A neural network-based biomarker association information extraction approach for cancer classification

A number of different approaches based on high-throughput data have been developed for cancer classification. However, these methods often ignore the underlying correlation between the expression levels of different biomarkers which are related to cancer. From a biological viewpoint, the modeling of these abnormal associations between biomarkers will play an important role in cancer classification. In this paper, we propose an approach based on the concept of Biomarker Association Networks (BAN) for cancer classification. The BAN is modeled as a neural network, which can capture the associations between the biomarkers by minimizing an energy function. Based on the BAN, a new cancer classification approach is developed. We validate the proposed approach on four publicly available biomarker expression datasets. The derived Biomarker Association Networks are observed to be significantly different for different cancer classes, which help reveal the underlying deviant biomarker association patterns responsible for different cancer types. Extensive comparisons show the superior performance of the BAN-based classification approach over several conventional classification methods.     

Differential gene expression identifies subgroups of ovarian carcinoma

Papillary serous ovarian carcinoma, the most common type of ovarian cancer, displays different biological behavior in different patients. This heterogeneity cannot be recognized by light microscopy. In this study, gene expression in 29 papillary serous ovarian carcinoma samples (21 invasive tumors and 8 borderline tumors), and 17 nonmalignant tissue types comprising 512 samples, was determined using Affymetrix U_133 oligonucleotide microarrays (Affymetrix, Inc., Santa Clara, Calif) representing approximately 40,000 known genes and expression sequence tags (ESTs). Differences in gene expression were quantified as the fold change in gene expression between the various sets of samples. A set of genes was identified that was over-expressed in the invasive ovarian carcinoma samples compared with the normal ovary samples. Principle component analysis of the set of invasive ovarian carcinomas using this set of genes revealed the existence of 2 major subgroups among the invasive ovarian carcinomas. A series of principle component analyses of the ovarian carcinomas using different gene sets composed of genes involved in different metabolic pathways also revealed the same 2 major subgroups of the invasive ovarian carcinomas. Review of the pathology by a single pathologist in a blinded manner suggested that these 2 subgroups differed in pathologic grade. Genes differentially expressed between the 2 ovarian carcinoma subsets were identified. Examination of gene expression in each ovarian carcinoma subset compared with that in 17 different normal tissue types (512 samples) revealed genes specifically over-expressed in ovarian carcinoma compared with these normal tissues. It is concluded that gene expression patterns may be useful in helping to further classify subtypes of papillary serous ovarian carcinoma that may have clinical significance. In addition, the genes identified as over-expressed in each set of serous ovarian carcinoma compared with normal tissues may represent potential biomarkers and/or targets for therapy.     

结肠癌的蛋白质组学研究

目的： 通过比较结肠癌组织与正常结肠组织的蛋白质组表达差异，寻找结肠癌相关的蛋白质，选择敏感的分子标志物。方法： 运用蛋白质组学技术，对8例结肠癌患者的结肠癌组织和正常结肠组织进行胶内差异双向电泳（2-D），选择差异表达超过2倍的蛋白质进行MALDI-TOF质谱分析和生物学信息分析。结果： 成功建立结肠癌和正常结肠组织的双向凝胶电泳图谱，结肠癌组织和正常组织凝胶电泳图谱中平均蛋白质斑点数分别为3289和3066，其中表达差异超过2倍的斑点共有31个，质谱分析和数据库检索共鉴定出18种蛋白质，包括keratin 8、S100A6、protein disulfide isomerase等。从功能分析，这些差异蛋白质与癌细胞的发生、增殖、分化、转移等相关。结论： 蛋白质组学能很好地显示结肠癌组织与正常结肠组织间的蛋白质表达差异，本研究鉴定的18种差异蛋白质有可能为研究结肠癌的生物学行为提供新的分子标记物。     

