Challenges and opportunities for oncology biomarker discovery

Nipple fluid for breast cancer diagnosis using the nanopore of Phi29 DNA-packaging motor

Detection of cancer in its early stage is a challenging task for oncologists. Inflammatory breast cancer has symptoms that are similar to mastitis and can be mistaken for microbial infection. Currently, the differential diagnosis between mastitis and Inflammatory breast cancer via nipple aspirate fluid (NAF) is difficult. Here, we report a label-free and amplification-free detection platform using an engineered nanopore of the phi29 DNA-packaging motor with biomarker Galectin3 (GAL3), Thomsen-Friedenreich (TF) binding peptide as the probe fused at its C-terminus. The binding of the biomarker in NAF samples from breast cancer patients to the probe results in the connector's conformational change with a current blockage of 32 %. Utilization of dwell time, blockage ratio, and peak signature enable us to detect basal levels of biomarkers from patient NAF samples at the single-molecule level. This platform will allow for breast cancers to be resolved at an early stage with accuracy and thoroughness.

Identification of stage-associated exosome miRNAs in colorectal cancer by improved robust and corroborative approach embedded miRNA-target network

Background

Colorectal cancer (CRC) is a common gastrointestinal tumor with high morbidity and mortality. At the molecular level, patients at different stages present considerable heterogeneity. Although the miRNA in exosome is an effective biomarker to reveal tumor progression, studies based on stage-associated exosome miRNA regulatory network analysis still lacking. This study aims to identify CRC stage-associated exosome miRNAs and reveal their potential function in tumor progression.

Methods

In this study, serum and cellular exosome miRNA expression microarrays associated with CRC were downloaded from GEO database. Stage-common (SC) and stage-specific (SS) differentially expressed miRNAs were extracted and their targets were identified based on 11 databases. Furthermore, miRNA SC and SS regulatory function networks were built based on the CRC phenotypic relevance of miRNA targets, and the corresponding transcription factors were identified. Concurrently, the potential stage-associated miRNAs were identified by receiver-operating characteristic (ROC) curve analysis, survival analysis, drug response analysis, ceRNA analysis, pathway analysis and a comprehensive investigation of 159 publications.

Results

Ten candidate stage-associated miRNAs were identified, with three SC (miR-146a-5p, miR-22-3p, miR-23b-3p) and seven SS (I: miR-301a-3p, miR-548i; IIIA: miR-23a-3p; IV: miR-194-3p, miR-33a-3p, miR-485-3p, miR-194-5p) miRNAs. Additionally, their targets were enriched in several vital cancer-associated pathways such as TGF-beta, p53, and hippo signaling pathways. Moreover, five key hotspot target genes (CCNA2, MAPK1, PTPRD, MET, and CDKN1A) were demonstrated to associated with better overall survival in CRC patients. Finally, miR-23b-3p, miR-301a-3p and miR-194-3p were validated being the most stably expressed stage-associated miRNAs in CRC serum exosomes, cell exosomes and tissues.

Conclusions

These CRC stage-associated exosome miRNAs aid to further mechanism research of tumor progression and provide support for better clinical management in patients with different stages.

Supervised Methods for Biomarker Detection from Microarray Experiments

Biomarkers are valuable indicators of the state of a biological system. Microarray technology has been extensively used to identify biomarkers and build computational predictive models for disease prognosis, drug sensitivity and toxicity evaluations. Activation biomarkers can be used to understand the underlying signaling cascades, mechanisms of action and biological cross talk. Biomarker detection from microarray data requires several considerations both from the biological and computational points of view. In this chapter, we describe the main methodology used in biomarkers discovery and predictive modeling and we address some of the related challenges. Moreover, we discuss biomarker validation and give some insights into multiomics strategies for biomarker detection.

Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles

(1) Background: Inter-tumour heterogeneity is one of cancer’s most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. (2) Methods: Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. (3) Results: Combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: paclitaxel (breast cancer), binimetinib (breast cancer) and cetuximab (colorectal cancer). Interestingly, each of these multi-gene ML models identifies some treatment-responsive PDXs not harbouring the best actionable mutation for that case. Thus, ML multi-gene predictors generally have much fewer false negatives than the corresponding single-gene marker. (4) Conclusions: As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if ML algorithms were also applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

Prediction of differentially expressed microRNAs in blood as potential biomarkers for Alzheimer's disease by meta-analysis and adaptive boosting ensemble learning

BACKGROUND

Blood circulating microRNAs that are specific for Alzheimer's disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs). However, non-reproducible and inconsistent reports of DEmiRNAs hinder biomarker development. The most reliable DEmiRNAs can be identified by meta-analysis. To enrich the pool of DEmiRNAs for potential AD biomarkers, we used a machine learning method called adaptive boosting for miRNA disease association (ABMDA) to identify eligible candidates that share similar characteristics with the DEmiRNAs identified from meta-analysis. This study aimed to identify blood circulating DEmiRNAs as potential AD biomarkers by augmenting meta-analysis with the ABMDA ensemble learning method.

METHODS

Studies on DEmiRNAs and their dysregulation states were corroborated with one another by meta-analysis based on a random-effects model. DEmiRNAs identified by meta-analysis were collected as positive examples of miRNA-AD pairs for ABMDA ensemble learning. ABMDA identified similar DEmiRNAs according to a set of predefined criteria. The biological significance of all resulting DEmiRNAs was determined by their target genes according to pathway enrichment analyses. The target genes common to both meta-analysis- and ABMDA-identified DEmiRNAs were collected to construct a network to investigate their biological functions.

RESULTS

A systematic database search found 7841 studies for an extensive meta-analysis, covering 54 independent comparisons of 47 differential miRNA expression studies, and identified 18 reliable DEmiRNAs. ABMDA ensemble learning was conducted based on the meta-analysis results and the Human MicroRNA Disease Database, which identified 10 additional AD-related DEmiRNAs. These 28 DEmiRNAs and their dysregulated pathways were related to neuroinflammation. The dysregulated pathway related to neuronal cell cycle re-entry (CCR) was the only statistically significant pathway of the ABMDA-identified DEmiRNAs. In the biological network constructed from 1865 common target genes of the identified DEmiRNAs, the multiple core ubiquitin-proteasome system, that is involved in neuroinflammation and CCR, was highly connected.

CONCLUSION

This study identified 28 DEmiRNAs as potential AD biomarkers in blood, by meta-analysis and ABMDA ensemble learning in tandem. The DEmiRNAs identified by meta-analysis and ABMDA were significantly related to neuroinflammation, and the ABMDA-identified DEmiRNAs were related to neuronal CCR.

Biomarkers in Pancreatic Cancer as Analytic Targets for Nanomediated Imaging and Therapy

As the increase in therapeutic and imaging technologies is swiftly improving survival chances for cancer patients, pancreatic cancer (PC) still has a grim prognosis and a rising incidence. Practically everything distinguishing for this type of malignancy makes it challenging to treat: no approved method for early detection, extended asymptomatic state, limited treatment options, poor chemotherapy response and dense tumor stroma that impedes drug delivery. We provide a narrative review of our main findings in the field of nanoparticle directed treatment for PC, with a focus on biomarker targeted delivery. By reducing drug toxicity, increasing their tumor accumulation, ability to modulate tumor microenvironment and even improve imaging contrast, it seems that nanotechnology may one day give hope for better outcome in pancreatic cancer. Further conjugating nanoparticles with biomarkers that are overexpressed amplifies the benefits mentioned, with potential increase in survival and treatment response.

An integrated target recognition and polymerase primer probe for microRNA detection

Extremely sensitive and visual measurements of microRNA (miRNA) in situ for early detection and monitoring of diseases remains a major challenge. To address this issue, this work reports a rapid, highly sensitive and selective microRNA (miRNA) biosensing strategy based on isothermal circular strand-displacement polymerization (ICSDP), and miRNA imaging was performed inside cells. In this work, a double hairpin DNA probe (HP1/HP2 complex) embedded with a sensing region and polymerase primer region was designed. Briefly, after the specific binding of target miRNA with the HP1/HP2 probe, HP1/HP2 itself can function as a primer to initiate the ICSDP with the help of Klenow Fragment (KF), yielding target miRNA for new rounds of ICSDP. In this process, one target can produce multiple signal outputs (1: n), achieving low abundance of miRNA detection. Under optimized conditions, the proposed strategy showed high sensitivity with a detection limit of 5 pM within 15 min and can also easily distinguish the control miRNA from the target miRNA. This method can be further applied to image the intracellular miRNA of interest in situ inside the cancer cells.

