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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
10,712.11
A''
2,116.96
A'
4,782.19
A1/2
6,922.16
CI10
14,235
CImax
629
h10
52
A1
35.25
A3
13.63
Data for the last 5 years (citations for the last 10 years) on December 2, 2023; A3 for period 2017-2021
Data for ARIS tenders ( 04.04.2019 – Programme tender, archive )
Database Linked records Citations Pure citations Average pure citations
WoS  612  18,655  16,848  27.53 
Scopus  694  23,667  21,352  30.77 
Researchers (22)
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 - 2023  24 
2.  25441  PhD Emanuela Boštjančič  Microbiology and immunology  Researcher  2021 - 2023  111 
3.  53798  Jure Brence  Computer science and informatics  Researcher  2021 - 2023  21 
4.  36220  PhD Martin Breskvar  Computer science and informatics  Researcher  2021 - 2023  34 
5.  53705  Alenka Dečman Cerar    Technical associate  2022 
6.  54662  Tina Draškovič  Oncology  Junior researcher  2021 - 2023 
7.  11130  PhD Sašo Džeroski  Computer science and informatics  Researcher  2021 - 2023  1,189 
8.  39720  Zdenka Flis    Technical associate  2022 
9.  27704  PhD Nina Hauptman  Chemistry  Head  2021 - 2023  98 
10.  31050  PhD Dragi Kocev  Computer science and informatics  Researcher  2021 - 2023  200 
11.  18455  Žiga Kušar  Neurobiology  Technical associate  2022 
12.  35470  PhD Jurica Levatić  Computer science and informatics  Researcher  2022 - 2023  42 
13.  27759  PhD Panče Panov  Computer science and informatics  Researcher  2021 - 2023  151 
14.  53702  Metod Perme    Technical associate  2022 
15.  38206  PhD Matej Petković  Computer science and informatics  Researcher  2021 - 2023  60 
16.  36541  PhD Alojz Šmid  Oncology  Researcher  2021 - 2023  72 
17.  11949  PhD Borut Štabuc  Oncology  Researcher  2021 - 2023  668 
18.  39597  PhD Jovan Tanevski  Computer science and informatics  Researcher  2021 - 2023  33 
19.  51957  PhD Ana Unkovič  Medical sciences  Researcher  2022 - 2023 
20.  51961  PhD Kristian Urh  Medical sciences  Researcher  2021 - 2023  20 
21.  12955  PhD Nina Zidar  Microbiology and immunology  Researcher  2021 - 2023  374 
22.  51028  PhD Margareta Žlajpah  Oncology  Technical associate  2021 - 2023  17 
Organisations (3)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0106  Jožef Stefan Institute  Ljubljana  5051606000  88,443 
2.  0312  University Medical Centre Ljubljana  Ljubljana  5057272000  75,485 
3.  0381  University of Ljubljana, Faculty of Medicine  Ljubljana  1627066  45,364 
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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