Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients

The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer

Objectives

To investigative the predictive ability of radiomics signature for preoperative staging (I-IIvs.III-IV) of primary colorectal cancer (CRC).

Methods

This study consisted of 494 consecutive patients (training dataset: n=286; validation cohort, n=208) with stage I–IV CRC. A radiomics signature was generated using LASSO logistic regression model. Association between radiomics signature and CRC staging was explored. The classification performance of the radiomics signature was explored with respect to the receiver operating characteristics(ROC) curve.

Results

The 16-feature-based radiomics signature was an independent predictor for staging of CRC, which could successfully categorize CRC into stage I-II and III-IV (p <0.0001) in training and validation dataset. The median of radiomics signature of stage III-IV was higher than stage I-II in the training and validation dataset. As for the classification performance of the radiomics signature in CRC staging, the AUC was 0.792(95%CI:0.741-0.853) with sensitivity of 0.629 and specificity of 0.874. The signature in the validation dataset obtained an AUC of 0.708(95%CI:0.698-0.718) with sensitivity of 0.611 and specificity of 0.680.

Conclusions

A radiomics signature was developed and validated to be a significant predictor for discrimination of stage I-II from III-IV CRC, which may serve as a complementary tool for the preoperative tumor staging in CRC.

Serum and tissue markers in colorectal cancer: State of art

Colorectal cancer (CRC) represents one of the most commonly diagnosed cancers worldwide. It is the second leading cause of cancer death in Western Countries. In the last decade, the survival of patients with metastatic CRC has improved dramatically. Due to the advent of new drugs (irinotecan and oxaliplatin) and target therapies (i.e. bevacizumab, cetuximab, panitumab, aflibercept and regorafenib), the median overall survival has risen from about 12 mo in the mid nineties to 30 mo recently. Molecular studies have recently widened the opportunity for testing new possible markers, but actually, only few markers can be recommended for practical use in clinic. In the next future, the hope is to have a complete panel of clinical biomarkers to use in every setting of CRC disease, and at the same time: 1) to receive information about prognostic significance by their expression and 2) to be oriented in the choice of the adequate treatment. Moreover, molecular analyses have shown that the natural history of all CRCs is not the same. Individual patients with same stage tumors may have different long-term prognosis and response to therapy. In addition, some prognostic variables are likely to be more important than others. Here we review the role of serum and tissue markers according to the recently published English literature. This paper is an extension of the article "Biological and clinical markers in colorectal cancer: state of art" by Cappellani A published in Jan 2010.

Serum profiling by mass spectrometry combined with bioinformatics for the biomarkers discovery in diffuse large B-cell lymphoma

The aim of this study was to identify potential serum biomarkers of diffuse large B-cell lymphoma (DLBCL) and to detect DLBCL therapy response biomarkers. DLBCL serum proteomic analysis was performed using the CM10 ProteinChip mass spectrometry (SELDI-TOF-MS) approach combined with bioinformatics. A total of 178 samples were analyzed in this study from untreated early stage DLBCL patients (38), patients with inflammatory lymphadenopathy (13), healthy donors (35), post-treatment non-relapsed DLBCL patients (53), and relapsed DLBCL patients (39). Model 1 formed by nine protein peaks (m/z: 6443, 5913, 6198, 4098, 7775, 9293, 5946, 5977, and 4628) could be used to distinguish DLBCL patients from healthy individuals with an accuracy of 95.89% (70/73). The diagnostic pattern constructed using the support vector machine including the nine proteins of model 1, showed a maximum Youden's Index. Model 2 formed by three protein peaks (m/z: 3942, 6639, and 4121) could be used to distinguish DLBCL patients from those with inflammatory lymphadenopathy with an accuracy of 94.12% (48/51). Model 3 formed by six protein peaks could distinguish patients with inflammatory lymphadenopathy from healthy individuals with an accuracy of 97.92% (47/48). Model 4 could be used to distinguish non-relapsed DLBCL patients from relapsed DLBCL patients with an accuracy of 84.78% (78/92). The four patterns were validated by leave-one-out cross-validation. These data demonstrate that the CM10 ProteinChip and SELDI-TOF-MS approach combined with bioinformatics can be used effectively to screen for the differential protein expression profiles of DLBCL patients and to predict the response to therapy.

