Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Uçar and et al., evaluate the predictive and prognostic value of pretreatment NPAR—a marker of systemic inflammation and nutritional status—in 194 patients with non-metastatic breast cancer (nMBC) undergoing neoadjuvant chemotherapy (NAC). The study covers a significant clinical gap by predicting resistance to neoadjuvant chemotherapy using a readily available, reasonably priced marker (NPAR). However, there are comments that need to be addressed prior to publication.

Here is a structured reviewer comment.

• Introduction page 2.
• Author has to add the study hypothesis prior to the aim of the study. e.g. elevated NPAR baseline correlates with chemoresistance and serves as an independent predictor of unfavorable long-term outcomes, including reduced overall and disease-free survival in the nMBC population.
• line 49; the author should write a suitable background about the NPAR as biomarker. More information about the bio-marker is required, such as the location (which gene) the family and which pathway effect,........etc

 

• Methodology
• The author should add the name of the hospital/center or institute were the sample collected. Also, should describe as well whether the study is single-center or multicentered.
• There is no description in Method Part how the authors evaluated these markers. Where is the expression? It is not clear how the author categorized ER, PR, HER2 Ki-67, LVI, PNI and ECE to positive and negative (yes and no) values. This need to clearly mentioned in the methodology as well as presented as supplementary Table
•  
• Results:
• Table 1: the author utilized 15 as a cut-off point for Ki-67. This should describe clearly on the methodology, including why use 15 not other cut-off point, and according to what (references).
• Table 1: the author wrote “Subtypes”. Should edited to “Molecular BC subtypes” and the molecular subtypes should write according to the WHO

Luminal-A like BC

Luminal-B BC (HER2-)

Luminal-B BC (HER2+)

HER2-enriched BC

Triple negative BC (TNBC)

• Discussion page 10; lines 207-211. “In the present study, we demonstrated the decisive role of the NPAR—a marker reflecting both systemic inflammation and nutritional status—on treatment success and survival outcomes in patients with non-metastatic breast cancer. Our findings indicated that elevated NPAR levels are associated with poor objective response (OR) rates, an increased risk of disease progression, and significantly lower OS and DFS. Furthermore, NPAR was identified as an independent prognostic marker affecting both disease-free and overall survival”. Repetition should be deleted.
• Limitations:
• Number of study sample (n=190) is limited and should be highlighted as a limitation for this present study.

Minor comments:

Abstract need more attention:

• Abstract: page 1 line 15. Provide the full name of nMBC.
• Abstract: page 1 line 17. Provide the full name of ROC.
• Abstract: page 1 line 20. Provide the full name of NAC.
• Abstract: page 1 line 21. Provide the full name of OS.

 

Author Response

                             Response to Reviewer 1

Introduction page 2. Author has to add the study hypothesis prior to the aim of the study. e.g. elevated NPAR baseline correlates with chemoresistance and serves as an independent predictor of unfavorable long-term outcomes, including reduced overall and disease-free survival in the nMBC population.

Response 1: We thank the reviewer for this valuable suggestion. In accordance with the reviewer’s recommendation, a clear study hypothesis has been added prior to the statement of the study aim in the Introduction section. Specifically, we hypothesized that elevated baseline NPAR levels are associated with chemoresistance and may serve as an independent predictor of unfavorable long-term outcomes, including reduced overall survival and disease-free survival in the nMBC population. The manuscript has been revised accordingly

line 49; the author should write a suitable background about the NPAR as biomarker. More information about the bio-marker is required, such as the location (which gene) the family and which pathway effect,........etc

Response 2:  We thank the reviewer for this valuable suggestion. In response to this comment, we have expanded the background section in the Introduction to provide a more comprehensive description of the neutrophil percentage-to-albumin ratio (NPAR) as a biomarker. Specifically, we added detailed information regarding the biological characteristics of neutrophils and albumin, including the genetic background of albumin (ALB gene located on chromosome 4q13.3), its membership in the albumin gene family, and its role as a negative acute-phase protein involved in inflammatory pathways. We also clarified the biological mechanisms underlying NPAR, emphasizing its ability to reflect systemic inflammation through neutrophil-mediated immune responses and reduced albumin levels associated with cytokine-mediated inflammatory signaling pathways (e.g., IL-6 and TNF-α pathways). Relevant literature has also been incorporated to support these statements. These additions can be found in the revised manuscript in the Introduction section

Methodology The author should add the name of the hospital/center or institute were the sample collected. Also, should describe as well whether the study is single-center or multicentered.

