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Usage Information

A mAb against surface-expressed FSHR engineered to engage adaptive immunity for ovarian cancer immunotherapy
Devivasha Bordoloi, Pratik S. Bhojnagarwala, Alfredo Perales-Puchalt, Abhijeet J. Kulkarni, Xizhou Zhu, Kevin Liaw, Ryan P. O’Connell, Daniel H. Park, Daniel W. Kulp, Rugang Zhang, David B. Weiner
Devivasha Bordoloi, Pratik S. Bhojnagarwala, Alfredo Perales-Puchalt, Abhijeet J. Kulkarni, Xizhou Zhu, Kevin Liaw, Ryan P. O’Connell, Daniel H. Park, Daniel W. Kulp, Rugang Zhang, David B. Weiner
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Research Article Oncology Therapeutics

A mAb against surface-expressed FSHR engineered to engage adaptive immunity for ovarian cancer immunotherapy

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Abstract

Despite advances in ovarian cancer (OC) therapy, recurrent OC remains a poor-prognosis disease. Because of the close interaction between OC cells and the tumor microenvironment (TME), it is important to develop strategies that target tumor cells and engage components of the TME. A major obstacle in the development of OC therapies is the identification of targets with expression limited to tumor surface to avoid off-target interactions. The follicle-stimulating hormone receptor (FSHR) has selective expression on ovarian granulosa cells and is expressed on 50%–70% of serous OCs. We generated mAbs targeting the external domain of FSHR using in vivo–expressed FSHR vector. By high-throughput flow analysis, we identified multiple clones and downselected D2AP11, a potent FSHR surface–targeted mAb. D2AP11 identifies important OC cell lines derived from tumors with different mutations, including BRCA1/2, and lines resistant to a wide range of therapies. We used D2AP11 to develop a bispecific T cell engager. In vitro addition of PBMCs and T cells to D2AP11-TCE induced specific and potent killing of different genetic and immune escape OC lines, with EC50s in the ng/ml range, and attenuated tumor burden in OC-challenged mouse models. These studies demonstrate the potential utility of biologics targeting FSHR for OC and perhaps other FSHR-positive cancers.

Authors

Devivasha Bordoloi, Pratik S. Bhojnagarwala, Alfredo Perales-Puchalt, Abhijeet J. Kulkarni, Xizhou Zhu, Kevin Liaw, Ryan P. O’Connell, Daniel H. Park, Daniel W. Kulp, Rugang Zhang, David B. Weiner

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Usage data is cumulative from December 2024 through December 2025.

Usage JCI PMC
Text version 832 244
PDF 128 49
Figure 449 8
Supplemental data 53 10
Citation downloads 114 0
Totals 1,576 311
Total Views 1,887

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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