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Kristen Shema

Graduate Student

With over 3 years of experience in preclinical drug development, I am driven to discover the next generation of therapeutic molecules that harness the power of the body's immune system to effect change in oncologic and autoimmune conditions. I look forward to employing the experience and diverse skillset that I've acquired in the positions that I've held in various sectors of the biotech and pharmaceutical industry to new opportunities in immunotherapeutic research and development.

Current Research

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Cancer immunotherapies are a class of therapeutic molecules designed to direct the patient’s own immune system to selectively kill tumor cells with great efficacy while also mitigating the risk of many off-target toxicities by solely interrupting tumor-specific processes. While the current class of immunotherapeutic checkpoint inhibitor blockade antibodies are extremely effective in targeting and killing certain cancer types, most patients are not effectively treated. The heterogeneity in patient responsiveness to these therapies have inspired researchers to create new technologies to pinpoint sources of this variability. Traditional tumor organoid cultures are often used for this purpose but suffer from some considerable limitations in recapitulating the tumor microenvironment in a biologically relevant way. Organoid culture techniques often preserve the tumor component, while immune cells and stromal cells die off. Given the relevance these cell types play in the efficacy of immune modulators, accounting for that heterogeneity is essential in performing therapeutic screening.

The Swartz Lab is developing a model of the tumor-immune interface called the TLI-Ivatar: a tumor, lymphatic, immune interface. The TLI-Ivatar is a high-throughput, perfused organoid culture system for analyzing whole tissue tumor and lymph node organoids. The goal of the project is to best recapitulate the complex interplay between tumors, lymphatic vessels, and the immune system within a dynamic, three dimensional space and to compare the model’s responses to patient response to better predict patient outcomes. The customizability of the model allows for researchers to not only screen immunotherapeutics in a high throughput manner on a single biological sample, but also can be used to establish a mechanistic understanding of immunotherapies by identifying the critical cell populations that potentiate immunotherapeutic effects. I aim to discover and enhance the predictive capacity of the current model with different classes of immunotherapeutics to inform the design of more personalized medicine.