scholarly journals Drug Sensitivity Assays of Human Cancer Organoid Cultures

2016 ◽  
pp. 339-351 ◽  
Author(s):  
Hayley E. Francies ◽  
Andrew Barthorpe ◽  
Anne McLaren-Douglas ◽  
William J. Barendt ◽  
Mathew J. Garnett
Keyword(s):  
Drug Sensitivity ◽  
Human Cancer ◽  
Organoid Cultures

Drug Sensitivity Assays of Human Cancer Organoid Cultures v1

protocols.io ◽  
2020 ◽  
Author(s):  
Hayley E. Francies ◽  
Andrew Barthorpe ◽  
Anne McLaren-Douglas ◽  
William J. Barendt ◽  
Mathew J. Garnett
Keyword(s):  
Drug Sensitivity ◽  
Human Cancer ◽  
Organoid Cultures

Erratum to: Drug Sensitivity Assays of Human Cancer Organoid Cultures

2018 ◽  
pp. 353-353 ◽  
Author(s):  
Hayley E. Francies ◽  
Andrew Barthorpe ◽  
Anne McLaren-Douglas ◽  
William J. Barendt ◽  
Mathew J. Garnett
Keyword(s):  
Drug Sensitivity ◽  
Human Cancer ◽  
Organoid Cultures

scholarly journals Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Juno M. Krahn ◽  
Igor Shats ◽  
Xiaoling Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Cancer Cell ◽  
Predictive Models ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
The Cancer Genome Atlas ◽  
Differential Sensitivity ◽  
Tumor Type

Abstract Background Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made. Results In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC50) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug. Conclusions We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

scholarly journals Identification of candidate predictive markers of anticancer drug sensitivity using a panel of human cancer cell lines

2003 ◽  
Vol 94 (12) ◽  
pp. 1074-1082 ◽  
Author(s):  
Shingo Dan ◽  
Mieko Shirakawa ◽  
Yumiko Mukai ◽  
Yoko Yoshida ◽  
Kanami Yamazaki ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Anticancer Drug ◽  
Drug Sensitivity ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Predictive Markers ◽  
Human Cancer Cell ◽  
Human Cancer Cell Lines

scholarly journals Successful Patient-Derived Organoid Culture of Gynecologic Cancers for Disease Modeling and Drug Sensitivity Testing

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2901
Author(s):  
Jianling Bi ◽  
Andreea M. Newtson ◽  
Yuping Zhang ◽  
Eric J. Devor ◽  
Megan I. Samuelson ◽  
...  
Keyword(s):  
Drug Sensitivity ◽  
Disease Modeling ◽  
Treatment Options ◽  
Gynecologic Cancer ◽  
Experimental Models ◽  
Trastuzumab Resistance ◽  
Sensitivity Testing ◽  
Drug Sensitivity Testing ◽  
Organoid Cultures ◽  
Line Setting

Developing reliable experimental models that can predict clinical response before treating the patient is a high priority in gynecologic cancer research, especially in advanced or recurrent endometrial and ovarian cancers. Patient-derived organoids (PDOs) represent such an opportunity. Herein, we describe our successful creation of 43 tumor organoid cultures and nine adjacent normal tissue organoid cultures derived from patients with endometrial or ovarian cancer. From an initial set of 45 tumor tissues and seven ascites fluid samples harvested at surgery, 83% grew as organoids. Drug sensitivity testing and organoid cell viability assays were performed in 19 PDOs, a process that was accomplished within seven days of obtaining the initial surgical tumor sample. Sufficient numbers of cells were obtained to facilitate testing of the most commonly used agents for ovarian and endometrial cancer. The models reflected a range of sensitivity to platinum-containing chemotherapy as well as other relevant agents. One PDO from a patient treated prior to surgery with neoadjuvant trastuzumab successfully predicted the patient’s postoperative chemotherapy and trastuzumab resistance. In addition, the PDO drug sensitivity assay identified alternative treatment options that are currently used in the second-line setting. Our findings suggest that PDOs could be used as a preclinical platform for personalized cancer therapy for gynecologic cancer patients.

scholarly journals Two-step multi-omics modelling of drug sensitivity in cancer cell lines to identify driving mechanisms

