The Impact of Genetic Variations on Radiotherapy Toxicity in Breast Cancer Patients: A Meta-Analysis of Acute and Late Skin Adverse Effects

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors present a very interesting meta-analysis of acute and late skin side effects of radiotherapy for breast cancer, focused on genetic variability. The paper is well written and engaging. We agree with the discussion and the limitations of the paper. 

Evaluation of AI-generated text suggests that 19% of the text resembles AI text such as : The ROBINS-I risk of bias assessment (Figure 3) offers critical insights into the quality of the studies contributing to the meta-analysis. Notably, many studies exhibited moderate to serious risks of bias, particularly in areas such as D1 (Confounding) and D5 (missing data). The high risk of bias in these domains is especially concerning, as inadequate control for confounding variables or incomplete data could substantially skew the observed associations.  A serious risk of bias in several studies likely contributes to the substantial heterogeneity detected by the random-effects model.
Overall, the paper can be accepted in its present form. 

Author Response

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions and corrections highlighted in the re-submitted file.

2. Point-by-point response to Comments and Suggestions for Authors

Comment #1. The authors present a very interesting meta-analysis of acute and late skin side effects of radiotherapy for breast cancer, focused on genetic variability. The paper is well written and engaging. We agree with the discussion and the limitations of the paper.

Answer #1. Thank you for your positive and encouraging feedback. We are pleased to hear that you found the meta-analysis engaging and well-written. We appreciate your agreement with our discussion and acknowledgment of the study's limitations. Your supportive comments reinforce the relevance of our work, and we remain committed to improving the clarity and impact of our research through thoughtful peer review.

Comment #2. Evaluation of AI-generated text suggests that 19% of the text resembles AI text such as: The ROBINS-I risk of bias assessment (Figure 3) offers critical insights into the quality of the studies contributing to the meta-analysis. Notably, many studies exhibited moderate to serious risks of bias, particularly in areas such as D1 (Confounding) and D5 (missing data). The high risk of bias in these domains is especially concerning, as inadequate control for confounding variables or incomplete data could substantially skew the observed associations.  A serious risk of bias in several studies likely contributes to the substantial heterogeneity detected by the random-effects model.

Answer #2. Thank you for your observation regarding the resemblance of certain passages in our manuscript to AI-generated text. As disclosed in our original submission, we used Grammarly to assist with grammar and phrasing improvements. This tool may suggest polished or standardized language, which can sometimes resemble AI-generated patterns, particularly in scientific writing where phrasing is often formulaic. We would like to clarify that all content, including interpretation and analysis, was developed by the authors based on a thorough review of the literature and our own meta-analytic work. The specific paragraph you referenced reflects original analysis of the ROBINS-I results and was not generated by AI.

Comment #3. Overall, the paper can be accepted in its present form.

Answer #3. Thank you very much for your positive feedback and for recommending acceptance of our manuscript in its current form. We greatly appreciate your time, thoughtful review, and support of our work.

Reviewer 2 Report

Comments and Suggestions for Authors

1. In paragraph 2.8, excluded studies - I don't see the point in citing them in the main body of the article, it's better to put them in the additional materials.
2. The Results section does not provide genetic markers identified in all included studies, they are only in the Discussion section. I think that tables 12 and 13 should be moved to the Results section.
3. Lines 372-374 repeat the information provided in Table 8. Lines 379-381 repeat the information in Table 9. Similarly, tables 10 and 11. Considering that the tables are small and do not contain additional information beyond that specified in the text, they can be deleted and the description left only in the text part. This will reduce the total number of tables in the manuscript and improve the perception of the information.

Author Response

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions and corrections highlighted in the re-submitted files.

2. Point-by-point response to Comments and Suggestions for Authors

Comment #1. In paragraph 2.8, excluded studies - I don't see the point in citing them in the main body of the article, it's better to put them in the additional materials.

