Screening drug effects in patient‐derived cancer cells links organoid responses to genome alterations
2017; Springer Nature; Volume: 13; Issue: 11 Linguagem: Inglês
10.15252/msb.20177697
ISSN1744-4292
AutoresJulia Jabs, Franziska Maria Zickgraf, Jeongbin Park, Steve Wagner, Xiaoqi Jiang, Katharina Jechow, Kortine Kleinheinz, Umut H. Toprak, Marc A. Schneider, Michael Meister, Saskia Spaich, Marc Sütterlin, Matthias Schlesner, Andreas Trumpp, Martin R. Sprick, Roland Eils, Christian Conrad,
Tópico(s)PARP inhibition in cancer therapy
ResumoArticle27 November 2017Open Access Transparent process Screening drug effects in patient-derived cancer cells links organoid responses to genome alterations Julia Jabs Julia Jabs orcid.org/0000-0002-4915-9966 Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany Search for more papers by this author Franziska M Zickgraf Franziska M Zickgraf orcid.org/0000-0003-4269-1316 Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) gGmbH, Heidelberg, Germany Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Jeongbin Park Jeongbin Park orcid.org/0000-0002-9064-4912 Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany Search for more papers by this author Steve Wagner Steve Wagner Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) gGmbH, Heidelberg, Germany Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Xiaoqi Jiang Xiaoqi Jiang Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Katharina Jechow Katharina Jechow Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany Search for more papers by this author Kortine Kleinheinz Kortine Kleinheinz orcid.org/0000-0002-1859-2281 Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany Search for more papers by this author Umut H Toprak Umut H Toprak Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Marc A Schneider Marc A Schneider orcid.org/0000-0001-8269-3821 Thoraxklinik at Heidelberg University Hospital, Member of the German Center for Lung Research (DZL), Heidelberg, Germany Search for more papers by this author Michael Meister Michael Meister Thoraxklinik at Heidelberg University Hospital, Member of the German Center for Lung Research (DZL), Heidelberg, Germany Search for more papers by this author Saskia Spaich Saskia Spaich Department of Gynaecology and Obstetrics, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany Search for more papers by this author Marc Sütterlin Marc Sütterlin orcid.org/0000-0003-0205-2552 Department of Gynaecology and Obstetrics, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany Search for more papers by this author Matthias Schlesner Matthias Schlesner orcid.org/0000-0002-5896-4086 Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Andreas Trumpp Andreas Trumpp Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) gGmbH, Heidelberg, Germany Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany German Cancer Consortium, Heidelberg, Germany Search for more papers by this author Martin Sprick Martin Sprick orcid.org/0000-0001-9691-7574 Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) gGmbH, Heidelberg, Germany Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany German Cancer Consortium, Heidelberg, Germany Search for more papers by this author Roland Eils Corresponding Author Roland Eils [email protected] orcid.org/0000-0002-0034-4036 Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany Heidelberg Center for Personalized Oncology, DKFZ-HIPO, DKFZ, Heidelberg, Germany Search for more papers by this author Christian Conrad Corresponding Author Christian Conrad [email protected] orcid.org/0000-0001-7036-342X Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany Search for more papers by this author Julia Jabs Julia Jabs orcid.org/0000-0002-4915-9966 Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany Search for more papers by this author Franziska M Zickgraf Franziska M Zickgraf orcid.org/0000-0003-4269-1316 Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) gGmbH, Heidelberg, Germany Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Jeongbin Park Jeongbin Park orcid.org/0000-0002-9064-4912 Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany Search for more papers by this author Steve Wagner Steve Wagner Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) gGmbH, Heidelberg, Germany Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Xiaoqi Jiang Xiaoqi Jiang Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Katharina Jechow Katharina Jechow Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany Search for more papers by this author Kortine Kleinheinz Kortine Kleinheinz orcid.org/0000-0002-1859-2281 Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany Search for more papers by this author Umut H Toprak Umut H Toprak Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Marc A Schneider Marc A Schneider orcid.org/0000-0001-8269-3821 Thoraxklinik at Heidelberg University Hospital, Member of the German Center for Lung Research (DZL), Heidelberg, Germany Search for more papers by this author Michael Meister