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Single-Cell Proteomic Profiling Identifies Combined AXL and JAK1 Inhibition as a Novel Therapeutic Strategy for Lung
Josephine A Taverna1, Chia-Nung Hung2,3, Daniel T DeArmond4
1Division of Hematology and Oncology, Department of Medicine, University of Texas Health Science Center, San Antonio, Texas.
Abstract:
Cytometry by time-of-flight (CyTOF) simultaneously measures multiple cellular proteins at the single-cell level and is used to assess intertumor and intratumor heterogeneity. This approach may be used to investigate the variability of individual tumor responses to treatments. Herein, we stratified lung tumor subpopulations based on AXL signaling as a potential targeting strategy. Integrative transcriptome analyses were used to investigate how TP-0903, an AXL kinase inhibitor, influences redundant oncogenic pathways in metastatic lung cancer cells. CyTOF profiling revealed that AXL inhibition suppressed SMAD4/TGFβ signaling and induced JAK1-STAT3 signaling to compensate for the loss of AXL. Interestingly, high JAK1-STAT3 was associated with increased levels of AXL in treatment-naïve tumors. Tumors with high AXL, TGFβ, and JAK1 signaling concomitantly displayed CD133-mediated cancer stemness and hybrid epithelial-to-mesenchymal transition features in advanced-stage patients, suggesting greater potential for distant dissemination. Diffusion pseudotime analysis revealed cell-fate trajectories among four different categories that were linked to clinicopathologic features for each patient. Patient-derived organoids (PDO) obtained from tumors with high AXL and JAK1 were sensitive to TP-0903 and ruxolitinib (JAK inhibitor) treatments, supporting the CyTOF findings. This study shows that single-cell proteomic profiling of treatment-naïve lung tumors, coupled with ex vivo testing of PDOs, identifies continuous AXL, TGFβ, and JAK1-STAT3 signal activation in select tumors that may be targeted by combined AXL-JAK1 inhibition. SIGNIFICANCE: Single-cell proteomic profiling of clinical samples may facilitate the optimal selection of novel drug targets, interpretation of early-phase clinical trial data, and development of predictive biomarkers valuable for patient stratification.
Insights
Cytometry by time-of-flight (CyTOF) identified AXL and JAK1-STAT3 signaling in lung tumors. Targeting these pathways with AXL and JAK inhibitors may improve treatment selection for advanced lung cancer patients.
Area of Science:
- Oncology
- Proteomics
- Cancer Signaling Pathways
Background:
- Cytometry by time-of-flight (CyTOF) enables single-cell protein analysis for assessing tumor heterogeneity.
- Understanding intertumor and intratumor variability is crucial for predicting treatment responses.
- AXL signaling is a potential therapeutic target in metastatic lung cancer.
Purpose of the Study:
- To stratify lung tumor subpopulations based on AXL signaling.
- To investigate the effects of the AXL kinase inhibitor TP-0903 on oncogenic pathways.
- To identify potential therapeutic strategies by analyzing signaling pathway dynamics.
Main Methods:
- CyTOF profiling to measure cellular proteins at the single-cell level.
- Integrative transcriptome analyses to study TP-0903 effects.
- Patient-derived organoid (PDO) models for ex vivo drug sensitivity testing.
- Diffusion pseudotime analysis for cell-fate trajectory assessment.
Main Results:
- AXL inhibition suppressed SMAD4/TGFβ signaling and induced compensatory JAK1-STAT3 signaling.
- High JAK1-STAT3 levels correlated with increased AXL in treatment-naïve tumors.
- Combined high AXL, TGFβ, and JAK1 signaling was linked to cancer stemness, EMT, and dissemination potential.
- PDOs from high AXL and JAK1 tumors showed sensitivity to TP-0903 and ruxolitinib (JAK inhibitor).
Conclusions:
- Single-cell proteomic profiling combined with PDO testing can identify actionable signaling pathways in lung tumors.
- Continuous AXL, TGFβ, and JAK1-STAT3 signaling in select tumors suggests a potential for combined AXL-JAK1 inhibition.
- This approach can aid in selecting novel drug targets and developing predictive biomarkers for patient stratification.
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