Proteomics as a guiding tool for more effective personalized therapy

Circulating Biomarkers for Cancer Detection: Could Salivary microRNAs Be an Opportunity for Ovarian Cancer Diagnostics?

MicroRNAs (miRNAs) are small non-coding RNAs with the crucial regulatory functions of gene expression at post-transcriptional level, detectable in cell and tissue extracts, and body fluids. For their stability in body fluids and accessibility to sampling, circulating miRNAs and changes of their concentration may represent suitable disease biomarkers, with diagnostic and prognostic relevance. A solid literature now describes the profiling of circulating miRNA signatures for several tumor types. Among body fluids, saliva accurately reflects systemic pathophysiological conditions, representing a promising diagnostic resource for the future of low-cost screening procedures for systemic diseases, including cancer. Here, we provide a review of literature about miRNAs as potential disease biomarkers with regard to ovarian cancer (OC), with an excursus about liquid biopsies, and saliva in particular. We also report on salivary miRNAs as biomarkers in oncological conditions other than OC, as well as on OC biomarkers other than miRNAs. While the clinical need for an effective tool for OC screening remains unmet, it would be advisable to combine within a single diagnostic platform, the tools for detecting patterns of both protein and miRNA biomarkers to provide the screening robustness that single molecular species separately were not able to provide so far.

DIGE Analysis of Clinical Specimens

Two-dimensional difference gel electrophoresis (2D-DIGE) is an elegant gel electrophoretic analytical tool for comparative protein assessment. It is based on two-dimensional gel electrophoresis (2D-GE) separation of fluorescently labeled protein extracts. The tagging procedures are designed to not interfere with the chemical properties of proteins with respect to their pI and electrophoretic mobility, once a proper labeling protocol is followed. The use of an internal pooled standard makes 2D-DIGE a highly accurate quantitative method enabling multiple protein samples to be separated on the same two-dimensional gel. Technical limitations of this technique (i.e., underrating of low abundant, high molecular mass and integral membrane proteins) are counterbalanced by the incomparable separation power which allows proteoforms and unknown PTM (posttranslational modification) identification. Moreover, the image matching and cross-gel statistical analysis generates robust quantitative results making data validation by independent technologies successful.

SAMHD1 as a prognostic and predictive biomarker in stage II colorectal cancer: A multicenter cohort study

Background

The identification of high-risk population patients is key to the personalized treatment options for the stage II colorectal cancers. The use of proteomics in the prognosis of patients with stage II colorectal cancer remains unclear.

Methods

Using quantitative proteomics, we analyzed proteins that are differentially expressed in the tumor and adjacent normal tissues of 11 paired colorectal cancer patients with and without recurrence selected by a nested case-control design. Of the 21 identified proteins, we selected one candidate protein. The association of the corresponding gene of the selected protein with overall survival (OS) and adjuvant chemotherapy was analyzed using two independent cohorts of patients with stages II colorectal cancer.

Results

Sterile α motif and histidine-aspartate domain-containing protein 1 (SAMHD1) was selected as the candidate biomarker. A group of 124 patients (12.5%) were stratified into SAMHD1-high subgroup. The 5-year OS rate of SAMHD1-high patients was lower than that of SAMHD1-low patients with stage II colorectal cancer (discovery cohort: hazard ratio [HR] = 2.89, 95% confidence interval [CI], 1.17-7.18, P = 0.016; validation cohort: HR = 2.25, 95% CI, 1.17-4.34, P = 0.013). The Cox multivariate analysis yielded similar results. In a pooled database, the 5-year OS rate was significantly different between patients with and without adjuvant chemotherapy among stage II SAMHD1-low tumors than in patients with stage II SAMHD1-high tumors (88% vs. 77%, P = 0.032).

Conclusions

SAMHD1-high expression could help in identifying patients with stage II colorectal cancer with poor prognosis and less benefit from adjuvant chemotherapy.

