Drs. Elana Fertig & Francesca Buffa
Dr. Elana Fertig, Associate Professor of Oncology and Applied Mathematics and Statistics Assistant Director of the Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, USA, will present the lecture "Uncovering hidden sources of transcriptional dysregulation arising from inter- and intra-tumor heterogeneity."
Dr. Francesca Buffa, Associate Professor, Computational Biology and Integrative Genomics, Department of Oncology, CRUK/MRC Institute, University of Oxford, will present the lecture "In-silico modelling of the tumour microenvironment."
Meet the speakers
Dr. Elana Fertig
Dr. Francesca Buffa
Dr. Elana Fertig's lecture
Heterogeneity poses a major challenge in translational research. For example, inter-tumor heterogeneity limits the biomarker discovery and intra-tumor heterogeneity enables therapeutic resistance. Moreover, in some cancers driver mutations are insufficient to account for the widespread transcriptional variation responsible for these outcomes. Thus, new computational tools to model transcriptional variation are essential. To address this we develop an innovative computational framework, Expression Variation Analysis (EVA), to model transcriptional dysregulation in cancer. Briefly, EVA quantifies transcriptional heterogeneity for one set of samples or cells from one phenotype using the expected dissimilarity between pairs of expression profiles. U-statistics theory can then quantify the statistical significance of the difference in transcriptional heterogeneity between phenotypes. We apply EVA to perform a comprehensive characterization of transcriptional variation in head and neck squamous cell carcinoma (HNSCC). At a pathway level, transcriptional variation in HNSCC tumors is higher than normal controls. Applying EVA to integrate ChIP-seq data with RNA-seq reveals that these pervasive transcriptional differences occur in enhancers. Similarly, applying EVA at a gene level to model splicing reveals more heterogeneity in transcript usage in tumor samples than normals. HPV- HNSCC tumors are unique in having mutations in genes that regulate the splicing machinery, and the HPV- tumors with these alterations have a greater number of dysregulated splice variants than those without. Nonetheless, the EVA analysis identifies a similar number of alternative splice variants in HPV+ as HPV- tumors suggesting an alternative mechanism of transcriptional heterogeneity in HPV+ disease. Adapting EVA to single cell data demonstrates that increased fibroblast composition is associated with greater variation in immune pathway activity in HNSCC. Moreover, we observe greater transcriptional heterogeneity in HNSCC primary tumors than lymph node metastasis consistent with a clonal outgrowth. We demonstrate that the statistical framework from EVA enables differential heterogeneity analysis in HNSCC ranging from pathway dysregulation, splice variation, epigenetic regulation, and single cell analysis. This algorithm provides a critical framework to model the hidden multi-molecular mechanisms underlying the complex patient outcomes that are pervasive in cancer.
Dr. Francesca Buffa's lecture
Despite progress in understanding many aspects of malignancy, resistance to therapy is still a frequent occurrence. Recognised causes of this resistance include 1) intra-tumour heterogeneity resulting in selection of resistant clones, 2) redundancy and adaptability of gene signalling networks, and 3) a dynamic and protective microenvironment. I will discuss how these aspects influence each other, and then focus on the tumour microenvironment.
The tumour microenvironment comprises a heterogeneous, dynamic and highly interactive system of cancer and stromal cells. One of the key physiological and micro-environmental differences between tumour and normal tissues is the presence of hypoxia, which not only alters cell metabolism but also affects DNA damage repair and induces genomic instability. Moreover, emerging evidence is uncovering the potential role of multiple stroma cell types in protecting the tumour primary niche.
I will discuss our work on in silico cancer models, which is using both experimental data and genomic data from large clinical cohorts of individuals to provide new insights into the role of the tumour microenvironment in cancer progression and response to treatment. I will then discuss how this information can help to improve patient stratification and develop novel therapeutic strategies.
Sunniva Bjørklund, postdoctoral fellow at the Institute for Cancer Research, will present her talk entitled "Alternative splicing and transcription in breast cancer."