Thyroid tumors: novel insights from proteomic studies

Several genomics-based techniques have been applied in the last decade to the molecular characterization of cancer, which has led to a variety of applications suitable for improved diagnosis, prognosis and prediction of outcome to treatment. Proteomics-based approaches have also been seen as crucial to the discovery of biomarkers for early diagnosis and prognosis of tumors, as well as for a better understanding of the molecular bases of cancer. Accordingly, proteomic techniques have been used extensively for a better molecular characterization of thyroid tumors. In this field, three main directions have been preceded: first, proteomic studies of model systems; second, proteomics of thyroid tumor specimens; and third, serum proteomics. In this review, we describe the most relevant results that have been obtained for tumors derived from thyroid follicular cells using various proteomic approaches.     

Use of DNA methylation for cancer detection: promises and challenges

DNA methylation is an important and reversible epigenetic modification, which regulates genomic stability and cellular plasticity. Faithful DNA methylation is essential for mammalian development and health, and perturbation of methylation dynamics contributes to the development of disease, including cancer. The discovery and validation of the biological indicators (biomarkers) for human cancers are essential steps in the development of methods for accurate subtype classification and outcome prediction in clinic oncology. While genetics (SNP, LOH and mutation) and expression profiling (mRNA and protein) of biomarkers have been extensively assessed for cancer diagnosis and prognosis, the potential for using epigenetic fingerprints for early diagnosis and outcome prediction in clinic oncology propels the exploration of using DNA methylation as a biomarker for cancer prognosis. Both the promises and challenges to realizing the clinical utility of the DNA methylation in cancer management are discussed in this review.     

SELDI-TOF serum proteomics and breast cancer: which perspective?

Despite the efforts of recent years, clinicians still lack reliable biomarkers for diagnosis, prognosis and prediction of clinical outcomes in breast cancer patients. Owing to the large number of people potentially involved in the management of this clinical problem, the search for noninvasive and repeatable laboratory assays has been intensive. Recently, the proteomic profiling performed by SELDI-TOF mass spectrometry, has been proposed in order to identify new clusters of serum markers that could be potentially useful in breast cancer management. The purpose of this special report is to review the literature on SELDI-TOF technology applied on serum coming from healthy people and breast cancer patients, in order to verify the clinical applicability of such approach. We conclude that potential new biomarkers, first of all for early diagnosis of breast cancer, need to be validated in larger clinical series.     

Functional relationship and gene ontology classification of breast cancer biomarkers

Breast cancer is a complex disease that still imposes a significant healthcare burden on women worldwide. The etiology of breast cancer is not known but significant advances have been made in the area of early detection and treatment. The advent of advanced molecular biology techniques, mapping of the human genome and availability of high throughput genomic and proteomic strategies opens up new opportunities and will potentially lead to the discovery of novel biomarkers for early detection and prognostication of breast cancer. Currently, many biomarkers, particularly the hormonal and epidermal growth factor receptors, are being utilized for breast cancer prognosis. Unfortunately, none of the biomarkers in use have sufficient diagnostic, prognostic and/or predictive power across all categories and stages of breast cancer. It is recognized that more useful information can be generated if tumors are interrogated with multiple markers. But choosing the right combination of biomarkers is challenging, because 1) multiple pathways are involved, 2) up to 62 genes and their protein products are potentially involved in breast cancer-related mechanisms and 3) the more markers evaluated, the more the time and cost involved. This review summarizes the current literature on selected biomarkers for breast cancer, discusses the functional relationships, and groups the selected genes based on a Gene Ontology classification.     

Proteomic analysis of tissue samples in translational breast cancer research

In the last decade, many proteomic technologies have been applied, with varying success, to the study of tissue samples of breast carcinoma for protein expression profiling in order to discover protein biomarkers/signatures suitable for: characterization and subtyping of tumors; early diagnosis, and both prognosis and prediction of outcome of chemotherapy. The purpose of this review is to critically appraise what has been achieved to date using proteomic technologies and to bring forward novel strategies – based on the analysis of clinically relevant samples – that promise to accelerate the translation of basic discoveries into the daily breast cancer clinical practice. In particular, we address major issues in experimental design by reviewing the strengths and weaknesses of current proteomic strategies in the context of the analysis of human breast tissue specimens.     

