NCI U01 CA-202144: Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics
The University of Utah team is 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. The team is 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 the team uncovers 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, the team will bring the patterns that they 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.
Physics-inspired mathematical frameworks. The team develops mathematical frameworks to uncover patterns in datasets arranged in two or more third- or higher-dimensional tables, known as tensors. Rather than simplifying the datasets, as is commonly done, the frameworks make use of their complex structure in order to tease out the patterns within them.
Genomic predictors of a patient's outcome. A recent comparison of the genomes of tumor and normal cells from the same sets of ovarian serous cystadenocarcinoma patients uncovered patterns of DNA copy-number alterations that were found to be correlated with a patient's survival and response to platinum-based chemotherapy. Among patients that were diagnosed at late stages, the DNA patterns distinguished short-term survivors, with a median survival time of three years, from long-term survivors, with a median survival time almost twice as long. Among patients treated with platinum, the DNA patterns distinguished those with platinum-resistant tumors, with a median survival time of three years, from those with platinum-sensitive tumors, with a median survival time of more than seven years.
Patterns of DNA copy-number alterations in personalized diagnostic and prognostic tests. Pending experimental revalidation, the team will bring the patterns that they 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.
The specific genes found to be perturbed may be actively involved in cancer development and progression, and could be the basis for drug therapies.
In the News
Press Release: J. Kiefer, "New Method Increases Accuracy of Ovarian Cancer Prognosis and Diagnosis," American Association for the Advancement of Science (AAAS) EurekAlert! (April 15, 2015).
Feature: R. Atkins, "Calculating Cancer Cures," National Academy of Engineering (NAE) Innovation Podcast and Radio Series (April 19, 2015).1
Feature: F. Pavlou, "Big Data, Hidden Knowledge," The Pathologist (June 15, 2015).2
From left to right: Orly Alter, Heidi A. Hanson, Elke A. Jarboe, Randy L. Jensen, Cheryl A. Palmer, Reha M. Toydemir, and Carl T. Wittwer.
Orly Alter, Ph.D., Principal Investigator
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.
Heidi A. Hanson, Ph.D., Co-Investigator
Dr. Hanson is a research assistant professor at the Huntsman Cancer Institute's Utah Population Database (UPDB) and an adjunct assistant professor of sociology and population health sciences at the University of Utah. As a biodemographer, she uses the UPDB's extensive demographic, genealogical, and health records in her research, to better understand the genetic and environmental risks of disease.
Elke A. Jarboe, M.D., Co-Investigator
Dr. Jarboe is an associate professor of pathology, and an adjunct associate professor of obstetrics and gynecology at the University of Utah School of Medicine. Her research has been focused on women's cancers since her postgraduate fellowships at the Brigham and Women's Hospital of the Harvard Medical School. She is a recipient of the Scully Young Investigator Award of the International Society of Gynecological Pathologists.
Randy L. Jensen, M.D., Ph.D., Co-Investigator
Dr. Jensen is a professor of neurosurgery, radiation oncology, and oncological sciences, and the director of the Neurosurgery Residency Program at the University of Utah School of Medicine. A neurosurgeon focusing on patients with brain tumors, he has treated more than a thousand patients, and has been involved in a number of brain cancer clinical trials. He serves on the editorial board of the Journal of Neuro-Oncology.
Cheryl A. Palmer, M.D., Co-Investigator
Dr. Palmer is a professor of pathology, the director of neuropathology in the Department of Pathology, as well as the director of the Pathology Residency Program at the University of Utah School of Medicine. She is a fellow of the American Academy of Neurology, and serves as the vice-president elect of the American Association of Neuropathologists.
Reha M. Toydemir, M.D., Ph.D., Co-Investigator
Dr. Toydemir is a clinical assistant professor of pathology at the University of Utah School of Medicine, board-certified in clinical cytogenetics. He is a fellow of the American College of Medical Genetics and Genomics.
Carl T. Wittwer, M.D., Ph.D., Co-Investigator
Dr. Wittwer is a professor of pathology at the University of Utah School of Medicine. A pioneer in modern nucleic acid analysis, he introduced several techniques used today worldwide in real-time PCR instruments, including rapid PCR, real-time "LightCycler" technology, and high resolution melting of PCR products. His many honors include the Ullman Award for Technology Innovation from the American Association of Clinical Chemistry.
Cherry blossoms at the University of Utah.