| Literature DB >> 27686855 |
Ada W Y Leung1,2, Tanya de Silva3,4, Marcel B Bally5,3,6,7, William W Lockwood8,9.
Abstract
Lung cancer is a heterogeneous disease consisting of multiple histological subtypes each driven by unique genetic alterations. Despite the development of targeted therapies that inhibit the oncogenic mutations driving a subset of lung cancer cases, there is a paucity of effective treatments for the majority of lung cancer patients and new strategies are urgently needed. In recent years, the concept of synthetic lethality has been established as an effective approach for discovering novel cancer-specific targets as well as a method to improve the efficacy of existing drugs which provide partial but insufficient benefits for patients. In this review, we discuss the concept of synthetic lethality, the various types of synthetic lethal interactions in the context of oncology and the approaches used to identify these interactions, including recent advances that have transformed the ability to discover novel synthetic lethal combinations on a global scale. Lastly, we describe the specific synthetic lethal interactions identified in lung cancer to date and explore the pharmacological challenges and considerations in translating these discoveries to the clinic.Entities:
Keywords: Combination treatments; Drug-drug interactions; Lung cancer; Synergy; Synthetic lethality
Year: 2016 PMID: 27686855 PMCID: PMC5041331 DOI: 10.1186/s12943-016-0546-y
Source DB: PubMed Journal: Mol Cancer ISSN: 1476-4598 Impact factor: 27.401
Fig. 1Synthetic Lethality: History and Evolution. The timeline indicates the major events that took place over the last century, from the first description of synthetic lethality to the recent development of technologies for high-throughput discoveries of synthetic lethal interactions
Fig. 2Types of Synthetic Lethal Interactions in the Context of Cancer. The various types of synthetic lethal interactions can be grouped into two categories: genetic-based and chemical-based. Genetic synthetic lethality is primarily based on cancer-specific genetic alterations (blue normal cells undergo genetic changes that result in transformation to red cancer cells) that become susceptible to further induced changes in gene expression resulting in synthetic lethality. Chemical synthetic lethality describes synthetic lethal interactions between inherent or induced genetic alterations and broad-spectrum therapeutics (chemosensitization) as well as synergistic outcomes from the use of two or more chemotherapeutics. Please see text for full description of each type of interaction. (LOF = loss-of-function, GOF = gain-of-function, passenger A1 = passenger gene deletion, A2 = isoform of deleted passenger A1, blue cell = normal cell, red cell = cancer cell, grey cell = dead cancer cell)
Synthetic Lethal Interactions Identified in Lung Cancer
| Interactor 1 | Interactor 2 | Type of Interaction | Method of Discovery | Lung Cancer Subtype | Year discovered | First Author | PMID |
|---|---|---|---|---|---|---|---|
| PAPSS1 | Cisplatin (or other DNA damaging agents) | Chemo-sensitization | RNAi Screen (siRNA) + Low-Dose Cisplatin | LAC | 2015 | Leung | 26220590 |
| CABYR | Cisplatin | Chemo-sensitization | RNAi Screen (siRNA) + Cisplatin | LAC | 2014 | Qian | 24362251 |
| dUTPase | FUdR/pemetrexed | Chemo-sensitization | Hypothesis Based | LAC; bronchioalveolar carcinoma; LCC | 2012 | Wilson | 22172489 |
| mKRAS | PKCi Aggegation (via Oncrasin-1 treatment) | GOF + GOF | Chemical library Compound Screen | LAC | 2008 | Guo | 18794128 |
| mKRAS | mEGFR | GOF + GOF | Computational - Mutual Exclusivity Analysis in Lung Cancer Genomic Data | LAC | 2015 | Unni/Lockwood | 26047463 |
| mKRAS | mBRAF | GOF + GOF | Hypothesis Based | LAC | 2016 | Cisowski | 26028035 |
| mEGFR | ARHG5 | GOF + LOF | Computational - EGFR Interactome Mapping | LAC | 2013 | Li | 24189400 |
| mKRAS | GATA2 | GOF + LOF | RNAi Screen (siRNA) | LAC | 2012 | Kumar | 22541434 |
| mKRAS | STK33 | GOF + LOF | RNAi Screen (shRNA) | LAC | 2009 | Scholl | 19490892 |
