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Genet SAAM, Visser E, Youssef-El Soud M, Belderbos HNA, Stege G, de Saegher MEA, Westeinde SCV', Brunsveld L, Broeren MAC, van de Kerkhof D, Eduati F, van den Borne BEEM, Scharnhorst V. Strengths and challenges in current lung cancer care: Timeliness and diagnostic procedures in six Dutch hospitals. Lung Cancer 2024; 189:107477. [PMID: 38271919 DOI: 10.1016/j.lungcan.2024.107477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Abstract
OBJECTIVES Timely diagnosis of lung cancer (LC) is crucial to achieve optimal patient care and outcome. Moreover, the number of procedures required to obtain a definitive diagnosis can have a large influence on the life expectancy of a patient. Here, adherence with existing Dutch guidelines for timeliness and type and number of invasive and imaging procedures was assessed. MATERIALS AND METHODS 1096 patients with suspected LC were enrolled in this multicenter prospective study (NL9146). The overall survival, time from referral to the first appointment with the pulmonologist, time to diagnosis and treatment, and the number of imaging and invasive procedures were evaluated. Patients were divided into different diagnostic groupsearly- and advanced stage non-small-cell lung cancer (NSCLC), small-cell lung cancer (SCLC), large cell neuroendocrine carcinoma of the lung (LCNEC), patients without LC and patients without a definitive diagnosis. RESULTS The majority of patients (66 %) received a definitive diagnosis within 5 weeks, although the time to diagnosis of early-stage LC patients and patients without LC was significantly longer comparted to advanced stage LC. An increase in invasive procedures was seen for early-stage LC compared to advanced stage LC and for 13 % of the advanced stage non-squamous NSCLC patients up to three additional invasive procedures were performed solely to obtain sufficient material for NGS. For patients without a definitive diagnosis, 50 % did undergo at least one invasive procedure, while 11 % did not wish to undergo any invasive procedures. CONCLUSION These insights could aid in improved LC diagnostics and efficient implementation of new techniques like liquid biopsy and artificial intelligence. This may lead to more timely LC care, a decreased number of invasive procedures, less variability between the diagnostic trajectory of different patients and aid in obtaining a definitive diagnosis for all patients.
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Affiliation(s)
- Sylvia A A M Genet
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Catharina Hospital Eindhoven, Eindhoven, The Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Esther Visser
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Catharina Hospital Eindhoven, Eindhoven, The Netherlands; Máxima Medical Center, Eindhoven/Veldhoven, The Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands
| | | | | | | | | | | | - Luc Brunsveld
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Maarten A C Broeren
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Máxima Medical Center, Eindhoven/Veldhoven, The Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands
| | - Daan van de Kerkhof
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Catharina Hospital Eindhoven, Eindhoven, The Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands; Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Volkher Scharnhorst
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Catharina Hospital Eindhoven, Eindhoven, The Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands; Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, The Netherlands.
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van Genderen MNG, Kneppers J, Zaalberg A, Bekers EM, Bergman AM, Zwart W, Eduati F. Agent-based modeling of the prostate tumor microenvironment uncovers spatial tumor growth constraints and immunomodulatory properties. NPJ Syst Biol Appl 2024; 10:20. [PMID: 38383542 PMCID: PMC10881528 DOI: 10.1038/s41540-024-00344-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/25/2024] [Indexed: 02/23/2024] Open
Abstract
Inhibiting androgen receptor (AR) signaling through androgen deprivation therapy (ADT) reduces prostate cancer (PCa) growth in virtually all patients, but response may be temporary, in which case resistance develops, ultimately leading to lethal castration-resistant prostate cancer (CRPC). The tumor microenvironment (TME) plays an important role in the development and progression of PCa. In addition to tumor cells, TME-resident macrophages and fibroblasts express AR and are therefore also affected by ADT. However, the interplay of different TME cell types in the development of CRPC remains largely unexplored. To understand the complex stochastic nature of cell-cell interactions, we created a PCa-specific agent-based model (PCABM) based on in vitro cell proliferation data. PCa cells, fibroblasts, "pro-inflammatory" M1-like and "pro-tumor" M2-like polarized macrophages are modeled as agents from a simple set of validated base assumptions. PCABM allows us to simulate the effect of ADT on the interplay between various prostate TME cell types. The resulting in vitro growth patterns mimic human PCa. Our PCABM can effectively model hormonal perturbations by ADT, in which PCABM suggests that CRPC arises in clusters of resistant cells, as is observed in multifocal PCa. In addition, fibroblasts compete for cellular space in the TME while simultaneously creating niches for tumor cells to proliferate in. Finally, PCABM predicts that ADT has immunomodulatory effects on macrophages that may enhance tumor survival. Taken together, these results suggest that AR plays a critical role in the cellular interplay and stochastic interactions in the TME that influence tumor cell behavior and CRPC development.
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Affiliation(s)
- Maisa N G van Genderen
- Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands
- Division of Oncogenomics, Oncode Institute, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Jeroen Kneppers
- Division of Oncogenomics, Oncode Institute, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Anniek Zaalberg
- Division of Oncogenomics, Oncode Institute, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Elise M Bekers
- Division of Pathology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Andries M Bergman
- Division of Oncogenomics, Oncode Institute, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Division of Medical Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
| | - Wilbert Zwart
- Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands.
- Division of Oncogenomics, Oncode Institute, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Institute for Complex Molecular Systems, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands.
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems, Eindhoven University of Technology, PO Box 513, 5600MB, Eindhoven, The Netherlands.
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3
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Mason M, Lapuente-Santana Ó, Halkola AS, Wang W, Mall R, Xiao X, Kaufman J, Fu J, Pfeil J, Banerjee J, Chung V, Chang H, Chasalow SD, Lin HY, Chai R, Yu T, Finotello F, Mirtti T, Mäyränpää MI, Bao J, Verschuren EW, Ahmed EI, Ceccarelli M, Miller LD, Monaco G, Hendrickx WRL, Sherif S, Yang L, Tang M, Gu SS, Zhang W, Zhang Y, Zeng Z, Das Sahu A, Liu Y, Yang W, Bedognetti D, Tang J, Eduati F, Laajala TD, Geese WJ, Guinney J, Szustakowski JD, Vincent BG, Carbone DP. A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer. J Transl Med 2024; 22:190. [PMID: 38383458 PMCID: PMC10880244 DOI: 10.1186/s12967-023-04705-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/05/2023] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.
