Knowledge-guided multi-scale independent component analysis for biomarker identification

<p>Abstract</p> <p>Background</p> <p>Many statistical methods have been proposed to identify disease biomarkers from gene expression profiles. However, from gene expression profile data alone, statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study. In this paper, we develop a novel strategy, namely knowledge-guided multi-scale independent component analysis (ICA), to first infer regulatory signals and then identify biologically relevant biomarkers from microarray data.</p> <p>Results</p> <p>Since gene expression levels reflect the joint effect of several underlying biological functions, disease-specific biomarkers may be involved in several distinct biological functions. To identify disease-specific biomarkers that provide unique mechanistic insights, a meta-data "knowledge gene pool" (KGP) is first constructed from multiple data sources to provide important information on the likely functions (such as gene ontology information) and regulatory events (such as promoter responsive elements) associated with potential genes of interest. The gene expression and biological meta data associated with the members of the KGP can then be used to guide subsequent analysis. ICA is then applied to multi-scale gene clusters to reveal regulatory modes reflecting the underlying biological mechanisms. Finally disease-specific biomarkers are extracted by their weighted connectivity scores associated with the extracted regulatory modes. A statistical significance test is used to evaluate the significance of transcription factor enrichment for the extracted gene set based on motif information. We applied the proposed method to yeast cell cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification.</p> <p>Conclusion</p> <p>We have proposed a novel method, namely knowledge-guided multi-scale ICA, to identify disease-specific biomarkers. The goal is to infer knowledge-relevant regulatory signals and then identify corresponding biomarkers through a multi-scale strategy. The approach has been successfully applied to two expression profiling experiments to demonstrate its improved performance in extracting biologically meaningful and disease-related biomarkers. More importantly, the proposed approach shows promising results to infer novel biomarkers for ovarian cancer and extend current knowledge.</p

Clinical proteomics for precision medicine: the bladder cancer case

Precision medicine can improve patient management by guiding therapeutic decision based on molecular characteristics. The concept has been extensively addressed through the application of –omics based approaches. Proteomics attract high interest, as proteins reflect a “real-time” dynamic molecular phenotype. Focusing on proteomics applications for personalized medicine, a literature search was conducted to cover: a) disease prevention, b) monitoring/ prediction of treatment response, c) stratification to guide intervention and d) identification of drug targets. The review indicates the potential of proteomics for personalized medicine by also highlighting multiple challenges to be addressed prior to actual implementation. In oncology, particularly bladder cancer, application of precision medicine appears especially promising. The high heterogeneity and recurrence rates together with the limited treatment options, suggests that earlier and more efficient intervention, continuous monitoring and the development of alternative therapies could be accomplished by applying proteomics-guided personalized approaches. This notion is backed by studies presenting biomarkers that are of value in patient stratification and prognosis, and by recent studies demonstrating the identification of promising therapeutic targets. Herein, we aim to present an approach whereby combining the knowledge on biomarkers and therapeutic targets in bladder cancer could serve as basis towards proteomics- guided personalized patient management

EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision

A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. 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. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network

The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods. In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process. MDNet includes an image model and a language model. The image model is proposed to enhance multi-scale feature ensembles and utilization efficiency. The language model, integrated with our improved attention mechanism, aims to read and explore discriminative image feature descriptions from reports to learn a direct mapping from sentence words to image pixels. The overall network is trained end-to-end by using our developed optimization strategy. Based on a pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we conduct sufficient experiments to demonstrate that MDNet outperforms comparative baselines. The proposed image model obtains state-of-the-art performance on two CIFAR datasets as well.Comment: CVPR2017 Ora

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.I recieved an honorarium from Roche Diagnostics for my participation in the advisory panel meeting leading to this pape

The immune microenvironment in mantle cell lymphoma : Targeted liquid and spatial proteomic analyses

