Analysis Of Drug Responses Research Articles

EGFR activity addiction facilitates anti-ERBB based combination treatment of squamous bladder cancer

Recent findings suggested a benefit of anti-EGFR therapy for basal-like muscle-invasive bladder cancer (MIBC). However, the impact on bladder cancer with substantial squamous differentiation (Sq-BLCA) and especially pure squamous cell carcinoma (SCC) remains unknown. Therefore, we comprehensively characterized pure and mixed Sq-BLCA (n = 125) on genetic and protein expression level, and performed functional pathway and drug-response analyses with cell line models and isolated primary SCC (p-SCC) cells of the human urinary bladder. We identified abundant EGFR expression in 95% of Sq-BLCA without evidence for activating EGFR mutations. Both SCaBER and p-SCC cells were sensitive to EGFR tyrosine kinase inhibitors (TKIs: erlotinib and gefitinib). Combined treatment with anti-EGFR TKIs and varying chemotherapeutics led to a concentration-dependent synergism in SCC cells according to the Chou-Talalay method. In addition, the siRNA knockdown of EGFR impaired SCaBER viability suggesting a putative “Achilles heel” of Sq-BLCA. The observed effects seem Sq-BLCA-specific since non-basal urothelial cancer cells were characterized by poor TKI sensitivity associated with a short-term feedback response potentially attenuating anti-tumor activity. Hence, our findings give further insights into a crucial, Sq-BLCA-specific role of the ERBB signaling pathway proposing improved effectiveness of anti-EGFR based regimens in combination with chemotherapeutics in squamous bladder cancers with wild-type EGFR-overexpression.

3D imaging of colorectal cancer organoids identifies responses to Tankyrase inhibitors.

Aberrant activation of the Wnt signalling pathway is required for tumour initiation and survival in the majority of colorectal cancers. The development of inhibitors of Wnt signalling has been the focus of multiple drug discovery programs targeting colorectal cancer and other malignancies associated with aberrant pathway activation. However, progression of new clinical entities targeting the Wnt pathway has been slow. One challenge lies with the limited predictive power of 2D cancer cell lines because they fail to fully recapitulate intratumoural phenotypic heterogeneity. In particular, the relationship between 2D cancer cell biology and cancer stem cell function is poorly understood. By contrast, 3D tumour organoids provide a platform in which complex cell-cell interactions can be studied. However, complex 3D models provide a challenging platform for the quantitative analysis of drug responses of therapies that have differential effects on tumour cell subpopulations. Here, we generated tumour organoids from colorectal cancer patients and tested their responses to inhibitors of Tankyrase (TNKSi) which are known to modulate Wnt signalling. Using compounds with 3 orders of magnitude difference in cellular mechanistic potency together with image-based assays, we demonstrate that morphometric analyses can capture subtle alterations in organoid responses to Wnt inhibitors that are consistent with activity against a cancer stem cell subpopulation. Overall our study highlights the value of phenotypic readouts as a quantitative method to asses drug-induced effects in a relevant preclinical model.

Establishment and characterization of an in vitro 3D ovarian cancer model for drug screening assays.

The acquired drug chemoresistance represents the main challenge of the ovarian cancer treatment. In addition, the absence of an adequate in vitro model able to reproduce the native tumor environment can contribute to the poor success rate of pre-clinical studies of new compounds. Three-dimensional (3D) culture models have been recently used for drug screening purposes due to their ability to reproduce the main characteristics of in vivo solid tumors. Here we describe the establishment and characterization of 3D ovarian cancer spheroids using different adenocarcinoma tumor cell lines (SKOV-3 and OVCAR-3 cells) in two different 3D scaffold-free methods: forced-floating in ultra-low attachment (ULA) plates and hanging drop (HD). Spheroids were evaluated in both 3D cultures in order to establish the best condition to perform the drug response analysis with Paclitaxel, a common drug used to treat ovarian cancer. SKOV-3 and OVCAR-3 spheroids with the desired characteristics (roundness close to 1.0 and diameter in the 200-500 μm range) were obtained using both methods after addition of the methylcellulose (MC) in the culture medium (0.25% and 0.5%, w/v). We also observed the presence of microvilli on the surface of the spheroids, higher presence of apoptotic cells and higher drug resistance, when compared with 2D cultures. The 3D cultures obtained seem to provide more reliable results in terms of drug response than those provided by 2D monolayer culture. The forced floating method was considered more suitable and straightforward to generate ovarian cancer spheroids for drug screening/cytotoxicity assays.

Proteome activity landscapes of tumor cell lines determine drug responses

Integrated analysis of genomes, transcriptomes, proteomes and drug responses of cancer cell lines (CCLs) is an emerging approach to uncover molecular mechanisms of drug action. We extend this paradigm to measuring proteome activity landscapes by acquiring and integrating quantitative data for 10,000 proteins and 55,000 phosphorylation sites (p-sites) from 125 CCLs. These data are used to contextualize proteins and p-sites and predict drug sensitivity. For example, we find that Progesterone Receptor (PGR) phosphorylation is associated with sensitivity to drugs modulating estrogen signaling such as Raloxifene. We also demonstrate that Adenylate kinase isoenzyme 1 (AK1) inactivates antimetabolites like Cytarabine. Consequently, high AK1 levels correlate with poor survival of Cytarabine-treated acute myeloid leukemia patients, qualifying AK1 as a patient stratification marker and possibly as a drug target. We provide an interactive web application termed ATLANTiC (http://atlantic.proteomics.wzw.tum.de), which enables the community to explore the thousands of novel functional associations generated by this work.

