Project acronym 3D-OA-HISTO
Project Development of 3D Histopathological Grading of Osteoarthritis
Researcher (PI) Simo Jaakko Saarakkala
Host Institution (HI) OULUN YLIOPISTO
Call Details Starting Grant (StG), LS7, ERC-2013-StG
Summary "Background: Osteoarthritis (OA) is a common musculoskeletal disease occurring worldwide. Despite extensive research, etiology of OA is still poorly understood. Histopathological grading (HPG) of 2D tissue sections is the gold standard reference method for determination of OA stage. However, traditional 2D-HPG is destructive and based only on subjective visual evaluation. These limitations induce bias to clinical in vitro OA diagnostics and basic research that both rely strongly on HPG.
Objectives: 1) To establish and validate the very first 3D-HPG of OA based on cutting-edge nano/micro-CT (Computed Tomography) technologies in vitro; 2) To use the established method to clarify the beginning phases of OA; and 3) To validate 3D-HPG of OA for in vivo use.
Methods: Several hundreds of human osteochondral samples from patients undergoing total knee arthroplasty will be collected. The samples will be imaged in vitro with nano/micro-CT and clinical high-end extremity CT devices using specific contrast-agents to quantify tissue constituents and structure in 3D in large volume. From this information, a novel 3D-HPG is developed with statistical classification algorithms. Finally, the developed novel 3D-HPG of OA will be applied clinically in vivo.
Significance: This is the very first study to establish 3D-HPG of OA pathology in vitro and in vivo. Furthermore, the developed technique hugely improves the understanding of the beginning phases of OA. Ultimately, the study will contribute for improving OA patients’ quality of life by slowing the disease progression, and for providing powerful tools to develop new OA therapies."
Summary
"Background: Osteoarthritis (OA) is a common musculoskeletal disease occurring worldwide. Despite extensive research, etiology of OA is still poorly understood. Histopathological grading (HPG) of 2D tissue sections is the gold standard reference method for determination of OA stage. However, traditional 2D-HPG is destructive and based only on subjective visual evaluation. These limitations induce bias to clinical in vitro OA diagnostics and basic research that both rely strongly on HPG.
Objectives: 1) To establish and validate the very first 3D-HPG of OA based on cutting-edge nano/micro-CT (Computed Tomography) technologies in vitro; 2) To use the established method to clarify the beginning phases of OA; and 3) To validate 3D-HPG of OA for in vivo use.
Methods: Several hundreds of human osteochondral samples from patients undergoing total knee arthroplasty will be collected. The samples will be imaged in vitro with nano/micro-CT and clinical high-end extremity CT devices using specific contrast-agents to quantify tissue constituents and structure in 3D in large volume. From this information, a novel 3D-HPG is developed with statistical classification algorithms. Finally, the developed novel 3D-HPG of OA will be applied clinically in vivo.
Significance: This is the very first study to establish 3D-HPG of OA pathology in vitro and in vivo. Furthermore, the developed technique hugely improves the understanding of the beginning phases of OA. Ultimately, the study will contribute for improving OA patients’ quality of life by slowing the disease progression, and for providing powerful tools to develop new OA therapies."
Max ERC Funding
1 500 000 €
Duration
Start date: 2014-02-01, End date: 2019-01-31
Project acronym Age Asymmetry
Project Age-Selective Segregation of Organelles
Researcher (PI) Pekka Aleksi Katajisto
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Starting Grant (StG), LS3, ERC-2015-STG
Summary Our tissues are constantly renewed by stem cells. Over time, stem cells accumulate cellular damage that will compromise renewal and results in aging. As stem cells can divide asymmetrically, segregation of harmful factors to the differentiating daughter cell could be one possible mechanism for slowing damage accumulation in the stem cell. However, current evidence for such mechanisms comes mainly from analogous findings in yeast, and studies have concentrated only on few types of cellular damage.
