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. 2025 Sep 29;4(6):100284. doi: 10.1016/j.cellin.2025.100284

Development of a comparative high-throughput screening platform for identifying glioblastoma subtype specific inhibitors

Zilai Wang a,⁎, Pin Zhang a, Kiira M Ratia b,d, Ahmad Daher c, Zongmin Zhao d, Paul R Carlier b,d,e, Yuwei Jiang f, Lijun Rong a,⁎⁎
PMCID: PMC12657333  PMID: 41321709

Abstract

Glioblastoma (GBM) is the most lethal primary malignant brain tumor in adults, with the development of effective therapeutic agents largely hampered by vast tumor heterogeneity and the impedance of efficient drug delivery by the blood-brain barrier (BBB). Our prior research has demonstrated that adult neural stem cells (NSCs) and oligodendrocyte precursor cells (OPCs) can act as cells of origin for two distinct GBM subtypes (Type 1and Type 2) in mice, with significant conservation to human Type I and Type II GBM in functional properties and distinct responses to the inhibition by Tucatinib and Dasatinib. Based on these findings, we have established a robust high-throughput screening (HTS) assay to identify lineage-dependent subtype-specific as well as lineage-independent small molecule inhibitors for therapeutic development. Reported in the current study, we conducted a HTS using a kinase inhibitor library (900 compounds) in Type 1 and Type 2 GBM cells. Our primary screen identified 84 common inhibitors, 11 Type 1-specific inhibitors, and 18 Type 2-specific inhibitors. The confirmation screen verified R406 and Ponatinib as selective inhibitors of Type 2 GBM cells, and this was further validated in dose-dependent assays. Additionally, R406 exhibited a synergistic effect with Tucatinib in Type 2 GBM cells, providing a rationale for combination therapy. Our study demonstrated the feasibility of identifying subtype-specific therapeutic vulnerabilities using cell-lineage based GBM models and laid the foundation for expanded HTS studies in larger scale screens in both mouse and human GBM subtypes.

Keywords: Glioblastoma, Kinase inhibitors, High-throughput screening, Subtype-specific therapy, R406, Ponatinib, Dasatinib, Tucatinib, Precision medicine

Graphical abstract

Image 1

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Highlights

  • •

    We established a high-throughput screen platform using lineage-based GBM models to identify subtype-specific inhibitors.

  • •

    R406 and Ponatinib selectively inhibit Type 2 GBM cells.

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    R406 exhibits a synergistic effect with Tucatinib in Type 2 GBM cells.

1. Introduction

Glioblastoma (GBM) is a grade IV glioma and the most common and malignant form of primary brain tumor, characterized by cellular and molecular heterogeneity (Brennan et al., 2013; Cancer Genome Atlas Research, 2008; Frattini et al., 2013; Neftel et al., 2019; Parsons et al., 2008; Weller et al., 2015). GBM was classified bioinformatically into four subtypes based on transcriptional profiling, namely the Classical, Mesenchymal, Proneural, and Neural, and was later refined by removing the Neural subtype (Phillips et al., 2006; Verhaak et al., 2010; Wang et al., 2017). However, survival of GBM patients has plateaued since 2011, suggesting the advances in GBM molecular profiling and classification have not yet been translated to improved patients’ survival at the population-level (Neth et al., 2022). This coincides with the fact that no targeted agents have been FDA approved for GBM treatment in the past decade (Gupta et al., 2019; Wang et al., 2021). Therefore, there is an urgent medical need to develop novel and effective therapeutics targeting GBM.

