavirus (HPV) causes the majority of oropharyngeal, cervical, and anal cancers, among others. These HPV-associated cancers cause substantial morbidity and mortality despite ongoing vaccination efforts. Aside from the earliest stage tumors, chemoradiation is used to treat most HPV-associated cancers across disease sites. Response rates are variable, and opportunities to improve oncologic control and reduce toxicity remain. HPV malignancies share multiple commonalities in oncogenesis and tumor biology that may inform personalized methods of screening, diagnosis, treatment and surveillance. In this review we discuss the current literature and identify promising molecular targets, prognostic and predictive clinical factors and biomarkers in HPV-associated oropharyngeal, cervical and anal cancer.

Colon Cancer Biomarkers: Implications for Personalized Medicine

The heterogeneity of colon cancers and their reactions presents both a challenge and promise for personalized medicine. The challenge is to develop effective biologically personalized therapeutics guided by predictive and prognostic biomarkers. Presently, there are several classes of candidate biomarkers, including genomic probes, inhibitory RNAs, assays for immunity dysfunction and, not to be forgotten, specific histopathologic and histochemical features. To develop effective therapeutics, candidate biomarkers must be qualified and validated in comparable independent cohorts, no small undertaking. This process and subsequent deployment in clinical practice involves not only the strong association of the biomarker with the treatment but also careful attention to the prosaic aspects of representative tumor site selection, obtaining a fully adequate sample which is preserved and prepared to optimize high quality analysis. In the future, the clinical utility of biomarker analytical results will benefit from associated clinical and basic science data with the assistance of artificial intelligence techniques. By application of an individualized, selected suite of biomarkers, comprehensively interpreted, individualized, more effective and less toxic therapy for colon cancer will be enabled, thereby fulfilling the promise of personalized medicine.

ERCC1 expression in advanced colorectal cancer and matched liver metastases

BACKGROUND

Platinum-based chemotherapy is part of the standard treatment for patients with colorectal cancer. ERCC1 is a potential predictive biomarker for platinum-based chemotherapy. The aim of this study was to examine interobserver agreement on ERCC1 protein expression in primary colorectal cancer as well as corresponding liver metastasis. Furthermore, comparison of ERCC1-expression in primary tumor and the corresponding liver metastasis was performed.

METHODS

Forty patients with primary colorectal cancers and corresponding liver metastases were included. One slide was stained with the anti-ERCC1 antibody, 4F9 clone (DAKO) and evaluated by two gastrointestinal pathology consultants and a pathology registrar separately. Interobserver agreement was evaluated for primary tumors and liver metastases using kappa (κ) statistics. Discordant scorings were reviewed, and consensus was obtained. The expression in primary tumor was compared with the corresponding liver metastases.

RESULTS

For the primary tumors agreement was found in 85% of the tumors corresponding to an unweighted kappa value of 0,79 (95% CI 0,64-0,94). For the liver metastases agreement was found in 76% corresponding to an unweighted kappa value of 0,64 (95% CI 0,49-0,79). When comparing primary tumors to the corresponding metastases, no concordance in ERCC1-expression was observed.

CONCLUSION

Interobserver agreement of ERCC1 expression was good for both primary tumors and liver metastases, which is crucial for a potential predictive biomarker. As no concordance between primary tumor and liver metastases was found it seems to be of high importance to use tissue from actual tumor burden for evaluation of ERCC1 expression. Further studies and correlation to clinical outcome are warranted.

Clinical biomarkers for cancer recognition and prevention: A novel approach with optical measurements

Cancer is the most important cause of death worldwide, and early cancer detection is the most fundamental factor for efficacy of treatment, prognosis, and increasing survival rate. Over the years great effort has been devoted to discovering and testing new biomarkers that can improve its diagnosis, especially at an early stage. Here we report the potential usefulness of new, easily applicable, non-invasive and relatively low-cost clinical biomarkers, based on abnormalities of oral mucosa spectral reflectance and fractal geometry of the vascular networks in several different tissues, for identification of hereditary non-polyposis colorectal cancer carriers as well for detection of other tumors, even at an early stage. In the near future the methodology/technology of these procedures should be improved, thus making possible their applicability worldwide as screening tools for early recognition and prevention of cancer.

