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Projects / Programmes source: ARIS

Determining the origin of liver metastases from liquid biopsy

Research activity

Code Science Field Subfield
3.04.00  Medical sciences  Oncology   

Code Science Field
3.02  Medical and Health Sciences  Clinical medicine 
Keywords
cancer, adenocarcinoma, epigenetic marker, metastasis, liquid biopsy, cell-free DNA, bioinformatics, liver tumors, circulating tumor cells
Evaluation (rules)
source: COBISS
Points
10,842.36
A''
2,287.63
A'
4,999.62
A1/2
7,334.8
CI10
15,318
CImax
659
h10
53
A1
36.83
A3
5.51
Data for the last 5 years (citations for the last 10 years) on September 15, 2024; A3 for period 2018-2022
Data for ARIS tenders ( 04.04.2019 – Programme tender, archive )
Database Linked records Citations Pure citations Average pure citations
WoS  628  19,306  17,398  27.7 
Scopus  711  24,495  22,069  31.04 
Researchers (24)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  33147  PhD Luka Bolha  Biochemistry and molecular biology  Researcher  2021 - 2024  27 
2.  25441  PhD Emanuela Boštjančič  Microbiology and immunology  Researcher  2021 - 2024  122 
3.  53798  Jure Brence  Computer science and informatics  Researcher  2021 - 2024  22 
4.  36220  PhD Martin Breskvar  Computer science and informatics  Researcher  2021 - 2023  36 
5.  53705  Alenka Dečman Cerar    Technical associate  2022 
6.  54662  Tina Draškovič  Oncology  Junior researcher  2021 - 2024 
7.  11130  PhD Sašo Džeroski  Computer science and informatics  Researcher  2021 - 2024  1,209 
8.  39720  Zdenka Flis    Technical associate  2022 
9.  27704  PhD Nina Hauptman  Chemistry  Head  2021 - 2024  108 
10.  31050  PhD Dragi Kocev  Computer science and informatics  Researcher  2021 - 2024  205 
11.  53530  Ana Kostovska  Computer science and informatics  Researcher  2024  44 
12.  18455  Žiga Kušar  Neurobiology  Technical associate  2022 - 2023 
13.  35470  PhD Jurica Levatić  Computer science and informatics  Researcher  2022 - 2023  44 
14.  53799  Martin Marzidovšek  Computer science and informatics  Researcher  2024  35 
15.  27759  PhD Panče Panov  Computer science and informatics  Researcher  2021 - 2024  157 
16.  53702  Metod Perme    Technical associate  2022 
17.  38206  PhD Matej Petković  Computer science and informatics  Researcher  2021 - 2023  67 
18.  36541  PhD Alojz Šmid  Oncology  Researcher  2021 - 2024  77 
19.  11949  PhD Borut Štabuc  Oncology  Researcher  2021 - 2024  680 
20.  39597  PhD Jovan Tanevski  Computer science and informatics  Researcher  2021 - 2024  38 
21.  51957  PhD Ana Unkovič  Medical sciences  Researcher  2022 - 2024 
22.  51961  PhD Kristian Urh  Medical sciences  Researcher  2021 - 2024  22 
23.  12955  PhD Nina Zidar  Microbiology and immunology  Researcher  2021 - 2024  392 
24.  51028  PhD Margareta Žlajpah  Oncology  Technical associate  2021 - 2024  22 
Organisations (3)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0381  University of Ljubljana, Faculty of Medicine  Ljubljana  1627066  49,127 
2.  0106  Jožef Stefan Institute  Ljubljana  5051606000  91,577 
3.  0312  University Medical Centre Ljubljana  Ljubljana  5057272000  78,447 
Abstract
Determining the origin of liver metastases from liquid biopsy Background Liver tumors are common and include primary and metastatic tumors. Exact differentiation of the tumor type is an essential step in choosing the optimal treatment. The most challenging is to distinguish among metastatic adenocarcinomas from various origins, and between metastatic adenocarcinomas and cholangiocarcinoma. This differentiation is sometimes difficult to make even if the most comprehensive clinical, laboratory, radiological, endoscopic and conventional pathological examinations are used, and the tumor is termed as cancer of unknown primary. Liver tumors are either primary tumors, including hepatocellular carcinoma and intrahepatic cholangiocarcinoma, or metastatic tumors, most commonly carcinomas, melanomas, lymphomas and sarcomas. It is sometimes difficult to distinguish between metastatic and primary liver carcinoma, particularly between metastatic adenocarcinoma and cholangiocarcinoma. However, given the different prognosis and treatment options, this discrimination is of vital importance. Carcinogenesis is accompanied by widespread genomic changes within the cell, including DNA alterations, protein expression and epigenetic changes (e.g. DNA methylation). These changes can be detected in circulating cancer byproducts in liquid biopsies: cell-free nucleic acids (cell-free tumor DNA, mRNA and miRNA), circulating tumor cells and extracellular vesicles. Many of these changes occur early in tumorigenesis and are highly pervasive across different tumor types. Therefore, a combination of different liquid biopsy biomarkers holds great promise for early cancer detection, primary tumor site discovery and treatment optimization. Hypotheses With bioinformatics analysis and machine learning methods we can identify genetic markers and patterns specific for each primary and metastatic liver tumor We can design custom-made genetic marker panel for discrimination between common malignant liver tumors and identify the origin of liver metastases Methods Our project proposes to use bioinformatics integration of genomics, transcriptomic and proteomics data for common primary and metastatic liver tumors, to decipher the diagnosis and primary tumor location. With bioinformatics tools, we will analyze available genomic data of approx. 2,000 samples of different primary tumor sites, which we will be used in further machine learning methods. This approach will help us uncover specific genomic patterns of each primary tumor and help us identify specific genomic biomarkers on which a custom-made marker panel will be designed. For clinical validation of panel, tissue and blood samples of patients with primary and metastatic liver tumor will be used. For detection of selected markers the next-generation sequencing, pyrosequencing and/or digital droplet PCR will be performed. Objectives To search for genomic and transcriptomic markers specific for a primary tumor with our own extensive bioinformatic analysis To identify genetic patterns for a specific primary tumor using cutting-edge machine learning methods To construct marker panels designed to discriminate among different primary and metastatic liver tumors To test the marker panels on tissue samples and liquid biopsy samples
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