Benardina Ndreshkjana

Supplementary Table S1 shows the clinical data of the CRC organoid-stroma cohort. Supplementary Table S2 shows the inventory of available materials and molecular analyses. Supplementary Table S3 shows the selected variant types for whole exome analysis. Supplementary Table S4 summarizes the genetic characterization of CRC models. Supplementary Table S5 shows the classification according to the consensus molecular subtypes (CMS). Supplementary Table S6 shows classification according to the CRC intrinsic subtypes (CRIS). Supplementary Table S7 summarizes the functional data in mono- and co-cultures. Supplementary Table S8 shows drug sensitivity of CRC organoids in co-culture with autologous and heterologous fibroblasts. Supplementary Table S9 show the drug sensitivity in all biobank models in mono- and co-culture. Supplementary Table S10 shows the cell viability data of the chemogenomic library screens in resistant co-culture models. Supplementary Table S11 shows the identified drug sensitivity and resistance signatures. Supplementary Table S12 lists all antibodies and primer sequences used in this study.

Supplementary Figure S1 shows light microscopic images of CRC organoids and CAFs. Supplementary Figure S2 shows immunostaining and RNA sequencing analysis of cultured CAFs. Supplementary Figure S3 shows chromosomal copy number changes in tumors and matched organoids. Supplementary Figure S4 shows the transcriptional variation among tumors and organoids. Supplementary Figure S5 shows the classification of cancer intrinsic subtypes (CRIS) in tumors, matched organoids and xenotransplants. Supplementary Figure S6 shows the tissue microarray analysis of the of tumor samples from the colorectal cancer organoid-stroma biobank cohort. Supplementary Figure S7 shows the association of growth characteristics with molecular features of colorectal cancer organoid-stroma biobank. Supplementary Figure S8 shows the shows CMS and CRIS classifications of tumors and of matched organoids in different contexts. Supplementary Figure S9 describes the establishment of a drug screening workflow in 3D organoid-stroma co-cultures. Supplementary Figure S10 describes the development of a dual luciferase assay to study cell viability simultaneously in organoids and CAFs. Supplementary Figure S11 demonstrates that the MET inhibitor BAY-474 sensitizes Gefitinib resistant co-cultures. Supplementary Figure S12 shows the association of identified sensitivity and resistance signatures with gene expression and prognosis in different CMS.

As cancer cell specific rewiring of metabolic networks creates potential therapeutic opportunities, we conducted a synthetic lethal screen utilizing inhibitors of metabolic pathways. Simultaneous administration of (R)-GNE-140 and BMS-986205 (Linrodostat) preferentially halted proliferation of ovarian cancer cells, but not of their non-oncogenically transformed progenitor cells. While (R)-GNE-140 inhibits lactate dehydrogenase (LDH)A/B and thus effective glycolysis, BMS-986205, in addition to its known inhibitory activity on Indoleamine 2,3-dioxygenase (IDO1), also restricts oxidative phosphorylation (OXPHOS), as revealed here. BMS-986205, which is being tested in multiple Phase III clinical trials, inhibits the ubiquinone reduction site of respiratory complex I and thus compromises mitochondrial ATP production. The energetic catastrophe caused by simultaneous interference with glycolysis and OXPHOS resulted in either cell death or the induction of senescence in tumor cells, with the latter being eliminated by senolytics. The frequent synergy observed with combined inhibitor treatment was comprehensively confirmed through testing on tumor cell lines from the DepMap panel and on human colorectal cancer organoids. These experiments revealed highly synergistic activity of the compounds in a third of the tested tumor cell lines, correlating with alterations in genes with known roles in metabolic regulation and demonstrating the therapeutic potential of metabolic intervention.

As cancer cell specific rewiring of metabolic networks creates potential therapeutic opportunities, we conducted a synthetic lethal screen utilizing inhibitors of metabolic pathways. Simultaneous administration of (R)-GNE-140 and BMS-986205 (Linrodostat) preferentially halted proliferation of ovarian cancer cells, but not of their non-oncogenically transformed progenitor cells. While (R)-GNE-140 inhibits lactate dehydrogenase (LDH)A/B and thus effective glycolysis, BMS-986205, in addition to its known inhibitory activity on Indoleamine 2,3-dioxygenase (IDO1), also restricts oxidative phosphorylation (OXPHOS), as revealed here. BMS-986205, which is being tested in multiple Phase III clinical trials, inhibits the ubiquinone reduction site of respiratory complex I and thus compromises mitochondrial ATP production. The energetic catastrophe caused by simultaneous interference with glycolysis and OXPHOS resulted in either cell death or the induction of senescence in tumor cells, with the latter being eliminated by senolytics. The frequent synergy observed with combined inhibitor treatment was comprehensively confirmed through testing on tumor cell lines from the DepMap panel and on human colorectal cancer organoids. These experiments revealed highly synergistic activity of the compounds in a third of the tested tumor cell lines, correlating with alterations in genes with known roles in metabolic regulation and demonstrating the therapeutic potential of metabolic intervention.

