Carino Gurjao and his team focus on developing and applying computational methods to analyze DNA, with the goal of unraveling the complexities of cancer evolution and progression.

Cancer is characterized by uncontrolled cell proliferation, driven by DNA mutations. Since DNA can be examined not only as a molecule but also as a sequence of letters, or even as a physical polymer, the study of tumor DNA lies at the intersection of multiple disciplines.

Research themes

With the advent of advanced sequencing technologies and the abundance of available genomic data, the study of DNA has entered an era of unprecedented excitement and activity. Nevertheless, to analyze this wealth of data, it is crucial to apply mathematical, statistical, and artificial intelligence tools with caution and to understand their limitations in order to ensure accurate and reliable analyses and interpretations.

Carino Gurjao and his team develop and use multimodal approaches to study 1) what shapes the mutational landscape of tumors and 2) how these landscapes can inform clinical decisions. To this end, Carino Gurjao and his team (3) also develop statistical models and computational methods to integrate large-scale genomic datasets.

Research objectives

Understanding how and where DNA mutations occur.

Dietary habits, lifestyle, and the microbiome can be genotoxic and leave an imprint on tumor DNA. The immune system can also shape the mutational landscape by eliminating cells with certain mutations (a theory known as the “neoantigen theory”). Furthermore, intrinsic characteristics of DNA, such as its 3D conformation and 2D base sequence, favor mutations at certain loci.

Harnessing mutational landscapes to inform clinical decisions.

In recent years, advances in genomic technology have revolutionized cancer treatment. In particular, immune-based therapies have demonstrated clinical benefits for a wide range of cancers, but are hampered by significant variability in patient response. This variability can be better understood and anticipated through the analysis of mutational landscapes, thereby enabling the personalization of therapies.