Proteomic Analysis Reveals That Metformin Suppresses PSMD2, STIP1, and CAP1 for Preventing Gastric Cancer AGS Cell Proliferation and Migration

Metformin is one of the most widely used anti-diabetic drugs in type-II diabetes treatment. The mechanism of decreasing blood glucose is believed to suppress hepatic gluconeogenesis by increasing muscular glucose uptake and insulin sensitivity. Recent studies suggest that metformin may reduce cancer risk; however, its anticancer mechanism in gastric cancers remains unclear. Here, we aim to evaluate the anticancer effects of metformin on human gastric adenocarcinoma (AGS) cells. Our results showed that metformin inhibited AGS cell proliferation in a dose-dependent manner. Using small-scale quantitative proteomics, we identified 177 differentially expressed proteins upon metformin treatment; among these, nine proteins such as 26S proteasome non-ATPase regulatory subunit 2 (PSMD2), stress-induced phosphoprotein 1 (STIP1), and adenylyl cyclase-associated protein 1 (CAP1) were significantly altered. We found that metformin induced cell cycle arrest at the G0/G1 phase, suppressed cell migration, and affected cytoskeleton distribution. Additionally, patients with highly expressed PSMD2, STIP1, and CAP1 have a poor clinical outcome. Our study provides a novel view of developing therapies for gastric cancer.

Identification of differentially expressed proteins in the locoregional recurrent esophageal squamous cell carcinoma by quantitative proteomics

Background

This study aimed to identify potential biomarkers associated with locoregional recurrence in patients with esophageal squamous cell carcinoma (ESCC) after radical resection.

Methods

We performed a quantitative proteomics analysis using isobaric tags for relative and absolute quantification (iTRAQ) with reversed-phase liquid chromatography-mass spectrometry (RPLC-MS) to identify differential expression proteins (DEPs) between a locoregional recurrence group and good prognosis group of ESCC after radical esophagectomy. The bioinformatics analysis was performed with ingenuity pathway analysis software (IPA) and Gene Ontology (GO) database using the software of MAS 3.0. Kaplan-Meier (KM) Plotter Online Tool (http://www.kmplot.com) was used to evaluate the relationship between the differential expression of proteins and survival in patients with ESCC.

Results

More than 400 proteins were quantitated of which 27 proteins had upregulated expression and 55 proteins had downregulated expression in the locoregional recurrence group compared to the good prognosis group. These 82 DEPs were associated with biological procession of cancer development including cellular movement, cellular assembly and organization, cellular function and maintenance, cellular growth and proliferation, cell death and survival, DNA replication recombination and repair, and so on. Of these DEPs, SPTAN1 and AGT proteins were identified to be associated with RFS in ESCC. SPTAN1 was positively associated with RFS and AGT was negatively associated with RFS. Expression of SPTAN1 tended to have favorable OS while expression of AGT tended to have poor OS.

Conclusions

Our results demonstrated that quantitative proteomics is an effective discovery tool to identify biomarkers for prognosis prediction in ESCC. However, it needs more studies with large populations of ESCC to validate these potential biomarkers.

Computer Tomography Radiomics-Based Nomogram in the Survival Prediction for Brain Metastases From Non-Small Cell Lung Cancer Underwent Whole Brain Radiotherapy

Prognostic parameters and models were believed to be helpful in improving the treatment outcome for patients with brain metastasis (BM). The purpose of this study was to investigate the feasibility of computer tomography (CT) radiomics based nomogram to predict the survival of patients with BM from non-small cell lung cancer (NSCLC) treated with whole brain radiotherapy (WBRT). A total of 195 patients with BM from NSCLC who underwent WBRT from January 2012 to December 2016 were retrospectively reviewed. Radiomics features were extracted and selected from pretherapeutic CT images with least absolute shrinkage and selection operator (LASSO) regression. A nomogram was developed and evaluated by integrating radiomics features and clinical factors to predict the survival of individual patient. Five radiomics features were screened out from 105 radiomics features according to the LASSO Cox regression. According to the optimal cutoff value of radiomics score (Rad-score), patients were stratified into low-risk (Rad-score <= −0.14) and high-risk (Rad-score > −0.14) groups. Multivariable analysis indicated that sex, karnofsky performance score (KPS) and Rad-score were independent predictors for overall survival (OS). The concordance index (C-index) of the nomogram in the training cohort and validation cohort was 0.726 and 0.660, respectively. An area under curve (AUC) of 0.786 and 0.788 was achieved for the short-term and long-term survival prediction, respectively. In conclusion, the nomogram based on radiomics features from CT images and clinical factors was feasible to predict the OS of BM patients from NSCLC who underwent WBRT.

Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer

Simple Summary

The heterogeneity of epithelial ovarian cancer and its associated molecular biological characteristics are continuously integrated in the development of therapy guidelines. In a next step, future therapy recommendations might also be able to focus on the patient’s systemic status, not only the tumor’s molecular pattern. Therefore, new methods to identify and validate host-related biomarkers need to be established. Using mass spectrometry, we developed and independently validated a blood-based proteomic classifier, stratifying epithelial ovarian cancer patients into good and poor survival groups. We also determined an age dependence of the prognostic performance of this classifier and its association with important biological processes. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response and could therefore be integrated into future processes of therapy planning.

Abstract

Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning.

Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis

About 20-40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value < 0.0001) and overall survival (p = 0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.

Oncoproteomics: Current status and future opportunities

Oncoproteomics is the systematic study of cancer samples using omics technologies to detect changes implicated in tumorigenesis. Recent progress in oncoproteomics is already opening new avenues for the identification of novel biomarkers for early clinical stage cancer detection, targeted molecular therapies, disease monitoring, and drug development. Such information will lead to new understandings of cancer biology and impact dramatically on the future care of cancer patients. In this review, we will summarize the advantages and limitations of the key technologies used in (onco)proteogenomics, (the Omics Pipeline), explain how they can assist us in understanding the biology behind the overarching "Hallmarks of Cancer", discuss how they can advance the development of precision/personalised medicine and the future directions in the field.

Systematic revelation of the protective effect and mechanism of Cordycep sinensis on diethylnitrosamine-induced rat hepatocellular carcinoma with proteomics

Cordyceps sinensis (C. sinensis) has been reported to treat liver diseases. Here, we investigated the inhibitory effect of C. sinensis on hepatocarcinoma in a diethylnitrosamine (DEN)-induced rat model with functional proteome tools.In the DEN-exposed group, levels of serum alanine aminotransferase and aspartate aminotransferase were increased while C. sinensis application remarkably inhibited the activities of these enzymes. Histopathological analysis also indicated that C. sinensis could substantially restore hypertrophic hepatocytes caused by DEN, suggesting that C. sinensis is effective in preventing DEN-induced hepatocarcinogenesis.We therefore comprehensively delineated the global protein alterations using a proteome platform. The most meaningful changes were found among proteins involved in oxidative stress and detoxification. Meanwhile, C. sinensis application could attenuate the carbonylation level of several enzymes as well as chaperone proteins. Network analysis implied that C. sinensis could obviously alleviate hepatocarcinoma via modulating redox imbalance, protein ubiquitination and tumor growth-associated transcription factors.Our findings provide new insight into the potential effects of C. sinensis in preventing carcinogenesis and might help in developing novel therapeutic strategies against chemical-induced hepatocarcinoma.

DIGE Analysis of Human Tissues

Two-dimensional difference gel electrophoresis (2-D DIGE) is an advanced and elegant gel electrophoretic analytical tool for comparative protein assessment. It is based on two-dimensional gel electrophoresis (2-DE) separation of fluorescently labeled protein extracts. The tagging procedures are designed to not interfere with the chemical properties of proteins with respect to their pI and electrophoretic mobility, once a proper labeling protocol is followed. The two-dye or three-dye systems can be adopted and their choice depends on specific applications. Furthermore, the use of an internal pooled standard makes 2-D DIGE a highly accurate quantitative method enabling multiple protein samples to be separated on the same two-dimensional gel. The image matching and cross-gel statistical analysis generates robust quantitative results making data validation by independent technologies successful.

