Personal Profile
Orly Alter
Track in Computational Systems and Synthetic Bioengineering
2004 Postdoctoral Fellow, Genetics, Stanford University
1999 Ph.D., Applied Physics, Stanford University
1989 B.Sc. magna cum laude, Physics, Tel Aviv University
Research
genetics, mathematics, physics
Current Research
Postdoctoral and Graduate Positions at the Genomic Signal Processing Lab
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.
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.
Selected Publications
Aiello KA, Ponnapalli SP, Alter O, Mathematically universal and biologically consistent astrocytoma genotype encodes for transformation and predicts survival phenotype. APL Bioeng 2018 Sep;2(3):
Aiello KA, Alter O, Platform-Independent Genome-Wide Pattern of DNA Copy-Number Alterations Predicting Astrocytoma Survival and Response to Treatment Revealed by the GSVD Formulated as a Comparative Spectral Decomposition. PLoS One 2016;11(10):e0164546
Sankaranarayanan P, Schomay TE, Aiello KA, Alter O, Tensor GSVD of patient- and platform-matched tumor and normal DNA copy-number profiles uncovers chromosome arm-wide patterns of tumor-exclusive platform-consistent alterations encoding for cell transformation and predicting ovarian cancer survival. PLoS One 2015;10(4):e0121396
Bertagnolli NM, Drake JA, Tennessen JM, Alter O, SVD identifies transcript length distribution functions from DNA microarray data and reveals evolutionary forces globally affecting GBM metabolism. PLoS One 2013;8(11):e78913
Lee CH, Alpert BO, Sankaranarayanan P, Alter O, GSVD comparison of patient-matched normal and tumor aCGH profiles reveals global copy-number alterations predicting glioblastoma multiforme survival. PLoS One 2012;7(1):e30098
Ponnapalli SP, Saunders MA, Van Loan CF, Alter O, A higher-order generalized singular value decomposition for comparison of global mRNA expression from multiple organisms. PLoS One 2011;6(12):e28072
Muralidhara C, Gross AM, Gutell RR, Alter O, Tensor decomposition reveals concurrent evolutionary convergences and divergences and correlations with structural motifs in ribosomal RNA. PLoS One 2011;6(4):e18768
Omberg L, Meyerson JR, Kobayashi K, Drury LS, Diffley JF, Alter O, Global effects of DNA replication and DNA replication origin activity on eukaryotic gene expression. Mol Syst Biol 2009;5:312
Omberg L, Golub GH, Alter O, A tensor higher-order singular value decomposition for integrative analysis of DNA microarray data from different studies. Proc Natl Acad Sci U S A 2007 Nov 20;104(47):18371-6
Alter O, Genomic signal processing: from matrix algebra to genetic networks. Methods Mol Biol 2007;377:17-60
Alter O, Discovery of principles of nature from mathematical modeling of DNA microarray data. Proc Natl Acad Sci U S A 2006 Oct 31;103(44):16063-4
Alter O, Golub GH, Singular value decomposition of genome-scale mRNA lengths distribution reveals asymmetry in RNA gel electrophoresis band broadening. Proc Natl Acad Sci U S A 2006 Aug 8;103(32):11828-33
Alter O, Golub GH, Reconstructing the pathways of a cellular system from genome-scale signals by using matrix and tensor computations. Proc Natl Acad Sci U S A 2005 Dec 6;102(49):17559-64
Alter O, Golub GH, Integrative analysis of genome-scale data by using pseudoinverse projection predicts novel correlation between DNA replication and RNA transcription. Proc Natl Acad Sci U S A 2004 Nov 23;101(47):16577-82
Alter O, Brown PO, Botstein D, Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms. Proc Natl Acad Sci U S A 2003 Mar 18;100(6):3351-6
Nielsen TO, West RB, Linn SC, Alter O, Knowling MA, O'Connell JX, Zhu S, Fero M, Sherlock G, Pollack JR, Brown PO, Botstein D, van de Rijn M, Molecular characterisation of soft tissue tumours: a gene expression study. Lancet 2002 Apr 13;359(9314):1301-7
Alter O, Brown PO, Botstein D, Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A 2000 Aug 29;97(18):10101-6