Response 3: We thank the reviewer for this helpful comment. In accordance with this suggestion, we have added the name of the hospital where the samples were collected and clarified that the study was conducted as a single-center study. This information has now been included in the Methods section of the revised manuscript

 

There is no description in Method Part how the authors evaluated these markers. Where is the expression? It is not clear how the author categorized ER, PR, HER2 Ki-67, LVI, PNI and ECE to positive and negative (yes and no) values. This need to clearly mentioned in the methodology as well as presented as supplementary table.

Response 4: We thank the reviewer for this important comment. We agree that the methodology describing the evaluation of pathological markers should be clearly specified. Therefore, we have revised the Materials and Methods section to provide detailed information on how ER, PR, HER2, Ki-67, lymphovascular invasion (LVI), perineural invasion (PNI), and extracapsular extension (ECE) were assessed and categorized. These markers were evaluated according to standard pathological criteria and international guidelines. In addition, a supplementary table summarizing the definitions and cut-off values used for each marker has been added to improve clarity.

 

 

Results:Table 1: the author utilized 15 as a cut-off point for Ki-67. This should describe clearly on the methodology, including why use 15 not other cut-off point, and according to what(references).

 

Response 5: We thank the reviewer for this important comment. In response, we have clarified the methodology regarding the Ki-67 cut-off value used in our study. Ki-67 expression was categorized using a cut-off value of 15% to distinguish low and high proliferative activity. This threshold was selected based on previous studies and expert consensus recommendations indicating that Ki-67 values around 14–15% are commonly used to differentiate luminal A-like from luminal B-like breast cancer subtypes and to stratify tumor proliferation activity. In particular, the St. Gallen International Expert Consensus has suggested thresholds in this range for clinical classification of breast cancer subtypes, and several studies have reported that Ki-67 values ≥15% are associated with higher proliferative activity and worse prognostic features. Accordingly, the rationale for using a 15% cut-off has now been clearly described in the Methods section of the revised manuscript, and appropriate references have been added.

 

Table 1: the author wrote “Subtypes”. Should edited to “Molecular BC subtypes” and the molecular subtypes should write according to the WHO Luminal-A like BC, Luminal-B BC (HER2-), Luminal-B BC (HER2+), HER2-enriched BC,Triple negative BC (TNBC)

Response 6: We thank the reviewer for this valuable comment. In accordance with this suggestion, the term “Subtypes” in Table 1 has been revised to “Molecular BC subtypes”. Furthermore, the molecular breast cancer subtypes have been reorganized and presented according to the WHO classification as follows: Luminal-A like BC, Luminal-B BC (HER2−), Luminal-B BC (HER2+), HER2-enriched BC, and Triple negative BC (TNBC). These changes have been implemented in the revised Table 1 of the manuscript.

Discussion page 10; lines 207-211. “In the present study, we demonstrated the decisive role of the NPAR—a marker reflecting both systemic inflammation and nutritional status—on treatment success and survival outcomes in patients with non-metastatic breast cancer. Our findings indicated that elevated NPAR levels are associated with poor objective response (OR) rates, an increased risk of disease progression, and significantly lower OS and DFS. Furthermore, NPAR was identified as an independent prognostic marker affecting both disease-free and overall survival”. Repetition should be deleted.

Response 7: We thank the reviewer for this insightful comment. In response, we have revised the Discussion opening paragraph to remove repetitive statements while clearly highlighting the main findings. The revised paragraph now concisely presents the prognostic significance of NPAR, its association with objective response, disease progression, and survival outcomes, and emphasizes its role as an independent predictor of both disease-free survival (DFS) and overall survival (OS). The revised text also integrates the clinical relevance of NPAR in risk stratification and treatment planning. These changes can be found in the Discussion section of the revised manuscript

 

Limitations:Number of study sample (n=190) is limited and should be highlighted as a limitation for this present study.

