Exploring the Effects of Cancer-Associated Mutations in Selected Cell Lines Using Online Computational Tools

doi:10.21203/rs.3.rs-5309600/v1

This research aimed to investigate the mutations in selected cancer-related genes across three different cell lines: A549, NCI-H23, and NCI-H460. The genes of interest included HSP90AA1, TBL1XR1, ZFHX3, DDR2, LIFR, and MYH11, with a focus on their mutation profiles and potential impacts on cancer development. Various online programs, including PolyPhen-2, Mutation Master, and DynaMut, were employed to analyze the functional effects of mutations on the proteins encoded by these genes.

Results indicated that HSP90AA1 and DDR2 had high Mutation Master scores, suggesting a strong association with disease. PolyPhen results showed that HSP90AA1 and ZFHX3 mutations were predicted to be probably damaging, while TBL1XR1 was classified as benign. The DynaMut analysis revealed significant changes in flexibility and stability for most mutations, particularly highlighting the destabilizing effects of TBL1XR1 and ZFHX3.

The Swiss-Model results showed full green structures upon superposition of wild-type and mutant proteins, indicating minimal structural differences. However, the absence of results for ZFHX3 in Mutation Master due to missing transcript data emphasizes the challenges faced in mutation research. The findings provide insights into the mutation landscape of these genes, suggesting potential avenues for further investigation in cancer research.

Cancer

mutation

HSP90AA1

TBL1XR1

ZFHX3

DDR2

LIFR

MYH11

PolyPhen-2

Mutation Master

DynaMut

Swiss-Model.

Cancer remains one of the most pressing global health concerns, being a leading cause of death and suffering worldwide. The genetic basis of cancer has become a critical focus of research, as mutations in specific genes can lead to tumor formation and progression (Wang et al., 2016). These mutations can alter protein function, stability, and interactions, contributing to the complex biology of cancer (Hanahan & Weinberg, 2011).

In this study, we specifically investigate three lung cancer cell lines: A549, NCI-H23, and NCI-H460. These cell lines are widely utilized in cancer research due to their ability to replicate the behavior of human lung tumors (Fong et al., 2020). Our research aims to identify and analyze significant mutations in cancer-related genes within these cell lines to better understand their roles in tumorigenesis.

To assess the impact of these mutations, we employed several online computational tools: PolyPhen-2, Mutation Master, Swiss-Model, and Dynamut. PolyPhen-2 predicts the potential impact of amino acid substitutions on the function of proteins, while Mutation Master evaluates mutation significance. Swiss-Model provides insights into the structural implications of these mutations, and Dynamut analyzes stability and flexibility changes in the protein structure (Adzhubei et al., 2010; Capriotti et al., 2013; Yang et al., 2015).

By comparing wild-type gene variants with their mutated forms, we aim to reveal how specific mutations may influence protein behavior and contribute to the development of lung cancer. This research will enhance our understanding of the molecular mechanisms that underpin cancer and potentially guide future therapeutic strategies.

3.1 Aims:

The primary aim of this research is to investigate the mutations in key cancer-associated genes across selected cancer cell lines. The focus is on understanding how these mutations might influence protein function and stability, which can provide insights into cancer development.

3.2 Objectives:

Identify Key Mutations:To identify and characterize mutations in the genes HSP90AA1, TBL1XR1, ZFHX3, DDR2, LIFR, and MYH11 in the A549, NCI-H23, and NCI-H460 cancer cell lines.

Assess the Impact of Mutations:To evaluate the potential impact of the identified mutations on protein function and stability using computational tools such as PolyPhen, Mutation Master, and DynaMut.

Compare Predictions Across Programs:To compare the results from different computational programs to determine consistency in predicting the damaging effects of the mutations.

Highlight Limitations of Current Databases:To address the challenges faced in mutation research, particularly the absence of transcript data for certain genes, and to emphasize the importance of maintaining comprehensive genomic databases.

