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PubMed2004

Role of oncoproteomics in the personalized management of cancer.

Oncoproteomics is the term used to describe the application of proteomic technologies in oncology and parallels the related field of oncogenomics. It is now contributing to the development of personalized management of cancer. Proteomic technologies are used for the identification of biomarkers in cancer, which will facilitate the integration of diagnosis and therapy of cancer. Molecular diagnostics, laser capture microdissection and protein biochips are among the technologies that are having an important impact on oncoproteomics. The discovery of protein patterns developed by the US Food and Drug Administration/National Cancer Institute Clinical Proteomics Program is capable of distinguishing cancer and disease-free states with high sensitivity and specificity and will also facilitate the development of personalized therapy of cancer. Examples of application are given for breast and prostate cancer and a selection of companies and their collaborations that are developing application of proteomics to personalized treatment of cancer are discussed. Continued refinement of techniques and methods to determine the abundance and status of proteins in vivo holds great promise for the future study of normal cells and the pathology of associated neoplasms. Personalized cancer therapy is expected to be in the clinic by the end of the first decade of the 21st century.

Computational Biology↗
PubMed2003

A data review and re-assessment of ovarian cancer serum proteomic profiling.

BACKGROUND: The early detection of ovarian cancer has the potential to dramatically reduce mortality. Recently, the use of mass spectrometry to develop profiles of patient serum proteins, combined with advanced data mining algorithms has been reported as a promising method to achieve this goal. In this report, we analyze the Ovarian Dataset 8-7-02 downloaded from the Clinical Proteomics Program Databank website, using nonparametric statistics and stepwise discriminant analysis to develop rules to diagnose patients, as well as to understand general patterns in the data that may guide future research. RESULTS: The mass spectrometry serum profiles derived from cancer and controls exhibited numerous statistical differences. For example, use of the Wilcoxon test in comparing the intensity at each of the 15,154 mass to charge (M/Z) values between the cancer and controls, resulted in the detection of 3,591 M/Z values whose intensities differed by a p-value of 10-6 or less. The region containing the M/Z values of greatest statistical difference between cancer and controls occurred at M/Z values less than 500. For example the M/Z values of 2.7921478 and 245.53704 could be used to significantly separate the cancer from control groups. Three other sets of M/Z values were developed using a training set that could distinguish between cancer and control subjects in a test set with 100% sensitivity and specificity. CONCLUSION: The ability to discriminate between cancer and control subjects based on the M/Z values of 2.7921478 and 245.53704 reveals the existence of a significant non-biologic experimental bias between these two groups. This bias may invalidate attempts to use this dataset to find patterns of reproducible diagnostic value. To minimize false discovery, results using mass spectrometry and data mining algorithms should be carefully reviewed and benchmarked with routine statistical methods.

Artificial Intelligence↗
PubMed2026

GICPIdb: an archival repository of multimodal data focusing on pathological images for gastrointestinal cancers.

INTRODUCTION: Deep learning (DL) shows great potential for predicting biomarkers from routine histopathological slides of gastrointestinal (GI) cancers. Yet most existing models are validated on limited patient cohorts, while pathological image annotation and molecular marker standardization demand substantial professional expertise. To address these gaps, we constructed the Gastrointestinal Cancer Pathological Image Archive (GICPIdb, gicpidb.shubuzuo.top), a dedicated database and web platform covering seven major GI cancer types. METHODS: High-quality hematoxylin and eosin (H&E)-stained whole-slide images were collected from multiple sources and uniformly processed. Image annotations were performed by board-certified pathologists following standardized protocols. GICPIdb offers five interactive web modules for data uploading, quality control, feature extraction, online annotation and AI-based prediction. Its intuitive interface supports data browsing, retrieval, visualization and downloading. RESULTS: The database houses 2,863 pathologist-annotated, uniformly processed, high-quality H&E stained images collected from 2,655 patients. Of these, 1,699 patients were sourced from The Cancer Genome Atlas (TCGA), 182 from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and 424 from China-Japan Friendship Hospital and 350 from Chifeng Municipal Hospital in Inner Mongolia, China. It also integrates data on over 50 key molecular markers (e.g., MSI, TMB) and prognostic labels related to survival, recurrence and metastasis. DISCUSSION: GICPIdb aims to promote the development of DL-driven AI tools for cancer research and clinical translation. The multi-institutional data collection and standardized annotation pipeline are expected to enhance the generalizability and reproducibility of AI-based prediction models across diverse patient populations.

deep learning↗
PubMed2006

A reagent resource to identify proteins and peptides of interest for the cancer community: a workshop report.