Michael Meister Thoraxklinik at Heidelberg University Hospital, Member of the German Center for Lung Research (DZL), Heidelberg, Germany Search for more papers by this author Saskia Spaich Saskia Spaich Department of Gynaecology and Obstetrics, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany Search for more papers by this author Marc Sütterlin Marc Sütterlin orcid.org/0000-0003-0205-2552 Department of Gynaecology and Obstetrics, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany Search for more papers by this author Matthias Schlesner Matthias Schlesner orcid.org/0000-0002-5896-4086 Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Search for more papers by this author Andreas Trumpp Andreas Trumpp Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) gGmbH, Heidelberg, Germany Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany German Cancer Consortium, Heidelberg, Germany Search for more papers by this author Martin Sprick Martin Sprick orcid.org/0000-0001-9691-7574 Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) gGmbH, Heidelberg, Germany Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany German Cancer Consortium, Heidelberg, Germany Search for more papers by this author Roland Eils Corresponding Author Roland Eils [email protected] orcid.org/0000-0002-0034-4036 Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany Heidelberg Center for Personalized Oncology, DKFZ-HIPO, DKFZ, Heidelberg, Germany Search for more papers by this author Christian Conrad Corresponding Author Christian Conrad [email protected] orcid.org/0000-0001-7036-342X Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany Search for more papers by this author Author Information Julia Jabs1,2,3, Franziska M Zickgraf4,5, Jeongbin Park1,3, Steve Wagner4,5, Xiaoqi Jiang6, Katharina Jechow1,2, Kortine Kleinheinz1,2,3, Umut H Toprak1, Marc A Schneider7, Michael Meister7, Saskia Spaich8, Marc Sütterlin8, Matthias Schlesner1, Andreas Trumpp4,5,9, Martin Sprick4,5,9, Roland Eils *,1,2,3,10 and Christian Conrad *,1,2 1Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany 2Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany 3Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany 4Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM) gGmbH, Heidelberg, Germany 5Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany 6Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany 7Thoraxklinik at Heidelberg University Hospital, Member of the German Center for Lung Research (DZL), Heidelberg, Germany 8Department of Gynaecology and Obstetrics, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany 9German Cancer Consortium, Heidelberg, Germany 10Heidelberg Center for Personalized Oncology, DKFZ-HIPO, DKFZ, Heidelberg, Germany *Corresponding author. Tel: +49 6221 42 3600; E-mail: [email protected] *Corresponding author. Tel: +49 6221 54 51304; E-mail: [email protected] Molecular Systems Biology (2017)13:955https://doi.org/10.15252/msb.20177697 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Cancer drug screening in patient-derived cells holds great promise for personalized oncology and drug discovery but lacks standardization. Whether cells are cultured as conventional monolayer or advanced, matrix-dependent organoid cultures influences drug effects and thereby drug selection and clinical success. To precisely compare drug profiles in differently cultured primary cells, we developed DeathPro, an automated microscopy-based assay to resolve drug-induced cell death and proliferation inhibition. Using DeathPro, we screened cells from ovarian cancer patients in monolayer or organoid culture with clinically relevant drugs. Drug-induced growth arrest and efficacy of cytostatic drugs differed between the two culture systems. Interestingly, drug effects in organoids were more diverse and had lower therapeutic potential. Genomic analysis revealed novel links between drug sensitivity and DNA repair deficiency in organoids that were undetectable in monolayers. Thus, our results highlight the dependency of cytostatic drugs and pharmacogenomic associations on culture systems, and guide culture selection for drug tests. Synopsis DeathPro, an automated microscopy-based assay resolves cell death and proliferation inhibition in 2D and 3D cultures. Drug screens using DeathPro provide insights into the impact of culture systems on drug effects and their links to genomic features. DeathPro resolves cytotoxic and cytostatic effects in drug screens with patient-derived ovarian and lung cancer cells, organoids and co-cultures with fibroblasts. Drug responses in cancer organoids are more diverse than in 2D cultured cells. Cytostatic drugs depend on culture systems, cytotoxic effects are independent of the culture format. Genomic analysis of cancer patients links DNA repair deficiency to drug sensitivity in organoids. Introduction Cell-based assays are a key tool in basic research and drug discovery, and are increasingly used in personalized oncology. In the last years, numerous anticancer