Detection of pancreatic cancer with normal carbohydrate antigen 19-9 using protein chip technology

AIM: To develop a method to differentiate pancreatic cancer patients from healthy or benign individuals when carbohydrate antigen (CA) 19-9 is normal.

METHODS: Forty-one serum samples from patients with pancreatic lesions and blood samples from 20 healthy individuals were collected at the first stage of the experiment according to the enrolment criteria. General characteristics and some clinical features were carefully compared to ensure that the results were reasonable. All the blood samples were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) combined with CM10 chips and a related bioinformatics analysis program to generate diagnostic models with different proteins. Forty-seven consecutive samples were tested at the next stage to verify the veracity and efficiency of the models.

RESULTS: The sex, age, and serum CA19-9 levels among the three groups (malignant, benign, and healthy) were statistically matched (P values were 0.957, 0.145, and 0.382, respectively). Two patterns were generated. Pattern 1 with four proteins theoretically had a specificity and sensitivity of 100% in distinguishing pancreatic cancer from healthy individuals, while it was 86.7% and 86.4%, respectively, in the subsequent practical verification. The positive predictive value (PPV) of the model was 86.4%. One of the four proteins was expressed highly in pancreatic cancer while the other three were expressed weakly. Pattern 2 consisted of six proteins that showed a specificity of 70.0% and sensitivity of 77.3% for differentiating malignancy from benign tumors. Its PPV reached 85.0%. Only one of these six proteins showed high expression in the malignant group.

CONCLUSION: SELDI-TOF-MS may facilitate diagnosis or differential diagnosis of pancreatic cancer when CA19-9 is normal. Pattern 1 may serve as a useful screening tool.

Proteomic analysis of primary colon cancer-associated fibroblasts using the SELDI-ProteinChip platform

OBJECTIVE

Cancer-associated fibroblasts (CAFs) are one of the hallmarks of the cancer microenvironment. Recent evidence has indicated that CAFs are more competent in enhancing cancer cell growth and migration than normal fibroblasts. However, the unique protein expression of CAFs has not been fully elucidated. This study aims to investigate the characterizations of colon CAFs by comparing the differential protein expression between CAFs and normal fibroblasts.

METHODS

Primary fibroblasts were isolated from surgical specimen of human colon cancer and matched normal colonic tissue. Purity of the cell population was verified through immunostain analysis. Total cell lysates and conditioned media from each group of cells were extracted, and protein expression analysis was conducted using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) ProteinChip platform.

RESULTS

Most primary cells showed typical fibroblast-like features after two weeks. Increased proportion of α-smooth muscle actin-positive myofibroblasts was detected within the CAFs in four of the six pairs of primary cells. Fibroblast activation protein was weakly expressed in most cells without differences. Using SELDI-TOF-MS ProteinChip platform, four protein peaks mass over charge ratio (m/z) 1142, 3011, 4035, and 4945 were detected in the total cell lysates, and two protein peaks m/z 1368 and 1389 were detected in the conditioned media. The potential candidate proteins found in the Swiss-Prot database include morphogenetic neuropeptides, FMRFamide-related peptides, insulin-like growth factor II, thymosin β-4-like protein 3, and tight junction-associated protein 1.

CONCLUSIONS

Using the SELDI-ProteinChip platform, differential protein expressions were identified in colon CAFs compared with normal colonic stromal fibroblasts. The complex proteomic alternations in colon CAFs may play important roles related to the colon cancer microenvironment.

Identification of Regional Lymph Node Involvement of Colorectal Cancer by Serum SELDI Proteomic Patterns

Background. To explore the application of serum proteomic patterns for the preoperative detection of regional lymph node involvement of colorectal cancer (CRC). Methods. Serum samples were applied to immobilized metal affinity capture ProteinChip to generate mass spectra by Surface-Enhanced Laser Desorption/ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS). Proteomic spectra of serum samples from 70 node-positive CRC patients and 75 age- and gender-matched node-negative CRC patients were employed as a training set, and a classification tree was generated by using Biomarker Pattern Software package. The validity of the classification tree was then challenged with a blind test set including another 65 CRC patients. Results. The software identified an average of 46 mass peaks/spectrum and 5 of the identified peaks at m/z 3,104, 3,781, 5,867, 7,970, and 9,290 were used to construct the classification tree. The classification tree separated effectively node-positive CRC patients from node-negative CRC patients, achieving a sensitivity of 94.29% and a specificity of 100.00%. The blind test challenged the model independently with a sensitivity of 91.43% a specificity of 96.67%. Conclusions. The results indicate that SELDI-TOF-MS can correctly distinguish node-positive CRC patients from node-negative ones and show great potential for preoperative screening for regional lymph node involvement of CRC.

