Cancer is characterized by the anarchic proliferation of cells, orchestrated by DNA mutations. Given that 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 crossroads of multiple disciplines.

Research themes

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

Carino Gurjao and his team are developing and using 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 are also developing statistical models and computational methods to integrate large-scale genomic datasets.

Research objectives

Understand 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 called the “neoantigen theory”). In addition, intrinsic DNA characteristics such as 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 shown clinical benefit across a wide range of cancers, but suffer from high variability in patient response. This variability can be better understood and anticipated thanks to the analysis of mutational landscapes, enabling therapies to be personalized.