Iron in glioblastoma macrophages
Graduate Research Assistant, Petronek Lab, Department of Radiation Oncology, University of IowaIowa City, Iowa · 2026 – present
My research focuses on glioblastoma, one of the most aggressive brain cancers. I study how immune cells inside and around brain tumors may help shape tumor progression, with a particular focus on macrophages. Macrophages are immune cells that normally help clean up damaged tissue, but in cancer they can sometimes be redirected by the tumor and become part of a supportive tumor environment.
A major focus of my work is understanding how these macrophages handle iron. Iron is essential for normal cell function, but in tumors it can also influence inflammation, tissue damage, immune suppression, and treatment response. Using computational biology and publicly available patient datasets, I study whether macrophages in glioblastoma show a specific iron-recycling program and where this program appears within the tumor.
My current work suggests that iron-related activity in glioblastoma is not simply coming from the cancer cells themselves. Instead, a strong iron-recycling signal appears in tumor-associated macrophages, especially in regions near dead or dying tumor tissue. By mapping these patterns across single-cell, bulk, anatomic, and proteomic datasets, I hope to better understand how macrophage iron metabolism contributes to the brain tumor microenvironment and how it may eventually guide more targeted translational studies.
Data I work across
Anatomic region data means measurements labeled by where in the tumor they were taken, so a signal can be placed in the tumor core, the edge, or the dying tissue between them.
GPx1 in colorectal cancer
Graduate Research Assistant, Cancer Biology Program, Carver College of Medicine, University of IowaIowa City, Iowa · 2024 – 2026
This work investigated GPx1 as a mediator of colon cancer progression and chemotherapy resistance. GPx1, short for glutathione peroxidase 1, is an enzyme that protects cells from oxidative damage. The same protection can allow a tumor cell to survive a drug intended to kill it.
The models were patient-derived organoids. An organoid is a small piece of living tissue grown in the lab from a patient tumor, and it reproduces the behavior of the original tumor more closely than a flat layer of cells does. I took tumor tissue through the whole path, from processing and dissociation of the fresh specimen to long-term culture, then used those organoids and matched cell lines to ask what happens when GPx1 is blocked.
The readouts covered both what the cells do and what they become. I ran qPCR, protein and immunoassays, and viability, proliferation and migration assays. On the flow cytometer I looked at cell cycle profiling, apoptosis with Annexin V and propidium iodide, and surface marker phenotyping. I also ran efficacy studies in PDX and CDX mouse models, which covered tumor implantation, dosing, caliper measurements, weight monitoring, and endpoint tissue harvest.
The last step was to take the results back to published multi-omics data and look for the molecular signatures that travel with GPx1-driven resistance.
Methods used here
Clinical shadowing, colorectal cancer care
Observer, Division of Gastrointestinal Surgery, University of Iowa Hospitals and ClinicsIowa City, Iowa · Fall 2025
I shadowed Kristina Guyton, MD, Clinical Associate Professor of Surgery, in colorectal cancer surgical care.
Patient-derived organoids begin as tumor tissue removed in an operation. Shadowing covered the clinical side of that path: how colorectal cancer presents, how the decision to operate is made, and how a specimen is handled before it reaches the laboratory.
Treatment was the other half of it. I followed how therapy is chosen after surgery, including how next-generation sequencing of a tumor guides that choice. Next-generation sequencing reads the mutations present in a patient's tumor. Those results narrow which drugs are likely to work and which are not, so the same diagnosis can lead to different treatment in two different patients.
This is the point where my laboratory work and the clinic meet. The drug-response experiments I ran in organoids and in mice ask the same question a molecular tumor board asks, on a different timescale and with a different kind of evidence.
Computational systems oncology at LUMS
Research Intern, then Undergraduate Research Lead, Biomedical Informatics and Engineering Research Laboratory, Lahore University of Management SciencesLahore, Pakistan · 2021 – 2023
My first research was computational. I joined the Biomedical Informatics and Engineering Research Laboratory as an intern and worked on TISON, a platform for simulating cancer at several scales at once. I developed algorithms for cellular circuit modeling inside it, debugged the front end, and co-authored the platform paper. I also built cancer gene interaction networks from TCGA data for head and neck squamous cell carcinoma and for colorectal cancer, which the lab used for pathway analysis and target finding.
For my senior year project I led a small team on autophagy in colorectal cancer. Autophagy is the process a cell uses to break down and recycle its own parts. In cancer it can either keep a cell alive or help kill it, and which one happens depends on the signaling around it. We built pathway models from the primary literature and public gene databases, then ran robustness and sensitivity analyses to find the nodes that decide the switch. Those nodes are the candidate drug targets.
Alongside that I spent a term in the Department of Life Sciences on a biostatistics project, comparing serum marker profiles across rheumatoid arthritis and periodontal disease cohorts using regression, cluster analysis, and gene ontology enrichment, to find where the two conditions overlap.