Proteomic approaches to tumor marker discovery

CONTEXT: Current tumor markers for ovarian cancer still lack adequate sensitivity and specificity to be applicable in large populations. High-throughput proteomic profiling and bioinformatics tools allow for the rapid screening of a large number of potential biomarkers in serum, plasma, or other body fluids. OBJECTIVE: To determine whether protein profiles of plasma can be used to identify potential biomarkers that improve the detection of ovarian cancer. DESIGN: We analyzed plasma samples that had been collected between 1998 and 2001 from patients with sporadic ovarian serous neoplasms before tumor resection at various International Federation of Gynecology and Obstetrics stages (stage I [n = 11], stage II [n = 3], and stage III [n = 29]) and from women without known neoplastic disease (n = 38) using proteomic profiling and bioinformatics. We compared results between the patients with and without cancer and evaluated their discriminatory performance against that of the cancer antigen 125 (CA125) tumor marker. RESULTS: We selected 7 biomarkers based on their collective contribution to the separation of the 2 patient groups. Among them, we further purified and subsequently identified 3 biomarkers. Individually, the biomarkers did not perform better than CA125. However, a combination of 4 of the biomarkers significantly improved performance (P < or =.001). The new biomarkers were complementary to CA125. At a fixed specificity of 94%, an index combining 2 of the biomarkers and CA125 achieves a sensitivity of 94% (95% confidence interval, 85%-100.0%) in contrast to a sensitivity of 81% (95% confidence interval, 68%-95%) for CA125 alone. CONCLUSIONS: The combined use of bioinformatics tools and proteomic profiling provides an effective approach to screen for potential tumor markers. Comparison of plasma profiles from patients with and without known ovarian cancer uncovered a panel of potential biomarkers for detection of ovarian cancer with discriminatory power complementary to that of CA125. Additional studies are required to further validate these biomarkers.     

The path to routine use of genomic biomarkers in the cancer clinic

It has been almost 15 years since the first microarray-based studies creating multigene biomarkers to subtype and predict survival of cancer patients. This Perspective looks at why only a handful of genomic biomarkers have reached clinical application and what advances are needed over the next 15 years to grow this number. I discuss challenges in creating biomarkers and reproducing them at the genomic and computational levels, including the problem of spatio-genomic heterogeneity in an individual cancer. I then outline the challenges in translating newly discovered genome-wide or regional events, like trinucleotide mutation signatures, kataegis, and chromothripsis, into biomarkers, as well as the importance of incorporating prior biological knowledge. Lastly, I outline the practical problems of pharmaco-economics and adoption: Are new biomarkers viewed as economically rational by potential funders? And if they are, how can their results be communicated effectively to patients and their clinicians? Genomic-based diagnostics have immense potential for transforming the management of cancer. The next 15 years will see a surge of research into the topics here that, when combined with a stream of new targeted therapies being developed, will personalize the cancer clinic.     

Molecular preservation by extraction and fixation, mPREF: a method for small molecule biomarker analysis and histology on exactly the same tissue

Background

Histopathology is the standard method for cancer diagnosis and grading to assess aggressiveness in clinical biopsies. Molecular biomarkers have also been described that are associated with cancer aggressiveness, however, the portion of tissue analyzed is often processed in a manner that is destructive to the tissue. We present here a new method for performing analysis of small molecule biomarkers and histology in exactly the same biopsy tissue.

Methods

Prostate needle biopsies were taken from surgical prostatectomy specimens and first fixed, each in a separate vial, in 2.5 ml of 80% methanol:water. The biopsies were fixed for 24 hrs at room temperature and then removed and post-processed using a non-formalin-based fixative (UMFIX), embedded, and analyzed by hematoxylin and eosin (H&E) and by immunohistochemical (IHC) staining. The retained alcohol pre-fixative was analyzed for small molecule biomarkers by mass spectrometry.

Results

H&E analysis was successful following the pre-fixation in 80% methanol. The presence or absence of tumor could be readily determined for all 96 biopsies analyzed. A subset of biopsy sections was analyzed by IHC, and cancerous and non-cancerous regions could be readily visualized by PIN4 staining. To demonstrate the suitability for analysis of small molecule biomarkers, 28 of the alcohol extracts were analyzed using a mass spectrometry-based metabolomics platform. All extracts tested yielded successful metabolite profiles. 260 named biochemical compounds were detected in the alcohol extracts. A comparison of the relative levels of compounds in cancer containing vs. non-cancer containing biopsies showed differences for 83 of the compounds. A comparison of the results with prior published reports showed good agreement between the current method and prior reported biomarker discovery methods that involve tissue destructive methods.

