In Silico Inference of Synthetic Cytotoxic Interactions from Paclitaxel Responses

Overview

Journal Int J Mol Sci

Publisher MDPI

Specialties Biochemistry
Chemistry
Molecular Biology

Date 2021 Jan 27

PMID 33499282

Authors

Jeong Hoon Lee

Kye Hwa Lee

Ju Han Kim

Affiliations

Soon will be listed here.

Abstract

To exploit negatively interacting pairs of cancer somatic mutations in chemotherapy responses or synthetic cytotoxicity (SC), we systematically determined mutational pairs that had significantly lower paclitaxel half maximal inhibitory concentration (IC) values. We evaluated 407 cell lines with somatic mutation profiles and estimated their copy number and drug-inhibitory concentrations in Genomics of Drug Sensitivity in Cancer (GDSC) database. The SC effect of 142 mutated gene pairs on response to paclitaxel was successfully cross-validated using human cancer datasets for urogenital cancers available in The Cancer Genome Atlas (TCGA) database. We further analyzed the cumulative effect of increasing SC pair numbers on the TP53 tumor suppressor gene. Patients with TCGA bladder and urogenital cancer exhibited improved cancer survival rates as the number of disrupted SC partners (i.e., SYNE2, SON, and/or PRY) of TP53 increased. The prognostic effect of SC burden on response to paclitaxel treatment could be differentiated from response to other cytotoxic drugs. Thus, the concept of pairwise SC may aid the identification of novel therapeutic and prognostic targets.

References

Ma P, Mumper R . Paclitaxel Nano-Delivery Systems: A Comprehensive Review. J Nanomed Nanotechnol. 2013; 4(2):1000164. PMC: 3806207. DOI: 10.4172/2157-7439.1000164. View

ONeil N, Bailey M, Hieter P . Synthetic lethality and cancer. Nat Rev Genet. 2017; 18(10):613-623. DOI: 10.1038/nrg.2017.47. View

Jones D, Raine K, Davies H, Tarpey P, Butler A, Teague J . cgpCaVEManWrapper: Simple Execution of CaVEMan in Order to Detect Somatic Single Nucleotide Variants in NGS Data. Curr Protoc Bioinformatics. 2016; 56:15.10.1-15.10.18. PMC: 6097605. DOI: 10.1002/cpbi.20. View

Mansilla S, Bataller M, Portugal J . Mitotic catastrophe as a consequence of chemotherapy. Anticancer Agents Med Chem. 2006; 6(6):589-602. DOI: 10.2174/187152006778699086. View

Burrell R, Swanton C . Tumour heterogeneity and the evolution of polyclonal drug resistance. Mol Oncol. 2014; 8(6):1095-111. PMC: 5528620. DOI: 10.1016/j.molonc.2014.06.005. View

Xu C, Shi R, Chen D, Sun Y, Wu Q, Wang T . Potential biomarkers for paclitaxel sensitivity in hypopharynx cancer cell. Int J Clin Exp Pathol. 2013; 6(12):2745-56. PMC: 3843255. View

Ahn E, DeKelver R, Lo M, Nguyen T, Matsuura S, Boyapati A . SON controls cell-cycle progression by coordinated regulation of RNA splicing. Mol Cell. 2011; 42(2):185-98. PMC: 3137374. DOI: 10.1016/j.molcel.2011.03.014. View

Fresno C, Fernandez E . RDAVIDWebService: a versatile R interface to DAVID. Bioinformatics. 2013; 29(21):2810-1. DOI: 10.1093/bioinformatics/btt487. View

Al-Lazikani B, Banerji U, Workman P . Combinatorial drug therapy for cancer in the post-genomic era. Nat Biotechnol. 2012; 30(7):679-92. DOI: 10.1038/nbt.2284. View

10.

Lee J, Park Y, Jung M, Lim S . Gene regulatory network analysis with drug sensitivity reveals synergistic effects of combinatory chemotherapy in gastric cancer. Sci Rep. 2020; 10(1):3932. PMC: 7054272. DOI: 10.1038/s41598-020-61016-z. View

11.

Kanehisa M, Goto S . KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999; 28(1):27-30. PMC: 102409. DOI: 10.1093/nar/28.1.27. View

12.

Wang R, Han Y, Zhao Z, Yang F, Chen T, Zhou W . Link synthetic lethality to drug sensitivity of cancer cells. Brief Bioinform. 2018; 20(4):1295-1307. DOI: 10.1093/bib/bbx172. View

13.

Manica M, Oskooei A, Born J, Subramanian V, Saez-Rodriguez J, Rodriguez Martinez M . Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders. Mol Pharm. 2019; 16(12):4797-4806. DOI: 10.1021/acs.molpharmaceut.9b00520. View

14.

Vaser R, Adusumalli S, Leng S, Sikic M, Ng P . SIFT missense predictions for genomes. Nat Protoc. 2015; 11(1):1-9. DOI: 10.1038/nprot.2015.123. View

15.

Jang K, Park M, Park J, Hwangbo H, Sung M, Kim S . Computational inference of cancer-specific vulnerabilities in clinical samples. Genome Biol. 2020; 21(1):155. PMC: 7386251. DOI: 10.1186/s13059-020-02077-1. View

16.

Ali M, Aittokallio T . Machine learning and feature selection for drug response prediction in precision oncology applications. Biophys Rev. 2018; 11(1):31-39. PMC: 6381361. DOI: 10.1007/s12551-018-0446-z. View

17.

Cibulskis K, Lawrence M, Carter S, Sivachenko A, Jaffe D, Sougnez C . Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013; 31(3):213-9. PMC: 3833702. DOI: 10.1038/nbt.2514. View

18.

Ecke T, Sachs M, Lenk S, Loening S, Schlechte H . TP53 gene mutations as an independent marker for urinary bladder cancer progression. Int J Mol Med. 2008; 21(5):655-61. View

19.

Park D, Kim J, Lew Y, Kim D, Namkoong S . Phase II trial of neoadjuvant paclitaxel and cisplatin in uterine cervical cancer. Gynecol Oncol. 2004; 92(1):59-63. DOI: 10.1016/j.ygyno.2003.09.015. View

20.

Guo J, Liu H, Zheng J . SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets. Nucleic Acids Res. 2015; 44(D1):D1011-7. PMC: 4702809. DOI: 10.1093/nar/gkv1108. View