I hypothesize that the chronological age of a subcellular component is a proxy for all the damage it has sustained. In order to secure regeneration, mammalian stem cells may therefore specifically sort old cellular material asymmetrically. To study this, I have developed a novel strategy and tools to address the age-selective segregation of any protein in stem cell division. Using this approach, I have already discovered that stem-like cells of the human mammary epithelium indeed apportion chronologically old mitochondria asymmetrically in cell division, and enrich old mitochondria to the differentiating daughter cell. We will investigate the mechanisms underlying this novel phenomenon, and its relevance for mammalian aging.
We will first identify how old and young mitochondria differ, and how stem cells recognize them to facilitate the asymmetric segregation. Next, we will analyze the extent of asymmetric age-selective segregation by targeting several other subcellular compartments in a stem cell division. Finally, we will determine whether the discovered age-selective segregation is a general property of stem cell in vivo, and it's functional relevance for maintenance of stem cells and tissue regeneration. Our discoveries may open new possibilities to target aging associated functional decline by induction of asymmetric age-selective organelle segregation.
Summary
Our tissues are constantly renewed by stem cells. Over time, stem cells accumulate cellular damage that will compromise renewal and results in aging. As stem cells can divide asymmetrically, segregation of harmful factors to the differentiating daughter cell could be one possible mechanism for slowing damage accumulation in the stem cell. However, current evidence for such mechanisms comes mainly from analogous findings in yeast, and studies have concentrated only on few types of cellular damage.
I hypothesize that the chronological age of a subcellular component is a proxy for all the damage it has sustained. In order to secure regeneration, mammalian stem cells may therefore specifically sort old cellular material asymmetrically. To study this, I have developed a novel strategy and tools to address the age-selective segregation of any protein in stem cell division. Using this approach, I have already discovered that stem-like cells of the human mammary epithelium indeed apportion chronologically old mitochondria asymmetrically in cell division, and enrich old mitochondria to the differentiating daughter cell. We will investigate the mechanisms underlying this novel phenomenon, and its relevance for mammalian aging.
We will first identify how old and young mitochondria differ, and how stem cells recognize them to facilitate the asymmetric segregation. Next, we will analyze the extent of asymmetric age-selective segregation by targeting several other subcellular compartments in a stem cell division. Finally, we will determine whether the discovered age-selective segregation is a general property of stem cell in vivo, and it's functional relevance for maintenance of stem cells and tissue regeneration. Our discoveries may open new possibilities to target aging associated functional decline by induction of asymmetric age-selective organelle segregation.
Max ERC Funding
1 500 000 €
Duration
Start date: 2016-05-01, End date: 2021-04-30
Project acronym Brain Health Toolbox
Project The Brain Health Toolbox: Facilitating personalized decision-making for effective dementia prevention
Researcher (PI) Alina Gabriela SOLOMON
Host Institution (HI) ITA-SUOMEN YLIOPISTO
Call Details Starting Grant (StG), LS7, ERC-2018-STG
Summary Preventing dementia and Alzheimer disease (AD) is a global priority. Previous single-intervention failures stress the critical need for a new multimodal preventive approach in these complex multifactorial conditions. The Brain Health Toolbox is designed to create a seamless continuum from accurate dementia prediction to effective prevention by i) developing the missing disease models and prediction tools for multimodal prevention; ii) testing them in actual multimodal prevention trials; and iii) bridging the gap between non-pharmacological and pharmacological approaches by designing a combined multimodal prevention trial based on a new European adaptive trial platform. Disease models and prediction tools will be multi-dimensional, i.e. a broad range of risk factors and biomarker types, including novel markers. An innovative machine learning method will be used for pattern identification and risk profiling to highlight most important contributors to an individual’s overall risk level. This is crucial for early identification of individuals with high dementia risk and/or high likelihood of specific brain pathologies, quantifying an individual’s prevention potential, and longitudinal risk and disease monitoring, also beyond trial duration. Three Toolbox test scenarios are considered: use for selecting target populations, assessing heterogeneity of intervention effects, and use as trial outcome. The project is based on a unique set-up aligning several new multimodal lifestyle trials aiming to adapt and test non-pharmacological interventions to different geographic, economic and cultural settings, with two reference libraries (observational - large datasets; and interventional - four recently completed pioneering multimodal lifestyle prevention trials). The Brain Health Toolbox covers the entire continuum from general populations to patients with preclinical/prodromal disease stages, and will provide tools for personalized decision-making for dementia prevention.