Suggest Future Research Directions:To propose further studies that could expand the understanding of mutations in cancer and validate the computational findings through experimental methods.

Cancer is a complex disease characterized by uncontrolled cell growth and proliferation. Many genes are implicated in the development and progression of cancer, and mutations in these genes can significantly impact their function and the cellular pathways they regulate. This literature review focuses on several key genes associated with cancer, including HSP90AA1, TBL1XR1, ZFHX3, DDR2, LIFR, and MYH11, to provide a better understanding of their roles in tumor biology.

HSP90AA1 (Heat Shock Protein 90 Alpha Family Class A Member 1) is an essential molecular chaperone that plays a critical role in protein folding and stabilization. It assists in the proper folding of numerous client proteins, many of which are involved in signaling pathways related to cancer, such as kinases and transcription factors. Research indicates that mutations or overexpression of HSP90AA1 can lead to the accumulation of misfolded proteins and contribute to tumorigenesis (Sonn et al., 2018). Moreover, HSP90AA1 has been recognized as a potential therapeutic target, as inhibiting its function may sensitize cancer cells to chemotherapy (Rizvi et al., 2018).

TBL1XR1 (Transducin-Like Enhancer of Split 1) is known for its role in the Wnt signaling pathway, which is crucial for regulating cell proliferation and differentiation. Mutations in TBL1XR1 can disrupt normal Wnt signaling, leading to uncontrolled cell growth and cancer progression. Recent studies have highlighted the importance of TBL1XR1 in colorectal cancer, suggesting that its mutations could serve as biomarkers for disease prognosis (Cao et al., 2020). Furthermore, targeting the aberrant signaling pathways caused by TBL1XR1 mutations may offer new avenues for therapeutic intervention.

ZFHX3 (Zinc Finger Homeobox 3) has emerged as an important player in cancer biology due to its involvement in various cellular processes, including transcriptional regulation and cell fate determination. Mutations in ZFHX3 have been linked to several cancers, including breast and endometrial cancer. These mutations can lead to loss of function or altered gene expression, contributing to tumor progression (Zhao et al., 2019). Understanding the specific mechanisms by which ZFHX3 mutations influence cancer development is crucial for developing targeted therapies.

DDR2 (Discoidin Domain Receptor Tyrosine Kinase 2) is a receptor tyrosine kinase that mediates cellular responses to collagen. It has been implicated in the development of lung cancer, where mutations in DDR2 have been associated with poor prognosis and increased tumor aggressiveness. Research indicates that targeting DDR2 mutations may provide new strategies for treating lung cancer patients, as these mutations can affect the response to certain therapies (Qin et al., 2020). Identifying DDR2 mutations in clinical samples could also help stratify patients for personalized treatment options.

LIFR (Leukemia Inhibitory Factor Receptor) is involved in various signaling pathways that regulate cell survival and proliferation. It has been suggested that mutations in LIFR can disrupt normal cytokine signaling, leading to tumorigenesis. Studies have shown that altered LIFR expression is associated with increased tumor growth in various cancers, making it a potential target for therapeutic intervention (González et al., 2017). Exploring the relationship between LIFR mutations and cancer progression could help identify new biomarkers for early detection.

MYH11 (Myosin Heavy Chain 11) is predominantly expressed in smooth muscle cells and plays a role in muscle contraction. Recent research has identified mutations in MYH11 that may contribute to tumor microenvironments and influence cancer cell behavior. These mutations can affect the tumor stroma and promote cancer cell migration and invasion (Hoxha et al., 2020). Investigating the role of MYH11 mutations in cancer may provide insights into the interactions between tumor cells and their surrounding environment.

In conclusion, the literature indicates that mutations in cancer-associated genes, such as HSP90AA1, TBL1XR1, ZFHX3, DDR2, LIFR, and MYH11, can significantly alter protein functions and contribute to cancer development. However, there remains a need for further research to fully understand the mechanisms by which these mutations affect cellular pathways and their implications for cancer treatment. This study aims to build upon existing knowledge by analyzing the mutational landscape of these genes across various cancer cell lines.