Through targeted mutation of three GBM relevant tumor suppressor genes, namely Trp53, Pten, and Nf1, we have created genetically engineered mouse models of two functionally different GBM subtypes (Type 1 and Type 2) defined by distinct cells of origin(Alcantara Llaguno et al., 2015; Wang et al., 2020). Using signatures of the two tumor types, we unveiled cognate GBM subtypes present in all human GBM data sets examined, and account for approximately 46% of human GBM cases. The relationship of the mouse to human cognate tumors is upheld by transcriptional profiles, tight association to the lineage of origin, and distinct functional properties of both the tumors and primary cultures. Among the conserved functional properties, the OPCs associated mouse Type 2 and human Type II GBM primary cultures utilize Neuregulin 1 (NRG1) activated ErbB3 signaling for growth, whereas the NSCs associated mouse Type 1 and human Type I cells grow the best on EGF. Further, we found that the ERBB2 inhibitor Tucatinib, and a multi-target tyrosine kinase inhibitor, Dasatinib, both can selectively inhibit mouse Type 2 and human Type II GBM cell growth in vitro and effectively abolishes downstream Ras and phosphoinositide-3-kinase (PI3K) activation while showing no measurable effects on mouse Type 1 or human Type I GBM cells (Wang et al., 2020). The identification of GBM subtypes that are supported by distinct receptor tyrosine kinases (RTKs) signaling underscores the need for development of a battery of inhibitors that target different RTKs, but also suggests a unique opportunity to identify subtype specific vulnerabilities underlying the differential RTK signaling as demonstrated by Tucatinib and Dasatinib. Using high-throughput screening (HTS) platforms, it is plausible to identify additional inhibitory small molecules that specifically target each individual RTK arm of influence under culture conditions supplied with ligand specific culture medium. We envisioned that performing screen and analysis on both Type 1 and Type 2 cells in a parallel setting will enable the reduction of false positives in hit selection and identification of subtype-specific inhibitory compounds.

In this study, we performed a HTS using a kinase inhibitor library to systematically identify subtype-specific as well as common therapeutic vulnerabilities in Type 1 and Type 2 GBM cells. We further validated key candidate compounds and identified R406 and Ponatinib as preferential inhibitors of Type 2 GBM. Additionally, we demonstrated the synergistic effect of R406 with Tucatinib in Type 2 cells, providing a rationale for combination therapy strategies in this GBM subtype. By identifying subtype-specific inhibitors, we aim to contribute to the advancement of precision medicine in GBM treatment. Our discovery platform set the stage for identifying more differential drug sensitivities of GBM subtypes in future larger scale of HTS and provide a framework for developing more targeted therapeutic approaches.

2. Results

2.1. Confirmation of distinct marker expression and drug response in Type 1 and Type 2 GBM cells

Primary mouse GBM Type 1 (3605, 2024, 1329) and Type 2 (3112, 775, 631) cells have been previously described (Wang et al., 2020), with distinct characteristics in their in vitro growth dependence on EGF or Nrg1, and in intracranial transplantation to recapitulate subtype specific histological features. We confirmed the subtype characteristic marker expression using Western blot analysis. Type 1 GBM cells exhibited higher expression of EGFR and Sox9, whereas Type 2 GBM cells showed elevated levels of Erbb3 and Sox10 (Fig. 1(A)). Next, we confirmed the differential sensitivity of the two GBM subtypes to Dasatinib, a dual Src/Bcr-Abl inhibitor, in a dose-dependent manner. Type 2 cells demonstrated greater sensitivity to Dasatinib treatment compared to Type 1 cells (Fig. 1(B)), highlighting subtype-specific differences in kinase signaling dependencies.

Fig. 1.

Fig. 1

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Confirmation of the representative marker expression differing Type 1 and Type 2 GBM and their distinct responses to Dasatinib treatment. (A) western blot showing the differential expression of EGFR and Sox9, and Erbb3 and Sox10 in three of each Type 1 and Type 2 GBM cells. (B) Confirmation of the dose dependent response of Type 1 and Type 2 GBM cells to Dasatinib treatment measured by ATP assays (CellTiter-Glo®; n = 3 replicates). Data are presented as mean±standard deviation (SD).