therapeutics developed from standard cell line screens in conventional 2D culture failed in clinical studies (Horvath et al, 2016). As a result, standard treatment and overall survival of advanced cancers like ovarian cancer (OC) has not changed for decades (Bowtell et al, 2015). To allow personalized therapy and improve drug development, new patient-derived models such as organoids (Gao et al, 2014; Van de Wetering et al, 2015; Schütte et al, 2017) and patient-derived xenografts (Alkema et al, 2015; Gao et al, 2015; Bruna et al, 2016) that recapitulate the heterogeneity and intrinsic drug sensitivity of the original tumour have started to replace the popular cancer cell lines. Patient-derived organoids may be grown as 3D cultures on hydrogels like Matrigel that mimic the extracellular matrix. Compared to 2D cell cultures, they have emerged as near-physiological models reflecting the gene expression, differentiation and structure of the primary tissue (Fatehullah et al, 2016). Nevertheless, due to increased workload, higher costs and the current lack of 3D assay methods, most drug screens are still performed in less physiological 2D cultures (Edmondson et al, 2014). Initial studies in ovarian cancer showed that cells cultured as cell aggregates are less sensitive to drugs than in monolayer culture (Loessner et al, 2010; Lee et al, 2013). The culture format thus shapes cellular drug responses and defines the translational power of a drug assay. However, this dependency cannot be studied in detail with widely used, unspecific viability assays that measure metabolic activity or cellular ATP as surrogate markers. Such assays show limited reproducibility and do not resolve actual drug effects of high therapeutic interest such as cell death and growth arrest (Haibe-Kains et al, 2013; Van de Wetering et al, 2015). Instead, recent advances in automated microscopy enable more sophisticated assays that can deconvolve drug effects in different culture formats. Here, we systematically compare drug effects in organoid and standard 2D culture using DeathPro, a confocal microscopy-based assay and image processing workflow to simultaneously study cell death and growth arrest in patient-derived material over time. Using DeathPro, we screened cells from nine high-grade serous OC patients with clinically relevant drugs and found that growth arrest and the efficacy of cytostatic drugs notably depend on the culture type. Remarkably, patient-specific genomic alterations correlated with drug effects observed in organoids, but not in 2D cell monolayers. Hence, combining refined assays like DeathPro with advanced models like cancer organoids could enhance drug screening in the context of personalized oncology and pharmacogenomics. Results Deconvolving drug-induced cell death and proliferation inhibition To resolve drug effects in patient cells and organoids, we developed an automated live cell assay and quantification workflow, which deconvolves drug-induced death and proliferation inhibition over time (DeathPro; Fig 1). To this end, cells were stained with Hoechst and counterstained with propidium iodide (PI) for dead cells and analysed at consecutive time points by confocal microscopy. To accurately quantify cell growth for each condition (Hafner et al, 2016), cells were imaged at the start and end of the drug treatment at the same position (Fig 1A). For high-throughput image analysis, we built an adaptable visual programming workflow that encompasses adaptive sequential thresholding and outlier filtering strategies to cope with heterogeneous cell morphologies and dye intensities. In the workflow, total areas covered by dead cells (PI-stained) and all cells (Hoechst or PI-stained) were determined from confocal images and used to calculate LD50 values and area under curve values for cell death (AUCd) and proliferation inhibition (AUCpi; Fig 1B). Figure 1. Drug-induced cell death and proliferation inhibition can be quantified from serial confocal images Schematic overview of drug testing in organoid culture with the DeathPro assay. Cells are grown on Matrigel for 4 days, stained with Hoechst (H) and propidium iodide (PI) and imaged at day 4, day 7 and day 10. Image gallery exemplifies OC12 organoid growth and cell death at start (day 4) and end of carboplatin treatment (day 7) and after carboplatin removal (day 10) using eight carboplatin concentrations or drug-free medium (ctrl). Confocal images are reduced to maximum intensity projections, and binary images of merged Hoechst (green) and PI (red) channel are shown. Image analysis for the DeathPro assay is based on area measurements in Hoechst and PI channels, and calculation of LD50, AUCd and AUCpi values to describe cell death and growth arrest. Drug response curve fitting and AUC values are illustrated for OC12 at time points depicted in (A). Data information: Grey and orange boxes in (A) correspond to the magnifications in (B). Scale bar is 200 μm. Download figure Download PowerPoint The DeathPro assay and workflow reliably resolved