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Original Publication Date
2026
Document Type
Presentation
Abstract
Sacituzumab govitecan functions as a biological "Trojan horse," using a deceptive targeting antibody to deliver lethal chemotherapeutic "soldiers" directly into aggressive breast cancer cells while sparing healthy tissue. However, therapeutic success is often undermined by inherent or acquired drug resistance. Using machine learning to analyze patient-derived samples, this study identifies the protein TC2N as a crucial marker of cellular vulnerability to this targeted delivery. Furthermore, evaluating long-term resistance mechanisms enables the development of secondary treatment strategies for non-responsive patients.
Accessibility Note
Content is digitally accessible 2026
Transcription
Next, we have Carson Walker. Cracking the Code of 'Smart' Cancer Therapy: Why Some Breast Tumors Resist Targeted Treatment. Carson is from the School of Medicine. Their advisor is Dr. Chuck Harrell. Each of you think of eight women in your life who are very important. These could be your mothers, sisters, neighbors, or other role models. Statistically speaking, one of those eight women will be diagnosed with breast cancer at some point in their lifetime. And many of those will be diagnosed with the most aggressive type of breast cancer, which I study. They will usually be treated with a chemotherapy drug, which will kill off the cancer cells, but it will also kill a lot of healthy cells as well. So researchers have developed a more targeted treatment called sacituzumab govitecan, which I will compare to a Trojan horse. The Trojan horse has an outside that is, well, it's, I guess, seems very innocent to these cancer cells and allows them to enter into the cancer cell carrying a load of toxic soldiers inside. And these toxic soldiers can fight off the mechanisms inside the cell to be able to break up the DNA and eventually kill the cancer cell. Unfortunately many patients will be resistant to this type of treatment even before the treatment reaches them. These resistant cells are very different from the patients who are sensitive, who would allow these Trojan horses to enter their cells and then allow the soldiers inside to be able to break up their DNA. And the first part of my thesis is looking at what's the difference between these sensitive and resistant cells and how we can target that to be able to continue treatment for these patients. So in that, we have a lot of patient samples that we receive and then we test to see how well they receive this drug and if the cancer cells are dying off and then we can compare using machine learning or AI models to see what genes are responsible, what are the differences that are responsible for the treatment success. Using that model we were able to find a protein called TC2N and when that's expressed more or has more of it or if a patient has more of that they're more likely to have this drug kill off those cancer cells. So that is one way that we can predict if a patient will be a good match for this treatment or if they won't. However, we know that cancer cells are very resilient, and they will overcome most treatments over time. So in that sense, the second part of my treatment, or the second part of my thesis comes in, where we are looking at what are the differences between these patients that acquire treatment over a long period of time and those that don't. With that, we can tell for these patients, what treatments they should receive. And we can test other therapeutics on these acquired resistance models so that we can see what their treatment plan should be down the road. So the two parts of my project include determining who should receive the treatment and what treatments they should get after resistance has been acquired. And using those together will give us a good opportunity to see what patients should get in the future. Thank you.
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Transcription
3MT_Finalist_Carson_Walker_Captions.srt (5 kB)
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Comments
12th Annual VCU 3MT® Competition, held on October 9, 2026.