Systematic biobanking, novel imaging techniques, and advanced molecular analysis for precise tumor diagnosis and therapy: The Polish MOBIT project

Nikliński, Jacek; Krętowski, Adam; Moniuszko, Marcin; Reszeć, Joanna; Michalska-Falkowska, Anna; Niemira, Magdalena; Ciborowski, Michał; Charkiewicz, Radosław; Jurgilewicz, Dorota; Kozłowski, Mirosław; Ramlau, Rodryg; Piwkowski, Cezary; Kwaśniewski, Mirosław; Kaczmarek, Monika M.; Ciereszko, Andrzej; Waśniewski, Tomasz; Mróz, Robert; Naumnik, Wojciech; Sierko, Ewa; Paczkowska, Magdalena; Kiśluk, Joanna; Sulewska, Anetta; Cybulski, Adam; Mariak, Zenon; Kędra, Bogusław; Szamatowicz, Jacek; Kurzawa, Paweł; Minarowski, Łukasz; Charkiewicz, Angelika Edyta; Mroczko, Barbara; Małyszko, Jolanta; Manegold, Christian; Pilz, Lothar R.; Allgayer, Heike; Abba, Mohammed; Juhl, Hartmut; Koch, Frauke

doi:10.1016/j.advms.2017.05.002

Cited by 19 publications

(20 citation statements)

References 24 publications

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“…Tissue samples were collected intraoperatively and processed immediately after surgical removal according to the systematic biobanking quality (28). After the macroscopic visual assessment, the tumors were divided into two sections.…”

Section: Methodsmentioning

confidence: 99%

Lymphatics-associated genes are downregulated at transcription level in non-small cell lung cancer

Kowalczuk

Laudański

et al. 2018

Oncol Lett

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The present study aimed to verify a possibility of ongoing lymphangiogenesis in non-small cell lung cancer (NSCLC) via examination of mRNA levels of a number of lymphangiogenesis-associated genes in tumors. It was hypothesized that transcriptional activation of these genes would occur in tumors that stimulate new lymphatic vessel formation. The study was performed on 140 pairs of fresh-frozen surgical specimens of cancer and unaffected lung tissues derived from NSCLC stage I-IIIA patients. mRNA levels were evaluated with the reverse transcription-quantitative polymerase chain reaction method and expressed as fold change differences between the tumor and normal tissues. Possible associations between expression and patient clinicopathological characteristics and survival were analyzed. In the NSCLC tissue samples, vascular endothelial growth factor (VEGF) C, VEGFD, VEGFR3, VEGFR2, VEGFR1, lymphatic vessel endothelial hyaluronan receptor 1, integrin subunit α 9, FOX2, neuropilin 2, fibroblast growth factor 2 genes were significantly downregulated (P<0.001 for all) compared with matched normal lung tissues, whereas mRNA levels for VEGFA, spleen associated tyrosine kinase, podoplanin, and prospero homeobox 1 genes were similar in both tissues. Neither lymph node status, nor disease pathological stage influenced expression, whereas more profound suppression of gene activities appeared to occur in squamous cell carcinomas compared with adenocarcinomas. The VEGFR1 mRNA expression level was significantly connected with patient survival in the univariate analysis, and was an independent prognostic factor for overall survival in the multivariate Cox's proportional hazards model (HR 2.103; 95% confidence interval: 1.005-4.401; P=0.049). The results support a hypothesis of absence of new lymphatic vessel formation inside growing NSCLC tumor mass, however do not exclude a possibility of lymphangiogenesis in narrow marginal tumor parts.