Supplementary Figure S1 shows light microscopic images of CRC organoids and CAFs. Supplementary Figure S2 shows immunostaining and RNA sequencing analysis of cultured CAFs. Supplementary Figure S3 shows chromosomal copy number changes in tumors and matched organoids. Supplementary Figure S4 shows the transcriptional variation among tumors and organoids. Supplementary Figure S5 shows the classification of cancer intrinsic subtypes (CRIS) in tumors, matched organoids and xenotransplants. Supplementary Figure S6 shows the tissue microarray analysis of the of tumor samples from the colorectal cancer organoid-stroma biobank cohort. Supplementary Figure S7 shows the association of growth characteristics with molecular features of colorectal cancer organoid-stroma biobank. Supplementary Figure S8 shows the shows CMS and CRIS classifications of tumors and of matched organoids in different contexts. Supplementary Figure S9 describes the establishment of a drug screening workflow in 3D organoid-stroma co-cultures. Supplementary Figure S10 describes the development of a dual luciferase assay to study cell viability simultaneously in organoids and CAFs. Supplementary Figure S11 demonstrates that the MET inhibitor BAY-474 sensitizes Gefitinib resistant co-cultures. Supplementary Figure S12 shows the association of identified sensitivity and resistance signatures with gene expression and prognosis in different CMS.

Supplementary Table S1 shows the clinical data of the CRC organoid-stroma cohort. Supplementary Table S2 shows the inventory of available materials and molecular analyses. Supplementary Table S3 shows the selected variant types for whole exome analysis. Supplementary Table S4 summarizes the genetic characterization of CRC models. Supplementary Table S5 shows the classification according to the consensus molecular subtypes (CMS). Supplementary Table S6 shows classification according to the CRC intrinsic subtypes (CRIS). Supplementary Table S7 summarizes the functional data in mono- and co-cultures. Supplementary Table S8 shows drug sensitivity of CRC organoids in co-culture with autologous and heterologous fibroblasts. Supplementary Table S9 show the drug sensitivity in all biobank models in mono- and co-culture. Supplementary Table S10 shows the cell viability data of the chemogenomic library screens in resistant co-culture models. Supplementary Table S11 shows the identified drug sensitivity and resistance signatures. Supplementary Table S12 lists all antibodies and primer sequences used in this study.

Supplementary Figure S1 shows light microscopic images of CRC organoids and CAFs. Supplementary Figure S2 shows immunostaining and RNA sequencing analysis of cultured CAFs. Supplementary Figure S3 shows chromosomal copy number changes in tumors and matched organoids. Supplementary Figure S4 shows the transcriptional variation among tumors and organoids. Supplementary Figure S5 shows the classification of cancer intrinsic subtypes (CRIS) in tumors, matched organoids and xenotransplants. Supplementary Figure S6 shows the tissue microarray analysis of the of tumor samples from the colorectal cancer organoid-stroma biobank cohort. Supplementary Figure S7 shows the association of growth characteristics with molecular features of colorectal cancer organoid-stroma biobank. Supplementary Figure S8 shows the shows CMS and CRIS classifications of tumors and of matched organoids in different contexts. Supplementary Figure S9 describes the establishment of a drug screening workflow in 3D organoid-stroma co-cultures. Supplementary Figure S10 describes the development of a dual luciferase assay to study cell viability simultaneously in organoids and CAFs. Supplementary Figure S11 demonstrates that the MET inhibitor BAY-474 sensitizes Gefitinib resistant co-cultures. Supplementary Figure S12 shows the association of identified sensitivity and resistance signatures with gene expression and prognosis in different CMS.

Supplementary Table S1 shows the clinical data of the CRC organoid-stroma cohort. Supplementary Table S2 shows the inventory of available materials and molecular analyses. Supplementary Table S3 shows the selected variant types for whole exome analysis. Supplementary Table S4 summarizes the genetic characterization of CRC models. Supplementary Table S5 shows the classification according to the consensus molecular subtypes (CMS). Supplementary Table S6 shows classification according to the CRC intrinsic subtypes (CRIS). Supplementary Table S7 summarizes the functional data in mono- and co-cultures. Supplementary Table S8 shows drug sensitivity of CRC organoids in co-culture with autologous and heterologous fibroblasts. Supplementary Table S9 show the drug sensitivity in all biobank models in mono- and co-culture. Supplementary Table S10 shows the cell viability data of the chemogenomic library screens in resistant co-culture models. Supplementary Table S11 shows the identified drug sensitivity and resistance signatures. Supplementary Table S12 lists all antibodies and primer sequences used in this study.

In colorectal cancers, the tumor microenvironment plays a key role in prognosis and therapy efficacy. Patient-derived tumor organoids (PDTO) show enormous potential for preclinical testing; however, cultured tumor cells lose important characteristics, including the consensus molecular subtypes (CMS). To better reflect the cellular heterogeneity, we established the colorectal cancer organoid–stroma biobank of matched PDTOs and cancer-associated fibroblasts (CAF) from 30 patients. Context-specific phenotyping showed that xenotransplantation or coculture with CAFs improves the transcriptomic fidelity and instructs subtype-specific stromal gene expression. Furthermore, functional profiling in coculture exposed CMS4-specific therapeutic resistance to gefitinib and SN-38 and prognostic expression signatures. Chemogenomic library screening identified patient- and therapy-dependent mechanisms of stromal resistance including MET as a common target. Our results demonstrate that colorectal cancer phenotypes are encrypted in the cancer epithelium in a plastic fashion that strongly depends on the context. Consequently, CAFs are essential for a faithful representation of molecular subtypes and therapy responses ex vivo. Significance: Systematic characterization of the organoid–stroma biobank provides a resource for context dependency in colorectal cancer. We demonstrate a colorectal cancer subtype memory of PDTOs that is independent of specific driver mutations. Our data underscore the importance of functional profiling in cocultures for improved preclinical testing and identification of stromal resistance mechanisms. This article is featured in Selected Articles from This Issue, p. 2109