Answer #1. Thank you for your comment and helpful suggestion. In response, we have moved the detailed table listing the excluded studies and their reasons for ineligibility to the appendix (Appendix A3), as recommended. However, we retained Section 2.8 ("Excluded Studies and Reasons for Ineligibility") in the main body of the manuscript to maintain transparency and alignment with PRISMA guidelines, which emphasize the importance of reporting exclusions. This section now serves to direct readers to the appendix for full details, while keeping the main text streamlined.

Comment #2. The Results section does not provide genetic markers identified in all included studies; they are only in the Discussion section. I think that tables 12 and 13 should be moved to the Results section.

Answer #2. Thank you for your valuable observation. In response to your comment, we have moved the previously labelled Tables 12 and 13 (now Tables 3 and 4) to the Results section under the newly added subsection 3.5. Statistically Significant Genetic Markers Identified (Lines 393 – 426). This change ensures that the statistical significative genetic markers identified across the included studies are clearly presented within the Results section, as expected. We have also added brief commentary summarizing the statistically significant associations reported in the included articles to provide context and improve clarity.

In addition, we have created a new subsection, 4.3. Pathway-Based Analysis of Radiogenomic Markers in Skin Toxicity (Line 582), which includes four sub-subsections: 4.3.1. DNA Repair Genes and Radiotherapy Toxicity (Line 596), 4.3.2. Circadian Rhythm and Radiotherapy Toxicity (Line 666), 4.3.3. Oxidative Stress Pathway in Radiation Skin Toxicity (Line 730), and 4.3.4. Inflammatory Gene Polymorphisms in Radiation Skin Toxicity (Line 777). In this section, we discuss and attempt to explain the potential mechanistic links between the SNPs and the acute or late side effects identified in this meta-analysis.

We appreciate your feedback, which helped us strengthen the structure and coherence of our manuscript.

Comment #3. Lines 372-374 repeat the information provided in Table 8. Lines 379-381 repeat the information in Table 9. Similarly, tables 10 and 11. Considering that the tables are small and do not contain additional information beyond that specified in the text, they can be deleted, and the description left only in the text part. This will reduce the total number of tables in the manuscript and improve the perception of the information.

Answer #3. Thank you for your thoughtful and constructive comment. In response, we have removed the former Tables 4, 5, 6, 7, 8, 9, 10, and 11 from the manuscript. The corresponding information has been retained and integrated into the text to preserve clarity and avoid redundancy. This change was made to streamline the presentation, reduce the overall number of tables, and improve the readability and flow of the Results section, particularly within the subsections addressing the pooled meta-analyses of acute and late side effects. We agree that this approach results in a more cohesive and reader-friendly structure.

Reviewer 3 Report

Comments and Suggestions for Authors

1. Line 314: Instead of “Table 1 above,” say “Table 1 (Study Characteristics).” Ensure this label is correctly positioned in the manuscript.
2. Line 323: Report the heterogeneity more interpretively:

"Substantial heterogeneity was observed (I² = 99.3%), indicating variability across studies beyond chance."

3. Figure 4 Legend (Lines 330–337): Streamline and remove repetition:

"Forest plot showing the individual and pooled ORs with 95% CIs for studies on acute radiotherapy-induced skin effects. The size of the markers reflects study weight."

4. Lines 346–353 (Funnel plot): Clarify that the funnel plot corresponds to the acute effects studies, not late effects (as it currently says). This appears to be a copy-paste error.

Author Response

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions and corrections highlighted in the re-submitted files.

2. Point-by-point response to Comments and Suggestions for Authors 

Comment #1. Line 314: Instead of “Table 1 above,” say “Table 1 (Study Characteristics).” Ensure this label is correctly positioned in the manuscript.

Answer #1. Thank you very much for your helpful observation. In response to your comment, we have adjusted the placement of the label to ensure a more coherent and reader-friendly structure. Additionally, we removed the term “above” to eliminate unnecessary directional language and to improve the overall clarity and professional tone of the manuscript (Lines 220–221). We appreciate your attention to these details, which has contributed to enhancing the quality of the text.

Comment #2. Line 323: Report the heterogeneity more interpretively: "Substantial heterogeneity was observed (I² = 99.3%), indicating variability across studies beyond chance."