Biomarkers for the lung cancer diagnosis and their advances in proteomics

Over a last decade, intense interest has been focused on biomarker discovery and their clinical uses. This interest is accelerated by the completion of human genome project and the progress of techniques in proteomics. Especially, cancer biomarker discovery is eminent in this field due to its anticipated critical role in early diagnosis, therapy guidance, and prognosis monitoring of cancers. Among cancers, lung cancer, one of the top three major cancers, is the one showing the highest mortality because of failure in early diagnosis. Numerous potential DNA biomarkers such as hypermethylations of the promoters and mutations in K-ras, p53, and protein biomarkers; carcinoembryonic antigen (CEA), CYFRA21-1, plasma kallikrein B1 (KLKB1), Neuron-specific enolase, etc. have been discovered as lung cancer biomarkers. Despite extensive studies thus far, few are turned out to be useful in clinic. Even those used in clinic do not show enough sensitivity, specificity and reproducibility for general use. This review describes what the cancer biomarkers are for, various types of lung cancer biomarkers discovered at present and predicted future advance in lung cancer biomarker discovery with proteomics technology.     

DNA Methylation Markers in Lung Cancer

Lung cancer is the most common cancer and the leading cause of cancer-related morbidity and mortality worldwide. As early symptoms of lung cancer are minimal and non-specific, many patients are diagnosed at an advanced stage. Despite a concerted effort to diagnose lung cancer early, no biomarkers that can be used for lung cancer screening and prognosis prediction have been established so far. As global DNA demethylation and gene-specific promoter DNA methylation are present in lung cancer, DNA methylation biomarkers have become a major area of research as potential alternative diagnostic methods to detect lung cancer at an early stage. This review summarizes the emerging DNA methylation changes in lung cancer tumorigenesis, focusing on biomarkers for early detection and their potential clinical applications in lung cancer.     

Proteomic analysis at the bedside: early detection of cancer

Proteomic technologies promise to accelerate rapidly a new era in molecular medicine, especially in the detection and discovery of disease-related biomarkers. These technologies have no bigger impact than in the field of human cancer research. Beyond lifestyle-associated prevention strategies, early detection of cancer has the most profound impact on the ultimate course of the disease: the earlier the cancer is detected, the better the prognosis. Today, new proteomic technologies are being used to discover new diagnostic and prognostic biomarkers for the early detection and treatment of cancer that will have important implications at the bedside.     

蛋白质芯片SELDI-TOFMS技术的研究进展及其在临床中的应用

蛋白质芯片为新一代的蛋白质组研究技术,由美国Ciphergen生物系统公司引进,表面增强激光解吸电离-飞行时间质谱(SELDI-TOFMS)提供一个高通量和高灵敏度的检测平台。投放至今虽短短10来年,但卓越的成果已广为医学科学界重视,尤其在恶性肿瘤的早期诊断、监控和预后研究上。蛋白质是细胞内执行生物功能的最终分子,蛋白质组学研究让人类更深入了解疾病和生命的本源,不断发现的特异性肿瘤标志物更为攻克癌症带来新希望。这里除对表面增强激光解吸电离_飞行时间质谱作较详尽的介绍外,更重点阐述其近年来蛋白质芯片近期的研究进展和在临床中的应用,并就其优劣和发展前景作出评估。     

Crucial considerations for pipelines to validate circulating biomarkers for breast cancer

Despite decades of progress in breast imaging, breast cancer remains the second most common cause of cancer mortality in women. The rapidly proliferative breast cancers that are associated with high relapse rates and mortality frequently present in younger women, in unscreened individuals, or in the intervals between screening mammography. Biomarkers exist for monitoring metastatic disease, such as CEA, CA27.29 and CA15-3, but there are no circulating biomarkers clinically available for early detection, prognosis, or monitoring for clinical relapse. There has been significant progress in the discovery of potential circulating biomarkers, including proteins, autoantibodies, nucleic acids, exosomes, and circulating tumor cells, but the vast majority of these biomarkers have not progressed beyond initial research discovery, and none have yet been approved for clinical use in early stage disease. Here, the authors review the crucial considerations of developing pipelines for the rapid evaluation of circulating biomarkers for breast cancer.     

Good practice guidelines for biomarker discovery from array data: a case study for breast cancer prognosis

Background

Biomarker discovery holds the promise for advancing personalized medicine as the biomarkers can help match patients to optimal treatment to improve patient outcomes. However, serious concerns have been raised because very few molecular biomarkers or signatures discovered from high dimensional array data can be successfully validated and applied to clinical use. We propose good practice guidelines as well as a novel tool for biomarker discovery and use breast cancer prognosis as a case study to illustrate the proposed approach.

Results

We applied the proposed approach to a publicly available breast cancer prognosis dataset and identified small numbers of predictive markers for patient subpopulations stratified by clinical variables. Results from an independent cross-platform validation set show that our model compares favorably to other gene signature and clinical variable based prognostic tools. About half of the discovered candidate markers can individually achieve very good performance, which further demonstrate the high quality of feature selection. These candidate markers perform extremely well for young patient with estrogen receptor-positive, lymph node-negative early stage breast cancers, suggesting a distinct subset of these patients identified by these markers is actually at high risk of recurrence and may benefit from more aggressive treatment than cur-rent practice.