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Section: Methodsmentioning

confidence: 99%

Lymphatics-associated genes are downregulated at transcription level in non-small cell lung cancer

Kowalczuk

Laudański

et al. 2018

Oncol Lett

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“…During the study program from 2015-2016, 114 patients with NSCLC were enrolled in the MOBIT project and underwent surgical tumour resection [19]. The mean age at diagnosis was 65.4 years, and 80 patients (70.2%) were male.…”

Section: Baseline Characteristicmentioning

confidence: 99%

“…Samples size factors were estimated, and counts were normalised using Relative Log Expression (RLE). Differential expression analysis assuming negative binominal distribution was performed with R packages DESeq2 [19], while the package EdgeR [23] (Trimmed Mean of M-values normalisation) was also used for verification of estimation robustness. After model fitting, dispersion estimates were obtained, and general linearised model was applied.…”

Section: Rna Sample Preparation and Sequencingmentioning

confidence: 99%

“…The broad goal of the current study was to identify transcriptome profile associated with the histological subtypes of NSCLC in a well-characterised group of patients from MOBIT project using RNA-seq. The Molecular Biomarkers for Individualized Therapy (MOBIT) [19] is an ambitious study targeting the development of personalised diagnostics based on high-quality biobanking and integrated analysis of 'omics' and imaging PET/MRI (Positron Emission Tomography-Magnetic Resonance Imaging) analysis, including the phenotypic, clinical, and epidemiological traits. As part of the project MOBIT, biospecimens of lung tumour and normal tissues were collected under strictly monitored conditions.…”

Section: Introductionmentioning

confidence: 99%

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Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

Niemira

Collin

Szałkowska

et al. 2019

Cancers

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212

166

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Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies consisting essentially of adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Although the diagnosis and treatment of ADC and SCC have been greatly improved in recent decades, there is still an urgent need to identify accurate transcriptome profile associated with the histological subtypes of NSCLC. The present study aims to identify the key dysregulated pathways and genes involved in the development of lung ADC and SCC and to relate them with the clinical traits. The transcriptional changes between tumour and normal lung tissues were investigated by RNA-seq. Gene ontology (GO), canonical pathways analysis with the prediction of upstream regulators, and weighted gene co-expression network analysis (WGCNA) to identify co-expressed modules and hub genes were used to explore the biological functions of the identified dysregulated genes. It was indicated that specific gene signatures differed significantly between ADC and SCC related to the distinct pathways. Of identified modules, four and two modules were the most related to clinical features in ADC and SCC, respectively. CTLA4, MZB1, NIP7, and BUB1B in ADC, as well as GNG11 and CCNB2 in SCC, are novel top hub genes in modules associated with tumour size, SUVmax, and recurrence-free survival. Our research provides a more effective understanding of the importance of biological pathways and the relationships between major genes in NSCLC in the perspective of searching for new molecular targets.

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“…Due to huge demand for the treatments and prevention of chronic diseases, mainly driven by aging of the population, there is a clear need for the new global integrative healthcare approaches [ 5 ]. Majority of the recent approaches to personalized medicine in oncology and other diseases relied on the various data types including the multiple types of genomic [ 6 - 10 ], transcriptomic [ 11 - 13 ], microRNA [ 14 ], proteomic [ 15 ], antigen [ 16 ], methylation [ 17 ], imaging [ 18 , 19 ], metagenomic [ 20 ], mitochondrial [ 21 ], metabolic [ 22 ], physiological [ 23 ] and other data. And while several attempts were made to evaluate the clinical benefit of the different methods [ 24 ] and multiple data types were used for evaluating the health status of the individual patients [ 25 ] including the widely popularized “Snyderome” project [ 26 ], none of these approaches are truly integrative on the population scale and compare the predictive nature and value of the various data types in the context of biomedicine.…”

Section: Introductionmentioning

confidence: 99%

Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare

Mamoshina

Ojomoko

Yanovich³

et al. 2017

Oncotarget

367

205

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The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.

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Systematic biobanking, novel imaging techniques, and advanced molecular analysis for precise tumor diagnosis and therapy: The Polish MOBIT project

Cited by 19 publications

References 24 publications

Lymphatics-associated genes are downregulated at transcription level in non-small cell lung cancer

Lymphatics-associated genes are downregulated at transcription level in non-small cell lung cancer

Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare

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