Summary
Preventing dementia and Alzheimer disease (AD) is a global priority. Previous single-intervention failures stress the critical need for a new multimodal preventive approach in these complex multifactorial conditions. The Brain Health Toolbox is designed to create a seamless continuum from accurate dementia prediction to effective prevention by i) developing the missing disease models and prediction tools for multimodal prevention; ii) testing them in actual multimodal prevention trials; and iii) bridging the gap between non-pharmacological and pharmacological approaches by designing a combined multimodal prevention trial based on a new European adaptive trial platform. Disease models and prediction tools will be multi-dimensional, i.e. a broad range of risk factors and biomarker types, including novel markers. An innovative machine learning method will be used for pattern identification and risk profiling to highlight most important contributors to an individual’s overall risk level. This is crucial for early identification of individuals with high dementia risk and/or high likelihood of specific brain pathologies, quantifying an individual’s prevention potential, and longitudinal risk and disease monitoring, also beyond trial duration. Three Toolbox test scenarios are considered: use for selecting target populations, assessing heterogeneity of intervention effects, and use as trial outcome. The project is based on a unique set-up aligning several new multimodal lifestyle trials aiming to adapt and test non-pharmacological interventions to different geographic, economic and cultural settings, with two reference libraries (observational - large datasets; and interventional - four recently completed pioneering multimodal lifestyle prevention trials). The Brain Health Toolbox covers the entire continuum from general populations to patients with preclinical/prodromal disease stages, and will provide tools for personalized decision-making for dementia prevention.
Max ERC Funding
1 498 268 €
Duration
Start date: 2019-02-01, End date: 2024-01-31
Project acronym CANCER SIGNALOSOMES
Project Spatially and temporally regulated membrane complexes in cancer cell invasion and cytokinesis
Researcher (PI) Johanna Ivaska
Host Institution (HI) TEKNOLOGIAN TUTKIMUSKESKUS VTT
Call Details Starting Grant (StG), LS1, ERC-2007-StG
Summary Cancer progression, characterized by uncontrolled proliferation and motility of cells, is a complex and deadly process. Integrins, a major cell surface adhesion receptor family, are transmembrane proteins known to regulate cell behaviour by transducing extracellular signals to cytoplasmic protein complexes. We and others have shown that recruitment of specific protein complexes by the cytoplasmic domains of integrins is important in tumorigenesis. Here our aim is to study three interrelated processes in cancer progression which involve integrin signalling, but which have not been elucidated earlier at all. 1) Integrins in cell division (cytokinesis). Since coordinated action of the cytoskeleton and membranes is needed both for cell division and motility, shared integrin functions can regulate both events. 2) Dynamic integrin signalosomes at the leading edge of invading cells. Spatially and temporally regulated, integrin-protein complexes at the front of infiltrating cells are likely to dictate the movement of cancer cells in tissues. 3) Transmembrane segments of integrins as scaffolds for integrin signalling. In addition to cytosolic proteins, integrins most likely interact with proteins within the membrane resulting into new signalling modalities. In this proposal we will use innovative, modern and even unconventional techniques (such as RNAi and live-cell arrays detecting integrin traffic, cell motility and multiplication, laser-microdissection, proteomics and bacterial-two-hybrid screens) to unravel these new integrin functions, for which we have preliminary evidence. Each project will give fundamentally novel mechanistic insight into the role of integrins in cancer. Moreover, these interdisciplinary new openings will increase our understanding in cancer progression in general and will open new possibilities for therapeutic intervention targeting both cancer proliferation and dissemination in the body.