5.1 Research Design

This study aimed to analyze mutations in key cancer-associated genes, specifically HSP90AA1, TBL1XR1, ZFHX3, DDR2, LIFR, and MYH11, across different cancer cell lines. The methods included data collection from various bioinformatics databases, mutation prediction tools, and structural analysis software.

5.2.1 Materials

1. Cancer Cell Lines: The study utilized three lung cancer cell lines:

○ A549 (human alveolar basal epithelial cells)

○ NCI-H23 (small cell lung carcinoma)

○ NCI-H460 (large cell lung carcinoma)

2. Bioinformatics Databases:

○ COSMIC (Catalogue of Somatic Mutations in Cancer): This database provided information on mutations in the selected genes across various cancers.

○ Ensembl: Used for retrieving transcript information and genomic sequences related to the selected genes.

3. Mutation Prediction Tools:

○ DynaMut: This tool was used to predict the impact of mutations on protein stability and flexibility. It provided values like ΔΔG, which indicates the change in free energy due to mutations.

○ PolyPhen: This tool was utilized to assess the potential impact of amino acid substitutions on the structure and function of the protein.

○ Mutation Master: It provided an overall score indicating whether mutations were disease-causing.

4. Protein Structure Analysis:

○ Swiss-Model: This software was used for homology modeling to predict the three-dimensional structure of proteins based on their amino acid sequences. It also allowed for the comparison of wild-type and mutant protein structures.

5.2.2 Methods

1. Data Collection:

○ Data on mutations in the selected genes were collected from the COSMIC database and cross-referenced with Ensembl for transcript details.

○ The mutation types and their corresponding sequences were recorded for further analysis.

2. Mutation Prediction:

○ Each identified mutation was analyzed using DynaMut and PolyPhen to evaluate its potential impact on protein function. This included checking the stability of the protein and the likelihood of the mutation being disease-causing.

○ Mutation Master was used to score the identified mutations, helping to determine their significance in cancer biology.

3. Structural Analysis:

○ The wild-type and mutant protein structures were generated using Swiss-Model. Superposition of these structures was performed to visualize the differences caused by mutations.

○ The analysis of the superposed images allowed for the assessment of structural alterations that could affect protein function.

4. Interpretation of Results:

○ The results from the mutation prediction tools and structural analysis were compiled to interpret the implications of the mutations on cancer development and progression.

○ A focus was placed on discussing the relevance of mutations in each gene concerning their known roles in cancer biology.

6.1 PolyPhen-2 results:

The following table shows the predictions made by PolyPhen-2 for the selected gene mutations, indicating their potential impact on protein function.

Gene

Cell Line

Ensembl Transcript

AA Mutation

CDS Mutation

PolyPhen HumDiv Score

HumDiv Sensitivity

HumDiv Specificity

Damage Prediction (HumDiv)

PolyPhen HumVar Score

HumVar Sensitivity

HumVar Specificity

Damage Prediction (HumVar)

HSP90AA1

A549

ENST00000334701.11

p.R477I

c.1430G>T

1

0

1

Probably damaging

0.991

0.5

0.95

Probably damaging

TBL1XR1

A549

ENST00000430069.5

p.T290A

c.868A>G

0.125

0.93

0.86

Benign

0.094

0.91

0.68

Benign

ZFHX3

A549

ENST00000641206.2

p.L1473F

c.4417C>T

0.985

0.74

0.96

Probably damaging

0.84

0.73

0.88

Possibly damaging

DDR2

NCI-H23

ENST00000367922.7

p.G101W

c.301G>T

1

0

1

Probably damaging

1

0

1

Probably damaging

LIFR

NCI-H460

ENST00000263409.8

p.L493F

c.1477C>T

0

1

0

Benign

0

1

0

Benign

MYH11

NCI-H460

ENST00000396324.7

p.D764E

c.2292C>A

0.165

0.92

0.87

Benign

0.653

0.79

0.84

Possibly damaging

Table 6.1.1 PolyPhen-2 Results for Selected Gene Mutations

6.2 Mutation Master results:

This table presents the Mutation Master scores, which predict whether the selected mutations are disease-causing or polymorphisms.