Answer #2. Thank you very much for your thoughtful and constructive suggestion. We sincerely appreciate your attention to the presentation of heterogeneity measures and fully agree that offering a more interpretive perspective can enhance the reader’s understanding of the results.

In preparing the manuscript, we made the decision to report heterogeneity in a concise manner within the Results section, aiming to maintain clarity and readability in what is already a highly data-intensive part of the text. We were concerned that adding extensive interpretative content at that stage might overwhelm the reader and detract from the flow of the main findings.

That said, we have not overlooked the importance of heterogeneity and its implications. We have addressed this in greater detail within the Discussion section, specifically in Subsection 4.1: Discussion of Radiotherapy-Induced Skin Acute Effects and Associated Genetic Markers (Lines 446–452), and Subsection 4.2: Discussion of Radiotherapy-Induced Skin Late Effects and Associated Genetic Markers (Lines 516–522).

Moreover, to further strengthen our response to this issue, we included a dedicated subsection—Section 4.5: Impact of Methodological Differences Among Included Studies (Lines 872–908)—where we explore the potential sources of the observed heterogeneity and reflect on its implications for the interpretation and generalizability of our meta-analytic findings.

We hope this approach addresses your concern by balancing clarity in results presentation with in-depth interpretation, and we are very grateful for your input, which has undoubtedly helped us improve the manuscript.

Comment #3. Figure 4 Legend (Lines 330–337): Streamline and remove repetition: "Forest plot showing the individual and pooled ORs with 95% CIs for studies on acute radiotherapy-induced skin effects. The size of the markers reflects study weight."

Answer #3. Thank you for your valuable comment. In response, we have merged the previous Figures 4 and 5 into a single, consolidated Figure 4, and combined Figures 6 and 7 into a new Figure 5. This adjustment was made to improve overall readability and minimize visual fragmentation of the results. Additionally, we revised the figure legends to eliminate redundancy, retaining only the essential explanatory details to ensure clarity and coherence. These changes aim to enhance the visual presentation while maintaining a concise and informative format.

Comment #4. Lines 346–353 (Funnel plot): Clarify that the funnel plot corresponds to the acute effects studies, not late effects (as it currently says). This appears to be a copy-paste error.

Answer #4. Thank you for bringing this to our attention. We sincerely apologize for the oversight—indeed, this was an inadvertent copy-paste error. We have now corrected the text to ensure both clarity and accuracy. We truly appreciate your careful and thorough review, which has been invaluable in improving the manuscript.

Reviewer 4 Report

Comments and Suggestions for Authors

Summary
This manuscript presents a meta-analysis investigating the association between genetic polymorphisms and radiotherapy-induced skin toxicities in breast cancer patients. While the authors conducted a statistically thorough and methodologically sound analysis using random-effects models, ROBINS-I for bias, and publication bias tests, several key limitations restrict the overall impact and novelty of the work.

Below are my comments and suggestions for improvement
- Lack of Substantive Findings in Results: The Results section largely summarizes effect sizes and heterogeneity statistics from previously published studies but does not clearly present novel or prioritized biomarkers emerging from the meta-analysis. While SNPs such as XRCC2, IFNG, ATM, TGFB1, and PER3 are noted, they are mentioned only post hoc in the Discussion section (e.g., Section 4.4), rather than presented and substantiated in the core results. This weakens the originality and clarity of the findings.
   Since the authors highlight these genes in the abstract, it is essential that they be supported with corresponding results in the main body. To ensure consistency and strengthen scientific impact, I strongly suggest restructuring the manuscript by integrating relevant parts of Section 4.4 into the Results. For example:
•    The statement: “A statistically significant association between radiation-induced skin toxicities [...] with SNPs in IFNG (rs2069705) and XRCC2 (rs2040639)” is a key finding that should appear in the Results, supported by meta-analysis statistics.
•    Similar statements such as “XRCC2 rs2040639 SNP […] showed a robust association with burning” also belong in the Results section, not in the interpretive Discussion.
In general, these findings should be clearly delineated as outputs of the authors' own meta-analysis and not simply discussed alongside literature.

