Clinical proteomics-driven precision medicine for targeted cancer therapy: current overview and future perspectives

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

RUPE-phospho: Rapid Ultrasound-Assisted Peptide-Identification-Enhanced Phosphoproteomics Workflow for Microscale Samples

Global phosphoproteome profiling can provide insights into cellular signaling and disease pathogenesis. To achieve comprehensive phosphoproteomic analyses with minute quantities of material, we developed a rapid and sensitive phosphoproteomics sample preparation strategy based on ultrasound. We found that ultrasonication-assisted digestion can significantly improve peptide identification by 20% due to the generation of longer peptides that can be detected by mass spectrometry. By integrating this rapid ultrasound-assisted peptide-identification-enhanced proteomic method (RUPE) with streamlined phosphopeptide enrichment steps, we established RUPE-phospho, a fast and efficient strategy to characterize protein phosphorylation in mass-limited samples. This approach dramatically reduces the sample loss and processing time: 24 samples can be processed in 3 h; 5325 phosphosites, 4549 phosphopeptides, and 1888 phosphoproteins were quantified from 5 μg of human embryonic kidney (HEK) 293T cell lysate. In addition, 9219 phosphosites were quantified from 1-2 mg of OCT-embedded mouse brain with 120 min streamlined RUPE-phospho workflow. RUPE-phospho facilitates phosphoproteome profiling for microscale samples and will provide a powerful tool for proteomics-driven precision medicine research.

Immunotherapy: an alternative promising therapeutic approach against cancers

The immune system interacts with cancer cells in multiple intricate ways that can shield the host against hyper-proliferation but can also contribute to malignancy. Understanding the protective roles of the immune system in its interaction with cancer cells can help device new and alternate therapeutic strategies. Many immunotherapeutic methodologies, including adaptive cancer therapy, cancer peptide vaccines, monoclonal antibodies, and immune checkpoint treatment, have transformed the traditional cancer treatment landscape. However, many questions remain unaddressed. The development of personalized combination therapy and neoantigen-based cancer vaccines would be the avant-garde approach to cancer treatment. Desirable chemotherapy should be durable, safe, and target-specific. Managing both tumor (intrinsic factors) and its microenvironment (extrinsic factors) are critical for successful immunotherapy. This review describes current approaches and their advancement related to monoclonal antibody-related clinical trials, new cytokine therapy, a checkpoint inhibitor, adoptive T cell therapy, cancer vaccine, and oncolytic virus.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

Molecular and genetic markers in hepatocellular carcinoma: In silico analysis to clinical validation (current limitations and future promises)

Hepatocellular carcinoma (HCC) is the second cause of cancer-related mortality. The diagnosis of HCC depends mainly on α-fetoprotein, which is limited in its diagnostic and screening capabilities. There is an urgent need for a biomarker that detects early HCC to give the patients a chance for curative treatment. New targets of therapy could enhance survival and create future alternative curative methods. In silico analysis provides both; discovery of biomarkers, and understanding of the molecular pathways, to pave the way for treatment development. This review discusses the role of in silico analysis in the discovery of biomarkers, molecular pathways, and the role the author has contributed to this area of research. It also discusses future aspirations and current limitations. A literature review was conducted on the topic using various databases (PubMed, Science Direct, and Wiley Online Library), searching in various reviews, and editorials on the topic, with overviewing the author’s own published and unpublished work. This review discussed the steps of the validation process from in silico analysis to in vivo validation, to incorporation into clinical practice guidelines. In addition, reviewing the recent lines of research of bioinformatic studies related to HCC. In conclusion, the genetic, molecular and epigenetic markers discoveries are hot areas for HCC research. Bioinformatics will enhance our ability to accomplish this understanding in the near future. We face certain limitations that we need to overcome.

