The number of primary tumor organoids varied between patient samples, with some tumors rendering thousands of primary organoids whereas others yielded only 10–20 primary organoids. This difference in derivation likely reflects the heterogeneous composition of tumors, with proliferative areas intermingled with regions of differentiated cells, stromal cells or necrosis. The growth rate of the organoids from patients 5 and 27 decreased over time, which prohibited their inclusion in the drug screen. All other organoids could be readily expanded and frozen to create a master cell bank. Upon thawing, cell survival was typically >80%. Unlike healthy tissue-derived organoids, tumor-derived organoids presented with a range of patient-specific morphologies, ranging from thin-walled cystic structures to compact organoids devoid of a lumen. H&E staining on primary tumors and the corresponding organoids revealed that the “cystic versus solid”-organization of the epithelium was generally preserved. Yet, marker expression analysis (KI67, OLFM4, KRT 20, Alcian blue) revealed heterogeneity both between patients and individual organoids within each culture ( Figure 1 B; Data S1 ).

Surgically resected tissue was obtained from previously untreated CRC patients. Tissue from rectal cancer patients was excluded because they routinely undergo irradiation before surgery. For multiple tissues, we observe that normal tissue-derived organoids outcompete tumor organoids under the optimized culture conditions, presumably due to genomic instability and resulting apoptosis in the latter. Combination of Wnt3A and the Wnt amplifier R-spondin1 is essential to grow organoids from normal epithelium. Over 90% of CRC cases harbor mutations that aberrantly activate the Wnt signaling pathway (), so we exploited the Wnt-dependency of normal colonic stem cells to selectively expand tumor organoids. A total of 22 tumor organoid cultures and 19 normal-adjacent organoid cultures were derived from 20 patients (P19 and P24 each carried two primary tumors separated by >10 cm; Figure 1 A). We successfully generated organoid cultures from 22 of 27 tumor samples. For one, we never observed growth. Four were lost due to bacterial/yeast infection. Since then, we have added next-generation antibiotics (see Experimental Procedures ) and currently observe an ∼90% success rate.

(B) Organoids architecture resembles primary tumor epithelium. H&E staining of primary tumor and the tumor organoids derived of these. A feature of most organoids is the presence of one or more lumens, resembling the tubular structures of the primary tumor (e.g., P8 and P19b). Tumors devoid of lumen give rise to compact organoids without lumen (P19a). Scale bar, 100 μM.

(A) Overview of the procedure. A total of 22 tumor organoids and 19 normal control organoids were derived and analyzed by exome-sequencing, RNA expression analysis and high-throughput drug screening. To determine the concordance between tumor organoids and primary tumor, DNA from the primary tumor was also isolated.

We found APC alterations in all but four of the organoids (P11, P19a/b, P28). Western blotting revealed P11 to express a truncated APC protein, pointing to a mutational event not covered by our exome-sequencing ( Figure S3 ). The wtAPC organoid P28 carries an activating mutation in CTNNB1 (T41A). In both P19a and P19b, we detected RNF43 mutations: frameshifts at aa positions 659 and 355, respectively. Only the latter is predicted to affect protein function.

Western blot for APC on a panel of tumor organoids. Extracts from the CRC cell line SW480 (stop codon at Q1338) and the kidney cell line 293T (wtAPC) were used as control.

Most CRC cases carry activating mutations in the WNT pathway: inactivation mutations in APC, FBXW7, AXIN2, and FAM123B, or activating mutations in CTNNB1 (). Gene fusions involving the Wnt-agonistic RSPO2 and RSPO3 genes have been observed in 5%–10% of CRC (). RNF43 encodes a negative regulator of the Wnt pathway, which serves to remove the Wnt receptor FZ in a negative feedback loop (). Recent sequencing efforts of gastric, ovary, and pancreatic neoplasias identified RNF43 mutations (), and RNF43 mutations have been observed in CRC (

Mutations of genes in DNA mismatch repair (MMR)-associated pathways are associated with a hypermutated phenotype (). Consistent with their classification as hypermutated CRC cases (), missense mutations were present in MSH3 in P7, and POLE mutations were detected in P10, P19a, and P19b. We did not observe mutations in MMR-associated genes in P24a and P24b and expression analysis showed normal levels of the pertinent genes. The culprit for hyper mutability thus remains to be identified for P24. The limited cohort size did not allow a statistical analysis for somatic copy number alterations to identify significant regions of amplification and deletions. However, manual inspection of the top regions identified by TCGA did reveal the presence of ERBB2-, MYC-, and IGF2-amplified organoids, as well as a reported gain of 13q in the non-hypermutated group ( Figure 3 C) In aggregate, these analyses demonstrate that organoid cultures faithfully capture the genomic features of the primary tumor from which they derive and much of the genomic diversity of CRC.