On the basis of discussions with representatives from all sectors of the cancer research community, the National Cancer Institute (NCI) recognizes the immense opportunities to apply proteomics technologies to further cancer research. Validated and well characterized affinity capture reagents (e.g. antibodies, aptamers, and affibodies) will play a key role in proteomics research platforms for the prevention, early detection, treatment, and monitoring of cancer. To discuss ways to develop new resources and optimize current opportunities in this area, the NCI convened the "Proteomic Technologies Reagents Resource Workshop" in Chicago, IL on December 12-13, 2005. The workshop brought together leading scientists in proteomics research to discuss model systems for evaluating and delivering resources for reagents to support MS and affinity capture platforms. Speakers discussed issues and identified action items related to an overall vision for and proposed models for a shared proteomics reagents resource, applications of affinity capture methods in cancer research, quality control and validation of affinity capture reagents, considerations for target selection, and construction of a reagents database. The meeting also featured presentations and discussion from leading private sector investigators on state-of-the-art technologies and capabilities to meet the user community's needs. This workshop was developed as a component of the NCI's Clinical Proteomics Technologies Initiative for Cancer, a coordinated initiative that includes the establishment of reagent resources for the scientific community. This workshop report explores various approaches to develop a framework that will most effectively fulfill the needs of the NCI and the cancer research community.

Biomedical Research↗
PubMed2006

Schizophrenia proteomics: biomarkers on the path to laboratory medicine?

Over two million Americans are afflicted with schizophrenia, a debilitating mental health disorder with a unique symptomatic and epidemiological profile. Genomics studies have hinted towards candidate schizophrenia susceptibility chromosomal loci and genes. Modern proteomic tools, particularly mass spectrometry and expression scanning, aim to identify both pathogenic-revealing and diagnostically significant biomarkers. Only a few studies on basic proteomics have been conducted for psychiatric disorders relative to the plethora of cancer specific experiments. One such proteomic utility enables the discovery of proteins and biological marker fingerprinting profiling techniques (SELDI-TOF-MS), and then subjects them to tandem mass spectrometric fragmentation and de novo protein sequencing (MALDI-TOF/TOF-MS) for the accurate identification and characterization of the proteins. Such utilities can explain the pathogenesis of neuro-psychiatric disease, provide more objective testing methods, and further demonstrate a biological basis to mental illness. Although clinical proteomics in schizophrenia have yet to reveal a biomarker with diagnostic specificity, methods that better characterize the disorder using endophenotypes can advance findings. Schizophrenia biomarkers could potentially revolutionize its psychopharmacology, changing it into a more hypothesis and genomic/proteomic-driven science.

Journal Article↗
PubMedDate not supplied

Proteomic strategies and their application in cancer research.

The understanding of carcinogenesis and tumor progression on a molecular basis needs a detailed study of proteins as effector molecules and as critical components of the multiple interconnected signaling pathways that drive the neoplastic phenotype. Thus, the proteomic approach represents a powerful tool for the challenge of the post-genomic era. The term "cancer proteome" refers to the collection of proteins expressed by a given cancer cell and should be considered as a highly dynamic entity within the cell, which affects a variety of cellular activities. The emerging proteomic analysis platforms including 2D-PAGE, mass spectrometry technologies, and protein microarrays represent powerful tools to study and understand cancer. These systems aim to not only identify, catalogue, and characterize cancer proteins, but also to unveil how they interact to affect overall tumor progression. Moreover, recent studies on various cancers have reported promising results concerning the detection of novel molecular biomarkers useful in the early diagnosis of cancer and in drug discovery. Thus, a new subdiscipline named clinical proteomics, concomitant with new molecular technologies that are developed, demonstrates promise to discover new cancer biomarkers. The early diagnosis of cancer, even in a premalignant state, is crucial for the successful treatment of this disease. For these reasons, it is clear that the identification of biomarkers for the early diagnosis of cancer should represent one of the main goals of this emerging field of study.

Animals↗
PubMed2004

The molecular scanner: concept and developments.