Performance Goals for an Adjunct Diagnostic Test to Reduce Unnecessary Biopsies After Screening Mammography: Analysis of Costs, Benefits, and Consequences

PURPOSE

Because benign biopsies resulting from false-positive mammographic findings are a known harm of breast cancer screening, physicians and test manufacturers are searching for ways to reduce their frequency. The aim of this study was to estimate potential costs and consequences associated with using an adjunct diagnostic test for triaging women with suspicious mammographic findings before biopsy.

METHODS

A decision model was developed to compare the use of an adjunct test before biopsy to the current standard of care for suspicious mammographic findings. The decision analysis was performed from the perspective of a national health payer, with a 1-year time horizon among women representative of the US screening population aged 40 to 79 years. Three primary outcomes were assessed: (1) incremental costs, (2) number of benign biopsies avoided, and (3) number of missed opportunities for diagnosing cancer per million women screened. Input parameters were obtained from the medical literature and expert opinion. Sensitivity analyses were performed to evaluate the effects of uncertainty in parameter estimates.

RESULTS

The base-case analysis demonstrated that the use of an adjunct diagnostic test with 95% sensitivity, 75% specificity, and a cost of $1,000 would eliminate 8,127 unnecessary breast biopsies per million women screened. However, this would cost the US health care system an additional $6,462,977 and result in 255 missed opportunities for diagnosing cancer per million women screened.

CONCLUSIONS

The addition of an adjunct test for triaging women for breast biopsy after abnormal findings on screening mammography would likely eliminate many unnecessary biopsies but also increase overall health care costs. This exploratory analysis highlights the fact that mammography remains a relatively inexpensive and effective breast cancer screening and diagnostic modality.

Predictive and prognostic cancer biomarkers revisited

While both prognostic and predictive cancer biomarkers predict clinical outcome, the term 'predictive biomarker' is reserved for the association of a specific therapy with a specific clinical outcome. The advent of genomic signatures and next generation sequencing as candidate predictive biomarkers has led to lengthy and expensive processes for biomarker qualification. The urgency to bring novel predictive cancer biomarkers to practice faster and cheaper requires strategies to lower the bar to biomarker implementation. Three strategies are suggested: identify biomarkers closely coupled to biologic mechanism associated with the clinical endpoint and scalable from cells to humans; identify biomarkers that can be reliably detected and quantified; and assess biomarkers by capacity to reduce toxicity as well as to increase therapy efficacy. Biomarker selection directly and closely related to production of end points by biologic mechanism demonstrated by a ladder of evidence should require less burden of proof clinically than biomarkers that are merely associative.

FGFR2, HER2 and cMet in gastric adenocarcinoma: detection, prognostic significance and assessment of downstream pathway activation

Receptor tyrosine kinase pathways are potential therapeutic targets in gastric adenocarcinoma patients. We evaluated HER2 and cMet protein expression, and FGFR2 gene amplification to assess their prognostic significance, and downstream mediators pS6 and pERK for their potential utility as pharmacodynamic biomarkers in patients with gastric adenocarcinoma. Tissue microarrays were constructed from resection samples of 184 patients who underwent surgery for gastric/gastro-oesophageal junction adenocarcinoma. Tissue cores were obtained from the tumour body (TB), luminal surface (LS) and invasive edge (IE), and immunohistochemical and fluorescence in situ hybridisation (FGFR2) analysis was performed. FGFR2 amplification was identified in 2 % of cases and associated with worse survival (P = 0.005). HER2 overexpression was observed in 10 % of cases and associated with increased survival (P = 0.041). cMet overexpression was observed in 4 % of cases and associated with worse survival (P < 0.001). On multivariate analysis, only cMet retained significance (P = 0.006). pS6 and pERK expression were observed in 73 % and 30 % of tumours, respectively, with no association with survival. HER2 (P = 0.004) and pERK (P = 0.001) expression differed between tumour regions with HER2 expression increased in the LS compared with the TB and IE. These findings confirm subpopulations in gastric adenocarcinoma with poor outcome that may benefit from specific therapeutic strategies. However, we found heterogeneous HER2, pS6 and pERK overexpression, which presents challenges for their use as predictive biomarkers in gastric biopsies. The potential downstream pharmacodynamic markers pS6 and pERK were expressed across tumour regions, providing evidence that resections and biopsies would yield comparative results in clinical trials.

