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Jožef Stefan
Postgraduate School

Jamova 39
SI-1000 Ljubljana

Phone: +386 1 477 31 00
Fax: +386 1 477 31 10


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You are invited to the 21th Institute colloquium in the academic year 2015/16. The colloquium will be held on Wednesday June 29, 2016 at 1 PM in the main Institute lecture hall, Jamova 39, Ljubljana.

prof. dr. Orly Alter

University of Utah, United States of America

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.

To read the abstract click Past colloquia are posted on

Cordially invited!