Literature DB >> 32561862

Discovering and validating cancer genetic dependencies: approaches and pitfalls.

Ann Lin1, Jason M Sheltzer2.   

Abstract

Cancer 'genetic dependencies' - genes whose products are essential for cancer cell fitness - are promising targets for therapeutic development. However, recent evidence has cast doubt on the validity of several putative dependencies that are currently being targeted in cancer clinical trials, underscoring the challenges inherent in correctly identifying cancer-essential genes. Here we review several common techniques and platforms for discovering and characterizing cancer dependencies. We discuss the strengths and drawbacks of different gene-perturbation approaches, and we highlight the use of poorly validated genetic and pharmacological agents as a common cause of target misidentification. A careful consideration of the limitations of current technologies and cancer models will improve our ability to correctly uncover cancer genetic dependencies and will facilitate the development of improved therapeutic agents.

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Year:  2020        PMID: 32561862     DOI: 10.1038/s41576-020-0247-7

Source DB:  PubMed          Journal:  Nat Rev Genet        ISSN: 1471-0056            Impact factor:   53.242


  15 in total

1.  In Vitro Silencing of lncRNA Expression Using siRNAs.

Authors:  Meike S Thijssen; Jennifer Bintz; Luis Arnes
Journal:  Methods Mol Biol       Date:  2021

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

Review 3.  Computational estimation of quality and clinical relevance of cancer cell lines.

Authors:  Lucia Trastulla; Javad Noorbakhsh; Francisca Vazquez; James McFarland; Francesco Iorio
Journal:  Mol Syst Biol       Date:  2022-07       Impact factor: 13.068

4.  Metabolic modeling-based drug repurposing in Glioblastoma.

Authors:  Claudio Tomi-Andrino; Alina Pandele; Klaus Winzer; John King; Ruman Rahman; Dong-Hyun Kim
Journal:  Sci Rep       Date:  2022-07-01       Impact factor: 4.996

5.  Genome-wide identification and analysis of prognostic features in human cancers.

Authors:  Joan C Smith; Jason M Sheltzer
Journal:  Cell Rep       Date:  2022-03-29       Impact factor: 9.995

Review 6.  Aneuploidy as a promoter and suppressor of malignant growth.

Authors:  Anand Vasudevan; Klaske M Schukken; Erin L Sausville; Vishruth Girish; Oluwadamilare A Adebambo; Jason M Sheltzer
Journal:  Nat Rev Cancer       Date:  2021-01-11       Impact factor: 69.800

7.  N-Terminal Acetyltransferases Are Cancer-Essential Genes Prevalently Upregulated in Tumours.

Authors:  Costas Koufaris; Antonis Kirmizis
Journal:  Cancers (Basel)       Date:  2020-09-15       Impact factor: 6.639

8.  Machine Learning and Systems Biology Approaches to Characterize Dosage-Based Gene Dependencies in Cancer Cells.

Authors:  Kevin Meng-Lin; Choong Yong Ung; Taylor M Weiskittel; Alex Chen; Cheng Zhang; Cristina Correia; Hu Li
Journal:  J Bioinform Syst Biol       Date:  2021-02-26

Review 9.  Synthetic Lethality in Cancer Therapeutics: The Next Generation.

Authors:  Jeremy Setton; Michael Zinda; Nadeem Riaz; Daniel Durocher; Michal Zimmermann; Maria Koehler; Jorge S Reis-Filho; Simon N Powell
Journal:  Cancer Discov       Date:  2021-04-01       Impact factor: 39.397

10.  Integrative oncogene-dependency mapping identifies RIT1 vulnerabilities and synergies in lung cancer.

Authors:  Athea Vichas; Amanda K Riley; Naomi T Nkinsi; Shriya Kamlapurkar; Phoebe C R Parrish; April Lo; Fujiko Duke; Jennifer Chen; Iris Fung; Jacqueline Watson; Matthew Rees; Austin M Gabel; James D Thomas; Robert K Bradley; John K Lee; Emily M Hatch; Marina K Baine; Natasha Rekhtman; Marc Ladanyi; Federica Piccioni; Alice H Berger
Journal:  Nat Commun       Date:  2021-08-09       Impact factor: 14.919

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