Summary
Cancer progression, characterized by uncontrolled proliferation and motility of cells, is a complex and deadly process. Integrins, a major cell surface adhesion receptor family, are transmembrane proteins known to regulate cell behaviour by transducing extracellular signals to cytoplasmic protein complexes. We and others have shown that recruitment of specific protein complexes by the cytoplasmic domains of integrins is important in tumorigenesis. Here our aim is to study three interrelated processes in cancer progression which involve integrin signalling, but which have not been elucidated earlier at all. 1) Integrins in cell division (cytokinesis). Since coordinated action of the cytoskeleton and membranes is needed both for cell division and motility, shared integrin functions can regulate both events. 2) Dynamic integrin signalosomes at the leading edge of invading cells. Spatially and temporally regulated, integrin-protein complexes at the front of infiltrating cells are likely to dictate the movement of cancer cells in tissues. 3) Transmembrane segments of integrins as scaffolds for integrin signalling. In addition to cytosolic proteins, integrins most likely interact with proteins within the membrane resulting into new signalling modalities. In this proposal we will use innovative, modern and even unconventional techniques (such as RNAi and live-cell arrays detecting integrin traffic, cell motility and multiplication, laser-microdissection, proteomics and bacterial-two-hybrid screens) to unravel these new integrin functions, for which we have preliminary evidence. Each project will give fundamentally novel mechanistic insight into the role of integrins in cancer. Moreover, these interdisciplinary new openings will increase our understanding in cancer progression in general and will open new possibilities for therapeutic intervention targeting both cancer proliferation and dissemination in the body.
Max ERC Funding
1 529 369 €
Duration
Start date: 2008-08-01, End date: 2013-07-31
Project acronym CUMTAS
Project Customized Micro Total Analysis Systems to Study Human Phase I Metabolism
Researcher (PI) Tiina Marjukka Sikanen
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Starting Grant (StG), LS9, ERC-2012-StG_20111109
Summary The goal of this project is to develop inexpensive, high-throughput technology to screen the thus far unexplored metabolic interactions between environmental and household chemicals and clinically relevant drugs. The main influential focus will be on human phase I metabolism (redox reactions) of common toxicants like agrochemicals and plasticizers. On the basis of their structural resemblance to pharmaceuticals and endogenous compounds, many of these chemicals are suspected to have critical effects on cytochrome P450 metabolism which is the main detoxification route of pharmaceuticals in man. However, with the current analytical instrumentation, screening of such large chemical pool would take several years, and new chemicals would be introduced faster than the old ones are screened. Thus, the main technological goal of this project is to develop novel, practically zero-cost analytical instruments that enable characterization of a compound’s metabolic profile at very high speed (<1 min/sample). This goal is achieved through miniaturization and high degree of integration of analytical instrumentation by microfabrication means, an approach often called lab(oratory)-on-a-chip. The microfabricated arrays are envisioned to incorporate all analytical key functions required (i.e., sample pretreatment, metabolic reaction, separation of the reaction products, detection) on a single chip. Thanks to the reduced dimensions, the amount of chemical waste and consumption of expensive reagents are significantly reduced. In this project, several different microfabrication techniques, from delicate cleanroom processes to extremely simple printing techniques, will be exploited to produce smart microfluidic designs and multifunctional surfaces. Towards the end of the project, more focus will be put on “printable microfluidics” which provides a truly low-cost approach for fabrication of highly customized microfluidic assays. Numerical modelling is also an integral part of the work.
Summary
The goal of this project is to develop inexpensive, high-throughput technology to screen the thus far unexplored metabolic interactions between environmental and household chemicals and clinically relevant drugs. The main influential focus will be on human phase I metabolism (redox reactions) of common toxicants like agrochemicals and plasticizers. On the basis of their structural resemblance to pharmaceuticals and endogenous compounds, many of these chemicals are suspected to have critical effects on cytochrome P450 metabolism which is the main detoxification route of pharmaceuticals in man. However, with the current analytical instrumentation, screening of such large chemical pool would take several years, and new chemicals would be introduced faster than the old ones are screened. Thus, the main technological goal of this project is to develop novel, practically zero-cost analytical instruments that enable characterization of a compound’s metabolic profile at very high speed (<1 min/sample). This goal is achieved through miniaturization and high degree of integration of analytical instrumentation by microfabrication means, an approach often called lab(oratory)-on-a-chip. The microfabricated arrays are envisioned to incorporate all analytical key functions required (i.e., sample pretreatment, metabolic reaction, separation of the reaction products, detection) on a single chip. Thanks to the reduced dimensions, the amount of chemical waste and consumption of expensive reagents are significantly reduced. In this project, several different microfabrication techniques, from delicate cleanroom processes to extremely simple printing techniques, will be exploited to produce smart microfluidic designs and multifunctional surfaces. Towards the end of the project, more focus will be put on “printable microfluidics” which provides a truly low-cost approach for fabrication of highly customized microfluidic assays. Numerical modelling is also an integral part of the work.
