Orly Alter is a Utah Science, Technology, and Research (USTAR) associate professor of bioengineering and human genetics at the Scientific Computing and Imaging Institute1 and the Huntsman Cancer Institute at the University of Utah, and the principal investigator of a National Cancer Institute (NCI) Physical Sciences in Oncology U01 project grant.2,3 Inventor of the "eigengene,"4,5,6 she pioneered the matrix7,8,9 and tensor10,11,12 modeling of large-scale molecular biological data, which, as she demonstrated, can correctly predict previously unknown physical,13,14,15 cellular,16,17,18,19 and evolutionary20,21 mechanisms.22,23,24 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,25,26,27 is recognized today as crucial to the field of gravitational wave detection.28,29
In her Genomic Signal Processing Lab, Alter develops novel, multi-tensor30,31,32,33 generalizations of the singular value decomposition, and uses them in the comparisons of, e.g., brain,34,35,36,37,38,39 lung,40,41 ovarian,42,43,44,45,46,47,48 and uterine cancer and normal genomes. She uncovers genome-scale patterns of DNA copy-number alterations that predict survival and response to treatment, statistically better than, and independent of, the best indicators in clinical use and existing laboratory tests. Her recent retrospective clinical trial experimentally validates the brain cancer pattern.49,50 Recurring alterations have been recognized as a hallmark of cancer for over a century, and observed in these cancers' genomes for decades; however, copy-number subtypes predictive of patients' outcomes were not identified before. The data had been publicly available, but the patterns remained unknown until the data were modeled by using the multi-tensor decompositions. This demonstrates that the decompositions underlie a mathematically universal description of the genotype-phenotype relationships in cancer that other machine learning methods miss.