Identification of the collagen family as prognostic biomarkers and immune-associated targets in gastric cancer

BackgroundGastric cancer has extremely high morbidity and mortality. Currently, it is lack of effective biomarkers and therapeutic targets for guiding clinical treatment. In this study, we aimed to identify novel biomarkers and therapeutic targets for gastric cancer. MethodsDifferentially expressed genes (DEGs) between gastric cancer and normal tissues were obtained from Gene Expression Omnibus (GEO). Core genes were identified by constructing protein-protein interaction network of DEGs. The expression of core genes was verified in Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN and clinical samples. Further, the mutation, DNA methylation, prognostic value, and immune infiltration of core genes were validated by cBioPortal, MethSurv, Kaplan-Meier plotter, and Tumor Immune Estimation Resource (TIMER) databases. Additionally, drug response analysis was performed by Cancer Therapy Response Portal (CTRP). ResultsA total of seven collagen family members were identified as core genes among upregulated genes. And copy number amplification may be involved in the upregulation of COL1A1 and COL1A2. Importantly, the collagen family was associated with the poor prognosis of patients with metastasis. Among them, COL1A1 had a higher hazard ratio (HR) for overall survival than other members (HR = 2.33). The correlation between DNA methylation levels at CpG sites of collagen family members and the prognosis was verified in gastric cancer. Besides, collagen family expression was positively correlated with macrophages infiltration and the expression of M2 macrophages markers. Further, collagen expression was related to the sensitivity and resistance of gastric cancer cell lines to certain drugs. ConclusionsThe collagen family, especially COL1A1, COL1A2, and COL12A1, may act as potential prognostic biomarkers and immune-associated therapeutic targets in gastric cancer.

Drug response analysis for scaffold-free cardiac constructs fabricated using bio-3D printer

Cardiac constructs fabricated using human induced pluripotent stem cells-derived cardiomyocytes (iPSCs-CMs) are useful for evaluating the cardiotoxicity of and cardiac response to new drugs. Previously, we fabricated scaffold-free three-dimensional (3D) tubular cardiac constructs using a bio-3D printer, which can load cardiac spheroids onto a needle array. In this study, we developed a method to measure the contractile force and to evaluate the drug response in cardiac constructs. Specifically, we measured the movement of the needle tip upon contraction of the cardiac constructs on the needle array. The contractile force and beating rate of the cardiac constructs were evaluated by analysing changes in the movement of the needle tip. To evaluate the drug response, contractile properties were measured following treatment with isoproterenol, propranolol, or blebbistatin, in which the movement of the needle tip was increased following isoproterenol treatment, but was decreased following propranolol or blebbistain, treatments. To evaluate cardiotoxicity, contraction and cell viability of the cardiac constructs were measured following doxorubicin treatment. Cell viability was found to decrease with decreasing movement of the needle tip following doxorubicin treatment. Collectively, our results show that this method can aid in evaluating the contractile force of cardiac constructs.

PD47-11 RAPID ORGANOID DEVELOPMENT, DRUG SCREENING, AND NEOADJUVANT CHEMOTHERAPY RESPONSE PREDICTION FOR PATIENTS WITH LOCALLY-ADVANCED BLADDER CANCER