Phosphoproteomics of Primary Cells Reveals Druggable Kinase Signatures in Ovarian Cancer

Our understanding of the molecular determinants of cancer is still inadequate because of cancer heterogeneity. Here, using epithelial ovarian cancer (EOC) as a model system, we analyzed a minute amount of patient-derived epithelial cells from either healthy or cancerous tissues by single-shot mass-spectrometry-based phosphoproteomics. Using a multi-disciplinary approach, we demonstrated that primary cells recapitulate tissue complexity and represent a valuable source of differentially expressed proteins and phosphorylation sites that discriminate cancer from healthy cells. Furthermore, we uncovered kinase signatures associated with EOC. In particular, CDK7 targets were characterized in both EOC primary cells and ovarian cancer cell lines. We showed that CDK7 controls cell proliferation and that pharmacological inhibition of CDK7 selectively represses EOC cell proliferation. Our approach defines the molecular landscape of EOC, paving the way for efficient therapeutic approaches for patients. Finally, we highlight the potential of phosphoproteomics to identify clinically relevant and druggable pathways in cancer.

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We analyze ex-vivo-cultured primary cells using phosphoproteomics

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We investigate epithelial ovarian cancer (EOC) and healthy tissue

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We uncover expression of cancer-specific proteins and kinase signatures

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The kinase CDK7 phosphorylates POLR2A and regulates EOC cell proliferation

LC-MSE, Multiplex MS/MS, Ion Mobility, and Label-Free Quantitation in Clinical Proteomics

Proteomic tools can only be implemented in clinical settings if high-throughput, automated, sensitive, and accurate methods are developed. This has driven researchers to the edge of mass spectrometry (MS)-based proteomics capacity. Here we provide an overview of recent achievements in mass spectrometric technologies and instruments. This includes development of high and ultra definition-MS^E (HDMS^E and UDMS^E) through implementation of ion mobility (IM) MS towards sensitive and accurate label-free proteomics using ultra performance liquid chromatography (UPLC). Label free UPLC-HDMS^E is less expensive than labeled-based quantitative proteomics and has no limits regarding the number of samples that can be analyzed and compared, which is an important requirement for supporting clinical applications.

Medical Machiavellianism: the tradeoff between benefit and harm with targeted chemotherapy

Machiavellianism is a word synonymous with the phrase "the end justifies the means", and in this article we have coined the term Medical Machiavellianism to describe the 'cruel-to-be-kind' administration of toxic chemotherapeutic agents in apparent violation of the precept first do no harm, while acknowledging the 'dirty hands' dilemma of having to decide between and choose the lesser of two evils in the setting of advanced cancer--i.e. to treat or not to treat. The perception that 'targeted' therapies are relatively non-toxic and therefore respect the Hippocratic First Commandment by virtue of their narrow selectivity is belied by their often inherent promiscuity, addressing multiple targets either inadvertently or deliberately, which may result in multiple side effects. The remarkable success of immunotherapy may have taken the bloom off the 'targeted agent' rose, however due to a lack of other approved treatment alternatives the toxicity of these agents may be overlooked or, at least, undervalued, especially given that the official measure of treatment success in oncology is overall survival (OS), not quality-of-life improvements. By analogy with the MACH-IV personality survey (1970), [1] which measures high and low Machiavellian orientation, we have defined in this article a rudimentary MACH scale for selected targeted chemotherapies, based on the means-to-ends ratio of toxicity and benefit. It is our hope that this comparison between targeted agents will itself function as a means to an end--to help oncologists strike the right balance between efficacy, toxicity and quality of life in the management of their patients.

Clinical proteomics in cancer: Where we are

Proteomics has emerged as a promising field in the post-genomic era. Notwithstanding the great advances provided by gene expression analysis in cancer, the lack of a correlation between gene expression and protein levels has highlighted the need for a proteomic focus on cancer. Although the increasing knowledge regarding cancer biology, a reliable marker to improve diagnosis, prognosis and treatment for cancer patients is not a reality at present. In this review, we address the main considerations regarding proteomics-based studies and their clinical applications on cancer research, highlighting some considerations related to strengths and limitations of proteomics-based studies and its application to clinical practice.