Detection of serological biomarkers by proximity extension assay for detection of colorectal neoplasias in symptomatic individuals

Background

Although the potential of biomarkers to aid in early detection of colorectal cancer (CRC) is recognized and numerous biomarker candidates have been reported in the literature, to date only few molecular markers have been approved for daily clinical use.

Methods

In order to improve the translation of biomarkers from the bench to clinical practice we initiated a biomarker study focusing on a novel technique, the proximity extension assay, with multiplexing capability and the possible additive effect obtained from biomarker panels. We performed a screening of 74 different biomarkers in plasma derived from a case–control sample set consisting of symptomatic individuals representing CRC patients, patients with adenoma, patients with non-neoplastic large bowel diseases and healthy individuals.

Results

After statistical evaluation we found 12 significant indicators of CRC and the receiver operating characteristic (ROC) curve of Carcinoembryonic antigen (CEA), Transferrin Receptor-1 (TFRC), Macrophage migration inhibitory factor (MIF), Osteopontin (OPN/SPP1) and cancer antigen 242 (CA242) showed additive effect. This biomarker panel identified CRC patients with a sensitivity of 56% at 90% specificity and thus the performance is sufficiently high to further investigate this combination of five proteins as serological biomarkers for detection of CRC. Furthermore, when applying the indicators to identify early-stage CRC a combination of CEA, TFRC and CA242 resulted in a ROC curve with an area under the curve of 0.861.

Conclusions

Five plasma protein biomarkers were found to be potential CRC discriminators and three of these were additionally found to be discriminators of early-stage CRC. These explorative data in symptomatic individuals demonstrates the feasibility of the multiplex proximity extension assay for screening of potential serological protein biomarkers and warrants independent analyses in a larger sample cohort, including asymptomatic individuals, to further validate the performances of our CRC biomarker panel.

Kinase activation in circulating cells: opportunities for biomarkers for diagnosis and therapeutic monitoring

A clinically useful tool to assay phosphorylation-dependent signaling in circulating cells has the potential to provide a wealth of information about a patient's health, including information unavailable by any other method. Patterns of kinase activation, such as the abnormal signaling characteristic of myeloproliferative disorders, may offer highly specific biomarkers for diagnosis or monitoring the efficacy of therapeutics. For assays of kinase activity in circulating leukocytes to be standardized, let alone made practical for the clinic, numerous technical hurdles must be overcome. In this review the current status of analysis of kinase signaling in circulating cells and recent progress in biomarker discovery and validation is discussed. Looking forward, the potential value of signaling patterns as complex biomarkers and the resulting need for future development of robust, multiplexed assays of kinase activation is addressed.

Breast cancer and protein biomarkers

Breast cancer is a healthcare concern of women worldwide. Despite procedures being available for diagnosis, prognosis and treatment of breast cancer, researchers are working intensively on the disease in order to improve the life quality of breast cancer patients. At present, there is no single treatment known to bring a definite cure for breast cancer. One of the possible solutions for combating breast cancer is through identification of reliable protein biomarkers that can be effectively used for early detection, prognosis and treatments of the cancer. Therefore, the task of identification of biomarkers for breast cancer has become the focus of many researchers worldwide.

Protein biomarkers of ovarian cancer: the forest and the trees

The goal of effective population-based screening for ovarian cancer remains elusive despite intense efforts aimed at improving upon biomarker and imaging modalities. While dozens of potential serum biomarkers for ovarian cancer have been identified in recent years, none have yet overcome the limitations that have hindered the clinical use of CA-125. Avenues of opportunity in biomarker development are emerging as investigators are beginning to appreciate the significance of remote, as well as local or regional, sources of biomarkers in the construction of diagnostic panels, as well as the importance of evaluating biomarkers in prediagnostic settings. As the list of candidate biomarkers of ovarian cancer continues to grow, refinements in the methods through which specific proteins are selected for further development as components of diagnostic panels are desperately sought. Such refinements must take into account both the bioinformatic and biological significance of each candidate. Approaches incorporating these considerations may potentially overcome the challenges to early detection posed by the histological heterogeneity of ovarian cancer. Here, we review the recent progress achieved in efforts to develop diagnostic biomarker panels for ovarian cancer and discuss the challenges that remain.