- High Heterogeneity and Lack of Sensitivity/Subgroup Analysis: The reported heterogeneity is extreme, and the authors acknowledge serious and moderate risk of bias across studies. However, no further effort is made to explore or mitigate this issue. Without sensitivity analyses (e.g., exclusion of lower-quality studies) or subgroup/meta-regression analyses (e.g., stratification by SNP class, study design, ethnicity, radiotherapy dose), the reliability of the pooled estimates is limited. I strongly recommend incorporating such analyses to clarify whether specific subgroups are driving the observed associations and improve interpretability and confidence in the meta-analytic findings.

- Limited Biological Insight: Although candidate genes like XRCC2, IFNG, and ATM are noted, the manuscript lacks deeper biological interpretation. Section 4.3-4.4 begins to address functional relevance, but without supporting pathway analysis, gene-set enrichment, or network-level insights, the manuscript misses an opportunity to link individual polymorphisms to underlying radiotoxicity mechanisms. Additionally, several important clinical or mechanistic observations are compelling but feel anecdotal without being systematically linked to the pooled data. The authors could enhance this section by adding pathway-level or functional annotation tools to contextualize implicated genes, organizing the biological insights more clearly into a dedicated Discussion subsection, or clarifying which findings were derived from their meta-analysis versus which are literature-based.

- Translational Relevance: The authors should more clearly articulate the novelty of this work relative to prior radiogenomic reviews or meta-analyses. It remains unclear whether this study introduces new insights or reinforces known associations. Also, if the manuscript intends to support clinical translation, then feasibility, validation, and generalizability should be briefly discussed. The REQUITE cohort and machine learning models are interesting, but these need better integration with the study’s actual findings.

Author Response

1. Summary

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions and corrections highlighted in the re-submitted files.

2. Point-by-point response to Comments and Suggestions for Authors

Comment #1. This manuscript presents a meta-analysis investigating the association between genetic polymorphisms and radiotherapy-induced skin toxicities in breast cancer patients. While the authors conducted a statistically thorough and methodologically sound analysis using random-effects models, ROBINS-I for bias, and publication bias tests, several key limitations restrict the overall impact and novelty of the work.

Answer #1. Thank you for your valuable comment and for recognizing the statistical and methodological rigor of our meta-analysis, including the use of random-effects models, ROBINS-I bias assessment, and publication bias testing. We appreciate your constructive feedback regarding the limitations that may affect the overall impact and novelty of the work. We have carefully considered these limitations and made corresponding revisions to the manuscript to address them. Detailed explanations and justifications in response to each specific limitation are provided in the relevant responses that follow. These adjustments aim to clarify the study’s contributions, strengthen its interpretation, and ensure transparency regarding its constraints. Thank you again for your thoughtful insights, which have helped improve the clarity and depth of our work.

Comment #2. Lack of Substantive Findings in Results: The Results section largely summarizes effect sizes and heterogeneity statistics from previously published studies but does not clearly present novel or prioritized biomarkers emerging from the meta-analysis. While SNPs such as XRCC2, IFNG, ATM, TGFB1, and PER3 are noted, they are mentioned only post hoc in the Discussion section (e.g., Section 4.4), rather than presented and substantiated in the core results. This weakens the originality and clarity of the findings.

   Since the authors highlight these genes in the abstract, it is essential that they be supported with corresponding results in the main body. To ensure consistency and strengthen scientific impact, I strongly suggest restructuring the manuscript by integrating relevant parts of Section 4.4 into the Results. For example:

• The statement: “A statistically significant association between radiation-induced skin toxicities [...] with SNPs in IFNG (rs2069705) and XRCC2 (rs2040639)” is a key finding that should appear in the Results, supported by meta-analysis statistics.
• Similar statements such as “XRCC2 rs2040639 SNP […] showed a robust association with burning” also belong in the Results section, not in the interpretive Discussion.

