Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review

Machine Learning Assisted MALDI Mass Spectrometry for Rapid Antimicrobial Resistance Prediction in Clinicals

Antimicrobial susceptibility testing (AST) plays a critical role in assessing the resistance of individual microbial isolates and determining appropriate antimicrobial therapeutics in a timely manner. However, conventional AST normally takes up to 72 h for obtaining the results. In healthcare facilities, the global distribution of vancomycin-resistant Enterococcus fecium (VRE) infections underscores the importance of rapidly determining VRE isolates. Here, we developed an integrated antimicrobial resistance (AMR) screening strategy by combining matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) with machine learning to rapidly predict VRE from clinical samples. Over 400 VRE and vancomycin-susceptible E. faecium (VSE) isolates were analyzed using MALDI-MS at different culture times, and a comprehensive dataset comprising 2388 mass spectra was generated. Algorithms including the support vector machine (SVM), SVM with L1-norm, logistic regression, and multilayer perceptron (MLP) were utilized to train the classification model. Validation on a panel of clinical samples (external patients) resulted in a prediction accuracy of 78.07%, 80.26%, 78.95%, and 80.54% for each algorithm, respectively, all with an AUROC above 0.80. Furthermore, a total of 33 mass regions were recognized as influential features and elucidated, contributing to the differences between VRE and VSE through the Shapley value and accuracy, while tandem mass spectrometry was employed to identify the specific peaks among them. Certain ribosomal proteins, such as A0A133N352 and R2Q455, were tentatively identified. Overall, the integration of machine learning with MALDI-MS has enabled the rapid determination of bacterial antibiotic resistance, greatly expediting the usage of appropriate antibiotics.

Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach

Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.

An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases

Multiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Multivariable model of postoperative delirium in cardiac surgery patients: proteomic and demographic contributions

Background

Delirium following cardiac surgery is common, morbid, and costly, but may be prevented with risk stratification and targeted intervention. Preoperative protein signatures may identify patients at increased risk for worse postoperative outcomes, including delirium. In this study, we aimed to identify plasma protein biomarkers and develop a predictive model for postoperative delirium in older patients undergoing cardiac surgery, while also uncovering possible pathophysiological mechanisms.

Methods

SOMAscan analysis of 1,305 proteins in the plasma from 57 older adults undergoing cardiac surgery requiring cardiopulmonary bypass was conducted to define delirium-specific protein signatures at baseline (PREOP) and postoperative day 2 (POD2). Selected proteins were validated in 115 patients using the ELLA multiplex immunoassay platform. Proteins were combined with clinical and demographic variables to build multivariable models that estimate the risk of postoperative delirium and bring light to the underlying pathophysiology.

Results

A total of 115 and 85 proteins from SOMAscan analyses were found altered in delirious patients at PREOP and POD2, respectively (p<0.05). Using four criteria including associations with surgery, delirium, and biological plausibility, 12 biomarker candidates (Tukey's fold change (|tFC|)>1.4, Benjamini-Hochberg (BH)-p<0.01) were selected for ELLA multiplex validation. Eight proteins were significantly altered at PREOP, and seven proteins at POD2 (p<0.05), in patients who developed postoperative delirium compared to non-delirious patients. Statistical analyses of model fit resulted in the selection of a combination of age, sex, and three proteins (angiopoietin-2 (ANGPT2); C-C motif chemokine 5 (CCL5); and metalloproteinase inhibitor 1 (TIMP1); AUC=0.829) as the best performing predictive model for delirium at PREOP. The delirium-associated proteins identified as biomarker candidates are involved with inflammation, glial dysfunction, vascularization, and hemostasis, highlighting the multifactorial pathophysiology of delirium.

Conclusion

Our study proposes a model of postoperative delirium that includes a combination of older age, female sex, and altered levels of three proteins. Our results support the identification of patients at higher risk of developing postoperative delirium after cardiac surgery and provide insights on the underlying pathophysiology. ClinicalTrials.gov ( NCT02546765 ).