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Affiliation(s)
- Mike Mason
- Bristol Myers Squibb, Princeton, NJ, USA
| | - Óscar Lapuente-Santana
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Anni S Halkola
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Wenyu Wang
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Raghvendra Mall
- Qatar Computing Research Institute, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar
- Department of Immunology, St. Jude Children's Research Hospital, P.O. Box 38105, Memphis, TN, USA
- Biotechnology Research Center, Technology Innovation Institute, P.O. Box 9639, Abu Dhabi, United Arab Emirates
| | - Xu Xiao
- School of Informatics, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Jacob Kaufman
- Department of Medicine, Duke University, Durham, NC, USA
- The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Jingxin Fu
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | | | - Han Chang
- Bristol Myers Squibb, Princeton, NJ, USA
| | | | | | | | | | - Francesca Finotello
- Institute of Molecular Biology, University of Innsbruck, Innsbruck, Austria
- Digital Science Center (DiSC), University of Innsbruck, Innsbruck, Austria
| | - Tuomas Mirtti
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- Department of Biomedical Engineering, School of Medicine, Emory University, Atlanta, GA, USA
| | - Mikko I Mäyränpää
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Jie Bao
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
| | - Emmy W Verschuren
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Eiman I Ahmed
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
| | - Michele Ceccarelli
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples "Federico II", 80125, Naples, Italy
- BIOGEM Institute of Molecular Biology and Genetics, Via Camporeale, Ariano Irpino, Italy
| | - Lance D Miller
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Atrium Health Wake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC, USA
| | - Gianni Monaco
- BIOGEM Institute of Molecular Biology and Genetics, Via Camporeale, Ariano Irpino, Italy
| | - Wouter R L Hendrickx
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
| | - Shimaa Sherif
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
| | - Lin Yang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ming Tang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | - Yi Zhang
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Zexian Zeng
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yang Liu
- Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Davide Bedognetti
- Human Immunology Department, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 26999, Doha, Qatar
- Department of Internal Medicine and Medical Specialties, University of Genoa, Genoa, Italy
| | - Jing Tang
- Faculty of Medicine, Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Teemu D Laajala
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland
- iCAN-Digital Precision Cancer Medicine Flagship, Helsinki, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, Turku, Finland
- Department of Pharmacology, Anschutz Medical Campus, University of Colorado, Denver, CO, USA
| | | | | | | | - Benjamin G Vincent
- Department of Medicine, Division of Hematology, Department of Microbiology and Immunology, Curriculum in Bioinformatics and Computational Biology, Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - David P Carbone
- The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA.
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Yelleswarapu M, Spinthaki S, de Greef TFA, Eduati F. Bilayer Microfluidic Device for Combinatorial Plug Production. J Vis Exp 2023. [PMID: 38108451 DOI: 10.3791/66154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023] Open
Abstract
Droplet microfluidics is a versatile tool that allows the execution of a large number of reactions in chemically distinct nanoliter compartments. Such systems have been used to encapsulate a variety of biochemical reactions - from incubation of single cells to implementation of PCR reactions, from genomics to chemical synthesis. Coupling the microfluidic channels with regulatory valves allows control over their opening and closing, thereby enabling the rapid production of large-scale combinatorial libraries consisting of a population of droplets with unique compositions. In this paper, protocols for the fabrication and operation of a pressure-driven, PDMS-based bilayer microfluidic device that can be utilized to generate combinatorial libraries of water-in-oil emulsions called plugs are presented. By incorporating software programs and microfluidic hardware, the flow of desired fluids in the device can be controlled and manipulated to generate combinatorial plug libraries and to control the composition and quantity of constituent plug populations. These protocols will expedite the process of generating combinatorial screens, particularly to study drug response in cells from cancer patient biopsies.
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Affiliation(s)
- Maaruthy Yelleswarapu
- Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology
| | - Sofia Spinthaki
- Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology
| | - Tom F A de Greef
- Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology
| | - Federica Eduati
- Department of Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology;
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Passier M, van Genderen MN, Zaalberg A, Kneppers J, Bekers EM, Bergman AM, Zwart W, Eduati F. Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model. Cancer Res Commun 2023; 3:1473-1485. [PMID: 37554550 PMCID: PMC10405859 DOI: 10.1158/2767-9764.crc-23-0097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/15/2023] [Accepted: 07/10/2023] [Indexed: 08/10/2023]
Abstract
Over 10% of men will be diagnosed with prostate cancer during their lifetime. Arising from luminal cells of the prostatic acinus, prostate cancer is influenced by multiple cells in its microenvironment. To expand our knowledge and explore means to prevent and treat the disease, it is important to understand what drives the onset and early stages of prostate cancer. In this study, we developed an agent-based model of a prostatic acinus including its microenvironment, to allow for in silico studying of prostate cancer development. The model was based on prior reports and in-house data of tumor cells cocultured with cancer-associated fibroblasts (CAF) and protumor and/or antitumor macrophages. Growth patterns depicted by the model were pathologically validated on hematoxylin and eosin slide images of human prostate cancer specimens. We identified that stochasticity of interactions between macrophages and tumor cells at early stages strongly affect tumor development. In addition, we discovered that more systematic deviations in tumor development result from a combinatorial effect of the probability of acquiring mutations and the tumor-promoting abilities of CAFs and macrophages. In silico modeled tumors were then compared with 494 patients with cancer with matching characteristics, showing strong association between predicted tumor load and patients' clinical outcome. Our findings suggest that the likelihood of tumor formation depends on a combination of stochastic events and systematic characteristics. While stochasticity cannot be controlled, information on systematic effects may aid the development of prevention strategies tailored to the molecular characteristics of an individual patient. Significance We developed a computational model to study which factors of the tumor microenvironment drive prostate cancer development, with potential to aid the development of new prevention strategies.
