Combine molecular biology and bioinformatics to study transitions from healthy skin to melanoma cancer cells

Tijana Randic. PhD supervisor: Stephanie Kreis

The aim of the project is to characterise melanoma development and identify predicting signals. Dynamic aspects of miRNA and mRNA expression patterns and their interplay over time have been a focus of SK’s group (Nazarov 2013). This DTU offers expertise from various scientific backgrounds and covers aspects of mathematical modelling directly connected to the dynamic aspects of melanoma development. This cancer generally begins with the transition of benign naevus or healthy skin into a primary melanoma lesion from where it progresses rapidly to metastatic disease if not excised or treated (Sekulic 2008). Once metastatised, melanoma has one of the most dismal prognosis and shortest life expectancy of all cancers (Siegel 2014).

Over the years SK’s group has acquired expertise in working with melanoma cell systems and fresh tissue material. It has established contacts with dermatological clinics for high quality patient samples (also over time). The group has in place state-of-the-art techniques and pipelines for analysing the miRNomes, transcriptomes and genomes from tissue and blood samples by NGS sequencing, qPCR arrays or microarrays. Exosomes can be readily isolated from tissue culture and patient samples and SK’s group is currently establishing the methodology for analysing exosome content. The molecular biological techniques required for analysis and validation of identified key players in transitional processes are available. Given the molecular biological and bioinformatic knowledge (Guan 2015), the cellular model systems, as well as the sequential clinical samples that are available in the group, this project will be able to contribute high-quality biological data to the overall DTU. The project will use these data to develop methods and models to predict critical transitions from a healthy/benign cell state to a cancerous/malignant state. The subproject will interact with the single-cell omics approach in disease dynamics by AS to perform single-cell analyses and identify potential cancer-specific characteristics of CTs.

The PhD student with a background in biology will first learn all necessary techniques using cultured cells and will participate in establishing different procedures to transform healthy melanocytes into tumour cells. Next, the student will generate the required data sets on sequential samples before moving onto patient samples. The analysis of NGS data sets will be performed in close collaboration with scientists from the LCSB and LSRU.

The subproject will provide the necessary data for the analysis of CT by theoretical approaches. In turn, prediction obtained by the modelling methods will then be tested to validate the framework of CTs in melanoma. Hence, the DTU members will support each other with regards to appropriate data analysis, which will ensure a tight coupling of theory development and the data generation process. The miRNA team will benefit from the mathematical/modelling expertise gathered in this consortium. Analysis of sequential data sets will be performed in close collaboration with members of this DTU so that data are prepared in a suitable way to permit downstream use in prediction models for CTs.