Strong advances have been made in the development of different cancer treatments with a variety of drug targets. However, not all treatments are equally effective for every patient, even for those with the same type of cancer, and some may experience negative side effects. This emphasizes the need to identify novel biomarkers to help select the most suitable treatment for each patient, thereby improving precision medicine. The Cancer Computational Biology group is researching the molecular differences between patients who respond well to a drug and those who develop resistance. The group is studying different treatment types, such as chemotherapy, hormone therapy, and immunotherapy, and analyzes genetic, epigenetic, and transcriptomic markers to identify patient differences.

The Cancer Computational Biology group is dedicated to improving our understanding of the molecular differences between cancer patients. To achieve this, they use multi-omics data & pathology images to explore the possibility of identifying new cancer subgroups. By identifying different subgroups, they aim to gain a better understanding of the unique pathways involved in each one, which could lead to the development of more specific and effective treatment targets. The use of multi-omics data plus histopathology images enables the group to integrate information from various sources, such as genomics, epigenomics, transcriptomics, proteomics and images, to gain a more comprehensive understanding of the underlying biological mechanisms involved in cancer development and progression. By leveraging this knowledge, they hope to develop more personalized and effective treatment options for cancer patients.

It is often impossible to obtain accurate measurements of all independent lifestyle and environmental factors in each patient, which results in limited availability of this information in cancer consortium data. As a consequence, in-depth research into the links between these factors and cancer incidence is restricted. Previous work by the Cancer Computational Biology Group has demonstrated the impact of lifestyle factors on epigenetic markers, such as miRNA expression, while we have explored the potential of DNA methylation as a mediator between lifestyle and non-communicable diseases during her PhD. To build upon this research, the Cancer Computational Biology Group is developing a new methodology that utilizes epigenetic markers as a proxy for lifestyle and environmental exposure, enabling a more comprehensive investigation into the association between these factors and cancer incidence. The results of this study have the potential to improve our understanding of cancer etiology and to inform the development of personalized prevention strategies that consider lifestyle and environmental factors.

In a Nature Medicine publication (2019), the Cancer Computational Biology group demonstrated the association of chromatin-modifying genes with chemotherapy resistance in breast cancer. The findings of the study suggest that epigenetic modifications play a crucial role in drug resistance and that targeting tumour suppressor genes through epigenetic drugs can potentially overcome this resistance. Building on this research, the group aims to further investigate the relationship between epigenetic modifications and drug resistance in breast cancer and other cancer types. The ultimate goal is to develop effective strategies that can help to personalize cancer treatment and improve patient outcomes.

The concept of synthetic lethality refers to the simultaneous presence of alterations in two genes that result in cell death, even though the alteration in each gene alone does not have significant consequences for cell survival. Synthetic lethality has been studied in cancer research for several years as a way to identify potential drug targets. However, most studies so far have focused on alterations in the genetic sequence of the genes involved. The Cancer Computational Biology group takes a different approach by investigating the implementation of multi-omics layers to identify synthetic lethality. Specifically, the group focuses on genes related to chromosomal regulation and implements a multi-omics scheme that investigates mutations, copy number aberrations, altered DNA methylation, and gene expression levels to identify synthetic lethality targets.