NCI U01 CA-202144: Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics
In the Genomic Signal Processing Lab, we invented the
and pioneered the
modeling of large-scale molecular biological data, which, as we demonstrated, can correctly predict previously unknown physical,10,11,12
Now, supported by a five-year, three and a half million-dollar National Cancer Institute (NCI) Physical Sciences in Oncology U01 project grant,22,23
we formulate and develop novel,
generalizations of the singular value decomposition, and use them in the comparisons of, e.g., adult and pediatric
and uterine cancer and normal genomes. We uncover 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. Our recent retrospective clinical trial experimentally validates the adult brain cancer pattern.43,44
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.
Recent Research in the News
- Press Release: J. Kiefer, "Genome-Wide Pattern Found in Tumors from Brain Cancer Patients Predicts Life Expectancy," American Association for the Advancement of Science (AAAS) EurekAlert! (May 15, 2020).
- Feature: A. J. Engler and D. E. Discher, "Rationally Engineered Advances in Cancer Research," Applied Physics Letters (APL) Bioengineering 2 (3), Special Topic: Bioengineering of Cancer preface 031601 (September 2018).
- Abstract: O. Alter, "Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics," Physical Sciences in Oncology Network (PS-ON) of the Cancer Research Institute (NCI) (September 2015).1
- Feature: F. Pavlou, "Big Data, Hidden Knowledge," The Pathologist (June 15, 2015).2
- Feature: R. Atkins, "Calculating Cancer Cures," National Academy of Engineering (NAE) Innovation Podcast and Radio Series (April 19, 2015).3
- 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).
- Review: M. Méchali, Faculty of 1000 evaluation 1728974 (February 2010).
- Feature: S. N. Dwivedi, "Rao Conference at the Interface between Statistics and the Sciences (Hyderabad, India, December 30, 2009 – January 2, 2010), Rao Best Poster Prize," International Biometric Society (IBS) Bulletin 27 (1), pp. 6–7 (January–March 2010).
- Synopsis: O. Alter, "2005 Linear Algebra and its Applications (LAA) Lecture," IMAGE: International Linear Algebra Society (ILAS) Bulletin 35, pp. 2–15 (December 2005).4
- Feature: M. E. Kilmer and C. D. Moravitz Martin, "Decomposing a Tensor," Society for Industrial and Applied Mathematics (SIAM) News 37 (9), (November 2004).
- Feature: J. Wixon and J. Ashurst, "Genome Informatics," Computational Functional Genomics 4 (5), pp. 509–514 (October 2003).
- Invited Commentary: L. Y. Dirix and A. T. van Oosterom, "Gene-Expression Profiling to Classify Soft-Tissue Sarcomas," Lancet 359 (9314), pp. 1263–1264 (April 2002).
- Feature: B. H. Ripin, "1998 Outstanding Doctoral Thesis Research in Atomic, Molecular, or Optical Physics (DAMOP) Award Finalists," American Physical Society (APS) News 7 (8), p. 5 (August–September 1998).