We use system biology approaches to search for integrated genomic blueprints that enable us to understand and predict how cancer will evolve and respond to treatment.
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.
The graph 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.
Cambridge 2012. Talk on integrated analysis of miRNA and mRNA expression profiling as a tool to identify prognostic markers and associated pathways:
London 2015. Big Data for Bioinformatics: Panel Discussion | Big Data Analytics Conference:
Francesca Buffa is an Associate Professor in the Department of Oncology, University of Oxford, where she leads the Computational Biology and Integrative Genomics Group. She is the recipient of a Cancer Research UK programme and a European Research Council consolidator award.
Professor Buffa research focuses on system biology approaches to search for integrated genomic blueprints that enable to understand and predict how cancer will evolve and respond to treatment.
After a Masters Degree in Theoretical Physics from the University of Turin, she completed her PhD in Physics and Mathematical Modelling at the Institute of Cancer Research within the University of London. She then undertook a postdoctoral research fellowship in Mathematical Modelling and Biostatistics at the Gray Cancer Institute, London, before joining the Molecular Oncology Laboratories at the Weatherall institute of Molecular Medicine, University of Oxford, first as a postdoctoral research fellow in Bioinformatics/Biostatistics then as a Group Leader. In addition to her research programme, she teaches at national and international Masters Courses and Advanced Schools, is a member of national and international panels, and acts as bioinformatics/biostatistics advisor for genomics clinical research studies. She has been invited to present her work at national and international conferences, and authored or co-authored over 100 publications, several in high impact journals.
Syed Haider, Alan McIntyre, Ruud G. P. M. van Stiphout, Laura M. Winchester, Simon Wigfield, Adrian L. Harris and Francesca M. Genomic alterations underlie a pan-cancer metabolic shift associated with tumour hypoxia. Genome Biology (2016) 17:140
Masiero M, Costa Simões F, Dong Han H, Snell C, Peterkin T, Bridges E, Mangala LS, Yen-Yao Wu S, Pradeep S, Li D, Han C, Dalton H, Lopez-Berestein G, Tuynman JB, Mortensen N, Li JL, Patient R, Sood AK, Banham AH, Harris AL and Buffa FM. A core human primary tumor angiogenesis signature identifies the endothelial orphan eceptor ELTD1 as a key regulator of angiogenesis. Cancer Cell. 2013 Aug 12;24(2):229-41. doi: 10.1016/j.ccr.2013.06.004. Epub 2013 Jul 18.
Favaro E, Bensaad K, Chong MG, Tennant DA, Ferguson DJ, Snell C, Steers G, Turley H, Li JL, Günther UL, Buffa FM, McIntyre A and Harris AL. Glucose utilization via glycogen phosphorylase sustains proliferation and prevents premature senescence in cancer cells. Cell Metab. 2012 Dec 5;16(6):751-64. doi: 10.1016/j.cmet.2012.10.017. Epub 2012 Nov 21.
Mehta S, Hughes NP, Buffa FM, Li SP, Adams RF, Adwani A, Taylor NJ, Levitt NC, Padhani AR, Makris A, Harris AL. Assessing early therapeutic response to bevacizumab in primary breast cancer using magnetic resonance imaging and gene expression profiles. J Natl Cancer Inst Monogr. 2011;2011(43):71-4. doi: 10.1093/jncimonographs/lgr027.
Google scholar: https://scholar.google.co.uk/citations?user=abd1LXcAAAAJ&hl=en&oi=ao
Research gate: https://www.researchgate.net/profile/Francesca_Buffa