Gene	Cell Line	Ensembl Transcript	AA Mutation	CDS Mutation	Alteration Type	Mutation Master Score	Prediction
HSP90AA1	A549	ENST00000334701.11	p.R477I	c.1430G>T	Single base exchange	97	Disease-causing
TBL1XR1	A549	ENST00000430069.5	p.T290A	c.868A>G	Single base exchange	58	Disease-causing
ZFHX3	A549	ENST00000641206.2	p.L1473F	c.4417C>T	Single base exchange	-	-
DDR2	NCI-H23	ENST00000367922.7	p.G101W	c.301G>T	Single base exchange	184	Disease-causing
LIFR	NCI-H460	ENST00000263409.8	p.L493F	c.1477C>T	Single base exchange	22	Polymorphism
MYH11	NCI-H460	ENST00000396324.7	p.D764E	c.2292C>A	Single base exchange	45	Disease-causing

Table 6.2.1 Mutation Master Predictions for Selected Genes

6.3 SWISS-MODEL Superposed images

The following figures depict the superimposed structures of wild-type and mutant proteins generated via Swiss-Model, with all green structures indicating minimal structural differences between the wild-type and mutant forms.

6.4 DynaMut results:

The table below outlines the ΔΔG values from the DynaMut analysis, highlighting the effect of mutations on the stability and flexibility of proteins.

Gene	Cell Line	ΔΔG (kcal/mol)	ΔΔG ENCoM (kcal/mol)	ΔΔG mCSM (kcal/mol)	ΔΔG SDM (kcal/mol)	ΔΔG DUET (kcal/mol)	ΔΔSVib ENCoM (kcal.mol⁻¹.K⁻¹)	Flexibility Change
HSP90AA1	A549	0.432	0.123	0.385	0.340	0.524	-0.154	Decrease in flexibility
TBL1XR1	A549	-0.786	-0.361	-1.656	0.330	-1.382	0.451	Increase in flexibility
ZFHX3	A549	-0.086	0.048	-0.678	-0.190	-0.728	-0.060	Decrease in flexibility
DDR2	NCI-H23	0.483	2.058	-1.166	-0.880	-1.146	-2.572	Decrease in flexibility
LIFR	NCI-H460	0.133	0.072	-0.501	-0.280	-0.399	-0.090	Decrease in flexibility
MYH11	NCI-H460	0.655	0.403	-0.478	-0.510	-0.414	-0.504	Decrease in flexibility

Table 6.4.1 DynaMut Analysis Results of Stability and Flexibility Changes in Protein Structures

Amino acids colored according to the vibrational entropy change upon mutation. BLUE represents a rigidification of the structure and RED a gain in flexibility

This research aimed to investigate mutations in several cancer-associated genes across three different cell lines: A549, NCI-H23, and NCI-H460. The primary objective was to assess how these mutations could impact protein function and stability, which is crucial for understanding cancer mechanisms and potential therapies.

PolyPhen Results:The PolyPhen analysis provided predictions on the functional impact of amino acid changes in the proteins. For instance, the mutation p.R477I in HSP90AA1 received a HumDiv score of 1, indicating it is "Probably damaging." This suggests that this mutation may significantly impair the protein's function, potentially contributing to cancer progression.

The mutation p.T290A in TBL1XR1 scored 0.125, predicting it to be "Benign." This indicates that this mutation is unlikely to have a significant effect on the protein's function, which suggests it may not play a role in disease development.

