Clinical proteomics and mass spectrometry profiling for cancer detection

Technology and Future of Multi-Cancer Early Detection

Cancer remains a significant global health challenge due to its high morbidity and mortality rates. Early detection is essential for improving patient outcomes, yet current diagnostic methods lack the sensitivity and specificity needed for identifying early-stage cancers. Here, we explore the potential of multi-omics approaches, which integrate genomic, transcriptomic, proteomic, and metabolomic data, to enhance early cancer detection. We highlight the challenges and benefits of data integration from these diverse sources and discuss successful examples of multi-omics applications in other fields. By leveraging these advanced technologies, multi-omics can significantly improve the sensitivity and specificity of early cancer diagnostics, leading to better patient outcomes and more personalized cancer care. We underscore the transformative potential of multi-omics approaches in revolutionizing early cancer detection and the need for continued research and clinical integration.

Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.

Systemic effects of ionizing radiation at the proteome and metabolome levels in the blood of cancer patients treated with radiotherapy: the influence of inflammation and radiation toxicity

PURPOSE

Blood is the most common replacement tissue used to study systemic responses of organisms to different types of pathological conditions and environmental insults. Local irradiation during cancer radiotherapy induces whole body responses that can be observed at the blood proteome and metabolome levels. Hence, comparative blood proteomics and metabolomics are emerging approaches used in the discovery of radiation biomarkers. These techniques enable the simultaneous measurement of hundreds of molecules and the identification of sets of components that can discriminate different physiological states of the human body. Radiation-induced changes are affected by the dose and volume of irradiated tissues; hence, the molecular composition of blood is a hypothetical source of biomarkers for dose assessment and the prediction and monitoring of systemic responses to radiation. This review aims to provide a comprehensive overview on the available evidence regarding molecular responses to ionizing radiation detected at the level of the human blood proteome and metabolome. It focuses on patients exposed to radiation during cancer radiotherapy and emphasizes effects related to radiation-induced toxicity and inflammation.

CONCLUSIONS

Systemic responses to radiation detected at the blood proteome and metabolome levels are primarily related to the intensity of radiation-induced toxicity, including inflammatory responses. Thus, several inflammation-associated molecules can be used to monitor or even predict radiation-induced toxicity. However, these abundant molecular features have a rather limited applicability as universal biomarkers for dose assessment, reflecting the individual predisposition of the immune system and tissue-specific mechanisms involved in radiation-induced damage.

Serum mass profile signature as a biomarker of early lung cancer

OBJECTIVES

Circulating molecular biomarkers of lung cancer may allow the pre-selection of candidates for computed tomography screening or increase its efficacy. We aimed to identify features of serum mass profile distinguishing individuals with early lung cancer from healthy participants of the lung cancer screening program.

METHODS

Blood samples were collected during a low-dose computed tomography (LD-CT) screening program performed by one institution (Medical University of Gdansk, Poland). MALDI-ToF mass spectrometry was used to characterize the low-molecular-weight (1000-14,000Da) serum fraction. The analysis comprised 95 patients with early stage lung cancer (including 30 screen-detected cases) and a matched group of 285 healthy controls. The cases were split into two independent cohorts (discovery and validation), analyzed separately 6 months apart.

RESULTS

Several molecular components of serum (putatively components of endogenous peptidome) discriminating patients with early lung cancer from controls were identified in a discovery cohort. This allowed building an effective cancer classifier as a model tuned to maximize negative predictive value, with an area under the curve (AUC) of 0.88, a negative predictive value of 100%, and a positive predictive value of 48%. However, the classifier performed worse in a validation cohort including independent sample sets (AUC 0.73, NPV 88% and PPV 30%).

CONCLUSIONS

We developed a serum mass profile-based signature identifying patients with early lung cancer. Although this marker has insufficient value as a stand-alone preselecting tool for LD-CT screening, its potential clinical usefulness in evaluation of indeterminate pulmonary nodules deserves further investigation.

Identification of serum proteome signatures of locally advanced and metastatic gastric cancer: a pilot study

BACKGROUND

The gastric cancer is one of the most common and mortal cancer worldwide. The initial asymptomatic development and further nonspecific symptoms result in diagnosis at the advanced stage with poor prognosis. Yet, no clinically useful biomarkers are available for this malignancy, and invasive gastrointestinal endoscopy remains the only reliable option at the moment. Hence, there is a need for discovery of clinically useful noninvasive diagnostic and/or prognostic tool as an alternative (or complement) for current diagnostic tools. Here we aimed to search for serum proteins characteristic for local and invasive gastric cancer.

