Literature DB >> 29953899

More than fishing for a cure: The promises and pitfalls of high throughput cancer cell line screens.

Alexander Ling1, Robert F Gruener2, Jessica Fessler3, R Stephanie Huang4.   

Abstract

High-throughput screens in cancer cell lines (CCLs) have been used for decades to help researchers identify compounds with the potential to improve the treatment of cancer and, more recently, to identify genomic susceptibilities in cancer via genome-wide shRNA and CRISPR/Cas9 screens. Additionally, rich genomic and transcriptomic data of these CCLs has allowed researchers to pair this screening data with biological features, enabling efforts to identify biomarkers of treatment response and gene dependencies. In this paper, we review the major CCL screening efforts and the large datasets these screens have made available. We also assess the CCL screens collectively and include a resource with harmonized CCL and compound identifiers to facilitate comparisons across screens. The CCLs in these screens were found to represent a wide range of cancer types, with a strong correlation between the representation of a cancer type and its associated mortality. Patient ages and gender distributions of CCLs were generally as expected, with some notable exceptions of female underrepresentation in certain disease types. Also, ethnicity information, while largely incomplete, suggests that African American and Hispanic patients may be severely underrepresented in these screens. Nearly all genes were targeted in the genetic perturbations screens, but the compounds used for the drug screens target less than half of known cancer drivers, likely reflecting known limitations in our drug design capabilities. Finally, we discuss recent developments in the field and the promise they hold for enabling future screens to overcome previous limitations and lead to new breakthroughs in cancer treatment.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Biomarkers; Cancer; Cell lines; Drug screens; Genetic perturbation screens; Pharmacogenomics

Mesh:

Substances:

Year:  2018        PMID: 29953899     DOI: 10.1016/j.pharmthera.2018.06.014

Source DB:  PubMed          Journal:  Pharmacol Ther        ISSN: 0163-7258            Impact factor:   12.310


  10 in total

1.  Drug screening and genome editing in human pancreatic cancer organoids identifies drug-gene interactions and candidates for off-label treatment.

Authors:  Christian K Hirt; Tijmen H Booij; Linda Grob; Patrik Simmler; Nora C Toussaint; David Keller; Doreen Taube; Vanessa Ludwig; Alexander Goryachkin; Chantal Pauli; Daniela Lenggenhager; Daniel J Stekhoven; Christian U Stirnimann; Katharina Endhardt; Femke Ringnalda; Lukas Villiger; Alexander Siebenhüner; Sofia Karkampouna; Marta De Menna; Janette Beshay; Hagen Klett; Marianna Kruithof-de Julio; Julia Schüler; Gerald Schwank
Journal:  Cell Genom       Date:  2022-02

Review 2.  Harnessing the predictive power of preclinical models for oncology drug development.

Authors:  Alexander Honkala; Sanjay V Malhotra; Shivaani Kummar; Melissa R Junttila
Journal:  Nat Rev Drug Discov       Date:  2021-10-26       Impact factor: 84.694

3.  A simplified transposon mutagenesis method to perform phenotypic forward genetic screens in cultured cells.

Authors:  Charlotte R Feddersen; Lexy S Wadsworth; Eliot Y Zhu; Hayley R Vaughn; Andrew P Voigt; Jesse D Riordan; Adam J Dupuy
Journal:  BMC Genomics       Date:  2019-06-17       Impact factor: 3.969

Review 4.  Machine learning approaches to drug response prediction: challenges and recent progress.

Authors:  George Adam; Ladislav Rampášek; Zhaleh Safikhani; Petr Smirnov; Benjamin Haibe-Kains; Anna Goldenberg
Journal:  NPJ Precis Oncol       Date:  2020-06-15

Review 5.  Pharmacogenetic and pharmacogenomic discovery strategies.

Authors:  Concetta Crisafulli; Petronilla Daniela Romeo; Marco Calabrò; Ludovica Martina Epasto; Saverio Alberti
Journal:  Cancer Drug Resist       Date:  2019-06-19

6.  Identification of hub genes for early detection of bone metastasis in breast cancer.

Authors:  Zitong Zhao; Haoran Yang; Guangling Ji; Shanshan Su; Yuqi Fan; Minghao Wang; Shengli Gu
Journal:  Front Endocrinol (Lausanne)       Date:  2022-09-29       Impact factor: 6.055

7.  Oncogene or tumor suppressor? Long noncoding RNAs role in patient's prognosis varies depending on disease type.

Authors:  Yingbo Huang; Alexander Ling; Siddhika Pareek; R Stephanie Huang
Journal:  Transl Res       Date:  2020-11-02       Impact factor: 7.012

8.  oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data.

Authors:  Danielle Maeser; Robert F Gruener; Rong Stephanie Huang
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 13.994

9.  Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action.

Authors:  Alexander Ling; R Stephanie Huang
Journal:  Nat Commun       Date:  2020-11-17       Impact factor: 14.919

10.  Identification of 5 Hub Genes Related to the Early Diagnosis, Tumour Stage, and Poor Outcomes of Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis.

Authors:  Rui Qiang; Zitong Zhao; Lu Tang; Qian Wang; Yanhong Wang; Qian Huang
Journal:  Comput Math Methods Med       Date:  2021-09-23       Impact factor: 2.238

  10 in total

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