Alex Douthart

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Hi, I'm Alexandria Douthart, a rising senior at Bainbridge High School, and I’m excited to be part of the DREAM High Program! I’ve really enjoyed learning about Systems Biology and R programming. In the future, I aspire to explore computational neuroscience and uncover the driving forces behind human intelligence and diseases like Alzheimer’s.

I am a social person, and enjoy spending free time with my friends and family. I also enjoy writing and have created original plays for the BHS “Winter One-Acts,” as well as drafting resolutions for our Model UN club. 

I feel incredibly fortunate to have had many educational opportunities and am passionate about making high-quality education accessible to everyone who seeks it. Beyond my studies, I volunteer my time to tutoring on Schoolhouse.world, a platform that offers free peer-to-peer academic help. Most recently, I was honored to become President of the Dialog Club, part of Schoolhouse.world's Dialogues program, where students from around the world participate in meaningful discussions on important topics, fostering empathy, communication, and understanding.

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.