Leveraging STRING Database API Queries to Elucidate Sensitivity and Resistance Mechanisms in Cancer Cells
Understanding the molecular mechanisms underlying cancer cell sensitivity and resistance to targeted therapies is crucial for the development of effective treatments. Protein-protein interaction (PPI) networks provide valuable insights into these mechanisms. This study demonstrates the use of optimized STRING database API queries to investigate key proteins and pathways associated with sensitivity and resistance in cancer cells harboring specific genetic mutations. Five focused queries were designed to explore interactions involving Aurora kinases, CDK4/6, KIF11, the MDM2-TP53 axis, and the RAS-MEK-ERK signaling pathway. The results highlight critical interactions and pathways that may contribute to drug response, emphasizing the significance of integrating PPI data to uncover potential therapeutic targets and enhance our understanding of cancer biology.
Cancer remains a leading cause of mortality worldwide, with treatment efficacy often hindered by the development of resistance to therapies. Genetic mutations in tumor suppressor genes such as RB1 and TP53 play pivotal roles in modulating cellular responses to anticancer agents. RB1 is a key regulator of cell cycle progression, and its loss can lead to uncontrolled cell proliferation [1]. TP53, often termed the "guardian of the genome," is crucial for DNA repair, apoptosis, and genomic stability; its mutation is associated with tumor development and resistance to therapy [2].
Investigating the protein-protein interactions (PPIs) involving these genes can reveal critical pathways that contribute to drug sensitivity or resistance. Protein interaction networks help in understanding complex biological processes and identifying potential targets for therapeutic intervention. By analyzing these networks, researchers can uncover novel interactions and pathways that may not be evident through traditional experimental approaches alone.
The STRING database is a comprehensive resource that curates known and predicted PPIs, offering tools for network visualization and functional enrichment analysis [3]. By leveraging the STRING API, researchers can customize queries to obtain specific interaction data relevant to their studies.
This article presents five optimized STRING API queries designed to elucidate the molecular mechanisms of sensitivity and resistance in cancer cells with RB1 and TP53 mutations. The queries focus on:
- Aurora kinases and RB1 in lung cancer sensitivity to Aurora kinase inhibitors.
- CDK4/6 and RB1 interactions contributing to resistance against CDK4/6 inhibitors.
- KIF11 and TP53 interactions affecting sensitivity to kinesin inhibitors.
- MDM2, MDM4, and TP53 interactions related to resistance to MDM2 inhibitors.
- RAS-MEK-ERK pathway components in signal transduction pathway analysis.
All data were retrieved using the STRING database version 11.5, which contains information on PPIs derived from various sources, including experimental data, computational prediction methods, and public text collections [3].
A comprehensive literature review was conducted to support the biological interpretations of the PPI networks. Databases such as PubMed and Google Scholar were used to identify relevant studies published up to October 2023. Keywords included combinations of gene/protein names (e.g., "RB1," "TP53," "Aurora kinases") and terms like "cancer," "protein-protein interaction," "drug resistance," and "cell cycle."
The following optimized queries were constructed to address specific research questions:
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Purpose: To visualize the interaction network of Aurora kinases (AURKA, AURKB, AURKC) and RB1 to understand their collective role in lung cancer cell sensitivity to Aurora kinase inhibitors.
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Query:
https://string-db.org/api/image/network?identifiers=AURKA%0aAURKB%0aAURKC%0aRB1&species=9606&add_white_nodes=5&required_score=700
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Purpose: To explore the interaction network of CDK4, CDK6, and RB1 to identify potential mechanisms underlying resistance to CDK4/6 inhibitors in RB1-mutated cells.
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Query:
https://string-db.org/api/image/network?identifiers=CDK4%0aCDK6%0aRB1&species=9606&add_white_nodes=10&network_flavor=confidence&required_score=700
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Purpose: To retrieve high-confidence interaction partners of KIF11 and TP53 that may enhance sensitivity to kinesin inhibitors in TP53-mutated cells.
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Query:
https://string-db.org/api/tsv/interaction_partners?identifiers=KIF11%0aTP53&species=9606&limit=30&required_score=700
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Purpose: To assess the significance of interactions among MDM2, MDM4, and TP53, providing insights into resistance mechanisms to MDM2 inhibitors in TP53-mutated cells.
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Query:
https://string-db.org/api/tsv/ppi_enrichment?identifiers=MDM2%0aMDM4%0aTP53&species=9606
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Purpose: To perform a functional enrichment analysis on key components of the RAS-MEK-ERK signaling pathway to identify overrepresented pathways related to sensitivity or resistance in different tumor suppressor mutation backgrounds.