 

Response 8: We thank the reviewer for this important comment. In response, we have highlighted the relatively small sample size (n=190) as a limitation in the revised manuscript and noted that future studies with larger cohorts are needed to validate our findings. This has been added to the Limitations section

                                                           Minor comments:

Abstract need more attention:Abstract: page 1 line 15. Provide the full name of nMBC. Abstract: page 1 line 17. Provide the full name of ROC. Abstract: page 1 line 20. Provide the full name of NAC. Abstract: page 1 line 21. Provide the full name of OS.

Response 9: We thank the reviewer for this helpful comment. In accordance with the suggestion, the full names of all abbreviations have been provided at their first occurrence in the Abstract. Specifically, the abbreviations non-metastatic breast cancer (nMBC), receiver operating characteristic (ROC), neoadjuvant chemotherapy (NAC), and overall survival (OS) have been defined when first mentioned. The Abstract has been revised accordingly.

Reviewer 2 Report

Comments and Suggestions for Authors

Major comments

1. Sample size and cohort selection

• The study includes 194 patients over a 20-year period in a single center. This appears to be quite small for such a long inclusion period and may raise concerns about selection bias. The authors should report how many breast cancer patients received neoadjuvant treatment in the center during this period, and clearly detail the inclusion and exclusion process (or why only such a small number of patients were included).

• Please justify the sample size and discuss whether a formal sample size/power consideration was performed for the main endpoints (OS, DFS, response to neoadjuvant therapy).

2. Imbalance between NPAR groups and statistical power

• The high-NPAR group consists of only 44 patients, compared with 150 patients in the low-NPAR group. This marked imbalance, combined with the overall modest sample size, may limit statistical power and the stability of effect estimates in survival and multivariable analyses.

• The authors should explicitly discuss how this imbalance affects the robustness of their findings and consider sensitivity analyses ( for example - alternative categorizations, treating NPAR as a continuous variable).

3. Univariate vs multivariate analyses and independent prognostic value

• Several clinicopathological factors (e.g., pCR, perineural invasion, lymphovascular invasion, tumor necrosis, extracapsular extension) show statistically significant associations in univariate analyses but lose significance in multivariate Cox regression for DFS and OS. This may suggest collinearity, and limited power.

• The authors should report the number of events for DFS and OS and justify the number of covariates included in the Cox models to avoid overfitting. They should also explore and report potential multicollinearity among variables.

• Importantly, if NPAR does not remain significant in multivariate models, it should not be presented as an independent prognostic factor. The abstract, results, and conclusions must clearly distinguish between univariate associations and adjusted results, and the wording of the conclusions should be toned down accordingly.

4. Kaplan–Meier curves versus Cox regression

• The Kaplan–Meier curves show statistically significant differences in survival between NPAR groups, whereas multivariate Cox regression does not confirm NPAR as an independent predictor. This discrepancy must be explicitly acknowledged and interpreted.

• The authors should clarify that the significant differences in Kaplan–Meier curves reflect unadjusted comparisons, while adjusted analyses may attenuate or abolish this effect due to confounding factors. The current text may overstate the prognostic role of NPAR.

5. Definition and validation of the NPAR cut-off (ROC analysis)

• The optimal NPAR cut-off was determined by ROC curve analysis in the same cohort in which it was subsequently tested, without any internal or external validation. This data-driven approach in a small, imbalanced sample carries a substantial risk of overfitting.

• The authors should specify for which endpoint the ROC analysis was performed (OS, DFS, response, or another outcome), report the corresponding AUC with 95% CI, and describe the criterion used to select the threshold.

6. Handling of NPAR as a variable

• The manuscript focuses on a dichotomized NPAR based on a single ROC-derived cut-off. Dichotomization of continuous variables usually reduces statistical power and may obscure dose–response relationships.

• The authors should consider additional analyses modelling NPAR as a continuous predictor in Cox regression for DFS and OS, and report whether NPAR remains associated with outcomes in this framework. This would provide a more robust assessment of its prognostic value.

7. Positioning within the existing literature

• Recent studies have investigated the association of NPAR with breast cancer incidence and prognosis. The introduction and discussion should better position the present study in relation to this existing studies in the literature, highlighting what is genuinely novel (especially focus on neoadjuvant-treated patients, specific endpoints or particular subgroups).