Max ERC Funding
1 499 668 €
Duration
Start date: 2013-05-01, End date: 2019-02-28
Project acronym DIADRUG
Project Insulin resistance and diabetic nephropathy - development of novel in vivo models for drug discovery
Researcher (PI) Sanna Lehtonen
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Starting Grant (StG), LS9, ERC-2009-StG
Summary Up to one third of diabetic patients develop nephropathy, a serious complication of diabetes. Microalbuminuria is the earliest sign of the complication, which may ultimately develop to end-stage renal disease requiring dialysis or a kidney transplant. Insulin resistance and metabolic syndrome are associated with an increased risk for diabetic nephropathy. Interestingly, glomerular epithelial cells or podocytes have recently been shown to be insulin responsive. Further, nephrin, a key structural component of podocytes, is essential for insulin action in these cells. Our novel findings show that adaptor protein CD2AP, an interaction partner of nephrin, associates with regulators of insulin signaling and glucose transport in glomeruli. The results suggest that nephrin and CD2AP are involved, by association with these proteins, in the regulation of insulin signaling and glucose transport in podocytes. We hypothesize that podocytes can develop insulin resistance and that disturbances in insulin response affect podocyte function and contribute to the development of diabetic nephropathy. The aim of this project is to clarify the mechanisms leading to development of insulin resistance in podocytes and to study the association between insulin resistance and the development of diabetic nephropathy. For this we will develop transgenic zebrafish and mouse models by overexpressing/knocking down insulin signaling-associated proteins specifically in podocytes. Further, we aim to identify novel drug leads to treat insulin resistance and diabetic nephropathy by performing high-throughput small molecule library screens on the developed transgenic fish models. The ultimate goal is to find a treatment to combat the early stages of diabetic nephropathy in humans.
Summary
Up to one third of diabetic patients develop nephropathy, a serious complication of diabetes. Microalbuminuria is the earliest sign of the complication, which may ultimately develop to end-stage renal disease requiring dialysis or a kidney transplant. Insulin resistance and metabolic syndrome are associated with an increased risk for diabetic nephropathy. Interestingly, glomerular epithelial cells or podocytes have recently been shown to be insulin responsive. Further, nephrin, a key structural component of podocytes, is essential for insulin action in these cells. Our novel findings show that adaptor protein CD2AP, an interaction partner of nephrin, associates with regulators of insulin signaling and glucose transport in glomeruli. The results suggest that nephrin and CD2AP are involved, by association with these proteins, in the regulation of insulin signaling and glucose transport in podocytes. We hypothesize that podocytes can develop insulin resistance and that disturbances in insulin response affect podocyte function and contribute to the development of diabetic nephropathy. The aim of this project is to clarify the mechanisms leading to development of insulin resistance in podocytes and to study the association between insulin resistance and the development of diabetic nephropathy. For this we will develop transgenic zebrafish and mouse models by overexpressing/knocking down insulin signaling-associated proteins specifically in podocytes. Further, we aim to identify novel drug leads to treat insulin resistance and diabetic nephropathy by performing high-throughput small molecule library screens on the developed transgenic fish models. The ultimate goal is to find a treatment to combat the early stages of diabetic nephropathy in humans.