Development of a nanobody-based immunoassay for the sensitive detection of fibrinogen-like protein 1

Immune checkpoint inhibition is an important strategy in cancer therapy. Blockade of CTLA-4 and PD-1/PD-L1 is well developed in clinical practice. In the last few years, LAG-3 has received much interest as an emerging novel target in immunotherapy. It was recently reported that FGL1 is a major ligand of LAG-3, which is normally secreted by the liver but is upregulated in several human cancers. FGL1 is a crucial biomarker and target for cancer immunotherapy. As the efficacy of immunotherapy is limited to specific types of patients, the subset of patients needs to be selected appropriately to receive precise treatment according to different biomarkers. To date, there is no test to accurately assess FGL1 expression levels. Nanobodies have some outstanding features, such as high stability, solubility and affinity for diagnostic and therapeutic applications. Here, we report the development and validation of a rapid, sensitive, and cost-effective nanobody-based immunoassay for the detection of FGL1 in human serum. In this study, human FGL1 recombinant protein was expressed and purified for the first time as an immunized antigen. Then, we constructed a nanobody phage display library and screened several nanobodies that bind FGL1 with high affinity. We selected two nanobodies targeting different epitopes of FGL1, one as a capture and the other conjugated with HRP as a probe. The double nanobody-based sandwich ELISA to detect the concentration of FGL1 showed a good response relationship in the range of 15.625–2000 ng/mL, and the recoveries from the spiked sample were in the range of 78% and 100%. This assay could be used as a potential approach for evaluating FGL1 expression for patient stratification and for predicting the therapeutic efficacy of targeting the LAG3/FGL1 axis.

Multi-omics analysis of brain tissue metabolome and proteome reveals the protective effect of gross saponins of Tribulus terrestris L. fruit against ischemic stroke in rat

ETHNOPHARMACOLOGICAL RELEVANCE

Gross Saponins of Tribulus terrestris L. Fruit (GSTTF) has been reported to have a protective effect against ischemic stroke, but the related mechanism is complex and still not fully investigated.

AIM OF THE STUDY

The combination of metabolomics and proteomics approach was applied to reveal the mechanisms of GSTTF in treating ischemic stroke.

MATERIALS AND METHODS

The metabolite and protein changes in brain tissue were analyzed by the LC-MS-based untargeted metabolomics method and tandem mass tags (TMT)-based quantitative proteomics technology. The multivariate statistical analysis and protein-protein interaction (PPI) analysis were conducted to screen out the biomarkers, and their related pathway was further investigated by the joint pathway analysis.

RESULTS

A total of 110 metabolites and 359 differential proteins, which were mainly associated with complement and coagulation cascades, sphingolipid metabolism, glycerophospholipid metabolism, glutathione metabolism, and platelet activation, etc. were screened out from the rat brain tissue. The PPI network exhibited that the protein F2, Fga, Fgb, Fgg, Plg, and C3, which are greatly involved in the complement and coagulation cascades, have a relatively high connectivity degree, indicating their importance in the process of middle cerebral artery occlusion (MCAO). The GSTTF exerted a protective effect against MCAO via modulating multiple proteins on this pathway. Moreover, F2 played a key role during the protective process and worth to be further investigated due to it has been reported as one of the therapeutic targets of ischemic stroke.

CONCLUSION

The present study could improve the understanding of the potential therapeutic mechanism of GSTTF against ischemic stroke.

Current updates on precision therapy for breast cancer associated brain metastasis: Emphasis on combination therapy

