Comment article by Marieke Kuijjer published in the Journal Cancer Research
Dr Kuijjer's article, 'Predicting Evolution Using Cell State Dynamics', examines research using state transitions to model cancer development and progression. The group has also recently published in the journals Cell Reports and Bioinformatics.
Dr Marieke Kuijjer. Photo: Oda Hveem
One of the biggest challenges in cancer is predicting its initiation and course of progression. In this issue of Cancer Research, Rockne and colleagues use state transition theory to predict how peripheral mononuclear blood cells in mice transition from a healthy state to acute myeloid leukemia. They found that critical transcriptomic perturbations could predict initiation and progression of the disease.
This is an important step toward accurately predicting cancer evolution, which may eventually facilitate early diagnosis of cancer and disease recurrence, and which could potentially inform on timing of therapeutic interventions.
Read the article, 'Predicting Cancer Evolution Using Cell State Dynamics', M. Kuijjer, Cancer Research (2020) in full at https://cancerres.aacrjournals.org/content/80/15/3072.long
Further publications for Dr Kuijjer in Cell Reports and Bioinformatics
The comment article comes after two further publications for Dr. Kuijjer, in the journals Cell Reports and Bioinformatics.
- Dr Kuijjer participated in a study to understand sex differences in gene regulation, which was published in Cell Reports. (https://pubmed.ncbi.nlm.nih.gov/32579922/) and also featured on GenomeWeb. The networks analyzed in this project were modeled with on the group's LIONESS too. The study was a very large effort and included over 8000 "big data" networks.
- The Kuijjer group's new miRNA regulatory network tool PUMA has now been accepted in the journal Bioinformatics: https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btaa571/5858977.