Supplemental Material Figure S1: PDOs used for molecular characterization and functional screening, related to Figure 1. 19 PDO lines were established and characterized by amplicon sequencing. 16 of those lines had fast enough growth rates for further analyses, CTG profiling worked for all of them. From one of the lines, we could not do image based profiling because there was not enough material. In image analysis, we lost another PDO line due to too many out of focus objects, that is why we end up with 14 analyzed (15 screened). The large Ki-Stem library was used only in 13 of the PDOs, which had better growth rates. Figure S2: Analysis of PDO morphological phenotypes, related to Figure 2. Feature plots of unperturbed PDO phenotypes. Descriptive feature plots show the median phenotype of unperturbed organoids derived from the same patient. Shown are six key features (Area, Phalloidin intensity, DAPI intensity, FITC intensity, FITC Haralic angular second moment (ASM) and FITC intensity 1-percentile) and their z-score relative to all profiled organoid lines. Figure S3: Viability profiling of PDOs with image based profiling and live-dead classification (LDC), related to Figure 3. a-c, Quality controls of Live-dead classification (LDC). Data from both, the large scale image based drug profiling (KiStem library, 464 drugs) with 13 PDO lines and from the image based clinical drug screening (Clinical Cancer library, 63 drugs in 5 concentrations) with 14 PDO lines are included with respect to LDC. a, Correlation of viability (fraction of viable organoids per well) between 2 biological replicates profiled with high-throughput imaging and classified by LDC. b, Pearson correlation coefficient between biological replicates of each line in viability screen with LDC. c, Viability of PDOs treated with DMSO (negative control) analyzed with LDC. The fraction of DMSO treated PDOs (correctly) classified as viable are shown for each individual PDO line. d, Accuracy of LDCs trained on image-features of all three available fluorescence channels compared to classifiers trained on single- channel data (actin/TRIC, DNA/DAPI, cell permeability/FITC) and on a combination of TRIC and DAPI data only. Single channel classifiers based on actin, DNA and permeability had mean accuarcies of 0.896, 0.864, and 0.885, respectively. The mean accuracy of LDC relying on a combination of Phalloidin and DAPI staining was 0.958, compared to 0.968 when cell permeability was included. Figure S4: Viability profiling of PDOs with CellTiter-Glo (CTG): Quality controls. Related to Figure 3. a-d, Quality controls of drug screen with metabolic viability (CellTiter-Glo, CTG) readout. 16 PDO lines were screened with the 63 drugs (Clinical Cancer library) in 5 concentrations per drug in parallel for read outs with imaging + LDC and CTG. a, Correlation of viability results obtained with CTG read out between 2 independent biological replicates performed for each PDO line. b, Pearson correlation coefficient of viability in 2 biological replicates, denoted for each individual PDO line screened. c, Average viability of plates screened in batches at different time points – no significant batch effects were detected. d, Average viability of positive controls (high-dose bortezomib) and negative controls (DMSO) in all screened plates after normalization of the dataset to DMSO controls. Figure S5: Comparison of image-based viability profiling with live dead classification (LDC) and metabolic viability profiling with CellTiter-Glo (CTG), related to Figure 3. a, Comparison of image-based profiling with LDC versus viability profiling using a metabolic (CellTiter- Glo, CTG) luminescence read-out. 15 PDO lines were profiled with 63 anticancer compounds in 5 concentrations in duplicates and viability was determined with imaging and LDC or CTG, in parallel. 14 PDO lines were suitable for analysis b, Pearson correlation of area under the dose-response curve (AUCs) calculated from dose-response curves from image-based screening with LDC (x-axis) or CTG luminescence-based viability screening (r = 0.87). Values of outlier compound methotrexate are marked in blue (compare below). c-e, Analysis of methotrexate, an example of a drug that had divergent response profiles in LDC and CTG screening. It showed strong toxicity in almost all PDO lines in CTG based experiments but had no visible effect on organoid viability based on the LDC. This difference may be explained by non-lethal metabolic effects of the drug. c, AUCs calculated from dose-response curves of all PDO lines according to luminescence viability (CTG) screening and image based screening (LDC). d, Representative example of dose-response curves (PDO line D054T) for methotrexate, determined by image-based screening (LDC) and