Cancer therapies have undergone a tremendous progress over the past decade. Precision medicine provides a more tailored approach, making the combination of existing therapies more precise. Different types of cancers are characterized by unique biomarkers that are targeted using various genomic approaches by clinicians and companies worldwide to achieve efficient treatment with minimal side effects. Precision medicine has two broad approaches namely stratified and personalized medicine. The driver mutations could vary within a subtype while the same driver mutations could be found across different subtypes. Precision medicine has recently gained a lot of importance for breast cancer therapy. Various kinds of mutations like hotspot mutations, gene alterations, gene amplification mutations are targeted to design a more specific therapy. Apart from these known gene mutations there are various unknown mutations. Thus, tumor heterogeneity can pose a challenge to precision medicine. For breast cancer, one of the most successful models developed in case of precision medicine is the anti-HER2 therapies as HER2 was considered to have the worst prognosis being highly malignant. But now due to the advent of HER2 receptor targeted therapies, it has a good prognosis. Moreover, precision medicine helps in identifying if the drug molecules being used for the treatment of one kind of cancer can be beneficial in the treatment of another kind of cancer as well, considering the signaling pathways and machinery is similar in most of the cancers. This reduces the time for new drug development and is economically more feasible. Precision medicine will prove to be very advantageous in case of brain metastasis.

Real-time lipid patterns to classify viable and necrotic liver tumors

Real-time tissue classifiers based on molecular patterns are emerging tools for fast tumor diagnosis. Here, we used rapid evaporative ionization mass spectrometry (REIMS) and multivariate statistical analysis (principal component analysis-linear discriminant analysis) to classify tissues with subsequent comparison to gold standard histopathology. We explored whether REIMS lipid patterns can identify human liver tumors and improve the rapid characterization of their underlying metabolic features. REIMS-based classification of liver parenchyma (LP), hepatocellular carcinoma (HCC), and metastatic adenocarcinoma (MAC) reached an accuracy of 98.3%. Lipid patterns of LP were more similar to those of HCC than to those of MAC and allowed clear distinction between primary and metastatic liver tumors. HCC lipid patterns were more heterogeneous than those of MAC, which is consistent with the variation seen in the histopathological phenotype. A common ceramide pattern discriminated necrotic from viable tumor in MAC with 92.9% accuracy and in other human tumors. Targeted analysis of ceramide and related sphingolipid mass features in necrotic tissues may provide a new classification of tumor cell death based on metabolic shifts. Real-time lipid patterns may have a role in future clinical decision-making in cancer precision medicine.

Alkaloids as Anticancer Agents: A Review of Chinese Patents in Recent 5 Years

BACKGROUND

In recent years, many novel alkaloids with anticancer activity have been found in China, and some of them are promising for developing as anticancer agents.

OBJECTIVE

This review aims to provide a comprehensive overview of the information about alkaloid anticancer agents disclosed in Chinese patents, and discusses their potential to be developed as anticancer drugs used clinically.

METHODS

Anticancer alkaloids disclosed in Chinese patents in recent 5 years were presented according to their mode of actions. Their study results published on PubMed, and SciDirect databases were presented.

RESULTS

More than one hundred anticancer alkaloids were disclosed in Chinese patents and their mode of action referred to arresting cell cycle, inhibiting protein kinases, affecting DNA synthesis and p53 expression, etc. Conclusion: Many newly found alkaloids displayed potent anticancer activity both in vitro and in vivo, and some of the anticancer alkaloids acted as protein kinase inhibitors or CDK inhibitors possess the potential for developing as novel anticancer agents.

In Silico Tools and Phosphoproteomic Software Exclusives

Proteomics and phosphoproteomics have been emerging as new dimensions of omics. Phosphorylation has a profound impact on the biological functions and applications of proteins. It influences everything from intrinsic activity and extrinsic executions to cellular localization. This post-translational modification has been subjected to detailed study and has been an object of analytical curiosity with the advent of faster instrumentation. The major strength of phosphoproteomic research lies in the fact that it gives an overall picture of the workforce of the cell. Phosphoproteomics gives deeper insights into understanding the mechanism behind development and progression of a disease. This review for the first time consolidates the list of existing bioinformatics tools developed for phosphoproteomics. The gap between development of bioinformatics tools and their implementation in clinical research is highlighted. The challenge facing progress is ideally believed to be the interdisciplinary arena this field of research is associated with. For meaningful solutions and deliverables, these tools need to be implemented in clinical studies for obtaining answers to pharmacodynamic questions, saving time, costs and energy. This review hopes to invoke some thought in this direction.