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0238961
Author(s):  
Nina Kusch ◽  
Andreas Schuppert
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Prediction Models ◽  
Human Cancer ◽  
Cancer Cell Lines ◽  
Heterogeneous Data ◽  
Omics Data ◽  
Classification Models ◽  
Data Types

Drug sensitivity prediction models for human cancer cell lines constitute important tools in identifying potential computational biomarkers for responsiveness in a pre-clinical setting. Integrating information derived from a range of heterogeneous data is crucial, but remains non-trivial, as differences in data structures may hinder fitting algorithms from assigning adequate weights to complementary information that is contained in distinct omics data. In order to counteract this effect that tends to lead to just one data type dominating supposedly multi-omics models, we developed a novel tool that enables users to train single-omics models separately in a first step and to integrate them into a multi-omics model in a second step. Extensive ablation studies are performed in order to facilitate an in-depth evaluation of the respective contributions of singular data types and of combinations thereof, effectively identifying redundancies and interdependencies between them. Moreover, the integration of the single-omics models is realized by a range of distinct classification algorithms, thus allowing for a performance comparison. Sets of molecular events and tissue types found to be related to significant shifts in drug sensitivity are returned to facilitate a comprehensive and straightforward analysis of potential computational biomarkers for drug responsiveness. Our two-step approach yields sets of actual multi-omics pan-cancer classification models that are highly predictive for a majority of drugs in the GDSC data base. In the context of targeted drugs with particular modes of action, its predictive performances compare favourably to those of classification models that incorporate multi-omics data in a simple one-step approach. Additionally, case studies demonstrate that it succeeds both in correctly identifying known key biomarkers for sensitivity towards specific drug compounds as well as in providing sets of potential candidates for additional computational biomarkers.

scholarly journals Correlation Between Drug Sensitivity Profiles of Ex Vivo Expanded Circulating Tumor Cells and Clinical Treatment Response in Pancreatic Ductal Adenocarcinoma Patients

2021 ◽  
Author(s):  
Yuan-Hung Wu ◽  
Yi-Ping Hung ◽  
Nai-Chi Chiu ◽  
Rheun-Chuan Lee ◽  
Chung-Pin Li ◽  
...  
Keyword(s):  
Clinical Response ◽  
Treatment Response ◽  
Tumor Cells ◽  
Drug Sensitivity ◽  
Ex Vivo ◽  
Clinical Treatment ◽  
Ductal Adenocarcinoma ◽  
Liquid Biopsies ◽  
Sensitivity Testing ◽  
Organoid Cultures

Abstract BackgroundPancreatic ductal adenocarcinoma (PDAC) is highly aggressive and has poor prognosis. There are few biomarkers to inform treatment decisions, and collecting tumor samples for genomic or drug sensitivity testing is challenging.MethodsCirculating tumor cells (CTCs) were prepared from the liquid biopsies of PDAC patients. These cells were subsequently expanded ex vivo to form CTC-derived organoid cultures, using a laboratory-developed biomimetic cell culture system. The CTC-derived organoids were tested for sensitivity to a PDAC panel of nine drugs, with tests conducted in triplicate, and a weighted cytotoxicity score (CTS) was calculated from the results. Clinical response to treatment in patients was evaluated using Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 criteria at the time of blood sampling and 3 months later. CTS was then correlated with clinical response, and analyzed using 2 × 2 contingency tables.ResultsA total of 41 liquid biopsies were collected from 31 patients, with 87.8% of liquid biopsies from patients with Stage 4 disease. CTC-derived organoid expansion was achieved in 3 weeks, with 87.8% culture efficiency. CTC-derived organoid cultures were positive for EpCAM staining and negative for CD45 staining in surface marker analysis. All patients had received a median of two lines of treatment prior to enrollment, and prospective utility analysis indicated significant correlation of CTS with clinical treatment response. Two representative case studies are also presented to illustrate the relevant clinical contexts.ConclusionsIn this study, CTCs were expanded from the liquid biopsies of PDAC patients with a high success rate. Drug sensitivity profiles from CTC-derived organoid cultures correlated meaningfully with treatment response. Further studies are warranted to validate the predictive potential for this approach.Trial RegistrationTaipei Medical University Hospital Protocol Record N201803020, registered on July 10, 2018; ClinicalTrials.gov Identifier: NCT04972461, retrospectively registered on July 22, 2021.

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