You have accessJournal of UrologyBladder Cancer: Basic Research & Pathophysiology IV (PD47)1 Apr 2020PD47-11 RAPID ORGANOID DEVELOPMENT, DRUG SCREENING, AND NEOADJUVANT CHEMOTHERAPY RESPONSE PREDICTION FOR PATIENTS WITH LOCALLY-ADVANCED BLADDER CANCER Kathryn A Marchetti*, Nathan Merrill, Nathalie Vandecan, Liwei Bao, Xu Cheng, Aaron Udager, Lindsey A Herrel, Jeffery Montgomery, Khaled Hafez, Todd Morgan, Alon Weizer, Ajjai Alva, Matthew Soellner, Sofia Merajver, and Samuel Kaffenberger Kathryn A Marchetti*Kathryn A Marchetti* More articles by this author , Nathan MerrillNathan Merrill More articles by this author , Nathalie VandecanNathalie Vandecan More articles by this author , Liwei BaoLiwei Bao More articles by this author , Xu ChengXu Cheng More articles by this author , Aaron UdagerAaron Udager More articles by this author , Lindsey A HerrelLindsey A Herrel More articles by this author , Jeffery MontgomeryJeffery Montgomery More articles by this author , Khaled HafezKhaled Hafez More articles by this author , Todd MorganTodd Morgan More articles by this author , Alon WeizerAlon Weizer More articles by this author , Ajjai AlvaAjjai Alva More articles by this author , Matthew SoellnerMatthew Soellner More articles by this author , Sofia MerajverSofia Merajver More articles by this author , and Samuel KaffenbergerSamuel Kaffenberger More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000934.011AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Neoadjuvant chemotherapy prior to radical cystectomy (RC) for muscle-invasive urothelial carcinoma is the standard of care despite a small absolute survival benefit and the potential for disease progression during chemotherapy. While progress has been made in the prediction of sensitivity to platinum-based chemotherapies, providing more accurate, personalized, and clinically-relevant chemotherapy response prediction is an unmet need. We present our early and ongoing experience with rapid, organoid-based drug-screening. METHODS: From patients undergoing a transurethral resection of bladder tumor or RC, 1 gram of tumor was procured and divided between DNA/RNA sequencing, organoid drug-screening, and patient-derived xenografts. Tissue was dissociated, filtered, and resuspended in organoid media for serial passage and drug screening. Drugs were tested at the maximum plasma concentration in human trials, so to provide physiologic relevance. Results were normalized to control such that a value of “100” indicated no difference in organoid viability compared to control, and a value of 0 indicated complete response. The number of drugs screened was dependent upon tissue available, but was often 20-30. RESULTS: Thus far, 25 patients have undergone rapid organoid development and drug testing. Drug response analyses were available on average 5-10 days following the procedure. Secondary to limited space, select drug response data from the 20 most recent patient organoid (org) samples are presented (Figure 1). All patients have DNA and RNA sequencing and PDX models in progress. CONCLUSIONS: Rapid organoid development and drug screening allows for the determination of neoadjuvant chemotherapy response within 5-10days. Use of this technique on tissue provided during disease work-up may further guide selection of effective therapeutic agents in patients with bladder cancer. This would overall minimize the morbidity of standard of care therapies. Source of Funding: none © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e929-e929 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Kathryn A Marchetti* More articles by this author Nathan Merrill More articles by this author Nathalie Vandecan More articles by this author Liwei Bao More articles by this author Xu Cheng More articles by this author Aaron Udager More articles by this author Lindsey A Herrel More articles by this author Jeffery Montgomery More articles by this author Khaled Hafez More articles by this author Todd Morgan More articles by this author Alon Weizer More articles by this author Ajjai Alva More articles by this author Matthew Soellner More articles by this author Sofia Merajver More articles by this author Samuel Kaffenberger More articles by this author Expand All Advertisement PDF downloadLoading ...

Measurement and models accounting for cell death capture hidden variation in compound response

Cancer cell sensitivity or resistance is almost universally quantified through a direct or surrogate measure of cell number. However, compound responses can occur through many distinct phenotypic outcomes, including changes in cell growth, apoptosis, and non-apoptotic cell death. These outcomes have divergent effects on the tumor microenvironment, immune response, and resistance mechanisms. Here, we show that quantifying cell viability alone is insufficient to distinguish between these compound responses. Using an alternative assay and drug-response analysis amenable to high-throughput measurement, we find that compounds with identical viability outcomes can have very different effects on cell growth and death. Moreover, additive compound pairs with distinct growth/death effects can appear synergistic when only assessed by viability. Overall, these results demonstrate an approach to incorporating measurements of cell death when characterizing a pharmacologic response.

Rapid organoid development, drug screening, and neoadjuvant chemotherapy response prediction for patients with locally advanced bladder cancer.

543 Background: Neoadjuvant chemotherapy prior to radical cystectomy (RC) for muscle-invasive urothelial carcinoma (UCCx) is the standard of care though the absolute survival benefit is small, and some patients progress during chemotherapy. While progress has been made in the prediction of sensitivity to platinum-based chemotherapies, providing more accurate, personalized, and clinically-relevant chemotherapy response prediction is an unmet need. We present our early and ongoing experience with rapid, organoid-based drug-screening. Methods: one gram of tumor was procured from patients undergoing TURBT or RC and divided between DNA/RNA sequencing, organoid drug-screening, and patient-derived xenografts. Tissue was dissociated, filtered, and resuspended in organoid media for serial passage and drug screening. Drugs were tested at “Cmax” concentration, which is the maximum plasma concentration in human trials so as to provide physiologic relevance. Results were normalized to control such that a value of 100 indicated no drug effect compared to control, and a value of 0 indicated complete response. Number of drugs screened was dependent upon tissue available, but was often 20-30. Results: 25 patients have undergone rapid organoid development and drug testing to date. Clinical correlations with chemotherapy response are ongoing. Drug response analyses were available 5-10 days following procedure. Select drug response data from the 9 most recent patient organoid samples are presented in the Table. DNA and RNA sequencing and PDX models are in progress. Conclusions: This platform allows for the rapid determination of neoadjuvant chemotherapy response and may further guide selection of therapeutic agents in patients with locally advanced bladder cancer.[Table: see text]

Patient-derived organoids for personalized drug screening in intrahepatic cholangiocarcinoma.

