Large genetic screens in model organisms have led to the identification of negative genetic interactions. Here, we describe a data integration workflow using data from genetic screens in model organisms to delineate drug combinations targeting synthetic lethal interactions in cancer.
A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of both synthetic lethal interactors leads to loss of cell viability or cell death. The best studied synthetic lethal interaction is between BRCA1/2 and PARP1, with PARP1 inhibitors being used in clinical practice to treat patients with BRCA1/2 mutated tumors. Large genetic screens in model organisms but also in haploid human cell lines have led to the identification of numerous additional synthetic lethal interaction pairs, all being potential targets of interest in the development of novel tumor therapies. One approach is to therapeutically target genes with a synthetic lethal interactor that is mutated or significantly downregulated in the tumor of interest. A second approach is to formulate drug combinations addressing synthetic lethal interactions. In this article, we outline a data integration workflow to evaluate and identify drug combinations targeting synthetic lethal interactions. We make use of available datasets on synthetic lethal interaction pairs, homology mapping resources, drug-target links from dedicated databases, as well as information on drugs being investigated in clinical trials in the disease area of interest. We further highlight key findings of two recent studies of our group on drug combination assessment in the context of ovarian and breast cancer.
Synthetic lethality defines an association of two genes, where loss of one gene does not affect viability, but loss of both genes leads to cell death. It was first described in 1946 by Dobzhansky while analyzing various phenotypes of drosophila by breeding homozygous mutants1. Mutants that did not produce viable offspring, although viable themselves, exhibited lethal phenotypes when crossed with certain other mutants, setting ground for the establishment of the theory of synthetic lethality. Hartwell and colleagues suggested that this concept might be applicable for cancer therapy in humans2. Pharmacologically provoked synthetic lethality could rely on just one mutation, given that the mutated gene’s synthetic lethal partner is targetable by a pharmacological compound. The first gene pair to enable pharmacological induction of synthetic lethality was BRCA(1/2) and PARP1. PARP1 functions as a sensor for DNA damage, and is tied to sites of double and single DNA strand-breaks, supercoils and crossovers3. BRCA1 and 2 play major roles in repair of DNA double-strand breaks through homologous recombination4. Farmer and colleagues published findings that cells deficient for BRCA1/2 were susceptible to PARP inhibition, while no cytotoxicity was observed in BRCA wild-type cells5. Ultimately, PARP inhibitors were approved for the treatment of BRCA deficient breast and ovarian cancer6,7. Further, synthetic lethality gene pairs leading to clinical approval of pharmacological compounds are much anticipated and a major area of recent cancer research efforts8.
Synthetic lethal gene interactions were modelled in multiple organisms including fruit flies, C. elegans and yeast2. Using various approaches including RNA-interference- and CRISPR/CAS-library knockouts, novel synthetic lethal gene pairs were discovered in recent years9,10,11. A protocol on the experimental procedures of RNAi in combination with CRISPR/CAS was recently published by Housden and colleagues12. Meanwhile, researchers also conducted large screens in haploid human cells to identify synthetic lethal interactions13,14. In silico methods like biological network analysis and machine learning have also shown promise in the discovery of synthetic lethal interactions15,16.
Conceptionally, one approach to make use of synthetic lethal interactions in the context of anti-tumor therapy is to identify mutated or non-functional proteins in tumor cells, making their synthetic lethal interaction partners promising drug targets for therapeutic intervention. Due to the heterogeneity of most tumor types, researchers have started the search for so-called synthetic lethal hub proteins. These synthetic lethal hubs have a number of synthetic lethal interaction partners that are either mutated and therefore non-functional or significantly downregulated in tumor samples. Addressing such synthetic lethal hubs holds promise in increasing drug efficacy or overcoming drug resistance as could be shown for instance in the context of vincristine resistant neuroblastoma17. A second approach to enhance drug treatment making use of the concept of synthetic lethal interactions is to identify drug combinations targeting synthetic lethal interactions. This could lead to new combinations of already approved single anti-tumor therapies and to the repositioning of drugs from other disease areas to the field of oncology.