Existing and novel biomarkers for precision medicine in systemic sclerosis

The discovery and validation of biomarkers resulting from technological advances in the analysis of genomic, transcriptomic, lipidomic and metabolomic pathways involved in the pathogenesis of complex human diseases have led to the development of personalized and rationally designed approaches for the clinical management of such disorders. Although some of these approaches have been applied to systemic sclerosis (SSc), an unmet need remains for validated, non-invasive biomarkers to aid in the diagnosis of SSc, as well as in the assessment of disease progression and response to therapeutic interventions. Advances in global transcriptomic technology over the past 15 years have enabled the assessment of microRNAs that circulate in the blood of patients and the analysis of the macromolecular content of a diverse group of lipid bilayer membrane-enclosed extracellular vesicles, such as exosomes and other microvesicles, which are released by all cells into the extracellular space and circulation. Such advances have provided new opportunities for the discovery of biomarkers in SSc that could potentially be used to improve the design and evaluation of clinical trials and that will undoubtedly enable the development of personalized and individualized medicine for patients with SSc.

RNA-Binding Motif Protein 6 is a Candidate Serum Biomarker for Pancreatic Cancer

PURPOSE

Early diagnosis is crucial to improve outcomes for pancreatic cancer patients (PC). The present study is designed to identify differently expressed peptides involved in PC as potential biomarkers.

EXPERIMENTAL DESIGN

The serum proteome of 22 PC patients, 12 pancreatitis patients (PP), and 45 healthy controls (HC) are analyzed using magnetic bead-based weak cation exchange (MB-WCX) and matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). Next, a supervised neural network (SNN) algorithm model is established by ClinProTools and the candidate biomarker identified using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Finally, the candidate biomarker is validated in tissue samples.

RESULTS

The SNN algorithm model discriminates PC from HC with 92.97% sensitivity and 94.55% specificity. Seventy-six differentially expressed peptides are identified, seven of which are significantly different among PC, PP, and HC (p < 0.05). Only one peak (m/z: 1466.99) tends to be upregulated in samples from HC, PP, and PC, which is identified as region of RNA-binding motif protein 6 (RBM6). In subsequent tissue analysis, it is verified that RBM6 expression is significantly higher in PC tissues than paracancerous tissue.

CONCLUSIONS AND CLINICAL RELEVANCE

The results indicate that RBM6 might serve as a candidate diagnostic biomarker for PC.

CLINICAL RELEVANCE

Methods used in this study could generate serum peptidome profiles of PC, PP, and HC, and present an approach to identify potential biomarkers for diagnosis of this malignancy.

Pattern identification of lung cancer patients based on body constitution questionnaires (BCQ) and glycoproteomics for precision medicine

BACKGROUND

The patient's pattern identification has been used for personalized medicine in traditional Korean medicine (TKM) and aims for patient-specific therapy by Korean medical doctors. The pattern identification in this trial will be diagnosed from body constitution questionnaire (BCQ) with a more objective diagnosis of it but this method still needs a more concrete scientific basis. Glycoproteins are well-known to be associated with diseases (especially cancers) so glycoproteomics can be applied to differentiate pattern identification types of lung cancer patients. Thus, for the first time proteomics approach will be applied to the pattern identification by comparing BCQ assessment in order to establish a scientific basis with clinical proteomics for precision medicine.