Confronting the Care Delivery Challenges Arising from Precision Medicine

Understanding the biology of cancer at the cellular and molecular levels, and the application of such knowledge to the patient, has opened new opportunities and uncovered new obstacles to quality cancer care delivery. Benefits include our ability to now understand that many, if not most, cancers are not one-size-fits-all. Cancers are a variety of diseases for which intervention may be very different. This approach is beginning to bear fruit in gynecologic cancers where we are investigating therapeutic optimization at a more focused level, that while not yet precision care, is perhaps much improved. Obstacles to quality care for patients come from many directions. These include incomplete understanding of the role of the mutant proteins in the cancers, the narrow spectrum of agents, broader mutational profiles in solid tumors, and sometimes overzealous application of the findings of genetic testing. This has been further compromised by the unbridled use of social media by all stakeholders in cancer care often without scientific qualification, where anecdote sometimes masquerades as a fact. The only current remedy is to wave the flag of caution, encourage all patients who undergo genetic testing, either germline or somatic, to do so with the oversight of genetic counselors and physician scientists knowledgeable in the pathways involved. This aspiration is accomplished with well-designed clinical trials that inform next steps in this complex and ever evolving process.

Ovarian cancer biomarker discovery based on genomic approaches

Ovarian cancer presents at an advanced stage in more than 75% of patients. Early detection has great promise to improve clinical outcomes. Although the advancing proteomic technologies led to the discovery of numerous ovarian cancer biomarkers, no screening method has been recommended for early detection of ovarian cancer. Complexity and heterogeneity of ovarian carcinogenesis is a major obstacle to discover biomarkers. As cancer arises due to accumulation of genetic change, understanding the close connection between genetic changes and ovarian carcinogenesis would provide the opportunity to find novel gene-level ovarian cancer biomarkers. In this review, we summarize the various gene-based biomarkers by genomic technologies, including inherited gene mutations, epigenetic changes, and differential gene expression. In addition, we suggest the strategy to discover novel gene-based biomarkers with recently introduced next generation sequencing.

Clinical implications of using molecular diagnostics for ovarian cancers

In the era of morphologic diagnostics, any epithelial tumor on or involving the ovaries was presumed to come from and be strictly of ovarian origin, apart from the rare but clearly metastatic tumors. Thus, many women who might have had small fallopian tube primary cancers that rapidly extended on to or into the ovary were deemed to have ovarian cancer. Now, as we begin to better understand that there are different types of cancers of nonuterine Muellerian origin, we expand upon the morphologic to add the molecular characteristics. Morphomolecular characteristics are being applied to drive clinical advances including development and optimization of predictive and prognostic biomarkers, redefinition of historical controls, and consideration of novel clinical trial designs. Ovarian cancer, not a common cancer to start with, is now subdivided into types, making ever smaller clinical cohorts. The first studies evaluating tubo-ovarian Muellerian cancers of morphomolecular types have begun. Deleterious mutations in BRCA1 or 2 have been validated as the first new predictive and prognostic biomarker of the high-grade serous ovarian cancer type and polyADPribose polymerase inhibitors, the first targeted agents for this morphomolecular entity. Similar progress is developing in other tubo-ovarian cancer types. This new knowledge is driving the building of a structure-function-type relationship that is generating novel clinically applicable hypotheses for testing.

Molecular profiling and commercial predication assays in ovarian cancer: still not ready for prime time?

