Abstract PO-093: Crenolanib improves PD-1 response and overall survival in immune checkpoint inhibitor resistant murine models of oral squamous cell carcinoma
Bibliographic record
Abstract
Abstract Background: Present line indications for pembrolizumab are for patients with metastatic or unresectable recurrent (i.e. incurable) head and neck squamous cell carcinoma (HNSCC). However, only a minority of patients on immunotherapy will realize a durable survival benefit because >80% of patients with metastatic HNSCC do not respond to PD-1 blockade. Tumor microenvironment mesenchymal stem cells (MSCs) and cancer associated fibroblasts (CAFs) have been reported significantly contribute to chemotherapy and radiation resistance. Moreover, MSCs have been shown to contribute to an immunosuppressive tumor microenvironment by upregulating PD-L1 in breast cancer models. We have previously shown crenolanib improves MSC mediated cisplatin resistance in vitro through modulation of MSC-mediated activation of AKT signaling, therefore we hypothesize that targeting MSCs may be of therapeutic benefit alone and in combination with anti-PD1 immunotherapy. Methods: Oral cancer was induced in the buccal space of C56/BL6 mice with the murine oral cancer cell lines MOC1 or PD-1 resistant cell line, MOC2. When tumors reached approximately 5 × 5 mm, mice were treated with 15 mg/kg (low dose) or 30 mg/kg crenolanib (high dose) for five consecutive days over 3 weeks. Tissue were harvested for immunohistochemistry and immunofluorescence analysis and ex vivo cell cultures prepared for analysis by western immunoblotting and flow cytometry. Results: There was a significant reduction in tumor volume in mice bearing MOC1 and MOC2 tumors (p<0.03) treated with either low or high dose crenolanib compared to vehicle control. Overall survival was also significantly improved in mice bearing MOC1 tumors treated with high dose crenolanib compared to mice treated with vehicle control (p<0.04) and approached significance in MOC2 mice treated with low dose crenolanib. Tumor sections were imaged by immunofluorescence microscopy. There was a decrease in expression of PDGFR-α on MOC1 tumor cells and α-SMA on tumor microenvironment stromal cells in mice treated with crenolanib compared to vehicle control, suggesting crenolanib targets both cell types. There was a significant reduction in tumor volume (p<0.0001) and improved overall survival (p<0.0004) in mice bearing MOC2 tumors treated with combination crenolanib plus pembrolizumab compared to vehicle plus pembrolizumab. Conclusions: Preliminary in vivo data suggests crenolanib may be efficacious when used in combination with anti-PD1 immunotherapy by inhibiting the immunosuppressive effects of tumor microenvironment MSCs. Citation Format: Xiangfeng Shen, Katherine Gonzalez, Rico Castillo, Ashlyn Rickard, Yvonne Mowery, Tammara Watts. Crenolanib improves PD-1 response and overall survival in immune checkpoint inhibitor resistant murine models of oral squamous cell carcinoma [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-093.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".