METHODS

Pre-treatment blood samples were collected from patients with diagnosed gastric adenocarcinoma at the different stage of disease: 35 patients with locally advanced cancer and 18 patients with metastatic cancer; 50 healthy donors were also included as a control group. The low-molecular-weight fraction of serum proteome (i.e., endogenous peptidome) was profiled by the MALDI-ToF mass spectrometry, and the whole proteome components were identified and quantified by the LC-MS/MS shotgun approach.

RESULTS

Multicomponent peptidome signatures were revealed that allowed good discrimination between healthy controls and cancer patients, as well as between patients with locally advanced and metastatic cancer. Moreover, a LC-MS/MS approach revealed 49 serum proteins with different abundances between healthy donors and cancer patients (predominantly proteins associated with inflammation and acute phase response). Furthermore, 19 serum proteins with different abundances between patients with locally advanced and metastatic cancer were identified (including proteins associated with cytokine/chemokine response and metabolism of nucleic acids). However, neither peptidome profiling nor shotgun proteomics approach allowed detecting serum components discriminating between two subgroups of patients with local disease who either developed or did not develop metastases during follow-up.

CONCLUSIONS

The molecular differences between locally advanced and metastatic gastric cancer, as well as more obvious differences between healthy individuals and cancer patients, have marked reflection at the level of serum proteome. However, we have no evidence that features of pre-treatment serum proteome could predict a risk of cancer dissemination in patients treated due to local disease. Nevertheless, presented data confirmed potential applicability of a serum proteome signature-based biomarker in diagnostics of gastric cancer.

MALDI-TOF-MS Platform for Integrated Proteomic and Peptidomic Profiling of Milk Samples Allows Rapid Detection of Food Adulterations

Adulteration of ovine, caprine, and buffalo milks with more common bovine material occurs for economic reasons and seasonal availability. Frauds are also associated with the use of powdered milk instead of declared, fresh material. In this context, various analytical methods have been adapted to dairy science applications with the aim to evaluate adulteration of milk samples, although time-consuming, suitable only for speciation or thermal treatment analysis, or useful for a specific fraud type. An integrated MALDI-TOF-MS platform for the combined peptidomic and proteomic profiling of milk samples is here presented, which allows rapid detection of illegal adulterations due to the addition of either nondeclared bovine material to water buffalo, goat, and ovine milks or of powdered bovine milk to the fresh counterpart. Peptide and protein markers of each animal milk were identified after direct analysis of a large number of diluted skimmed and/or enriched diluted skimmed filtrate samples. In parallel, markers of thermal treatment were characterized in different types of commercial milks. Principal components scores of ad hoc prepared species- or thermal treatment-associated adulterated milk samples were subjected to partial least-squares regression, permitting a fast accurate estimate of the fraud extents in test samples at either protein and peptide level. With respect to previous reports on MALDI-TOF-MS protein profiling methodologies for milk speciation, this study extends that approach to the analysis of the thermal treatment and introduces an independent, complementary peptide profiling measurement, which integrates protein data with additional information on peptides, validating final results and ultimately broadening the method applicability.

Partial-Body Irradiation in Patients with Prostate Cancer Treated with IMRT Has Little Effect on the Composition of Serum Proteome

Partial body irradiation during cancer radiotherapy (RT) induces a response of irradiated tissues that could be observed at the level of serum proteome. Here we aimed to characterize the response to RT in group of patients treated because of prostate cancer. Five consecutive blood samples were collected before, during, and after the end of RT in a group of 126 patients who received definitive treatment with a maximum dose of 76 Gy. Serum peptidome, which was profiled in the 2000–16,000 Da range using MALDI-MS. Serum proteins were identified and quantified using the shotgun LC-MS/MS approach. The majority of changes in serum peptidome were detected between pre-treatment samples and samples collected after 3–4 weeks of RT (~25% of registered peptides changed their abundances significantly), yet the intensity of observed changes was not correlated significantly with the degree of acute radiation toxicity or the volume of irradiated tissues. Furthermore, there were a few serum proteins identified, the abundances of which were different in pre-RT and post-RT samples, including immunity and inflammation-related factors. Observed effects were apparently weaker than in comparable groups of head and neck cancer patients in spite of similar radiation doses and volumes of irradiated tissues in both groups. We concluded that changes observed at the level of serum proteome were low for this cohort of prostate cancer patients, although the specific components involved are associated with immunity and inflammation, and reflect the characteristic acute response of the human body to radiation.