Conclusions

The Molecular Preservation by Extraction and Fixation (mPREF) method allows for the analysis of small molecule biomarkers from exactly the same tissue that is processed for histopathology.     

卵巢癌中ETV4基因高表达对患者预后的临床意义

目的　探讨ETV4在卵巢癌中的表达及与预后的关系。 方法　分别利用BioGPS数据库、Oncomine数据库、癌症细胞系百科全书（CCLE）和Kaplan-Meier Plotter数据库分析ETV4基因在正常人体组织中的表达、卵巢癌组织中的表达，以及卵巢癌患者预后生存分析；利用cBioPortal数据库分析ETV4在卵巢癌中基因突变和预后关系。 结果　BioGPS数据库分析结果显示ETV4在正常卵巢组织低表达；Oncomine数据库检索出340项，ETV4表达水平有差异意义的结果32项，其中ETV4表达增高30项，ETV4表达降低2项；对符合设定条件的2项研究进行Meta分析发现，ETV4在卵巢癌组织中呈高表达状态（P<0.05）；CCLE分析结果显示ETV4在卵巢癌细胞中高表达；Kaplan-Meier Plotter数据库结果显示，ETV4高表达组的卵巢癌患者总体生存时间（OS）与无病生存时间（DFS）较低表达组均显著延长（P<0.05）。cBioPortal数据库结果显示ETV4在卵巢癌中基因突变率为2.5%，但对卵巢癌患者的OS与DFS均无显著影响（P>0.05）。 结论　ETV4在卵巢癌组织中高表达，且具有显著延长卵巢癌患者OS与DFS的作用，可作为卵巢癌临床预后的候选标志物；ETV4基因的突变对卵巢癌患者的OS与DFS无显著影响。     

MALDI mass spectrometric imaging of biological tissue sections

In biomedical research, the discovery of new biomarkers and new drugs demands analytical techniques with high sensitivity together with increased throughput. The possibility to localize or to follow changes in organisms at the molecular level by imaging component distributions of specific tissues, is of prime importance to unravel biochemical pathways and develop new treatments and drugs. Established molecular imaging techniques such as MRI and PET are already widely used, however their need for molecular probes to report the presence of the analytes of interest precludes the simultaneous exploration of different biomolecules. Matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI MSI) takes full advantage of the high sensitivity of mass spectrometry instrumentation but also of the ability of the latter to simultaneously detect a wide range of compounds, almost regardless from their nature and mass. To perform MALDI MSI, sections of biological tissues are introduced in an MALDI MS instrument, where the UV pulsed laser of the MALDI source is used to raster over a selected area while acquiring mass spectra of the ablated ions at every image point. From this array of spectra, hundreds of analyte-specific images can be generated based on the selected masses. MALDI MSI can be used to track biomarkers such as peptides or proteins but also to map drug/tissue interactions. In this paper, an overview of the possibilities of MSI will be given. As an example, MSI on brain tissue sections for the study of Alzheimer's disease (AD) will be shown. Mapping of amyloid peptides as a new approach for drug lead optimization will be presented. Target identification thanks to MSI will be introduced and the last part will be dedicated to the molecular scanner approach, which gives access to high-mass range by combining tissue blotting and digestion in a one-step process.     

