CHARACTERISING IMMUNE EVASION MECHANISMS IN CANCER
In this presentation, I will discuss our work characterizing the heterogeneity we observe at the tumor-immune interface in metastatic cancer, and our efforts in elucidating its molecular underpinnings. Focusing on high-grade serous ovarian cancer (HGSOC), we have performed immunogenomics analysis of treatment-naive and paired pre/post-chemotherapy treated samples. In treatment-naive HGSOC, we find that immune cell-excluded and inflammatory microenvironments co-exist within the same individuals and at the same tumor sites, indicating ubiquitous variability in immune signaling. Analysis of tumor microenvironment cell composition, DNA copy number, mutations and gene expression show that immune cell exclusion is associated with amplification of Myc target genes and increased expression of canonical Wnt signaling in treatment-naive HGSOC. Following neoadjuvant chemotherapy, we find increased natural killer cell infiltration and oligoclonal expansion of T cells. I will discuss these and other unpublished results demonstrating the role of the tumor microenvironment in treatment response in ovarian cancer. Furthermore, my objective is to present and discuss computational and experimental methods for ‘omics analysis of tumour-immune interactions.
Martin Miller joined Cancer Research UK Cambridge Institute at the University of Cambridge as a Junior Group Leader in 2015 after completing his post-doctoral training in Computational Biology at Memorial Sloan Kettering Cancer Center with Prof. Chris Sander. Martin’s research is focussed on understanding how cancer cells manipulate their surroundings and escape anti-tumour control mechanisms. His group applies experimental and computational approaches to characterise cell-cell interactions in the tumour microenvironment of ovarian and pancreatic cancer, which has led to the development of several proteomic and immunogenomic methods. Over the next years, his team will apply such technologies to investigate tumour-immune cell interactions and help identify therapeutic targets that exploit the power of the immune system in controlling cancer growth and spread.
DECODING MUTATIONAL PROCESSES IN CANCER
Cancer genomes accumulate a large number of somatic mutations resulting from imperfection of DNA processing during normal cell cycle as well as from carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. Identifying mutagenic processes shaping a cancer genome is an important step towards understanding tumorigenesis. However, the relation between these processes and the imprint (or signature) they leave on a genome is complicated by the fact that mutagenic processes are context dependent and not necessarily additive. To address these challenges, we took several complementary approaches allowing us to link mutational signatures to their causes. I will discuss our approaches based on gene expression modules, mutated subnetworks, and the recently introduced RePrint – the first attempt to capture properties of composite signatures where DNA repair deficiency is superimposed with exogenous mutagenic processes.
Teresa Przytycka is a Senior Investigator at National Institutes of Health. She received PhD in Computer Science from the University of British Columbia, Vancouver. Her early research carrier focused on theory of algorithms - first at UC Riverside and subsequently at Odense University, Denmark. She was awarded a Sloan Foundation and the U.S. Department of Energy Fellowship to jumpstart computational biology research which she used to study protein folding at Johns Hopkins University (with Prof. George Rose). Currently, she heads the Algorithmic Methods in Molecular and Systems Biology section of National Library of Medicine. The research in her group focuses on developing computational methods facilitating the understanding of cancer, gene regulation and interaction, and on developing methods for the analysis of new types of high-throughput experimental data.
María Rodríguez Martínez
ARTIFICIAL INTELLIGENCE APPROACHES FOR THE ANALYSIS OF SINGLE-CELL PROTEOMIC (CyTOF) DATA IN CANCER
In recent years, the recent availability of large amounts of data together with technical developments facilitating the implementation and training of more performant models have made possible the broad application of sophisticated computational models to a vast set of problems.
In this talk, I will present current activities of the Computational Systems Biology group at IBM Research, Zurich, focused on the analysis of single-cell proteomic data (CyTOF), which allows the simultaneous quantification of dozens of markers in millions of cells. I will first introduce CellCycleTRACER, a method that enables the correction of confounding factors in single cell data, such as the cell cycle and volume heterogeneity. Next, I will present an ambitious work that characterized the tumour cellular ecosystem of breast cancer tumours. In this work, we analysed the CyTOF profiles of ~26 millions of single cells using a tumour cell-centric and an immune cell-centric antibody panel, discovering that many tumours harboured unique tumour cell phenotypes that emphasized their “tumour ecosystem individuality”. The data also enabled the description of the tumour cell and immune heterogeneity with unprecedented resolution and deepened our understanding of the complex relationships within the breast tumour ecosystem. Finally, I will discuss how modified graphical model approaches can be used to reconstruct the signalling dynamics elicited by drug perturbation experiments.
Dr. María Rodrίguez Martίnez (female) is the technical lead of the group of Systems Biology at IBM Research - Zürich. A physicist by training, she did her PhD and a first postdoc in High Energy Physics at the Institut d’Astrophysique de Paris and Hebrew University respectively. In 2006, she transitioned into Systems Biology as a postdoc at the Weizmann Institute of Science and later at Columbia University.
She is the Technical Leader of Systems Biology at IBM – Research Zurich and an associated member of the Department of Biology at ETH since 2014. Her current research focuses on the development of computational and statistical approaches to unravel cancer molecular mechanisms using high-throughput multi-omics datasets and single-cell molecular data. In recent years, her team has focused on the development of mathematical and statistical models to unravel cancer heterogeneity, to develop new approaches for personalized drug design and for the analysis of single cell data. She is currently the technical coordinator of an H2020 grant, iPC, focused on developing personalized medicine approaches for pediatric cancers.