The approval of immune checkpoint inhibitors has expanded treatment options for renal cell carcinoma (RCC), but new therapies that target RCC stemness and promote anti-tumor immunity are needed. Previous findings demonstrate that doublecortin-like kinase 1 (DCLK1) regulates stemness and is associated with RCC disease progression. Herein, we demonstrate that small-molecule kinase inhibitor DCLK1-IN-1 strongly inhibits DCLK1 phosphorylation and downregulates pluripotency factors and cancer stem cell (CSC) or epithelial-mesenchymal transition (EMT)-associated markers including c-MET, c-MYC, and N-Cadherin in RCC cell lines. Functionally, DCLK1-IN-1 treatment resulted in significantly reduced colony formation, migration, and invasion. Additionally, assays using floating or Matrigel spheroid protocols demonstrated potent inhibition of stemness. An analysis of clinical populations showed that DCLK1 predicts RCC survival and that its expression is correlated with reduced CD8+ cytotoxic T-cell infiltration and increases in M2 immunosuppressive macrophage populations. The treatment of RCC cells with DCLK1-IN-1 significantly reduced the expression of immune checkpoint ligand PD-L1, and co-culture assays using peripheral blood monocytes (PBMCs) or T-cell expanded PBMCs demonstrated a significant increase in immune-mediated cytotoxicity alone or in combination with anti-PD1 therapy. Together, these findings demonstrate broad susceptibility to DCLK1 kinase inhibition in RCC using DCLK1-IN-1 and provide the first direct evidence for DCLK1-IN-1 as an immuno-oncology agent.

Colorectal cancer (CRC) is a major source of morbidity and mortality, characterized by intratumoral heterogeneity and the presence of cancer stem cells (CSCs). Bufalin has potent activity against many tumors, but studies of its effect on CRC stemness are limited. We explored bufalin’s function and mechanism using CRC patient-derived organoids (PDOs) and cell lines. In CRC cells, bufalin prevented nuclear translocation of β-catenin and down-regulated CSC markers (CD44, CD133, LGR5), pluripotency factors, and epithelial–mesenchymal transition (EMT) markers (N-Cadherin, Slug, ZEB1). Functionally, bufalin inhibited CRC spheroid formation, aldehyde dehydrogenase activity, migration, and invasion. Network analysis identified a C-Kit/Slug signaling axis accounting for bufalin’s anti-stemness activity. Bufalin treatment significantly downregulated C-Kit, as predicted. Furthermore, overexpression of C-Kit induced Slug expression, spheroid formation, and bufalin resistance. Similarly, overexpression of Slug resulted in increased expression of C-Kit and identical functional effects, demonstrating a pro-stemness feedback loop. For further study, we established PDOs from diagnostic colonoscopy. Bufalin differentially inhibited PDO growth and proliferation, induced apoptosis, restored E-cadherin, and downregulated CSC markers CD133 and C-Myc, dependent on C-Kit/Slug. These findings suggest that the C-Kit/Slug axis plays a pivotal role in regulating CRC stemness, and reveal that targeting this axis can inhibit CRC growth and progression.

Background Cancer molecular subtyping plays a critical role in individualized patient treatment. In previous studies, high-throughput gene expression signature-based methods have been proposed to identify cancer subtypes. Unfortunately, the existing ones suffer from the curse of dimensionality, data sparsity, and computational deficiency. Methods To address those problems, we propose a computational framework for colorectal cancer subtyping without any exploitation in model complexity and generality. A supervised learning framework based on deep learning (DeepCSD) is proposed to identify cancer subtypes. Specifically, based on the differentially expressed genes under cancer consensus molecular subtyping, we design a minimalist feed-forward neural network to capture the distinct molecular features in different cancer subtypes. To mitigate the overfitting phenomenon of deep learning as much as possible, L1 and L2 regularization and dropout layers are added. Results For demonstrating the effectiveness of DeepCSD, we compared it with other methods including Random Forest (RF), Deep forest (gcForest), support vector machine (SVM), XGBoost, and DeepCC on eight independent colorectal cancer datasets. The results reflect that DeepCSD can achieve superior performance over other algorithms. In addition, gene ontology enrichment and pathology analysis are conducted to reveal novel insights into the cancer subtype identification and characterization mechanisms. Conclusions DeepCSD considers all subtype-specific genes as input, which is pathologically necessary for its completeness. At the same time, DeepCSD shows remarkable robustness in handling cross-platform gene expression data, achieving similar performance on both training and test data without significant model overfitting or exploitation of model complexity.

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