Abstract B148: Activity of the TAM kinase-targeting compound, SLC-391, is mediated by the engagement of the immune system in CT-26 syngeneic mouse model
Bibliographic record
Abstract
Abstract Overexpression of TAM family receptor tyrosine kinases such as Axl has been reported in numerous types of human cancer, and found to correlate with tumor progression and prognosis, metastasis, and drug resistance. Aside from the role in cell growth and survival, TAM kinases have long been recognized for their immunosuppressive activity as well. Hence, downregulation of their activity is expected to unleash antitumor immunity in the tumor microenvironment. As one of the several TAM-targeting small-molecule inhibitors identified, SLC-391 displays a relatively strong activity against Axl, as evidenced by the reductions of phosphotransferase activity in radiometric biochemical assay and the level of Axl Y779 phosphorylation in the cell-based assay. Contradictorily, proliferation of CT-26 murine colon carcinoma cells seemed to be unaffected by the compound in the thymidine incorporation assay with an IC50 of ~10 μM. Interestingly, SLC-391 inhibited CT-26 tumor growth by 37% in a 15-day efficacy study in which the compound was administered at 50 mg/kg p.o.. In comparison, a PD-1 antibody delayed tumor growth by 27%. Tumor-infiltrating lymphocyte phenotyping revealed increases in the number of NK cells and the ratio of M1/M2-polarized macrophages in SLC-391 treatment group, followed by the rise of CD8+ T/Treg ratio and reduction in immunosuppressive myeloid cells. This is indicative of sequential engagement and stimulation of proinflammatory innate immune response and adaptive immune response. In addition, a synergistic antitumor effect was observed when the anti-PD-1 insensitive CT-26 syngeneic model was treated with a combination of SLC-391 and an anti-PD-1 antibody and the overall survival rate of the combination group was prolonged dramatically in comparison with the vehicle control group. To summarize, the antitumor activity of SLC-391 is at least in part mediated by reversing the immunosuppressive tumor microenvironment in CT-26 colon carcinoma model. Citation Format: Shenshen Lai, Rick Li, Paromita Raha, Yuxiang Hu, Jun Yan, Hong Zhang, Anthony Marotta, Zaihui Zhang. Activity of the TAM kinase-targeting compound, SLC-391, is mediated by the engagement of the immune system in CT-26 syngeneic mouse model [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr B148.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".