Alain Mange

Autoantibody signatures: progress and perspectives for early cancer detection.

•  Introduction •  Unknown origins of autoantibody production in cancer •  The use of proteomics to identify autoantibodies •  Clinical utility of autoantibody signatures for early detection of cancer ‐  Autoantibodies as potential cancer biomarkers ‐  Defining autoantibody signatures in cancer: still many challenges •  Autoantibody detection to build screening tests in high‐risk populations •  Conclusion and perspectives

HDL Proteome in Hemodialysis Patients: A Quantitative Nanoflow Liquid Chromatography-Tandem Mass Spectrometry Approach

Aside from a decrease in the high-density lipoprotein (HDL) cholesterol levels, qualitative abnormalities of HDL can contribute to an increase in cardiovascular (CV) risk in end-stage renal disease (ESRD) patients undergoing chronic hemodialysis (HD). Dysfunctional HDL leads to an alteration of reverse cholesterol transport and the antioxidant and anti-inflammatory properties of HDL. In this study, a quantitative proteomics approach, based on iTRAQ labeling and nanoflow liquid chromatography mass spectrometry analysis, was used to generate detailed data on HDL-associated proteins. The HDL composition was compared between seven chronic HD patients and a pool of seven healthy controls. To confirm the proteomics results, specific biochemical assays were then performed in triplicate in the 14 samples as well as 46 sex-matched independent chronic HD patients and healthy volunteers. Of the 122 proteins identified in the HDL fraction, 40 were differentially expressed between the healthy volunteers and the HD patients. These proteins are involved in many HDL functions, including lipid metabolism, the acute inflammatory response, complement activation, the regulation of lipoprotein oxidation, and metal cation homeostasis. Among the identified proteins, apolipoprotein C-II and apolipoprotein C-III were significantly increased in the HDL fraction of HD patients whereas serotransferrin was decreased. In this study, we identified new markers of potential relevance to the pathways linked to HDL dysfunction in HD. Proteomic analysis of the HDL fraction provides an efficient method to identify new and uncharacterized candidate biomarkers of CV risk in HD patients.

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

BackgroundPrenatal 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.ResultsThe 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.ConclusionThis 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.

Proteomic analysis of RCL2 paraffin‐embedded tissues

Histopathological diagnosis in most of the worlds hospitals is based upon formalin‐fixed and paraffin‐embedded (FFPE) tissues. Although this standard fixation and embedding procedure keeps the tissue in excellent form for morphological and immunohistological analysis, FFPE is inappropriate for nucleic acids and protein studies. We investigated the potential value of RCL2, a new non‐toxic fixative, for sparing proteins preserved in paraffin‐embedded tissues. Normal colonic mucosa tissue was fixed in RCL2 prior to paraffin embedding (RCL2P), and then processed for quality and quantity of protein conservation, as compared to frozen and FFPE tissues using complementary proteomic analysis approaches. Using 4 different protein extraction protocols, RCL2P tissue consistently showed the highest protein yield. Similar protein patterns were observed with RCL2P and frozen tissues using mono and bi‐dimensional electrophoresis. Moreover, membrane, cytoplasmic and nuclear proteins, as well as phosphorylated proteins, were successfully detected using western‐blot. Furthermore, protein patterns observed by mass spectrometry analysis after laser‐captured microdissection were found to be identical for frozen and RCL2‐fixed tissues. At last, immunohistochemistry using various antibodies showed comparable results between both tissue storage methods. We concluded that RCL2 has great potential for performing both morphological and molecular analyses on the same archival paraffin‐embedded tissue sample, and can be a new method for investigating protein biomarkers.

Proteomic approaches to identify biomarkers predictive of radiotherapy outcomes

To be highly successful, a radiotherapeutic dose must be sufficiently large to destroy radioresistant tumors, yet avoid injuring the surrounding healthy tissue. However, many patients exhibit high radiosensitivity and may develop radiation-induced early and late side effects. Because the identification of these radiosensitive patients remains largely problematic, general radiotherapy protocols currently limit the dose given, which risks delivering an insufficient dose to a significant number of less sensitive patients. Therefore, one of the main current challenges of radiobiology is to predict a patient’s tumor radioresistance and normal tissue radiosensitivity to tailor a personalized treatment to that individual. Although predictive assays exist, none has demonstrated highly significant results that would be useful in a clinical setting. Therefore, proteomics represents a promising approach for identifying new relevant predictive biomarkers. In this review, the authors first explain the main characteristics of tumor radioresistance and normal tissue radiosensitivity. The authors next describe the existing predictive assays. Finally, the proteomics studies performed to date to identify new biomarkers that probably predicts radiotherapy outcomes are discussed.