Radiotherapy-related changes in serum proteome patterns of head and neck cancer patients; the effect of low and medium doses of radiation delivered to large volumes of normal tissue

Background

Conformal intensity-modulated radiation therapy (IMRT) involves irradiation of large volume of normal tissue with low and medium doses, biological relevance of which is not clear yet. Serum proteome features were used here to study the dose-volume effects in patients irradiated with IMRT due to head and neck cancer.

Methods

Blood samples were collected before and during RT, and also about one month and one year after the end of RT in a group of 72 patients who received definitive treatment. Serum proteome profiles were analyzed using MALDI-ToF mass spectrometry in 800–14,000 Da range.

Results

Major changes in serum proteome profiles were observed between pre-treatment samples and samples collected one month after RT. Radiation-related changes in serum proteome features were affected by low-to-medium doses delivered to a large fraction of body mass. Proteome changes were associated with intensity of acute radiation toxicity, indicating collectively that RT-related features of serum proteome reflected general response of patient’s organism to irradiation. However, short-term dose-related changes in serum proteome features were not associated significantly with the long-term efficacy of the treatment.

Conclusions

The effects of low and medium doses of radiation have been documented at the level of serum proteome, which is a reflection of the patient’s whole body response.

Identification of kininogen-1 as a serum biomarker for the early detection of advanced colorectal adenoma and colorectal cancer

Background

Serum markers represent potential tools for the detection of colorectal cancer (CRC). The aim of this study was to obtain proteomic expression profiles and identify serum markers for the early detection of CRC.

Methods

Proteomic profiles of serum samples collected from 35 healthy volunteers, 35 patients with advanced colorectal adenoma (ACA), and 40 patients with CRC were compared using Clinprot technology. Using enzyme-linked immunosorbent assays (ELISAs), 366 sera samples were additionally analyzed, and immunohistochemistry studies of 400 tissues were used to verify the expression of kininogen-1 and its value in the early detection of CRC.

Results

Predicting models were established among the three groups, and kininogen-1 was identified as a potential marker for CRC using Clinprot technology. ELISAs also detected significantly higher serum kininogen-1 levels in ACA and CRC patients compared to controls (P<0.05). Furthermore, the area under the receiver operating characteristic curve (AUC) for serum kininogen-1 in the diagnosis of ACA was 0.635 (P = 0.003), and for serum carcinoembryonic antigen (CEA) was 0.453 (P = 0.358). The sensitivity, specificity, and accuracy of serum kininogen-1 for diagnosing Duke’s stage A and B CRC was 70.13%, 65.88%, and 67.90%, respectively, whereas serum CEA was 38.96%, 85.88%, and 63.58%, respectively. Moreover, immunohistochemistry showed that expression of kininogen-1 was significantly higher in CRC and ACA tissues than in normal mucosa (48.39% vs. 15.58% vs. 0%, P<0.05).

Conclusions

These results suggest that Clinprot technology provides a useful tool for the diagnosis of CRC, and kininogen-1 is a potential serum biomarker for the early detection of advanced colorectal adenoma and CRC.

Identification of proteins expressed differently among surgically resected stage I lung adenocarcinomas

RATIONALE

Among patients with surgically resected stage I lung adenocarcinoma, some succumb to early recurrence, while others survive for more than 5 years. Few markers to predict prognoses in these patients have been accepted. Recent advances in proteomic methodologies offer a unique chance to identify new candidate biomarkers. The aim of this study is to find differences in protein expression in resected lung cancer tissue of stage I adenocarcinoma from patients with no recurrence for more than 5 years and from those with early recurrence.

METHODS

Lung cancer tissues were obtained from 15 patients with pathologically confirmed stage I adenocarcinoma. The patients were divided into two groups, those with recurrence within 36 months (early recurrence group, n=9) and those that were disease-free for over 5 years (disease free group, n=6). Tissue proteins were separated by a two-dimensional electrophoresis long gel system (30 × 40 cm) with set ranges (3-10 NL) and examined by nano-LC-ESI-MS/MS. Western blot assays were performed to validate these proteins.

