This summer, Layla Ismail and Suwayda Said worked with various cancer datasets procured by genomics, clinical, and proteomics data. Both interns developed a clear understanding of systems biology by working with diverse data. 

One data source came from Glioblastoma cells. Glioblastoma (GBM) is a highly complex and lethal cancer with a 10% chance of survival after five years. Due to this, scientists have devoted countless efforts to analyzing GBM and what makes up its tumor microenvironment. By analyzing the cell heterogeneity using scRNA-seq data (a sequencing method that allows us to look at gene expression), we can examine the efficacy of available GBM treatments.

This summer, Layla worked on completing, optimizing, and publishing two learning modules using research data from Glioblastoma cells. One module was an extension of an activity made by James Park. It focused on characterizing Glioblastoma cell heterogeneity in response to drug treatment. Through this, both interns learned to analyze the efficacy of two drug treatments compared to the control subpopulation. Layla gained inspiration from this and opted to create an advanced module focusing on stem cell marker expression in Glioblastoma tumor samples in R. She analyzed the function of stem cell markers and the cell proliferation pathway they are associated with. Both learning modules can be accessed here:

• Characterizing GBM Cell Heterogeneity in Response to Drug Treatment

• Analyzing the Presence of Glioblastoma Stem Cell Markers in the Tumor Microenvironment 

She also worked with Jasmine Juette on using data processing to analyze Tumor Mutation Burden. Tumor Mutation Burden is the number of somatic mutations per megabase of the sequenced tumor genome. Tumor cells with high mutation burden effectively fight off immunotherapy, making it extremely difficult to block their cell proliferation pathway. TMBs are used as a biomarker to see how patients are reacting to the immunotherapy given. She worked with a clinical dataset filled with information from 1600+ patients. The outcome of this analysis was recognizing the effect of TMB on the survival rate of patients in this dataset and building a machine-learning model that reflected the original data. 

Layla and Suwayda also worked on a National Cancer Institute (a subdivision of the NIH) project with the ST-Analytics team at ISB. This project focuses on analyzing sequential immunotherapies as cancer treatment options as compared to monotherapies or non-sequential combination therapies. They collaborated with scientists from Baliga Lab, Shmulevich Lab, Heath Lab, Wei Lab, and researchers from Yale and UCLA. The goal was to understand this multi-institutional project to help others learn about innovative immuno-oncology systems biology research.

One subgoal was to learn about the 20+ people who have come together to work on this project over the next five years. The intern pair interviewed nine people who are part of the ISB team and created this map, using the tool Cytoscape, which showcases the connections between postdocs, interns, leads, PIs, etc, at ISB. They thought showcasing the wide range of experience, skills, and education across the ST-Analytics team at ISB and everyone working on the U54 NIH project would be essential. It's important to note that this only showcases 1 of the three institutions working on U54, so an actual map of everyone working on the project is even more complex.

During the interviews, Layla and Suwayda gained insight into potential careers. They received fantastic advice especially relevant to young adults interested in science. Thank you to all who gave their precious time for the benefit of students. Here is a word cloud demonstrating specific pieces of wisdom.

In addition to learning about the people and career options for cancer research, both interns also learned about the new technologies used to profile and image tumor samples. This connected to the broader goal of learning about systems biology. Not only do researchers need to understand which genes are turned on and off to combat cancer, but they also need to understand the tumor microenvironment. The tumor microenvironment comprises different cells and structures, like immune cells, blood vessels, and connective tissue.  All are moving about and actively supporting the growth and spread of cancer.

MAPK (Mitogen-Activated Protein Kinase) is a protein that is part of a pathway that sends signals in many processes, including cell growth and proliferation. Other proteins included are BRAF and the enzyme MEK. Mutations in genes coding for proteins can cause abnormal activation of this pathway, causing cell overgrowth. That is where different cancer treatments can come into play. Experimental projects currently taking place as a part of the U54 project analyze a specific type of immunotherapy: immune checkpoint blockers. Suwayda took some notes from Next Generation Science's curriculum standards for scientific education in classrooms across the United States. She hopes to incorporate knowledge of disciplinary core ideas, cross-cutting concepts, and student-led interactive practices. "A critical element to being in research transcends acquiring knowledge and peaks at transferring it." To Suwayda, making these learning modules is important because cancer is a disease that has wrecked many lives. A statistic from the CDC says that one in five Americans will get skin cancer by age seventy. This project is about melanoma, which is incredibly destructive since it has the lowest survival rate of all skin cancers. Adolescents must know what it is and what preventative steps they can take today.

The header image was created by Boris Aguilar, a Senior Research Scientist at ISB working in the Shmulevich lab and Heber Lima da Rocha, a Postdoctoral Researcher at Indiana University working in the Macklin lab. This image depicts an Agent-Based Model, where we model the tumor in silico. In this specific example, we categorize each melanoma tumor cell as an ‘agent’ that predicts the actions of the in vivo cell.