The complex interplay of the tumour and immune cells affects tumour growth, progression, and response to treatment. Restorationof effective immune response forms the basis of onco-immunology, which further enabled the development of immunotherapy. Inthe era of precision medicine, pin-pointing patient biological heterogeneity especially in relation to patient-specific immunemicroenvironment is a necessity for the discovery of novel biomarkers and for development of patient stratification tools for targetedtherapeutics. Mantle cell lymphoma (MCL) is a rare and aggressive subtype of B-cell lymphoma with poor survival and high relapserates. Previous investigations of MCL have largely focused on the tumour itself and explorations of the immune microenvironmenthave been limited. This thesis and the included five papers, investigates multiple aspects of the immune microenvironment withrespect to proteomic analysis performed on tissue and liquid biopsies of diagnostic and relapsed/refractory (R/R) MCL cohorts.Analyses based on liquid biopsies (serum) in particular are relevant for aggressive cases such as in relapse, where invasiveprocedures for extracting tissues is not recommended. Thus, paper I-II probes the possibility of using serum for treatment andoutcome-associated biomarker discovery in R/R MCL, using a targeted affinity-based protein microarray platform quantifyingimmune-regulatory and tumor-secretory proteins in sera. Analysis performed in paper I using pre-treatment samples, identifies 11-plex biomarker signature (RIS – relapsed immune signature) associated with overall survival. Further integration of RIS with mantlecell lymphoma international prognostic index (MIPI) led to the development of MIPIris index for the stratification of R/R MCL intothree risk groups. Moreover, longitudinal analysis can be important in understanding how patient respond to treatment and thiscan further guide therapeutic interventions. Thus, paper II is a follow-up study wherein longitudinal analyses was performed onpaired samples collected at pre-treatment (baseline) and after three months of chemo-immunotherapy (on-treatment). We showhow genetic aberrations can influence systemic profiles and thus integrating genetic information can be crucial for treatmentselection. Furthermore, we observe that the inter-patient heterogeneity associated with absolute values can be circumvented byusing velocity of change to capture general changes over time in groups of patients. Thus, using velocity of change in serumproteins between pre- and on-treatment samples identified response biomarkers associated with minimal residual disease andprogression. While exploratory analysis using high dimensional omics-based data can be important for accelerating discovery,translating such information for clinical utility is a necessity. Thus, in paper III, we show how serum quantification can be usedcomplementary tissue-identified prognostic biomarkers and this can enable faster clinical implementation. Presence of CD163+M2-like macrophages has shown to be associated with poor outcome in MCL tissues. We show that higher expression of sCD163levels in sera quantified using ELISA, is also associated with poor outcome in diagnostic and relapsed MCL. Furthermore, wesuggest a cut-off for sCD163 levels that can be used for clinical utility. Further exploration of the dynamic interplay of tumourimmunemicroenvironment is now possible using spatial resolved omics for tissue-based analysis. Thus, in paper IV and V, weanalyse cell-type specific proteomic data collected from tumour and immune cells using GeoMx™ digital spatial profiler. In paperIV, we show that presence as well as spatial localization of CD163+ macrophage with respect to tumour regions impactsmacrophage phenotypic profiles. Further modulation in the profile of surrounding tumour and T-cells is observed whenmacrophages are present in the vicinity. Based on this analysis, we suggest MAPK pathway as a potential therapeutic target intumours with CD163+ macrophages. Immune composition can be defined not just by the type of cells, but also with respect tofrequency and spatial localization and this is explored in paper V with respect to T-cell subtypes. Thus, in paper V, we optimizeda workflow of multiplexed immunofluorescence image segmentation that allowed us to extract cell metrics for four subtypes ofCD3+ T-cells. Using this data, we show that higher infiltration of T-cells is associated with a positive outcome in MCL. Moreover,by combining image derived metrics to cell specific spatial omics data, we were able to identify immunosuppressivemicroenvironment associated with highly infiltrated tumours and suggests new potential targets of immunotherapy with respect toIDO1, GITR and STING. In conclusion, this thesis explores systemic and tumor-associated immune microenvironment in MCL, fordefining patient heterogeneity, developing methods of patient stratification and for identifying novel and actionable biomarkers

QUANTITATIVE PROTEOMIC ANALYSES OF HUMAN PLASMA: APPLICATION OF MASS SPECTROMETRY FOR THE DISCOVERY OF CLINICAL DELIRIUM BIOMARKERS

The biomarker discovery pipeline is a multi-step endeavor to identify potential diagnostic or prognostic markers of a disease. Although the advent of modern mass spectrometers has revolutionized the initial discovery phase, a significant bottleneck still exists when validating discovered biomarkers. In this doctoral research, I demonstrate that the discovery, verification and validation of biomarkers can all be performed using mass spectrometry and apply the biomarker pipeline to the context of clinical delirium. First, a systematic review of recent literature provided a birds-eye view of untargeted, discovery proteomic attempts for biomarkers of delirium in the geriatric population. Here, a comprehensive search from five databases yielded 1172 publications, from which eight peer-reviewed studies met our defined inclusion criteria. Despite the paucity of published studies that applied systems- biology approaches for biomarker discovery on the subject, lessons learned and insights from this review was instrumental in the study designing and proteomics analyses of plasma sample in our cohort. We then performed a targeted study on four biomarkers for their potential mediation role in the occurrence of delirium after high-dose intra-operative oxygen treatment. Although S100B calcium binding protein (S100B), gamma enolase (ENO2), chitinase-3-like protein 1 (CHI3L1) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1) have well-documented associations with delirium, we did not find any such associations in our cohort. Of note, this study demonstrates that the use of targeted approaches for the purposes of biomarker discovery, rather than an untargeted, systems-biology approach, is unavoidably biased and may lead to misleading conclusions. Lastly, we applied lessons learned and comprehensively profiled the plasma samples of delirium cases and non-delirium cases, at both pre- and post-surgical timepoints. We found 16 biomarkers as signatures of cardiopulmonary bypass, and 11 as potential diagnostic candidates of delirium (AuROC = 93%). We validated the discovered biomarkers on the same mass spectrometry platform without the use of traditional affinity-based validation methods. Our discovery of novel biomarkers with no know association with delirium such as serum amyloid A1 (SAA1) and A2 (SAA2), pepsinogen A3 (PEPA3) and cathepsin B (CATB) shed new lights on possible neuronal pathomechanisms