Human urine proteomics: building a list of human urine cancer biomarkers

In the last decade, several reports have focused on the identification and characterization of proteins present in urine. In an effort to build a list of proteins of interest as biomarkers, we reviewed the largest urine proteomes and built two updated lists of proteins of interest (available as supplementary tables). The first table includes a consensus list of 443 proteins found in urine by independent laboratories and reported on the top three largest urine proteomes currently published. This consensus list of proteins could serve as biomarkers to diagnose, monitor and manage a number of diseases. Here, we focus on a reduced list of 35 proteins with potential interest as cancer biomarkers in urine following two criteria: first, proteins previously detected in urine using bottom-up proteomic experiments, and second, those suggested as cancer protein biomarkers in human plasma. In an effort to standardize the information presented and its use in future studies, here we include the updated International Protein Index (v. 3.80) and primary Swiss-Prot accession numbers, official gene symbols and recommended full names. The main variables that influence urine proteomic experiments are also discussed.

Microfluidics using spatially defined arrays of droplets in one, two, and three dimensions

Spatially defined arrays of droplets differ from bulk emulsions in that droplets in arrays can be indexed on the basis of one or more spatial variables to enable identification, monitoring, and addressability of individual droplets. Spatial indexing is critical in experiments with hundreds to millions of unique compartmentalized microscale processes--for example, in applications such as digital measurements of rare events in a large sample, high-throughput time-lapse studies of the contents of individual droplets, and controlled droplet-droplet interactions. This review describes approaches for spatially organizing and manipulating droplets in one-, two-, and three-dimensional structured arrays, including aspiration, laminar flow, droplet traps, the SlipChip, self-assembly, and optical or electrical fields. This review also presents techniques to analyze droplets in arrays and applications of spatially defined arrays, including time-lapse studies of chemical, enzymatic, and cellular processes, as well as further opportunities in chemical, biological, and engineering sciences, including perturbation/response experiments and personal and point-of-care diagnostics.

Molecular markers for early detection

A common belief is that the earlier that cancer is detected, the better the chance exists for reduced mortality and morbidity. The advent of new and emerging molecular, genetic, and imaging technologies has broadened the possible strategies for early detection and prevention, but a beneficial impact on mortality needs to be supported by clinical evidence. Molecular markers are being identified that are enhancing our ability to predict and detect cancer before it develops and at the earliest signs of impending carcinogenic transformation. Of the innumerable molecular markers in development, a standalone early detection marker with acceptable sensitivity and specificity is available for bladder cancer, although for most cancer sites there are promising avenues of research that will likely produce results in the next decade. The perfect molecular marker would be one that is inherently related to the disease, specifically to the processes of malignant tumorigenesis or to the defense mechanisms of the individual. For example, mutations associated with increased cancer risk often produce gene products that interfere with tumor-suppressor pathways (eg, DNA repair or cell-cycle control) or support oncogenic pathways (eg, through genetic instability or silencing the apoptotic pathway). Finding molecular markers associated with these processes, and where in the process they produce their actions, can lead to interventions based on maintaining support for the normal process and interrupting the action of the products of the mutation. The search for molecular markers for cancer prevention and early detection presents a formidable challenge that requires a systematic and scientifically sound validation process. The search encompasses a broad range of scientific disciplines, including biochemistry, genetics, histology, immunology, informatic technologies, and epidemiology; strategies to identify and understand molecular markers are approached with multidisciplinary teams focused on understanding the mechanistic basis of cancer and the processes and pathways that underlie carcinogenesis.