581 Background: Despite standard treatment with gemcitabine and cisplatin, median survival for unresectable Intrahepatic Cholangiocarcinoma (ICC) is < 1 year. Clearly, novel therapeutic strategies are urgently needed. The paucity of targetable mutations in ICC and the as yet unproven benefit of genetically targeted drugs led us to ask whether a reliable clinical benefit may be revealed by patient-specific therapeutic testing in novel models of ICC. Here we describe our ability to establish patient-derived three-dimensional organoid cultures (PDO) that enable individualized identification of active single agents or drug combinations in surrogate models of ICC. Methods: To model patient-specific drug responses, we used the freshly resected ICCs from small samples of single patient tumors to generate PDXs and PDOs, small spheroidal clusters of tumor cells grown in vitro. We have employed a high-throughput drug screening platform using AI-enhanced robotics (Yamaha Motor Corporation) to identify and distribute single, uniformly sized PDOs into 384-well ultra-low adherent plates. This is coupled with a TECAN D300e drug dispenser that rapidly delivers nanoliter volumes of a 34-drug panel, thereby facilitating rapid, reliable drug response analyses. Results: Our data show that PDOs retain characteristic genomic and histological features of the patients’ tumors. Drug responses were specific to each patient tumor, but PDOs from all patients responded to a greater or lesser degree to mTOR inhibition, suggesting that this pathway is important in ICC. The responses of PDO to the mTOR inhibitor Sapanisertib (INK128), was recapitulated in the same patient’s PDX. Further, INK128 was synergistic with gemcitabine in patient 970 PDOs as well as in vivo in PDX also from patient 970. Conclusions: As it is believed that PDX can predict patient responses to drugs, our results suggest that PDO may also predict patient drug responses. The establishment of PDO may allow economical patient-specific, high throughput drug screens that could ultimately inform clinical practice. [Table: see text]

Identifying Drug Sensitivity Subnetworks with NETPHIX

Phenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. One of the important challenges in the area is to predict drug response on a personalized level and to understand the causes of different responses. The pathway-centric view of cancer significantly advanced the understanding of genotype-phenotype relationships. However, most network module identification methods in cancer focus on identifying subnetworks that include general cancer drivers, hence cannot be applied directly for the analysis of continuous features like drug response. On the other hand, existing genome wide association approaches or drug response prediction algorithms do not fully utilize the complex properties of cancer mutational landscape and cannot pinpoint the genetic alterations that are causal to drug sensitivity. To address these challenges, we developed a computational method, named NETPHIX (NETwork-to-PHenotype assocIation with eXclusivity), which aims to identify subnetworks of genes whose genetic alterations are associated with a continuous cancer phenotype. Leveraging the properties of cancer mutations and the interactions among genes, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction in formation among genes, NETPHIX modules are functionally coherent, and can thus provide important insights into drug action.

Functional Precision Medicine in AML: Technical Performance Evaluation for in Vitro Diagnostics Using High-Throughput Image-Based Screening of Primary Patient Cells

Introduction: Identification of the right drug, for the right patient, at the right time is the defining goal for precision and personalized medicine programs. Many tools are currently being pursued for this including next-gen sequencing and other -omics approaches to determine target availability (Moscow et al, 2018 Nat Rev Clin Oncol.), functional drug response profiling in viable patient samples (Snijder et al, 2017 Lancet Haem), or a combination of both (Schmidl et al, 2019 Nat Chem Bio). Of those, functional precision medicine approaches are only just beginning to gain clinical interest, and because of this, nearly all programs lack systematic testing in prospective clinical studies (Letai, 2017 Nat Rev Med). One of the first functional precision medicine approaches to be tested in a small prospective clinical study was the use of high-throughput image-based screening and single cell analysis of drug response in primary tissues ("Pharmacoscopy") as a tool to prospectively rank drug options for patients with late-stage hematological indications (Snijder et al 2017, Lancet Haem, NCT03096821). In this study, taking Pharmacoscopy data into account for treatment selection led to a higher overall response rate (88% vs. 24%) and longer progression free survival (22.6 vs. 5.7 weeks) compared to the previous line of therapy. In order for functional precision medicine programs to reach widespread routine use, ultimately the development of fully-certified in vitro diagnostic test products will be required. This requires amongst others a systematic understanding of i) the technical robustness of functional drug testing approaches and ii) a detailed understanding of how pre-analytical sample handling influences assay results. Here, we set out to investigate two crucial questions that arose in the course of systematic assay development for AML: a) do CD34+ cells derived from peripheral blood and bone marrow AML patients respond differently to short term ex vivo treatment with small molecule drugs and b) does the overall response of these cells change upon cryopreservation? These issues are of high relevance even outside the setting of functional precision medicine development programs given worldwide biobanking efforts, and the lack of robust systematic model comparison. Methods: Fresh patient blood and bone marrow samples of newly diagnosed as well as relapsed and refractory patients with AML were collected at Medical University of Graz under appropriate ethics approval. Samples were divided into two parts and one part used immediately, the other cryopreserved as viable cells. The response of CD34+ cells against 140 different small molecule drugs (two concentrations and three technical replicates) was evaluated using single-cell high content microscopy (Allcyte's "Pharmacoscopy" platform) in both peripheral blood and bone marrow as well as freshly used and previously viably frozen samples. Drug response was determined by fitting to generalised linear and mixed models. Further, RNA is isolated from additional patient samples before and after biobanking for Nanostring analysis. Results: Overall, across all tested patient samples, we observed a high correlation between drug response tested in blood and bone marrow samples from this group of patient samples, indicating, at least for AML, no strong niche dependence for the drugs tested. Additionally, we also observed a high correlation between drug response tested in fresh and frozen tissues (Figure 1). The differences that did appear were, in particular, in drugs targeting metabolic and cell-growth dependent functions, amongst others. Conclusions: Understanding how biobanking affects the ex vivo drug response of primary human tissues is paramount to being able to understand how far translational efforts can go using tissues and functional drug response assays. We provide systematic data supporting the use of cryopreserved samples for the functional analysis of drug response in AML. This is a fundamental question associated with the use of biobanks for investigations of primary patient material. Figure 1 Disclosures Krall: Allcyte: Employment, Equity Ownership. Meszaros:Allcyte: Employment. Vilagos:Allcyte: Employment. Sill:Astellas: Other: Advisory board; Novartis: Other: Advisory board; Astex: Other: Advisory board; AbbVie: Other: Advisory board. Vladimer:Allcyte: Employment, Equity Ownership, Patents & Royalties: EP3198276A1.