In the case of ZFHX3, the mutation p.L1473F scored 0.985 for HumDiv, also suggesting a probably damaging effect. This finding raises concerns regarding the potential role of this mutation in cancer. Conversely, the mutation p.G101W in DDR2 scored 1, indicating it is "Probably damaging." This suggests that DDR2 mutations could lead to functional disruptions, contributing to tumorigenesis.

LIFR showed a mutation p.L493F that received a HumDiv score of 0, indicating a benign prediction. This suggests that this specific alteration is unlikely to significantly impact the protein's function. On the other hand, the mutation p.D764E in MYH11 received a HumVar score of 0.653, indicating it is "Possibly damaging," suggesting a potential impact on function.

Mutation Master Results:The Mutation Master analysis categorized mutations based on their predicted pathogenicity. Mutations in HSP90AA1 and TBL1XR1 were both labeled as "Disease-causing," implying a strong link to cancer. The mutation in ZFHX3, although not yielding results due to a missing transcript, is significant because findings from other programs indicated potential implications for protein function and stability. Similarly, DDR2 mutations were classified as "Disease-causing," indicating a possible role in cancer progression.

DynaMut Results:The DynaMut analysis provided essential data on how mutations could affect protein stability and flexibility. For HSP90AA1, a ΔΔG value of 0.432 kcal/mol was observed, suggesting that this mutation may stabilize the protein. This finding is interesting because a stable protein can often maintain its function, suggesting that this particular mutation might not be as harmful as predicted by PolyPhen.

In contrast, TBL1XR1 exhibited a notably destabilizing ΔΔG value of -0.786 kcal/mol, indicating that this mutation could significantly affect the protein's stability and potentially lead to dysfunctional interactions that promote cancer development. ZFHX3 showed a ΔΔG of -0.086 kcal/mol, indicating a destabilizing effect, which could lead to loss of function or abnormal protein behavior.

For DDR2, a ΔΔG of 0.483 kcal/mol suggests potential stability; however, the damaging prediction highlights the complexity of its role in cancer. LIFR displayed a ΔΔG of 0.133 kcal/mol, suggesting slight stabilization, while the mutation p.D764E in MYH11 showed a ΔΔG of 0.655 kcal/mol, which may indicate stability but could also suggest a functional impairment based on other analyses.

The analysis also assessed the ΔΔSVib values, which provide insight into changes in molecular flexibility due to mutations. For HSP90AA1, a decrease of -0.154 kcal/mol.K was noted, indicating that the mutation may lead to a more rigid protein structure. Conversely, TBL1XR1 demonstrated an increase of 0.451 kcal/mol.K, suggesting enhanced flexibility that might alter its function and interactions with other molecules.

All the superposed images from the Swiss Model analysis displayed a full green structure, suggesting that the overall shape of the proteins remained stable despite the mutations. However, the predicted damaging effects from PolyPhen and Mutation Master indicate that the mutations could still have serious implications for protein function.

In summary, the findings highlight that mutations in HSP90AA1, TBL1XR1, and DDR2 are likely to have damaging effects that could contribute to cancer progression. The mutations in ZFHX3 and MYH11 also warrant attention, with potential impacts on protein function that could influence cancer mechanisms. Although LIFR and some other mutations were predicted to be benign, the overall analysis provides valuable insights into how specific mutations can affect cancer-related proteins, which could be important for developing targeted therapies in the future.

8.1 Discussion

This research focused on investigating mutations in key cancer-associated genes across three different cell lines: A549, NCI-H23, and NCI-H460. The findings suggest that specific mutations in these genes can potentially contribute to cancer development. Notably, the mutations in HSP90AA1, TBL1XR1, and DDR2 were found to be likely damaging, indicating their importance in cancer biology. The results from the PolyPhen, Mutation Master, and DynaMut analyses provided a comprehensive view of how these mutations might affect protein function and stability.