Introduction to the development and validation of predictive biomarker models from high-throughput data sets

High-throughput technologies can routinely assay biological or clinical samples and produce wide data sets where each sample is associated with tens of thousands of measurements. Such data sets can be mined to discover biomarkers and develop statistical models capable of predicting an endpoint of interest from data measured in the samples. The field of biomarker model development combines methods from statistics and machine learning to develop and evaluate predictive biomarker models. In this chapter, we discuss the computational steps involved in the development of biomarker models designed to predict information about individual samples and review approaches often used to implement each step. A practical example of biomarker model development in a large gene expression data set is presented. This example leverages BDVal, a suite of biomarker model development programs developed as an open-source project (see http://bdval.org /).

Mass Spectrometry in Diagnostic Oncoproteomics

Diagnostic oncoproteomics is the application of proteomic techniques for the diagnosis of malignancies. A new mass spectrometric technology involves surface enhanced laser desorption ionization combined with time-of flight mass analysis (SELDI-TOF-MS), using special protein chips. After the description of the relevant principles of the technique, including approaches to proteomic pattern diagnostics, applications are reviewed for the diagnosis of ovarian, breast, prostate, bladder, pancreatic, and head and neck cancers, and also several other malignancies. Finally, problems and prospects of the approach are discussed.

Targeted proteomic strategy for clinical biomarker discovery

The high complexity and large dynamic range of blood plasma proteins currently prohibit the sensitive and high-throughput profiling of disease and control plasma proteome sample sets large enough to reliably detect disease indicating differences. To circumvent these technological limitations we describe here a new two-stage strategy for the mass spectrometry (MS) assisted discovery, verification and validation of disease biomarkers. In an initial discovery phase N-linked glycoproteins with distinguishable expression patterns in primary normal and diseased tissue are detected and identified. In the second step the proteins identified in the initial phase are subjected to targeted MS analysis in plasma samples, using the highly sensitive and specific selected reaction monitoring (SRM) technology. Since glycosylated proteins, such as those secreted or shed from the cell surface are likely to reside and persist in blood, the two-stage strategy is focused on the quantification of tissue derived glycoproteins in plasma. The focus on the N-glycoproteome not only reduces the complexity of the analytes, but also targets an information-rich subproteome which is relevant for remote sensing of diseases in the plasma. The N-glycoprotein based biomarker discovery and validation workflow reviewed here allows for the robust identification of protein candidate panels that can finally be selectively monitored in the blood plasma at high sensitivity in a reliable, non-invasive and quantitative fashion.

Candidate List of yoUr Biomarker (CLUB): A Web-based Platform to Aid Cancer Biomarker Research

CLUB (“Candidate List of yoUr Biomarkers”) is a freely available, web-based resource designed to support Cancer biomarker research. It is targeted to provide a comprehensive list of candidate biomarkers for various cancers that have been reported by the research community. CLUB provides tools for comparison of marker candidates from different experimental platforms, with the ability to filter, search, query and explore, molecular interaction networks associated with cancer biomarkers from the published literature and from data uploaded by the community. This complex and ambitious project is implemented in phases. As a first step, we have compiled from the literature an initial set of differentially expressed human candidate cancer biomarkers. Each candidate is annotated with information from publicly available databases such as Gene Ontology, Swiss-Prot database, National Center for Biotechnology Information’s reference sequences, Biomolecular Interaction Network Database and IntAct interaction. The user has the option to maintain private lists of biomarker candidates or share and export these for use by the community. Furthermore, users may customize and combine commonly used sets of selection procedures and apply them as a stored workflow using selected candidate lists. To enable an assessment by the user before taking a candidate biomarker to the experimental validation stage, the platform contains the functionality to identify pathways associated with cancer risk, staging, prognosis, outcome in cancer and other clinically associated phenotypes. The system is available at http://club.bii.a-star.edu.sg.