Associations between Microrna Expression, Disease Progression and Ex Vivo Drug Response in Multiple Myeloma

Targeted drug treatment strategies have significantly prolonged the overall survival rate among multiple myeloma (MM) patients. However, high relapse rates and multiple drug resistance still pose major challenges. Although, the underlying molecular features of the disease have been explored both at the genomic and transcriptomic levels, the functional role of microRNAs (miRNA) in MM disease progression and prognosis is yet to be investigated at a personalized level. In earlier studies, microRNAs have been implicated to regulate gene expression and were determined to play crucial roles in the biology of MM by acting as oncogenes or tumor suppressors. Nevertheless, considering the heterogeneity of MM, little is known about the roles of miRNAs in controlling MM disease progression and drug response at an individualized systems level. We collected bone marrow aspirates from MM patients at diagnosis (n=20) and relapse (n=25) after informed consent and following approved protocols in accordance with the Declaration of Helsinki. CD138+ plasma cells were enriched from the bone marrow samples and used for miRNA-sequencing and drug sensitivity and resistance testing (DSRT). The miRNA was prepared from the CD138+ cells and subjected to sequencing using Illumina compatible technologies. DSRT was performed and responses to 83 clinically approved drugs and investigational compounds were measured as drug sensitivity scores (DSS) as described previously (Majumder et al., Oncotarget 2017). The pairwise comparative analysis of miRNA expression and drug responses was performed using Spearman's rank-order correlations, to elucidate significant associations of miRNA expression with drug sensitivity and resistance. Additionally, using DEseq2 the differential miRNA expression was determined for the newly diagnosed and relapse samples to deconvolute the role of miRNAs in MM disease progression. The comparative analysis of the miRNA expression and drug sensitivity scores revealed statistically significant associations between miRNA expression and drug sensitivity measures with the Spearman coefficient (r) ranging from -0.71 to 0.64 (adjusted p-value ≤ 0.05) (Figure 1A). Negative associations were more prevalent, with 40 miRNAs negatively associated with ≥1 drug response from the total of 30 predicted drugs. miR-486, which is known to be an effective biomarker in diagnosis and prognosis of multiple cancer types (Jiang et al., Oncotarget 2018), was found to have significant negative correlation (r= -0.71 to -0.52, p-value ≤ 0.01) with the responses of 14 drugs. Similarly, negative correlation was observed for miR-144 with 12 drugs and miR-584 with 9 drugs. We observed that PI3K/mTOR inhibitors and HDAC inhibitors were common amongst all the significant negative correlations predicted. Specifically, the PI3K/mTOR inhibitors apitosilib, omipalisib and buparlisib were found to be negatively associated with the expression of 18, 14 and 7 miRNAs respectively. These observations can lead to the understanding of miRNA mediated regulation of molecular pathways involved in drug resistance. Differential miRNA expression analysis between newly diagnosed and relapse MM samples revealed the involvement of miRNAs in disease progression. The analysis resulted in total of 31 significant differentially expressed miRNAs with fold change ≥2 and adjusted p-value ≤ 0.1 (Figure 1B). Several miRNAs known to play crucial roles in cancer diagnosis and prognosis were found to be significantly upregulated in the relapse samples. In particular, 25 miRNAs were upregulated, including following miR-17/92 cluster members: miR-18b, miR-20a, miR-92b and miR-106a, which are known to have an oncogenic role in various cancer types (Mogilyansky & Rigoutsos, Cell Death and Differentiation 2013). Interestingly, 12/31 differentially regulated miRNAs were located on chromosome X. Although cytogenetic alteration data predicted that chromosome 1q gain is significantly prominent in the relapse samples (p-value = 0.009), only 3/31 differentially regulated miRNAs were located on chromosome 1. These results demonstrate the role of miRNAs in regulating drug response and disease progression in multiple myeloma. Monitoring miRNA expression profiles in MM patients can facilitate the assessment of treatment outcome and prognosis, and miRNAs could potentially be useful prognostic and treatment biomarkers for MM. Disclosures Silvennoinen: Amgen: Research Funding; Bristol-Myers Squibb (BMS): Research Funding; Takeda: Research Funding; Celgene: Research Funding. Heckman:Celgene: Research Funding; Novartis: Research Funding; Oncopeptides: Research Funding; Orion Pharma: Research Funding.