Development and use of biomarkers in oncology drug development

Successful development and use of biomarkers will improve the productivity of oncology drug development. Recognition of the importance of biomarkers for speeding drug development is reflected in the precise definitions and concepts proposed by an NIH Working Group to standardize terminology and promote a more coherent and systematic approach to the development and use of biomarkers. Potential clinical biomarkers of drug efficacy are often identified through pre-clinical studies or basic research. Identification of potential biomarkers for use in oncology is moving rapidly forward through continuing advances in clinical imaging technologies, especially molecular and functional imaging. Other rapid advances are a product of the growing availability of new scientific reagents for established technologies and of high-throughput genomic and proteomic technologies that can generate hundreds of potential biomarkers for further evaluation. In certain cases, conventional clinical diagnostic techniques or assays can be adapted for use in pre-clinical models to evaluate their ability to serve as biomarkers for predicting clinical responses to new drug candidates. Evaluation (pre-clinical and clinical) of a potential biomarker is often the longest stage of biomarker development, and standards for evaluation or validation depend on the intended use and stage of clinical development. Biomarkers verified for use in preclinical studies can be used to help select appropriate animal models and lead compounds. Biomarkers verified for use in clinical trials can confirm a drug's pharmacological or biological mechanism of action, guide protocol design, aid patient and dose selection, and help to minimize safety risks. Oncology drug development can be optimized by using a tiered set of clinical biomarkers that predict compound efficacy and safety with increasing confidence at each rise in tier thereby aiding corporate decision-making about advancing compounds. In oncology, a special class of extensively evaluated biomarkers of efficacy (surrogate endpoints) that generally correlate with desired clinical outcomes can be used as a basis for corporate decisions as well as for gaining accelerated provisional regulatory approval of a drug.     

Zwitterionic polymer-coated immunobeads for blood-based cancer diagnostics

Both total plasma and tumor-derived microvesicle (TMV)-associated miRNAs have been proposed as potential blood-based biomarkers for cancer diagnosis. However, there has been no comparison of the two types of miRNAs for biomarker discovery because of technological challenges of isolating TMVs from human plasma. The effective isolation of TMVs can be hardly achieved with conventional immunobead-based methods due to the high content of plasma proteins. In the current study, zwitterionic sulfobetaine-conjugated immunobeads are prepared using cluster of differentiation 83 (CD83) as a candidate protein marker for breast cancer-derived microvesicles. The zwitterionic immunobeads are more than 10-fold efficient for isolating TMVs from clinical plasma samples by suppressing nonspecific protein binding than conventional immunobeads. Early-stage breast cancer can be distinguished from benign breast disease by using the sulfobetaine-modified immunobeads, whereas conventional immunobeads show poor discriminatory performance. Further, we demonstrate that miRNAs in the form of TMVs offer a major improvement over total plasma miRNAs for early cancer detection. The analyses of miRNA expression levels show that in total, 6 miRNAs are significantly upregulated in the CD83-positive microvesicles of breast cancer patients, whereas differential miRNA expression is not detected on using total plasma RNA. The results indicate that our zwitterionic immunobead platform may constitute a powerful tool to identify circulating biomarkers and open a new avenue for highly sensitive blood-based cancer diagnostics.     

A patient-like orthotopic implantation nude mouse model of highly metastatic human ovarian cancer

Clinically relevant animal models of human cancer are important for studies of cancer biology, invasion and metastasis, and for investigating new forms of prognostic diagnosis and therapy. An ovarian tumor line (RMG-1: human clear cell carcinoma of the ovary) previously grown subcutaneously was implanted ortho-topically as intact tissue into the ovarian capsule of 22 nude mice. The tumors showed progressive growth at the orthotopic site in all animals. Tumor-associated serum galactosyltransferase (GAT) tended to be posi-tive in all nude -mice. The tumors invaded or metastasized to the contralateral ovary, retroperitoneum, mesentery and peritoneum, and omentum, and metastasized to the subcutaneous tissue, lymph nodes and distant organs including the liver, kidney, pancreas, and diaphragm. In striking contrast, subcutaneous trans-plantation of this tumor resulted in growth in only 2 of 5 animals with local lymph node and kidney involve-ment but no retroperitoneal or peritoneal involvement. These findings suggest that orthotopic implantation provides a suitable micro-environment in which ovarian cancer can express its intrinsic clinically-relevant properties. This approach is relevant to the clinical features of ovarian cancer and is thought to be a useful model for studies of therapy for this cancer.© Kluwer Academic Publishers 1998     

Ectopic expressed long non-coding RNA H19 contributes to malignant cell behavior of ovarian cancer