Conclusion

The results show that by following good practice guidelines, we can identify highly predictive genes in high dimensional breast cancer array data. These predictive genes have been successfully validated using an independent cross-platform dataset.

     

Challenges to developing proteomic-based breast cancer diagnostics

Over the past decade, multiple genetic and histological approaches have accelerated development of new breast cancer diagnostics and treatment paradigms. Multiple distinct genetic subtypes of breast cancers have been defined, and this has progressively led toward more personalized medicine in regard to treatment options. There still remains a deficiency in the development of molecular diagnostic assays that can be used for breast cancer detection and pretherapy clinical decisions. In particular, the type of cancer-specific biomarker typified by a serum or tissue-derived protein. Progress in this regard has been minimal, especially in comparison to the rapid advancements in genetic and histological assays for breast cancers. In this review, some potential reasons for this large gap in developing protein biomarkers will be discussed, as well as new strategies for improving these approaches. Improvements in the study design of protein biomarker discovery strategies in relation to the genetic subtypes and histology of breast cancers is also emphasized. The current successes in use of genetic and histological assays for breast cancer diagnostics are summarized, and in that context, the current limitations of the types of breast cancer-related clinical samples available for protein biomarker assay development are discussed. Based on these limitations, research strategies emphasizing identification of glycoprotein biomarkers in blood and MALDI mass spectrometry imaging of tissues are described.     

分子诊断技术在乳腺癌检测中的最新进展

乳腺癌是一种严重危害女性健康的恶性肿瘤,对其致病基因的检测有助于肿瘤早期诊断、精准治疗及预后评估.本文总结了近年来乳腺癌相关的热点基因,并对相关基因的分子诊断技术、检测方法及应用进行了综述.首次评述了数字PCR方法用于乳腺癌分子检测的进展.全面对比不同分子诊断技术的差别与优缺点,为乳腺癌关键基因的检测提供指导建议与理论支持,并对未来发展趋势做出展望.     

Metabolomic studies of breast cancer in murine models: A review

BackgroundMetabolomic strategies have been extensively used to search for biomarkers of disease, including cancer, in biological complex mixtures such as cells, tissues and biofluids. In breast cancer research, murine models are of great value and metabolomics has been increasingly applied to characterize tumor or organ tissues, or biofluids, for instance to follow-up metabolism during cancer progression or response to specific therapies.Scope of reviewThis review briefly introduces the different murine models used in breast cancer research and proceeds to present the metabolomic studies reported so far to describe the deviant metabolic behavior associated to breast cancer, in each type of model: xenografts (cell- or patient-derived), spontaneous (naturally-occurring or genetically engineered) and carcinogen-induced. The type of sample and strategies followed are identified, as well as the main findings from of study.Major conclusionsMetabolomics has gradually become relevant in characterizing murine models of breast cancer, using either Nuclear Magnetic Resonance (NMR) or Mass Spectromety (MS). Both tissue and biofluids are matrixes of interest in this context, although in some type of models, reports have focused primarily on the former. The aims of tissue studies have comprised the search for mechanistic knowledge of carcinogenesis, metastasis development and response/resistance to therapies. Biofluid metabolomics has mainly aimed at finding non-invasive biomarkers for early breast cancer detection or prognosis determination.General significanceMetabolomics provides exquisite detail on murine tumor and systemic metabolism of breast cancer. This knowledge paves the way for the discovery of new biomarkers, potentially translatable to in vivo non-invasive patient follow-up.     

Proteomics in prostate cancer biomarker discovery

Despite advances in molecular medicine, genomics, proteomics and translational research, prostate cancer remains the second most common cause of cancer-related mortality for men in the Western world. Clearly, early detection, targeted treatment and post-treatment monitoring are vital tools to combat this disease. Tumor markers can be useful for diagnosis and early detection of cancer, assessment of prognosis, prediction of therapeutic effect and treatment monitoring. Such tumor markers include prostate-specific antigen (prostate), cancer antigen (CA)15.3 (breast), CA125 (ovarian), CA19.9 (gastrointestinal) and serum α-fetoprotein (testicular cancer). However, all of these biomarkers lack sensitivity and specificity and, therefore, there is a large drive towards proteomic biomarker discovery. Current research efforts are directed towards discovering biosignatures from biological samples using novel proteomic technologies that provide high-throughput, in-depth analysis and quantification of the proteome. Several of these studies have revealed promising biomarkers for use in diagnosis, assessment of prognosis, and targeting treatment of prostate cancer. This review focuses on prostate cancer proteomic biomarker discovery and its future potential.     