In general, these findings should be clearly delineated as outputs of the authors' own meta-analysis and not simply discussed alongside literature.

Answer #2. Thank you for this insightful comment. In response, we have restructured the manuscript to better align the core findings with the highlighted SNPs and to ensure consistency between the Results and Discussion sections. Specifically, we have relocated the former Tables 12 and 13—which summarize the statistically significant SNPs identified in our meta-analysis—to the Results section under a newly added subsection titled “3.5. Statistically Significant Genetic Markers Identified” (now Tables 3 and 4) (Lines 393-426). This directly addresses the need to present prioritized and novel biomarkers as core outcomes of our meta-analysis.

While we briefly described some of these associations in the Results narrative, we opted not to expand extensively in text to maintain the clarity and structure of the Results section. Instead, the supporting data are now presented concisely in the referenced tables, providing readers with a clear view of the SNPs’ relevance, directionality, and statistical significance.

Section 4.3. (Former section 4.4.) (Line 582) now focuses more appropriately on integrating these findings with existing literature, discussing mechanistic implications, biological plausibility, and relevance for future predictive models. This structural adjustment ensures that the Results section highlights the key novel findings of the meta-analysis, while the Discussion interprets them within the broader radiogenomic context—thereby improving both the originality and clarity of the manuscript.

Comment #3. High Heterogeneity and Lack of Sensitivity/Subgroup Analysis: The reported heterogeneity is extreme, and the authors acknowledge serious and moderate risk of bias across studies. However, no further effort is made to explore or mitigate this issue. Without sensitivity analyses (e.g., exclusion of lower-quality studies) or subgroup/meta-regression analyses (e.g., stratification by SNP class, study design, ethnicity, radiotherapy dose), the reliability of the pooled estimates is limited. I strongly recommend incorporating such analyses to clarify whether specific subgroups are driving the observed associations and improve interpretability and confidence in the meta-analytic findings.

Answer #3. Thank you very much for your thoughtful and constructive comment regarding the high heterogeneity and the absence of sensitivity and subgroup/meta-regression analyses. We fully acknowledge the importance of these approaches in improving the interpretability, reliability, and scientific rigor of meta-analytic findings, particularly in complex fields like radiogenomics.

Prior to submitting the manuscript, we had conducted a sensitivity analysis by excluding studies identified as having a high risk of bias. Initially, we chose not to include this analysis in the manuscript due to the persistently high heterogeneity and broader confidence intervals that followed, which we felt did not improve interpretability. However, based on your insightful recommendation, we have now integrated a dedicated subsection — Section 3.2: Impact of Excluding High-Risk Studies on Meta-Analysis Results (Lines 294-322) — where we present a comparative analysis of pooled estimates with and without high-risk studies.

In this sensitivity analysis, we found that excluding high-risk studies resulted in only a marginal decrease in heterogeneity for late effects (I² dropped from 99.45% to 92.75%) and for acute effects (from 99.3% to 97.18%). Despite these reductions, heterogeneity remained high, and the statistical power was significantly reduced. This was reflected in the drop in fail-safe N (from 30,445 to 721 for late effects and from 27,989 to 2,150 for acute effects) and broader confidence intervals, which increased uncertainty. Interestingly, pooled ORs increased slightly (from 1.44 to 1.97 for late toxicities, and from 1.53 to 1.83 for acute toxicities), indicating stronger effect estimates, albeit with wider confidence intervals.

Considering these findings, we decided to retain the pooled analysis including all studies for the main interpretation of results. This approach offers greater statistical power and more stable effect estimates, even when higher-risk studies are included.

To further address the high heterogeneity, we introduced a new subsection — Section 4.5: Impact of Methodological Differences Among Included Studies (Lines 872-908) — where we discuss potential sources of heterogeneity in depth. These include:

• Variability in radiotherapy protocols and doses,
• Diverse grading criteria for skin toxicities (e.g., RTOG, CTCAE, VAS, LENT-SOMA, etc.),
• Differences in statistical analysis methods,
• Demographic variability (e.g., age ranges),
• Study design differences (mostly retrospective observational vs. a few case-control studies),
• And the retrospective nature of all included studies, which inherently increases the risk of bias and data inconsistency.

