flavonoid compound library l7700  (Selleck Chemicals)

Journal: bioRxiv

Article Title: A Multi-Kinase Inhibitor Screen Identifies mTOR Signaling as a Vulnerability in Advanced Prostate Cancer

doi: 10.64898/2026.10.13.758818

Figure Lengend Snippet: (A) Radial bar plot showing the distribution of screened compounds across annotated target pathways. Grey bars indicate the total number of compounds assigned to each pathway, and red bars indicate the number of compounds within each pathway that induced cell killing in AXL KO PC3 cells, as defined by the prespecified hit criteria (SSMD < −3 at both concentrations). The radial axis represents compound counts, with gridlines at 50, 100, 150, 200, and 250. The PI3K/AKT/mTOR pathway, highlighted in red text, yielded the highest number of hits (12 compounds; *** P < 0.0001). Pathway-level enrichment of hits relative to library representation was assessed using Fisher’s exact test with FDR correction for multiple comparisons across 22 pathways.

Article Snippet: We therefore performed an unbiased high-throughput screen of a 1,814 compound annotated kinase inhibitor library (Selleckchem) in isogenic AXL KO and AXL WT cells to identify pharmacological vulnerabilities associated with AXL loss; schematic in Supplemental Fig. 2C.

Techniques:

Journal: bioRxiv

Article Title: A Multi-Kinase Inhibitor Screen Identifies mTOR Signaling as a Vulnerability in Advanced Prostate Cancer

doi: 10.64898/2026.10.13.758818

Figure Lengend Snippet: (A) Heatmap of the 22 compounds meeting the hit criterion in the kinase inhibitor screen in AXL KO PC3 cells at 0.5 µM (left) and 2.5 µM (right). Columns represent DMSOvtreated controls and compound-treated replicates, and rows represent individual compounds. Colors indicate optical density normalized to the mean of the vehicle controls on each replicate plate, ranging from blue (no effect) through white to red (effect). Boxed values to the right of each panel indicate the SSMD for each compound; more negative values indicate greater reductions in viability relative to vehicle controls. Compounds were classified as hits if SSMD < −3 at both concentrations. (B) Sensitivity of AXL-low and AXL-high cancer cell lines to seven hit compounds identified in the kinase screen and evaluated in the PRISM drug repurposing screen (DepMap, 20Q2): bardoxolone methyl, taselisib, dasatinib, Torin-2, pictilisib (GDC-0941), VE-822, and MK-8745. AXL -low cell lines were significantly more sensitive to taselisib and bardoxolone methyl, whereas AXL- high cell lines were more sensitive to dasatinib. Cell lines were stratified by AXL expression (DepMap Public 24Q4) into AXL -Low (first quartile, log 2 (TPM + 1) < 0.4; red; n = 40) and AXL- High (third quartile, log 2 (TPM + 1) > 6.4; gray; n = 155) groups. Boxplots show the area under the dose-response curve, with lower AUC values indicating greater drug sensitivity. Each point represents one cell line. For each drug, AUC values were converted to drug-specific z-scores, and cell lines with z-scores < −1 were classified as sensitive. The proportions of sensitive cell lines were compared between groups using two-sided Fisher’s exact tests. * P < 0.05, ** P < 0.01; ns, not significant. P values were not adjusted for multiple comparisons. (C) Dose-response viability curves for six compounds preferentially active following AXL loss (SR-4835, dasatinib, cinobufagin, MCB-613, uprosertib, and bardoxolone methyl) in AXL WT (gray) and AXL KO (red) PC3 cells following 72 h of treatment. (D) Dose-response viability curves for Torin-2 in AXL WT (gray) and AXL KO (red) PC3 cells. Points represent mean ±SD. The IC 50 values were 10.23 nM for AXL KO cells and 186.5 nM for AXL WT cells. In (C) and (D) , viability is normalized to vehicle-treated controls, and data are presented as mean ± SD from 2-3 biological replicates. Curves were fitted by four parameter logistic nonlinear regression (GraphPad PRISM) and the half maximal values (IC 50 ) with 95% confidence intervals were interpolated from the fitted curves.

Article Snippet: We therefore performed an unbiased high-throughput screen of a 1,814 compound annotated kinase inhibitor library (Selleckchem) in isogenic AXL KO and AXL WT cells to identify pharmacological vulnerabilities associated with AXL loss; schematic in Supplemental Fig. 2C.

Techniques: Sensitivity, Expressing