Fiber Optic Particle Plasmon Resonance-Based Immunoassay Using a Novel Multi-Microchannel Biochip

A novel multi-microchannel biochip fiber-optic particle plasmon resonance (FOPPR) sensor system for the simultaneous detection of multiple samples. The system integrates a novel photoelectric system, a lock-in module, and an all-in-one platform incorporating optical design and mechanical design together to improve system stability and the sensitivity of the FOPPR sensor. The multi-microchannel FOPPR biochip has been developed by constructing a multi-microchannel flow-cell composed of plastic material to monitor and analyze five samples simultaneously. The sensor system requires only 30 μL of sample for detection in each microchannel. Moreover, the total size of the multi-microchannel FOPPR sensor chip is merely 40 mm × 30 mm × 4 mm; thus, it is very compact and cost-effective. The analysis was based on calibration curves obtained from real-time sensor response data after injection of sucrose solution, streptavidin and anti-dinitrophenyl (anti-DNP) antibody of known concentrations over the chips. The results show that the multi-microchannel FOPPR sensor system not only has good reproducibility (coefficient of variation (CV) < 10%), but also excellent refractive index resolution (6.23 ± 0.10 × 10⁻⁶ refractive index unit (RIU)). The detection limits are 2.92 ± 0.28 × 10⁻⁸ g/mL (0.53 ± 0.01 nM) and 7.48 ± 0.40 × 10⁻⁸ g/mL (0.34 ± 0.002 nM) for streptavidin and anti-DNP antibody, respectively.

Nanoparticle-enabled blood tests for early detection of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is often detected too late to allow adequate treatments with the result that patients are condemned to sufferings and early death. Most efforts have been therefore aimed at identifying sensitive PDAC biomarkers. Although biomarkers have numerous advantages, sample size, intra-individual variability, existence of several biases and confounding variables and cost of investigation make their clinical application challenging. In recent years, nanotechnology is providing new options for early cancer detection. Among recent discoveries, the concept is emerging that the protein corona, i.e. the layer of plasma proteins that surrounds nanomaterials in bodily fluids, is personalized. In particular, the protein corona of cancer patients is significantly different from that of healthy individuals. Herein, we review this concept with a particular focus on clinical relevance. We also discuss the recently developed nanoparticle-enabled blood (NEB) tests that demonstrated to be promising in discriminating PDAC patients from healthy volunteers by global change of the nanoparticle-protein corona. We conclude with a critical discussion of research perspectives aimed at further improving the prediction ability of the test.

Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic

Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine - most notably, oncology and cardiovascular diseases - allowing rapid early detection and supporting the evolution of biomarker-guided, precision-medicine-based targeted therapies. In Alzheimer disease (AD), breakthroughs in biomarker identification and validation include cerebrospinal fluid and PET markers of amyloid-β and tau proteins, which are highly accurate in detecting the presence of AD-associated pathophysiological and neuropathological changes. However, the high cost, insufficient accessibility and/or invasiveness of these assays limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multistage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This Review summarizes the efforts of an international working group that aimed to survey the current landscape of blood-based AD biomarkers and outlines operational steps for an effective academic-industry co-development pathway from identification and assay development to validation for clinical use.

Extracellular vesicles and their nucleic acids for biomarker discovery

Extracellular vesicles (EVs) are a heterogenous population of vesicles originate from cells. EVs are found in different biofluids and carry different macromolecules, including proteins, lipids, and nucleic acids, providing a snap shot of the parental cells at the time of release. EVs have the ability to transfer molecular cargoes to other cells and can initiate different physiological and pathological processes. Mounting lines of evidence demonstrated that EVs' cargo and machinery is affected in disease states, positioning EVs as potential sources for the discovery of novel biomarkers. In this review, we demonstrate a conceptual overview of the EV field with particular focus on their nucleic acid cargoes. Current knowledge of EV subtypes, nucleic acid cargo and pathophysiological roles are outlined, with emphasis placed on advantages against competing analytes. We review the utility of EVs and their nucleic acid cargoes as biomarkers and critically assess the newly available advances in the field of EV biomarkers and high throughput technologies. Challenges to achieving the diagnostic potential of EVs, including sample handling, EV isolation, methodological considerations, and bioassay reproducibility are discussed. Future implementation of 'omics-based technologies and integration of systems biology approaches for the development of EV-based biomarkers and personalized medicine are also considered.