Detection of renal allograft dysfunction with characteristic protein fingerprint by serum proteomic analysis

This study aimed to diagnose renal allograft dysfunction with specific biomarkers by serum proteomic analysis. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) and bioinformatics (support vector machine and leave-one cross validation) were used to analyze serum proteome. Enrolled patients included 38 biopsy-proved acute rejection (BPAR), 10 acute tubular necrosis (ATN), 24 subclinical rejection (SCR) and 29 stable control recipients verified by protocol biopsy. A characteristic protein profile can be detected in each renal allograft dysfunction group. BPAR patients were differentiated from stable patients with markers of 9710.1, 4971, 6675.5, 8563.8, 6709.2, 9319 and 4476.7 Da with high sensitivity and specificity. ATN can be clearly distinguished from BPAR and stable control. Subclinical rejection differentiated from stable control with markers of 9193.1, 2759.1, 8464.6 Da. The independent blind test yielded with high specificity and sensitivity for each group. Serum proteome analysis by SELDI-TOF MS combined with bioinformatics in renal allograft dysfunction is valuable and promising. Specific markers were detected in each group. Identification of these proteins may prove useful as diagnostic markers for allograft dysfunction and better to elucidate the mechanism of acute rejection.

Prospects of using proteome technologies in the diagnostics of sexually transmitted infections and skin diseases

The article presents data from the literature describing up-to-date syphilis diagnostics methods used in the Russian Federation. It also describes main proteome techniques and gives the results of applying proteome technologies in the diagnostics of diseases including infectious ones, and prospects and opportunities for using direct proteome profiling to develop a new method for syphilis diagnostics are analyzed.

Laser-capture microdissection and protein extraction for protein fingerprint of OSCC and OLK

To find new biomarkers and establish histopathology protein fingerprint models for early detection of oral squamous cell carcinoma (OSCC), laser capture microdissection (LCM) technology was utilized in 21 OSCC tissues and 7 oral leukoplaque (OLK) tissues as well as their adjacent normal tissues. Each sample was then detected by SELDI-TOF-MS technology and CM10 protein chip as well as bioinformatics tools. Three proteomic biomarker patterns were identified. Pattern 1 distinguishes patients with OLK from healthy individuals. Pattern 2 distinguishes patients with OSCC from healthy individuals. Pattern 3 distinguishes patients with OSCC from patients with OLK. The analysis yielded both a specificity and a sensitivity of 90.48% for pattern 1, a specificity of 100.00% and a sensitivity of 85.71% for pattern 2, and a specificity of 100.00% and a sensitivity of 85.71% for pattern 3. Proteome mass/charge 3714, 3515, and 4944 built the distinguished protein peaks between the OSCC tumor and adjacent normal tissues. The accuracy of the blind prediction was 90.48% (38/42). M/Z 15122 and 7569 built the distinguished protein peaks between the OLK and adjacent normal tissues. M/Z 3738 and 11366 built the distinguished protein peaks between the OSCC and the OLK. By employing LCM technology combined with SELDI-TOF-MS technology and bioinformatics approaches, histopathology would not only facilitate the discovery of better biomarkers for OSCC and OLK, but also provide a useful tool for molecular diagnosis by potential biomarker.

Proteomic diagnosis models from serum for early detection of oral squamous cell carcinoma

The early diagnosis of oral squamous cell carcinoma (OSCC) is crucial to prevent deformity and malfunction post-surgery, as well as to allow clinicians to make a rapid decision about treatment. The aim of this study was to search a serum diagnostic model at a molecular level for OSCC. After collection and processing of serum from 28 OSCC patients and 32 healthy volunteers in the Department of Stomatology at the university hospital, samples were detected using Surface Enhanced Laser Desorption Ionization Time of Flight Mass Spectrometry (SELDI-TOF-MS) technology with CM10 protein chip and bioinformatics. Seven protein mass peaks were screened out to build a serum diagnosis model with a significant P value, respectively, and the sensitivity, specificity, and total accuracy were 93.75%, 92.86%, and 93.33%. The use of serum protein fingerprint provides a promising approach for early diagnostics, which could benefit determining preventative and therapeutic stages of patients with OSCC.

