Cortes Ciriano Group

Cancer genomics

The group develops computational tools to characterise the patterns of mutations and genome instability processes in human cancers through the analysis of genome sequencing data from clinical samples and preclinical models.


The group develops computational tools to characterise the mechanisms and consequences of genome instability processes in human cancers through the analysis of genome sequencing data from clinical samples and preclinical models using artificial intelligence and statistical methods. Our ultimate goals are to deliver computational tools in the field of cancer genomics to study cancer evolution and facilitate the discovery of novel biomarkers and therapeutic targets, and analytical methods applicable in clinical settings to improve cancer diagnosis and treatment selection. Our research activities are articulated around 3 main areas:

Genomic instability and immune escape. Complex alterations of the number and structure of chromosomes are common in human cancers, but are rarely observed in normal tissues, thus hinting at their critical role in triggering malignant transformation. In our group, we develop computational methods to elucidate the molecular mechanisms involved in the generation of complex patterns of genomic instability in cancer, study how such alterations alter the differentiation dynamics and function of cells, and identify the mechanisms whereby human cancers avoid the attack of the immune system and response to immunotherapies. For example, one area we are currently interested in is the application of long-read sequencing technologies to study the interplay between complex genomic aberrations and epigenetic changes. To this aim, we are developing computational methods to analyse long-read sequencing data from large collections of clinical samples.

Early detection of cancer. We are also interested in the identification of genomic and epigenomic patterns that could be used for the early detection of cancer using liquid biopsy methods. A major focus of the group is the development of minimally invasive tests to realise the vision that one day all cancers will be detected early (when there are more chances of successful treatment) using simple tests, such as a blood test. Currently, we are working towards the development of technically simple, fast turnaround, multi-omic methods based on nanopore sequencing for the early detection of cancer and minimal residual disease monitoring, which we are prospectively validating in clinical cohorts.

Single-cell genomics. A third research area in the group is related to the application of single-cell sequencing technologies to elucidate the patterns of clonal evolution in human cancers and study the functional consequences of somatic alterations at single-cell resolution. We develop tools to determine the clonal architecture of human cancers, the relative timing of somatic mutations and their impact on cell phenotypes, and estimate the fitness (selection) associated to such mutations using mathematical modelling. Such knowledge helps us unravel which genomic aberrations trigger neoplastic transformation and the expansion of cancer cells in a quantitative manner, with implications to our understanding of cancer progression and the development of early detection methods.

We are a highly collaborative group, and enjoy working with committed collaborators with complementary expertise to ours. In fact, much of our work is developed in close collaboration with experimental biologists and clinicians on campus and beyond. Thus, we are always happy to discuss potential collaborations aligned with the goals of the group outlined above.

About Dr Isidro Cortés-Ciriano

Isidro Cortés-Ciriano joined EMBL-EBI as Research Group Leader in June 2019 after completing postdoctoral training at Harvard Medical School, under the supervision of Prof. Peter Park, and at the University of Cambridge, under the supervision of Prof. Andreas Bender. Isidro completed his PhD at the Pasteur Institute in 2015. Isidro’s expertise includes biology, genomics and statistical modelling.