PMCID string | Title string | Sentences string |
|---|---|---|
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Single-agent plates measure each drug across 10 doses, while combination plates only measure drug pairs at a relative 1:1 ratio—corresponding to the diagonal of a 10 × 10 matrix. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Single-agent response values are mapped onto dose-response curves and integrated with combination data to assemble the sparse matrix (Supplementary Fig. 3c, d), which effectively cuts the required number of measurements by 90%. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | By dramatically decreasing the experimental resource consumption per combination, sparse mode increases throughput and efficiency. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For example, the workflow supports up to 135 single agents, which yields 9045 combinations and would require just 73 plates when using six replicates of single-agent plates (Supplementary Fig. 3e, f). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Consequently, sparse mode’s miniaturized design and efficient plate usage make ultra-large-scale combination screens available with minimal resources. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To enhance the utility of our sparse screening workflow, we developed an ensemble machine learning model capable of predicting the non-measured response values within a sparse matrix, effectively filling in the gaps to reconstruct the completed matrix. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | We leveraged the dense mode combination data to train the model, which provided fully-measured 10 × 10 matrices alongside their corresponding single-agent responses. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | From this, we curated a training set comprising 552 matrices (198,720 measurements) and a test set of 184 matrices (66,240 measurements) (Fig. 3a). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Each non-measured index within the sparse matrix is predicted by a dedicated regression model within the ensemble, allowing all 90 models to be finely tuned for their respective targets (Fig. 3b, see “Methods” section).Fig. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | 3Machine learning supplements sparse screening data.a Composition of training and test sets used to develop the ensemble model. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The training set includes 552 fully-measured 10 × 10 combination matrices (light orange, 75% of total data), while the test set has 184 matrices (dark orange, 25% of total data). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | b Overview of the ensemble machine learning model architecture. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Ninety individual models are each designed to predict the response of a single non-measured index of a sparse matrix. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Fully-measured 10 × 10 matrices from the training set are downsampled to reflect the 30 measured values collected in sparse mode (treated as features) and the index a given model aims to predict (treated as the outcome variable). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Each model undergoes hyperparameter tuning and is fit with the XGBoost regression model (see “Methods” section). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | c Model performance measured by R across the 10 respective folds for each of the 90 models. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Box plot center line represents the median (0.947), bounds are 25th and 75th percentiles (0.934 and 0.959), and whiskers extend to the most extreme data points within 1.5× the interquartile range from the box edges. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The minimum is 0.860, the maximum is 0.983. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | d Variable importance (VI) contributed by the 30 measured values toward the model predicting index (represented by a yellow circle) across the test set. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | e Comparison of observed versus predicted cell death response values for the model predicting index across the test set. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | f–i Like (d, e) showing the VI and predictive performance for models and , respectively. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | j Violin plot summarizing the Euclidean distances between the target index of a model and its corresponding feature index with the maximum importance value, across all 90 models. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The dashed line represents the median Euclidean distance (d = 2) across all samples, with model highlighted as an example (red dot). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | k Example of the model predicting index , illustrating the Euclidean distance (d = 2) from its target index to the feature index with the maximum importance value. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | l Overall predictive performance of the model across all samples in the test set (n = 184). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | m Overview of the sparse mode workflow, integrating the experimental generation of sparse matrices with the machine learning-based imputation of non-measured response values. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | a Composition of training and test sets used to develop the ensemble model. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The training set includes 552 fully-measured 10 × 10 combination matrices (light orange, 75% of total data), while the test set has 184 matrices (dark orange, 25% of total data). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | b Overview of the ensemble machine learning model architecture. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Ninety individual models are each designed to predict the response of a single non-measured index of a sparse matrix. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Fully-measured 10 × 10 matrices from the training set are downsampled to reflect the 30 measured values collected in sparse mode (treated as features) and the index a given model aims to predict (treated as the outcome variable). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Each model undergoes hyperparameter tuning and is fit with the XGBoost regression model (see “Methods” section). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | c Model performance measured by R across the 10 respective folds for each of the 90 models. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Box plot center line represents the median (0.947), bounds are 25th and 75th percentiles (0.934 and 0.959), and whiskers extend to the most extreme data points within 1.5× the interquartile range from the box edges. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The minimum is 0.860, the maximum is 0.983. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | d Variable importance (VI) contributed by the 30 measured values toward the model predicting index (represented by a yellow circle) across the test set. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | e Comparison of observed versus predicted cell death response values for the model predicting index across the test set. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | f–i Like (d, e) showing the VI and predictive performance for models and , respectively. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | j Violin plot summarizing the Euclidean distances between the target index of a model and its corresponding feature index with the maximum importance value, across all 90 models. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The dashed line represents the median Euclidean distance (d = 2) across all samples, with model highlighted as an example (red dot). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | k Example of the model predicting index , illustrating the Euclidean distance (d = 2) from its target index to the feature index with the maximum importance value. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | l Overall predictive performance of the model across all samples in the test set (n = 184). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | m Overview of the sparse mode workflow, integrating the experimental generation of sparse matrices with the machine learning-based imputation of non-measured response values. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Our model demonstrated a high predictive accuracy, with a median R of 0.945 across 10-fold cross-validation (Fig. 3c). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Analyzing variable importance (VI) revealed that predictions were most strongly influenced by measured values closest to the target index. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | For instance, the model predicting the response at row 1, column 2 (Model), primarily relied on data from its two nearest measured indices (Fig. 3d). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Despite relying heavily on only two of the 30 measured values, this model achieved strong predictive performance (r = 0.98) between the observed and predicted values in the test set (Fig. 3e). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Similar patterns were observed throughout the ensemble, with strong performance across models predicting values near the diagonal (Fig. 3f, g) and those in the lower-right quadrant of the matrix (Fig. 3h, i). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | A broader analysis of all models confirmed a proximity-based dependency, with most predictions relying on measured indices within a Euclidean distance of 1 or 2 from the target index (Fig. 3j, k). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | These findings underscore the importance of leveraging local features for predicting sparse matrix values and highlight the advantage of training dedicated models for each of the 90 non-measured indices, enabling highly targeted predictions (Supplementary Data 2). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To evaluate the ensemble’s overall performance, we compared observed and predicted cell death responses across all non-measured indices in the test set. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This yielded a strong correlation (r = 0.98, Supplementary Data 3) (Fig. 3l), demonstrating the ensemble’s reliability in capturing key combination response effects. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | By predicting non-measured values, the ensemble model enriches the sparse screening workflow, providing detailed insights that enhance the interpretability of synergy results while preserving resource efficiency (Fig. 3m). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To demonstrate the scalability and practical utility of Combocat, we applied the sparse mode workflow to screen 9045 drug combinations in the neuroblastoma cell line CHP-134 (Supplementary Data 4), representing the largest dense combination screen reported in a single cell line. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | We used 135 small molecules, encompassing approved chemotherapeutics, investigational compounds, and neuroblastoma-selective agents nominated from our earlier CRISPR screens. ( |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Supplementary Data 5). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Synergy was quantified for each pair by their mean Blissadj. ( |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | score—the adjusted Bliss synergy averaged across the matrix diagonal (10 measured dose pairs) (Fig. 4a). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Most combinations centered near zero (Fig. 4b), indicating a predominance of additive or non-synergistic interactions and aligning with prior findings that strong synergy is a rare phenomenon. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Fig. 4Ultrahigh-throughput screen of 9045 combinations in CHP-134 cells.a Heatmap of the mean adjusted Bliss synergy scores (Blissadj.) |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | across experimentally observed 10 dose pairs for each of the 9045 tested combinations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | b–e Histograms of the mean Blissadj. ( |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | , Moran’s I, QC flag count (observed), and mean % cell death, respectively, across the combination data. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | c–e highlight filters applied to exclude (gray) or include (green) combinations in the final hit list. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | f Filtered combinations (n = 594), ranked by their mean Blissadj. ( |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | scores. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The top 15 hits are labeled above. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | g–n Comparison of the top 2 hits from sparse mode (upper matrices) re-screened using dense mode (lower matrices). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The cell death (g, h, k, l) and synergy (i, j, m, n) matrices are compared between the two modes. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | a Heatmap of the mean adjusted Bliss synergy scores (Blissadj.) |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | across experimentally observed 10 dose pairs for each of the 9045 tested combinations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | b–e Histograms of the mean Blissadj. ( |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | , Moran’s I, QC flag count (observed), and mean % cell death, respectively, across the combination data. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | c–e highlight filters applied to exclude (gray) or include (green) combinations in the final hit list. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | f Filtered combinations (n = 594), ranked by their mean Blissadj. ( |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | scores. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The top 15 hits are labeled above. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | g–n Comparison of the top 2 hits from sparse mode (upper matrices) re-screened using dense mode (lower matrices). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The cell death (g, h, k, l) and synergy (i, j, m, n) matrices are compared between the two modes. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To prioritize drug combinations for further exploration, we used three filtering criteria that captured spatially coherent synergy patterns (Moran’s I), data reliability (QC flag counts), and biological relevance (mean cell death response, Fig. 4c–e, see “Methods” section). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | After filtering, 594 top-scoring pairs remained, ranked by mean Blissadj. ( |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | (Fig. 4f). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | To validate these sparse mode predictions, we re-screened a subset of 40 combinations in dense mode: the top 30 from sparse mode (expected to display the highest synergy) plus 10 randomly selected combinations that had been excluded. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Measuring these combinations in a fully sampled 10 × 10 format enabled a direct comparison to sparse mode results. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Most top-ranked drug pairs retained strong synergy patterns, confirming that sparse mode can effectively prioritize synergistic combinations (Supplementary Fig. 4a). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | In contrast, the 10 random pairs exhibited weaker synergy in dense mode, matching their lower sparse mode scores. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | We additionally compared single-agent dose-response curves for the 47 unique drugs in the validation set and observed close alignment (rho = 0.858) of IC50 values and fitted dose-response curves (Supplementary Fig. 4b, c), reinforcing the consistency of single-agent measurements obtained in sparse mode. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Among the validated hits, the ATM inhibitor AZD1390 in combination with the PARP inhibitors Olaparib or Rucaparib stood out for their pronounced synergy (Fig. 4g, i, k, m). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This aligns mechanistically with targeting complementary DNA damage repair pathways: PARP inhibition leads to accumulation of single-strand breaks, while ATM inhibition impairs the double-strand break repair response. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Re-testing these combinations in dense mode confirmed high agreement (Fig. 4h, j, l, n), meaning strong, reliable synergies could be identified from the starting set of over 9000 screened combinations. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Collectively, these data establish Combocat’s sparse mode as an efficient and scalable means of uncovering synergy even in ultra-large-scale screens. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Combocat presents a step toward more comprehensive, flexible, and scalable drug combination screening. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | By uniting acoustic liquid handling and machine learning-assisted inference, we have demonstrated the feasibility of screening thousands of combinations while retaining the resolution and interpretability crucial to discovering strong synergistic interactions. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The principles underpinning Combocat are broadly applicable, with a design that supports compatibility with various assay readouts that align with specified volumes and plate formats—such as luminescence, absorbance (Supplementary Fig. 6), fluorescence, mass spectrometry, or high-content imaging. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Furthermore, the open-source acoustic liquid handler protocols ensure adaptability to other drug dispensing platforms with comparable capabilities, establishing Combocat as a scalable foundation for diverse experimental applications. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | The functionality and workflow of Combocat are designed to be straightforward, including its minimal and intuitive analytical pipeline (Supplementary Fig. 5a). |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | This streamlined architecture, along with open-source and detailed documentation, ensures that researchers can easily implement and customize Combocat experiments according to their needs. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | Our goal is to pursue continual refinement and expansion of the platform. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | One of the most compelling aspects of Combocat lies in its potential for community-driven advancement. |
PMC12705714 | An open-source screening platform accelerates discovery of drug combinations | As more research groups generate dense combination data, these datasets can be anonymized and contributed back to the community for re-training and improving the ensemble machine learning model (Supplementary Fig. 5b). |
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