Short of early detection to allow curative primary intervention, the other major barrier to further success in treatment of ovarian cancers is matching the best treatment to the proper ovarian cancer type and to the individual patient. There are several decades of experience applying in vitro chemoresponse testing for solid tumors including ovarian cancer. This concept, first described in 1979, has yet to receive level one evidence supporting its application, despite the testing of numerous assays commercially as well as in academic centers and its use for tens of thousands of patients at a significant cost. The approach-rather than undergoing rigorous scientific examination-is now being muddied by the development of commercial molecular profiling assays from which treatment suggestions are provided. Molecular profiling as a research tool has added value to our understanding and treatment of patients with ovarian cancer. Morphologic and histochemical characterizations coupled now with increasing knowledge of ovarian cancer type-specific molecular patterns is improving our ability to properly diagnosis ovarian cancer type and thus guide therapy. With the exception of the role of germ-line and possibly somatic BRCA1 and BRCA2 mutations and their true predictiveness for probable response to poly(ADP-ribose) polymerase inhibition, molecular typing and profiling has yet to identify druggable molecular targets in ovarian cancer. Its use should be continued as a research and learning tool, and its results should be subjected to clinical trial validation. For very different reasons, neither chemoresponse assays nor molecular profiling are ready for prime time, yet.

Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors

Background

Tumor classification based on their predicted responses to kinase inhibitors is a major goal for advancing targeted personalized therapies. Here, we used a phosphoproteomic approach to investigate biological heterogeneity across hematological cancer cell lines including acute myeloid leukemia, lymphoma, and multiple myeloma.

Results

Mass spectrometry was used to quantify 2,000 phosphorylation sites across three acute myeloid leukemia, three lymphoma, and three multiple myeloma cell lines in six biological replicates. The intensities of the phosphorylation sites grouped these cancer cell lines according to their tumor type. In addition, a phosphoproteomic analysis of seven acute myeloid leukemia cell lines revealed a battery of phosphorylation sites whose combined intensities correlated with the growth-inhibitory responses to three kinase inhibitors with remarkable correlation coefficients and fold changes (> 100 between the most resistant and sensitive cells). Modeling based on regression analysis indicated that a subset of phosphorylation sites could be used to predict response to the tested drugs. Quantitative analysis of phosphorylation motifs indicated that resistant and sensitive cells differed in their patterns of kinase activities, but, interestingly, phosphorylations correlating with responses were not on members of the pathway being targeted; instead, these mainly were on parallel kinase pathways.

Conclusion

This study reveals that the information on kinase activation encoded in phosphoproteomics data correlates remarkably well with the phenotypic responses of cancer cells to compounds that target kinase signaling and could be useful for the identification of novel markers of resistance or sensitivity to drugs that target the signaling network.

Ovarian cancer: can proteomics give new insights for therapy and diagnosis?

The study of the ovarian proteomic profile represents a new frontier in ovarian cancer research, since this approach is able to enlighten the wide variety of post-translational events (such as glycosylation and phosphorylation). Due to the possibility of analyzing thousands of proteins, which could be simultaneously altered, comparative proteomics represent a promising model of possible biomarker discovery for ovarian cancer detection and monitoring. Moreover, defining signaling pathways in ovarian cancer cells through proteomic analysis offers the opportunity to design novel drugs and to optimize the use of molecularly targeted agents against crucial and biologically active pathways. Proteomic techniques provide more information about different histological types of ovarian cancer, cell growth and progression, genes related to tumor microenvironment and specific molecular targets predictive of response to chemotherapy than sequencing or microarrays. Estimates of specificity with proteomics are less consistent, but suggest a new role for combinations of biomarkers in early ovarian cancer diagnosis, such as the OVA1 test. Finally, the definition of the proteomic profiles in ovarian cancer would be accurate and effective in identifying which pathways are differentially altered, defining the most effective therapeutic regimen and eventually improving health outcomes.

Meeting the challenge of ascites in ovarian cancer: new avenues for therapy and research

Malignant ascites presents a considerable clinical challenge to the management of ovarian cancer, but also provides a wealth of opportunities for translational research. The accessibility of ascitic fluid and its cellular components make it an excellent source of tumour tissue for the investigation of prognostic and predictive biomarkers, pharmacodynamic markers and for molecular profiling analysis. In this Opinion article, we discuss recent advances in our understanding of its pathophysiology, the development of new methods to characterize its molecular features and how these findings can be used to improve the treatment of malignant ascites, particularly in the context of ovarian cancer.