Max ERC Funding
2 000 000 €
Duration
Start date: 2009-11-01, End date: 2014-10-31
Project acronym DOGPSYCH
Project Canine models of human psychiatric disease: identifying novel anxiety genes with the help of man's best friend
Researcher (PI) Hannes Tapani Lohi
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Starting Grant (StG), LS2, ERC-2010-StG_20091118
Summary Anxiety disorders include different forms of pathological fear and anxiety and rank among the most common health concerns in human medicine. Millions of people become affected every year, and many of them do not respond to treatments. Anxiety disorders are heritable, but genetically complex. As a result, traditional gene mapping methods in the human population with prominent locus and allelic heterogeneity have not succeeded. Similarly, rodents have provided some insights into the circuitry of anxiety, but naturally occurring versions do not exist and gene deletion studies have not provided adequate models. To break through and identify new anxiety genes, I propose a novel and unique approach that resorts to man s best friend, dog. Taking advantage of the exaggerated genetic homogeneity characteristic of purebred dogs, recent genomics tools and the existence of naturally occurring heritable behaviour disorders in dogs can remedy the current lack of a suitable animal model of human psychiatric disorders. I propose to collect and perform a genome-wide association study in four breed-specific anxiety traits in dogs representing the three major forms of human anxiety: compulsive pacing and tail-chasing, noise phobia, and shyness corresponding to human OCD, panic disorder and social phobia, respectively. Canine anxiety disorders respond to human medications and other phenomenological studies suggest a share biological mechanism in both species. The proposed research has the potential to discover new genetic risk factors, which eventually will shed light on the biological basis of common neuropsychiatric disorders in both dog and human, provide insight into etiological mechanisms, enable identification of individuals at high-risk for adverse health outcomes, and facilitate development of tailored treatments.
Summary
Anxiety disorders include different forms of pathological fear and anxiety and rank among the most common health concerns in human medicine. Millions of people become affected every year, and many of them do not respond to treatments. Anxiety disorders are heritable, but genetically complex. As a result, traditional gene mapping methods in the human population with prominent locus and allelic heterogeneity have not succeeded. Similarly, rodents have provided some insights into the circuitry of anxiety, but naturally occurring versions do not exist and gene deletion studies have not provided adequate models. To break through and identify new anxiety genes, I propose a novel and unique approach that resorts to man s best friend, dog. Taking advantage of the exaggerated genetic homogeneity characteristic of purebred dogs, recent genomics tools and the existence of naturally occurring heritable behaviour disorders in dogs can remedy the current lack of a suitable animal model of human psychiatric disorders. I propose to collect and perform a genome-wide association study in four breed-specific anxiety traits in dogs representing the three major forms of human anxiety: compulsive pacing and tail-chasing, noise phobia, and shyness corresponding to human OCD, panic disorder and social phobia, respectively. Canine anxiety disorders respond to human medications and other phenomenological studies suggest a share biological mechanism in both species. The proposed research has the potential to discover new genetic risk factors, which eventually will shed light on the biological basis of common neuropsychiatric disorders in both dog and human, provide insight into etiological mechanisms, enable identification of individuals at high-risk for adverse health outcomes, and facilitate development of tailored treatments.
Max ERC Funding
1 381 807 €
Duration
Start date: 2010-10-01, End date: 2015-09-30
Project acronym DrugComb
Project Informatics approaches for the rational selection of personalized cancer drug combinations
Researcher (PI) Jing TANG
Host Institution (HI) HELSINGIN YLIOPISTO
Call Details Starting Grant (StG), LS7, ERC-2016-STG
Summary Making cancer treatment more personalized and effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We critically need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. This project will develop mathematical and computational tools to identify drug combinations that can be used to provide personalized and more effective therapeutic strategies that may prevent acquired resistance. Utilizing molecular profiling and pharmacological screening data from patient-derived leukaemia and ovarian cancer samples, I will develop model-based clustering methods for identification of patient subgroups that are differentially responsive to first-line chemotherapy. For patients resistant to chemotherapy, I will develop network modelling approaches to predict the most potential drug combinations by understanding the underlying drug target interactions. The drug combination prediction will be made for each patient and will be validated using a preclinical drug testing platform on patient samples. I will explore the drug combination screen data to identify significant synergy at the therapeutically relevant doses. The drug combination hits will be mapped into signalling networks to infer their mechanisms. Drug combinations with selective efficacy in individual patient samples or in sample subgroups will be further translated into in treatment options by clinical collaborators. This will lead to novel and personalized strategies to treat cancer patients.