Unearthing new genomic markers of drug response by improved measurement of discriminative power

Background

Oncology drugs are only effective in a small proportion of cancer patients. Our current ability to identify these responsive patients before treatment is still poor in most cases. Thus, there is a pressing need to discover response markers for marketed and research oncology drugs. Screening these drugs against a large panel of cancer cell lines has led to the discovery of new genomic markers of in vitro drug response. However, while the identification of such markers among thousands of candidate drug-gene associations in the data is error-prone, an appraisal of the effectiveness of such detection task is currently lacking.

Methods

Here we present a new non-parametric method to measuring the discriminative power of a drug-gene association. Unlike parametric statistical tests, the adopted non-parametric test has the advantage of not making strong assumptions about the data distorting the identification of genomic markers. Furthermore, we introduce a new benchmark to further validate these markers in vitro using more recent data not used to identify the markers.

Results

The application of this new methodology has led to the identification of 128 new genomic markers distributed across 61% of the analysed drugs, including 5 drugs without previously known markers, which were missed by the MANOVA test initially applied to analyse data from the Genomics of Drug Sensitivity in Cancer consortium.

Conclusions

Discovering markers using more than one statistical test and testing them on independent data is unusual. We found this helpful to discard statistically significant drug-gene associations that were actually spurious correlations. This approach also revealed new, independently validated, in vitro markers of drug response such as Temsirolimus-CDKN2A (resistance) and Gemcitabine-EWS_FLI1 (sensitivity).

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0336-z) contains supplementary material, which is available to authorized users.

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

Identification and Clinical Translation of Biomarker Signatures: Statistical Considerations

Powerful machine learning tools exist to extract biological patterns for diagnosis or prediction from high-dimensional datasets. Simultaneous advances in high-throughput profiling technologies have led to a rapid acceleration of biomarker discovery investigations across all areas of medicine. However, the translation of biomarker signatures into clinically useful tools has thus far been difficult. In this chapter, several important considerations are discussed that influence such translation in the context of classifier design. These include aspects of variable selection that go beyond classification accuracy, as well as effects of variability on assay stability and sample size. The consideration of such factors may lead to an adaptation of biomarker discovery approaches, aimed at an optimal balance of performance and clinical translatability.

The analytical validation of the Oncotype DX Recurrence Score assay

In vitro diagnostic multivariate index assays are highly complex molecular assays that can provide clinically actionable information regarding the underlying tumour biology and facilitate personalised treatment. These assays are only useful in clinical practice if all of the following are established: analytical validation (i.e., how accurately/reliably the assay measures the molecular characteristics), clinical validation (i.e., how consistently/accurately the test detects/predicts the outcomes of interest), and clinical utility (i.e., how likely the test is to significantly improve patient outcomes). In considering the use of these assays, clinicians often focus primarily on the clinical validity/utility; however, the analytical validity of an assay (e.g., its accuracy, reproducibility, and standardisation) should also be evaluated and carefully considered. This review focuses on the rigorous analytical validation and performance of the Oncotype DX^® Breast Cancer Assay, which is performed at the Central Clinical Reference Laboratory of Genomic Health, Inc. The assay process includes tumour tissue enrichment (if needed), RNA extraction, gene expression quantitation (using a gene panel consisting of 16 cancer genes plus 5 reference genes and quantitative real-time RT-PCR), and an automated computer algorithm to produce a Recurrence Score^® result (scale: 0–100). This review presents evidence showing that the Recurrence Score result reported for each patient falls within a tight clinically relevant confidence interval. Specifically, the review discusses how the development of the assay was designed to optimise assay performance, presents data supporting its analytical validity, and describes the quality control and assurance programmes that ensure optimal test performance over time.

Unraveling Molecular Differences of Gastric Cancer by Label-Free Quantitative Proteomics Analysis