These methodological disparities — particularly the diverse definitions and assessment tools for skin toxicity — appear to be a more significant driver of heterogeneity than the inclusion of high-risk studies alone. We have made this point clear in our discussion to highlight the structural limitations inherent in the radiogenomic literature and underscore the challenges of harmonizing such data across studies.

Before submitting the manuscript, we also carefully considered several stratification strategies, including those based on study design, ethnicity, radiotherapy dose, and SNP classification. However, after evaluating the composition and reporting within the included studies, we ultimately decided not to perform or present these subgroup/meta-regression analyses in the current version of the manuscript for several specific and methodological reasons.

Regarding study design, most included studies were observational in nature, with only two classifieds specifically as case-control studies. Given that case-control designs are widely recognized as a subtype of observational research and share core methodological features with retrospective cohort studies—such as reliance on previously collected data, similar bias structures, and comparable statistical approaches—we considered stratification by study design to be of limited added value. Furthermore, both case-control studies included in our meta-analysis reported outcomes and methodologies that were highly comparable to those used in the broader group of retrospective observational studies. Therefore, performing a stratified analysis by design would have introduced redundancy without yielding substantial insight or reducing heterogeneity in a meaningful way. We hope the reviewer understands that our decision was driven not by oversight but by a careful weighing of analytical utility versus potential redundancy, with the aim of preserving clarity and avoiding overinterpretation of marginal distinctions.

Regarding ethnicity, we fully acknowledge the importance of genetic ancestry in radiogenomic studies. However, this factor proved particularly complex in our dataset. Many of the included studies involved multinational cohorts or populations of mixed or unreported ethnicity. In some cases, the same study pooled participants from multiple countries (with either similar or distinct ancestral backgrounds) but did not stratify results by ethnic group. Due to this lack of granularity and consistency in the reporting of ethnic composition—and given the risk of misclassification—we found it methodologically inappropriate and potentially misleading to attempt subgroup analyses based on ethnicity. We hope the reviewer understands that our decision stems from caution and scientific integrity rather than oversight.

As for radiotherapy dose stratification, most studies used treatment regimens clustered around a standard total dose of approximately 50 Gy. While some studies reported ranges varied modestly (typically between 40–60 Gy), these variations were often related to protocol-specific boost dosing or minor fractionation adjustments rather than fundamentally different treatment strategies. Additionally, due to inconsistent reporting of total dose, fractionation schedules, and treatment durations across studies, it was not feasible to create coherent and meaningful subgroups. We concluded that attempting dose-based stratification under these circumstances could introduce more bias than clarity.

Lastly, regarding SNP classification or grouping by functional pathway, we absolutely agree that this is a promising avenue for future investigation. In fact, we are currently working on a follow-up meta-analysis specifically aimed at stratifying radiogenomic risk by biological pathways and molecular cascades, which we believe will offer a more nuanced understanding of how these variants operate collectively within specific DNA repair or inflammatory mechanisms. For this reason, and to avoid redundancy or premature conclusions, we felt it was more appropriate not to include incomplete or exploratory subgroup data in this manuscript.

We truly value the reviewer’s suggestion, and we recognize its potential to enhance the translational impact of radiogenomic studies like ours. We sincerely hope that our reasoning is well-received and that our decision not to include these analyses at this stage is understood as a choice made in the interest of methodological rigor and scientific clarity.

Comment #4. Limited Biological Insight: Although candidate genes like XRCC2, IFNG, and ATM are noted, the manuscript lacks deeper biological interpretation. Section 4.3-4.4 begins to address functional relevance, but without supporting pathway analysis, gene-set enrichment, or network-level insights, the manuscript misses an opportunity to link individual polymorphisms to underlying radiotoxicity mechanisms. Additionally, several important clinical or mechanistic observations are compelling but feel anecdotal without being systematically linked to the pooled data. The authors could enhance this section by adding pathway-level or functional annotation tools to contextualize implicated genes, organizing the biological insights more clearly into a dedicated Discussion subsection, or clarifying which findings were derived from their meta-analysis versus which are literature-based.