Humoral response to cancer as a tool for biomarker discovery.

There is an important need to find relevant biomarkers that show high sensitivity and specificity for early diagnosis and prognosis of cancer. An immune response to cancer is elicited in humans, as demonstrated in part by the identification of autoantibodies against a number of tumor-associated antigens in sera from patients with different types of cancer. Identification of tumor-associated antigens and their cognate autoantibodies is a promising strategy for the discovery of relevant biomarkers. During the past few years, proteomic approaches, including SEREX, SERPA and, more recently, protein microarrays, have been the dominant strategies used to identify tumor-associated antigens and their cognate autoantibodies. In this review, we aim to describe advantages, drawbacks, and recent improvements of these approaches for the study of humoral responses.

Identification and validation of new autoantibodies for the diagnosis of DCIS and node negative early‐stage breast cancers

Evidence of circulating autoantibodies in cancer patient sera has created opportunities for exploiting them as biomarkers. We report the identification and the clinical validation of an autoantibody panel in newly diagnosed patients with early‐stage breast cancer. Proteomic approach and serological screening of a discovery set of sera (n = 80) were performed to identify tumor‐associated antigens (TAAs). Autoantibody levels were then measured in an independent validation set (n = 182) against a panel of five TAAs by enzyme‐linked immunosorbent assay. Sixty‐seven antigens that elicited a specific humoral response in breast cancer were identified and five antigens (GAL3, PAK2, PHB2, RACK1 and RUVBL1) were selected for validation. GAL3 and RACK1 showed significantly increased reactivity in early‐stage breast cancer. When combined, the five markers significantly discriminated early‐stage cancer from healthy individuals (AUC = 0.81; 95% CI [0.74–0.86]). Interestingly, this value was high in both node‐negative early‐stage primary breast cancer (AUC = 0.81; 95% CI [0.72–0.88]) and ductal carcinoma in situ (AUC = 0.85; 95% CI [0.76–0.95]) populations. This autoantibody panel could be useful as a diagnostic tool in a screening strategy of early‐stage invasive breast cancer and preinvasive breast cancer. It could be particularly appropriate in complement to mammography for women with high breast density.

Identifying autoantibody signatures in cancer: a promising challenge

Biomarkers that show high sensitivity and specificity are needed for the early diagnosis and prognosis of cancer. An immune response to cancer is elicited in humans, as demonstrated, in part, by the identification of autoantibodies against a number of tumor-associated antigen (TAAs) in sera from patients with different types of cancer. Identification of TAAs and their cognate autoantibodies is a promising strategy for the discovery of relevant biomarkers. During the past few years, three proteomic approaches, including serological identification of antigens by recombinant expression cloning (SEREX), serological proteome analysis (SERPA) and, more recently, protein microarrays, have been the dominant strategies used to identify TAAs and their cognate autoantibodies. In this review, we aim to describe the advantages, drawbacks and recent improvements of these approaches for the study of humoral responses. Finally, we discuss the definition of autoantibody signatures to improve sensitivity for the development of clinically relevant tests.

FKBP family proteins as promising new biomarkers for cancer

FK506-binding proteins (FKBPs) belong to the immunophilin family and bind immunosuppressive drugs, such as FK506 and rapamycin. These proteins, through interactions with steroid hormone receptors, kinases, or other cellular factors, play important roles in various physiological processes and, more interestingly, in pathological processes in mammals. Accumulating evidence has implicated some FKBP members in a variety of processes, such as the cell cycle and survival and apoptotic signaling pathways, particularly in cancers. After the deregulation of their expression was observed in cancer tissues, it became increasingly clear that FKBP members played an important role in tumorigenesis and the response to chemotherapies and radiotherapies and that FKBP members could act as oncogenes or tumor suppressors depending on the tissue type. A wealth of data from in vitro and clinical studies is paving the way for novel, promising roles of FKBPs as diagnostic, prognostic or therapy-monitoring cancer biomarkers.

Highly sensitive detection of melanoma based on serum proteomic profiling

PurposeThere 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.MethodsWe 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.ResultsIn 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.ConclusionsAlthough 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.