A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

OBJECTIVE

To develop a new bioinformatic tool based on a data-mining approach for extraction of the most informative proteins that could be used to find the potential biomarkers for the detection of cancer.

METHODS

Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality reduction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test.

RESULTS

From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%.

CONCLUSION

The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.

Applications of SELDI-MS technology in oncology

Considerable interest, speculation and controversy have been generated utilising surface-enhanced laser desorption/ionization in conjunction with mass spectrometry (SELDI-MS) for the diagnosis, prognosis and therapeutic monitoring of cancer and offers an attractive approach to cancer biomarker discovery from tissues and biological fluids. This technology utilises a combination of mass spectrometry and chromatography to facilitate protein profiling of complex biological mixtures. Compared to some other more traditional proteomic platforms, such as 2D polyacrylamide gel electrophoresis, it has a high-throughput capability and can resolve low-mass proteins. However, a considerable number of challenging issues related to the design of studies, including reproducibility, sensitivity, specificity, variation in sample collection, processing and storage, have been reported as problematic with this technology; albeit some of these concerns could perhaps also be lauded against other proteomic approaches that have attempted to address complex protein mixtures, such as plasma. Applications, successes and limitations of SELDI-MS in both clinical and basic science arenas will be reviewed in this article.

Deep proteome profiling of sera from never-smoked lung cancer patients

Previous studies on the serum proteome are hampered by the huge dynamic range of concentration of different protein species. The use of Equalizer Beads coupled with a combinatorial library of ligands has been shown to allow access to many low-abundance proteins or polypeptides undetectable by classical analytical methods. This study focused on never-smoked lung cancer, which is considered to be more homogeneous and distinct from smoking-related cases both clinically and biologically. Serum samples obtained from 42 never-smoked lung cancer patients (28 patients with active untreated disease and 14 patients with tumor resected) were compared with those from 30 normal control subjects using the pioneering Equalizer Beads technology followed by subsequent analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Eighty-five biomarkers were significantly different between lung cancer and normal control. The application of classification algorithms based on significant biomarkers achieved good accuracy of 91.7%, 80% and 87.5% in class-prediction with respect to presence or absence of disease, subsequent development of metastasis and length of survival (longer or shorter than median) respectively. Support vector machine (SVM) performed best overall. We have proved the feasibility and convenience of using the Equalizer Beads technology to study the deep proteome of the sera of lung cancer patients in a rapid and high-throughput fashion, and which enables detection of low abundance polypeptides/proteins biomarkers. Coupling with classification algorithms, the technologies will be clinically useful for diagnosis and prediction of prognosis in lung cancer.

Building the power house: recent advances in mitochondrial studies through proteomics and systems biology

The emerging field of systems biology seeks to develop novel approaches to integrate heterogeneous data sources for effective analysis of complex living systems. Systemic studies of mitochondria have generated a large number of proteomic data sets in numerous species, including yeast, plant, mouse, rat, and human. Beyond component identification, mitochondrial proteomics is recognized as a powerful tool for diagnosing and characterizing complex diseases associated with these organelles. Various proteomic techniques for isolation and purification of proteins have been developed; each tailored to preserve protein properties relevant to study of a particular disease type. Examples of such techniques include immunocapture, which minimizes loss of posttranslational modification, 4-iodobutyltriphenylphosphonium labeling, which quantifies protein redox states, and surface-enhanced laser desorption ionization-time-of-flight mass spectrometry, which allows sequence-specific binding. With the rapidly increasing number of discovered molecular components, computational models are also being developed to facilitate the organization and analysis of such data. Computational models of mitochondria have been accomplished with top-down and bottom-up approaches and have been steadily improved in size and scope. Results from top-down methods tend to be more qualitative but are unbiased by prior knowledge about the system. Bottom-up methods often require the incorporation of a large amount of existing data but provide more rigorous and quantitative information, which can be used as hypotheses for subsequent experimental studies. Successes and limitations of the studies reviewed here provide opportunities and challenges that must be addressed to facilitate the application of systems biology to larger systems.