Dean Lee, Monika Manne, Roshan Kumar, Rebecca Silver, Alexandra Staskus, Julianna Crivello, Zhiyuan Wang, Dohyun Lee, Ross Fulton, Zhizhan Gu, Christos Hatzis, Francisco Adrian, Andreas Raue, Liang Schweizer
- The discovery of predictive biomarkers of response is critical for forecasting patient benefit from novel immune-modulatory therapeutics. However, their discovery is hindered by the complexity of the drug modes of action and the lack of adequate biological models.
We designed an approach to define biomarkers of response by subjecting dissociated tissue from human tumors to T cell-targeting biologics or their combinations, followed by single-cell transcriptomic profiling and TCR clonotype characterization. Responding cells are identified as those showing treatment-specific shifts in gene expression profiles. Specifically for T cells, TCR clonotypes can be used to match responding T cells in treatment conditions to their sister clones in the baseline state. Comparing the baseline gene expression profiles between T cell clonotypes that responded to the treatment and those that did not allows the discovery of gene expression signatures that can predict response.
We processed over 20The discovery of predictive biomarkers of response is critical for forecasting patient benefit from novel immune-modulatory therapeutics. However, their discovery is hindered by the complexity of the drug modes of action and the lack of adequate biological models.
We designed an approach to define biomarkers of response by subjecting dissociated tissue from human tumors to T cell-targeting biologics or their combinations, followed by single-cell transcriptomic profiling and TCR clonotype characterization. Responding cells are identified as those showing treatment-specific shifts in gene expression profiles. Specifically for T cells, TCR clonotypes can be used to match responding T cells in treatment conditions to their sister clones in the baseline state. Comparing the baseline gene expression profiles between T cell clonotypes that responded to the treatment and those that did not allows the discovery of gene expression signatures that can predict response.
We processed over 20 tumor samples obtained from cancer patients and treated them ex vivo with two novel biologics currently under clinical development - HFB301001, a potentially best-in-class 2nd generation OX40 agonist, and HFB200301, a potentially first-in-class TNFR2 agonist. We applied our biomarker discovery strategy to the pooled scRNA-seq and scTCR-seq data from these samples to define predictive signatures of response to these drugs. To further validate this strategy, we also generated single-cell data in our ex vivo system to characterize response to anti-PD-1 treatment. In the anti-PD-1 predictive response signature, we identified genes involved in inflammatory response and genes in the pathway of other co-inhibitory checkpoints. Application of the anti-PD-1 predictive response signature to bulk transcriptomic data from clinical studies with checkpoint inhibitors successfully stratified patients into two groups with significantly different risk of progression. The predictive response signatures for the two novel agonistic antibodies shared genes involved in inflammatory pathway with the anti-PD-1 signature, but also contained other distinct gene sets. Application of these predictive response signatures to bulk transcriptomic data from TCGA was able to stratify patients across cancer indications by predicted likelihood of response to individual treatments or combinations. The predictive biomarkers of response for the novel OX40 and TNFR2 agonist antibodies will be validated in our Phase I clinical trials.
Ex vivo single-cell profiling coupled with TCR clonotype characterization enabled the discovery of predictive response signatures that informed patient selection strategies for the early clinical development of novel therapeutics.…