The absence of results for ZFHX3 due to a missing transcript highlights a significant challenge in mutation research. However, the presence of results from other programs indicates that it may still play a role in cancer mechanisms. This situation emphasizes the need for more comprehensive genomic databases to ensure that all relevant data is accessible for researchers.

8.2 Future Scope

Future research could explore additional genes associated with cancer to build a more extensive understanding of the genetic landscape of tumors. Expanding the analysis to include more cell lines or patient-derived samples could provide insights into the variability of mutations across different cancer types. Furthermore, using advanced computational tools and machine learning algorithms may enhance the predictive power of mutation effects on protein function.

Additionally, future studies could aim to validate these computational predictions through experimental methods. In vitro studies could help confirm the functional impacts of the identified mutations, allowing researchers to better understand how these alterations contribute to cancer progression. Investigating the interactions between mutated proteins and other cellular components could also provide valuable insights into their roles in cancer biology.

8.3 Limitations

Despite the valuable insights gained from this research, several limitations should be acknowledged. One major limitation is the reliance on computational predictions, which may not always accurately reflect real-world biological scenarios. While tools like PolyPhen, Mutation Master, and DynaMut are helpful, they are not infallible, and experimental validation is necessary to confirm the effects of mutations.

Additionally, the analysis was limited to three cell lines, which may not fully represent the diversity of mutations seen in various cancer types. Future studies should consider analyzing a broader range of cell lines or clinical samples to gain a more comprehensive understanding of the mutations' impact on cancer biology.

Furthermore, the lack of transcript data for ZFHX3 demonstrates the importance of maintaining and updating genomic databases. This gap can hinder research efforts and limit the ability to make informed conclusions about specific genes.

In conclusion, while this research provided valuable insights into the potential impact of mutations in key cancer-related genes, further studies are needed to validate these findings and explore additional genetic factors involved in cancer. Addressing the limitations and expanding the scope of research will be crucial in advancing our understanding of cancer mechanisms and developing targeted therapies.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Funding

No funding was received for this study.

Author Contribution

I, Rohid Premkumar, as the sole researcher, conceptualised the study, conducted all computational analyses, including mutation analysis using PolyPhen, Mutation Master, Swiss-Model, and DynaMut, and interpreted the data. I also prepared all figures, wrote the main manuscript text, and reviewed and approved the final manuscript for submission.

Data Availability

Data that support the findings of this study are derived from publicly available databases. The analysis utilized mutation data from the COSMIC database, along with transcript information from Ensembl. The relevant links to access these databases are as follows:COSMIC: https://cancer.sanger.ac.uk/cosmicA549 Cell Line- COSMIC Sample ID: COSS2776223NCI-H23 Cell Line- COSMIC Sample ID: COSS1998461NCI-H460 Cell Line- COSMIC Sample ID: COSS1998463Ensembl: https://www.ensembl.org/A549 Cell Line:HSP90AA1 Transcript ID- ENST00000334701.11TBL1XR1 Transcript ID- ENST00000430069.5ZFHX3 Transcript ID- ENST00000641206.2 NCI-H23 Cell Line:DDR2 Transcript ID- ENST00000367922.7NCI-H460 Cell Line:LIFR Transcript ID- ENST00000263409.8MYH11 Transcript ID- ENST00000396324.7These datasets analyzed during the current study are publicly accessible.

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No competing interests reported.

Exploring the Effects of Cancer-Associated Mutations in Selected Cell Lines Using Online Computational Tools

Status:

Version 1

Abstract

Figures

2. Introduction

3. Aims and Objectives

3.1 Aims:

3.2 Objectives:

4. Review of Literature

5. Methods and Materials

5.1 Research Design

5.2.1 Materials

5.2.2 Methods

6. Results

7. Interpretation of Results

8. Discussion, Future Scope and Limitations

8.1 Discussion

8.2 Future Scope

8.3 Limitations

Declarations

Ethics approval and consent to participate

Consent for publication

Funding

Author Contribution

Data Availability

References

Additional Declarations

Status:

Version 1