2.2. Development of a HTS platform

Given that one of the essential characteristics differing Type 1 and Type 2 GBM is their differential expression of EGFR and ERBB3 and distinct dependence on EGF and Nrg1 activated signaling, and that RTKs are key regulators of cellular signaling pathways that control diverse effect such as proliferation, survival, differentiation, and metabolism and so on, we choose to use viability assay as the primary readout for the screen since it can comprehensively capture the functional impact of kinase inhibition unbiasedly on overall cell survival, incorporating not only direct effects on proliferation but also broader consequences on apoptotic pathways, metabolic adaptations, and resistance mechanisms. To this end, we designed to use a Cell-Titer-Glo luminescent assay to measure intracellular ATP levels as surrogate for cellular viability, taking advantage of established protocols of HTS on antiviral inhibitors and drug discovery efforts on cancers and hypertension (Arslan et al., 2013; Cheng et al., 2016; Wang et al., 2014; Yang et al., 2017) on format and liquid handling. We used 384-well format for the HTS, Type 1 and Type 2 GBM cells are assayed in the same DMEM/F12 serum free medium supplemented with B27 and N2, plus EGF, Nrg1, and PDGF-AA. We used AZD3759 (Zorifertinib) as positive controls for Type 1 cells, and Tucatinib or Dasatinib was used as positive control for Type 2 cells (Fig. S1A).

We have optimized the HTS parameters such as cell density, medium volume, and incubation time in the 384-well plate format to achieve a high signal/background ratio. To establish an optimal screen condition on the 384-well plate format, we started with examining of cell numbers of 500/well, 1000/well, 2000/well, and 4000/well, and culture medium volume of 30 μL/well, 40 μL/well, 50 μL/well, and 60 μL/well with a 4-day incubation time post treatment (Fig. S1B-C). We determined that 500 cells/well would provide the best separation of Type 1 cells vs. Type 2 cells on Tucatinib treatment. In terms of culture volume, we noted bigger variations in results from 30 μL/well cultures, and no significant differences were found among 40 μL/well, 50 μL/well, and 60 μL/well conditions. For the convenience of liquid handling and compound dilution, we decided to use 40 μL/well for the screen (Fig. S1B-C). We also compared the effect of using poly-D-lysine coated plate vs non-coated plate on cell growth and response to Tucatinib treatment, and concluded that coating does not make significant differences on the cell growth and treatment response (Fig. S1D). To evaluate the effect of incubation time on signal-to-noise ratio (S/N), we compared the results from incubation time of 4 days, 5 days, 6 days, and 7 days, and determined that 5 days incubation post plating/treatment is in the linear range and has the benefit of time efficiency while achieving decent S/N index (Fig. S2).

2.3. HTS identifies subtype-specific and common GBM hits

We performed a viability-based HTS assay using a kinase inhibitor library containing 900 compounds, a curated collection of small molecules including both FDA-approved drugs and investigational compounds covering major kinase families designed to target a wide range of protein kinases, which are key regulators of cellular signaling pathways involved in cancer progression, proliferation, and survival. The assay was conducted in a 384-well format in a HTS flow as depicted in Fig. 2(A) using CellTiter-Glo to quantify ATP levels as a measure of viability (Fig. 2(A)). Compounds were arranged on three 384-well plates each for Type 1 and Type 2 cells, with 500 cells and 40 μL of culture medium per well, and final drug concentration at 1 μM. We achieved Z′ scores greater than 0.53 in all three plates of Type 2 cells, and observed variable but decent scores in plates of Type 1 cells (Fig. S3). The Z′-factor was determined from measurement of the assay signal in the DMSO controls (100% viability) and the measurement of the assay signal in the presence of AZD3759 (Type 1 cell plates) or Dasatinib (Type 2 cells plates) (Zhang et al., 1999). We define a compound as “hit” when the compound shows 70% or greater inhibitory effect (or 30% or less viability) (see method). From the primary screen, 84 compounds exhibited inhibitory effects on both subtypes, while 11 compounds were specific to Type 1 and 18 were specific to Type 2 GBM cells (Fig. 2, Fig. 3(B) and Fig. 3). The dot plots in Fig. 3 highlighted the distribution of drug responses across Type 1 (Fig. 3(A)) and Type 2 (Fig. 3(B)) GBM cells. Negative controls (DMSO-treated cells) and positive controls (AZD3759 for Type 1 and Dasatinib for Type 2) confirmed assay robustness.