carboplatin-induced cell death and proliferation inhibition in OC organoids generated by culturing patient-derived cells on Matrigel (Fig 1). In addition, we performed pilot drug screens in OC patient cells from mouse xenografts and in 2D co-cultures with fibroblasts to validate our DeathPro concept in other common personalized cancer models (Fig EV1A–D). Moreover, we resolved drug effects in lung cancer organoids to verify that the DeathPro workflow can be applied to patient cells from different cancer entities (Fig EV1E and F). Click here to expand this figure. Figure EV1. DeathPro assay resolves drug responses in ovarian cancer co-cultures, xenograft-derived cells and lung cancer organoids Representative images of primary ovarian cancer (OC12) or pleural effusion-derived cells (PE20) seeded together with human ovary (HOF) or lung fibroblasts (IMR90, both blue channel) to model cellular interactions in the primary tumour or lung metastasis. The DeathPro assay solely analyses drug response of Hoechst and PI-stained ovarian cancer nuclei (green and red channel) and excludes live fibroblasts (CellTracker Green stained, blue channel). DeathPro resolves cell death and growth arrest in co-culture pilot drug screens. Cells were treated for 72 h with indicated drugs. Drug test with OC patient-derived xenograft model. Ascites from mice intraperitoneally injected with OC12 was harvested, seeded onto Matrigel and used for drug screening from day 1–4 postseeding. Representative images depict untreated (ctrl) or 2 mM carboplatin-treated cells stained with Hoechst (green) and PI (red) at day 4. DeathPro resolves cytotoxic and cytostatic effects in mouse OC12 ascites cells. A pilot drug screen similar to the co-cultures screens (A, B) was performed as described in (C). Growth of ascites cells is heterogeneous in controls and drug-treated conditions. Lung cancer cells lines derived from lymph node (LN2106) or lung tumour (T2427) were cultured in 3D on Matrigel for 7 days and stained with Hoechst (H, green) and propidium iodide (PI, red). Pilot drug screen in 3D cultured lung cancer cells from patients. Drug responses and cell growth measured after 72 or 144 h for drugs as indicated. For better visualization, logarithmic LD50 values were normalized so that 1 and 0 correspond to minimum and maximum dose, respectively. Data information: Scale bar is 100 μm. Download figure Download PowerPoint By using live cell dyes, patient cells or organoids can be directly used for screening and do not have to be genetically modified to express fluorescent proteins. To exclude the possibility that either dye alters cell behaviour, we tested their effect on OC organoids. Hoechst and PI did not affect organoid growth but increased cell death (Fig EV2A and B), which is accounted for in AUCd measurements by normalization to the untreated control (Fig 1B). Additionally, cytotoxic effects induced by 11 drugs correlated well between long-term and short-term stained organoids (Pearson correlation 0.81–0.95) indicating that both dyes do not interfere with drug-induced cell death measured by DeathPro (Fig EV2C and D). Imaging OC12 organoids only at the end or additionally at the beginning of the drug treatment did neither alter organoid growth nor cell death (Fig EV2A). To achieve low phototoxicity and high throughput of DeathPro, we chose to acquire confocal images at low resolution and to analyse 2D image projections. To validate that this coarse procedure captures complex 3D phenotypes, we experimentally compared the DeathPro strategy to "slice-wise" analysis of confocal image stacks. In the tested conditions, we detected similar cell death ratios with both approaches (Appendix Fig S1). Thus, the DeathPro imaging strategy can be used to efficiently determine drug effects in screens but at the cost of a potential bias which we cannot exclude for all conditions. Click here to expand this figure. Figure EV2. DeathPro dyes Hoechst and PI do not affect organoid growth and drug responses A, B. In the absence of drugs, Hoechst (H) and PI do not change growth but lead to more cell death in organoids. (A) OC12 organoids were stained at day 4 or day 7 with H and PI and imaged already at day 4 or only at day 7 to compare the influence of imaging on growth and cell death. Both were quantified at day 7. (B) Organoids were stained at day 4, 7 or 10, and growth and cell death were quantified at day 7 or 10 as indicated. C, D. Staining organoids with H and PI has no effect on cytotoxic responses to drugs. DeathPro assay was performed with OC12 organoids and the indicated drugs. Cell death was determined at day 7 (C) or day 10 (D) and compared between organoids stained at treatment begin (day 4) or end at day 7 (C) or day 10 after drug washout (D). Data information: n.s., not significant, *P < 0.05 in two-tailed Welch's t-test. Rp, Pearson correlation coefficient. Black line (x = y) is depicted as reference for perfect correlation. Download figure Download PowerPoint Drug-induced growth arrest in ovarian cancer patient