Cancer Genomic Medicine

Cancer Genomic Medicine - overview

Though our collective understanding of the mechanisms of cancer and cancer treatment has grown tremendously over the decades, there remains numerous ways that existing models and modes of therapy fall short of what is needed to fully address this important public health issue. The continued innovation and growth in high-through data technologies such as next generation sequencing, high-throughput screening, and mass spectrometry offers researchers at IBM and elsewhere the opportunity to improve cancer models and develop systems that integrate and analyze data to better target patient-specific cancers.

IBM researchers are developing and applying both experimental and computational approaches to store, analyze these data, and to assign structural and functional information to key genome components. By developing a range of data analytics, computer science algorithms, and machine learning techniques - both supervised and unsupervised, IBM can better identify and predict relevant cancer driver events, therapeutic targets, and therapeutic responses.

Within our group, though we have broad interests and expertise, we are particularly interested in developing systems to support physicians understand their patients omic data using Watson for Genomics, better understanding the mechanisms of immunotherapy response, and working to unravel the factors of drug resistance.