V sredo, 29.6.2016, ob 13h vljudno vabimo na predavanje dr. ORLY ALTER (Scientific Computing and Imaging Institute and the Huntsman Cancer Institute, University of Utah) z naslovom Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics. Predavanje bo potekalo v veliki predavalnici Instituta Jožef Stefan, Jamova 39, Ljubljana.
Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics
We are developing new mathematical frameworks to do what no others currently can, that is, create a single coherent model from multiple high-dimensional datasets, known as tensors. The frameworks – comparative spectral decompositions – generalize those that underlie the theoretical description of the physical world. We are using the frameworks to compare and contrast datasets recording different aspects of a single disease, such as genomic profiles of multiple cell types from the same set of patients, measured more than once by several different methods. By using the complex structure of the datasets, rather than simplifying them as is commonly done, the frameworks enable the separation of patterns of DNA alterations – which occur only in the tumor genomes – from those that occur in the genomes of normal cells in the body, and from variations caused by experimental inconsistencies. The patterns that we uncover in the data are expected to offer answers to the open question of the relation between a tumor’s genome and a patient’s outcome. For example, recent comparisons of the genomes of tumor and normal cells from the same sets of ovarian and, separately, glioblastoma brain cancer patients uncovered patterns of DNA copy-number alterations that were found to be correlated with a patient’s survival and response to chemotherapy. For three decades prior, the best predictor of ovarian cancer survival was the tumor’s stage; more than a quarter of ovarian tumors are resistant to the platinum-based chemotherapy, the first-line treatment, yet no diagnostic existed to distinguish resistant from sensitive tumors before the treatment. For five decades prior, the best prognostic indicator of glioblastoma was the patient’s age at diagnosis. The ovarian and brain cancer data were published, but the patterns remained unknown until the team applied their comparative spectral decompositions. Pending experimental revalidation, we will bring the patterns that we uncover to the clinic, to be used in personalized diagnostic and prognostic pathology laboratory tests. The tests would predict a patient’s survival and response to therapy, and doctors could tailor treatment accordingly.
Dr. Orly Alter:
Dr. Alter is a USTAR associate professor of bioengineering and human genetics at the Scientific Computing and Imaging Institute and the Huntsman Cancer Institute at the University of Utah. Inventor of the “eigengene,” she pioneered the matrix and tensor modeling of large-scale molecular biological data, which, as she demonstrated, can be used to correctly predict previously unknown cellular mechanisms. Dr. Alter received her Ph.D. in applied physics at Stanford University, and her B.Sc. magna cum laude in physics at Tel Aviv University. Her Ph.D. thesis on “Quantum Measurement of a Single System,” which was published by Wiley-Interscience as a book, is recognized today as crucial to the field of gravitational wave detection.