Literature DB >> 25759023

In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities.

Carlota Rubio-Perez1, David Tamborero1, Michael P Schroeder1, Albert A Antolín2, Jordi Deu-Pons1, Christian Perez-Llamas1, Jordi Mestres2, Abel Gonzalez-Perez1, Nuria Lopez-Bigas3.   

Abstract

Large efforts dedicated to detect somatic alterations across tumor genomes/exomes are expected to produce significant improvements in precision cancer medicine. However, high inter-tumor heterogeneity is a major obstacle to developing and applying therapeutic targeted agents to treat most cancer patients. Here, we offer a comprehensive assessment of the scope of targeted therapeutic agents in a large pan-cancer cohort. We developed an in silico prescription strategy based on identification of the driver alterations in each tumor and their druggability options. Although relatively few tumors are tractable by approved agents following clinical guidelines (5.9%), up to 40.2% could benefit from different repurposing options, and up to 73.3% considering treatments currently under clinical investigation. We also identified 80 therapeutically targetable cancer genes.
Copyright © 2015 Elsevier Inc. All rights reserved.

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Year:  2015        PMID: 25759023     DOI: 10.1016/j.ccell.2015.02.007

Source DB:  PubMed          Journal:  Cancer Cell        ISSN: 1535-6108            Impact factor:   31.743


  150 in total

1.  Anticancer drugs: Advancing precision medicine in silico.

Authors:  Megan Cully
Journal:  Nat Rev Drug Discov       Date:  2015-04-24       Impact factor: 84.694

Review 2.  Precision medicine for metastatic breast cancer--limitations and solutions.

Authors:  Monica Arnedos; Cecile Vicier; Sherene Loi; Celine Lefebvre; Stefan Michiels; Herve Bonnefoi; Fabrice Andre
Journal:  Nat Rev Clin Oncol       Date:  2015-07-21       Impact factor: 66.675

Review 3.  Whole-Genome Sequencing in Cancer.

Authors:  Eric Y Zhao; Martin Jones; Steven J M Jones
Journal:  Cold Spring Harb Perspect Med       Date:  2019-03-01       Impact factor: 6.915

4.  Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics.

Authors:  Michael Q Ding; Lujia Chen; Gregory F Cooper; Jonathan D Young; Xinghua Lu
Journal:  Mol Cancer Res       Date:  2017-11-13       Impact factor: 5.852

Review 5.  Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes.

Authors:  A Sveen; S Kilpinen; A Ruusulehto; R A Lothe; R I Skotheim
Journal:  Oncogene       Date:  2015-08-24       Impact factor: 9.867

6.  Predicting Drug Response in Human Prostate Cancer from Preclinical Analysis of In Vivo Mouse Models.

Authors:  Antonina Mitrofanova; Alvaro Aytes; Min Zou; Michael M Shen; Cory Abate-Shen; Andrea Califano
Journal:  Cell Rep       Date:  2015-09-17       Impact factor: 9.423

7.  PRL3-zumab, a first-in-class humanized antibody for cancer therapy.

Authors:  Min Thura; Abdul Qader Omer Al-Aidaroos; Wei Peng Yong; Koji Kono; Abhishek Gupta; You Bin Lin; Kousaku Mimura; Jean Paul Thiery; Boon Cher Goh; Patrick Tan; Ross Soo; Cheng William Hong; Lingzhi Wang; Suling Joyce Lin; Elya Chen; Sun Young Rha; Hyun Cheol Chung; Jie Li; Sayantani Nandi; Hiu Fung Yuen; Shu-Dong Zhang; Yeoh Khay Guan; Jimmy So; Qi Zeng
Journal:  JCI Insight       Date:  2016-06-16

8.  Systematic Prioritization of Druggable Mutations in ∼5000 Genomes Across 16 Cancer Types Using a Structural Genomics-based Approach.

Authors:  Junfei Zhao; Feixiong Cheng; Yuanyuan Wang; Carlos L Arteaga; Zhongming Zhao
Journal:  Mol Cell Proteomics       Date:  2015-12-09       Impact factor: 5.911

9.  Cancer genomics: opportunities for medicinal chemistry?

Authors:  Lirong Wang; Xiang-Qun Xie
Journal:  Future Med Chem       Date:  2016-03-15       Impact factor: 3.808

10.  A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines.

Authors:  Eirini Tsirvouli; Vasundra Touré; Barbara Niederdorfer; Miguel Vázquez; Åsmund Flobak; Martin Kuiper
Journal:  Front Mol Biosci       Date:  2020-10-09
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