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Query:
https://string-db.org/api/tsv/enrichment?identifiers=BRAF%0aMAP2K1%0aMAP2K2%0aMAPK3%0aMAPK1%0aKRAS%0aNRAS%0aHRAS&species=9606
- Identifiers Separation: Gene symbols are separated using
%0a(URL-encoded newline character) to ensure proper parsing by the API. - Species:
species=9606specifies that the data pertains to Homo sapiens. - Additional Nodes:
add_white_nodesparameter is adjusted to include a manageable number of interactors, focusing on direct and high-confidence interactions. - Confidence Score:
required_score=700is used to filter for high-confidence interactions (scores range from 0 to 1000). - Network Flavor:
network_flavor=confidenceensures edges reflect interaction confidence rather than evidence count.
The queries were executed, and the resulting data were collected in the specified formats (image or TSV). For Queries 3 and 4, the raw data obtained from the API responses were used for validation and further analysis. Network images were examined for interaction patterns, while TSV files were analyzed for functional enrichment, interaction significance, and validation of the hypothesized interactions.
Statistical analyses were interpreted with caution, especially for small protein sets where p-values might not fully capture biological significance. Alternative validation approaches included cross-referencing findings with other databases (e.g., BioGRID, IntAct) and consulting published experimental data.
The network image generated revealed direct and indirect interactions between AURKA, AURKB, AURKC, and RB1. Key findings include:
- Direct Interactions: RB1 showed interactions with AURKA and AURKB, suggesting a regulatory relationship where Aurora kinases may phosphorylate RB1, leading to its inactivation and promoting cell cycle progression [4].
- Additional Interactors: High-confidence proteins such as PLK1 (Polo-like kinase 1) and CDC20 (Cell division cycle 20) were identified. These proteins are critical regulators of mitosis and have been implicated in cancer progression [5,6].
Biological Interpretation:
The interaction between RB1 and Aurora kinases suggests that in RB1-mutated lung cancer cells, the deregulation of cell cycle checkpoints may enhance sensitivity to Aurora kinase inhibitors. Targeting Aurora kinases could restore control over cell proliferation in these cells.
Value:
This network highlights potential pathways through which Aurora kinase inhibitors may exert effects in RB1-mutated lung cancer cells, providing targets for therapeutic intervention.
The generated network illustrated the interactions among CDK4, CDK6, and RB1, along with additional interactors like CCND1 (Cyclin D1) and CDKN2A (p16^INK4a^):
- CDK4/6-RB1 Axis: The network confirmed the critical role of RB1 in regulating cell cycle progression via CDK4/6-mediated phosphorylation. Loss of RB1 function can lead to uncontrolled cell division despite CDK4/6 inhibition [7].
- Resistance Mechanisms: The identification of CDKN2A, an inhibitor of CDK4/6, suggests that its loss or inactivation could contribute to resistance by removing inhibitory control over CDK activity [8].
Biological Interpretation:
In RB1-mutated cells, the absence of functional RB1 renders CDK4/6 inhibitors less effective, as the downstream target of these kinases is missing. This suggests that resistance to CDK4/6 inhibitors in RB1-deficient cancers may require alternative therapeutic strategies.
Value:
Understanding these interactions provides insights into resistance mechanisms, potentially guiding the development of combination therapies to overcome resistance, such as targeting downstream effectors or parallel pathways.
The TSV output from Query 3 provided a list of high-confidence interaction partners for KIF11 and TP53. The data included STRING identifiers, preferred names, and interaction scores.
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Top Interactors:
- DLGAP5 (Score: 0.997): Involved in mitotic spindle formation.
- CEP55 (Score: 0.997): Plays a role in cytokinesis and cell division.
- BUB1B (Score: 0.994): Essential for spindle checkpoint function.
- KIF23 (Score: 0.994): A kinesin motor protein involved in cytokinesis.
- ASPM (Score: 0.989): Associated with mitotic spindle regulation.
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Functional Insights:
- Many of the top interactors are involved in mitosis and cell cycle regulation, suggesting that KIF11 functions within a network critical for cell division.
- High interaction scores indicate strong evidence supporting these interactions.
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Top Interactors:
- MDM2 (Score: 0.999): E3 ubiquitin-protein ligase that negatively regulates TP53.
