Biomarkers for early detection of high risk cancers: from gliomas to nasopharyngeal carcinoma

Synthetic lipoprotein as nano-material vehicle in the targeted drug delivery

High-density lipoprotein (HDL) and low-density lipoprotein (LDL), as human endogenous lipoprotein particles, have low toxicity, high selectivity, and good safety. They can avoid the recognition and clearance of human reticuloendothelial system. These synthetic lipoproteins (sLPs) have been attracted extensive attention as the nanovectors for tumor-targeted drug and gene delivery. Herein, recent advances in the field of anticancer based on these two lipid proteins and recombinant lipoproteins (rLPs) as target delivery vectors were analyzed and discussed.

Early response index: a statistic to discover potential early stage disease biomarkers

Background

Identifying disease correlated features early before large number of molecules are impacted by disease progression with significant abundance change is very advantageous to biologists for developing early disease diagnosis biomarkers. Disease correlated features have relatively low level of abundance change at early stages. Finding them using existing bioinformatic tools in high throughput data is a challenging task since the technology suffers from limited dynamic range and significant noise. Most existing biomarker discovery algorithms can only detect molecules with high abundance changes, frequently missing early disease diagnostic markers.

Results

We present a new statistic called early response index (ERI) to prioritize disease correlated molecules as potential early biomarkers. Instead of classification accuracy, ERI measures the average classification accuracy improvement attainable by a feature when it is united with other counterparts for classification. ERI is more sensitive to abundance changes than other ranking statistics. We have shown that ERI significantly outperforms SAM and Localfdr in detecting early responding molecules in a proteomics study of a mouse model of multiple sclerosis. Importantly, ERI was able to detect many disease relevant proteins before those algorithms detect them at a later time point.

Conclusions

ERI method is more sensitive for significant feature detection during early stage of disease development. It potentially has a higher specificity for biomarker discovery, and can be used to identify critical time frame for disease intervention.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1712-y) contains supplementary material, which is available to authorized users.

Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers

Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high-throughput data (or big data). Detailed comparisons of various types of biomarkers as well as their applications are also discussed.

Immune surveillance of nasopharyngeal carcinoma (NpC)

In the U.S., nasopharyngeal carcinoma (NpC) kills >7,600 each year. Deaths are predominantly among adult men, and in most cases, early detection and treatment can save lives. Despite the annual spending of approximately 3.2 billion dollars on head and neck cancer research, NpC remains a neglected disease since its fatality rates are among the lowest nation wide. The relative survival rates from NpC have not improved in the U.S. in the last 20 years. Infection with Epstein Barr Virus (EBV) is an important co-factor in the etiology of NpC. In other regions of the word (e.g., South-East Asia, Latin America), EBV infection and NpCrelated prevalence and mortality are substantially higher and more alarming. Epidemiological data indicate high prevalence of EBV infection and increased risk for NpC among Central and South American and Asian immigrants in the U.S., and also predict a sharp increase in NpC incidence in the next decade. To face this emerging threat, it is important to develop and validate novel modes of detection and intervention for NpC. To this end, we characterized the proteomic signature of NpC, and of the tumor infiltrating lymphocytes of the CD8+, activated (CD38+, mTOR+) and regulatory immune cell (FoxP3+) phenotype. Paraffinized biopsies were processed, and tissue microarrays constructed and tested by immunohistochemistry and triimmunohistofluorescence for a battery of signaling markers, including AKT and PI3K, in conjunction with EBV status and ANKRD11, an NpC susceptibility biomarker. Microphotographs, analyzed and quantified by confocal microscopy and fractal analysis, suggest new avenues for immunotherapies of NpC.

Proteomic signature of Temporomandibular Joint Disorders (TMD): Toward diagnostically predictive biomarkers