Gastric cancer (GC) has significant morbidity and mortality worldwide and especially in China. Its molecular pathogenesis has not been thoroughly elaborated. The acknowledged biomarkers for diagnosis, prognosis, recurrence monitoring and treatment are lacking. Proteins from matched pairs of human GC and adjacent tissues were analyzed by a coupled label-free Mass Spectrometry (MS) approach, followed by functional annotation with software analysis. Nano-LC-MS/MS, quantitative real-time polymerase chain reaction (qRT-PCR), western blot and immunohistochemistry were used to validate dysregulated proteins. One hundred forty-six dysregulated proteins with more than twofold expressions were quantified, 22 of which were first reported to be relevant with GC. Most of them were involved in cancers and gastrointestinal disease. The expression of a panel of four upregulated nucleic acid binding proteins, heterogeneous nuclear ribonucleoprotein hnRNPA2B1, hnRNPD, hnRNPL and Y-box binding protein 1 (YBX-1) were validated by Nano-LC-MS/MS, qRT-PCR, western blot and immunohistochemistry assays in ten GC patients’ tissues. They were located in the keynotes of a predicted interaction network and might play important roles in abnormal cell growth. The label-free quantitative proteomic approach provides a deeper understanding and novel insight into GC-related molecular changes and possible mechanisms. It also provides some potential biomarkers for clinical diagnosis.

Drug discovery in ophthalmology: past success, present challenges, and future opportunities

BACKGROUND

Drug discovery has undergone major transformations in the last century, progressing from the recognition and refinement of natural products with therapeutic benefit, to the systematic screening of molecular libraries on whole organisms or cell lines and more recently to a more target-based approach driven by greater knowledge of the physiological and pathological pathways involved. Despite this evolution increasing challenges within the drug discovery industry are causing escalating rates of failure of development pipelines.

DISCUSSION

We review the challenges facing the drug discovery industry, and discuss what attempts are being made to increase the productivity of drug development, including a refocusing on the study of the basic biology of the disease, and an embracing of the concept of 'translational research'. We consider what ophthalmic drug discovery can learn from the sector in general and discuss strategies to overcome the present limitations. This includes advances in the understanding of the pathogenesis of disease; improvements in animal models of human disease; improvements in ophthalmic drug delivery and attempts at patient stratification within clinical trials. As we look to the future, we argue that investment in ophthalmic drug development must continue to cover the whole translational spectrum (from 'bench to bedside and back again') with recognition that both biological discovery and clinical understanding will drive drug discovery, providing safe and effective therapies for ocular disease.

Effectiveness of an individualized program of muscular strength and endurance with aerobic training for improving germ cell cancer-related fatigue in men undergoing chemotherapy: EFICATEST study protocol for a randomized controlled trial

Background

Patients with testicular germ cell cancer (GCC) have a high cure rate; however, cancer-related fatigue is the most common complication among patients with GCC undergoing treatment with chemotherapy. Although exercise is widely recommended, information about the physio-pathological effects of cancer therapy on skeletal muscle is very limited. Our aim is to evaluate the effects of an individualized program of muscular strength and endurance with aerobic training on cancer-related fatigue.

Methods/Design

The present study is a randomized controlled trial comparing an individualized program of muscular strength and endurance with aerobic training compared to a control group. We will conduct this trial in patients undergoing chemotherapy, recruited by the Department of Oncology of Virgen de la Victoria Hospital (Málaga). Patients will be included and evaluated before the first cycle of chemotherapy and assigned randomly to the experimental or control group. Cancer-related fatigue, physical condition and biological samples will be measured at the beginning and at the end of an 8-week intervention by the same evaluator, who will be unaware of the allocation of participants to each group. Furthermore, there will be monitoring for 6 months (24 weeks) after training for all outcome variables.

Discussion

This study hopes to offer patients with GCC an individualized exercise program with aerobic training for cancer-related fatigue. Such a scheme, if beneficial, could be implemented successfully within public health.

Trial registration

ClinicalTrials.gov Identifier: NCT02433197. Date of registration: 13 April 2015.

From Molecules to Patients: The Clinical Applications of Translational Bioinformatics

OBJECTIVE

In order to realize the promise of personalized medicine, Translational Bioinformatics (TBI) research will need to continue to address implementation issues across the clinical spectrum. In this review, we aim to evaluate the expanding field of TBI towards clinical applications, and define common themes and current gaps in order to motivate future research.

METHODS

Here we present the state-of-the-art of clinical implementation of TBI-based tools and resources. Our thematic analyses of a targeted literature search of recent TBI-related articles ranged across topics in genomics, data management, hypothesis generation, molecular epidemiology, diagnostics, therapeutics and personalized medicine.

RESULTS

Open areas of clinically-relevant TBI research identified in this review include developing data standards and best practices, publicly available resources, integrative systemslevel approaches, user-friendly tools for clinical support, cloud computing solutions, emerging technologies and means to address pressing legal, ethical and social issues.