- CDKN1A (p21) (Score: 0.999): Mediates TP53-dependent cell cycle arrest.
- EP300 (Score: 0.999): A histone acetyltransferase that co-activates TP53.
- MDM4 (Score: 0.999): Regulates TP53 activity similarly to MDM2.
- ATM (Score: 0.999): Involved in DNA damage response and activates TP53.
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Functional Insights:
- The interactions reinforce TP53's role in DNA damage response, apoptosis, and cell cycle regulation.
- The high scores reflect well-established interactions.
Biological Interpretation:
The data validate that KIF11 interacts with proteins involved in mitotic processes. In TP53-mutated cells, where cell cycle checkpoints are compromised, targeting KIF11 and its interactors could disrupt mitosis, leading to cell death. The high-confidence interactions of TP53 with MDM2 and MDM4 support the rationale for targeting the MDM2-TP53 axis in cancer therapy.
Value:
The query response data confirm the predicted interactions, providing validation for the potential co-targeting of KIF11 and its partners to enhance the efficacy of kinesin inhibitors in TP53-mutated cancers.
The PPI enrichment analysis (Query 4) output provided the following results:
- Number of Nodes: 3
- Number of Edges: 3
- Average Node Degree: 2.0
- Local Clustering Coefficient: 1.0
- Expected Number of Edges: 1
- P-value: 0.077
Interpretation:
- Connectivity: The network has a high average node degree and clustering coefficient, indicating that all three proteins are interconnected.
- Statistical Significance: The p-value of 0.077 suggests that the observed connectivity is higher than expected by chance, but it does not reach the conventional threshold for statistical significance (p < 0.05).
- Statistical Limitations: The small size of the protein set (only three proteins) limits the statistical power of the enrichment analysis. With such a small sample, even highly connected networks may not yield statistically significant p-values.
Biological Interpretation:
Despite the p-value not being below 0.05, the complete interconnectivity among MDM2, MDM4, and TP53 is biologically significant. These proteins are known to form a regulatory network where MDM2 and MDM4 negatively regulate TP53 activity through ubiquitination and degradation [13]. The high local clustering coefficient (1.0) indicates that each protein is connected to every other protein in the network.
Alternative Validation:
- Cross-Referencing Databases: The interactions among MDM2, MDM4, and TP53 are corroborated by other databases such as BioGRID and IntAct, which provide experimental evidence supporting these interactions.
- Published Experimental Data: Numerous studies have experimentally validated the interactions within this axis [13,16], reinforcing the biological importance regardless of the statistical p-value.
Value:
The query response data support the biological importance of the MDM2/MDM4-TP53 axis in cancer biology. The lack of statistical significance in the p-value reflects limitations of the statistical model rather than the absence of meaningful interactions.
The functional enrichment analysis identified overrepresented pathways and Gene Ontology (GO) terms:
- MAPK Signaling Pathway: Highly enriched (p-value 2.23e-15), confirming the centrality of this pathway in the queried proteins.
- Cell Proliferation and Differentiation: GO terms related to "regulation of cell population proliferation" (GO:0042127, p-value 2.36e-07) and "regulation of cell differentiation" (GO:0045595, p-value 0.00015) were significantly overrepresented.
- Additional Pathways: Enrichment of pathways related to cancer, such as "pathways in cancer" (KEGG:05200), indicating the relevance of these proteins in oncogenic processes.
Biological Interpretation:
Alterations in the RAS-MEK-ERK pathway contribute to uncontrolled cell proliferation, survival, and differentiation—all hallmarks of cancer [14]. The enrichment of these pathways underscores their importance in cancer progression and potential as therapeutic targets.
Value:
These results provide a deeper understanding of how alterations in the RAS-MEK-ERK pathway contribute to cancer progression and treatment responses, informing potential therapeutic strategies that could be tailored based on tumor suppressor mutation status.
This study demonstrates the utility of tailored STRING API queries in exploring complex PPI networks relevant to cancer biology. By incorporating the query response data for Queries 3 and 4, we validated the interactions and enhanced the reliability of our findings.
The PPI enrichment analysis for the MDM2/MDM4-TP53 network did not reach conventional statistical significance (p-value = 0.077). This limitation is primarily due to the small size of the protein set. Statistical models used in enrichment analyses are more robust with larger datasets; thus, small networks may not yield significant p-values despite biologically significant interactions.
To mitigate this limitation, alternative validation approaches were employed:
- Cross-Referencing with Other Databases: Confirmed the interactions in BioGRID and IntAct, which provide experimental evidence.