The most commonly altered genes in CRC () were well represented in the organoid cultures ( Figure 3 B; Tables S1 I and S1J). Inactivating alterations to the tumor suppressors APC, TP53, FBXW7, and SMAD4, as well as activating mutations in KRAS (codon 12 and 146) and PIK3CA (codon 545 and 1047) were observed. Activating mutations in BRAF and TGFBR1/2 mutations were observed in the hypermutated organoids, consistent with previous reports for primary CRC ().

Discordant mutations were assessed for their likely biological significance in cancer, based on Cancer Gene Census and data reported from the PanCancer analysis of 5,000 whole exomes (). Only 4% (27/679) of discordant mutations found in organoids affected cancer-related genes, including a third hit to APC, which was already biallelically inactivated in P14, SMAD4 mutation in P16, and POLE mutation in P19b ( Table S1 H). Cancer-significant genes that were discordant in the biopsy represented 4.4% (12/271) ( Table S1 H). The discordant mutations had a mean allelic frequency of 10.3% and 34.1% for the biopsy and organoids, respectively. This could represent the enrichment or depletion of a sub-clonal population in the organoid culture present within the original tumor, as well as acquisition of additional mutations during derivation or propagation.

Somatic variants within the coding regions in organoid cultures were highly concordant with the corresponding biopsy specimen for both hypermutated and non-hypermutated patients (median = 0.88 frequency of concordance, range 0.62–1.00) ( Figure 3 A; Table S1 E). Indeed, combined analysis of SCNAs and single nucleotide variants (SNVs) to infer Cancer Cell Fractions (CCF) () in the biopsy and tumor organoids, revealed that the common CRC driver mutations were maintained in culture. In 13 out of 14 organoid-biopsy pairs tested, tumor subclones sharing common CRC drivers were detected in the biopsy. In 50% of the organoids, a dominant subclone from the biopsy was present, likely representing sampling during derivation but it could also indicate loss in culture ( Figures S2 A and S2B; Tables S1 F and S1G). Transcriptome analysis of single organoids showed subtle differences in gene expression within an organoid culture, confirming their heterogeneous composition. The differences in overall gene expression were more pronounced in the organoids derived from the hypermutant tumors ( Figure S2 C).

(C) Single-organoid transcriptome profiling. mRNA sequencing was performed on single organoids from 9 patients. A heatmap of correlations between organoids shows organoids derived from the same patient have similar gene expression profiles. Correlations between organoids derived from the same patient are higher than correlations between organoids from different patients (Pearsons correlation 0.918 ± 0.040 versus 0.785 ± 0.064 respectively), indicating that heterogeneity in gene expression between organoids derived from the same patient is smaller than the differences from patient to patient.

(B) Representative CCFs demonstrating the organoid cultures derived from a particular subclone present within the original tumor biopsy. The lineages are inferred from the CCF fractions.

(A) Representative Cancer Cell Fractions (CCF) demonstrating maintenance of subclonal populations in organoid cultures in comparison to the original tumor biopsy (). Red bubbles indicate clusters of coding alterations that co-occur at that corresponding cancer cell fraction. The color scale is indicated on the right side of the plot. Mutations that are shared between the biopsy and organoid are diagonal where y = x. Mutations found only in the biopsy are on the x axis and mutations found only in the organoids are on the y axis. The major CRC driver genes are shared between biopsy and organoid, other relevant caner related genes are listed for the discordant and subclonal CCFs. The lineages represent our best interpretation of the different populations inferred from the CCF.

(B) Overview of the mutations found in the tumor organoids. The hash-mark in each box represents each allele and whether it was subject to deletion, mutation, frame-shift alteration, nonsense mutation or splice site mutation. Those alterations present in >10% of cases are compared to the percentage of cases reported by the TCGA CRC.Indicates discordant mutations targeting the same gene between the two sites in P19 and P24. See also Tables S1 I and S1J.