METHODS

This observational trial will at first diagnose the pattern identification types of lung cancer patients with BCQ assessment and then elucidate their relationships with proteomics. Blood samples will be collected before surgery along with clinical information of participants. The patients' pattern identification in TKM will be diagnosed from BCQ assessment. Then, lung cancer patients will be divided and pooled into 3 lung cancer entire (LCE) groups according to their pattern identification types (Xu, Stasis, or Gentleness). Three lung cancer representative (LCR) groups will be selected and pooled from each LCE group by selecting those with the same control factors. The 3 LCE groups and the 3 LCR groups from lung cancer patients will be independently analyzed through the glycoproteomics approach based on the patients' pattern identification. Glycoproteins from the 6 groups will be identified through proteomics approach and then categorized for analysis.

DISCUSSION

This study intends to diagnose pattern identification of patients in TKM with BCQ assessment and proteomics approach. The identification of the glycoproteins in each group will lead to the scientific foundation of personalized medicine in TKM according to patients' pattern identification for lung cancer therapy. We intend to(1) diagnose the pattern identification types of lung cancer patients with BCQ under the framework of TKM;(2) evaluate BCQ assessment with glycoproteomics approach for precision medicine.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03384680. Registered 27 December 2017. Retrospectively registered.

Proteomics and phosphoproteomics in precision medicine: applications and challenges

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

Molecular insights into cancer drug resistance from a proteomics perspective

INTRODUCTION

Resistance to chemotherapy and development of specific and effective molecular targeted therapies are major obstacles facing current cancer treatment. Comparative proteomic approaches have been employed for the discovery of putative biomarkers associated with cancer drug resistance and have yielded a number of candidate proteins, showing great promise for both novel drug target identification and personalized medicine for the treatment of drug-resistant cancer. Areas covered: Herein, we review the recent advances and challenges in proteomics studies on cancer drug resistance with an emphasis on biomarker discovery, as well as understanding the interconnectivity of proteins in disease-related signaling pathways. In addition, we highlight the critical role that post-translational modifications (PTMs) play in the mechanisms of cancer drug resistance. Expert opinion: Revealing changes in proteome profiles and the role of PTMs in drug-resistant cancer is key to deciphering the mechanisms of treatment resistance. With the development of sensitive and specific mass spectrometry (MS)-based proteomics and related technologies, it is now possible to investigate in depth potential biomarkers and the molecular mechanisms of cancer drug resistance, assisting the development of individualized therapeutic strategies for cancer patients.

Histone-lysine N-methyltransferase SETD7 is a potential serum biomarker for colorectal cancer patients

Background

There is an urgent need for the identification of new, clinically useful biomarkers of CRC to enhance diagnostic and prognostic capabilities.

Methods

We performed proteomic profiling on serum samples from paired pre- and post-operative CRC patients, colorectal polyps patients and healthy controls using an approach combining magnetic bead-based weak cation exchange and matrix-assisted laser desorption ionization-time of flight mass spectrometry. We next performed liquid chromatography-electrospray ionization-tandem mass spectrometry to identify the proteins and selected potential biomarker based on bioinformatics analysis of the TCGA and GEO dataset. We examined SETD7 expression in serum and tissue samples by ELISA and immunohistochemistry respectively and explored the biological function of SETD7 in vitro.

Findings

85 differentially expressed peptides were identified. Five peptides showing the most significant changes in abundance across paired pre- and post-operation CRC patients, colorectal polyps patients and healthy controls were identified as peptide regions of FGA, MUC5AC and SETD7. Bioinformatics analysis suggested that the up-regulation of SETD7 in CRC is relatively specific. Validation studies showed that SETD7 expression increased from healthy controls to those with colorectal polyps and finally CRC patients, and decreased after surgery. The sensitivity and specificity of SETD7 were 92.17% and 81.08%, with a high diagnostic value (AUC = 0.9477). In addition, SETD7 expression was significantly correlated with tumor stage and microsatellite instability. Knockdown of SETD7 inhibited cancer cell proliferation, induced G1/S cell cycle arrest and increased apoptosis.

Interpretation

Our data indicate that SETD7 could serve as a potential diagnostic and prognostic biomarker for CRC.