Digital microfluidic hydrogel microreactors for proteomics

Proteolytic digestion is an essential step in proteomic sample processing. While this step has traditionally been implemented in homogeneous (solution) format, there is a growing trend to use heterogeneous systems in which the enzyme is immobilized on hydrogels or other solid supports. Here, we introduce the use of immobilized enzymes in hydrogels for proteomic sample processing in digital microfluidic (DMF) systems. In this technique, preformed cylindrical agarose discs bearing immobilized trypsin or pepsin were integrated into DMF devices. A fluorogenic assay was used to optimize the covalent modification procedure for enzymatic digestion efficiency, with maximum efficiency observed at 31 μg trypsin in 2-mm diameter agarose gel discs. Gel discs prepared in this manner were used in an integrated method in which proteomic samples were sequentially reduced, alkylated, and digested, with all sample and reagent handling controlled by DMF droplet operation. Mass spectrometry analysis of the products revealed that digestion using the trypsin gel discs resulted in higher sequence coverage in model analytes relative to conventional homogenous processing. Proof-of-principle was demonstrated for a parallel digestion system in which a single sample was simultaneously digested on multiple gel discs bearing different enzymes. We propose that these methods represent a useful new tool for the growing trend toward miniaturization and automation in proteomic sample processing.

Salivary Proteomic Signatures of Oral Squamous Cell Carcinoma

Delay in diagnosing oral squamous cell carcinoma (OSCC) can be still identified as a major cause of its high morbidity and mortality. To date, the early diagnosis for OSCC is mainly based on clinical oral examination and there is an urgent need for reliable markers; thus, advancements in molecular technologies has set the stage for investigating new markers, as well as new diagnostic matrices. The aim of the present study is to investigate the presence of proteomic signatures of OSCC in saliva and their use as potential biomarkers for early and non-invasive diagnosis. Saliva from 45 OSCC patients and 30 healthy controls was analysed by SELDI-TOF mass spectrometry and ProteinChip® technology. A supervised multivariate statistical analysis (Classification and Regression Tree - CART) was used to build models for discriminating between OSCC and controls, and between early (ES-OSCC) and late stage (LS-OSCC) cancers. The peptide with 8041 Da mass was 22-fold more expressed in OSCC, thus being a suitable potential biomarker. Classification and regression analysis allowed to build a model that was capable of correctly classifying all cancers and controls in an independent testing set, using the 8041 m/z peak as splitter. Eleven peaks were also differently expressed between ES-OSCCand LS-OSCC, but, basing on these differences, it was not possible to build an algorithm to predict tumour staging. These findings confirm that saliva proteome in OSCC patients is different from healthy controls and these variations might reflect different stages of disease progression and are worthy of further validation as diagnostic and prognostic biomarkers.

Clinicopathological pattern and Annexin A2 and Cdc42 status in patients presenting with differentiation and lymphnode metastasis of esophageal squamous cell carcinomas

Annexin A2 and Cdc42 were identified by 2-dimensional electrophoresis (2-DE) and MALDI-TOF-MS between esophageal squamous cell carcinomas (ESCC) and corresponding normal esophagus mucosa in our previous study. To assess clinico-pathological pattern and Annexin A2 and Cdc42 status with respect to cell differentiation and lymphnode metastasis in patients with ESCC. The expression of Annexin A2 and Cdc42 in 22 pairs of fresh ESCC and matched tissues were detected by qRT-PCR and western blot, respectively. And it was further confirmed by immunohistochemistry with 175 pairs of formalin-fixed, paraffin-embedded ESCC. Results showed that Annexin A2 expression was significantly down-regulated, and Cdc42 was up-regulated in ESCC compared to matched control on both mRNA and protein level (P < 0.05), which was in accordance with our previous results on proteomics data. Additionally, Annexin A2 and Cdc42 expression was significantly correlated with lymphoid node metastasis (P < 0.05) and pathological differentiation (P < 0.05). Taken together, we proposed that the aberrant expression of Annexin A2 and Cdc42 played a role in carcinogenesis, differentiation and metastasis of ESCC, which implied its potential target for clinical biomarkers in differentiation and lymph node metastasis.