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Affiliation(s)
- Margot Passier
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Maisa N.G. van Genderen
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Anniek Zaalberg
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
| | - Jeroen Kneppers
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
| | - Elise M. Bekers
- Division of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Andries M. Bergman
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
- Division of Medical Oncology, Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
| | - Wilbert Zwart
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
- Division of Medical Oncology, Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Division of Medical Oncology, Netherlands Cancer Institute, Plesmanlaan, Amsterdam, the Netherlands
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Dinis Fernandes C, Schaap A, Kant J, van Houdt P, Wijkstra H, Bekers E, Linder S, Bergman AM, van der Heide U, Mischi M, Zwart W, Eduati F, Turco S. Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer. Cancers (Basel) 2023; 15:3074. [PMID: 37370685 DOI: 10.3390/cancers15123074] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/11/2023] [Accepted: 05/16/2023] [Indexed: 06/29/2023] Open
Abstract
Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.
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Affiliation(s)
- Catarina Dinis Fernandes
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Annekoos Schaap
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Joan Kant
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Petra van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Hessel Wijkstra
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Department of Urology, Amsterdam University Medical Centers, 1100 DD Amsterdam, The Netherlands
| | - Elise Bekers
- Department of Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Simon Linder
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Andries M Bergman
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- Division of Medical Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Uulke van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Massimo Mischi
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Wilbert Zwart
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Federica Eduati
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Simona Turco
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
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7
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Visser E, Genet SAAM, de Kock RPPA, van den Borne BEEM, Youssef-El Soud M, Belderbos HNA, Stege G, de Saegher MEA, van 't Westeinde SC, Brunsveld L, Broeren MAC, van de Kerkhof D, Deiman BALM, Eduati F, Scharnhorst V. Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer. Lung Cancer 2023; 178:28-36. [PMID: 36773458 DOI: 10.1016/j.lungcan.2023.01.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 01/11/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023]
Abstract
OBJECTIVES Pathologic subtyping of tissue biopsies is the gold standard for the diagnosis of lung cancer (LC), which could be complicated in cases of e.g. inconclusive tissue biopsies or unreachable tumors. The diagnosis of LC could be supported in a minimally invasive manner using protein tumor markers (TMs) and circulating tumor DNA (ctDNA) measured in liquid biopsies (LBx). This study evaluates the performance of LBx-based decision-support algorithms for the diagnosis of LC and subtyping into small- and non-small-cell lung cancer (SCLC and NSCLC) aiming to directly impact clinical practice. MATERIALS AND METHODS In this multicenter prospective study (NL9146), eight protein TMs (CA125, CA15.3, CEA, CYFRA 21-1, HE4, NSE, proGRP and SCCA) and ctDNA mutations in EGFR, KRAS and BRAF were analyzed in blood of 1096 patients suspected of LC. The performance of individual and combined TMs to identify LC, NSCLC or SCLC was established by evaluating logistic regression models at pre-specified positive predictive values (PPV) of ≥95% or ≥98%. The most informative protein TMs included in the multi-parametric models were selected by recursive feature elimination. RESULTS Single TMs could identify LC, NSCLC and SCLC patients with 46%, 25% and 40% sensitivity, respectively, at pre-specified PPVs. Multi-parametric models combining TMs and ctDNA significantly improved sensitivities to 65%, 67% and 50%, respectively. CONCLUSION In patients suspected of LC, the LBx-based decision-support algorithms allowed identification of about two-thirds of all LC and NSCLC patients and half of SCLC patients. These models therefore show clinical value and may support LC diagnostics, especially in patients for whom pathologic subtyping is impossible or incomplete.
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Affiliation(s)
- Esther Visser
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Catharina Hospital Eindhoven, Eindhoven, the Netherlands; Máxima Medical Center, Eindhoven/Veldhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands.
| | - Sylvia A A M Genet
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Catharina Hospital Eindhoven, Eindhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Remco P P A de Kock
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Catharina Hospital Eindhoven, Eindhoven, the Netherlands; Máxima Medical Center, Eindhoven/Veldhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands
| | | | | | | | | | | | | | - Luc Brunsveld
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Maarten A C Broeren
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Máxima Medical Center, Eindhoven/Veldhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands
| | - Daan van de Kerkhof
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Catharina Hospital Eindhoven, Eindhoven, the Netherlands
| | - Birgit A L M Deiman
- Catharina Hospital Eindhoven, Eindhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands; Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Volkher Scharnhorst
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Catharina Hospital Eindhoven, Eindhoven, the Netherlands; Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands; Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, the Netherlands
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8
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Visser E, de Kock R, Genet S, Borne BVD, Soud MYE, Belderbos H, Stege G, de Saegher M, ’t Westeinde SV, Broeren M, Eduati F, Deiman B, Scharnhorst V. Up-front mutation detection in circulating tumor DNA by droplet digital PCR has added diagnostic value in lung cancer. Transl Oncol 2022; 27:101589. [PMID: 36413862 PMCID: PMC9679361 DOI: 10.1016/j.tranon.2022.101589] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/28/2022] [Accepted: 11/11/2022] [Indexed: 11/21/2022] Open
Abstract
Identification of actionable mutations in advanced stage non-squamous non-small-cell lung cancer (NSCLC) patients is recommended by guidelines as it enables treatment with targeted therapies. In current practice, mutations are identified by next-generation sequencing of tumor DNA (tDNA-NGS), which requires tissue biopsies of sufficient quality. Alternatively, circulating tumor DNA (ctDNA) could be used for mutation analysis. This prospective, multicenter study establishes the diagnostic value of ctDNA analysis by droplet digital PCR (ctDNA-ddPCR) in patients with primary lung cancer. CtDNA from 458 primary lung cancer patients was analyzed using a panel of multiplex ddPCRs for EGFR (Ex19Del, G719S, L858R, L861Q and S768I), KRAS G12/G13 and BRAF V600 mutations. For 142 of 175 advanced stage non-squamous NSCLC patients tDNA-NGS results were available to compare to ctDNA-ddPCR. tDNA-NGS identified 98 mutations, of which ctDNA-ddPCR found 53 mutations (54%), including 32 of 45 (71%) targetable driver mutations. In 2 of these 142 patients, a mutation was found by ctDNA-ddPCR only. In 33 advanced stage patients lacking tDNA-NGS results, ctDNA-ddPCR detected 15 additional mutations, of which 7 targetable. Overall, ctDNA-ddPCR detected 70 mutations and tDNA-NGS 98 mutations in 175 advanced NSCLC patients. Using an up-front ctDNA-ddPCR strategy, followed by tDNA-NGS only if ctDNA-ddPCR analysis is negative, increases the number of mutations found from 98 to 115 (17%). At the same time, up-front ctDNA-ddPCR reduces tDNA-NGS analyses by 40%, decreasing the need to perform (additional) biopsies.