- Consulting Literature: Numerous studies have established the critical role of the MDM2/MDM4-TP53 axis in cancer [13,16].
These approaches highlight that statistical significance in computational analyses should be interpreted alongside biological context and empirical evidence.
The high-confidence interaction partners identified for KIF11 and TP53 confirm the proposed roles of these proteins in mitosis and cell cycle regulation. The data support the hypothesis that targeting KIF11 and its interactors could be a viable strategy to induce mitotic arrest and apoptosis in TP53-mutated cancer cells [12]. The validation through STRING data and cross-referencing with published studies strengthens the case for further experimental investigation.
To enhance the robustness of the findings, future studies could:
- Experimental Validation: Conduct laboratory experiments such as co-immunoprecipitation, Western blotting, and cell viability assays to confirm interactions and functional effects.
- Integration with Omics Data: Combine PPI data with transcriptomic and proteomic datasets to assess the expression levels and activity of the proteins in specific cancer types.
- Computational Modeling: Use network modeling and simulations to predict the impact of targeting specific proteins on cell survival and proliferation.
The findings of this study have potential implications for personalized medicine. By understanding the specific PPI networks altered in tumors with RB1 or TP53 mutations, therapies can be tailored to target these vulnerabilities. For instance:
- RB1-Mutated Cancers: Combining Aurora kinase inhibitors with agents targeting PLK1 or CDC20 may improve outcomes.
- TP53-Mutated Cancers: Targeting KIF11 and its interactors may induce mitotic catastrophe in cancer cells lacking proper cell cycle checkpoints.
These strategies could enhance treatment efficacy and overcome resistance mechanisms.
Optimized STRING API queries, combined with validation using query response data and literature support, are powerful tools for dissecting the molecular underpinnings of cancer cell sensitivity and resistance to therapies. This approach facilitates the identification and validation of key proteins and pathways, informing the development of targeted treatments and combination therapies. Integrating PPI data into cancer research enhances our understanding of disease mechanisms and supports the advancement of personalized medicine. However, computational predictions should be complemented with experimental studies to confirm their biological significance and translate findings into clinical applications.
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- Gorgun, G., et al. The critical role of CDK1 in Aurora A kinase-mediated mitotic lethality in multiple myeloma cells. Cancer Research, 2010.
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The authors acknowledge the STRING database team for providing a valuable resource for protein-protein interaction data and the broader scientific community for contributions to cancer biology research.
The authors declare no conflict of interest.
Supplementary figures and tables generated from the STRING queries are available upon request. These include detailed network diagrams, lists of interaction partners with confidence scores, and additional statistical analyses.
@article{10.1093/nar/gkac1000,
author = {Szklarczyk, Damian and Kirsch, Rebecca and Koutrouli, Mikaela and Nastou, Katerina and Mehryary, Farrokh and Hachilif, Radja and Gable, Annika L and Fang, Tao and Doncheva, Nadezhda T and Pyysalo, Sampo and Bork, Peer and Jensen, Lars J and von Mering, Christian},
title = "{The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest}",
journal = {Nucleic Acids Research},
volume = {51},
number = {D1},
pages = {D638-D646},
year = {2022},
month = {11},
abstract = "{Much of the complexity within cells arises from functional and regulatory interactions among proteins. The core of these interactions is increasingly known, but novel interactions continue to be discovered, and the information remains scattered across different database resources, experimental modalities and levels of mechanistic detail. The STRING database (https://string-db.org/) systematically collects and integrates protein–protein interactions—both physical interactions as well as functional associations. The data originate from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All of these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. The data can be accessed via the website, but also programmatically and via bulk downloads. The most recent developments in STRING (version 12.0) are: (i) it is now possible to create, browse and analyze a full interaction network for any novel genome of interest, by submitting its complement of encoded proteins, (ii) the co-expression channel now uses variational auto-encoders to predict interactions, and it covers two new sources, single-cell RNA-seq and experimental proteomics data and (iii) the confidence in each experimentally derived interaction is now estimated based on the detection method used, and communicated to the user in the web-interface. Furthermore, STRING continues to enhance its facilities for functional enrichment analysis, which are now fully available also for user-submitted genomes.}",
issn = {0305-1048},
doi = {10.1093/nar/gkac1000},
url = {https://doi.org/10.1093/nar/gkac1000},
eprint = {https://academic.oup.com/nar/article-pdf/51/D1/D638/48440966/gkac1000.pdf},
}