metabolic viability screening (CTG). The range of values for two biological replicates are shaded in grey. e, Representative images of organoids treated with DMSO (control) and methotrexate at the highest concentration. Images show viable PDO line D054T in both conditions; Cyan = DAPI, magenta = Phalloidin, yellow = cell permeability; scale bar: 50µmscale-bar: 200µm. f-g, Analysis of PDO proliferation and methotrexate drug effect: It has been discussed that non-toxic inhibition of cell proliferation can induce overestimated cell toxicity measurements depending on differences in cellular proliferation rate. We found methotrexate to be among the two compounds in our dataset in which treatment sensitivity could be directly associated with differences in organoid doubling time. f, Doubling times of PDO lines determined by a CTG-based short term proliferation assay. g, Association of methotrexate sensitivity and PDO proliferation rate. After excluding D021T01 from further analysis because of its extremely long proliferation rate, the spearman correlation coefficient was calculated. There was a significant negative correlation between doubling time and treatment sensitivity (SCC = -0.67, p = 0.0054 two- sided Spearman's rank correlation rho test). Figure S6: Drug viability profiling in PDOs, differential responses and associations with molecular PDO characteristics, related to Figure 4. a, Clustering of differential drug effects on 14 PDO lines. Viability of PDOs treated with a library of 63 drugs in 5 concentrations (Clinical cancer library) was determined by high-throughput imaging and LDC. Centered AUCs were calculated for each compound before unsupervised clustering was performed. Dotted lines indicate where Figure 6c has been cropped. b, Clustering of differential drug effects on 16 PDO lines. Viability of PDOs treated with a library of 63 drugs in 5 concentrations (Clinical cancer library) was determined by metabolic viability assay (CTG). Centered AUCs were calculated for each compound before unsupervised clustering was performed. c, Dynamic range of drug effects (AUCmax-AUCmin for every drug), a cut-off was set for analyses of associations between drug effects and differentially expressed genes. d-f, Nutlin3a response is associated with TP53 mutation status. Viability was analyzed with both, metabolic viability assay (CTG) and high-throughput imaging with LDC. CTG data are shown here, LDC data are shown in Figure 7. d, Comparison of AUC values from metabolic viability screening between 16 PDO lines with mutant (n = 6) and wild-type (n = 10) TP53. Each dot represents one organoid line. Horizontal red bars indicate the group means. Statistical significance was tested using a permutation test with 10,000 Monte Carlo resamples and the false discovery was controlled using the Benjamini-Hochberg method. e, Volcano plot of differentially expressed genes between 16 organoid lines that are more or less sensitive to nutlin3a treatment determined by CTG drug profiling. Blue dots indicate genes that were more highly expressed in organoid lines sensitive to the drug treatment. Yellow dots indicate genes that were found to be expressed more highly in organoid lines with increased resistance to the drug treatment. Statistical significance was assessed using a moderated t-test. The horizontal lines indicate 5% false discovery rate (FDR). f, Dose-response curve of nutlin3a determined by determined by high- throughput imaging and LDC. TP53 mutated cases are highlighted in black. g-i, Examples of dose response curves from targeted and conventional anticancer agents not currently used in colorectal cancer care showing differential responses between PDO lines (responder = black, non-responder = grey). Viability was determined by metabolic CTG profliling. The same analyses performed on high- throughput imaging and LDC data are presented in Figure 4. Figure S7: High throughput imaging identifies compounds by mode of action with distinct multiparametric phenotypes, related to Figure 5. a, Number of active drugs for all profiled organoid lines. 13 PDO lines were analyzed after perturbation with a library of 464 experimental compounds targeting stem cell and developmental pathways. b, The plot from Figure 4c. A map of related compound induced phenotypes across all organoid lines. Shown are multiple clusters with small angles between the aggregated normal vectors (i.e. similar compound induced phenotypes). Selected clusters enriched with the same perturbed molecular target are color- labeled. Clusters enriched with MEK inhibitors, CDK inhibitors, mTOR inhibitors, EGFR inhibitors and GSK3 inhibitors are zoomed out to show compounds exhibiting similar compound related phenotypes induced by effects on- or off the primary target. Fisher’s exact