• The authors should clearly state whether similar NPAR cut-offs have been reported previously and how their findings compare with those of other cohorts.

• In the Discussion, a substantial part of the text is devoted to studies on other malignancies (such as bladder, colorectal, head and neck cancers) when comparing NPAR-related findings. While these references are useful to support the general concept of inflammation–nutrition indices, the discussion should be more clearly focused on breast cancer.

• The authors should incorporate and critically discuss the growing body of literature specifically addressing NPAR in breast cancer, including recent large epidemiologic and clinical studies assessing its association with breast cancer risk, distant metastasis, and prognosis, and better position their findings in that context.

• However, comparisons with results in other tumor types should be kept concise and clearly labelled as extrapolations, while the main comparison framework should be the existing evidence in breast cancer.

9. Limitations

• The current Limitations section is inadequate and requires substantial expansion and reorientation. While it acknowledges the retrospective design and single-center nature, it fails to address several critical methodological weaknesses:

  • Small sample size and imbalance: 194 patients over 20 years is modest, and the high-NPAR group (n=44) severely limits statistical power, model stability, and the capability to generalise. 

  • Loss of significance in multivariable analysis: NPAR and other factors significant in univariate/Kaplan–Meier analyses do not retain independent prognostic value in Cox models – this must be candidly discussed rather than downplayed.

  • Overfitting risk from ROC-derived cutoff: The data-driven cutoff lacks validation (internal or external) in this small cohort.

  • Selection bias: Justify cohort size against total NAC patients in the center.

• The current text overstates strengths ('strong evidence', 'high predictive power') while understating weaknesses, then transitions into unsubstantiated clinical recommendations. Revise to provide a balanced, transparent limitations paragraph first, followed by a separate 'Strengths/Clinical implications' paragraph with appropriately cautious language.”

Comments on the Quality of English Language

Minor comments

• In the Materials and Methods section (around lines 84–88, page 3), where breast cancer staging is described, a standard reference for the staging system used should be cited (respectively - the AJCC Cancer Staging Manual, 8th edition). Please specify explicitly which AJCC edition was applied and add the corresponding reference.

• In the Discussion part, the first paragraph (approximately lines 197–211, page 10) contains several sentences that are identically repeated, without meaningful rephrasing. Similar sentences reappear in lines 222–229 (page 11). This thing reduces clarity and makes the manuscript harder to follow. Please revise the first paragraph of the Discussion (lines 197–211) and the subsequent paragraphs (lines 222–229) to remove repeated sentences and improve conciseness and readability. 

• Also, other relevant studies and discussion should be included here, which may highlight the importance of the studied subject.

• Please revise the English language for clarity and grammar throughout the manuscript; a professional language editing service may be helpful.

Author Response

Response to Reviewer 2

                                      Major comments

1. Sample size and cohort selection

The study includes 194 patients over a 20-year period in a single center. This appears to be quite small for such a long inclusion period and may raise concerns about selection bias. The authors should report how many breast cancer patients received neoadjuvant treatment in the center during this period, and clearly detail the inclusion and exclusion process (or why only such a small number of patients were included). Please justify the sample size and discuss whether a formal sample size/power consideration was performed for the main endpoints (OS, DFS, response to neoadjuvant therapy).

Response 1: We thank the reviewer for this important comment. During the study period (2004–2024), a substantially larger number of breast cancer patients were treated at our center, and a considerable proportion received neoadjuvant chemotherapy. However, only patients fulfilling all predefined inclusion criteria and having complete clinical, laboratory, and long-term follow-up data were included in the final analysis. After applying these criteria, 194 patients were eligible for the present study. We have now clarified the cohort selection process in the Methods section (Study Population). Because of the retrospective design of the study, a formal a priori sample size calculation was not performed. Instead, all eligible patients meeting the study criteria were included. This clarification has also been added to the Statistical Analysis section.

1. Imbalance between NPAR groups and statistical power

The high-NPAR group consists of only 44 patients, compared with 150 patients in the low-NPAR group. This marked imbalance, combined with the overall modest sample size, may limit statistical power and the stability of effect estimates in survival and multivariable analyses. The authors should explicitly discuss how this imbalance affects the robustness of their findings and consider sensitivity analyses ( for example - alternative categorizations, treating NPAR as a continuous variable).