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Affiliation(s)
- Esther Visser
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands,Catharina Hospital Eindhoven, Eindhoven, the Netherlands,Máxima Medical Center, Eindhoven, Veldhoven, the Netherlands,Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands,Corresponding author at: Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
| | - Remco de Kock
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands,Catharina Hospital Eindhoven, Eindhoven, the Netherlands,Máxima Medical Center, Eindhoven, Veldhoven, the Netherlands,Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands
| | - Sylvia Genet
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands,Catharina Hospital Eindhoven, Eindhoven, the Netherlands,Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands,Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | | | | | | | | | | | | | - Maarten Broeren
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands,Máxima Medical Center, Eindhoven, Veldhoven, the Netherlands,Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands,Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands,Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands,Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Birgit Deiman
- Catharina Hospital Eindhoven, Eindhoven, the Netherlands,Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands
| | - Volkher Scharnhorst
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands,Catharina Hospital Eindhoven, Eindhoven, the Netherlands,Expert Center Clinical Chemistry Eindhoven, Eindhoven, the Netherlands,Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands,Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, the Netherlands
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9
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Kolmar L, Autour A, Ma X, Vergier B, Eduati F, Merten CA. Technological and computational advances driving high-throughput oncology. Trends Cell Biol 2022; 32:947-961. [PMID: 35577671 DOI: 10.1016/j.tcb.2022.04.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/11/2022] [Accepted: 04/20/2022] [Indexed: 01/21/2023]
Abstract
Engineering and computational advances have opened many new avenues in cancer research, particularly when being exploited in interdisciplinary approaches. For example, the combination of microfluidics, novel sequencing technologies, and computational analyses has been crucial to enable single-cell assays, giving a detailed picture of tumor heterogeneity for the very first time. In a similar way, these 'tech' disciplines have been elementary for generating large data sets in multidimensional cancer 'omics' approaches, cell-cell interaction screens, 3D tumor models, and tissue level analyses. In this review we summarize the most important technology and computational developments that have been or will be instrumental for transitioning classical cancer research to a large data-driven, high-throughput, high-content discipline across all biological scales.
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Affiliation(s)
- Leonie Kolmar
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Alexis Autour
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Xiaoli Ma
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Blandine Vergier
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
| | - Christoph A Merten
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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10
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Lapuente-Santana Ó, van Genderen M, Hilbers PA, Finotello F, Eduati F. Interpretable systems biomarkers predict response to immune-checkpoint inhibitors. Patterns (N Y) 2021; 2:100293. [PMID: 34430923 PMCID: PMC8369166 DOI: 10.1016/j.patter.2021.100293] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/22/2021] [Accepted: 05/31/2021] [Indexed: 02/07/2023]
Abstract
Cancer cells can leverage several cell-intrinsic and -extrinsic mechanisms to escape immune system recognition. The inherent complexity of the tumor microenvironment, with its multicellular and dynamic nature, poses great challenges for the extraction of biomarkers of immune response and immunotherapy efficacy. Here, we use RNA-sequencing (RNA-seq) data combined with different sources of prior knowledge to derive system-based signatures of the tumor microenvironment, quantifying immune-cell composition and intra- and intercellular communications. We applied multi-task learning to these signatures to predict different hallmarks of immune responses and derive cancer-type-specific models based on interpretable systems biomarkers. By applying our models to independent RNA-seq data from cancer patients treated with PD-1/PD-L1 inhibitors, we demonstrated that our method to Estimate Systems Immune Response (EaSIeR) accurately predicts therapeutic outcome. We anticipate that EaSIeR will be a valuable tool to provide a holistic description of immune responses in complex and dynamic systems such as tumors using available RNA-seq data.
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Affiliation(s)
- Óscar Lapuente-Santana
- Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands
| | - Maisa van Genderen
- Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands
| | - Peter A.J. Hilbers
- Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands
| | - Francesca Finotello
- Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands
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11
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Genet SA, Visser E, van den Borne BE, Soud MYE, Belderbos HN, Stege G, de Saegher ME, Eduati F, Broeren MA, van Dongen J, Brunsveld L, van de Kerkhof D, Scharnhorst V. Correction of the NSE concentration in hemolyzed serum samples improves its diagnostic accuracy in small-cell lung cancer. Oncotarget 2020; 11:2660-2668. [PMID: 32676167 PMCID: PMC7343637 DOI: 10.18632/oncotarget.27664] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 06/15/2020] [Indexed: 12/29/2022] Open
Abstract
Neuron-specific enolase (NSE) is a well-known biomarker for the diagnosis, prognosis and treatment monitoring of small-cell lung cancer (SCLC). Nevertheless, its clinical applicability is limited since serum NSE levels are influenced by hemolysis, leading to falsely elevated results. Therefore, this study aimed to develop a hemolysis correction equation and evaluate its role in SCLC diagnostics. Two serum pools were spiked with increasing amounts of hemolysate obtained from multiple individuals. A hemolysis correction equation was obtained by analyzing the relationship between the measured NSE concentration and the degree of hemolysis. The equation was validated using intentionally hemolyzed serum samples, which showed that the correction was accurate for samples with an H-index up to 30 μmol/L. Correction of the measured NSE concentration in patients suspected of lung cancer caused an increase in AUC and a significantly lower cut-off value for SCLC detection when compared to uncorrected results. Therefore, a hemolysis correction equation should be used to correct falsely elevated NSE concentrations. Results of samples with an H-index above 30 μmol/L should not be reported to clinicians. Application of the equation illustrates the importance of hemolysis correction in SCLC diagnostics and questions the correctness of the currently used diagnostic cut-off value.