Summary
Making cancer treatment more personalized and effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We critically need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. This project will develop mathematical and computational tools to identify drug combinations that can be used to provide personalized and more effective therapeutic strategies that may prevent acquired resistance. Utilizing molecular profiling and pharmacological screening data from patient-derived leukaemia and ovarian cancer samples, I will develop model-based clustering methods for identification of patient subgroups that are differentially responsive to first-line chemotherapy. For patients resistant to chemotherapy, I will develop network modelling approaches to predict the most potential drug combinations by understanding the underlying drug target interactions. The drug combination prediction will be made for each patient and will be validated using a preclinical drug testing platform on patient samples. I will explore the drug combination screen data to identify significant synergy at the therapeutically relevant doses. The drug combination hits will be mapped into signalling networks to infer their mechanisms. Drug combinations with selective efficacy in individual patient samples or in sample subgroups will be further translated into in treatment options by clinical collaborators. This will lead to novel and personalized strategies to treat cancer patients.
Max ERC Funding
1 500 000 €
Duration
Start date: 2017-06-01, End date: 2022-05-31
Project acronym DynaOmics
Project From longitudinal proteomics to dynamic individualized diagnostics
Researcher (PI) Laura Linnea Maria Elo-Uhlgren
Host Institution (HI) TURUN YLIOPISTO
Call Details Starting Grant (StG), LS7, ERC-2015-STG
Summary Longitudinal omics data hold great promise to improve biomarker detection and enable dynamic individualized predictions. Recent technological advances have made proteomics an increasingly attractive option but clinical longitudinal proteomic datasets are still rare and computational tools for their analysis underdeveloped. The objective of this proposal is to create a roadmap to detect clinically feasible protein markers using longitudinal data and effective computational tools. A biomedical focus is on early detection of Type 1 diabetes (T1D). Specific objectives are:
1) Novel biomarker detector using longitudinal data. DynaOmics introduces novel types of multi-level dynamic markers that are undetectable in conventional single-time cross-sectional studies (e.g. within-individual changes in abundance or associations), develops optimization methods for their robust and reproducible detection within and across individuals, and validates their utility in well-defined samples.
2) Individualized disease risk prediction dynamically. DynaOmics develops dynamic individualized predictive models using the multi-level longitudinal proteome features and novel statistical and machine learning methods that have previously not been used in this context, including joint models of longitudinal and time-to-event data, and one-class classification type techniques.
3) Dynamic prediction of T1D. DynaOmics builds a predictive model of dynamic T1D risk to assist early detection of the disease, which is crucial for developing future therapeutic and preventive strategies. T1D typically involves a relatively long symptom-free period before clinical diagnosis but current tools to predict early T1D risk have restricted power.
The objectives involve innovative and unconventional approaches and address major unmet challenges in the field, having high potential to open new avenues for diagnosis and treatment of complex diseases and fundamentally novel insights towards precision medicine.
Summary
Longitudinal omics data hold great promise to improve biomarker detection and enable dynamic individualized predictions. Recent technological advances have made proteomics an increasingly attractive option but clinical longitudinal proteomic datasets are still rare and computational tools for their analysis underdeveloped. The objective of this proposal is to create a roadmap to detect clinically feasible protein markers using longitudinal data and effective computational tools. A biomedical focus is on early detection of Type 1 diabetes (T1D). Specific objectives are:
1) Novel biomarker detector using longitudinal data. DynaOmics introduces novel types of multi-level dynamic markers that are undetectable in conventional single-time cross-sectional studies (e.g. within-individual changes in abundance or associations), develops optimization methods for their robust and reproducible detection within and across individuals, and validates their utility in well-defined samples.
2) Individualized disease risk prediction dynamically. DynaOmics develops dynamic individualized predictive models using the multi-level longitudinal proteome features and novel statistical and machine learning methods that have previously not been used in this context, including joint models of longitudinal and time-to-event data, and one-class classification type techniques.