The temporomandibular joint (TMJ) articulates the mandible with the maxilla. Temporomandibular joint disorders (TMD) are dysfunctions of this joint, which range from acute to chronic inflammation, trauma and dislocations, developmental anomalies and neoplasia. TMD manifest as signs and symptoms that involve the surrounding muscles, ligaments, bones, synovial capsule, connective tissue, teeth and innervations proximal and distal to this joint. TMD induce proximal and distal, chronic and acute, dull or intense pain and discomfort, muscle spasm, clicking/popping sounds upon opening and closing of the mouth, and chewing or speaking difficulties. The trigeminal cranial nerve V, and its branches provide the primary sensory innervation to the TMJ. Our clinical work suggests that the auriculotemporal (AT) nerve, a branch of the mandibular nerve, the largest of the three divisions of the trigeminal nerve, plays a critical role in TMD sequelae. The AT nerve provides the somatosensory fibers that supply the joint, the middle ear, and the temporal region. By projecting fibers toward the otic ganglion, the AT nerve establishes an important bridge to the sympathetic system. As it courses posteriorly to the condylar head of the TMJ, compression, injury or irritation of the AT nerve can lead to significant neurologic and neuro-muscular disorders, including Tourette's syndrome,Torticolli, gait or balance disorders and Parkinson's disease. Here, we propose that a proteomic signature of TMD can be obtained by assessing certain biomarkers in local (e.g., synovial fluid at the joint) and distal body fluids (e.g., saliva, cerebrospinal fluid), which can aid TMD diagnosis and prognosis.

The role of the microenvironment in tumor immune surveillance

The evidence appears compelling that the microenvironment, and associated biological cellular and molecular factors, may contribute to the progression of a variety of tumors. The effects of the microenvironment may directly influence the plasticity of T cell lineages, which was recently discussed (O'Shea & Paul, 2010 [4]). To review the putative role of the microenvironment in modulating the commitment of tumor immune surveillance, we use the model of oral premalignant lesions.

Relationship between human oral lichen planus and oral squamous cell carcinoma at a genomic level: a datamining study

The leader gene approach is a data mining method based on the systematic search for genes involved in a specific process and their ranking according to the number of interconnections with the other genes identified. The genes with the strongest interconnections are termed leader genes, since they may be supposed to play an important role in the process. The potential of malignant progression of OLP to oral squamous cell carcinoma (OSCC) is still not completely clear. In this study, the leader gene approach is applied to investigate the association between OLP and OSCC at a molecular level. Results were integrated with those obtained in an experimental analysis (see paper 1 of this series). Genes involved in OLP and OSCC were identified by systematic queries to dedicated databases. Interconnections among identified genes were calculated and given a confidence value using STRING database. Leader genes were identified by clustering genes according to their interconnections. This theoretical analysis shows that OLP and OSCC share two leader genes: TP53 and CDKN1A, involved in the PI3K signalling events mediated by AKT pathway. This finding and those obtained in the experimental analysis suggest the possible involvement of some key genes/proteins LCK, PIK3CA, BIRC5, TP53 and CDKN1A in the malignant progression from OLP to OSCC. Moreover, these findings support the role of some molecular pathways, namely IL2 signalling events mediated by PI3K, PI3K signalling events mediated by AKT, and, possibly, Aurora A signalling in the association between OLP and OSCC.

LCK, survivin and PI-3K in the molecular biomarker profiling of oral lichen planus and oral squamous cell carcinoma

T cell signaling is critical in oral lichen planus (OLP) based on the pathogenesis of this chronic inflammatory autoimmune mucocutaneous lesion. Lck plays a key role in T cell signaling; ultimately this signaling affects other targets such as PI-3K. Excessive activity in PI-3K inhibits apoptosis and promotes uncontrolled cell growth. Molecular biomarker profiling in OLP, Chronic Interface Mucosities (CIM), Epithelial Dysplasia (EpD) and Oral Squamous Cell Carcinoma (SCCA) with application of the principle of biomarker voting may represent a new frontier in the diagnosis, assessment and the arguable debate of OLP transformation to cancer. The presence of Lck, PI-3K and Survivin, a cancer specific anti-apoptotic protein was assessed, using immunohistochemistry and tissue micro-array on patient samples, in OLP, SCCA, CIM and EpD. Lck expression was very high in 78.6 % of OLP patients compared to 3.7% in SCCA; PI-3K was high in 63% of SCCA, 100% of EpD, and 35.7% OLP cases. Survivin was high in 64.3% of OLP cases, 96.3% of SCCA, and 100% of EpD. CIM cases may be slightly different molecularly to OLP. Taken together, our data suggest that biomarker protein voting can be effectively used to isolate high-risk OLP cases. Specifically, we show data with four remarkable cases demonstrating that molecular factors are predictive of histopathology. We conclude that it is safer to treat OLP as premalignant lesions, to adopt aggressive treatment measure in histopathologic described well and moderately differentiated SCCA, and to monitor progress of these diseases molecularly using individualized auto-proteomic approach. The use of Lck inhibitors in OLP management needs to be investigated in the future.