| mKRAS | TBK1 | GOF + LOF | RNAi Screen (shRNA) | LAC | 2009 | Barbie | 19847166 |
| mKRAS | PLK1 | GOF + LOF | RNAi Screen (shRNA) | LAC | 2009 | Luo | 19490893 |
| mKRAS | WT1 | GOF + LOF | RNAi Screen (shRNA) | LAC | 2010 | Vicent | 20972333 |
| mKRAS | CDK4 | GOF + LOF | RNAi Screen (shRNA) | LAC | 2010 | Puyol | 20609353 |
| MYC | PRKDC | GOF + LOF | RNAi Screen (shRNA) | SCLC | 2014 | Zhou | 25495526 |
| mEGFR | PRKCSH + EGFR-i | GOF + LOF + LOF | RNAi Screen (shRNA) + Gefitinib | LAC | 2014 | Sudo | 25528770 |
| mEGFR | NF-kB + EGFR-i | GOF + LOF + LOF | RNAi Screen (shRNA/siRNA) + EGFR TKI | LAC | 2011;2015 | Bivona | 21430781 |
| mKRAS | BCL-XL + MEK-i | GOF + LOF + LOF | RNAi Screen (shRNA) + Selumetinib | LAC | 2013 | Corcoran | 23245996 |
| ATM | DNAPK | LOF + LOF | Hypothesis Based | LAC | 2013 | Riabinska | 23761041 |
| ATM/p53 | ATR (under DNA damaging conditions) | LOF + LOF | Hypothesis Based | LAC | 2011 | Reaper | 21490603 |
| EGFR | Tankyrase 1 | LOF + LOF | RNAi Screen (shRNA) + Gefitinib | LAC | 2012 | Casas-Selves | 22738915 |
| MAX | BRG1 | LOF + LOF | Computational - Global gene expression analysis; cancer databases | SCLC | 2014 | Romero | 24362264 |
| RB1 | CDKN2A | LOF + LOF | Computational - Proteome/transcriptome profiling | LAC; SCLC | 2016 | Kim | 26647789 |
| LKB1 | phenformin | LOF + LOF | Compound library screen | LAC | 2013 | Shackelford | 23352126 |
| BRM/SMARCA2 | BRG1 | LOF + LOF/collateral | RNAi Screen (shRNA) | LAC | 2014 | Hoffman | 24520176 |
| PSMA1 (proteosome subunit) | Radiation | Radio-sensitization | RNAi Screen (shRNA) | LAC; LCC | 2013 | Cron | 24040035 |
| Cisplatin | Irinotecan | Synergistic Interaction | Hypothesis Based | SCLC | 2002 | Noda | 11784874 |
GOF gain-of-function, LOF loss-of-function, LAC lung adenocarincoma, SCLC small-cell lung cancer, LCC large cell carcinoma, m-(gene) mutant variant of the gene, (gene)-i inhibitor of gene product
Fig. 3Considerations when validating synthetic lethal targets. Several factors should be considered when deciding whether or not to translate a synthetic lethal discovery to therapeutics. If the target was discovered from an RNAi screen, off-target effects should be eliminated by testing individual siRNA duplexes, using pools of siRNAs, or even testing the interaction using small molecules if available (a). Secondly, the synthetic lethality should be verified in a panel of cell lines for the indication(s) of interest to assess potential applications of the therapeutic strategy of interest (b). The therapeutic window should also be assessed to ensure that synthetic lethality occurs in a cancer-specific manner (c). When developing pharmaceuticals for the target of interest, it is crucial to understand whether it is the enzymatic activity or a specific interaction that is responsible for the synthetic lethality observed (d). Finally, synthetic lethality might be dependent on the extent of genetic alteration. This dose dependency should be explored and addressed when designing and developing therapeutics for synthetic lethal targets (e)
Fig. 4In Vivo Considerations for Synthetic Lethal Therapeutics. When using two or more therapeutics, it is important to determine the drug combination ratios at which synergy occur (a). This should be done in a panel of cell lines for the indication(s) of interest. Synergism may also be dependent on the timing of the administration of the different therapeutics (b). Another challenge that needs to be addressed is the issue associated with drug penetration into the entire tumour (c). As a result of poorly organized vasculature, concentration gradients will be generated upon treatment and outcomes of synthetic lethal approaches may be limited by the inability to induce sufficient genetic alterations in all cells of the targeted population. Finally, while synthetic lethal approaches are promising, certain populations of the tumour may survive treatment due to intra-tumoural heterogeneity which makes them insensitive to the specific treatment regimen (d)