389P - The establishment of patient-derived organoid models and drug response of resectable non-small cell lung cancer

BackgroundPatient-derived cancer organoid (PDOs) models have proven with powerful research value and significant clinical application prospects. However, we still know little about organoid models of non-small cell lung cancer (NSCLC). This study aims to characterize the consistency of genomic variations between the primary tumours and PDOs, and to explore utility of PDOs as preclinical models to predict the treatment response for the precision medicine. MethodsTumour samples were collected for organoid culture. Primary tumour and PDO samples were analysed by whole exon sequencing (WES). C-MET overexpression of tissue was test by immunohistochemistry. Antineoplastic drugs were tested by the PDOs. Cell viability was measured by Cell Titer Glo assay 7-10 days after drug treatment. Heatmap of log IC50 values were calculated from drug response analyses of PDOs by applying nonlinear regression (curve fit). ResultsA total of 7 patients (pts) (I-III stage) were enrolled. 7 paired surgical tumour and PDOs were analysed, respectively. Comparison of gene mutations of top 20 ranked genes related with lung cancer, the concordance were over 80% between tumour and PDOs in 5 pts. The concordances of the other 2 pts were less than 50%. Both tissues and PDOs harbored driver mutations in 4 pts (2 EGFR L858R, 1 EGFR EX20 ins and 1 KRAS G12C ). Drug screen was carried out by using 26 antineoplastic drugs in the 7 PDOs in vitro. Of the 2 PDOs with EGFR L858R, one displayed the most significant response to Gefitinib, the other showed resistance to Gefitinib but significant response to Osimertinib, whose matched tissue showed c-MET overexpression indicating a mechanism of resistant to Gefitinib. The PDO with EGFR EX20 ins also indicated resistance to Gefitinib but significant response to Osimertinib in accordance with public articles. ConclusionsPatient-derived lung cancer organoids could provide us a practical model system for studying NSCLC and predict treatment response for personal precision medicine. Legal entity responsible for the studyThe authors. FundingHas not received any funding. DisclosureAll authors have declared no conflicts of interest.

Pheno-seq \u2013 linking visual features and gene expression in 3D cell culture systems

Patient-derived 3D cell culture systems are currently advancing cancer research since they potentiate the molecular analysis of tissue-like properties and drug response under well-defined conditions. However, our understanding of the relationship between the heterogeneity of morphological phenotypes and the underlying transcriptome is still limited. To address this issue, we here introduce “pheno-seq” to directly link visual features of 3D cell culture systems with profiling their transcriptome. As prototypic applications breast and colorectal cancer (CRC) spheroids were analyzed by pheno-seq. We identified characteristic gene expression signatures of epithelial-to-mesenchymal transition that are associated with invasive growth behavior of clonal breast cancer spheroids. Furthermore, we linked long-term proliferative capacity in a patient-derived model of CRC to a lowly abundant PROX1-positive cancer stem cell subtype. We anticipate that the ability to integrate transcriptome analysis and morphological patho-phenotypes of cancer cells will provide novel insight on the molecular origins of intratumor heterogeneity.

Abstract 2490: R2: Genomics analysis and visualization platform

Abstract Given the complexity, omics data like RNA-seq are often analyzed by bioinformaticians, and not the wetlab researchers that performed the experiments. With the biomedical researcher (with limited or no bioinformatics skills) in mind as the end-user, we developed the user-friendly, publicly accessible R2 platform (http://r2.amc.nl), enabling biomedical scientists to work with their own data from anywhere at any time. R2 (cited in >850 peer-reviewed scientific publications) consists of an omics database, coupled to an extensive set of interactive tools to analyze/visualize the datasets. Interactive analyses within the software are highly connected, allowing quick navigation between various aspects of the datamining process. Besides password protected private data, R2 also contains an extensive body of publicly available omics data, enabling users to relate their findings to other diseases / tissues, or as validation in integrative analyses. Next to gene expression (bulk as well as single cell), R2 is also being employed for the integration, analysis and visualization of copynumber, SNP, methylation, miRNA, drug response, ChIP-seq, and NGS exome/WGS DNA sequencing data. Analyses include correlation, differential expression, gene sets, gene ontology, transcription factor binding sites, tSNE, PCA, k-means, Kaplan Meier survival scans, signature creation etc. Visualizations include various gene oriented plots, heatmaps, Circos plots, embedded genome browser, Venn diagrams, etc. The real power of the platform lies in the chaining of results of analyses; e.g. the results of a differential expression analysis can be further trimmed down using a Kaplan Meier analysis that in turn can be used for a Gene Ontology over-representation analysis. Furthermore, the webserver allows for overviews across different datasets (such as MegaSampler and 2D distribution), where a user can harness the power of thousands of measurements. Recently, integrated analyses for personalized medicine, where copy number, mRNA expression, methylation and mutation data are combined into a comprehensive overview, occasionally supplemented with PDX/organoid drug response data. These serve clinical decision making for a patient, but as growing cohorts also form a treasure trove for scientific discovery. R2 provides a central starting point from where data mining and analysis paths can be followed within one environment. Data/analyses can be shared between R2-users via our community options, making R2 an outstanding environment for discovery, hypothesis testing and scientific collaboration. Citation Format: Jan Koster, Richard Volckmann, Danny Zwijnenburg, Piet Molenaar, Rogier Versteeg. R2: Genomics analysis and visualization platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2490.