Integrative analysis of exogenous, endogenous, tumour and immune factors for precision medicine

Immunotherapy strategies targeting immune checkpoints such as the CTLA4 and CD274 (programmed cell death 1 ligand 1, PD-L1)/PDCD1 (programmed cell death 1, PD-1) T-cell coreceptor pathways are revolutionising oncology. The approval of pembrolizumab use for solid tumours with high-level microsatellite instability or mismatch repair deficiency by the US Food and Drug Administration highlights promise of precision immuno-oncology. However, despite evidence indicating influences of exogenous and endogenous factors such as diet, nutrients, alcohol, smoking, obesity, lifestyle, environmental exposures and microbiome on tumour-immune interactions, integrative analyses of those factors and immunity lag behind. Immune cell analyses in the tumour microenvironment have not adequately been integrated into large-scale studies. Addressing this gap, the transdisciplinary field of molecular pathological epidemiology (MPE) offers research frameworks to integrate tumour immunology into population health sciences, and link the exposures and germline genetics (eg, HLA genotypes) to tumour and immune characteristics. Multilevel research using bioinformatics, in vivo pathology and omics (genomics, epigenomics, transcriptomics, proteomics and metabolomics) technologies is possible with use of tissue, peripheral blood circulating cells, cell-free plasma, stool, sputum, urine and other body fluids. This immunology-MPE model can synergise with experimental immunology, microbiology and systems biology. GI neoplasms represent exemplary diseases for the immunology-MPE model, given rich microbiota and immune tissues of intestines, and the well-established carcinogenic role of intestinal inflammation. Proof-of-principle studies on colorectal cancer provided insights into immunomodulating effects of aspirin, vitamin D, inflammatory diets and omega-3 polyunsaturated fatty acids. The integrated immunology-MPE model can contribute to better understanding of environment-tumour-immune interactions, and effective immunoprevention and immunotherapy strategies for precision medicine.

Current status and future directions of cancer immunotherapy

In the past decades, our knowledge about the relationship between cancer and the immune system has increased considerably. Recent years' success of cancer immunotherapy including monoclonal antibodies (mAbs), cancer vaccines, adoptive cancer therapy and the immune checkpoint therapy has revolutionized traditional cancer treatment. However, challenges still exist in this field. Personalized combination therapies via new techniques will be the next promising strategies for the future cancer treatment direction.

Proteome Heterogeneity in Colorectal Cancer

Tumor heterogeneity is an important feature of colorectal cancer (CRC) manifested by dynamic changes in gene expression, protein expression, and availability of different tumor subtypes. Recent publications in the past 10 years have revealed proteome heterogeneity between different colorectal tumors and within the same tumor site. This paper reviews recent research works on the proteome heterogeneity in CRC, which includes the heterogeneity within a single tumor (intratumor heterogeneity), between different anatomical sites at the same organ, and between primary and metastatic sites (intertumor heterogeneity). The potential use of proteome heterogeneity in precision medicine and its implications in biomarker discovery and therapeutic outcomes will be discussed. Identification of the unique proteome landscape between and within individual tumors is imperative for understanding cancer biology and the management of CRC patients.

Four Principles Regarding an Effective Treatment of Aging

The question whether aging is a disease or not, has been asked by many professionals who are involved in the study of age-related degeneration. However, not only an agreement on this remains elusive, but also effective clinical treatments against human aging have not been forthcoming. In this Opinion paper I suggest that the complexity involved in aging is such that we need to remodel our thinking to involve a much more 'systems-oriented' approach. I explore four main principles which should be employed by those who are working on finding treatments against agerelated degeneration. First, I discuss the problems encountered in translating laboratory research into effective therapies for humans. Second, I propose that a 'systems-thinking' method needs to be more extensively employed, instead of relying exclusively on the current reductionist one. Third, it is submitted that we must learn from the history of life-extension research, and not blindly follow contemporary paradigms, which may lead us into yet more 'dead ends' with regards to therapies. Finally, I suggest that, we may need to employ certain universal notions and use these in order to gain insights into the mechanics of a possible therapy against age-related degeneration. Examples may be the principle of hormesis, those of degeneracy, exaptation, and others from cybernetic or systems science domains. By using this four-pronged approach we liberate our thinking from the shackles of existing common mistakes and fallacies, and we open the way for a fresh approach that may lead us towards entirely new paradigms for providing clinically effective therapies against agerelated degeneration.