(A) Concordance of somatic mutations detected in organoid and corresponding biopsies. Bar graph represents the proportion of coding alterations that are concordant between the biopsy and the corresponding organoid culture and those that are found only in organoid or biopsy specimen. N/A indicates cases in which exome-sequencing was not performed on the corresponding biopsy.

Genomic DNA was isolated from tumor and matched normal organoid cultures for whole-exome sequencing in order to identify tumor-specific somatic mutations (). Genomic DNA from the corresponding biopsy specimens were available for comparative analysis for 16 of these cases ( Table S1 A). The mutation rates per Mb varied widely for different tumor organoids (range 2.0–77.9), with a median value of 3.7 in the tumor organoids, similar to the median rate of 3.6 in the biopsy samples ( Figure 2 A; Table S1 B). Mutations were predominantly CpG to T transitions, consistent with results from large-scale CRC sequencing ( Figures S1 A and S1B; Table S1 C). Of the 22 tumor organoids, six displayed hypermutation (>10 mutations/Mb): P7, P10 and the organoids from the two patients with two tumors each (P19a and P19b, P24a and P24b). Interestingly, the P19a and P19b tumors share TP53 R273C and BRAF V600E alterations, suggesting they arose from the same somatically altered progenitor cell but then diverged to acquire independent secondary alterations ( Figures S1 C and S1D). In contrast, the P24a and P24b tumors share 80% (469/590) of somatic alterations but then have discordant driving alterations in APC and TP53, indicating that the hypermutator phenotype may have been present prior to the acquisition of growth promoting mutations ( Figures S1 E and S1F). The frequency of hypermutated organoid cultures in our patient panel (20%; 4 of 20) agreed with the reported frequency in a much larger cohort of clinical samples and display comparable somatic copy number alterations (SCNAs) ( Figure 2 B; Table S1 D) (). The successful derivation of both hypermutated and non-hypermutated organoids implies an absence of culture-based bias.

(F) Venn diagram showing the concordance of coding mutations shared between the biopsy and organoids derived from either the P24a or P24b site.

(D) Venn diagram showing the concordance of coding mutations shared between the biopsy and organoids derived from either the P19a or P19b site. Genes listed are cancer relevant genes as identified by the Cancer Gene Consensus and

(B) Comparison of somatic copy-number alterations found in the biopsies and corresponding organoids (Biop/Org) and TCGA CRC in hypermutated and non-hypermutated samples.

(A) Whole exome sequencing of the tumor and corresponding biopsy, when available, revealed the presence of hypermutated (>10 mutations/Mb) and non-hypermutated subtypes within the organoids. Comparable rates of mutations were observed in the tumor organoid (O) and tumor biopsy (B). Organoids without corresponding biopsy are indicated in with red (O).

Several CRC classifications have been proposed based on RNA expression. We combined expression data from organoid samples and TCGA tissue samples and classified these in subtypes using the gene signatures by Figure 4 C displays the subtyping of the 22 organoid samples and 431 TCGA RNA sequencing (RNA-seq) tumor tissue samples. The heatmap shows the normalized scores of genes by samples, both sorted by subtype (see Experimental Procedures ). Organoid samples were spread across the subtypes, with the transit-amplifying (TA) subtype being most frequently represented. The enterocyte subtype was not represented. In addition, the RNA expression data allowed expression analysis of individual genes in organoids. MLH1 expression was absent from two tumor organoids from patient 19 as well as from patient 7 (that is also mutant in MSH3) ( Figure S4 ). In the two tumor organoids from P24, we did not detect expression changes in MLH1 or any other MSI-associated gene.

Organoid cultures consist purely of epithelial cells. Therefore, the system allows for direct gene expression analysis without a contamination from mesenchyme, blood vessels, immune cells, etc. Normal colon-derived and tumor-derived organoids were plated under identical conditions in complete medium (+Wnt). After 3 days, RNA was analyzed using Affymetrix single transcript arrays. Figure 4 A shows the correlation heatmap of the organoid samples. Normal colon-derived organoids clustered tightly together, while the tumor-derived organoids exhibited much more heterogeneity. Next, we searched for genes differentially expressed between normal and tumor organoids. Normal colon-derived organoids ( Figure 4 B) expressed genes of differentiated cells (e.g., the goblet cell markers MUC1 and MUC4 and the colonocyte marker CA2). Genes enriched in tumor organoids included cancer-associated genes such as PROX1, BAMBI, and PTCH1 and the Wnt target gene APCDD1 ().