Protein profiling in respiratory disease: techniques and impact

Multifactorial diseases such as respiratory disease call for a global analysis of such disorders. Recent advances in protein profiling techniques may allow for early diagnosis of respiratory disease, which is crucial for intervention and treatment. In order to reduce false-positive rates, clinical diagnosis requires a high degree of sensitivity and specificity to be an effective screening tool. Protein profiles identified by ProteinChip^® (Ciphergen Biosystems) technology coupled with mass spectrometry affords a global analysis of clinical samples and is beginning to reach acceptable levels of sensitivity and specificity. Combining the profile with another diagnostic tool enhances the effectiveness of protein profiles to classify disease. Although current efforts have centered on serum protein profiling, the local environment of the lung may be better reflected in proteins of bronchoalveolar lavage or sputum. Identification of biomarkers of disease by protein profiling anaylses may lead to an understanding of the mechanisms of this disease and contribute to the discovery of new therapeutics for the prevention and treatment of disease. Advancing these analyses are techniques such as ProteinChip mass spectrometry, laser capture microdissection, tissue microarrays and fluorescently labeled antibody bead arrays, which enable the direct global analysis of complex mixtures. Effective high-throughput and ease of use of clinical testing will arrive with improvements in bioinformatics and decreases in instrumentation costs.     

Discovery of biomarkers for gastric cancer: a proteomics approach

Gastric cancer is the second leading cause of cancer-related deaths worldwide. Although many treatment options exist for patients with gastric tumors, the incidence and mortality rate of gastric cancer are on the rise. The early stages of gastric cancer are non-symptomatic, and the treatment response is unpredictable. This situation is further aggravated by a lack of diagnostic biomarkers that can aid in the early detection and prognosis of gastric cancer and in the prediction of chemoresistance. Moreover, clinical surgical specimens are rarely obtained, and traditional biomarkers of gastric cancer are not very effective. Many studies in the field of proteomics have contributed to the discovery and establishment of powerful diagnostic tools (e.g., ProteinChip array) in the management of cancer. The evolution in proteomic technologies has not only enabled the screening of a large number of samples but also enabled the identification of pathologically significant proteins, such as phosphoproteins, and the quantitation of difference in protein expression under different conditions. Multiplexed assays are used widely to accurately fractionate various complex samples such as blood, tissue, cells, and Helicobacter pylori-infected specimens to identify differentially expressed proteins. Biomarker detection studies have substantially contributed to the areas of secretome, metabolome, and phosphoproteome. Here, we review the development of potential biomarkers in the natural history of gastric cancer, with specific emphasis on the characteristics of target protein convergence.     

Efficient screening of predictive biomarkers for individual treatment selection

The development of molecular diagnostic tools to achieve individualized medicine requires identifying predictive biomarkers associated with subgroups of individuals who might receive beneficial or harmful effects from different available treatments. However, due to the large number of candidate biomarkers in the large‐scale genetic and molecular studies, and complex relationships among clinical outcome, biomarkers, and treatments, the ordinary statistical tests for the interactions between treatments and covariates have difficulties from their limited statistical powers. In this paper, we propose an efficient method for detecting predictive biomarkers. We employ weighted loss functions of Chen et al. to directly estimate individual treatment scores and propose synthetic posterior inference for effect sizes of biomarkers. We develop an empirical Bayes approach, namely, we estimate unknown hyperparameters in the prior distribution based on data. We then provide efficient screening methods for the candidate biomarkers via optimal discovery procedure with adequate control of false discovery rate. The proposed method is demonstrated in simulation studies and an application to a breast cancer clinical study in which the proposed method was shown to detect the much larger numbers of significant biomarkers than existing standard methods.     

Liquid Biopsies: Emerging role and clinical applications in solid tumours

Late detection and lack of precision diagnostics are the major challenges in cancer prevention and management. Biomarker discovery in specific cancers, especially at the pre-invasive stage, is vital for early diagnosis, positive treatment response, and good disease prognosis. Traditional diagnostic measures require invasive procedures such as tissue excision using a needle, an endoscope, and/or surgical resection which can be unsafe, expensive, and painful. Additionally, the presence of comorbid conditions in individuals might render them ineligible for undertaking a tissue biopsy, and in some cases, it is difficult to access tumours depending on the site of occurrence. In this context, liquid biopsies are being explored for their clinical significance in solid malignancies management. These non-invasive or minimally invasive methods are being developed primarily for identification of biomarkers for early diagnosis and targeted therapeutics. In this review, we have summarised the use and importance of liquid biopsy as significant tool in diagnosis, prognosis prediction, and therapeutic development. We have also discussed the challenges that are encountered and future perspective.