Fig. 2.

Fig. 2

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A diagram of screen set up and outline of primary and confirmation screen results. (A) A diagram showing the flow through process of the HTS, the assay is based on viability measuring of cellular ATP levels using Cell-Titer Glo. Briefly, the library compounds were deposited individually to the 384-well assay plate at 40 nL at 1 mM using an Echo 650T liquid handler. On day one, Type 1 (3605) and Type 2 (3112) cells were plated at 500 cells/well in 40 μL assay medium, and incubated in 37 °C with 5% CO2 incubator for 5 days. On day 5, 5 μL of Cell Titer Glo substrate was added to each well, plates were set on rocking bed shaking for 10 min, and luminescence signal was captured with a plate reader. (B) From primary screen, we identified 84 common hits, 11 hits specific for Type 1 cells, and 18 specific hits (with 70% inhibition cut off) for Type 2 cells. The 113 cherry-picked compounds were proceeded for the confirmation screen in extended 3 Type 1 cells (3605, 2024, 1329) and 3 Type 2 cells (3112, 775, 631), and 3 hits for Type 2 specific and 46 common hits for both Type 1 and Type 2 cells were confirmed (with 70% inhibition cut off).

Fig. 3.

Fig. 3

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Dot plot compiling the results of primary screen. Three plates of Type 1 cells (A) and three plates of Type 2 cells (B) assayed on Kinase inhibitor library were shown. Cells treated with DMSO were used as negative controls, AZD3759 and Dasatinib were used as positive controls for Type 1 and Type 2 cells, respectively.

A total of 113 cherry-picked compounds underwent a secondary confirmation screen in an extended list of Type 1 cells (3605, 2024, 1329) and Type 2 cells (3112, 775, 631) at 1 μM concentration in 384-well plates. Dot plot analysis reaffirmed differential responses between Type 1 and Type 2 cells (Fig. 4(A)). Z’ score analysis further validated the robustness of screening results (Fig. 4(B)). Using a 70% inhibition cutoff, 46 compounds were confirmed as common hits for both Type 1 and Type 2 cells, three compounds (Ponatinib, Dasatinib, PD0166285) were confirmed as Type 2 specific hits, increasing to seven (Ponatinib, Dasatinib, R406 free base, PD0166285, XMD8-92, R406, Cediranib) when the threshold was lowered to 60% (Fig. 4(C)). As an established Type 2 cells specific inhibitor and positive control used in the study, Dasatinib was also reaffirmed as a hit from the kinase inhibitor library, demonstrating the reliability of our screen platform. A summary of confirmed hits, including compounds dropped due to intra-group variability, is provided in Table 1 and Table S1.

Fig. 4.

Fig. 4

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Summary of confirmation screen on 113cherry-pickedcompounds. (A) Dot plot of confirmation screen on 3 Type 1 cells (3605, 2024, 1329) and 3 Type 2 cells (3112, 775, 631). (B) Z′ scores of confirmation screen on 3 Type 1 cells and 3 Type 2 cells. (C) Venn diagram of hits identified in confirmation screen for Type 2 cells. Three compounds were confirmed when 70% inhibition cut off was used, and 7 compounds were confirmed when 60% inhibition cut off was used.

Table 1.

Summary of confirmation screen for Type 1 specific hits, Type 2 specific hits, and common hits for both; number of compounds dropped out were noted for variation within Type 1 group, Type 2 group, or in both groups.