CONCLUSIONS

There is a need for further research bridging the gap from foundational TBI-based theories and methodologies to clinical implementation. We have organized the topic themes presented in this review into four conceptual foci - domain analyses, knowledge engineering, computational architectures and computation methods alongside three stages of knowledge development in order to orient future TBI efforts to accelerate the goals of personalized medicine.

Application of metabolomics in drug resistant breast cancer research

The metabolic profiles of breast cancer cells are different from normal mammary epithelial cells. Breast cancer cells that gain resistance to therapeutic interventions can reprogram their endogenous metabolism in order to adapt and proliferate despite high oxidative stress and hypoxic conditions. Drug resistance in breast cancer, regardless of subgroups, is a major clinical setback. Although recent advances in genomics and proteomics research has given us a glimpse into the heterogeneity that exists even within subgroups, the ability to precisely predict a tumor’s response to therapy remains elusive. Metabolomics as a quantitative, high through put technology offers promise towards devising new strategies to establish predictive, diagnostic and prognostic markers of breast cancer. Along with other “omics” technologies that include genomics, transcriptomics, and proteomics, metabolomics fits into the puzzle of a comprehensive systems biology approach to understand drug resistance in breast cancer. In this review, we highlight the challenges facing successful therapeutic treatment of breast cancer and the innovative approaches that metabolomics offers to better understand drug resistance in cancer.

Abnormal levels of heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) in tumour tissue and blood samples from patients diagnosed with lung cancer

Lung cancer is the second most common type of cancer in the world and is the most common cause of cancer-related death in both men and women. Research into causes, prevention and treatment of lung cancer is ongoing and much progress has been made recently in these areas, however survival rates have not significantly improved. Therefore, it is essential to develop biomarkers for early diagnosis of lung cancer, prediction of metastasis and evaluation of treatment efficiency, as well as using these molecules to provide some understanding about tumour biology and translate highly promising findings in basic science research to clinical application. In this investigation, two-dimensional difference gel electrophoresis and mass spectrometry were initially used to analyse conditioned media from a panel of lung cancer and normal bronchial epithelial cell lines. Significant proteins were identified with heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1), pyruvate kinase M2 isoform (PKM2), Hsc-70 interacting protein and lactate dehydrogenase A (LDHA) selected for analysis in serum from healthy individuals and lung cancer patients. hnRNPA2B1, PKM2 and LDHA were found to be statistically significant in all comparisons. Tissue analysis and knockdown of hnRNPA2B1 using siRNA subsequently demonstrated both the overexpression and potential role for this molecule in lung tumorigenesis. The data presented highlights a number of in vitro derived candidate biomarkers subsequently verified in patient samples and also provides some insight into their roles in the complex intracellular mechanisms associated with tumour progression.

Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids

Background

Computational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids.

Methodology

A positive set of abstracts was defined by the terms ‘breast cancer’ and ‘lung cancer’ in conjunction with 14 separate ‘biofluids’ (bile, blood, breastmilk, cerebrospinal fluid, mucus, plasma, saliva, semen, serum, synovial fluid, stool, sweat, tears, and urine), while a negative set of abstracts was defined by the terms ‘(biofluid) NOT breast cancer’ or ‘(biofluid) NOT lung cancer.’ More than 5.3 million total abstracts were obtained from PubMed and examined for biomarker-disease-biofluid associations (34,296 positive and 2,653,396 negative for breast cancer; 28,355 positive and 2,595,034 negative for lung cancer). Biological entities such as genes and proteins were tagged using ABNER, and processed using Python scripts to produce a list of putative biomarkers. Z-scores were calculated, ranked, and used to determine significance of putative biomarkers found. Manual verification of relevant abstracts was performed to assess our method’s performance.

Results

Biofluid-specific markers were identified from the literature, assigned relevance scores based on frequency of occurrence, and validated using known biomarker lists and/or databases for lung and breast cancer [NCBI’s On-line Mendelian Inheritance in Man (OMIM), Cancer Gene annotation server for cancer genomics (CAGE), NCBI’s Genes & Disease, NCI’s Early Detection Research Network (EDRN), and others]. The specificity of each marker for a given biofluid was calculated, and the performance of our semi-automated literature mining method assessed for breast and lung cancer.

