USTAR Confluence 2015: Where innovative ideas seed, grow and thrive

I will present my most recent work on astrocytoma prognosis and the use of comparative spectral decompositions as a tool for personalized medicine at the 2015 USTAR Confluence conference next Wednesday, September 30th here in Salt Lake City. This is an exciting opportunity to share my work with Utah's innovators and entrepreneurs. 

The Utah Science Technology and Research (USTAR) initiative is a long-term, state-funded investment to strengthen Utah's "knowledge economy" and generate high-paying jobs based on three program areas. The first area involves funding for strategic investments at the University of Utah and Utah State University to recruit world-class researchers. The second area is to build state-of-the-art interdisciplinary facilities at these institutions for the innovation teams. The third program area involves teams that work with companies and entrepreneurs across the state to promote science, innovation, and commercialization activities. 

Grace Hopper Celebration 2015 -- Our Time to Lead

I am thrilled to announce that I will be attending the 2015 Grace Hopper Celebration (GHC) of Women in Computing as a 2015 Scholar sponsored by the National Science Foundation. This annual meeting is hosted by the Anita Borg Institute and will be held next month in Houston, Texas. It is the largest gathering of female technologists and this year's record-breaking celebration will include over 12,000 women! 

You can find my profile (along with all of the other 2015 Scholars) here!

Poster Presentation at BMES 2015!

Next month, I will present my most recent work on astrocytoma brain cancer at the 2015 Biomedical Engineering Society (BMES) Annual Meeting in Tampa, FL. I am presenting my poster titled "Cross-Platform DNA Copy-Number Alterations Predict Astrocytoma Survival and Response to Chemotherapy" in the Personalized Medicine and Biomarkers in Cancer poster session on Friday, October 9, 2015.

Ovarian cancer study published in PLoS One!

Our work on ovarian cancer was recently published in PLoS One! It was also featured in an AAAS EurekAlert! available here

This paper defines a novel generalization of the singular value decomposition for two column-matched and row-independent tensors. This algorithm is data-driven, meaning that it can be applied to any data with this structure, allowing researchers in fields as diverse as geophysics and finance to identify and study fundamental patterns in their data.  Here, we apply it to genomic profiles from ovarian cancer patients and identify chromosome arm-wide patterns of copy number aberrations that predict patient survival and response to platinum-based chemotherapy.   

See the full open-access paper here.
Supplementary materials for this work are available here.

Abstract: The number of large-scale high-dimensional datasets recording different aspects of a single disease is growing, accompanied by a need for frameworks that can create one coherent model from multiple tensors of matched columns, e.g., patients and platforms, but independent rows, e.g., probes. We define and prove the mathematical properties of a novel tensor generalized singular value decomposition (GSVD), which can simultaneously find the similarities and dissimilarities, i.e., patterns of varying relative significance, between any two such tensors. We demonstrate the tensor GSVD in comparative modeling of patient- and platform-matched but probe-independent ovarian serous cystadenocarcinoma (OV) tumor, mostly high-grade, and normal DNA copy-number profiles, across each chromosome arm, and combination of two arms, separately. The modeling uncovers previously unrecognized patterns of tumor-exclusive platform-consistent co-occurring copy-number alterations (CNAs). We find, first, and validate that each of the patterns across only 7p and Xq, and the combination of 6p+12p, is correlated with a patient's prognosis, is independent of the tumor's stage, the best predictor of OV survival to date, and together with stage makes a better predictor than stage alone. Second, these patterns include most known OV-associated CNAs that map to these chromosome arms, as well as several previously unreported, yet frequent focal CNAs. Third, differential mRNA, microRNA, and protein expression consistently map to the DNA CNAs. A coherent picture emerges for each pattern, suggesting roles for the CNAs in OV pathogenesis and personalized therapy. In 6p+12p, deletion of the p21-encoding CDKN1A and p38-encoding MAPK14 and amplification of RAD51AP1 and KRAS encode for human cell transformation, and are correlated with a cell's immortality, and a patient's shorter survival time. In 7p, RPA3 deletion and POLD2 amplification are correlated with DNA stability, and a longer survival. In Xq, PABPC5 deletion and BCAP31 amplification are correlated with a cellular immune response, and a longer survival.