cells is culture-dependent To systematically assess the influence of extracellular matrix on patient cell responses, we used the DeathPro assay to screen patient-derived OC cell lines (PDCLs) in standard 2D culture or as cancer organoids. PDCLs were established from metastatic serous ovarian cancers, maintained in 2D culture and seeded on Matrigel to generate "cancer organoids" (FIGO stage IIIc-IV, Table EV1, Fig 2A). Additionally, we included human ovarian surface epithelial cells (HOSEpiC) to assess potential side-effects such as cytotoxicity in normal cells. Seeded on Matrigel, HOSEpiC developed into spheres whereas PDCLs formed morphologically diverse "cancer organoids" (Fig 2B) that expressed the tumour markers CA-125 and WT1 (Appendix Fig S2). Figure 2. Culture type shapes drug-induced growth arrest in ovarian cancer patient cells Simplified overview of generation and cultivation of patient-derived ovarian cancer cell lines (PDCLs) from different sites (OC: primary tumour, Asc: ascites, PE: pleural effusion). Patient material was taken directly into 2D culture (Asc211, PE306) or amplified by xenografting into mice. PDCLs are maintained in 2D culture but can be grown as ovarian cancer organoids on Matrigel. Morphology of ovarian cancer organoids and normal ovarian epithelial cells (HOSEpiC) on Matrigel 7 days after seeding. Green (Hoechst) and red (PI) channels are merged. Drug responses (cell death: AUCd, growth arrest: AUCpi) measured with DeathPro assay after 72-h drug treatment in patient cells cultured as monolayers (2D) or ovarian cancer organoids (3D). Comparison of drug-induced cell death (AUCd) and growth arrest (AUCpi) in 2D vs. 3D. Drug responses measured in ovarian cancer organoids (3D) after 72-h drug treatment followed by 72-h drug removal. Comparison of drug-induced cell death and growth arrest in 2D vs. 3D after drug removal. Data information: All values shown are means of two independent biological replicates. HOSE, HOSEpiC; Rp, Pearson correlation coefficient; C + P, carboplatin + paclitaxel. Download figure Download PowerPoint Ovarian cancer organoids or 2D cultured PDCLs were screened twice for 22 drugs or drug combinations (Table EV2) currently used or under investigation for treatment of OC. LD50 and cell death (AUCd) values were highly reproducible across all drugs and patients in 2D and organoid culture (Pearson correlation 0.86–0.97, Fig EV3A), whereas growth arrest (AUCpi) showed slightly lower correlation (Pearson correlation 0.67–0.76, Fig EV3B). Click here to expand this figure. Figure EV3. Reproducibility of DeathPro drug screens A, B. Scatterplots of AUCd, logLD50 and AUCpi values of biological replicates determined in 2D and 3D culture screens of patient-derived ovarian cancer cell lines and HOSEpiC (220 measurements per time point: 22 drug dilution series on 10 cell lines) with corresponding Pearson correlation coefficients (Rp). The black line (x = y) is depicted as reference for perfect correlation. C. Growth of patient-derived OC cell lines and HOSEpiC in organoid culture or as cell monolayers (2D). Growth within 72 or 144 h was measured from day 4 on after seeding in 3D or from day 1–4 in 2D (n = 4, mean ± SD shown). Download figure Download PowerPoint Based on the DeathPro results, we compared all drug effects determined in OC patient cells between 2D culture and 3D culture (Fig 2C). In both screens, drugs induced more growth arrest than cell death (Fig 2D). Due to low drug-induced cell death, LD50 values could not be determined in 20–30% of all conditions (Fig EV4A). After 72-h drug treatment, cell death was slightly lower in organoids than that in 2D cultures (Figs 2C and EV4B). Surprisingly, death upon drug treatment strongly correlated in 2D and 3D culture whereas drug-induced growth arrest varied greatly with culture type (Fig 2D, Pearson correlation 0.85 vs. 0.475). Since drug-induced cell death was growth-dependent and organoids grew slowly compared to cells in 2D culture (Fig EV3C), we measured organoid responses a second time after drug removal in 3D (Fig 2E, Appendix Fig S3A). After washout, drug effects increased in most patient organoids (Appendix Fig S3A and B) as they either intensify with time or continue to be induced by residual compounds in Matrigel. Still, cytotoxicity levels resembled those in 2D culture (Fig 2F, Pearson correlation 0.755). Likewise, LD50s measured in 3D culture before and after drug removal highly correlated with LD50s in 2D culture (Fig EV4C and D, Pearson correlation 0.872, 0.822). In contrast, growth inhibition again differed after drug removal (Fig 2F, Pearson correlation 0.525). Overall, growth arrest was the major drug effect in OC cells and was culture type-dependent, whereas cell death was similar between culture types. Click here to expand this figure. Figure EV4. Drug sensitivity described by LD50 is similar in cells cultured in 2D or as organoids A. LD50 values determined after 72-h drug exposure, or 72-h drug removal in OC
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