In this article, we present a step-by-step procedure to yield a list of drug combinations that target synthetic lethal interaction pairs. In this workflow, we (i) use data on synthetic lethal interactions from BioGRID and (ii) information on homologous genes from Ensembl, (iii) retrieve drug-target pairs from DrugBank, (iv) build disease-drug associations from ClinicalTrials.gov, and (v) hence generate a set of drug combinations addressing synthetic lethal interactions. Lastly, we provide drug combinations in the context of ovarian and breast cancer in the representative results section.
1. Retrieving synthetic lethal gene pairs
Column number | Column header name |
3 | Gene Name |
12 | Species |
13 | Drug IDs |
Table 1: Relevant columns of the BioGRID datafile.
2. Translating synthetic lethal gene pairs to human orthologs
3. Mapping synthetic lethal interaction partners to drugs
Column number | Column header name |
3 | Gene Name |
12 | Species |
13 | Drug IDs |
4. Establishing the set of currently tested drug combinations in clinical trials
5. Identification of drug combinations targeting synthetic lethal interactions
6. Testing selected new drug combinations in vitro
Our group has recently published two studies applying the workflow depicted in this manuscript to identify drug combinations targeting synthetic lethal interactions in the context of ovarian and breast cancer24,25. In the first study, we evaluated drug combinations that are currently tested in late stage clinical trials (phase III and IV) or already being used in clinical practice to treat ovarian cancer patients regarding their impact on synthetic lethal interactions. In addition, we identified drug combinations that are currently not being tested in clinical trials but provide a rationale from the perspective of targeting synthetic lethal interactions. We therefore evaluated all possible drug combinations choosing drugs from the pool of all compounds in late stage ovarian cancer trials. We identified a unique set of 61 drug combinations that had been investigated in 68 late stage ovarian cancer trials. Twelve out of these 61 drug combinations addressed at least one synthetic lethal interaction. 84 additional drug combinations were proposed to address synthetic lethal interactions without being investigated in clinical trials to this date. 21 unique drugs contributed to the 84 identified drug combinations targeting a set of 39 synthetic lethal interactors as given in Figure 1.
Figure 1: Network of proposed novel drug combinations in the context of ovarian cancer. Figure 1 displays synthetic lethal interactions where interactors are addressed by two drugs currently not being tested in clinical trials. Synlet interactions are displayed in red, whereas drug-target links are indicated by grey edges. Dotted lines represent synthetic lethal interactions being addressed by other drug combinations in late stage ovarian cancer clinical trials. These investigated drug combinations are indicated with an asterisk (*), each in combination with paclitaxel with the additional investigated combination of cediranib and olaparib being indicated by a circle (o) [adapted from 25]. Please click here to view a larger version of this figure.
Using the same workflow in a second study, we identified 243 promising drug combinations targeting 166 synthetic lethal gene pairs in the context of breast cancer. We experimentally tested selected drug combinations regarding their impact on cell viability and apoptosis in two breast cancer cell lines. In particular, the proposed low-toxicity drug combination of celecoxib and zoledronic acid showed cytotoxicity beyond additive effects in breast cancer cell lines as determined by their combinatorial index. Results of viability and apoptosis assays for this drug combination are displayed in Figure 2.
Figure 2: Impact of celecoxib and zoledronic acid on viability and apoptosis in SKBR-3 cells. (A) Viability assay results for celecoxib (CEL), zoledronic acid (ZOL) and the combination of zoledronic acid and celecoxib (ZOL + CEL) in SKBR-3 breast cancer cell lines. Low and high CEL concentrations used were 50µM and 75µM. Low and high ZOL concentrations used were 500µM and 750µM. The drug combination had a significant synergistic effect on cell viability (** p < 0.001). (B, C) Annexin V (ANXA5) and 7-AAD stainings of SKBR-3 cells treated with CEL, ZOL, and the drug combination ZOL + CEL. The percentage of 7-AADpos/ANXA5pos cells was increased after treatment with the drug combination ZOL + CEL [adapted from 24]. Please click here to view a larger version of this figure.