RESULTS

Twelve protein spots were up-regulated and 8 were down-regulated in the disease-free group as compared with the recurrence group. Of the 12 up-regulated proteins, haptoglubin, tau-tubulin kinase-2 (TTBK2), thymidine phosphorylase, annexin-1, PIN1, CAPG, and SEC23 were validated by Western blot. Among the 8 down-regulated proteins, serpinB6 and trangelin-2 were validated.

CONCLUSIONS

A total of 9 differentially expressed proteins were successfully extracted, identified, and confirmed from stage I lung adenocarcinoma tissues. The increased or decreased expression of these proteins according to prognosis may be the basis for further studies of proteomics in developing prognostic biomarkers.

MALDI-MS-Based Profiling of Serum Proteome: Detection of Changes Related to Progression of Cancer and Response to Anticancer Treatment

Mass spectrometry-based analyses of the low-molecular-weight fraction of serum proteome allow identifying proteome profiles (signatures) that are potentially useful in detection and classification of cancer. Several published studies have shown that multipeptide signatures selected in numerical tests have potential values for diagnostics of different types of cancer. However due to apparent problems with standardization of methodological details, both experimental and computational, none of the proposed peptide signatures analyzed directly by MALDI/SELDI-ToF spectrometry has been approved for routine diagnostics. Noteworthy, several components of proposed cancer signatures, especially those characteristic for advanced cancer, were identified as fragments of blood proteins involved in the acute phase and inflammatory response. This indicated that among cancer biomarker candidates to be possibly identified by serum proteome profiling were rather those reflecting overall influence of a disease (and the therapy) upon the human organism, than products of cancer-specific genes. Current paper focuses on changes in serum proteome that are related to response of patient's organism to progressing malignancy and toxicity of anticancer treatment. In addition, several methodological issues that affect robustness and interlaboratory reproducibility of MS-based serum proteome profiling are discussed.

Radiation-related changes in serum proteome profiles detected by mass spectrometry in blood of patients treated with radiotherapy due to larynx cancer

The study aimed to detect features of human serum proteome that were associated with exposure to ionizing radiation. The analyzed group consisted of 46 patients treated with radical radiotherapy for larynx cancer; patients were irradiated with total doses in a range from 51 to 72 Gy. Three consecutive blood samples were collected from each patient: before the start, 2 weeks after the start, and 4-6 weeks after the end of radiotherapy. The low-molecular-weight fraction of the serum proteome (2,000-13,000 Da) was analyzed by the MALDI-ToF mass spectrometry. Proteome profiles of serum samples collected before the start of radiotherapy and during the early stage of the treatment were similar. In marked contrast, mass profiles of serum samples collected several weeks after the end of the treatment revealed clear changes. We found that 41 out of 312 registered peptide ions changed their abundance significantly when serum samples collected after the final irradiation were compared with samples collected at the two earlier time points. We also found that abundances of certain serum peptides were associated with total doses of radiation received by patients. The results of this pilot study indicate that features of serum proteome analyzed by mass spectrometry have potential applicability as a retrospective marker of exposure to ionizing radiation.

New lipidomic approaches in cystic fibrosis

Lipid analysis has been a crucial source of information in cystic fibrosis (CF). New methodologies for qualitative and quantitative lipidomics allow evaluation of a large number of samples, of special interest in patient screening for diagnostic and prognostic biological markers, as well as in cell physiology. In this chapter, two new complementary approaches are described: matrix-assisted laser desorption coupled to time of flight (MALDI-TOF-ClinProTools™) and liquid chromatography coupled to ion trap mass spectrometry (LC-MS( n )). MALDI-TOF-ClinProTools™ offers a large unbiased screening for the discovery of potential lipid alterations in diseased patients. LC-MS( n ) represents a state-of-the-art lipidomic tool for the identification and quantification of such alterations. The combination of both may open new perspectives in the quest for lipids participating in CF pathogenesis, therapy targets, and biomarkers.