Approaches aimed at deciphering the proteome have illustrated the need for relatively complex and highly sensitive methodologies. The major elements of proteome analysis, such as powerful protein separation and enzymatic processing, mass spectrometry and dedicated bioinformatics have been assembled in the development of the molecular scanner. This highly flexible and data-rich approach has combined the power of electrophoretic protein separation, the simultaneous digestion and transfer of proteins through an enzymatic membrane, the immediate use of the MALDI mass spectrometer to scan a collecting membrane, and the development of dedicated bioinformatics tools to perform protein identification and molecular imaging of the proteome. Clinical applications of the molecular scanner have also started to be developed for disease diagnosis in biological material.

Animals↗
PubMed2004

All about DIGE: quantification technology for differential-display 2D-gel proteomics.

2D polyacrylamide gel electrophoresis has been the traditional workhorse of proteomics, allowing for the resolution of several thousand proteins in a single gel. Difference gel electrophoresis is an emerging technology that allows for accurate quantification with statistical confidence while controlling for nonbiologic variation, and also increases the dynamic range and sensitivity of traditional 2D polyacrylamide gel electrophoresis. With inclusion of an internal standard formed from equal amounts of every sample in an experiment, difference gel electrophoresis technology also allows for repetitive measurements and multivariable analyses to be quantitatively analyzed in one co-ordinated experiment, yielding statistically-significant changes in protein expression related to many disease states. This technique promises to be an important tool in clinical proteomics and the study of the mechanism of disease, investigating diagnostic biomarkers and pinpointing novel therapeutic targets.

Animals↗
PubMed2005

Application of MALDI-TOF mass spectrometry in screening and diagnostic research.

During the last years, mass spectrometry has revolutionised protein biochemistry and has advanced to a superior tool for the identification and detailed analysis of peptides and proteins. The high throughput allowed by some mass spectrometry platforms has enabled the important step from analysis of individual proteins to proteomics. Recently, an additional field of mass spectrometry applications has emerged - namely screening and diagnostic research. In contrast to protein identification, screening applications have to detect analyte molecules of defined molecular weights which can be calculated beforehand, for example by means of chemical structures. Here, the accuracy and sensitivity of mass spectrometry has to be combined with the requirements of high-throughput analyses, in particular speed and automation. These criteria are especially fulfilled by state of the art matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) instruments. The first high throughput screening (HTS) application proved to be genotyping of single nucleotide polymorphisms. The same principle was later applied for several quality control issues, for example for oligonucleotides, peptide or compound libraries. This development has culminated in the screening and profiling of complex biomarker patterns in clinical proteomics to detect a molecular fingerprint for specific diseases in biological samples. Thus, mass spectrometry based methods are expected to enable a very early diagnosis of diseases with minimally invasive methods of investigation. This type of high end screening application has the potential to revolutionise the early diagnosis of many diseases. Here, we give an overview of the application of mass spectrometry in the fields of screening and diagnostic research.

Biomedical Research↗
PubMed2006

Multiplexed mass spectrometric immunoassay in biomarker research: a novel approach to the determination of a myocardial infarct.

Reported here is the development of a multiplexed mass spectrometric immunoassay (MSIA) for the detection of myocardial infarction (MI). The assay is the product of a study that systematically progresses from biomarker discovery--to identification and verification--to assay design, data analysis, and statistical challenge. During targeted population proteomics investigations, two novel biomarkers, serum amyloid A1alpha and S-sulfated transthyretin, were found to be responsive to MI. These putative markers were subsequently screened in larger cohorts of individuals to verify their responsiveness toward MI. Upon verification, a multiplexed assay was designed that was capable of simultaneously monitoring the new markers plus a previously established MI-marker (myoglobin). The multiplexed MSIA was applied to two 96-sample sets comprised of 48-MI/48-healthy and 19-MI/77-healthy, which served as training and case cohorts, respectively. Data evaluation using either preset reference levels or multivariate analysis exhibited sensitivities and specificities of >97%. These findings illustrate the importance of using systematic approaches in clinical proteomics to discover biomarkers and produce high-performance assays relevant to disease.

Biomarkers↗
PubMed2005

Two-dimensional electrophoresis database of fluorescence-labeled proteins of colon cancer cells.

We constructed a novel database of the proteome of DLD-1 colon cancer cells by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) of fluorescence-labeled proteins followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) analysis. The database consists of 258 functionally categorized proteins corresponding to 314 protein spots. The majority of the proteins are oxidoreductases, cytoskeletal proteins and nucleic acid binding proteins. Phosphatase treatment showed that 28% of the protein spots on the gel are phosphorylated, and mass spectrometric analysis identified 21 of them. Proteins of DLD-1 cells and of laser-microdissected colon cancer tissues showed similar distribution on 2D gels, suggesting the utility of our database for clinical proteomics.

