PMCID string | Title string | Sentences string |
|---|---|---|
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This communal effort could result in a dynamic resource capable of delivering progressively more accurate synergy estimates with minimal experimental overhead. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | While Combocat enables high-throughput combination screening in various contexts, some limitations are important to acknowledge. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | First, the accuracy of sparse mode inference is inherently dependent on the quality and diversity of the training data. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | We tested three progressively stringent stratifications of our dense mode dataset (Supplementary Fig. 7a–c) and found essentially identical predictive performance (r≈0.97, Supplementary Fig. 7d–f), demonstrating no simple data leakage in predicting responses in Neuroblastoma. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | However, as new data are collaboratively added, preventing leakage through continuous hold-out testing will be essential to maintain the model’s robustness and generalizability. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Second, although dense and sparse modes’ experimental measurements largely agree, they will differ in some circumstances. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Sparse mode uses miniaturized volumes and a different plate format, which can occasionally introduce shifts in drug potency or dynamic range. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This can be related to technical factors like compound dispersion in smaller volumes, plate layout effects, or variations in cell confluence in smaller wells. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Finally, while Combocat supports Bliss and Loewe synergy metrics, each carries assumptions and limitations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For example, Bliss can misestimate synergy for drugs targeting similar pathways, and Loewe cannot calculate synergy in the absence of dose-equivalence (Supplementary Fig. 8a, b)—making it undefined in 61.5% of sparse mode dose combinations tested (Supplementary Fig. 8c). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | We compared the mean synergy scores in our combinations passing QC and filtering steps (n = 800) and observed a modest agreement between the Bliss and Loewe models (Spearman rho = −0.672, p < 2.2e-16, Supplementary Fig. 8d). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Importantly, additional reference models exist such as Highest Single Agent (HSA), Zero Interaction Potency (ZIP)(which models changes in potency of the dose-response curves), and others, which can be evaluated in future extensions of the platform. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | We envision leveraging Combocat’s scalability to investigate more of the vast unexplored drug combination space, mapping out the synergy landscapes of thousands of drug combinations across many cell lines (Supplementary Fig. 5c). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | By integrating sparse mode workflows with continuously evolving machine learning models, rich synergy datasets can be generated, capable of capturing complex biological responses. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Such comprehensive synergy maps will empower researchers to identify conserved interactions, reveal lineage-specific vulnerabilities, and guide the rational selection of combination therapies. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This study complied with all relevant ethical regulations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The CHP-134 cell line was purchased from Sigma (catalog #6122002) and maintained in RPMI 1640 medium (Corning, catalog #10-041-CM) supplemented with 10% FBS (Corning, catalog #35-015-CV) at 37 °C with 5% CO2. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Mycoplasma was routinely tested using the MycoAlert mycoplasma detection kit (Lonza, catalog # LT07-118), and the cells were verified negative for contamination. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For experiments with bacterial cultures, the Escherichia coli BW25113 strain (Horizon Discovery Ltd.) was cultured at 37 °C in Mueller-Hinton (MH) agar or broth (BBL). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Cultures in exponential phase (OD600 0.4–0.6) were diluted to OD600 of 0.0005 for plating in 384-well format. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Compounds were sourced from commercial vendors (see Supplementary Data 5). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Stocks were solubilized in DMSO and stored at −80 °C until 24 h before use, at which point they were thawed at room temperature and centrifuged at 1100 RPM for 5 min. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Across all assays, compounds were screened at 10 concentrations using 3-fold serial dilutions, typically with an upper limit of 25 µM. Most compounds were screened below and above their physiologically-relevant doses, and in a way positioning their suspected IC50s as the midpoint to attempt achieving a complete dose-response effect. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The choice of 135 compounds used in sparse mode comes from drugs targeting the top neuroblastoma-selective sensitizers from earlier CRISPR-anchor screens. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Specifically, drugs were chosen using the following approach:Our previous CRISPR knockout-anchor screens investigated 18 cell lines using 7 neuroblastoma standard-of-care drugs and 1 investigational compound (Doxorubicin, Etoposide, Retinoic Acid, Topotecan, Vincristine, Cisplatin, Phosphoramide Mustard, and JQAD1). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The CRISPR knockout library was designed to target 655 known druggable genes. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | These screens were designed to identify druggable gene knockouts that sensitize cancer cells to standard-of-care neuroblastoma drugs. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To identify these high-potential drug-sensitizing gene knockouts from the resulting dataset, we applied a custom Bayesian hierarchical model (described in the “Methods” section of Lee and Wright et al.) to nominate genes with differential sensitization effects in the Neuroblastoma group, relative to the outgroup cell. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | We then compiled the list of drugs that targeted the resulting neuroblastoma-selective sensitizing gene knockouts. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For genes targetable by >1 drug, the list was reduced manually, considering factors such as clinical approval, redundancy with other drugs, number of analogs, and compound availability. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Finally, we manually added high-priority investigational compounds to the list after consultation with multiple pediatric oncologists at St. Jude, yielding the final number of 135 unique drugs. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The list of drugs is available in Supplementary Data 5. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Our previous CRISPR knockout-anchor screens investigated 18 cell lines using 7 neuroblastoma standard-of-care drugs and 1 investigational compound (Doxorubicin, Etoposide, Retinoic Acid, Topotecan, Vincristine, Cisplatin, Phosphoramide Mustard, and JQAD1). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The CRISPR knockout library was designed to target 655 known druggable genes. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | These screens were designed to identify druggable gene knockouts that sensitize cancer cells to standard-of-care neuroblastoma drugs. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To identify these high-potential drug-sensitizing gene knockouts from the resulting dataset, we applied a custom Bayesian hierarchical model (described in the “Methods” section of Lee and Wright et al.) to nominate genes with differential sensitization effects in the Neuroblastoma group, relative to the outgroup cell. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | We then compiled the list of drugs that targeted the resulting neuroblastoma-selective sensitizing gene knockouts. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For genes targetable by >1 drug, the list was reduced manually, considering factors such as clinical approval, redundancy with other drugs, number of analogs, and compound availability. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Finally, we manually added high-priority investigational compounds to the list after consultation with multiple pediatric oncologists at St. Jude, yielding the final number of 135 unique drugs. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The list of drugs is available in Supplementary Data 5. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To enable comprehensive and reproducible measurement of pairwise dose combinations in a 10 × 10 matrix format, we designed a 384-well plate template that accommodates three replicate 10 × 10 combination matrices in a 384-well plate format, along with three replicates of the dose-response curves for each single-agent and twelve replicates of each positive and negative control (Fig. 1c). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | In each well, 200 nL of total compound was transferred via an Echo 655 acoustic liquid handler (Beckman Coulter): 100 nL of compound + 100 nL of DMSO for single-agent wells, or 100 nL of each compound for combination wells. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This was followed by 40 μL of cells, added using a Multidrop combi (Thermo Fisher). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Maintaining a 40 μL volume of cells minimized edge effects arising from evaporation. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Each dense mode plate tested exactly one drug combination, making the total plate count for a dense mode experiment scale linearly with the number of drug pairs. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Sparse mode was designed to increase throughput and reduce resource usage by miniaturizing the assay into a 1536-well plate format (Supplementary Fig. 3a, b). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Three key features enable ultra-large combination screens in sparse mode:Miniaturized volumes: Only 20 nL of compound is transferred (10 nL of each drug for combinations or 10 nL of drug + 10 nL of DMSO for single-agent wells), followed by 4 μL of cells. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Edge wells are filled with culture medium to mitigate evaporation. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Doses are interleaved to minimize well-to-well signal contamination. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Measurement of the diagonal only: Each 10 × 10 matrix is represented by 10 dose pairs that combine each drug at a relative 1:1 ratio. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For example, the 6th-highest tested dose of Drug 1 with the 6th-highest tested dose of Drug 2. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This reduces the total measured dose pairs by 90%, compared to a fully-measured 10 × 10 matrix (Supplementary Fig. 3d).Separate single-agent and combination plates: Single-agent and combination plates are tested separately, with single-agents testing each drug’s 10-dose series, and combinations testing only the diagonal dose pairs. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This allows single agents to be mapped onto combination matrices without being re-measured. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For example, to screen the combinations A_B and A_C, dense mode would measure A twice, whereas sparse mode would measure A once. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Within-plate controls facilitate normalization before integration into assembled matrices. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Miniaturized volumes: Only 20 nL of compound is transferred (10 nL of each drug for combinations or 10 nL of drug + 10 nL of DMSO for single-agent wells), followed by 4 μL of cells. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Edge wells are filled with culture medium to mitigate evaporation. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Doses are interleaved to minimize well-to-well signal contamination. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Measurement of the diagonal only: Each 10 × 10 matrix is represented by 10 dose pairs that combine each drug at a relative 1:1 ratio. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For example, the 6th-highest tested dose of Drug 1 with the 6th-highest tested dose of Drug 2. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This reduces the total measured dose pairs by 90%, compared to a fully-measured 10 × 10 matrix (Supplementary Fig. 3d). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Separate single-agent and combination plates: Single-agent and combination plates are tested separately, with single-agents testing each drug’s 10-dose series, and combinations testing only the diagonal dose pairs. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This allows single agents to be mapped onto combination matrices without being re-measured. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For example, to screen the combinations A_B and A_C, dense mode would measure A twice, whereas sparse mode would measure A once. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Within-plate controls facilitate normalization before integration into assembled matrices. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The total number of plates required for a sparse mode screen can be calculated as:1[12pt] $$=c(r+)})$$=crn135+n2135where c is the total number of cell lines, r is the total number of single-agent plate replicates, and n is the number of unique drugs. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The 135 value comes directly from the number of unique drugs able to fit within the usable (non-edge) wells of the 1536-well plates. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For both dense and sparse modes, compounds were transferred into empty 384- or 1536-well plates (Corning) using the Echo 655 acoustic liquid handler with Combocat protocol files (available at combocat.stjude.org). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | After compound transfer, cells were dispensed at seeding densities of 1000 cells/well in 384-well plates or 200 cells/well in 1536-well plates. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For E. coli cells (which were only screened in 384-well plates), an OD600 of 0.0005 was used. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | In 1536-well plates, cells were strained through a 70 µM cell strainer before being dispensed. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The final DMSO concentration after cell addition was 0.5% in all wells. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Plates were incubated at 37 °C for 72 h. Cell viability was assessed after the 3-day treatment using CellTiter-Glo (or BacTiter-Glo for bacterial cells) by adding either 25 µL (to 384-well plates) or 2 µL (to 1536-well plates) of prepared reagent and reading luminescence on an EnVision plate reader (PerkinElmer). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For the absorbance-based readout of cell viability, 8 µL of MTS reagent (Abcam) at 0.5× concentration was added to the cells at the endpoint time. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The MTS reagent was incubated with cells for 3 h, and plates were read at 490 nanometers on a Cytation 5 plate reader (BioTek). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The quality of all screened assay plates was assessed using the Z′ metric, which measures quality as a function of signal window and data variation, given by:2[12pt] $$^ }=1-_}+_}}_}-_}|}$$′=1−3σNeg+3σPosμNeg−μPoswhere σNeg/Pos and μNeg/Pos represent the standard deviation and means of the controls, respectively. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | A Z′ value of ≥0.5 is generally regarded as an excellent separation of controls. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The Combocat ensemble machine learning model was developed as a collection of 90 individual models, each built to predict one of the 90 non-measured indices of a sparse matrix. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To construct these models, an input dataset was first assembled containing all fully-measured data collected via dense mode, totaling over 800 combination matrices and their single-agent response values (Supplementary Data 1). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The input dataset was first filtered to remove any matrices with a mean cell death value < 10%. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Next, this dataset was split into 75% for training and 25% for the test set, respectively. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This resulted in a training set of 552 matrices (198,720 measurements) and a test set of 184 matrices (66,240 measurements). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Note that while the combination matrix itself has dimensions of 10 × 10, including the single-agent data effectively extends this into an 11 × 11 grid, where the 1st column and 11th row represent the single-agent responses (Supplementary Fig. 9a). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This format was used for associating a model with its respective index. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Each model was trained on 30 features: response values from 10 single-agent indices each from Drug 1 and Drug 2, and 10 indices corresponding to the matrix diagonal, which represent each drug’s concentration combined at a relative 1:1 ratio (Supplementary Fig. 9b). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | These 30 features correspond to the 30 indices measured experimentally in the sparse mode workflow (Supplementary Fig. 3d) and were used across the input data to generate the models predicting the 90 total indices of a sparse matrix (Supplementary Fig. 9c). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The training of each model involved fitting the tree-based regression model XGBoost. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Hyperparameter tuning was enabled to optimize model performance, which was accomplished using a space-filling grid design to efficiently test 40 combinations of the hyperparameters (detailed in Supplementary Data 6). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | A 10-fold cross-validation was used to select the optimal set of hyperparameters based on the lowest R value. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Once identified, each model was trained using its respective optimal hyperparameters on the entire training set. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The performance of each model was evaluated across all folds, gauged by R and RMSE. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For each model, the variable importance was measured across each of the 30 features (30 measured response values from sparse matrices) to evaluate their predictive impact (Supplementary Data 7). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Next, the trained models were validated on their test sets to ensure their predictive capability by comparing the observed vs. predicted cell death response values. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The comparison of observed vs. predicted response values across the 90 models collectively was used to summarize the predictive performance of the ensemble model. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Finally, the ensemble model was serialized in a format that allows for rapid deployment for future sparse matrix predictions. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | All model generation was performed using the tidymodels framework (tidymodels.org). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To ensure data integrity and mitigate the impact of spurious measurements, we implemented a rigorous quality control (QC) procedure focusing on three key metrics: single-agent standard deviation, residuals from single-agent dose-response curves, and monotonicity of the cell death response. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For each single-agent drug, we first calculated the standard deviation of the percentage cell death (% cell death) values across all replicates at each dose level, resulting in ten standard deviation values per agent corresponding to the ten doses tested. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Any dose with a percentage cell death standard deviation exceeding a predefined threshold (Tdefault) of 29 was flagged for disqualification. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | All combination data points involving that particular dose were also flagged for disqualification (Supplementary Fig. 2e). |
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