% Inhibition Confirmed Dropped out b/c variation in T1 group Dropped out b/c variation in T2 group Dropped out b/c variation in both T1&T2 groups
Type 1 specific T1>80, T2<50 0/11 2/11 2/11 7/11
Type 2 specific T2>80, T1<50 3/18 12/18 1/18 2/18
Common hits T1>80, T2>80 35/84 18/84 9/84 22/84
Type 1 specific T1>70, T2<50 0/11 2/11 4/11 5/11
Type 2 specific T2>70, T1<50 3/18 12/18 2/18 1/18
Common hits T1>70, T2>70 46/84 20/84 7/84 11/84
Type 1 specific T1>60, T2<50 0/11 2/11 4/11 5/11
Type 2 specific T2>60, T1<50 7/18 8/18 2/18 1/18
Common hits T1>60, T2>60 56/84 22/84 1/84 5/84
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2.4. R406 and Ponatinib selectively inhibit Type 2 GBM cells in a dose-dependent manner

To further validate the screen result, we purchased commercially available compounds and investigated their inhibitory effect on extended Type 1 (3605, 2024, 1329) and Type 2 (3112, 775, 631) cells in serial dilution assays. Two kinase inhibitors, R406 and Ponatinib, were confirmed as preferential inhibitors of Type 2 GBM cells in dose dependent manner, with both compounds exhibited stronger growth inhibition in Type 2 cells (3112, 775, 631) compared to Type 1 cells (3605, 2024, 1329) (Fig. 5(A)–(B)).

Fig. 5.

Fig. 5

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R406 and Ponatinib were confirmed as Type 2 cells preferential inhibitors in concentration dependent manner tested in extended set of Type 1 and Type 2 cells. (A) R406 preferentially inhibits Type 2 cells (3112, 775, 631) growth in concentration dependent manner compared to Type 1 cells (3605, 2024, 1329) measured by ATP assays (CellTiter-Glo®; n = 3 replicates). Data are presented as mean±SD. (B) Ponatinib preferentially inhibits Type 2 cells (3112, 775, 631) growth in concentration dependent manner compared to Type 1 cells (3605, 2024, 1329) measured by ATP assays (CellTiter-Glo®; n = 3 replicates). Data are presented as mean±SD.

2.5. Synergistic effect of R406 and Tucatinib in Type 2 GBM cells

Given the preferential activity of R406 in Type 2 GBM, we tested its potential synergy with Tucatinib, a HER2 inhibitor. Combination treatment with R406 and Tucatinib in serial dilution assay resulted in enhanced inhibition of Type 2 cell growth (Fig. 6), with synergy confirmed by co-inhibition index calculations (CI score 0.557 < 1, suggesting synergy). In sharp contrast, Type 1 cells are resistant to the treatment of R406 and Tucatinib individually or in combination, further highlighted the subtype specific vulnerability of Type 2 cells.

Fig. 6.

Fig. 6

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R406 works synergistically with Tucatinib to inhibit the growth of Type 2 GBM cells. Combination treatment of R406 and Tucatinib at series dilution was performed to determine their effect on Type 2 cell growth. The co-inhibition index was calculated and showed that R406 and Tucatinib work synergically. (A) Summarized data tables of combination treatment matrix at different R406 and Tucatinib concentrations. (B) Curves showing the differential response of Type 1 and Type 2 cells on each of the individual or combination treatment of R406 and Tucatinib measured by ATP assays (CellTiter-Glo®; n = 6 replicates). Data are presented as mean±SD.