Response 2: We appreciate the reviewer’s comment regarding the imbalance between NPAR groups. In our cohort, the number of patients with high NPAR was smaller than that of the low-NPAR group, which may influence statistical power and the stability of effect estimates. We have now explicitly acknowledged this issue as a limitation in the Discussion section. We agree that larger cohorts with more balanced group distributions would allow more robust estimation of the prognostic effect of NPAR.

1. Univariate vs multivariate analyses and independent prognostic value

Several clinicopathological factors (e.g., pCR, perineural invasion, lymphovascular invasion, tumor necrosis, extracapsular extension) show statistically significant associations in univariate analyses but lose significance in multivariate Cox regression for DFS and OS. This may suggest collinearity, and limited power.The authors should report the number of events for DFS and OS and justify the number of covariates included in the Cox models to avoid overfitting. They should also explore and report potential multicollinearity among variables.Importantly, if NPAR does not remain significant in multivariate models, it should not be presented as an independent prognostic factor. The abstract, results, and conclusions must clearly distinguish between univariate associations and adjusted results, and the wording of the conclusions should be toned down accordingly.

Response 3: We thank the reviewer for this important comment. The number of outcome events for both overall survival (OS) and disease-free survival (DFS) has now been explicitly reported in the Results section. To minimize the risk of overfitting, only variables that were significant in the univariate analysis and clinically meaningful were included in the multivariate Cox regression models. In addition, we clarified in the Methods section that potential multicollinearity among variables was evaluated prior to model construction.

1. Kaplan–Meier curves versus Cox regression

The Kaplan–Meier curves show statistically significant differences in survival between NPAR groups, whereas multivariate Cox regression does not confirm NPAR as an independent predictor. This discrepancy must be explicitly acknowledged and interpreted. The authors should clarify that the significant differences in Kaplan–Meier curves reflect unadjusted comparisons, while adjusted analyses may attenuate or abolish this effect due to confounding factors. The current text may overstate the prognostic role of NPAR.

Response 4: We appreciate the reviewer’s observation regarding the differences between Kaplan–Meier and Cox regression analyses. Kaplan–Meier curves represent unadjusted comparisons between groups, whereas Cox regression models account for potential confounding factors. We have now clarified this distinction in the Discussion section and emphasized that the adjusted Cox regression results provide a more conservative estimate of the independent prognostic effect of NPAR.

1. Definition and validation of the NPAR cut-off (ROC analysis)

The optimal NPAR cut-off was determined by ROC curve analysis in the same cohort in which it was subsequently tested, without any internal or external validation. This data-driven approach in a small, imbalanced sample carries a substantial risk of overfitting. The authors should specify for which endpoint the ROC analysis was performed (OS, DFS, response, or another outcome), report the corresponding AUC with 95% CI, and describe the criterion used to select the threshold.

Response 5: We thank the reviewer for this insightful comment. The ROC analysis was performed using overall survival as the endpoint, and the cut-off value was determined using the minimum Euclidean distance criterion. We have now clarified this in the Methods section. We also acknowledge the risk of overfitting associated with deriving the cut-off in the same dataset and have added this point as a limitation in the Discussion.

1. Handling of NPAR as a variable

The manuscript focuses on a dichotomized NPAR based on a single ROC-derived cut-off. Dichotomization of continuous variables usually reduces statistical power and may obscure dose–response relationships. The authors should consider additional analyses modelling NPAR as a continuous predictor in Cox regression for DFS and OS, and report whether NPAR remains associated with outcomes in this framework. This would provide a more robust assessment of its prognostic value.

Response 6: We agree with the reviewer that dichotomization of continuous variables may reduce statistical power. In the present study, NPAR was categorized based on a ROC-derived threshold to facilitate clinical interpretability. Nevertheless, we acknowledge that modeling NPAR as a continuous variable could provide additional insights, and we have now included this point in the Discussion as a direction for future research.