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Complex interactions of cellular players in chronic Graft-versus-Host Disease

Chronic Graft-versus-Host Disease is a life-threatening inflammatory condition that affects many patients after allogeneic hematopoietic stem cell transplantation. Although we have made substantial progress in understanding disease pathogenesis and the role of specific immune cell subsets, treatment options are still limited. To date, we lack a global understanding of the interplay between the different cellular players involved, in the affected tissues and at different stages of disease development and progression. In this review we summarize our current knowledge on pathogenic and protective mechanisms elicited by the major involved immune subsets, being T cells, B cells, NK cells and antigen presenting cells, as well as the microbiome, with a special focus on intercellular communication of these cell types via extracellular vesicles as up-and-coming fields in chronic Graft-versus-Host Disease research. Lastly, we discuss the importance of understanding systemic and local aberrant cell communication during disease for defining better biomarkers and therapeutic targets, eventually enabling the design of personalized treatment schemes.

Multi-omics Integration and Epilepsy: Towards a Better Understanding of Biological Mechanisms

The epilepsies are a group of complex neurological disorders characterised by recurrent seizures. Approximately 30% of patients fail to respond to anti-seizure medications, despite the recent introduction of many new drugs. The molecular processes underlying epilepsy development are not well understood and this knowledge gap impedes efforts to identify effective targets and develop novel therapies against epilepsy. Omics studies allow a comprehensive characterisation of a class of molecules. Omics-based biomarkers have led to clinically validated diagnostic and prognostic tests for personalised oncology, and more recently for non-cancer diseases. We believe that, in epilepsy, the full potential of multi-omics research is yet to be realised and we envisage that this review will serve as a guide to researchers planning to undertake omics-based mechanistic studies.

Pathology-supported genetic testing for the application of breast cancer pharmacodiagnostics: family counselling, lifestyle adjustments and change of medication

BACKGROUND

Pathology-supported genetic testing (PSGT) enables transitioning of risk stratification from the study population to the individual.

RESEARCH DESIGN AND METHODS

We provide an overview of the translational research performed in postmenopausal breast cancer patients at increased risk of osteoporosis due to aromatase inhibitor therapy, as the indication for referral. Both tumour histopathology and blood biochemistry levels were assessed to identify actionable disease pathways using whole exome sequencing (WES).

RESULTS

The causes and consequences of inadequate vitamin D levels as a modifiable risk factor for bone loss were highlighted in 116 patients with hormone receptor-positive breast cancer. Comparison of lifestyle factors and WES data between cases with vitamin D levels at extreme upper and lower ranges identified obesity as a major discriminating factor, with the lowest levels recorded during winter. Functional polymorphisms in the vitamin D receptor gene contributed independently to therapy-related osteoporosis risk. In a patient with invasive lobular carcinoma, genetic counselling facilitated investigation of the potential modifying effect of a rare CDH1 variant co-occurring withBRCA1 c.66dup (p.Glu23ArgfsTer18).

CONCLUSION

Validation of PSGT as a three-pronged pharmacodiagnostics tool for generation of adaptive reports and data reinterpretation during follow-up represents a new paradigm in personalised medicine, exposing significant limitations to overcome.

DNA Methylation of Window of Implantation Genes in Cervical Secretions Predicts Ongoing Pregnancy in Infertility Treatment

Window of implantation (WOI) genes have been comprehensively identified at the single cell level. DNA methylation changes in cervical secretions are associated with in vitro fertilization embryo transfer (IVF-ET) outcomes. Using a machine learning (ML) approach, we aimed to determine which methylation changes in WOI genes from cervical secretions best predict ongoing pregnancy during embryo transfer. A total of 2708 promoter probes were extracted from mid-secretory phase cervical secretion methylomic profiles for 158 WOI genes, and 152 differentially methylated probes (DMPs) were selected. Fifteen DMPs in 14 genes (BMP2, CTSA, DEFB1, GRN, MTF1, SERPINE1, SERPINE2, SFRP1, STAT3, TAGLN2, TCF4, THBS1, ZBTB20, ZNF292) were identified as the most relevant to ongoing pregnancy status. These 15 DMPs yielded accuracy rates of 83.53%, 85.26%, 85.78%, and 76.44%, and areas under the receiver operating characteristic curves (AUCs) of 0.90, 0.91, 0.89, and 0.86 for prediction by random forest (RF), naïve Bayes (NB), support vector machine (SVM), and k-nearest neighbors (KNN), respectively. SERPINE1, SERPINE2, and TAGLN2 maintained their methylation difference trends in an independent set of cervical secretion samples, resulting in accuracy rates of 71.46%, 80.06%, 80.72%, and 80.68%, and AUCs of 0.79, 0.84, 0.83, and 0.82 for prediction by RF, NB, SVM, and KNN, respectively. Our findings demonstrate that methylation changes in WOI genes detected noninvasively from cervical secretions are potential markers for predicting IVF-ET outcomes. Further studies of cervical secretion of DNA methylation markers may provide a novel approach for precision embryo transfer.

