Finding ways to detect cancer from blood samples

Doctoral student Parisa Mapar, studying both at the University of Helsinki and Amsterdam UMC, develops algorithms to analyse DNA from the tumour present in the blood samples.

 Parisa Mapar: what is the focus of your doctoral research?  

The focus of my research is the application of machine learning techniques to liquid biopsy data for the early detection of cancer and its recurrence. I investigate the data obtained from blood draws to find biomarkers indicating signs of cancer. Specifically, I develop algorithms to analyse circulating tumour DNA, which is DNA from the tumour present in the blood samples.   

The goal is to identify the presence of cancer, determine its type, and predict recurrence. This approach is non-invasive and cost-effective, so it is an excellent method for cancer screening and monitoring.  

How did you end up studying your field and what inspired you to pursue a doctorate?  

My sister, who is six years older than I am and is currently a Postdoctoral Fellow at Harvard Medical School, studied electrical engineering in Iran. Her passion for computer science and electrical engineering profoundly influenced my decision to pursue a bachelor's degree in computer engineering.  

After that, my fascination with the potential of computational methods in solving complex biological problems sparked my interest in bioinformatics. I got really interested in bioinformatics during my master's studies at Aalto University.  

Currently, I am doing a joint doctorate, which I started in 2021 at the University of Helsinki and expanded in 2023 to include Amsterdam UMC and Vrije Universiteit in the Netherlands. In the Netherlands, the allotted time for completing the doctorate is four years, so I have three more years to go.  I am fortunate to have three supervisors guiding my research from three different countries: one at the University of Manchester, one at Amsterdam UMC, and one at the Institute for Molecular Medicine Finland (FIMM) in Helsinki.  

How is your work important for the wider community?  

My work has significant implications for public health. Early detection of cancer and its recurrence can significantly improve patient outcomes, reduce healthcare costs, and lower cancer mortality rates. This is especially important in populations with limited access to traditional diagnostic methods.   

Since it is a non-invasive approach, multiple blood samples can be taken over time to monitor tumour growth or treatment effects, providing extra information for personalised cancer treatment.  

What are your plans for the future? Where do you see yourself after you have received your doctorate?  

After finishing my PhD, I envision myself continuing in the field of biomedical research, either in an academic setting or within the biotech industry. I hope to one day manage my own team, as my heart truly lies in research.  

What advice would you give to someone just starting their doctoral journey?  

My first piece of advice is to choose a topic you are genuinely passionate about. A PhD journey is challenging and demanding, and it is your interest and passion that will keep you going. Take your time to be sure of your topic before starting your PhD journey.   

Also, be open about the struggles you face during your PhD. This helps both you and others in similar situations.   

Seek mentorship whenever possible and attend PhD gatherings for peer advice and support.   

For me, a real eye-opener was the once-in-a-lifetime opportunity to attend the Global Young Scientists Summit in Singapore in January 2024. Meeting Nobel Prize winners and other prominent researchers in person was amazing! It was very inspiring to see how they view science as a global collaboration rather than a competition.  

Most importantly, remember to balance your work with personal well-being.   

Parisa Mapar
  • born in Iran, lives currently in the Netherlands  
  • BSc in Computer Engineering at Azad University, Tehran, in 2012, major in hardware engineering.  
  • MSc in Computer, Communication and Information Sciences at Aalto University with Major in communications engineering and Minor in machine learning and data mining in 2018.  Master's thesis topic: Machine learning for enzyme promiscuity.  
  • doctoral student since 2021 in the Integrative Life Science programme at University of Helsinki and since 2023 at Amsterdam UMC and Vrije Universiteit, the Netherlands (joint doctoral student). Doctoral thesis topic: Analysis of cell-free DNA fragmentation patterns through machine learning methods to detect cancer.  
  • working in the Institute for Molecular Medicine Finland (FIMM) and in the Cancer Center Amsterdam 
  • Funding: University of Helsinki Doctoral School and foundations
  • Parisa Mapar in the University of Helsinki Research Portal