Amino Acid Sequence↗
PubMed2025

UALCAN Mobile, an app for cancer proteogenomic data analysis.

Cancer is a complex disease affecting various organs and is a major cause of death worldwide. During cancer initiation, disease progression, and tumor metastasis, various genomic and proteomic alterations are observed. Recent technological advances have led to the generation of large amounts of molecular data, including genomics and transcriptomics. These large-scale datasets can be utilized to analyze and identify sub-class-specific cancer biomarkers and targets. However, there is a need for the development of user-friendly tools for large-scale data analysis, disseminating the analyzed data in a visualizable format to cancer researchers with no programming skills. We developed UALCAN, a comprehensive platform that allows users to integrate disparate data to better understand the genes, proteins, and pathways perturbed in cancer and make discoveries of potential biomarkers and targets. In the current study, we describe the development of the UALCAN Mobile application (app) that will provide cancer transcriptomic data obtained from The Cancer Genome Atlas (TCGA) project to evaluate protein-coding gene expression based on various stratifications, including stage, grade, race, gender, and molecular-subtypes across over 30 types of cancers. In addition, the UALCAN mobile provides data analysis options for epigenetic changes due to DNA promoter methylation and Clinical Proteomic Tumor Analysis Consortium (CPTAC) cancer proteomic data. The app provides access to large cancer molecular datasets on the go. To find changes in the expression of causative genes and proteins and to identify biomarkers and therapeutic targets, UALCAN mobile app will be extremely valuable. The "UALCAN Mobile" app is free to use and can be downloaded from both the iOS/Apple and the Android Play Store and has been downloaded over 100 times in each of iOS and android app stores.

app↗
PubMed2026

A weakly supervised deep learning-based recurrence prediction and risk stratification of lung adenocarcinoma from pathology whole-slide images.

BACKGROUND: Accurate prediction of postoperative recurrence in lung adenocarcinoma (LUAD) is essential for guiding clinical decision-making and improving patient outcomes. Although various predictive models have been developed, most rely on complex genomic analyses and high-dimensional clinical data. The complexity of these approaches substantially limits their feasibility for routine clinical use. To address this clinical challenge, this study aims to predict postoperative recurrence using routinely available hematoxylin and eosin (H&E)-stained images and characterize the associated biological features. METHODS: A total of 329 patients who underwent curative resection at the First Affiliated Hospital of Wenzhou Medical University (FHWMU) were retrospectively enrolled and randomly assigned to training and internal validation cohorts in a 7:3 ratio. An independent external validation cohort comprising 70 patients from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) was included. Three patch-level feature extractors (Inception_V3, ResNet18, and DenseNet121) were evaluated within a weakly supervised multiple-instance learning (MIL) framework incorporating automated region-of-interest (ROI) detection on segmented whole-slide images (WSIs). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), Kaplan-Meier (KM) survival analysis, and multivariable Cox proportional hazards regression. Transcriptomic profiling and gene set enrichment analysis (GSEA) were conducted to investigate biological differences between risk groups. RESULTS: The model achieved AUCs of 0.923 in the training cohort, 0.891 in the internal validation cohort, and 0.847 in the external validation cohort. The model effectively stratified patients into high- and low-risk groups with significantly different recurrence-free survival (RFS) across all cohorts (all P&#x2009;<&#x2009;0.001) and retained prognostic value within AJCC stages I-III. Transcriptomic analyses revealed consistent enrichment of cell cycle-related pathways and neutrophil extracellular trap (NET) formation in high-risk patients across both institutional and CPTAC cohorts, aligning with distinct biological profiles of the model-derived risk stratification. CONCLUSIONS: This weakly supervised deep learning framework enables accurate and externally validated prediction of postoperative recurrence in LUAD using routinely available histopathological images, and integration of histopathological features with molecular analyses enhances biological interpretability. This work provides a clinically accessible and cost-effective tool for postoperative risk assessment in LUAD patients.

Humans↗
PubMed2002

Tagless extraction-retentate chromatography: a new global protein digestion strategy for monitoring differential protein expression.