Recent studies have highlighted the role of long non-coding RNAs (lncRNAs) in carcinogenesis and have suggested that genes of this class might be used as biomarkers in cancer. However, whether lncRNAs are involved in ovarian cancer (OC) remains largely unknown. In the present study, we focused on lncRNAH19 and investigated the expression and functional role of H19 in OC. H19 expression was measured in 70 pairs of ovarian cancer tissue samplescompared with normal controls by real-time quantitative RT-PCR. The effects of H19 on ovarian cancer cells were studied by RNA interference approach. Apoptosis and cell cycle were analyzed by flow cytometry. Cells viability was evaluated using cell counting Kit-8. Our results demonstrated that that H19 silencing inhibited OV90 and SKOV3 OC cell proliferation in vitro. Further investigation into the mechanisms responsible for the growth inhibitory effects by H19 silencing revealed that its knockdown resulted in the induction of cell cycle arrest and apoptosis through certain cell cycle-related and apoptosis-related proteins. Together, our data suggest that LncRNAH19 plays an important role in OC cell proliferation and contributes to a better understanding of the importance of dysregulated lncRNAs in OC progression.     

Cancer immunotherapy: A need for peripheral immunodynamic monitoring

Immunotherapy has become an important approach for treating different tumours which has shown significant efficacy in numerous clinical trials, especially those using new checkpoint inhibitors and adoptive cell therapy, which have rapidly become widespread after being approved. However, analysis of peripheral immune biomarkers before and after immunotherapy and their relationship to clinical responses and disease prognosis have rarely been performed in clinical trials. In this review, we examine dynamic changes in the immune system before and after therapy by analyzing recent clinical trials of immunotherapy in patients with cancer that focused on checkpoint inhibitors and adoptive cell therapy. Our aim was to identify circulating biomarkers which can specifically predict clinical response and prognosis, as well as toxicities of immunotherapy. Through this approach, we hope to advance our understanding of the mechanisms of immunotherapy with the goal of developing individualized treatment for cancer patients.     

Anti-tumor antibodies in ovarian cancer

Anti-tumor antibodies have potential as cancer biomarkers. There is relatively limited identification of anti-tumor antibodies in response to ovarian cancer, compared with studies for other cancers. There is also very limited information on the prevalence of anti-tumor antibodies among ovarian cancer patients. Although most anti-tumor antibodies react with antigens common to both tumor and normal tissue, the anti-tumor response tends to be confined to individuals with ovarian cancer, similar to other cancers. Antibodies to HOXA7, a differentiation antigen, have the highest reported prevalence in ovarian cancer (67%). Antibodies to other ubiquitous antigens including NY-ESO-1, Ep-CAM (epithelial cell adhesion molecule), HSP-90 (heat shock protein 90), and mutated p53 have been identified in ovarian cancer. Anti-tumor antibody specificity reflects the heterogeneity of antigen expression in tumors. Tests based on panels of a combination of anti-tumor antibodies may be more predictive for ovarian cancer, as no single specificity accounts for ovarian tumors. In addition to characterization of anti-tumor antibodies as diagnostic markers, study of anti-tumor antibodies is likely to provide insights into mechanisms of tumor development. There is evidence of antibodies to tumor antigens and of activated T cells, suggesting immune recognition of tumor antigens occurred. Nonetheless, as tumors are not 'rejected', it is likely that there are alterations in the immune system. The basis for tumor growth in the face of immune activity remains to be determined.     

一种基于有监督奇异值分解和随机森林的卵巢癌磷脂代谢物特征提取方法

卵巢癌是一种常见的妇科肿瘤,死亡率占各类妇科肿瘤的首位。选取既有较高的分类疾病模式能力又具有生物学关联的特征肿瘤标志物用于肿瘤的诊断是目前研究的重点。本研究针对卵巢癌磷脂代谢物数据的问题,提出了一种融合有监督奇异值分解和基于信息增益的随机森林决策的方法用于特征标志物的选择。首先应用有监督奇异值分解计算各标志物的权重值,并根据权重值粗选出候选标志物;其次应用基于信息增益的随机森林决策理论从候选标志物中选出特征标志物;最后通过SVM分类器测试,分类率高达90%以上。本研究方法与其他常用方法比较具有一定优势,其中一个明显的特点是所选特征标志物不但保持了较高的分类率,而且具有生物学关联意义,从而证实本研究方法具有较高的可行性和实用性。     