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Affiliation(s)
- Sylvia A.A.M. Genet
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
- Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands
| | - Esther Visser
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
- Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands
| | | | | | | | | | | | - Federica Eduati
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
- Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands
| | - Maarten A.C. Broeren
- Máxima Medical Center, Eindhoven/Veldhoven, The Netherlands
- Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands
| | - Joost van Dongen
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
- Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands
| | - Luc Brunsveld
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
- Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands
| | - Daan van de Kerkhof
- Catharina Hospital Eindhoven, Eindhoven, The Netherlands
- Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands
| | - Volkher Scharnhorst
- Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, The Netherlands
- Catharina Hospital Eindhoven, Eindhoven, The Netherlands
- Expert Center Clinical Chemistry Eindhoven, Eindhoven, The Netherlands
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12
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Abstract
Immunotherapy with checkpoint blockers (ICBs), aimed at unleashing the immune response toward tumor cells, has shown a great improvement in overall patient survival compared to standard therapy, but only in a subset of patients. While a number of recent studies have significantly improved our understanding of mechanisms playing an important role in the tumor microenvironment (TME), we still have an incomplete view of how the TME works as a whole. This hampers our ability to effectively predict the large heterogeneity of patients' response to ICBs. Systems approaches could overcome this limitation by adopting a holistic perspective to analyze the complexity of tumors. In this Mini Review, we focus on how an integrative view of the increasingly available multi-omics experimental data and computational approaches enables the definition of new systems-based predictive biomarkers. In particular, we will focus on three facets of the TME toward the definition of new systems biomarkers. First, we will review how different types of immune cells influence the efficacy of ICBs, not only in terms of their quantification, but also considering their localization and functional state. Second, we will focus on how different cells in the TME interact, analyzing how inter- and intra-cellular networks play an important role in shaping the immune response and are responsible for resistance to immunotherapy. Finally, we will describe the potential of looking at these networks as dynamic systems and how mathematical models can be used to study the rewiring of the complex interactions taking place in the TME.
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Affiliation(s)
- Óscar Lapuente-Santana
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
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13
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Eduati F, Jaaks P, Wappler J, Cramer T, Merten CA, Garnett MJ, Saez-Rodriguez J. Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies. Mol Syst Biol 2020; 16:e9690. [PMID: 33438807 DOI: 10.15252/msb.209690] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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14
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Eduati F, Jaaks P, Wappler J, Cramer T, Merten CA, Garnett MJ, Saez‐Rodriguez J. Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies. Mol Syst Biol 2020; 16:e8664. [PMID: 32073727 PMCID: PMC7029724 DOI: 10.15252/msb.20188664] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 01/27/2020] [Accepted: 01/28/2020] [Indexed: 12/22/2022] Open
Abstract
Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.
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Affiliation(s)
- Federica Eduati
- European Molecular Biology Laboratory (EMBL)Genome Biology UnitHeidelbergGermany
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)HinxtonUK
- Joint Research Centre for Computational Biomedicine (JRC‐COMBINE)Faculty of MedicineRWTH Aachen UniversityAachenGermany
- Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
| | | | - Jessica Wappler
- Department SurgeryMolecular Tumor BiologyRWTH University HospitalAachenGermany
| | - Thorsten Cramer
- Department SurgeryMolecular Tumor BiologyRWTH University HospitalAachenGermany
- ESCAM – European Surgery Center Aachen MaastrichtAachenGermany
- ESCAM – European Surgery Center Aachen MaastrichtMaastrichtThe Netherlands
| | - Christoph A Merten
- European Molecular Biology Laboratory (EMBL)Genome Biology UnitHeidelbergGermany
| | | | - Julio Saez‐Rodriguez
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)HinxtonUK
- Joint Research Centre for Computational Biomedicine (JRC‐COMBINE)Faculty of MedicineRWTH Aachen UniversityAachenGermany
- Institute for Computational BiomedicineFaculty of MedicineBIOQUANT‐CenterHeidelberg UniversityHeidelbergGermany
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15
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Finotello F, Eduati F. Multi-Omics Profiling of the Tumor Microenvironment: Paving the Way to Precision Immuno-Oncology. Front Oncol 2018; 8:430. [PMID: 30345255 PMCID: PMC6182075 DOI: 10.3389/fonc.2018.00430] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 09/13/2018] [Indexed: 12/20/2022] Open
Abstract
The tumor microenvironment (TME) is a multifaceted ecosystem characterized by profound cellular heterogeneity, dynamicity, and complex intercellular cross-talk. The striking responses obtained with immune checkpoint blockers, i.e., antibodies targeting immune-cell regulators to boost antitumor immunity, have demonstrated the enormous potential of anticancer treatments that target TME components other than tumor cells. However, as checkpoint blockade is currently beneficial only to a limited fraction of patients, there is an urgent need to understand the mechanisms orchestrating the immune response in the TME to guide the rational design of more effective anticancer therapies. In this Mini Review, we give an overview of the methodologies that allow studying the heterogeneity of the TME from multi-omics data generated from bulk samples, single cells, or images of tumor-tissue slides. These include approaches for the characterization of the different cell phenotypes and for the reconstruction of their spatial organization and inter-cellular cross-talk. We discuss how this broader vision of the cellular heterogeneity and plasticity of tumors, which is emerging thanks to these methodologies, offers the opportunity to rationally design precision immuno-oncology treatments. These developments are fundamental to overcome the current limitations of targeted agents and checkpoint blockers and to bring long-term clinical benefits to a larger fraction of cancer patients.
