The Taub Faculty of Computer Science Events and Talks

Cancer is a worldwide burden with several million deaths annually and the situation is set to worsen globally as the population ages, with a projected increase of 45% to 2030 according to the WHO. Current cancer management is mainly focused on intervention after tumors have been detected; however, there is a drive towards very early detection and intervention to reduce the risk of metastatic disease. Novel methodology is emerging to allow modulation of tumor progression through targeting of molecular pathways in particular molecular subclasses. A major challenge for cancer prevention and treatment success is that the development and progression are complex biological/evolutionary processes, involving tumors that are composite organ systems with dynamical genomes shaped by gene aberrations, epigenetic changes, cellular biological context, characteristics specific to the individual patient, and environmental influences. Conventional biological and statistical approaches are not designed to unravel the complicated interactions involved in cellular functioning. In particular, biological interactions will increasingly have to be sought at and between several systemic levels. Failure to capture such connections may seriously compromise our ability to distinguish between normal variation and disease-related perturbations in a pathway. In a systems biology perspective, tumor development and progression depends on an interplay between different endogenous processes at various levels, as well as external stimuli.

Ultimately, we would like to be able to predict the effect of any given perturbation of this system. While this is a very ambitious goal,the perspective of systems biology is nevertheless of great importance,as we need to move towards more systematic studies of complex interactions in order to better understand the entirety of the processes. A substantial gap exists between the abundance and complexity of the data typically available for a patient/tumor and the sophistication of the statistical and computational tools available to handle and interpret this information. High-throughput molecular data at various levels, including DNA, mRNA, miRNA, protein and methylation are being generated on a steadily increasing number of breast cancer patients. Several molecular markers have been identified and are used to develop profiles associated with tumor aggressiveness, response to therapy and patient outcome. Pooling datasets, combining profiles at various levels, and analyzing the data in a compendium rather than in isolation can lead to more reliable molecular signatures and thereby more specific diagnosis and treatment of breast cancer patients. It is usually not feasible to obtain data at all systemic levels on all patients and on normal as well as tumor tissue. More typically, some measurements are available on one group of individuals and other measurements are available on another group. Sometimes, groups partially overlap so that more measurements are available on a smaller subset of individuals. A major challenge in the analysis of multilevel molecular data is to develop methods and algorithms that allow joint analysis of such partially overlapping data sets. In this talk, we will discuss some of the challenges in this context and provide own examples of how these may be approached. We will also share some speculations concerning how to handle such problems in the future.