Exosomes carrying immunoinhibitory proteins and their role in cancer

Recent emergence of exosomes as information carriers between cells has introduced us to a new previously unknown biological communication system. Multi‐directional cross‐talk mediated by exosomes carrying proteins, lipids and nucleic acids between normal cells, cells harbouring a pathogen or cancer and immune cells has been instrumental in determining outcomes of physiological as well as pathological conditions. Exosomes play a key role in the broad spectrum of human diseases. In cancer, tumour‐derived exosomes carry multiple immunoinhibitory signals, disable anti‐tumour immune effector cells and promote tumour escape from immune control. Exosomes delivering negative signals to immune cells in cancer, viral infections, autoimmune or other diseases may interfere with therapy and influence outcome. Exosomes can activate tissue cells to produce inhibitory factors and thus can suppress the host immune responses indirectly. Exosomes also promise to be non‐invasive disease biomarkers with a dual capability to provide insights into immune dysfunction as well as disease progression and outcome.     

Autoantibodies against stress‐induced phosphoprotein‐1 as a novel biomarker candidate for ovarian cancer

Detection of autoantibodies against tumor‐associated antigens (TAA) has recently been shown to be a powerful tool for early detection of various cancers. The aim of this study was to investigate the possibility of using autoantibodies against TAA as novel biomarkers by a proteomics‐based approach in patients with ovarian cancer. We used two‐dimensional differential gel electrophoresis analysis of immuno‐precipitated tumor antigens (2D‐DITA) to compare the levels of autoandibodies in pretreatment and posttreatment sera of patients with ovarian cancers. The identified autoantibodies were validated by SYBR Green real‐time polymerase chain reaction (PCR) and immunohistochemistry (IHC). We further evaluated the level of autoantibody in sera of 68 ovarian cancer patients by an enzyme‐linked immunosorbent assay (ELISA). The autoantibody directed against stress‐induced phosphoprotein‐1 (STIP‐1) emerged as a novel biomarker candidate for ovarian cancer. SYBR Green PCR and IHC confirmed that the STIP‐1 mRNA and protein expression levels were significantly up‐regulated in ovarian cancers compared with normal and benign tumors (P = 0.003 and P < 0.001, respectively). A preliminary ELISA study showed that the serum levels of anti‐STIP‐1 autoantibodies were significantly elevated in ovarian cancer patients compared with healthy controls (P = 0.03). The results suggest that 2D‐DITA is a useful tool to detect autoantibodies and that STIP‐1 is a potential biomarker candidate for ovarian cancers. © 2010 Wiley‐Liss, Inc.     

Ovarian tissue banking for cancer patients: fertility preservation, not just ovarian cryopreservation

While ovarian tissue cryopreservation has commonly been equated with fertility preservation in cancer patients, there is a range of alternative options to preserve fertility. Based on the type and timing of chemotherapy, the type of cancer, the patient's age and the partner status, a different strategy of fertility preservation may be needed. If the patient has a partner or accepts donor sperm, embryo cryopreservation should be considered first, since this is a clinically well established procedure. Despite relatively low pregnancy rates, when there is time for ovarian stimulation and the patient is single, oocyte cryopreservation may also be preferred to ovarian tissue banking. In breast cancer patients, tamoxifen or aromatase inhibitors can be used for ovarian stimulation prior to oocyte or embryo cryopreservation. In endometrial cancer patients, aromatase inhibitors may be the only choice for ovarian stimulation. When only pelvic radiotherapy is used, ovarian transposition can be performed, but the success rates vary because of scatter radiation and vascular compromise. Lack of FSH and GnRH receptors on primordial follicles and oocytes does not make gonadal suppression an effective strategy of gonadal protection. Fertility preservation should be an integral part of improving the quality of life in cancer survivors; however, it is neither possible nor ethical to recommend the same recipe for every cancer patient.