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Affiliation(s)
- Francesca Finotello
- Biocenter, Division for Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
| | - Federica Eduati
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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16
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Eduati F, Utharala R, Madhavan D, Neumann UP, Longerich T, Cramer T, Saez-Rodriguez J, Merten CA. A microfluidics platform for combinatorial drug screening on cancer biopsies. Nat Commun 2018; 9:2434. [PMID: 29934552 PMCID: PMC6015045 DOI: 10.1038/s41467-018-04919-w] [Citation(s) in RCA: 133] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 06/05/2018] [Indexed: 02/06/2023] Open
Abstract
Screening drugs on patient biopsies from solid tumours has immense potential, but is challenging due to the small amount of available material. To address this, we present here a plug-based microfluidics platform for functional screening of drug combinations. Integrated Braille valves allow changing the plug composition on demand and enable collecting >1200 data points (56 different conditions with at least 20 replicates each) per biopsy. After deriving and validating efficient and specific drug combinations for two genetically different pancreatic cancer cell lines and xenograft mouse models, we additionally screen live cells from human solid tumours with no need for ex vivo culturing steps, and obtain highly specific sensitivity profiles. The entire workflow can be completed within 48 h at assay costs of less than US$ 150 per patient. We believe this can pave the way for rapid determination of optimal personalized cancer therapies.
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Affiliation(s)
- Federica Eduati
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, Cambridge, United Kingdom
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, 69117, Heidelberg, Germany
- Department of Biomedical Engineering, Eindhoven University of Technology, 5600MB, Eindhoven, The Netherlands
| | - Ramesh Utharala
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, 69117, Heidelberg, Germany
| | - Dharanija Madhavan
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, 69117, Heidelberg, Germany
| | - Ulf Peter Neumann
- Department of Surgery, RWTH University Hospital, 52057, Aachen, Germany
- ESCAM - European Surgery Center Aachen Maastricht, Aachen, Germany and Maastricht, The Netherlands
| | - Thomas Longerich
- Institute of Pathology, RWTH University Hospital, 52057, Aachen, Germany
- Institute of Pathology, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Thorsten Cramer
- ESCAM - European Surgery Center Aachen Maastricht, Aachen, Germany and Maastricht, The Netherlands
- Molecular Tumor Biology, Department Surgery, RWTH University Hospital, 52057, Aachen, Germany
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, Cambridge, United Kingdom.
- Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, 52057, Aachen, Germany.
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, BIOQUANT-Center, 69120, Heidelberg, Germany.
| | - Christoph A Merten
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstrasse 1, 69117, Heidelberg, Germany.
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17
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Traynard P, Tobalina L, Eduati F, Calzone L, Saez-Rodriguez J. Logic Modeling in Quantitative Systems Pharmacology. CPT Pharmacometrics Syst Pharmacol 2017; 6:499-511. [PMID: 28681552 PMCID: PMC5572374 DOI: 10.1002/psp4.12225] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 06/01/2017] [Accepted: 06/15/2017] [Indexed: 12/12/2022]
Abstract
Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).
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Affiliation(s)
- Pauline Traynard
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Luis Tobalina
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany
| | - Federica Eduati
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Laurence Calzone
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Julio Saez-Rodriguez
- RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen, Germany.,European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK
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18
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Eduati F, Doldàn-Martelli V, Klinger B, Cokelaer T, Sieber A, Kogera F, Dorel M, Garnett MJ, Blüthgen N, Saez-Rodriguez J. Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type-Specific Dynamic Logic Models. Cancer Res 2017; 77:3364-3375. [PMID: 28381545 DOI: 10.1158/0008-5472.can-17-0078] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Revised: 03/17/2017] [Accepted: 03/31/2017] [Indexed: 12/20/2022]
Abstract
Genomic features are used as biomarkers of sensitivity to kinase inhibitors used widely to treat human cancer, but effective patient stratification based on these principles remains limited in impact. Insofar as kinase inhibitors interfere with signaling dynamics, and, in turn, signaling dynamics affects inhibitor responses, we investigated associations in this study between cell-specific dynamic signaling pathways and drug sensitivity. Specifically, we measured 14 phosphoproteins under 43 different perturbed conditions (combinations of 5 stimuli and 7 inhibitors) in 14 colorectal cancer cell lines, building cell line-specific dynamic logic models of underlying signaling networks. Model parameters representing pathway dynamics were used as features to predict sensitivity to a panel of 27 drugs. Specific parameters of signaling dynamics correlated strongly with drug sensitivity for 14 of the drugs, 9 of which had no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by coblockade of GSK3, which was not found based on associations with genomic data. These results suggest that to better understand the cancer resistance and move toward personalized medicine, it is essential to consider signaling network dynamics that cannot be inferred from static genotypes. Cancer Res; 77(12); 3364-75. ©2017 AACR.
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Affiliation(s)
- Federica Eduati
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Victoria Doldàn-Martelli
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom.,Departamento de Física de la Materia Condensada, Condensed Matter Physics Center (IFIMAC) and Instituto Nicolás Cabrera, Facultad de Ciencias, Universidad Autónoma de Madrid, Madrid, Spain
| | - Bertram Klinger
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Integrative Research Institute (IRI) Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Thomas Cokelaer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom
| | - Anja Sieber
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Integrative Research Institute (IRI) Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Fiona Kogera
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Mathurin Dorel
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Integrative Research Institute (IRI) Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
| | - Mathew J Garnett
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, United Kingdom
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany. .,Integrative Research Institute (IRI) Life Sciences and Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, United Kingdom. .,Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, Aachen, Germany
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19
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Abstract
BACKGROUND The insulin signalling pathway (ISP) is an important biochemical pathway, which regulates some fundamental biological functions such as glucose and lipid metabolism, protein synthesis, cell proliferation, cell differentiation and apoptosis. In the last years, different mathematical models based on ordinary differential equations have been proposed in the literature to describe specific features of the ISP, thus providing a description of the behaviour of the system and its emerging properties. However, protein-protein interactions potentially generate a multiplicity of distinct chemical species, an issue referred to as "combinatorial complexity", which results in defining a high number of state variables equal to the number of possible protein modifications. This often leads to complex, error prone and difficult to handle model definitions. RESULTS In this work, we present a comprehensive model of the ISP, which integrates three models previously available in the literature by using the rule-based modelling (RBM) approach. RBM allows for a simple description of a number of signalling pathway characteristics, such as the phosphorylation of signalling proteins at multiple sites with different effects, the simultaneous interaction of many molecules of the signalling pathways with several binding partners, and the information about subcellular localization where reactions take place. Thanks to its modularity, it also allows an easy integration of different pathways. After RBM specification, we simulated the dynamic behaviour of the ISP model and validated it using experimental data. We the examined the predicted profiles of all the active species and clustered them in four clusters according to their dynamic behaviour. Finally, we used parametric sensitivity analysis to show the role of negative feedback loops in controlling the robustness of the system. CONCLUSIONS The presented ISP model is a powerful tool for data simulation and can be used in combination with experimental approaches to guide the experimental design. The model is available at http://sysbiobig.dei.unipd.it/ was submitted to Biomodels Database ( https://www.ebi.ac.uk/biomodels-main/ # MODEL 1604100005).
