Spatial Transcriptomics of Immune Cell Distribution in Non-Small Cell Lung Cancer Identifies Tertiary Lymphoid Structures and Its Density and Area Fraction Were Associated with Neoadjuvant Therapy Response

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Editor and Authors,

It was my pleasure to review this manuscript titled “Spatial Transcriptomics of Immune Cell Distribution in Non-Small Cell Lung Cancer identifies Tertiary Lymphoid Structure and its Density and Area Fraction were associated with Neoadjuvant Therapy Response” by Dr. Zelin and his colleagues from Shanghai, China.

In this work the authors investigated the spatial organization of immune cells and tertiary lymphoid structures (TLS) in non-small cell lung cancer (NSCLC) by using spatial transcriptomics, multiplex immunohistochemistry (mIHC), and immunohistochemistry (IHC). They were able to analyze two spatial transcriptomic samples (including one lung squamous cell carcinoma (LUSC) and one lung adenocarcinoma (LUAD) subtypes) both from patients responding to neoadjuvant chemo-immunotherapy, and subsequently validated their findings using a cohort of 66 NSCLC patients.

The authors then report their findings as:

TLS can be identified adequately through spatial transcriptomic analysis

Activated B and T cells localize within TLS, whereas plasma cells and macrophages predominantly localize outside of TLS.

Antigen-presenting machinery (APM) genes are enriched within the TLS.

Mature TLS (mTLS) are larger than immature TLS and contain more B cells in this study.

Increased TLS/mTLS density and area fraction within the tumor bed are associated with favorable response to neoadjuvant therapy.

I found the topic to be very stimulating and close to my own research interests and the analysis and methodological /investigative approach is appropriate. However, there are a few issues that I found need addressing and improving prior to publication. Specifically:

1. The authors used only two spatial transcriptomic samples from two responders (one LUSC and one LUAD) onto which they based their analysis and mechanistic conclusions! This to me is the biggest limitation of the study as no non-responder spatial transcriptomic sample were evaluated!! This limitation need to be addressed by the authors and justify why it was done!
2. The authors seem to over-extend some of their conclusions and statements! Specifically, the title and the central message of the manuscript suggests that TLS characteristics are associated with induction therapy response! But, as mentioned above spatial transcriptomics were only performed in responders and moreover only pathology-based TLS quantification was used to base the response association analysis on!
3. The TLS identification strategy defined it through cluster 6, B-cell and T-cell scores and TLS signature scores. This to me is reasonable but stronger evidence may be needed to clarify how specific cluster 6 is and if alternative clustering parameters were utilized.
4. I believe the analysis provides insufficient adjustment for clinical co-founders such as histology (LUAD vs LUSC), stage, PD-L1 expression, smoking history, treatment type and age and sex! This introduces bios into the analysis! I would suggest a more robust logistical regression analysis be performed!
5. Moreover, the APM activation conclusions are not fully supported. The authors infer activation based on the TAP1 and TAP2, B2M and TAPBP but the HLA-A, -B and –C were absent from the panel and no antigen levels and presentation were measured.
6. The authors make the suggestion that plasma cells migrate outside of TLS after differentiation!  While possible and plausible the data do not unequivocally show this
7. In terms of language and writing up the text needs significant edits that need to be done. For example, the authors use the term responsible group and non-responsible group but these terms are incorrect and instead responder and non-responder groups should be used!
8. The title is too long and convoluted/difficult to understand! I suggest it is changed to: “Spatial Transcriptomic Analysis of Tertiary Lymphoid Structures in Non-Small Cell Lung Cancer Reveals Associations Between TLS Density, Maturation, and Response to Neoadjuvant Therapy”
9. The TLS criteria and thresholds used (> 120 cells, > 20 B-Cells, ect) need to be more clearly explained and justified with literature evidence if possible!!
10. The figures are small and the color scales are unclear. They are difficult to read and understand! Please upload higher resolution images!
11. Finally, the discussion seems small and truncated in scope! The authors do not compare their findings with prior NSCLC TLS literature as they should! They should discuss limitations more extensively and should explain why mTLS localization in the tumor bed is important!