3) Dynamic prediction of T1D. DynaOmics builds a predictive model of dynamic T1D risk to assist early detection of the disease, which is crucial for developing future therapeutic and preventive strategies. T1D typically involves a relatively long symptom-free period before clinical diagnosis but current tools to predict early T1D risk have restricted power.
The objectives involve innovative and unconventional approaches and address major unmet challenges in the field, having high potential to open new avenues for diagnosis and treatment of complex diseases and fundamentally novel insights towards precision medicine.
Max ERC Funding
1 499 869 €
Duration
Start date: 2016-06-01, End date: 2021-05-31
Project acronym EnDeCAD
Project Enhancers Decoding the Mechanisms Underlying CAD Risk
Researcher (PI) Minna Unelma KAIKKONEN-MÄÄTTÄ
Host Institution (HI) ITA-SUOMEN YLIOPISTO
Call Details Starting Grant (StG), LS4, ERC-2018-STG
Summary In recent years, genome-wide association studies (GWAS) have discovered hundreds of single nucleotide polymorphisms (SNPs) which are significantly associated with coronary artery disease (CAD). However, the SNPs identified by GWAS explain typically only small portion of the trait heritability and vast majority of variants do not have known biological roles. This is explained by variants lying within noncoding regions such as in cell type specific enhancers and additionally ‘the lead SNP’ identified in GWAS may not be the ‘the causal SNP’ but only linked with a trait associated SNP. Therefore, a major priority for understanding disease mechanisms is to understand at the molecular level the function of each CAD loci. In this study we aim to bring the functional characterization of SNPs associated with CAD risk to date by focusing our search for causal SNPs to enhancers of disease relevant cell types, namely endothelial cells, macrophages and smooth muscle cells of the vessel wall, hepatocytes and adipocytes. By combination of massively parallel enhancer activity measurements, collection of novel eQTL data throughout cell types under disease relevant stimuli, identification of the target genes in physical interaction with the candidate enhancers and establishment of correlative relationships between enhancer activity and gene expression we hope to identify causal enhancer variants and link them with target genes to obtain a more complete picture of the gene regulatory events driving disease progression and the genetic basis of CAD. Linking these findings with our deep phenotypic data for cardiovascular risk factors, gene expression and metabolomics has the potential to improve risk prediction, biomarker identification and treatment selection in clinical practice. Ultimately, this research strives for fundamental discoveries and breakthrough that advance our knowledge of CAD and provides pioneering steps towards taking the growing array of GWAS for translatable results.
Summary
In recent years, genome-wide association studies (GWAS) have discovered hundreds of single nucleotide polymorphisms (SNPs) which are significantly associated with coronary artery disease (CAD). However, the SNPs identified by GWAS explain typically only small portion of the trait heritability and vast majority of variants do not have known biological roles. This is explained by variants lying within noncoding regions such as in cell type specific enhancers and additionally ‘the lead SNP’ identified in GWAS may not be the ‘the causal SNP’ but only linked with a trait associated SNP. Therefore, a major priority for understanding disease mechanisms is to understand at the molecular level the function of each CAD loci. In this study we aim to bring the functional characterization of SNPs associated with CAD risk to date by focusing our search for causal SNPs to enhancers of disease relevant cell types, namely endothelial cells, macrophages and smooth muscle cells of the vessel wall, hepatocytes and adipocytes. By combination of massively parallel enhancer activity measurements, collection of novel eQTL data throughout cell types under disease relevant stimuli, identification of the target genes in physical interaction with the candidate enhancers and establishment of correlative relationships between enhancer activity and gene expression we hope to identify causal enhancer variants and link them with target genes to obtain a more complete picture of the gene regulatory events driving disease progression and the genetic basis of CAD. Linking these findings with our deep phenotypic data for cardiovascular risk factors, gene expression and metabolomics has the potential to improve risk prediction, biomarker identification and treatment selection in clinical practice. Ultimately, this research strives for fundamental discoveries and breakthrough that advance our knowledge of CAD and provides pioneering steps towards taking the growing array of GWAS for translatable results.
Max ERC Funding
1 498 647 €
Duration
Start date: 2019-01-01, End date: 2023-12-31