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Affiliation(s)
- Barbara Di Camillo
- Department of Information Engineering, University of Padova, Via Gradenigo 6A, Padova, 35131, Italy
| | - Azzurra Carlon
- Department of Information Engineering, University of Padova, Via Gradenigo 6A, Padova, 35131, Italy.,Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Florence, Italy
| | - Federica Eduati
- Department of Information Engineering, University of Padova, Via Gradenigo 6A, Padova, 35131, Italy.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Gianna Maria Toffolo
- Department of Information Engineering, University of Padova, Via Gradenigo 6A, Padova, 35131, Italy.
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20
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Eduati F, Mangravite LM, Wang T, Tang H, Bare JC, Huang R, Norman T, Kellen M, Menden MP, Yang J, Zhan X, Zhong R, Xiao G, Xia M, Abdo N, Kosyk O, Friend S, Dearry A, Simeonov A, Tice RR, Rusyn I, Wright FA, Stolovitzky G, Xie Y, Saez-Rodriguez J. Erratum: Prediction of human population responses to toxic compounds by a collaborative competition. Nat Biotechnol 2015; 33:1109. [PMID: 26448092 PMCID: PMC7608305 DOI: 10.1038/nbt1015-1109a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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21
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Cokelaer T, Bansal M, Bare C, Bilal E, Bot BM, Chaibub Neto E, Eduati F, de la Fuente A, Gönen M, Hill SM, Hoff B, Karr JR, Küffner R, Menden MP, Meyer P, Norel R, Pratap A, Prill RJ, Weirauch MT, Costello JC, Stolovitzky G, Saez-Rodriguez J. DREAMTools: a Python package for scoring collaborative challenges. F1000Res 2015; 4:1030. [PMID: 27134723 PMCID: PMC4837986 DOI: 10.12688/f1000research.7118.2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/01/2016] [Indexed: 01/30/2023] Open
Abstract
UNLABELLED DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of March 2016, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform at https://www.synapse.org. AVAILABILITY DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools/dreamtools.
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Affiliation(s)
- Thomas Cokelaer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI),Wellcome Trust Genome Campus, Cambridge, UK; Bioinformatics and Biostatistics Hub, C3BI, Institut Pasteur, Paris, France
| | - Mukesh Bansal
- Department of Systems Biology, Columbia University, New York, USA
| | | | - Erhan Bilal
- IBM, TJ Watson, Computational Biology Center, New York, USA
| | | | | | - Federica Eduati
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI),Wellcome Trust Genome Campus, Cambridge, UK
| | - Alberto de la Fuente
- Leibniz Institute for Farm Animal Biology, Institute of Genetics and Biometry, Dummerstorf, Germany
| | - Mehmet Gönen
- Oregon Health & Science University, Portland, OR, USA
| | - Steven M Hill
- MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
| | | | - Jonathan R Karr
- Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Robert Küffner
- Institute of Bioinformatics and Systems Biology, German Research Center for Environmental Health, Munich, Germany
| | - Michael P Menden
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI),Wellcome Trust Genome Campus, Cambridge, UK
| | - Pablo Meyer
- IBM, TJ Watson, Computational Biology Center, New York, USA
| | - Raquel Norel
- IBM, TJ Watson, Computational Biology Center, New York, USA
| | | | | | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology and Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children's Hospital, Cincinnati, OH, USA
| | - James C Costello
- Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Gustavo Stolovitzky
- IBM, TJ Watson, Computational Biology Center, New York, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI),Wellcome Trust Genome Campus, Cambridge, UK; RWTH Aachen University Medical Hospital, Joint Research Centre for Computational Biomedicine (JRCCOMBINE), Aachen, Germany
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22
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Di Camillo B, Eduati F, Nair SK, Avogaro A, Toffolo GM. Leucine modulates dynamic phosphorylation events in insulin signaling pathway and enhances insulin-dependent glycogen synthesis in human skeletal muscle cells. BMC Cell Biol 2014; 15:9. [PMID: 24646332 PMCID: PMC3994550 DOI: 10.1186/1471-2121-15-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2013] [Accepted: 03/12/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Branched-chain amino acids, especially leucine, are known to interact with insulin signaling pathway and glucose metabolism. However, the mechanism by which this is exerted, remain to be clearly defined. In order to examine the effect of leucine on muscle insulin signaling, a set of experiments was carried out to quantitate phosphorylation events along the insulin signaling pathway in human skeletal muscle cell cultures. Cells were exposed to insulin, leucine or both, and phosphorylation events of key insulin signaling molecules were tracked over time so as to monitor time-related responses that characterize the signaling events and could be missed by a single sampling strategy limited to pre/post stimulus events. RESULTS Leucine is shown to increase the magnitude of insulin-dependent phosphorylation of protein kinase B (AKT) at Ser473 and glycogen synthase kinase (GSK3β) at Ser21-9. Glycogen synthesis follows the same pattern of GSK3β, with a significant increase at 100 μM leucine plus insulin stimulus. Moreover, data do not show any statistically significant increase of pGSK3β and glycogen synthesis at higher leucine concentrations. Leucine is also shown to increase the magnitude of insulin-mediated extracellularly regulated kinase (ERK) phosphorylation; however, differently from AKT and GSK3β, ERK shows a transient behavior, with an early peak response, followed by a return to the baseline condition. CONCLUSIONS These experiments demonstrate a complementary effect of leucine on insulin signaling in a human skeletal muscle cell culture, promoting insulin-activated GSK3β phosphorylation and glycogen synthesis.
