Scarlett Luo

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Scarlett Luo attended this program during the summer before her senior year, at Edmonds-Woodway High School. She is a full International Baccalaureate (IB) diploma student, and finds science very fascinating. The relevant coursework she's taken in the IB program includes: IB Marine Biology, IB Chemistry, and IB Biology. Cancer specifically, has always been an area of interest for Scarlett due to personal experience being a member of an immediate family member, who has had cancer. Outside of school, she's an advocate for Asian American Native Hawaiian and Pacific Islander (AANHPI) representation in education. Her latest work is a children's book, Mia's Family Tree: Leaves of Legacy, and at its peak, was ranked #52 on Amazon for Children's Books in Immigration. She hopes to continue AANHPI representation and advocacy in the future. 

Through hands-on programming, DREAM-High Scholars visualize and analyze genomics, clinical, and physical data from breast cancer cells. DREAM-High is a partnership between the Columbia Center for Cancer Systems Therapeutics, the Palazzo Strozzi Foundation USA, the Stanford Center for Cancer Systems Biology, and the Institute for Systems Biology. 

In the DREAM-High program, Scholars learn to program in R, a language for statistical computing and graphics. They manipulate and write code in a cloud-based RStudio environment to analyze a wide range of data on breast cancer patients and cancer cell lines. 

I created heat maps as colorized representations of data matrices. I reordered features and observations so that similar entities are close to each other in the graph.  Heat maps make it easy to visualize and understand complex data.

I loaded and examined a data frame of clinical information from 1,082 breast cancer patients from The Cancer Genome Atlas (TCGA). I summarized clinical measurements on both the patients, such as  gender and age, and the patients’ tumors, such as estrogen receptor status and histology.

I performed an integrative analysis of clinical measurements and gene expression data for 1,082 patients in the TCGA Breast Cancer cohort. By calculating heat maps and annotating them with clinical information, I detected patterns in the patients' expression profiles across 18,351 genes that correspond to luminal and triple negative breast cancers.

I discovered biological processes that distinguish cancer cell lines based on the aggressiveness of the cancers they model. For both breast cancer and colon cancer cell lines, I calculated, visualized, and functionally annotated differential gene expression profiles with data from the Physical Sciences in Oncology Cell Line Characterization Study.

I built linear regression models that are predictive of breast cancer survival from the METABRIC breast cancer dataset. I found that gene expression profiles of certain cancer genes are predictive of prognosis. Inclusion of additional features in my model increased its explanatory power.