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Francesca Buffa

Computational biology

High throughput technology has driven a scientific revolution in both clinical and molecular research. Remarkably, it is now possible to obtain multiple levels of information about the entire genome of individual cancers, and to characterise a genomic blueprint for different cancer types. This revolution has enabled the accumulation of cancer genomic ‘big data’ and related knowledge at a speed never experienced before in biological science.

Integrating these data has the potential to dramatically improve understanding of the differences between cancers, and of the molecular mechanisms underlying response and resistance to treatment. Specifically, we aim to understand:

  • how and where the blueprint of the genome is functional,
  • how this function is impaired or changed in cancer,
  • what is the role of the tumour microenvironment in driving these changes, and
  • how this information can be used to improve clinical practice.

The tumour microenvironment encompasses a heterogeneous, dynamic and highly interactive system of cancer and stroma cells. One of the key physiological and microenvironmental differences between tumour and normal tissues is the presence of hypoxia, which induces genomic instability, and DNA amplification and damage, whilst altering DNA repair.

Hypoxia drives selective growth of more aggressive cancer cells, and thus enables tumour progression. It also causes therapeutic resistance, including resistance to radiotherapy treatment. In this respect, we have previously identified gene signatures (e.g. Masiero et al Cancer Cell 2013) that are being validated in prospective studies and translated into biomarkers to identify hypoxic tumours and tailor their treatment.

The Hypoxia-inducible transcription factor 1 (HIF1) is one of the main regulators of the transcriptional response to hypoxia. Whilst our previous studies on the transcriptional response to hypoxia have defined commonly activated pathways across cancer types, large heterogeneity between cancers has also been observed. We are now studying in greater details this heterogeneity in thousands of clinical samples from 10 major cancer types, and deriving both common but also tumour specific signatures.

Hypoxia along with acidosis increases clonal selection resulting in aggressive cancer phenotypes, but the drivers of this selection are not fully understood and likely to be different across cancer types. In order to elucidate mechanistic insights into the dysregulated pathways in hypoxic tumours, it is crucial to study genomic aberrations alongside transcriptional and post-transcriptional changes.

Our recent studies (e.g. Haider et al, Genome Biology, 2016) demonstrate that by studying the aberrant cancer genomic landscape together with transcriptional deregulation in hypoxic tumours, we can identify factors potentially driving adaptation to hypoxia in cancer. Following this, we are now using complex integrative methods combining genomic data with transcriptional, post-transcriptional, and functional assay data, together with clinical knowledge and functional genomics approaches to improve our understanding of the selection pressure generated by hypoxia.

While our principal aim is to build integrated models of cancer which could improve our understanding of the disease and generate novel hypotheses, we also develop new approaches and computational tools to aid the translation of this big data generated knowledge into beneficial use in the clinic for treatment of patients.

Computational biology 1

 The graph above shows the interplay between genes regulated by hypoxia in a cancer cell. Genes are shown as circles, while their relationship with co-partners genes which help fuel progression of cancer is shown by green edges. The network demonstrates complex interplay which emerges when cancer cell undergoes hypoxia following poor blood supply, and patients with such deregulation are shown to have poorer outcome. A computational algorithm previously developed by us (Buffa et al.) was used, which aims at unravelling such interplays by starting with known key-player genes in cancer (seeds). It extends the network by finding other genes in human genome (>25,000) exhibiting similar patterns of expression, and further limiting to those genes having highest number of associates/co-partners. This way, a highly active network is identified which is shown to contain primary culprits of driving aggressive cancers.

We adopt system biology and system medicine approaches to integrate results from functional assays, clinical knowledge with genomics, transcriptomics and other phenotypic data. This involves analysis of large clinical and experimental cohorts using sophisticated computational and statistical techniques. The graph above shows an example network model of the interplay between genes regulated by hypoxia in a cancer cell. Genes are shown as circles, while their relationship with co-partners genes which help fuel progression of cancer is shown by green edges. The network demonstrates complex interplay which emerges when cancer cell undergoes hypoxia following poor blood supply, and patients with such deregulation are shown to have poorer outcome. A computational algorithm previously developed by us (Buffa et al.) was used, which aims at unravelling such interplays by starting with known key-player genes in cancer (seeds). It extends the network by finding other genes in human genome (>25,000) exhibiting similar patterns of expression, and further limiting to those genes having highest number of associates/co-partners. This way, a highly active network is identified which is shown to contain primary culprits of driving aggressive cancers.