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Affiliation(s)
| | | | | | | | - Gianna M Toffolo
- Department of Information Engineering, University of Padua, Padua, Italy.
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23
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Guziolowski C, Videla S, Eduati F, Thiele S, Cokelaer T, Siegel A, Saez-Rodriguez J. Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming. Bioinformatics 2013; 29:2320-6. [PMID: 23853063 PMCID: PMC3753570 DOI: 10.1093/bioinformatics/btt393] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2013] [Revised: 06/17/2013] [Accepted: 07/04/2013] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions. RESULTS We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. AVAILABILITY caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. SUPPLEMENTARY INFORMATION Supplementary materials are available at Bioinformatics online. CONTACT santiago.videla@irisa.fr.
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24
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Eduati F, De Las Rivas J, Di Camillo B, Toffolo G, Saez-Rodriguez J. Integrating literature-constrained and data-driven inference of signalling networks. ACTA ACUST UNITED AC 2012; 28:2311-7. [PMID: 22734019 PMCID: PMC3436796 DOI: 10.1093/bioinformatics/bts363] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
MOTIVATION Recent developments in experimental methods facilitate increasingly larger signal transduction datasets. Two main approaches can be taken to derive a mathematical model from these data: training a network (obtained, e.g., from literature) to the data, or inferring the network from the data alone. Purely data-driven methods scale up poorly and have limited interpretability, whereas literature-constrained methods cannot deal with incomplete networks. RESULTS We present an efficient approach, implemented in the R package CNORfeeder, to integrate literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. Our method extends a given network with links derived from the data via various inference methods, and uses information on physical interactions of proteins to guide and validate the integration of links. We apply CNORfeeder to a network of growth and inflammatory signalling. We obtain a model with superior data fit in the human liver cancer HepG2 and propose potential missing pathways. AVAILABILITY CNORfeeder is in the process of being submitted to Bioconductor and in the meantime available at www.cellnopt.org. CONTACT saezrodriguez@ebi.ac.uk SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Federica Eduati
- Department of Information Engineering, University of Padova, Padova, 31050, Italy
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25
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Eduati F, Di Camillo B, Karbiener M, Scheideler M, Corà D, Caselle M, Toffolo G. Dynamic modeling of miRNA-mediated feed-forward loops. J Comput Biol 2012; 19:188-99. [PMID: 22300320 DOI: 10.1089/cmb.2011.0274] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Given the important role of microRNAs (miRNAs) in genome-wide regulation of gene expression, increasing interest is devoted to mixed transcriptional and post-transcriptional regulatory networks analyzing the combinatorial effect of transcription factors (TFs) and miRNAs on target genes. In particular, miRNAs are known to be involved in feed-forward loops (FFLs), where a TF regulates a miRNA and they both regulate a target gene. Different algorithms have been proposed to identify miRNA targets, based on pairing between the 5' region of the miRNA and the 3'UTR of the target gene, and correlation between miRNA host genes and target mRNA expression data. Here we propose a quantitative approach integrating an existing method for mixed FFL identification based on sequence analysis with differential equation modeling approach that permits us to select active FFLs based on their dynamics. Different models are assessed based on their ability to properly reproduce miRNA and mRNA expression data in terms of identification criteria, namely: goodness of fit, precision of the estimates, and comparison with submodels. In comparison with standard approaches based on correlation, our method improves in specificity. As a case study, we applied our method to adipogenic differentiation gene expression data providing potential novel players in this regulatory network. Supplementary Material for this article is available at www.liebertonline.com/cmb.
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Affiliation(s)
- Federica Eduati
- Department of Information Engineering, University of Padova, Padova, Italy
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26
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De Palo G, Eduati F, Zampieri M, Di Camillo B, Toffolo G, Altafini C. Adaptation as a genome-wide autoregulatory principle in the stress response of yeast. IET Syst Biol 2011; 5:269-79. [PMID: 21823758 DOI: 10.1049/iet-syb.2009.0050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
The gene expression response of yeast to various types of stresses/perturbations shows a common functional and dynamical pattern for the vast majority of genes, characterised by a quick transient peak (affecting primarily short genes) followed by a return to the pre-stimulus level. Kinetically, this process of adaptation following the transient excursion can be modelled using a genome-wide autoregulatory mechanism by means of which yeast aims at maintaining a preferential concentration in its mRNA levels. The resulting feedback system explains well the different time constants observable in the transient response, while being in agreement with all the known experimental dynamical features. For example, it suggests that a very rapid transient can be induced also by a slowly varying concentration of the gene products.
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Affiliation(s)
- G De Palo
- SISSA International School for Advanced Studies, Trieste, Italy
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27
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Abstract
The task of the DREAM4 (Dialogue for Reverse Engineering Assessments and Methods) “Predictive signaling network modeling” challenge was to develop a method that, from single-stimulus/inhibitor data, reconstructs a cause-effect network to be used to predict the protein activity level in multi-stimulus/inhibitor experimental conditions. The method presented in this paper, one of the best performing in this challenge, consists of 3 steps: 1. Boolean tables are inferred from single-stimulus/inhibitor data to classify whether a particular combination of stimulus and inhibitor is affecting the protein. 2. A cause-effect network is reconstructed starting from these tables. 3. Training data are linearly combined according to rules inferred from the reconstructed network. This method, although simple, permits one to achieve a good performance providing reasonable predictions based on a reconstructed network compatible with knowledge from the literature. It can be potentially used to predict how signaling pathways are affected by different ligands and how this response is altered by diseases.
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Affiliation(s)
- Federica Eduati
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Alberto Corradin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Barbara Di Camillo
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Gianna Toffolo
- Department of Information Engineering, University of Padova, Padova, Italy
- * E-mail:
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