Biomarker-driven development of new therapies for autoimmune diseases: current status and future promises

INTRODUCTION

Auto-immune diseases are complex and heterogeneous. Various types of biomarkers can be used to support precision medicine approaches to autoimmune diseases, ensuring that the right patient receives the most appropriate therapy to improve treatment outcomes.

AREAS COVERED

We review the recent progress made in modeling several autoimmune diseases such as Systemic Lupus Erythematosus, primary Sjogren Syndrome, and Rheumatoid Arthritis following extensive molecular profiling of large cohorts of patients. From this knowledge, BMKs are being identified which support diagnostic as well as patient stratification and prediction of response to treatment. The identification of biomarkers should be initiated early in drug development and properly validated during subsequent clinical trials. To ensure the robustness and reproducibility of biomarkers, the PERMIT Consortium recently established recommendations highlighting the importance of relevant study design, sample size, and appropriate validation of analytical methods.

EXPERT OPINION

The integration by AI-powered analytics of massive data provided by multi-omics technologies, high-resolution medical imaging and sensors borne by patients will eventually allow the identification of clinically relevant BMKs, likely in the form of combinatorial predictive algorithms, to support future drug development for autoimmune diseases.

Cervical Secretion Methylation Is Associated with the Pregnancy Outcome of Frozen-Thawed Embryo Transfer

The causes of implantation failure remain a black box in reproductive medicine. The exact mechanism behind the regulation of endometrial receptivity is still unknown. Epigenetic modifications influence gene expression patterns and may alter the receptivity of human endometrium. Cervical secretions contain endometrial genetic material, which can be used as an indicator of the endometrial condition. This study evaluates the association between the cervical secretion gene methylation profile and pregnancy outcome in a frozen-thawed embryonic transfer (FET) cycle. Cervical secretions were collected from women who entered the FET cycle with a blastocyst transfer (36 pregnant and 36 non-pregnant women). The DNA methylation profiles of six candidate genes selected from the literature review were measured by quantitative methylation-specific PCR (qMSP). Bioinformatic analysis of six selected candidate genes showed significant differences in DNA methylation between receptive and pre-receptive endometrium. All candidate genes showed different degrees of correlation with the pregnancy outcomes in the logistic regression model. A machine learning approach showed that the combination of candidate genes' DNA methylation profiles could differentiate pregnant from non-pregnant samples with an accuracy as high as 86.67% and an AUC of 0.81. This study demonstrated the association between cervical secretion methylation profiles and pregnancy outcomes in an FET cycle and provides a basis for potential clinical application as a non-invasive method for implantation prediction.

Recommendations for robust and reproducible preclinical research in personalised medicine

BACKGROUND

Personalised medicine is a medical model that aims to provide tailor-made prevention and treatment strategies for defined groups of individuals. The concept brings new challenges to the translational step, both in clinical relevance and validity of models. We have developed a set of recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine.

METHODS

These recommendations have been developed following four main steps: (1) a scoping review of the literature with a gap analysis, (2) working sessions with a wide range of experts in the field, (3) a consensus workshop, and (4) preparation of the final set of recommendations.

RESULTS

Despite the progress in developing innovative and complex preclinical model systems, to date there are fundamental deficits in translational methods that prevent the further development of personalised medicine. The literature review highlighted five main gaps, relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. We identified five points of focus for the recommendations, based on the consensus reached during the consultation meetings: (1) clinically relevant translational research, (2) robust model development, (3) transparency and education, (4) revised regulation, and (5) interaction with clinical research and patient engagement. Here, we present a set of 15 recommendations aimed at improving the robustness of preclinical methods in translational research for personalised medicine.