3. Discussion

GBM remains a major therapeutic challenge due to its high heterogeneity and intrinsic resistance to conventional therapies. In this study, we leveraged the cell lineage based GBM subtypes, Type 1 and Type 2, and a kinase inhibitor library to identify subtype specific drug sensitivities in Type 1 and Type 2 GBM cells. We have developed a robust, cell-based HTS platform. The differential susceptibility of Type 1 and Type 2 cells to Tucatinib/Dasatinib treatment, as demonstrated in this study, provides a solid foundation for an in vitro and in vivo platform to screen for and characterize Type 1 and Type 2 GBM specific inhibitory compounds. The advantage of having both Type 1 and Type 2 cells in the HTS in a parallel setting, is that it enables to reduce the number of false positives in hit selection, and identify subtype-specific inhibitory compounds. This platform provides an exciting opportunity for developing therapeutics for GBM with more potency and precision in the near future. Our findings reveal critical differences in kinase dependencies between the subgroups and highlight R406 and Ponatinib as additional inhibitors of Type 2 GBM. These results underscore the importance of subtype-specific therapeutic development strategies and provide a solid foundation for future larger scale of HTS.

Among the validated hits, R406 and Ponatinib emerged as potential candidates for Type 2 GBM treatment. R406, a spleen tyrosine kinase (SYK) inhibitor, originally developed as the active metabolite of fostamatinib, an oral prodrug approved for the treatment of immune thrombocytopenia (ITP)(Newland and Mcdonald, 2020). R406 was also identified as an effective inhibitor against glioma stem cells (GSCs) and found to elicit metabolic shift toward OXPHOS and induced ROS accumulation via disruption of either Syk/PI3K pathway in Syk-positive GSCs or PI3K/Akt signaling in Syk-negative GSCs (Sun et al., 2019). Since Syk is not differentially expressed between Type 1 and Type 2 GBM cells, we speculate that the subtype specific effect of R406 sits on a Syk independent mechanism. The exact mode of action of R406 in Type 2 GBM remains to be determined. Similarly, Ponatinib, a multi-kinase inhibitor originally developed for BCR-ABL-driven leukemias, has demonstrated activity against FGFR- and VEGFR-driven pathways, both of which are implicated in GBM progression (Kantarjian et al., 2024; Bastola et al., 2025; Wang et al., 2021). While both Ponatinib and Dasatinib are multi-kinase inhibitors with distinct target profiles, they share significant common targets such as Src family kinases, Abl kinases, PDGFRs, and c-Kit. Given the overlapped targets and that they both function as preferential inhibitors for Type 2 GBM, they provide a unique tool set to pinpoint the exact mechanism of their mode of action in Type 2 GBM and the subtype specific vulnerabilities. Thus, the HTS platform not only offers mining opportunities to identify GBM subtype specific hits for therapeutic development, it also generates probes to understand the differing signaling of Type 1 and Type 2 GBM in biology in lineage dependent context.

Furthermore, we demonstrated that R406 exhibits a synergistic effect with Tucatinib in Type 2 GBM cells, suggesting that this dual inhibition may be an effective strategy for overcoming resistance mechanisms in Type 2 GBM. Having been recognized as a complex phenotype rooted in the coexistence of multiple signaling routes, GBM requires drug combinations targeting each of the representative signaling components. Looking forward, a better understanding of mechanisms that lead to synergistic effects of drug combinations and strategies to identify drug synergy is highly desirable for GBM treatment.

Despite these promising findings, our study has limitations. While our HTS identified several potent kinase inhibitors, further validation in patient-derived xenograft models and clinical samples is necessary to confirm their therapeutic relevance. Additionally, mechanistic studies are needed to elucidate the downstream effects of R406 and Ponatinib in Type 2 GBM cells. Lastly, our cell lineage based GBM stratification strategy covers about 46% of total GBM cases, we look forward to expanding our cell-of-origin based GBM subtypes and collection of cells for the stratification of GBM subset, making it more comprehensive to discover subtype-specific inhibitory compounds.

In conclusion, our study laid a solid foundation for larger HTS in the future for subtype-specific inhibitory small molecules for GBM therapeutic agent development and highlighted the potential of precision medicine approaches in GBM treatment.

4. Materials and methods

4.1. Cell culture

Type 1 and Type 2 GBM primary cultures were established previously (Wang et al., 2020), and were cultured in DMEM/F12 serum free medium supplemented with B27 and N2, plus EGF, FGF, PDGF-AA, and NRG1 (10 ng/mL each) in 5% oxygen, 37 °C incubator. For cultures in coated plates, 10 μg/mL Laminin diluted in PBS was used to pre-coat the plates at least 2 h at 37 °C.