Abstract 2689: High-content imaging and single-cell analysis of drug response ex vivo is predictive of clinical outcome for hematologic cancer patients

Abstract The ability to predict clinical drug response is a fundamental goal of precision cancer medicine. Genetics has performed well in identifying the presence of targets and stratifying patients into clinical trials. However, particularly for late-stage and relapsed cancer patients, many genetic driven trials have yet to report improvements in clinical response. In parallel, directly measuring cytotoxicity of drugs ex vivo in patient samples with functional technologies has been gaining traction during the past five years, with some programs showing translational success. In our approach, we use high throughput and high content microscopy to quantify single-cell phenotypic biomarkers of cancer cells, and healthy cells, that are indicative of cell death in order to determine a differential drug response. Cancer cells and healthy cells are distinguished using fluorescently-labeled extracellular-marker targeted diagnostic antibodies, and no long term ex vivo culture is necessary as the assay lasts only 24 hours. The cytotoxicity of cancer cells under drug treatment can be scored and compared to any cell death occurring in healthy cells within the same patient sample. As all is assessed by imaging of each individual cell within the same sample and set-up, the healthy cells of the patients can be used as internal controls, statistical analysis covers millions of events and images can be re-mined and re-analysed in future. In a prospective basket trial testing 140 drugs in triplicate at two concentrations (768 tests, yielding 15.000 images per patient), we ranked drugs prospectively for late-stage patients diagnosed with hematological cancers. In this interim analysis, 15 (88%) of 17 patients receiving guided treatment had an overall response compared with four (24%) of 17 patients with their most recent regimen. 12 (71%) of 17 patients had a progression-free survival ratio of 1·3 or higher, and median progression-free survival increased by four times, from 5.7 weeks to 22.6 weeks. While continued follow-up of the study is warranted, this clinical use case foreshadows the success of functional drug screening using single-cell imaging, and further, big data medicine. A unique byproduct of this big data medicine approach, is large pan-indication off-labeling drug-to-patient maps. These maps describe both on- and off-target drug responses, but also indication areas that lack ex vivo drug response - potentially enabling clinical re-use of drugs, and elucidating mechanism of action of drugs where it is unknown. The functional screening data over many patients, indications, and drugs, combined with clinical response data, can eventually be used for larger predictive studies. Citation Format: Gregory I. Vladimer, Berendd Snijder, Nikolaus Krall, Christoph Kornauth, Stefan Kubicek, Ulrich Jäger, Philipp B. Staber, Giulio Superti-Furga. High-content imaging and single-cell analysis of drug response ex vivo is predictive of clinical outcome for hematologic cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2689.

Abstract 4732: CDK4/6 inhibition with lerociclib (G1T38) enhances response to PI3K or ERK inhibitors in high-throughput, ex vivo pancreatic PDX screens

Abstract Introduction: In pancreatic ductal adenocarcinoma (PDAC), mutations in KRAS and deletion or promoter methylation of the CDKN2A gene - resulting in deregulation of the p16/CDK4/CDK6/RB axis - each occur in over 90% of cases. Therefore, combination therapy approaches targeting KRAS effectors and CDK4/6 may be a promising treatment strategy in a majority of PDAC patients. Lerociclib (G1T38) is an oral and selective CDK4/6 inhibitor in clinical development as a potential backbone therapy for multiple combination regimens in cancer. Here, we used a powerful and reliable ex vivo drug screening platform, the live tissue sensitivity assay (LTSA), to evaluate the ability of lerociclib to synergize with inhibitors of KRAS effectors across a panel of 24 well-characterized PDAC patient-derived xenografts (PDXs). Methods: PDX tumors were sectioned into uniform tissue slices at 200 µm thickness and arrayed in 96-well plates. For each PDX model, tumor tissue slices were treated with 0.3 µM, 1 µM, and 3 µM of lerociclib, alone and in combination with identical concentrations of a PI3K (pictilisib), mTOR (AZD2014), MEK (trametinib), ERK (ulixertinib), BRAF (vemurafenib) or EGFR (erlotinib) inhibitor for 72 hours. The viabilities of individual tissue slices were measured with PrestoBlue® reagent. A quantitative drug response analysis approach was implemented in which “responder” PDXs were defined as those exhibiting a statistically significant reduction in area under the dose-response curve (AUC) for the combination treatment compared to the most efficacious single agent. Statistical significance was defined as p < 0.05 using Student’s t-test. Results: Treatment of 24 PDAC PDXs using the LTSA revealed a range of sensitivities to each inhibitor as a single agent. When KRAS effectors and CDK4/6 were inhibited in tandem, statistically significant (p < 0.05) enhancements of efficacy were observed when lerociclib was added to pictilisib (21% of PDX models tested), ulixertinib (15%), vemurafenib (10%), erlotinib (5%), trametinib (4%), or AZD2014 (4%). These ex vivo data narrowed the focus of follow-up in vivo work; studies of PDX models in mice treated with lerociclib +/- pictilisib or ulixertinib are ongoing to validate these results. Moreover, differential gene expression and mutational analysis for responders versus non-responders identified through the PDAC PDX LTSA platform are underway, to elucidate molecular factors that predict tumor response or enrich for populations that are sensitive to CDK4/6 and PI3K/ERK inhibitors. Conclusion: The LTSA platform was used to evaluate drug combinations in viable, PDX tumor tissue. We found that lerociclib significantly enhances the response to PI3K or ERK inhibition in a substantial proportion of the tested PDAC PDXs as measured by AUC analysis in the LTSA platform, supporting future clinical evaluation of these combinations. Citation Format: Bingbing Dai, Daniel M. Freed, Jessica A. Sorrentino, Jithesh Jose Augustine, Tara G. Hughes, Ya'an Kang, Patrick J. Roberts, Jason B. Fleming, Michael P. Kim. CDK4/6 inhibition with lerociclib (G1T38) enhances response to PI3K or ERK inhibitors in high-throughput, ex vivo pancreatic PDX screens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4732.