CONCLUSIONS

Appropriate preclinical models should be an integral contributor to interventional clinical trial success rates, and predictive translational models are a fundamental requirement to realise the dream of personalised medicine. The implementation of these guidelines is ambitious, and it is only through the active involvement of all relevant stakeholders in this field that we will be able to make an impact and effectuate a change which will facilitate improved translation of personalised medicine in the future.

Variation of DNA methylation on the IRX1/2 genes is responsible for the neural differentiation propensity in human induced pluripotent stem cells

Introduction

Human induced pluripotent stem cells (hiPSCs) are useful tools for reproducing neural development in vitro. However, each hiPSC line has a different ability to differentiate into specific lineages, known as differentiation propensity, resulting in reduced reproducibility and increased time and funding requirements for research. To overcome this issue, we searched for predictive signatures of neural differentiation propensity of hiPSCs focusing on DNA methylation, which is the main modulator of cellular properties.

Methods

We obtained 32 hiPSC lines and their comprehensive DNA methylation data using the Infinium MethylationEPIC BeadChip. To assess the neural differentiation efficiency of these hiPSCs, we measured the percentage of neural stem cells on day 7 of induction. Using the DNA methylation data of undifferentiated hiPSCs and their measured differentiation efficiency into neural stem cells as the set of data, and HSIC Lasso, a machine learning-based nonlinear feature selection method, we attempted to identify neural differentiation-associated differentially methylated sites.

Results

Epigenome-wide unsupervised clustering cannot distinguish hiPSCs with varying differentiation efficiencies. In contrast, HSIC Lasso identified 62 CpG sites that could explain the neural differentiation efficiency of hiPSCs. Features selected by HSIC Lasso were particularly enriched within 3 Mbp of chromosome 5, harboring IRX1, IRX2, and C5orf38 genes. Within this region, DNA methylation rates were correlated with neural differentiation efficiency and were negatively correlated with gene expression of the IRX1/2 genes, particularly in female hiPSCs. In addition, forced expression of the IRX1/2 impaired the neural differentiation ability of hiPSCs in both sexes.

Conclusion

We for the first time showed that the DNA methylation state of the IRX1/2 genes of hiPSCs is a predictive biomarker of their potential for neural differentiation. The predictive markers for neural differentiation efficiency identified in this study may be useful for the selection of suitable undifferentiated hiPSCs prior to differentiation induction.

Feasibility of Precision Medicine in Hypertension Management-Scope and Technological Aspects

Personalized management of diseases by considering relevant patient features enables optimal treatment, instead of management according to an average patient. Precision management of hypertension is important, because both susceptibility to complications and response to treatment vary between individuals. While the use of genomic and proteomic personal features for widespread precision hypertension management is not practical, other features, such as age, ethnicity, and cardiovascular diseases, have been utilized in guidelines for hypertension management. In precision medicine, more blood-pressure-related clinical and physiological characteristics in the patient's profile can be utilized for the determination of the threshold of hypertension and optimal treatment. Several non-invasive and simple-to-use techniques for the measurement of hypertension-related physiological features are suggested for use in precision management of hypertension. In order to provide precise management of hypertension, accurate measurement of blood pressure is required, but the available non-invasive blood pressure measurement techniques, auscultatory sphygmomanometry and oscillometry, have inherent significant inaccuracy-either functional or technological-limiting the precision of personalized management of hypertension. A novel photoplethysmography-based technique for the measurement of systolic blood pressure that was recently found to be more accurate than the two available techniques can be utilized for more precise and personalized hypertension management.

Tackling the translational challenges of multi-omics research in the realm of European personalised medicine: A workshop report

Personalised medicine (PM) presents a great opportunity to improve the future of individualised healthcare. Recent advances in -omics technologies have led to unprecedented efforts characterising the biology and molecular mechanisms that underlie the development and progression of a wide array of complex human diseases, supporting further development of PM. This article reflects the outcome of the 2021 EATRIS-Plus Multi-omics Stakeholder Group workshop organised to 1) outline a global overview of common promises and challenges that key European stakeholders are facing in the field of multi-omics research, 2) assess the potential of new technologies, such as artificial intelligence (AI), and 3) establish an initial dialogue between key initiatives in this space. Our focus is on the alignment of agendas of European initiatives in multi-omics research and the centrality of patients in designing solutions that have the potential to advance PM in long-term healthcare strategies.