Proteomic characterization of Her2/neu-overexpressing breast cancer cells

The receptor tyrosine kinase HER2 is an oncogene amplified in invasive breast cancer and its overexpression in mammary epithelial cell lines is a strong determinant of a tumorigenic phenotype. Accordingly, HER2-overexpressing mammary tumors are commonly indicative of a poor prognosis in patients. Several quantitative proteomic studies have employed two-dimensional gel electrophoresis in combination with MS/MS, which provides only limited information about the molecular mechanisms underlying HER2/neu signaling. In the present study, we used a SILAC-based approach to compare the proteomic profile of normal breast epithelial cells with that of Her2/neu-overexpressing mammary epithelial cells, isolated from primary mammary tumors arising in mouse mammary tumor virus-Her2/neu transgenic mice. We identified 23 proteins with relevant annotated functions in breast cancer, showing a substantial differential expression. This included overexpression of creatine kinase, retinol-binding protein 1, thymosin 4 and tumor protein D52, which correlated with the tumorigenic phenotype of Her2-overexpressing cells. The differential expression pattern of two genes, gelsolin and retinol binding protein 1, was further validated in normal and tumor tissues. Finally, an in silico analysis of published cancer microarray data sets revealed a 23-gene signature, which can be used to predict the probability of metastasis-free survival in breast cancer patients.

Mass spectrometry-based analysis of therapy-related changes in serum proteome patterns of patients with early-stage breast cancer

Background

The proteomics approach termed proteome pattern analysis has been shown previously to have potential in the detection and classification of breast cancer. Here we aimed to identify changes in serum proteome patterns related to therapy of breast cancer patients.

Methods

Blood samples were collected before the start of therapy, after the surgical resection of tumors and one year after the end of therapy in a group of 70 patients diagnosed at early stages of the disease. Patients were treated with surgery either independently (26) or in combination with neoadjuvant chemotherapy (5) or adjuvant radio/chemotherapy (39). The low-molecular-weight fraction of serum proteome was examined using MALDI-ToF mass spectrometry, and then changes in intensities of peptide ions registered in a mass range between 2,000 and 14,000 Da were identified and correlated with clinical data.

Results

We found that surgical resection of tumors did not have an immediate effect on the mass profiles of the serum proteome. On the other hand, significant long-term effects were observed in serum proteome patterns one year after the end of basic treatment (we found that about 20 peptides exhibited significant changes in their abundances). Moreover, the significant differences were found primarily in the subgroup of patients treated with adjuvant therapy, but not in the subgroup subjected only to surgery. This suggests that the observed changes reflect overall responses of the patients to the toxic effects of adjuvant radio/chemotherapy. In line with this hypothesis we detected two serum peptides (registered m/z values 2,184 and 5,403 Da) whose changes correlated significantly with the type of treatment employed (their abundances decreased after adjuvant therapy, but increased in patients treated only with surgery). On the other hand, no significant correlation was found between changes in the abundance of any spectral component or clinical features of patients, including staging and grading of tumors.

Conclusions

The study establishes a high potential of MALDI-ToF-based analyses for the detection of dynamic changes in the serum proteome related to therapy of breast cancer patients, which revealed the potential applicability of serum proteome patterns analyses in monitoring the toxicity of therapy.

Proteomics in asthma

Proteomic approaches have already been successfully implemented in areas such as cancer research. Surprisingly, only a few proteomics analyses have been published reporting on the protein profiles associated with asthma. Although proteomics has its limitations and experimental challenges, it can successfully contribute to the understanding of a complex disease such as asthma. We have reviewed the current literature that has reported the use of proteomic techniques to identify proteins that may contribute to altered lung function in asthma. Only a few of these studies have used proteomic techniques on human tissues associated with asthma, while most research has been performed with animal models of asthma. Proteomic applications have been used as a complimentary technique to verify the suspected candidate proteins involved in asthma. In addition, novel proteins have been identified as potential therapeutic targets. Future collaboration between the different scientific disciplines using proteomic studies of animal models of asthma and confirmation of these findings in human tissues will significantly contribute to the understanding of the etiology of asthma and lead to the development of new therapeutic strategies for this highly prevalent disease.

Application of slicing of one-dimensional gels with subsequent slice-by-slice mass spectrometry for the proteomic profiling of human liver cytochromes P450

Sequential thin slicing of one-dimensional electrophoresis gels followed by slice-by-slice mass spectrometry to allow protein identification was used to produce a proteomic map for cytochromes P450. Parallel MALDI-TOF-MS and LC-MS/MS analyses were performed. Combination of the two MS methods increased the quality of protein identification. We have proposed an efficient approach to obtain a comprehensive profile of drug-metabolizing enzymes in the liver that can be used to differentiate between polymorphic variants of cytochromes P450.