Answer #4. Thank you for your valuable feedback. In response, we have restructured the former Section 4.4 and renamed it as Section 4.3: Pathway-Based Analysis of Radiogenomic Markers in Skin Toxicity (Line 582). This revised section now includes four focused sub-subsections: 4.3.1. DNA Repair Genes and Radiotherapy Toxicity (Line 596), 4.3.2. Circadian Rhythm and Radiotherapy Toxicity (Line 666), 4.3.3. Oxidative Stress Pathway in Radiation Skin Toxicity (Line 730), and 4.3.4. Inflammatory Gene Polymorphisms in Radiation Skin Toxicity (Line 777).

This structural refinement was made to better align the manuscript’s organization with your suggestion. Specifically, it allows us to integrate the key genetic findings of the meta-analysis with current literature, exploring mechanistic implications, biological plausibility, and potential relevance for predictive modeling. We believe this revision enhances the manuscript’s clarity and interpretive depth by ensuring that the Results section remains focused on presenting the novel findings, while the newly restructured Discussion interprets those findings in a broader radiogenomic context.

We are grateful for your insight, which has helped improve both the scientific rigor and overall readability of the manuscript.

Comment #5. Translational Relevance: The authors should more clearly articulate the novelty of this work relative to prior radiogenomic reviews or meta-analyses. It remains unclear whether this study introduces new insights or reinforces known associations. Also, if the manuscript intends to support clinical translation, then feasibility, validation, and generalizability should be briefly discussed. The REQUITE cohort and machine learning models are interesting, but these need better integration with the study’s actual findings.

Answer #5. Thank you for this valuable comment. In response, we revised several parts of the manuscript to emphasize the translational relevance and novel contributions of our work more clearly.

We would like to clarify that the contribution of our study—whether introducing new insights or reinforcing known associations—is explicitly addressed in Section 4.3. Pathway-Based Analysis of Radiogenomic Markers in Skin Toxicity (Lines 582–810) (former subsection 4.4.). In this section, we systematically interpret the associations identified through our meta-analysis within key biological pathways, offering mechanistic explanations that extend beyond simple statistical reporting and thus provide added scientific value.

Furthermore, we have revised the Conclusions section (Lines 991–1002) to highlight the novelty and utility of this meta-analysis more clearly. Specifically, we emphasized how our integrative and pathway-oriented approach contributes to the current understanding of radiogenomic predictors of skin toxicity, and how it may inform the development of future personalized radiotherapy strategies.

We would like to clarify that the former Section 4.3 has now been repositioned and updated as the current Section 4.4: Relevance and Future Potential of Genetic Markers Identified. In this revised section, we explore the broader implications of the genetic associations identified in our meta-analysis, including their potential role in guiding future personalized radiotherapy approaches. Additionally, we have expanded this section to draw a more explicit comparison with the REQUITE project, highlighting key similarities in methodology and outcomes between our findings and this large-scale prospective study. We discuss how the aggregation of small-effect-size SNPs, including those with non-significant individual odds ratios, can still yield clinically meaningful predictive power—an insight echoed in our pooled results showing a 53% increased risk for acute and 44% for late toxicities among mutation carriers. We also incorporated a more detailed discussion on the concept and utility of Polygenic Risk Scores (PRS) (Lines 848–870). This inclusion further supports the shift toward multifactorial risk modeling and reinforces the translational relevance of our findings.

Lastly, we introduced a new section, 4.6. Feasibility, Validity, and Generalizability of the Meta-Analysis Findings (Lines 910-947), to specifically address these critical aspects. This section discusses the strengths and limitations related to the included studies' methodological variability, retrospective design, and population diversity, ensuring a more comprehensive evaluation of the meta-analysis’s translational potential.

We sincerely appreciate your insights, which have contributed to a clearer and more forward-looking presentation of our work.