If the issues raised above are adequately addressed then I feel the manuscript has potential for publication as it pertains to a clinically interesting question. However, as it stands now, major revision is needed for it to move forward. Thank you.

Author Response

Dear reviewer:

Thank you for taking your precious time to review our manuscript (Manuscript ID: cancers-4383527, Title: Spatial Transcriptomics of Immune Cell Distribution in Non-Small Cell Lung Cancer identifies Tertiary Lymphoid Structure and its density and area fraction were associated with neoadjuvant Therapy Response), and for offering us very valuable suggestions on our article. Below, we provide our point-by-point responses to each comment. In the revised manuscript, all changes will be highlighted.

Comments1：The authors used only two spatial transcriptomic samples from two responders (one LUSC and one LUAD) onto which they based their analysis and mechanistic conclusions! This to me is the biggest limitation of the study as no non-responder spatial transcriptomic sample were evaluated!! This limitation need to be addressed by the authors and justify why it was done!

Reply1：

Thank you for raising this important question. We had indeed attempted to perform spatial transcriptome sequencing on samples from the non-responder group within an independent cohort. However, the resulting data quality was poor: the median number of genes detected per spot was only 1,669, which is substantially lower than that of the LUSC (3,700) and LUAD (5,898) samples. Including such low-quality data would introduce substantial bias, so we excluded it from the manuscript.

Furthermore, due to budget limitations, we were unable to sequence additional samples at this stage. Hence, only the two samples (LUSC and LUAD from responders) were included. We hope the editor and reviewers can understand this limitation.

Comments2：The authors seem to over-extend some of their conclusions and statements! Specifically, the title and the central message of the manuscript suggests that TLS characteristics are associated with induction therapy response! But, as mentioned above spatial transcriptomics were only performed in responders and moreover only pathology-based TLS quantification was used to base the response association analysis on!

Comments8：The title is too long and convoluted/difficult to understand! I suggest it is changed to: “Spatial Transcriptomic Analysis of Tertiary Lymphoid Structures in Non-Small Cell Lung Cancer Reveals Associations Between TLS Density, Maturation, and Response to Neoadjuvant Therapy”

Reply 2&8 :

Thank you for your suggestions. We agree that a more objective description of the results is needed. Since the associations were led by the pathological quantification rather than the spatial transcriptomic analysis, we have revised the title to a more conservative, descriptive statement: “Spatial Transcriptomics of Immune Cell Distribution in NSCLC Identifies Tertiary Lymphoid Structures; Pathological Quantification of TLS Shows Their Density and Area Fraction Are Associated with Neoadjuvant Therapy Response”

Also, in the manuscript, we will carefully consider and revise the words used throughout the text to ensure objectivity. All the changes and modifications will be highlighted.

Comments3：The TLS identification strategy defined it through cluster 6, B-cell and T-cell scores and TLS signature scores. This to me is reasonable but stronger evidence may be needed to clarify how specific cluster 6 is and if alternative clustering parameters were utilized.

Reply3:

Your comment was very much to the point. Indeed, we varied the number of clusters (k) from 6 to 10, while keeping the resolution parameter constant. At k=6 or 7, the TLS region merged with adjacent tissues; at k=9 or 10, it fragmented into sub-clusters. At k=8, cluster 6 achieved the optimal performance: it captured the entirety of the T/B-cell aggregated area while maintaining a clear boundary that distinguished it from neighboring non-TLS tissues.

As you rightly noted, our evaluation was not limited to B-cell, T-cell, and TLS signature scores—we additionally performed histological comparative analyses. Mapping cluster 6 onto H&E sections of LUSC and LUAD samples verified its consistent localization within lymphoid aggregate regions. Side-by-side comparison with mIHC staining further confirmed that cluster 6 was located within TLS regions.