Perspectives and challenges in patient stratification in Alzheimer’s disease

Background

Patient stratification is the division of a patient population into distinct subgroups based on the presence or absence of particular disease characteristics. As patient stratification can be used to account for the underlying pathology of a disease, it can help physicians to tailor therapeutic interventions to individuals and optimize their care management and treatment regime. Alzheimer’s disease, the most common form of dementia, is a heterogeneous disease and its management benefits from patient stratification in clinical trials, and the development of personalized care and treatment strategies for people living with the disease.

Main body

In this review, we discuss the importance of the stratification of people living with Alzheimer’s disease, the challenges associated with early diagnosis and patient stratification, and the evolution of patient stratification once disease-modifying therapies become widely available.

Conclusion

Patient stratification plays an important role in drug development in clinical trials and may play an even larger role in clinical practice. A timely diagnosis and stratification of people living with Alzheimer’s disease is paramount in determining people who are at risk of progressing from mild cognitive impairment to Alzheimer’s dementia. There are key issues associated with stratifying patients which include the heterogeneity and complex neurobiology behind Alzheimer’s disease, our inadequately prepared healthcare systems, and the cultural perceptions of Alzheimer’s disease. Stratifying people living with Alzheimer’s disease may be the key in establishing precision and personalized medicine in the field, optimizing disease prevention and pharmaceutical treatment to slow or stop cognitive decline, while minimizing adverse effects.

Identification of Critical Biomarkers and Immune Infiltration in Rheumatoid Arthritis Based on WGCNA and LASSO Algorithm

Rheumatoid arthritis(RA) is the most common inflammatory arthritis, and a significant cause of morbidity and mortality. RA patients' synovial inflammation contains a variety of genes and signalling pathways that are poorly understood. It was the goal of this research to discover the major biomarkers related to the course of RA and how they connect to immune cell infiltration. The Gene Expression Omnibus was used to download gene microarray data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) regression were used to identify hub markers for RA. Single-sample GSEA was used to examine the infiltration levels of 28 immune cells and their connection to hub gene markers. The hub genes' expression in RA-HFLS and HFLS cells was verified by RT-PCR. The CCK-8 assay was applied to determine the roles of hub genes in RA. In this study, we identified 21 differentially expressed genes (DEGs) in RA. WGCNA yielded two co-expression modules, one of which exhibited the strongest connection with RA. Using a combination of differential genes, a total of 6 intersecting genes was discovered. Six hub genes were identified as possible biomarkers for RA after a lasso analysis was performed on the data. Three hub genes, CKS2, CSTA, and LY96, were found to have high diagnostic value using ROC curve analysis. They were shown to be closely related to the concentrations of several immune cells. RT-PCR confirmed that the expressions of CKS2, CSTA and LY96 were distinctly upregulated in RA-HFLS cells compared with HFLS cells. More importantly, knockdown of CKS2 suppressed the proliferation of RA-HFLS cells. Overall, to help diagnose and treat RA, it's expected that CKS2, CSTA, and LY96 will be available, and the aforementioned infiltration of immune cells may have a significant impact on the onset and progression of the disease.

Evaluating Translational Methods for Personalized Medicine—A Scoping Review

The introduction of personalized medicine, through the increasing multi-omics characterization of disease, brings new challenges to disease modeling. The scope of this review was a broad evaluation of the relevance, validity, and predictive value of the current preclinical methodologies applied in stratified medicine approaches. Two case models were chosen: oncology and brain disorders. We conducted a scoping review, following the Joanna Briggs Institute guidelines, and searched PubMed, EMBASE, and relevant databases for reports describing preclinical models applied in personalized medicine approaches. A total of 1292 and 1516 records were identified from the oncology and brain disorders search, respectively. Quantitative and qualitative synthesis was performed on a final total of 63 oncology and 94 brain disorder studies. The complexity of personalized approaches highlights the need for more sophisticated biological systems to assess the integrated mechanisms of response. Despite the progress in developing innovative and complex preclinical model systems, the currently available methods need to be further developed and validated before their potential in personalized medicine endeavors can be realized. More importantly, we identified underlying gaps in preclinical research relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. To achieve a broad implementation of predictive translational models in personalized medicine, these fundamental deficits must be addressed.