Multiplex quantitative analysis of stroma-mediated cancer cell invasion, matrix remodeling, and drug response in a 3D co-culture model of pancreatic tumor spheroids and stellate cells

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a stroma-rich carcinoma, and pancreatic stellate cells (PSCs) are a major component of this dense stroma. PSCs play significant roles in metastatic progression and chemoresistance through cross-talk with cancer cells. Preclinical in vitro tumor model of invasive phenotype should incorporate three-dimensional (3D) culture of cancer cells and PSCs in extracellular matrix (ECM) for clinical relevance and predictability.MethodsPANC-1 cells were cultured as tumor spheroids (TSs) using our previously developed minipillar chips, and co-cultured with PSCs, both embedded in collagen gels. Effects of PSC co-culture on ECM fiber network, invasive migration of cancer cells, and expression of epithelial-mesenchymal transition (EMT)-related proteins were examined. Conditioned media was also analyzed for secreted factors involved in cancer cell-PSC interactions. Inhibitory effect on cancer cell invasion was compared between gemcitabine and paclitaxel at an equitoxic concentration in PANC-1 TSs co-cultured with PSCs.ResultsCo-culture condition was optimized for the growth of TSs, activation of PSCs, and their interaction. Increase in cancer cell invasion via ECM remodeling, invadopodia formation and EMT, as well as drug resistance was recapitulated in the TS-PSC co-culture, and appeared to be mediated by cancer cell-PSC interaction via multiple secreted factors, including IL-6, IL-8, IGF-1, EGF, TIMP-1, uPA, PAI-1, and TSP-1. Compared to gemcitabine, paclitaxel showed a greater anti-invasive activity, which was attributed to suppresion of invadopodia formation in cancer cells as well as to PSC-specific cytotoxicity abrogating its paracrine signaling.ConclusionsHere, we established 3D co-culture of TSs of PANC-1 cells and PSCs using minipillar histochips as a novel tumoroid model of PDAC. Our results indicate usefulness of the present co-culture model and multiplex quantitative analysis method not only in studying the role of PSCs and their interactions with tumor cell towards metastatic progression, but also in the drug evaluation of stroma-targeting drugs.

CFTR Expression Analysis for Subtyping of Human Pancreatic Cancer Organoids.

Background Organoid cultures of human pancreatic ductal adenocarcinoma (PDAC) have become a promising tool for tumor subtyping and individualized chemosensitivity testing. PDACs have recently been grouped into different molecular subtypes with clinical impact based on cytokeratin-81 (KRT81) and hepatocyte nuclear factor 1A (HNF1A). However, a suitable antibody for HNF1A is currently unavailable. The present study is aimed at establishing subtyping in PDAC organoids using an alternative marker. Methods A PDAC organoid biobank was generated from human primary tumor samples containing 22 lines. Immunofluorescence staining was established and done for 10 organoid lines for cystic fibrosis transmembrane conductance regulator (CFTR) and KRT81. Quantitative real-time PCR (qPCR) was performed for CFTR and HNF1A. A chemotherapeutic drug response analysis was done using gemcitabine, 5-FU, oxaliplatin, and irinotecan. Results A biobank of patient-derived PDAC organoids was established. The efficiency was 71% (22/31) with 68% for surgical resections and 83% for fine needle aspirations. Organoids could be categorized into the established quasimesenchymal, exocrine-like, and classical subtypes based on KRT81 and CFTR immunoreactivity. CFTR protein expression was confirmed on the transcript level. CFTR and HNF1A transcript expression levels positively correlated (n = 10; r = 0.927; p = 0.001). PDAC subtypes of the primary tumors and the corresponding organoid lines were identical for most of the cases analyzed (6/7). Treatment with chemotherapeutic drugs revealed tendencies but no significant differences regarding drug responses. Conclusions Human PDAC organoids can be classified into known subtypes based on KRT81 and CFTR immunoreactivity. CFTR and HNF1A mRNA levels correlated well. Furthermore, subtype-specific immunoreactivity matched well between PDAC organoids and the respective primary tumor tissue. Subtyping of human PDACs using CFTR might constitute an alternative to HNF1A and should be further investigated.