Comments4: I believe the analysis provides insufficient adjustment for clinical co-founders such as histology (LUAD vs LUSC), stage, PD-L1 expression, smoking history, treatment type and age and sex! This introduces bios into the analysis! I would suggest a more robust logistical regression analysis be performed!

Reply4:

Thank you for this critical methodological concern. We fully recognize that unadjusted clinical confounders may introduce bias.

Unfortunately, due to the retrospective nature of our pathological archive, detailed records for smoking history, PD-L1 status, treatment regimens, and precise pathological staging were not uniformly available for all cases. With only age, pathological classification, and sex accessible, a robust multivariate logistic regression is not statistically feasible for our current dataset.

To address this, we have removed causal claims from our manuscript and have redefined our study as a descriptive spatial molecular characterization in both the Abstract and Discussion. We have also clearly highlighted the lack of these clinical covariates as a limitation. We believe this honest reframing appropriately aligns our conclusions with the scope of the available data.

Comments5: Moreover, the APM activation conclusions are not fully supported. The authors infer activation based on the TAP1 and TAP2, B2M and TAPBP but the HLA-A, -B and –C were absent from the panel and no antigen levels and presentation were measured.

Reply5:

Thank you for this insightful comment. We agree that our initial conclusion regarding "APM activation" was an overstatement, as it was based on the upregulated expression of TAP1, TAP2, B2M, and TAPBP. To address this concern, we have taken the following steps to revise the wording and strengthen our evidence.

Firstly, we have modified the section title into “Antigen presenting machinery (APM) related genes were highly expressed in TLS accompanied by highly expression of upstream and downstream genes of MHC class I within TLS in the tumor bed of NSCLC patients.”

Secondly, we attempted to find more evidence to support it in the spatial transcriptome data. We compared the expression levels of NLRC5 and CD69 inside and outside the TLS. The former gene activates the expression of HLA, while the latter gene is a marker for the early activation of T cells after the interaction between MHC class I molecules and T‑cell receptor.

      However, we must acknowledge the limitation of our spatial transcriptomic profiling. Since the probe panel lacks probes targeting HLA genes, we are unable to offer direct spatial proof of HLA upregulation. We plan to comprehensively discuss this constraint and clearly state this limitation in the revised discussion.

Comments 6: The authors make the suggestion that plasma cells migrate outside of TLS after differentiation!  While possible and plausible the data do not unequivocally show this

Reply 6:

Thank you for this insightful suggestion. We agree that spatial transcriptomics is unable to track the sequence of IgG molecules, and thus our current dataset does not provide direct evidence for its migration.

Our findings indicate that activated B cells are mainly distributed within the TLS, while plasma cells are mainly located outside the TLS.

 If the migratory process reported in previous studies¹ operates in our system, it would offer a plausible explanation for this pattern. We acknowledge that this interpretation remains speculative until functionally validated. In the Discussion, we will make it clear that our findings show a possibility, not a causal link.

Comments7: In terms of language and writing up the text needs significant edits that need to be done. For example, the authors use the term responsible group and non-responsible group but these terms are incorrect and instead responder and non-responder groups should be used!

Reply7:

Thank you for your criticism and suggestions. We will carefully revise the language and words in the article to ensure precision and standardization. All the changes and modifications will be highlighted.

Comments9: The TLS criteria and thresholds used (> 120 cells, > 20 B-Cells, ect) need to be more clearly explained and justified with literature evidence if possible!!

Reply9:

Thank you for your suggestion. There is no unified standard for the division of cell number and area in TLS. The smallest visible report is 45 lymphocytes, with a minimum TLS area of 6,245μm^{2  2}. A paper published in Nature Cancer included TLS with more than 50 lymphocytes in the statistics³.

However, in the NSCLC tissue, there are some unique characteristics in immune cell distribution. Specifically, we discovered some black deposits in the tissue, and CD3-positive cells were found to be clustered along these deposits. Without a strict TLS threshold, these structures, which have significant histological differences from conventional TLS, would also be included in the statistics.