CKS2 and S100A12: Two Novel Diagnostic Biomarkers for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic systematicness autoimmunity disease with joint inflammation. RA etiology is still unknown. Early and exact diagnosing is still hard to reach. In the paper, we purposed to discover novel diagnosis biological marker for RA. Two open, usable gene expression profiles of human RA as well as controlled specimens (dataset GSE17755 as well as GSE93272) were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 331 RA and 88 control samples. Functional enrichment analysis was applied to explore the possible function of DEGs. Expression levels as well as diagnosis values of biological marker in RA were further verified in our cohort by the use of RT-PCR and ROC assays. We identified 13 DEGs between RA samples and control samples. 13 DEGs were remarkably abundant in NF-kappa B signal pathway. Among the 13 DEGs, CKS2, S100A12, LY96, and ANXA3 exhibited a strong diagnostic ability in screening RA specimens from normal specimens using all AUC > 0.8. Moreover, we confirmed that the expression of CKS2 and S100A12 was distinctly upregulated in RA specimens contrasted to normal specimens. Overall, serum CKS2 and S100A12 could be used as novel diagnosis biological markers for RA patients.

Study designs for clinical trials applied to personalised medicine: a scoping review

OBJECTIVE

Personalised medicine (PM) allows treating patients based on their individual demographic, genomic or biological characteristics for tailoring the 'right treatment for the right person at the right time'. Robust methodology is required for PM clinical trials, to correctly identify groups of participants and treatments. As an initial step for the development of new recommendations on trial designs for PM, we aimed to present an overview of the study designs that have been used in this field.

DESIGN

Scoping review.

METHODS

We searched (April 2020) PubMed, Embase and the Cochrane Library for all reports in English, French, German, Italian and Spanish, describing study designs for clinical trials applied to PM. Study selection and data extraction were performed in duplicate resolving disagreements by consensus or by involving a third expert reviewer. We extracted information on the characteristics of trial designs and examples of current applications of these approaches. The extracted information was used to generate a new classification of trial designs for PM.

RESULTS

We identified 21 trial designs, 10 subtypes and 30 variations of trial designs applied to PM, which we classified into four core categories (namely, Master protocol, Randomise-all, Biomarker strategy and Enrichment). We found 131 clinical trials using these designs, of which the great majority were master protocols (86/131, 65.6%). Most of the trials were phase II studies (75/131, 57.2%) in the field of oncology (113/131, 86.3%). We identified 34 main features of trial designs regarding different aspects (eg, framework, control group, randomisation). The four core categories and 34 features were merged into a double-entry table to create a new classification of trial designs for PM.

CONCLUSIONS

A variety of trial designs exists and is applied to PM. A new classification of trial designs is proposed to help readers to navigate the complex field of PM clinical trials.

Liquid Biopsy in Type 2 Diabetes Mellitus Management: Building Specific Biosignatures via Machine Learning

BACKGROUND

The need for minimally invasive biomarkers for the early diagnosis of type 2 diabetes (T2DM) prior to the clinical onset and monitoring of β-pancreatic cell loss is emerging. Here, we focused on studying circulating cell-free DNA (ccfDNA) as a liquid biopsy biomaterial for accurate diagnosis/monitoring of T2DM.

METHODS

ccfDNA levels were directly quantified in sera from 96 T2DM patients and 71 healthy individuals via fluorometry, and then fragment DNA size profiling was performed by capillary electrophoresis. Following this, ccfDNA methylation levels of five β-cell-related genes were measured via qPCR. Data were analyzed by automated machine learning to build classifying predictive models.

RESULTS

ccfDNA levels were found to be similar between groups but indicative of apoptosis in T2DM. INS (Insulin), IAPP (Islet Amyloid Polypeptide-Amylin), GCK (Glucokinase), and KCNJ11 (Potassium Inwardly Rectifying Channel Subfamily J member 11) levels differed significantly between groups. AutoML analysis delivered biosignatures including GCK, IAPP and KCNJ11 methylation, with the highest ever reported discriminating performance of T2DM from healthy individuals (AUC 0.927).

CONCLUSIONS

Our data unravel the value of ccfDNA as a minimally invasive biomaterial carrying important clinical information for T2DM. Upon prospective clinical evaluation, the built biosignature can be disruptive for T2DM clinical management.