Serum peptide profiles during progression of chronic hepatitis B virus infection to liver failure

Chronic hepatitis B virus (HBV)-infected patients with liver failure have a poor prognosis, and no satisfactory biomarkers are available for diagnosis before the end-stage. We explored serum peptide profiling for diagnosis and prediction of progression to liver failure in HBV-infected patients. Serum samples (164) from healthy subjects (n = 20), or subjects with chronic hepatitis B without cirrhosis and liver failure [chronic hepatitis B subjects without cirrhosis and liver failure (CHB); n = 33], with compensated liver cirrhosis (compensated liver cirrhosis (LC); n = 35), with acute-on-chronic liver failure [acute-on-chronic liver failure (ACLF); n = 38] or with chronic liver failure [chronic liver failure (CLF), n = 38] were applied to ClinProt magnetic beads, and bound peptides/proteins were analyzed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Our classification diagnostic models of liver disease were generated based on the Genetic Algorithm (GA) and Quick Classifier Algorithm (QC). Differentially expressed peptides were found among all test groups, with patterns of difference that readily distinguished between healthy and various HBV-associated liver disease samples. The model generated seven characteristic peptide peaks at 4053 m/z, 3506 m/z, 4963 m/z, 9289 m/z, 2628 m/z, 3193 m/z and 6432 m/z, giving overall predictive capability of 54.27%. Two-way comparisons of LC, ACLF or CLF vs CHB had predictive capabilities of 79.8%, 91.41% and 97.99%, respectively. Comparisons of ACLF or CLF vs LC were predictive at 87.72% and 82.18%, respectively and ACLF vs CLF was predictive at 75.05%. These classification diagnostic models generated by different peptide peaks were further validated in blinded tests with 67-100% accuracy. Serum peptide patterns vary during progression of chronic HBV infection to liver failure and may be used to distinguish different stages of the disease.

Serum protein signature may improve detection of ductal carcinoma in situ of the breast

Ductal carcinoma in situ (DCIS) of the breast is part of a spectrum of preinvasive lesions that originate within normal breast tissue and progress to invasive breast cancer. The detection of DCIS is important for the reduction of mortality from breast cancer, but the diagnosis of preinvasive breast tumors is hampered by the lack of an adequate detection method. To identify the changes in protein expression during the initial stage of tumorigenesis and to identify the presence of new DCIS markers, we analysed serum from 60 patients with breast cancer and 60 normal controls using mass spectrometry. A 23-protein index was generated that correctly distinguishes the DCIS and control groups with sensitivities and specificities in excess of 80% in two independent cohorts. Two candidate peptides were purified and identified as platelet factor 4 (PF-4) and complement C3a(desArg) anaphylatoxin (C3a(desArg)) using liquid chromatography-tandem mass spectrometry (LC-MS/MS). In an independent serum set of 165 patients, PF-4 and C3a(desArg) were significantly upregulated in DCIS compared with non-cancerous controls, as validated using western blot and enzyme-linked immunosorbent assay. We conclude that our serum protein-based test, used in conjunction with image-based screening practices, could improve the sensitivity and specificity of breast cancer detection.

Mass spectrometry-based serum proteome pattern analysis in molecular diagnostics of early stage breast cancer

Background

Mass spectrometric analysis of the blood proteome is an emerging method of clinical proteomics. The approach exploiting multi-protein/peptide sets (fingerprints) detected by mass spectrometry that reflect overall features of a specimen's proteome, termed proteome pattern analysis, have been already shown in several studies to have applicability in cancer diagnostics. We aimed to identify serum proteome patterns specific for early stage breast cancer patients using MALDI-ToF mass spectrometry.

Methods

Blood samples were collected before the start of therapy in a group of 92 patients diagnosed at stages I and II of the disease, and in a group of age-matched healthy controls (104 women). Serum specimens were purified and the low-molecular-weight proteome fraction was examined using MALDI-ToF mass spectrometry after removal of albumin and other high-molecular-weight serum proteins. Protein ions registered in a mass range between 2,000 and 10,000 Da were analyzed using a new bioinformatic tool created in our group, which included modeling spectra as a sum of Gaussian bell-shaped curves.