(C) CRC molecular subtypes are represented by the organoid panel. Genes by samples heat map of normalized gene expression of 22 organoid samples and 431 TCGA RNA-seq tumor tissue samples, organized by subtype. Within each subtype, samples are sorted by their mean gene expression for the signature genes associated with that specific subtype.

(B) MA plot of logged normal versus tumor gene expression. p values are computed with the R package limma, by comparing normal versus tumor gene expression. Cancer-associated genes (e.g., APCDD1, PROX1, and PTCH1) are shown in the top half.

(A) Correlation heat map of normal organoids versus tumor organoids based on 2,186 genes (the top 10% of genes in terms of SD). The normal organoids are very highly correlated with each other, whereas the tumor samples exhibit more heterogeneity. The colors represent pairwise Pearson correlations after the expression values have been logged and mean-centered for every gene. The hierarchical clustering is based on one minus correlation distance. The affix N = normal, T = tumor.

Isolation of a novel human gene, APCDD1, as a direct target of the beta-Catenin/T-cell factor 4 complex with probable involvement in colorectal carcinogenesis.

Unlike most other WNT pathway mutations, RNF43 mutations yield a cell that is hypersensitive to—yet still dependent on—secreted WNT. Array data confirmed the expression of several WNTs by the organoids ( Figure S5 A). The O-acyltransferase Porcupine is required for the secretion of WNTs and its inhibition prevents autocrine/paracrine activation of the pathway (). The small molecule porcupine inhibitor IWP2 () was tested on a small panel of the tumor organoids and strongly affected the RNF43 mutant P19b organoid ( Figure 5 A). This observation implied that porcupine inhibition may be evaluated for treatment of the small subset of cancer patients mutant in RNF43.

(C) Scatterplots of the correlation in (1-AUC) values for three compounds (GDC0941, obatoclax mesylate, and trametinib) screened twice during every screening run. Values are the mean of three technical replicates.

(B) Scatterplot of (1-AUC) values for all technical replicates of drug screening data. Plots show the correlation between the three different technical replicates and each data point represents the (1-AUC) value for an individual organoid.

(A) Autocrine/paracrine WNT signaling in P19b. A small panel of tumor organoids was incubated with increasing amounts of the Porcupine inhibitor IWP2. Growth of the RNF43 mutant P19b was inhibited, indicative of dependency on autocrine/paracrine WNT signaling. Error bars indicate the SD of triplicate measurements. See also Figure S5

(A) Heatmap of WNT gene expression (log2) for the tumor organoid cohort. Expression of 19 WNT members was analyzed. All tumor organoids show relatively high WNT expression.

Organoid Proof-of-Concept Drug Screen

Prompted by this, we developed a robotized drug sensitivity screen in 3D-organoid culture and correlated drug sensitivity with genomic features to identify molecular signatures associated with altered drug response. Organoid cultures were gently disrupted and plated on BME-coated 384-well plates in a 2% BME solution. Organoids were left overnight before being drugged and left for 6 days before measuring cell number using CellTiter-Glo reagent. Drug sensitivity was represented by the half-maximal inhibitory concentration (IC 50 ), the slope of the dose-response curve, and area under the dose-response curve (AUC).

A bespoke 83 compound library was assembled for screening, including drugs in clinical use (n = 25), chemotherapeutics (n = 10), drugs previously investigated in or currently undergoing studies in clinical trials (n = 29), and experimental compounds to a diverse range of cancer targets (n = 29) ( Table S2 A). The library included the anti-EGFR antibody cetuximab, used clinically for KRAS/NRAS/BRAF wild-type CRC, as well as oxaliplatin and 5-FU, first line chemotherapeutics for CRC treatment. In total, 19 of 20 tumor organoids (from 18 different patients) were successfully screened in experimental triplicate, generating >5,000 measurements of organoid-drug interactions ( Table S2 B).