1. Positioning within the existing literature

Recent studies have investigated the association of NPAR with breast cancer incidence and prognosis. The introduction and discussion should better position the present study in relation to this existing studies in the literature, highlighting what is genuinely novel (especially focus on neoadjuvant-treated patients, specific endpoints or particular subgroups). The authors should clearly state whether similar NPAR cut-offs have been reported previously and how their findings compare with those of other cohorts. In the Discussion, a substantial part of the text is devoted to studies on other malignancies (such as bladder, colorectal, head and neck cancers) when comparing NPAR-related findings. While these references are useful to support the general concept of inflammation–nutrition indices, the discussion should be more clearly focused on breast cancer. The authors should incorporate and critically discuss the growing body of literature specifically addressing NPAR in breast cancer, including recent large epidemiologic and clinical studies assessing its association with breast cancer risk, distant metastasis, and prognosis, and better position their findings in that context. However, comparisons with results in other tumor types should be kept concise and clearly labelled as extrapolations, while the main comparison framework should be the existing evidence in breast cancer.

Response 7: We thank the reviewer for this helpful suggestion. We have revised the Introduction and Discussion sections to better position our findings within the existing literature on NPAR in breast cancer. In particular, we expanded the discussion of recent studies investigating the association between NPAR and breast cancer prognosis and emphasized the novelty of our study in focusing on patients receiving neoadjuvant chemotherapy.

8. Limitations

The current Limitations section is inadequate and requires substantial expansion and reorientation. While it acknowledges the retrospective design and single-center nature, it fails to address several critical methodological weaknesses: Small sample size and imbalance: 194 patients over 20 years is modest, and the high-NPAR group (n=44) severely limits statistical power, model stability, and the capability to generalise.  Loss of significance in multivariable analysis: NPAR and other factors significant in univariate/Kaplan–Meier analyses do not retain independent prognostic value in Cox models – this must be candidly discussed rather than downplayed. Overfitting risk from ROC-derived cutoff: The data-driven cutoff lacks validation (internal or external) in this small cohort. Selection bias: Justify cohort size against total NAC patients in the center. The current text overstates strengths ('strong evidence', 'high predictive power') while understating weaknesses, then transitions into unsubstantiated clinical recommendations. Revise to provide a balanced, transparent limitations paragraph first, followed by a separate 'Strengths/Clinical implications' paragraph with appropriately cautious language.”

Response 8: We thank the reviewer for this helpful suggestion. We have revised the  Discussion sections to better position our limitations.  İn multivariate analyses  NPAR is significant for OS (HR 3.79; p= 0.002) and DFS ( HR 2.80; p= 0.003).

                            Minor comments

In the Materials and Methods section (around lines 84–88, page 3), where breast cancer staging is described, a standard reference for the staging system used should be cited (respectively - the AJCC Cancer Staging Manual, 8th edition). Please specify explicitly which AJCC edition was applied and add the corresponding reference.

Response  9: We thank the reviewer for this helpful comment. The staging system used in our study has now been clarified in the Materials and Methods section. We explicitly state that tumor staging was performed according to the American Joint Committee on Cancer (AJCC) Cancer Staging Manual, 8th edition, and the corresponding reference has been added to the manuscript.

In the Discussion part, the first paragraph (approximately lines 197–211, page 10) contains several sentences that are identically repeated, without meaningful rephrasing. Similar sentences reappear in lines 222–229 (page 11). This thing reduces clarity and makes the manuscript harder to follow. Please revise the first paragraph of the Discussion (lines 197–211) and the subsequent paragraphs (lines 222–229) to remove repeated sentences and improve conciseness and readability. Also, other relevant studies and discussion should be included here, which may highlight the importance of the studied subject.

Response 10: We thank the reviewer for pointing out the repeated sentences in the Discussion section. We carefully revised the first paragraph and the following sections of the Discussion to remove duplicated sentences and improve clarity and conciseness. In addition, we expanded the discussion by incorporating additional relevant studies addressing prognostic biomarkers and inflammatory indices in breast cancer to better contextualize our findings within the existing literature.

Please revise the English language for clarity and grammar throughout the manuscript; a professional language editing service may be helpful.

Response 11: We agree with the reviewer that the clarity of the manuscript could be improved. Accordingly, the entire manuscript has been thoroughly revised and edited for English grammar, usage, and spelling. Special attention was paid to sentence structure and technical terminology to ensure the findings are communicated clearly. We believe the linguistic quality of the revised manuscript now meets the required academic standards.

 

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

I am pleased with the answers the authors provided to my previous report.