Results

We have identified features of serum proteome patterns that were significantly different between blood samples of healthy individuals and early stage breast cancer patients. The classifier built of three spectral components that differentiated controls and cancer patients had 83% sensitivity and 85% specificity. Spectral components (i.e., protein ions) that were the most frequent in such classifiers had approximate m/z values of 2303, 2866 and 3579 Da (a biomarker built from these three components showed 88% sensitivity and 78% specificity). Of note, we did not find a significant correlation between features of serum proteome patterns and established prognostic or predictive factors like tumor size, nodal involvement, histopathological grade, estrogen and progesterone receptor expression. In addition, we observed a significantly (p = 0.0003) increased level of osteopontin in blood of the group of cancer patients studied (however, the plasma level of osteopontin classified cancer samples with 88% sensitivity but only 28% specificity).

Conclusion

MALDI-ToF spectrometry of serum has an obvious potential to differentiate samples between early breast cancer patients and healthy controls. Importantly, a classifier built on MS-based serum proteome patterns outperforms available protein biomarkers analyzed in blood by immunoassays.

Highly sensitive detection of melanoma based on serum proteomic profiling

PURPOSE

There is no available tumor marker that can detect primary melanoma. Proteomics analysis has been proposed as a novel tool that would lead to the discovery of potential new tumor markers.

METHODS

We developed a serum proteomic fingerprinting approach coupled with a classification method to determine whether proteomic profiling could discriminate between melanoma and healthy volunteers. A total of 108 serum samples from 30 early-stage [American Joint Committee on Cancer (AJCC) stage I or II] and 30 advanced-stage (AJCC stage III or IV) melanoma patients and 48 healthy volunteers were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) utilizing protein chip technology and artificial neural networks.

RESULTS

In a first step, a multiprotein classifier was built using a training set of 30 pathologically confirmed melanoma and 24 healthy volunteer serum samples, resulting in good classification accuracy for correct diagnosis and stage classification assignment. Subsequently, our multiprotein classifier was tested in an independent validation set of 30 melanoma and 24 non-cancer serum samples patients, maintained in a good diagnostic accuracy of 98.1% (sensitivity 96.7%, specificity 100%), and 100% stage I/II classification assignment.

CONCLUSIONS

Although results remain to be confirmed in larger collective patient cohorts, we could demonstrate the usefulness of proteomic profiling as a sensitive and specific assay to detect melanoma, including non-metastatic melanoma, from the serum.

Proteomic profile determination of autosomal aneuploidies by mass spectrometry on amniotic fluids

BACKGROUND

Prenatal diagnosis of chromosomal abnormalities by cytogenetic analysis is time-consuming, expensive, and requires highly qualified technicians. Rapid diagnosis of aneuploidies followed by reassurance of women with normal results can be performed by molecular analysis of uncultured foetal cells. In the present study, we developed a proteomic fingerprinting approach coupled with a statistical classification method to improve diagnosis of aneuploidies, including trisomies 13, 18, and 21, in amniotic fluid samples.

RESULTS

The proteomic spectra obtained from 52 pregnant women were compiled, normalized, and mass peaks with mass-to-charge ratios between 2.5 and 50 kDa identified. Peak information was combined together and analysed using univariate statistics. Among the 208 expressed protein peaks, 40 differed significantly between aneuploid and non aneuploid samples, with AUC diagnostic values ranging from 0.71 to 0.91. Hierarchical clustering, principal component analysis and support vector machine (SVM) analysis were performed. Two class predictor models were defined from the training set, which resulted in a prediction accuracy of 92.3% and 96.43%, respectively. Using an external and independent validation set, diagnostic accuracies were maintained at 87.5% and 91.67%, respectively.

CONCLUSION

This pilot study demonstrates the potential interest of protein expression signature in the identification of new potential biological markers that might be helpful for the rapid clinical management of high-risk pregnancies.