4.2. Western blot

Cells were directly lysed in cultured wells with cold lysis buffer (50 mM Tris (pH 7.5), 150 mM NaCl, 1% NP40, 10% Glycerol, with Protease inhibitor cocktail and Phospho-inhibitor (Biorad), incubated on ice for 30 min and centrifuged at 12,000×g for 10 min at 4 °C. Supernatants were collected, mixed with 2× Laemmli sample buffer (Biorad), denatured on 65 °C heating block for 20 min 4%–20% Precast protein gels (Biorad) were used for gel running, nitrocellulose membranes were used for transfer, with constant current at 400 mA for 2 h in 4 °C. 5% non-fat milk in TBST (TBS with 0.1% tween20) was used for blocking and dilution of primary and second antibodies. Membranes were blocked at room temperature (RT) for 1 h and incubated with primary antibody overnight in 4 °C cold room on shaker. Secondary antibodies were applied after wash and incubated at RT for 2 h. Membranes were then washed with TBST and signals were developed with Clarity ECL substrate (Biorad) and detected with ChemiDoc MP Imaging System (Biorad).

4.3. Compound library and controls

The TargetMol 900-compound kinase inhibitor library used in this study is a curated collection of small molecules designed to target a wide range of protein kinases, which are key regulators of cellular signaling pathways involved in cancer progression, proliferation, and survival. This library includes both FDA-approved drugs and investigational compounds covering major kinase families. The 900 unique compounds were arrayed in three 384-well plates at 1-mM concentration in DMSO in 40 nL (at final concentration of 1 μM upon adding of 40 μL medium with cells), leaving columns 1, 2, 23, and 24 with DMSO. The positive control drug for this assay, AZD3759 for Type 1 cells (in wells at A1, A2, B1, B2, C1, C2, and N23, N24, O23, O24, P23, P24), and Dasatinib for Type 2 cells (in wells at N1, N2, O1, O2, P1, P2, and A23. A24, B23, B24, C23, C24), were solubilized at 10 mM in DMSO, at a final concentration of 1 μM.

4.4. High-throughput screen

The 900 compounds of the Kinase inhibitor library were spotted individually into three 384-well plates using an Echo 650T liquid handler in two duplicates. On day one, Type 1 (3605) and Type 2 (3112) cells were plated manually using muti-channel pipette at 500 cells/well in 40 μL assay medium, and incubated in 37 °C 5% CO2 incubator for 5 days. On day 5, 5 μL of Cell Titer Glo substrate was added to each well, plates were set on rocking bed shaking for 10 min. Luciferase activity was measured by an EnVision plate reader (PerkinElmer).

4.5. Data analysis

The luminescence signal in each well was captured on the EnVision plate reader. The data were exported as comma separated values files and analyzed as previously described (Wang et al., 2014). The median of luminescence signals of all samples in each plate was calculated, and then the signal in each well was normalized by the plate sample median. The Z′ score was calculated from the normalized signals from DMSO and positive control (PC) wells on each plate with the following equation: Z’ = 1 – [3∗(σDMSO+ σPC)/|μDMSO– μPC|), where σ and μ are the standard deviation and mean of the normalized signals. Percentage of inhibition (% inhibition) was calculated as 100 × (1 – normalized signal). Hit compound lists were generated separately from screens of Type 1 and Type 2 cells by applying various % inhibition cutoffs, and shared hits and subtype-specific hits were picked by comparing these lists. Our rationale for selecting a 70% inhibition threshold in primary screen is threefold: (1) we aim to identify the strongest and most reliable starting points rather than every possible active molecule; (2) we prioritize minimizing false positives, while acknowledging the trade-off of potentially missing weaker true inhibitors; and (3) from a cost-effectiveness perspective, a higher threshold ensures that only the most promising candidates advance to the confirmation screen, thereby saving significant time and resources.

