NCI U01 CA-202144: Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics

In the Genomic Signal Processing Lab, we invented the "eigengene,"1,2,3  and pioneered the matrix4,5,6  and tensor7,8,9,10,11,12  modeling of large-scale molecular biological data, which, as we demonstrated, can be used to correctly predict previously unknown physical,13,14,15  cellular,16,17,18,19,20  and evolutionary21,22  mechanisms.23,24,25 

Now, supported by a five-year, three million-dollar National Cancer Institute (NCI) Physical Sciences in Oncology U01 project grant,26,27  we develop novel, multi-tensor generalizations of the singular value decomposition, and use them in the comparisons of brain,28,29,30,31  lung,32, ovarian, 33,34,35,36,37,38,39  and uterine cancer and normal genomes, to uncover 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. 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, illustrating the universal ability of these decompositions — generalizations of the frameworks that underlie the theoretical description of the physical world — to find what other methods miss.

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