The role of osteopontin in tumor progression and metastasis in breast cancer

The use of cancer biomarkers to anticipate the outlines of disease has been an emerging issue, especially as cancer treatment has made such positive steps in the last few years. Progress in the development of consistent malignancy markers is imminent because advances in genomics and bioinformatics have allowed the examination of immense amounts of data. Osteopontin is a phosphorylated glycoprotein secreted by activated macrophages, leukocytes, and activated T lymphocytes, and is present in extracellular fluids, at sites of inflammation, and in the extracellular matrix of mineralized tissues. Several physiologic roles have been attributed to osteopontin, i.e., in inflammation and immune function, in mineralized tissues, in vascular tissue, and in kidney. Osteopontin interacts with a variety of cell surface receptors, including several integrins and CD44. Binding of osteopontin to these cell surface receptors stimulates cell adhesion, migration, and specific signaling functions. Overexpression of osteopontin has been found in a variety of cancers, including breast cancer, lung cancer, colorectal cancer, stomach cancer, ovarian cancer, and melanoma. Moreover, osteopontin is present in elevated levels in the blood and plasma of some patients with metastatic cancers. Therefore, suppression of the action of osteopontin may confer significant therapeutic activity, and several strategies for bringing about this suppression have been identified. This review looks at the recent advances in understanding the possible mechanisms by which osteopontin may contribute functionally to malignancy, particularly in breast cancer. Furthermore, the measurement of osteopontin in the blood or tumors of patients with cancer, as a way of providing valuable prognostic information, will be discussed based on emerging clinical data.

A list of candidate cancer biomarkers for targeted proteomics

We have compiled from literature and other sources a list of 1261 proteins believed to be differentially expressed in human cancer. These proteins, only some of which have been detected in plasma to date, represent a population of candidate plasma biomarkers that could be useful in early cancer detection and monitoring given sufficiently sensitive specific assays. We have begun to prioritize these markers for future validation by frequency of literature citations, both total and as a function of time. The candidates include proteins involved in oncogenesis, angiogenesis, development, differentiation, proliferation, apoptosis, hematopoiesis, immune and hormonal responses, cell signaling, nucleotide function, hydrolysis, cellular homing, cell cycle and structure, the acute phase response and hormonal control. Many have been detected in studies of tissue or nuclear components; nevertheless we hypothesize that most if not all should be present in plasma at some level. Of the 1261 candidates only 9 have been approved as "tumor associated antigens" by the FDA. We propose that systematic collection and large-scale validation of candidate biomarkers would fill the gap currently existing between basic research and clinical use of advanced diagnostics.

Optically Encoded Particles and Their Applications in Multiplexed Biomedical Assays

In the future, the rapid discovery of new cures, vaccines, and diagnostics for common diseases will depend on the ability of biomedical researchers to investigate complex mixtures of proteins or DNA. The need to measure the abundance of these entities, together with their level of interaction, has driven the development of new research tools that enable simultaneous analysis of multiple analytes (multiplexing). Optically encoded particles are emerging as the multiplexing tools of choice, especially for clinical research. In this Review, an overview of various new optical encoding methods will be presented, together with important biomedical applications in which particle-based assays are currently being used.

Detection and identification of heat shock protein 10 as a biomarker in colorectal cancer by protein profiling

Although colorectal cancer is one of the best-characterized tumors with regard to the multistep progression, it remains one of the most frequent and deadly neoplasms. For a better understanding of the molecular mechanisms behind the process of tumorigenesis and tumor progression, changes in protein expression between microdissected normal and tumorous colonic epithelium were analyzed. Cryostat sections from colorectal tumors, adenoma tissue, and adjacent normal mucosa were laser-microdissected and analyzed using ProteinChip Arrays. The derived MS profiles exhibited numerous statistical differences. One peak showing significantly high expression in the tumor was purified by reverse-phase chromatography and SDS-PAGE. The protein band of interest was passively eluted from the gel and identified as heat shock protein 10 (HSP 10) by tryptic digestion, peptide mapping, and MS/MS analysis. This tumor marker was further characterized by immunohistochemistry. Analysis of HSP 10-positive tissue by ProteinChip technology confirmed the identity of this protein. This work demonstrates that biomarker in colorectal cancer can be detected, identified, and assessed by a proteomic approach comprising tissue microdissection, protein profiling, and immunological techniques. In our experience, histological defined microdissected tissue areas should be used to identify proteins that might be responsible for tumorigenesis.

Non-invasive cancer detection: Strategies for the identification of novel cancer markers

In the last few years powerful technologies have emerged and are being applied for biomarker discovery. The purpose of this article is to discuss and help focus the strategies applying these novel technologies for the identification of potential biomarkers for early detection of cancer.

