A Novel Approach to Predict Checkpoint PD-L1 Responses Using Cancer Genomics Information

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Researchers in the College of Dentistry at the University of Iowa and Cellworks Group, Inc. have partnered to develop computational and experimental models that accurately predict checkpoint PD-L1 expression.

"Computational vs. experimental biomarker trends are highly correlated and predictive responses are verified by transwell co-cultures."

Researchers in the College of Dentistry at the University of Iowa and Cellworks Group, Inc. have partnered to develop computational and experimental models that accurately predict checkpoint PD-L1 expression. PD-L1 is an immunosuppressive biomarker found on cancer cells whose high expression has correlated to longer clinical response in patients which respond to these class of treatments.

These findings, presented at the 57th American Society of Hematology (ASH) Annual Meeting and Exposition on December 5, 2015, demonstrate that immune checkpoint ligand PD-L1 and other immunosuppressive biomarker responses can be predicted based on genomic (e.g., mutation and copy number variation) information of the patient tumor using computational predictive technology. Using genomic profiles of multiple myeloma cancer cell lines, the predicted immunosuppressive biomarkers were prospectively validated in the laboratory experiments on these cells in vitro. Adding immune cells to multiple myeloma cell lines in a unique proprietary co-culture system forms the basis of a functional assay that can predict likelihood of response to checkpoint immunotherapy (Provisional Patent 62/250,997; November 4, 2015).

The Cellworks simulation approach provides big data interpretation solution for precision medicine beyond data transformation. The ability to create patient computational avatars and digital simulation of treatments with a lab validation workflow is a big differentiator.

The combined technology is timely, particularly to pharmaceutical partners who want to test their checkpoint inhibitors prospectively or retrospectively in these models to analyze completed clinical studies to identify inclusion and exclusion genomic predictors to improve the percentage of responders to these immune therapies.

The approach takes advantage of the impetus towards sequencing and profiling tumor samples and addresses clinical needs where patient cancer cells are not available for laboratory experimentation. Using both oral squamous cell carcinoma and multiple myeloma cell lines PD-L1 responses were accurately predicted, demonstrating that immune checkpoint ligand PD-L1, and other immunosuppressive biomarker responses are related to cell genomics and play an integral role in treatment outcomes by influencing cell signaling and downstream effects on PD-L1 expression.

Many cancers protect themselves from the immune system by overriding host immune checkpoints. These checkpoints, involving co-receptors on T-cells and ligands on tumor cells, are therefore important targets for immunotherapeutic treatment of cancers through the use of checkpoint inhibitors. Checkpoint inhibitors are molecules that can block the binding between checkpoint receptors and ligands. However, checkpoint inhibitors like PD-L1 inhibitors can have a response rate below 20.5% in patients. Therefore, the success of current therapy depends upon a precision medicine approach: finding the right treatment for the right patient within a reasonable time.

Prediction of checkpoint inhibitor responses in cancer patients could help in choosing appropriate personalized checkpoint inhibitor treatments. These assay models would also allow for the screening of combination treatments involving more than one immunotherapeutic agent or a combination of immunotherapeutic and chemotherapeutic agents to increase the clinical response rates of patients with low response rates to a single immunotherapy treatment. In all, these assays may identify efficacious checkpoint inhibitors targeted to the patients’ cancer mutational profile, save precious treatment time, reduce costs from ineffective treatments, and improve long term prognosis.

For more information, please contact:
Kim Alan Brogden, PhD; Director, Dows Institute for Dental Research; Professor, Department of Periodontics; N423 DSB, College of Dentistry; 801 Newton Road; The University of Iowa; Iowa City, IA 52242. (T): 319-335-8077. (F): 319-335-8895. (E): kim-brogden(at)uiowa(dot)edu

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Kim A. Brogden
University of Iowa
+1 319-335-8077
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