Proteomics in clinical prostate research

The incidence of early prostate cancer (PCa) has increased rapidly in recent years. The majority of newly diagnosed PCa are in early tumor phase. Presently, we do not have adequate biomarkers to assess tumor aggressiveness in individual cases. Consequently, too many patients are given curatively intended treatment. An exploration of the human proteome may provide clinically useful markers. 2-DE has been successfully used for analysis of the protein phenotype using clinical samples. Proteins are separated according to size and charge, gels are compared by image analysis, protein spots of interest are excised, and proteins identified by MS. This method is exploratory and allows protein identification. However, low-abundance proteins are difficult to detect and 2-DE is currently too labor-intensive for routine use. In recent years, nongel based techniques, such as LC-MS, SELDI-MS, and protein arrays have emerged. They require smaller sample sizes and can be more automated than 2-DE. In this review, we describe studies of the protein expression of benign prostatic tissue and PCa, which is likely to serve as the first step in prognostic biomarker discovery. The prostate proteome is still far from a complete mapping which would enhance our understanding of PCa biology.

Proteomics in uveal melanoma research: opportunities and challenges in biomarker discovery

Uveal melanoma (UM) is the most frequent primary intraocular tumor in adult humans. Despite the significant advances in diagnosis and treatment of UM in the last decades, the prognosis of UM sufferers is still poor. Metastatic liver disease is the leading cause of death in UM and can develop after a long disease-free interval, suggesting the presence of occult micrometastasis. Proteomics technology has opened new opportunities for elucidating the molecular mechanism of complex diseases, such as cancer. This article will review the recent developments in biomarker discovery for UM research by proteomics. In the last few years, the first UM proteomics-based analyses have been launched, yielding promising results. An update on recent developments on this field is presented.

What does it need to be a biomarker? Relationships between resolution, differential quantification and statistical validation of protein surrogate biomarkers

The separation of proteins with the aim of discovering surrogate biomarkers defining differences between various stages of biological materials is the core occupation of every project in Proteomics. There are numerous recent publications suggesting a wide array of separation technologies, ranging from 2-DE, MS-linked LC, CE or chip-based surface-enhanced laser desorption ionization claiming to be useful for this purpose, and addressing the urgent clinical, diagnostic or toxicological needs for such surrogates. However, many potential biomarkers emerging from proteomic studies did not survive validation in, for example, large-scale clinical studies or simply independent experiments, and at the same time being tested in settings with case numbers bigger than perhaps a few hundreds. The major problems of protein biomarkers are associated with the huge dynamic range of possible concentrations and the ever-increasing number of molecular species due to post-translational modifications. In particular, the chemical diversity of the latter imposes a necessity of improved resolution of separation technologies, because otherwise the crucial quantitative information is lost in pools of poorly resolved peptides. Here, we present and analyze some examples of successful developments of protein biomarkers, and show the prerequisites and necessary considerations while moving protein candidates from purely descriptive phenomena to a stage of validated surrogate biomarkers. This includes a detailed discussion of requirements regarding resolution of initial separation techniques, linear dynamic range and statistics of differential quantification, but also the subsequent clinical validation, testing the biomarker in clinical settings and using large numbers of patient samples.

Proteomics in neurosciences

This review provides an outline of the most important proteomic applications in the study of neurodegenerative disorders including Alzheimer's (AD), Parkinson's (PD), Huntington's (HD), and prion diseases, and also discusses advances in cancer and addiction. One of the scopes is to illustrate the potential of proteomics in the biomarkers discovery of these diseases. Finally, this article comments the advantages and drawbacks of the most commonly used techniques and methods for samples preparation.

Investigation of the advanced functionalities of a hybrid quadrupole orthogonal acceleration time-of-flight mass spectrometer

Time-of-flight mass spectrometry (ToF-MS) has gained wide acceptance in many fields of chemistry, proteomics, metabolomics and small molecule analysis. ToF-MS, however, has some inherent advantages and drawbacks. Numerous developments have been made to hybrid ToF instruments to improve their capabilities. We have used a quadrupole orthogonal acceleration ToF (Q-oa-ToF) instrument to assess developments made to improve resolution, dynamic range and signal-to-noise (S/N) ratios (i.e. sensitivity). Higher mass resolution can improve the analysis of mixtures containing compounds with similar m/z values and improved mass accuracy gives greater confidence for structural elucidation applications. Wide dynamic ranges are necessary for the analysis of unknown samples or samples that vary widely in analyte concentrations. The performance of the advanced functionalities for routine structural elucidation in terms of resolution, dynamic range and S/N ratios was investigated using test compounds. The results presented in this work demonstrate and validate the use of these new enhancements for Q-ToF instruments and also show their limitations.