Genomic biomarkers for cancer assessment: implementation challenges for laboratory practice

Genomic biomarkers are an emerging class of laboratory tests, which present special implementation challenges for clinical laboratory services, compared to conventional laboratory tests. These challenges, which include analytical, bioinformatics, bioethical, interpretation and commercialization issues, represent real obstacles to widespread implementation of these tests. Technical challenges include the capacity to detect and identify many different kinds of markers for different diseases in a short time period, capacity to identify simultaneously gene rearrangements, amplification, inhibition, deletions and replications. Bioinformatics challenges include rapid analysis of genomic data, as well as the cross reference to other genomic data, and to other laboratory tests. Bioethical issues relate to consent to retain and use genetic data, which may be obtained inadvertently during analysis for genomic markers. Interpretation challenges include observations that the particular genomic markers may not be independent variables, as other undetected genomic alterations could invalidate or alter genomic marker interpretation. Further, as early experience with predictive genetic markers for cancer has shown, proprietary commercial interests may conflict with public health values of identifying genomic markers in subject populations. Based on our 10 years of experience with genomic biomarkers, important implementation strategies for genomic markers include development of:Standard high throughput analyzers capable of detecting any alteration of any genomic variant at any time. Bioinformatics analysis online, coupled to stored patient data. Laboratory service framework that preserves confidentiality but integrates genomic data with other laboratory tests. Laboratory service framework, which links consents, genomic analysis, reports to both specimen and data repositories. Overall, the laboratory service challenges for genomic markers are to manage very large analytical sets and very large data sets in finite time with responsible interpretation, all within finite funding. To meet these challenges, implementation strategies beyond the one disease, one diagnosis, one genomic marker concept must begin now.

A Technical Triade for Proteomic Identification and Characterization of Cancer Biomarkers

Biomarkers are needed to elucidate the biological background and to improve the detection of cancer. Therefore, we have analyzed laser-microdissected cryostat sections from head and neck tumors and adjacent mucosa on ProteinChip arrays. Two differentially expressed proteins (P = 3.34 x 10(-5) and 4.6 x 10(-5)) were isolated by two-dimensional gel electrophoresis and identified as S100A8 (calgranulin A) and S100A9 (calgranulin B) by in-gel proteolytic digestion, peptide mapping, tandem mass spectrometry analysis, and immunodepletion assay. The relevance of these single marker proteins was evaluated by immunohistochemistry. Positive tissue areas were reanalyzed on ProteinChip arrays to confirm the identity of these proteins. As a control, a peak with low P was identified as calgizzarin (S100A11) and characterized in the same way. This technical triade of tissue microdissection, ProteinChip technology, and immunohistochemistry opens up the possibility to find, identify, and characterize tumor relevant biomarkers, which will allow the movement toward the clonal heterogeneity of malignant tumors. Taking this approach, proteins were identified that might be responsible for invasion and metastasis.

Mass spectrometry-based clinical proteomics

In recent years, mass spectrometry (MS) has been recognized as a 'Gold Standard' tool for the identification and analysis of individual proteins in expression proteomics studies. Moreover, MS has proven useful for the analysis of nucleic acids for single nucleotide polymorphism (SNP) genotyping purposes. With the increased usage of MS as a standard tool for life science applications and the advancement of MS instrumentation, sample preparation and bioinformatics, MS technology has entered novel screening and discovery application areas that are beyond the traditional protein identification and characterization applications. The areas of clinical diagnostics and predictive medicine are just two prime examples of these fields. Predictive markers or biomarkers for early diagnosis of diseases are of growing importance for the human healthcare community. The goal of using MS in clinical proteomics is to generate protein profiles (mass to charge [m/z] ratio versus signal intensity) from readily available body fluids like serum, saliva and urine to detect changes in protein levels that reflect changes in the disease states. Whereas the results originating from individual protein markers may be intriguing, data resulting from the analysis of complex, multiple biomarker patterns may be unequivocal. These biomarker patterns are hidden in complex mass spectra and are not always obvious to the human eye. Sophisticated bioinformatics algorithms have to be applied to determine these unique biomarker patterns. Here, we review the latest developments concerning the use of MS for the discovery of biomarker patterns and for the identification of individual biomarkers in the field of clinical proteomics applications.