We have outlined a workflow to identify drug combinations impacting synthetic lethal interactions. This workflow makes use of (i) data on synthetic lethal interactions from model organisms, (ii) information of human orthologs, (iii) information on drug-target associations, (iv) drug information on clinical trials in the context of cancer, as well as (v) on information of drug-disease and gene-disease associations extracted from scientific literature. The consolidated information can be used to evaluate the impact of a given drug combination under investigation on synthetic lethal gene pairs. In addition, consolidated data can be used to evaluate a set of drugs currently being investigated or tested in clinical trials in the context of cancer to find combinations targeting the most relevant synthetic lethal interactions, therefore having a higher chance of impacting tumor cell survival. Lastly, the data generated can be used to screen for drug combinations consisting of drugs not initially developed for tumor treatment, thus providing a way for a computationally driven drug repositioning case.
For each step in the data integration workflow we present key data sources to complete the full data workflow but point out that the workflow can be further enhanced at various stages by making use of additional data sources. In our workflow we extracted synthetic lethal interaction pairs from the BioGRID database18. We specifically focused on interactions of experiment types “synthetic lethality” and “negative genetic”. Information in BioGRID on synthetic lethal interactions contains datasets from large genetic screen as for example a dataset published by Costanzo and colleagues26, which is also available in the DRYGIN database27, as well as data on single synthetic lethal interactions as described in individual experiments in scientific literature. There are additional data sources collecting and storing synthetic lethal interactions, as for example SynLethDB28. Further, on the level of orthology mapping, a large number of different tools and databases exist. We present a way to make use of Ensembl biomart to map synthetic lethal interaction partners identified in model organisms to their corresponding human orthologs. Other orthology databases and services include NCBI’s HomoloGene database29, the OMA orthology database from the Swiss Institute of Bioinformatics30, or the InParanoid ortholog groups database maintained by the Stockholm Bioinformatics Center31. In our workflow, we focused on synthetic lethal interactions from multiple model organisms, with the largest number of synthetic lethal interactions coming from yeast. One might consider restricting the input set for the orthology mapping to data from mouse and rat only, which are evolutionary closer to humans. An additional way of defining the input set of synthetic lethal interactions is to only focus on synthetic lethal interactions being conserved in multiple species, thereby increasing the chances that the synthetic lethal interaction is truly positive. This on the other hand might reduce the set of synthetic lethal interactions dramatically, as there is already a large difference in the identified synthetic lethal interactions between S. cerevisiae and S. pombe. Another approach is to be not too stringent at the beginning and to even extend the set of experimental synthetic lethal interactions by machine learning algorithms as we did in the two studies listed in the representative results section. In brief, a random forest model was used to predict synthetic lethal interactions for human genes for which no orthologous genes existed in yeast. The random forest model was trained on the set of synthetic lethal interaction pairs from yeast and their orthologous human genes using data on pathway associations, Gene Ontology assignment as well as disease and drug associations as described previously24,25. This allowed us to consider human genes for which no ortholog mapping information was available in our integration workflow. A widely used database storing information on drug-target associations is DrugBank, which is also the primary source of interactions in the workflow. Other databases holding to some extent complementary information on drug targets are the Therapeutic Target Database (TTD)32 or ChEMBL33. Major components of the workflow are also incorporated in the e.valuation platform from emergentec and SynLethDB, which has been developed by researchers from Nanyang Technological University. The last update of SynLethDB in 2015, however, was based on the datasets stored in the download section on their respective webpage28.