Clinical multi-omics strategies for the effective cancer management

Cancer is a global health issue as a multi-factorial complex disease, and early detection and novel therapeutic strategies are required for more effective cancer management. With the development of systemic analytical -omics strategies, the therapeutic approach and study of the molecular mechanisms of carcinogenesis and cancer progression have moved from hypothesis-driven targeted investigations to data-driven untargeted investigations focusing on the integrated diagnosis, treatment, and prevention of cancer in individual patients. Predictive, preventive, and personalized medicine (PPPM) is a promising new approach to reduce the burden of cancer and facilitate more accurate prognosis, diagnosis, as well as effective treatment. Here we review the fundamentals of, and new developments in, -omics technologies, together with the key role of a variety of practical -omics strategies in PPPM for cancer treatment and diagnosis.

BIOLOGICAL SIGNIFICANCE

In this review, a comprehensive and critical overview of the systematic strategy for predictive, preventive, and personalized medicine (PPPM) for cancer disease was described in a view of cancer prognostic prediction, diagnostics, and prevention as well as cancer therapy and drug responses. We have discussed multi-dimensional data obtained from various resources and integration of multisciplinary -omics strategies with computational method which could contribute the more effective PPPM for cancer. This review has provided the novel insights of the current applications of each and combined -omics technologies, which showed their powerful potential for the establishment of PPPM for cancer.

Energy sensing pathways: Bridging type 2 diabetes and colorectal cancer?

The recently rapid increase of obesity and type 2 diabetes mellitus has caused great burden to our society. A positive association between type 2 diabetes and risk of colorectal cancer has been reported by increasing epidemiological studies. The molecular mechanism of this connection remains elusive. However, type 2 diabetes may result in abnormal carbohydrate and lipid metabolism, high levels of circulating insulin, insulin growth factor-1, and adipocytokines, as well as chronic inflammation. All these factors could lead to the alteration of energy sensing pathways such as the AMP activated kinase (PRKA), mechanistic (mammalian) target of rapamycin (mTOR), SIRT1, and autophagy signaling pathways. The resulted impaired SIRT1 and autophagy signaling pathway could increase the risk of gene mutation and cancer genesis by decreasing genetic stability and DNA mismatch repair. The dysregulated mTOR and PRKA pathway could remodel cell metabolism during the growth and metastasis of cancer in order for the cancer cell to survive the unfavorable microenvironment such as hypoxia and low blood supply. Moreover, these pathways may be coupling metabolic and epigenetic alterations that are central to oncogenic transformation. Further researches including molecular pathologic epidemiologic studies are warranted to better address the precise links between these two important diseases.

Molecular pathological epidemiology: new developing frontiers of big data science to study etiologies and pathogenesis

Molecular pathological epidemiology (MPE) is an integrative field that utilizes molecular pathology to incorporate interpersonal heterogeneity of a disease process into epidemiology. In each individual, the development and progression of a disease are determined by a unique combination of exogenous and endogenous factors, resulting in different molecular and pathological subtypes of the disease. Based on "the unique disease principle," the primary aim of MPE is to uncover an interactive relationship between a specific environmental exposure and disease subtypes in determining disease incidence and mortality. This MPE approach can provide etiologic and pathogenic insights, potentially contributing to precision medicine for personalized prevention and treatment. Although breast, prostate, lung, and colorectal cancers have been among the most commonly studied diseases, the MPE approach can be used to study any disease. In addition to molecular features, host immune status and microbiome profile likely affect a disease process, and thus serve as informative biomarkers. As such, further integration of several disciplines into MPE has been achieved (e.g., pharmaco-MPE, immuno-MPE, and microbial MPE), to provide novel insights into underlying etiologic mechanisms. With the advent of high-throughput sequencing technologies, available genomic and epigenomic data have expanded dramatically. The MPE approach can also provide a specific risk estimate for each disease subgroup, thereby enhancing the impact of genome-wide association studies on public health. In this article, we present recent progress of MPE, and discuss the importance of accounting for the disease heterogeneity in the era of big-data health science and precision medicine.