A new global protein digestion and selective peptide extraction strategy for the purpose of monitoring differential protein expression, coined as tagless extraction-retentate chromatography, is introduced. Target protein populations are firstly digested under reduced and alkylated conditions, and resultant peptides selectively extracted via covalent attachment to methionine residues by bromoacetyl reactive groups tethered to the surface of glass beads packed in small reaction vessels. After conjugation, reactive beads are stringently washed to remove nonspecifically bound peptides and then later treated with beta-mercaptoethanol to release captured methionine peptides in their nascent state, without complicating affinity tags. Recovered methionine containing peptides are profiled using the surface-enhanced laser desorption/ionization (SELDI) retentate chromatography mass spectrometry (RCMS) method. Selected peptides are further studied employing ProteinChip tandem mass spectrometry (MS/MS) analysis to identify their parent proteins. This approach has been applied to an Escherichia coli lysate model system and has demonstrated facility in reducing global digest complexity, sensitivity to low protein expression levels, and significant quantitative capability. It is envisioned that tagless extraction-RCMS will evolve to be a valuable approach for both basic research and clinical proteomics endeavors.

Amino Acid Sequence↗
PubMed2005

Analysis and understanding of high-dimensionality data by means of multivariate data analysis.

Multivariate analysis such as principal-components analysis (PCA) and partial-least-squares-discriminant analysis (PLS-DA) have been applied to peptidomics data from clinical urine samples subjected to LC/MS analysis. We show that it is possible to use these methods to get information from a complex set of clinical data. The aim of the work is to use this information as a first step in the further search for clinical biomarker data. It is possible to identify peptide-biomarker fingerprints related to disease diagnosis and progression. Further, we review clinical proteomics and pharmacogenomics data analyzed with the same multivariate approach.

Data Interpretation, Statistical↗
PubMed2006

Application of mass spectrometry to the discovery of biomarkers for detection of prostate cancer.

There has been an impressive emergence of mass spectrometry based technologies applied toward the study of proteins. Equally notable is the rapid adaptation of these technologies to biomedical approaches in the realm of clinical proteomics. Concerted efforts toward the elucidation of the proteomes of organ sites or specific disease state are proliferating and from these efforts come the promise of better diagnostics/prognostics and therapeutic intervention. Prostate cancer has been a focus of many such studies with the promise of improved care to patients via biomarkers derived from these proteomic approaches. The newer technologies provide higher analytical capabilities, employ automated liquid handling systems, fractionation techniques and bioinformatics tools for greater sensitivity and resolving power, more robust and higher throughput sample processing, and greater confidence in analytical results. In this prospects, we summarize the proteomic technologies applied to date in prostate cancer, along with their respective advantages and disadvantages. The development of newer proteomic strategies for use in future applications is also discussed.

Biomarkers, Tumor↗
PubMedDate not supplied

Current and emerging commercial optical biosensors.

The field of commercial optical biosensors is rapidly evolving, with new systems and detection methods being developed each year. This review outlines the currently available biosensor hardware and highlights unique features of each platform. Affinity-based biosensor technology, with its high sensitivity, wide versatility and high throughput, is playing a significant role in basic research, pharmaceutical development, and the food and environmental sciences. Likewise, the increasing popularity of biosensors is prompting manufacturers to develop new instrumentation for dedicated applications. We provide a preview of some of the emerging commercial systems that are dedicated to drug discovery, proteomics, clinical diagnostics and routine biomolecular interaction analysis.

Biosensing Techniques↗
PubMed2005

Serum protein profiling by miniaturized solid-phase extraction and matrix-assisted laser desorption/ionization mass spectrometry.

Serum profiling by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) holds promise as a clinical tool for early diagnosis of cancer and other human diseases. Sample preparation is key to achieving reproducible and well-resolved signals in MALDI-MS; a prerequisite for translation of MALDI-MS based diagnostic methods to clinical applications. We have investigated a number of MALDI matrices and several miniaturized solid-phase extraction (SPE) methods for serum protein concentration and desalting with the aim of generating reproducible, high-quality protein profiles by MALDI-MS. We developed a simple protocol for serum profiling that combines a matrix mixture of 2,5-dihydroxybenzoic acid and alpha-cyano-4-hydroxycinnamic acid with miniaturized SPE and MALDI-MS. Functionalized membrane discs with hydrophobic, ion-exchange or chelating properties allowed reproducible MALDI mass spectra (m/z 1000-12,000) to be obtained from serum. In a proof-of-principle application, SPE with chelating material and MALDI-MS identified protein peaks in serum that had been previously reported for distinguishing a person diagnosed with breast cancer from a control. These preliminary results indicate that this simple SPE/MALDI-MS method for serum profiling provides a versatile and scalable platform for clinical proteomics.

Blood Proteins↗