Also, we have revised the wording in the Method section. "Defining TLS was not our original intention. We only included TLS that exceeded the threshold for statistical analysis to avoid bias. Since all the TLS divisions are completed manually, the area threshold can eliminate the subjective decisions made during the TLS division process.

Comments10: The figures are small and the color scales are unclear. They are difficult to read and understand! Please upload higher resolution images!

Reply10:

          Thank you for your criticism. We will improve our figures.

Comments11: Finally, the discussion seems small and truncated in scope! The authors do not compare their findings with prior NSCLC TLS literature as they should! They should discuss limitations more extensively and should explain why mTLS localization in the tumor bed is important!

Reply11:

We appreciate the reviewers’ comments. In the revised Discussion, we will better compare our results with prior studies on NSCLC TLS, expand the limitations section, and clarify the significance of mTLS localization within the tumor bed.

1. Meylan M, Petitprez F, Becht E, et al. Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer. Immunity. 2022;55(3):527-541.e5. doi:10.1016/j.immuni.2022.02.001
2. Barmpoutis P, Di Capite M, Kayhanian H, et al. Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer. PLoS One. 2021;16(9):e0256907. doi:10.1371/journal.pone.0256907
3. Vanhersecke L, Brunet M, Guégan JP, et al. Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression. Nat Cancer. 2021;2(8):794-802. doi:10.1038/s43018-021-00232-6

Reviewer 2 Report

Comments and Suggestions for Authors

This study used spatial transcriptomics, multiplex immunohistochemistry, and immunohistochemistry to characterize tertiary lymphoid structures (TLS) in NSCLC and demonstrated that activated B and T cells are enriched within TLS, whereas plasma cells and macrophages are predominantly located outside TLS, with antigen-presenting machinery genes showing higher expression within TLS. Furthermore, higher TLS and mature TLS (mTLS) density and area fraction in the tumor bed were significantly associated with better response to neoadjuvant chemoimmunotherapy, suggesting that TLS maturation and B-cell enrichment may contribute to antitumor immunity and treatment efficacy. However, some parts of the manuscript need to be revised before it can be accepted.

Major:

1. The manuscript does not include a Simple Summary section on the first page. Please add this section in accordance with the journal's formatting requirements.
2. The manuscript should explain why only one LUSC and one LUAD sample were selected for spatial transcriptomic analysis and whether these cases are representative of the entire cohort.
3. Spatial transcriptomic analyses were limited to responder samples. Inclusion of non-responders would be necessary to determine whether the observed TLS-associated immune architecture is truly linked to treatment response.
4. The conclusions regarding the contribution of TLS and APM activation to treatment response are primarily based on correlative observations and would benefit from functional validation.
5. The criteria used to define TLS (total cells ≥120, B cells ≥20, and area ≥10,000 μm²) require further justification. Since TLS quantification is central to the study conclusions, the authors should provide a clear rationale, supporting references, and evidence that the findings are robust to alternative threshold definitions.
6. The conclusion that APM is activated within TLS is primarily inferred from gene expression data and HLA-A staining. Additional functional evidence would be needed to confirm enhanced antigen presentation and its contribution to local antitumor immune responses.
7. The statistical analysis may not adequately account for the non-independence of TLS observations within the same patient. Given that multiple TLS were analyzed per patient, statistical approaches that account for intra-patient clustering (e.g., mixed-effects models) should be considered.

Minor:

1. The manuscript repeatedly uses "responsible group" and "non-responsible group." The correct terminology should be "responder" and "non-responder" groups throughout the manuscript.
2. Numerous grammatical errors and awkward expressions are present.
3. More details regarding patient recruitment, inclusion/exclusion criteria, treatment regimens, and follow-up duration should be provided.
4. Several spatial transcriptomic and heatmap figures contain small labels and low-resolution annotations, making interpretation difficult.
5. Key study limitations, including the limited spatial transcriptomic sample size, retrospective design, and lack of functional validation, are not adequately discussed and should be acknowledged.