A way to rank identified drug combinations and targeted synthetic lethal interaction pairs is using the association of synthetic lethal partners and/or drugs with the disease of interest via literature mining methods. In our work on the evaluation of drug combinations in the context of ovarian cancer, we ranked novel proposed drug combinations based on the number of publications on ovarian cancer mentioning either one of the two synthetic lethal interactors of a respective drug combination. MeSH annotation in Pubmed can be used to identify publications for a specific disease using the exact disease terms as given in the major MeSH branch C. Information on genes in the identified publications can be extracted using NCBI’s gene2pubmed mapping file as described elsewhere34. Further, there are dedicated databases holding gene-disease and/or drug-disease links such as the Comparative Toxicogenomics Database35, DisGeNET36, or the e.valuation software platform. Ranking of drug combinations based on disease associations is one way of supporting the final selection of drug combinations for experimental testing. Additional aspects need to be considered when selecting drug combinations for further testing, like for example individual toxicity profiles of the drugs or expression status of synthetic lethal interactors in the respective target organ.
In the representative results section, we present data for the drug combination of celecoxib and zoledronic acid, which was identified following the workflow to identify drug combinations in the context of breast cancer. This particular drug combination was selected for experimental testing due to the low toxicity profiles of both compounds. We used various concentrations in in-vitro experiments to evaluate the impact of the drug combination on cell viability and apoptosis. Ideally, drug concentrations could be significantly lowered for individual drugs to minimize side effects while at the same time maximizing efficacy by combining two drugs. Seeing impact on viability at lower doses is even more meaningful, as drug concentrations used for in vitro testing could be criticized to be supratherapeutical, that are not reached in in vivo models. However, the concentrations were chosen based on cell culture experiments with these given drugs in the literature. Drug dosing may further influence what targets are primarily affected, as most compounds have more than one drug target, potentially impacting a large set of known and unknown downstream molecules as well. Drug combinations showing synergistic effects on cell viability in in-vitro cell culture systems should therefore be further investigated in 3D or in-vivo models.
Summarizing, we present a workflow that integrates information from different data sources to evaluate and propose drug combinations targeting synthetic lethal interactions. To date, the largest information on synthetic lethal interactions is still coming from model organisms, requiring a mandatory orthology mapping step to the human genome. First screens in human haploid cells have led to the identification of synthetic lethal interactions in human cells. Additionally, the CRISPR/CAS technology has opened new ways of studying synthetic lethal interactions on a cellular level. With more high quality biological synthetic lethal interaction data becoming available, we propose that data integration efforts such as ours will transform clinical cancer treatment in the future, by discovering novel and clinically meaningful synthetic lethal gene pairs aside from BRCA(1/2)/PARP1.
The authors have nothing to disclose.
Funding for developing the data integration workflow was obtained from European Community’s Seventh Framework Programme under grant agreement nu. 279113 (OCTIPS). Adaption of data within this publication was kindly approved by Public Library of Sciences Publications and Impact Journals, LLC.
BioGRID | n/a | n/a | thebiogrid.org |
ClinicalTrials.gov | n/a | n/a | ClinicalTrials.gov |
DrugBank | n/a | n/a | drugbank.ca |
Ensembl BioMart | n/a | n/a | ensembl.org |
for alternative computational databases please refer to the manuscript | |||
7-AAD | ebioscience | 00-6993-50 | |
AnnexinV-APC | BD Bioscience | 550474 | |
celecoxib | Sigma-Aldrich | PZ0008-25MG | |
CellTiter-Blue Viability Assay | Promega | G8080 | |
FACS Canto II | BD Bioscience | n/a | |
fetal bovine serum | Fisher Scientific/Gibco | 16000044 | |
FloJo Software | FloJo LLC | V10 | |
McCoy's 5a Medium Modified | Fisher Scientific/Gibco | 16600082 | |
penicillin G/streptomycin sulfate | Fisher Scientific/Gibco | 15140122 | |
SKBR-3 cells | American Type Culture Collection (ATCC) | ATCC HTB-30 | |
zoledronic acid | Sigma-Aldrich | SML0223-50MG | |
further materials or equipment will be made available upon request |