Conclusions

We developed a semi-automated process for determining a list of putative biomarkers for breast and lung cancer. New knowledge is presented in the form of biomarker lists; ranked, newly discovered biomarker-disease-biofluid relationships; and biomarker specificity across biofluids.

Progress and perspectives of biomarker discovery in Chinese medicine research

Biomarker discovery in Chinese medicine (CM) has recently attracted a great deal of attention, owing to the promise of high-throughput technologies development and the potential of Chinese herbal medicine. Furthermore, it seems that pattern classification in CM might be serving as inspirational analogy and a practical guide, which might contribute to biomarkers discovery rather than just being used as diagnostic method. Although much work is still needed to identify markers, efforts are now being directed towards discovering biomarkers or biomarkers based network that could target herbal formulae. In this article, we review progress in biomarker discovery development, discuss current biomarker discovery in CM highlighting challenges and opportunities of pattern classification and presenting a perspective of the future integrative modeling approaches as an emerging trend in biomarker discovery.

Molecular biomarkers in clinical development: what could we learn from the clinical trial registry?

Aim: Objective of this research is to assess whether the trend of stratified medicine widely discussed in scientific literature is translated into real clinical trials registered in ClinicalTrials.gov . Methods: By semi-automatic screening of over 150,000 trials, we filtered trials with stratified biomarker to analyze their therapeutic focus, major drivers and elucidated the impact of stratified biomarker programs on trial duration and completion. Results: >5% of trials are using molecular biomarker for stratification; duration of such trials is longer. 21% of them are done in late stages and Oncology is the major focus. Conclusion: Trials with stratified biomarker in drug development has quadrupled in last decade but represents a small part of all interventional trials reflecting multiple co-developmental challenges of therapeutic compounds and companion diagnostics.

The hallmarks of osteoarthritis and the potential to develop personalised disease-modifying pharmacological therapeutics

Osteoarthritis (OA) is an age-related condition and the leading cause of pain, disability and shortening of adult working life in the UK. The incidence of OA increases with age, with 25% of the over 50s population having OA of the knee. Despite promising preclinical data covering various molecule classes, there is regrettably at present no approved disease-modifying OA drugs (DMOADs). With the advent of next generation sequencing technologies, other therapeutic areas, in particular oncology, have experienced a paradigm shift towards defining disease by its molecular composition. This paradigm shift has enabled high resolution patient stratification and supported the emergence of personalised or precision medicines. In this review we evaluate the potential for the development of OA therapeutics to undergo a similar paradigm shift given that OA is increasingly being recognised as a heterogeneous disease affecting multiple joint tissues. We highlight the evidence for the role of these tissues in OA pathology as different "hallmarks" of OA biology and review the opportunities to identify and develop targeted disease-modifying pharmacological therapeutics. Finally, we consider whether it is feasible to expect the emergence of personalised disease-modifying medicines for patients with OA and how this might be achieved.

Recent advances in modeling languages for pathway maps and computable biological networks

As our theories of systems biology grow more sophisticated, the models we use to represent them become larger and more complex. Languages necessarily have the expressivity and flexibility required to represent these models in ways that support high-resolution annotation, and provide for simulation and analysis that are sophisticated enough to allow researchers to master their data in the proper context. These languages also need to facilitate model sharing and collaboration, which is currently best done by using uniform data structures (such as graphs) and language standards. In this brief review, we discuss three of the most recent systems biology modeling languages to appear: BEL, PySB and BCML, and examine how they meet these needs.

From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine

With the significant advancement of high-throughput technologies and diagnostic techniques throughout the past decades, molecular underpinnings of many disorders have been identified. However, translation of patient-specific molecular mechanisms into tailored clinical applications remains a challenging task, which requires integration of multi-dimensional molecular and clinical data into patient-centric models. This task becomes even more challenging when dealing with complex diseases such as neurodegenerative disorders. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. We argue that integrative disease modeling will be an indispensable part of any P4 medicine research and development in the near future and that it supports the shift from descriptive to causal mechanistic diagnosis and treatment of complex diseases. For each 'P' in predictive, preventive, personalized and participatory (P4) medicine, we demonstrate how integrative disease modeling can contribute to addressing the real-world issues in development of new predictive, preventive, personalized and participatory measures. With the increasing recognition that application of integrative systems modeling is the key to all activities in P4 medicine, we envision that translational bioinformatics in general and integrative modeling in particular will continue to open up new avenues of scientific research for current challenges in P4 medicine.