Harnessing Artificial Intelligence to Accelerate Synthetic Cell Therapies for Cancer and Beyond Daniel J Baker

Format:

Book

Thesis/Dissertation

Author/Creator:

Baker, Daniel (Daniel J.), author.

Contributor:

University of Pennsylvania. Cell and Molecular Biology., degree granting institution.

Language:

English

Subjects (All):

0800.

0982.

0992.

Local Subjects:

0800.

0982.

0992.

Physical Description:

1 electronic resource (84 pages)

Contained In:

Dissertations Abstracts International 87-07B

Place of Publication:

Ann Arbor : ProQuest Dissertations and Theses, 2025

Language Note:

English

Summary:

CAR T cells have demonstrated curative potential in hematologic cancers and increasing efficacy in solid tumors and non-malignant diseases. This coupled with the long-term safety track record of these therapies further signal the paradigm shifting significance for medicine at large. However, target identification remains a critical bottleneck for broadening the reach of these therapies. We hypothesized that given the amount of data available, and the use of artificial intelligence we could build a nomination pipeline to identify tractable targets. To address this we developed a systematic, artificial intelligence-driven approach for rational and high-throughput CAR T target discovery by integrating single-cell RNA sequencing datasets from human melanoma and healthy tissues. This list was refined using public datasets to optimize tumor expression, tissue specificity, and clinical feasibility. Large language models were applied to prioritize and nominate targets with broad therapeutic promise. Glycoprotein non-metastatic melanoma protein B (GPNMB) emerged as the target most often nominated, exhibiting expression across hematologic and solid tumors. We engineered a novel human CAR T cell targeting GPNMB, which showed potent antitumor activity in mouse models of histiocytic lymphoma, melanoma, and colorectal adenocarcinoma. These findings establish a scalable, human-in-the-loop pipeline for CAR T target discovery and support the clinical translation of GPNMB-directed CAR T cells as a pan-cancer therapeutic. This work strongly positions the field to leverage artificial intelligence for novel indications to broaden the reach of cellular therapies

Notes:

Advisors: June, Carl H.; Arany, Zoltan; Bassing, Craig H. Committee members: Baur, Joseph A.; Berger, Shelley L.; Fraietta, Joseph A.; Maus, Marcela V.

Source: Dissertations Abstracts International, Volume: 87-07, Section: B.

Ph.D. University of Pennsylvania 2025

Vendor supplied data

Local Notes:

School code: 0175

ISBN:

9798276001593

Access Restriction:

Restricted for use by site license