Proteomic Biomarker Identification in Cerebrospinal Fluid for Leptomeningeal Metastases with Neurological Complications

Leptomeningeal metastases (LM) from solid tumours, lymphoma and leukaemia are characterized by multifocal neurological deficits with a high mortality rate. Early diagnosis and initiation of treatment are essential to kerb neurological deterioration. However, this is not always possible as 25% of cerebrospinal fluid samples produce false-negative results at first cytological examination. The identification of biomarkers that allow stratification of individuals according to risk for developing LM would be a major benefit. Proteomic-based approaches are now in increasing use for this purpose, and these are reviewed in this chapter with a focus on cerebrospinal fluid (CSF) analyses. The construction of a CSF proteome disease database would also facilitate analysis of other neurological disorders.

Evaluation of salivary oxidative parameters in overweight and obese young adults

Background

Obesity is characterized by a deposition of abnormal or excessive fat in adipose tissue, and is linked with a risk of damage to several metabolic and pathological processes associated with oxidative stress. To date, salivary oxidative biomarkers have been minimally explored in obese individuals. Thus, the aim of this study was to assess the concentrations of salivary oxidative biomarkers (ferric-reducing antioxidant power, uric acid, sulfhydryl groups) and lipid peroxidation in obese and overweight young subjects.

Materials and methods

Levels of lipid peroxidation, ferric-reducing antioxidant power, uric acid, and SH groups were determined in the saliva and serum of 149 young adults, including 54 normal weight, 27 overweight, and 68 obese individuals. Anthropometric measurements were also evaluated.

Results

Salivary levels of ferric-reducing antioxidant power, sulfhydryl groups, and lipid peroxidation, as well as serum levels of ferric-reducing antioxidant power, uric acid, and lipid peroxidation were higher in obese patients when compared with individuals with normal weight. There were correlations between salivary and serum ferric-reducing antioxidant power and salivary and serum uric acid in the obese and normal-weight groups.

Conclusions

Our results indicate that the increase in salivary levels of ferric-reducing antioxidant power, sulfhydryl groups, and lipid peroxidation, and serum levels of ferric-reducing antioxidant power, uric acid, and lipid peroxidation could be related to the regulation of various processes in the adipose tissue. These findings may hold promise in identifying new oxidative markers to assist in diagnosing and monitoring overweight and obese patients.

Integrative analysis of the colorectal cancer proteome: potential clinical impact

INTRODUCTION

Colorectal cancer (CRC) is one of the common types of cancer that affects a significant proportion of the population and is a major contributor to cancer related mortality. The relatively poor survival rate of CRC could be improved through the identification of clinically useful biomarkers. Areas covered: This review highlights the need for biomarkers and discusses recent proteomics discoveries in the aspects of CRC clinical practice including diagnosis, prognosis, therapy, screening and molecular pathological epidemiology (MPE). Studies have been evaluated in relation to biomarker target, methodology, sample selection, limitations, and potential impact. Finally, the progress in proteomic approaches is briefly discussed and the main difficulties facing the translation of proteomics biomarkers into the clinical practice are highlighted. Expert commentary: The establishment of specific guidelines, best practice recommendations and the improvement in proteomic strategies will significantly improve the prospects for developing clinically useful biomarkers.