We incorporated a number of controls into the assay design. The median Z factor score, a measure of assay plate quality, across all screening plates was 0.62 (n = 119; upper and lower quartile = 0.85 and 0.3, respectively), consistent with an experimentally robust assay. We did observe some unexplained organoid-specific variation in assay plate quality. Dose-response measurements were performed in experimental triplicate or duplicate (on separate plates) and replicate AUC values were highly correlated (Pearson correlation [Rp] > 0.87) ( Figure 5 B). Furthermore, the compounds trametinib, GDC0941, and obatoclax mesylate were screened twice independently on separate assay plates and a good correlation was observed between the experimentally determined AUC values (Rp = 0.79, 0.71, and 0.76, respectively) ( Figure 5 C).

50 values demonstrated a diverse range of sensitivities across the organoids and identified three major sub-groups ( 50 of 0.5 μM, comparable to IC 50 values of BRAF V600E colorectal cancer cell lines (range 0.004–2.55 μM; average 0.96 μM). Figure 6 Heatmap of IC 50 s of All 85 Compounds against 19 Colorectal Cancer Organoids Show full caption (A) Organoids have been clustered based on their IC 50 values across the drug panel. The drug names and their nominal target(s) are provided in the bottom panel. (B) Drugs with the same nominal targets have similar activity profiles across the organoid panel. (1-AUC) values are plotted for inhibitor of PI3K (GDC0941 and BYL719), IGF1R (OSI-906 and BMS-536924), EGFR (cetuximab and gefitinib), and BRAF (PLX4720 and dabrafenib). See also Tables S2 A and S2B. As a first validation, the only tumor organoid in the panel that was sensitive to the Porcupine inhibitor LGK974 was P19b ( Figure S5 B), confirming the observations made with IWP2 ( Figure 5 A). The clustering of compounds based on their ICvalues demonstrated a diverse range of sensitivities across the organoids and identified three major sub-groups ( Figure 6 A). One group was associated with sensitivity to a majority of the compounds (organoids P8, P7, and P19a), in contrast to the cluster (P31, P11) exhibiting insensitivity. The remaining organoids had intermediate sensitivity. Interestingly, the multifocal tumors P19a and P19b, derived from the same patient and both carrying the BRAF V600E mutation, differed in their overall drug response profile. We observed clustering of drugs that inhibit the IGF1R and PI3K-AKT signaling pathways ( Figure 6 A), and compounds with similar nominal targets had comparable activity across the organoid collection. For example, a similar sensitivity pattern was observed for the PI3K inhibitors GDC0941 and BYL719 (α-selective), the IGF1R inhibitors OSI-906 and BMS-536924, EGFR inhibitors cetuximab and gefitinib, and the BRAF inhibitors dabrafenib and PLX4720 ( Figure 6 B). All but one of the organoids displayed a lack of sensitivity to BRAF inhibition. P19a, a BRAF V600E mutant organoid, displayed partial sensitivity to dabrafenib with an ICof 0.5 μM, comparable to ICvalues of BRAF V600E colorectal cancer cell lines (range 0.004–2.55 μM; average 0.96 μM).

50 values and slopes of the corresponding dose-response curves, with MSI-status as a covariate. Complete drug sensitivity and genomic data sets were available for 18 organoids and used for this analysis. The analysis included 16 genes identified as mutated, amplified, or deleted in CRC (referred to as mutant genes) as described by Lawrence et al. (2014) Lawrence M.S.