CRediT authorship contribution statement

Zilai Wang: Writing – review & editing, Writing – original draft, Validation, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Pin Zhang: Data curation. Kiira M. Ratia: Visualization, Validation, Formal analysis. Ahmad Daher: Writing – review & editing, Resources. Zongmin Zhao: Writing – review & editing. Paul R. Carlier: Writing – review & editing, Resources. Yuwei Jiang: Writing – review & editing, Resources. Lijun Rong: Writing – review & editing, Supervision, Resources, Conceptualization, Funding acquisition.

Declaration of competing interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Acknowledgement

This research was partially supported by UICentre Drug Discovery (U2D2) Program award (#2023-U2D201), UICC/American Cancer Society Institutional Research Grant Pilot Project award (#IRG-22-149-01-IRG), and the Warren and Clara Cole Early Faculty Investigator Award to Wang Z.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cellin.2025.100284.

Contributor Information

Zilai Wang, Email: zilai@uic.edu.

Lijun Rong, Email: lijun@uic.edu.

Appendix A. Supplementary data

The following is/are the supplementary data to this article:

Supplemental Table S1

Compiled confirmation screen results of 3 Type 1 versus 3 Type 2 cells.

mmc1.xlsx (31.2KB, xlsx)
Supplemental Table S2

Data table of R406 and Tucatinib combination treatment in Type 2 (3112) cells.

mmc2.xlsx (25.7KB, xlsx)
Supplemental Table S3

Data table of R406 and Tucatinib combination treatment in Type 1 (3605) cells.

mmc3.xlsx (26.3KB, xlsx)

Fig. S1.

Fig. S1

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Optimization of screen parameters. A) Type 1 (3605) cells are resistant whereas Type 2 (3112) cells are sensitive to Tucatinib treatment; both Type 1 and 2 cells are sensitive to AZD3759; 293T cells are resistant for both AZD3759 and Tucatinib (CellTiter-Glo®; n = 8 replicates). B) Testing the effect of different culture medium volume at 30 μL, 40 μL, 50 μL, and 60 μL per well in 384-well plate on drug response (CellTiter-Glo®; n = 4 replicates). C) Testing the effect of different seeding density of cells at 500, 1000, 2000, and 4000 cells per well in 384-well plate on drug response (CellTiter-Glo®; n = 8 replicates). D) Testing the effect of coating versus non-coating plate on drug response (CellTiter-Glo®; n = 4 replicates).

Fig. S2.

Fig. S2

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Optimization of HTS assay on incubation time and S/N. (A) Test of incubation time of 4-, 5-, 6- and 7-days post Tucatinib treatment on the separation of Type 1 (3605) and Type 2 (3112) cells response (CellTiter-Glo®; n = 8 replicates). (B) Type 1 and (C)Type 2 cells on their response to AZD3759 treatment incubated for 4, 5, 6, and 7 days (CellTiter-Glo®; n = 8 replicates). (D-E) Effect of incubation time on the signal/noise ratio (S/N) for Type 2 cells on AZD3759 treatment (D), Tucatinib treatment (E), and Type 1 cells on AZD3759 (F) treatment (calculated with dataset in A-C).

Fig. S3.

Fig. S3

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Z′ scores of primary screen of 3 Type 1 cell plates and 3 Type 2 cell plates.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Table S1

Compiled confirmation screen results of 3 Type 1 versus 3 Type 2 cells.

mmc1.xlsx (31.2KB, xlsx)
Supplemental Table S2

Data table of R406 and Tucatinib combination treatment in Type 2 (3112) cells.

mmc2.xlsx (25.7KB, xlsx)
Supplemental Table S3

Data table of R406 and Tucatinib combination treatment in Type 1 (3605) cells.

mmc3.xlsx (26.3KB, xlsx)

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