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et al. In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Figure 7 Gene-Drug Associations and Differential Drug Sensitivity Profiles of Interest Show full caption (A) Association of TP53 mutational status with nutlin-3a response. Viability response curves of the altered (blue) and wild-type organoids (gray) as well as scatter plots of cell line IC 50 (μM) values are shown. IC50 values are on a natural logarithmic scale. Each circle represents one cell line, red bars indicate geometric means of IC 50 values and black bold bars indicate median log IC 50 values. Box top/low bounds indicate upper/lower quartiles, and whiskers (indicated by the dashed lines) extend to extreme values (minimal and maximal) excluding outliers (i.e., whose value is more than 3/2 times the upper quartile and less than 3/2 times the lower quartile). Purple bar positions on the y axis indicate means +/− log IC 50 SD. (B) Immunohistochemical staining showing stabilization of TP53 in organoid P18. Scale bar, 100 μM. (C) Association of KRAS status and cetuximab response. Colors and symbols coding is the same as (A). (D) Dose-response curves after 6 days treatment with MK2206, AZD8931, and gemcitabine. (E) Reproducibility of drug response profiles for 11 drugs. The Pearson correlation score of (1-AUC) values from the primary screen compared to (1-AUC) values from validation screens are used for comparison. The validation screen was performed twice (run 1 and 2) with >1 month elapsed between each screen. NA, data unavailable for this drug. (F) The correlation of 1-AUC values from the primary and validation screens for AZD8931, gemcitabine, and nutlin-3a. See also Figures S6 and S7 and Tables S3 S4 , and S5 To identify genetic correlates between individual oncogenic mutations and drug response, we performed a multivariate analysis of variance (MANOVA) incorporating ICvalues and slopes of the corresponding dose-response curves, with MSI-status as a covariate. Complete drug sensitivity and genomic data sets were available for 18 organoids and used for this analysis. The analysis included 16 genes identified as mutated, amplified, or deleted in CRC (referred to as mutant genes) as described by Table S3 ). The MANOVA identified a subset (12 of 864, ∼1%) of gene-drug associations as statistically significant (p < 0.005, incorporating a 30% false discovery rate [FDR]) ( Table S4 ). These results were further filtered based on the magnitude of the effect size on the ICvalues of wild-type versus mutant cell line populations (effect size >2; Cohen’s D), and correlations identified due to a singlet outlier organoids were removed. This resulted in the identification of one high confidence gene-drug association already reported in the literature (). Loss-of-function mutations of the tumor suppressor TP53 were associated with resistance to nutlin-3a (p = 0.0018), an inhibitor of MDM2 ( Figure 7 A). Of the four organoids that were wild-type for TP53 by DNA sequencing, only P18 was (unexpectedly) insensitive to nutlin-3a. However, immunohistochemistry of p53 in P18 revealed the protein to be stabilized, indicative of functional inactivation of the p53 pathway ( Figure 7 B).

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et al. Effects of KRAS, BRAF, NRAS, and PIK3CA mutations on the efficacy of cetuximab plus chemotherapy in chemotherapy-refractory metastatic colorectal cancer: a retrospective consortium analysis. Vecchione, 2014 Vecchione L. Optimization of anti-EGFR treatment of advanced colorectal cancer. Figure S6 Association of KRAS Status and BIBW2992 Response, Related to Figure 7 We could also readily detect resistance to the anti-EGFR inhibitors cetuximab and BIBW2992 (afatinib) in the setting of KRAS mutant organoids (p = 0.008/FDR 37% and p = 0.029/FDR 54%, respectively), although these associations were below statistical significance when considering an FDR <30% ( Figures 7 C and S6 ). Of the KRAS wild-type organoids, a subset 2/10 was insensitive to cetuximab, including P19b that has a BRAF mutation, a known mediator of cetuximab resistance (). For the remaining organoid, further mechanisms beyond mutated KRAS/NRAS/BRAF are likely to be involved in cetuximab resistance ().

Figure S7 Secondary Screen, Related to Figure 7 Show full caption Dose-response graphs of drugs screened in a secondary assay (Run 1: 10 drugs, 14 organoids; Run 2: 11 drugs, 16 organoids). Also shown are scatterplots of the 1-AUC values from the primary and secondary screens to determine degree of reproducibility between the two screens. We also identified a number of compounds with differential activity in the absence of an apparent genetic biomarker ( Figure 7 D). For example, a subset of organoids was exquisitely sensitive to the AKT1/2 inhibitor MK2206. Similarly, we observed distinct subsets of organoids that are exquisitely sensitive to the pan-ERBB inhibitor AZD8931 and the chemotherapeutic gemcitabine. We also performed a validation screen with 11 of the original 83 compounds across the organoid panel and compared the measured responses ( Figure S7 Table S5 ). We observed positive correlation for all compounds and nine exhibited good to fair reproducibility as indicated by an Rp of 0.5 or greater ( Figures 7 E and 7F). Variation within the assay was likely due to inherent technical noise, biological variation, and sensitivity to outlier data points due to the small number of organoids.

In summary, the successful application of organoids in a systematic and unbiased high-throughput drug screen to identify clinically relevant biomarkers demonstrates the feasibility and utility of organoid technology for investigating the molecular basis of drug response. Furthermore, the identification of putative novel molecular markers has opened avenues for further investigation of drug sensitivity in CRC. The current analysis is still constrained by the relatively small number of patients. The derivation of a significantly larger organoid collection would increase the representation of rare genotypes and the statistical power to detect molecular markers of drug response.