Author Response

Dear reviewer:

Thank you for taking your precious time to review our manuscript (Manuscript ID: cancers-4383527, Title: Spatial Transcriptomics of Immune Cell Distribution in Non-Small Cell Lung Cancer identifies Tertiary Lymphoid Structure and its density and area fraction were associated with neoadjuvant Therapy Response), and for offering us very valuable suggestions on our article. Below, we provide our point-by-point responses to each comment. In the revised manuscript, all changes will be highlighted.

Major:

Comments 1: The manuscript does not include a Simple Summary section on the first page. Please add this section in accordance with the journal's formatting requirements.

Reply 1:

Thank you for reminding us about the Simple Summary. We will add this section to the revised manuscript.

Comments 2: The manuscript should explain why only one LUSC and one LUAD sample were selected for spatial transcriptomic analysis and whether these cases are representative of the entire cohort.

Comments3：Spatial transcriptomic analyses were limited to responder samples. Inclusion of non-responders would be necessary to determine whether the observed TLS-associated immune architecture is truly linked to treatment response.

Reply 2&3:

Thank you for raising these two important questions.

We had indeed attempted to perform spatial transcriptome sequencing on more samples from the non-responder group within an independent cohort. However, the resulting data quality was poor: the median number of genes detected per spot was only 1,669, which is substantially lower than that of the LUSC (3,700) and LUAD (5,898) samples. Including such low-quality data would introduce substantial bias, so we excluded it from the manuscript. Furthermore, due to budget limitations, we were unable to sequence additional samples at this stage. Hence, only the two samples (LUSC and LUAD from responders) were included. We hope the editor and reviewers can understand this limitation.

Comments 4: The conclusions regarding the contribution of TLS and APM activation to treatment response are primarily based on correlative observations and would benefit from functional validation.

Comments 6: The conclusion that APM is activated within TLS is primarily inferred from gene expression data and HLA-A staining. Additional functional evidence would be needed to confirm enhanced antigen presentation and its contribution to local antitumor immune responses.

Reply 4&6:

Thank you for your suggestion. We fully agree that functional validation is important and it would significantly enhance our findings. However, we have not been able to perform these experiments due to the very limited and precious viable tissue. Furthermore, establishing a TLS model specifically for NSCLC in mice remains a challenge, and to our knowledge, no such model has been reported.

To partially address this concern and provide additional mechanistic insight, we have now performed in silico analyses to explore the potential upstream regulators and downstream effectors of APM. Specifically, we compared the expression levels of NLRC5 and CD69 inside and outside the TLS. The former gene activates the expression of HLA, while the latter gene is a marker for the early activation of T cells after the interaction between MHC class I molecules and T‑cell receptor.

However, we must acknowledge the limitation of our spatial transcriptomic profiling. Since the probe panel lacks probes targeting HLA genes, we are unable to offer direct spatial proof of HLA upregulation. We plan to comprehensively discuss this constraint and clearly state this limitation in the revised discussion.

Comments 5: The criteria used to define TLS (total cells ≥120, B cells ≥20, and area ≥10,000 μm²) require further justification. Since TLS quantification is central to the study conclusions, the authors should provide a clear rationale, supporting references, and evidence that the findings are robust to alternative threshold definitions.

Reply 5:

Thank you for your suggestion.

There is no unified standard for the division of cell number and area in TLS. The smallest reported threshold is 45 lymphocytes, with a minimum TLS area of 6,245μm^{2 1}. A paper published in Nature Cancer included TLS with more than 50 lymphocytes in the statistics².

However, in the NSCLC tissue, there are some unique characteristics in immune cell distribution. Specifically, we discovered some black deposits in the tissue, and CD3-positive cells were found to be clustered along these deposits. Without a strict TLS threshold, these structures, which have significant histological differences from conventional TLS, would also be included in the statistics.

Also, we have revised the wording in the Method section. "Defining TLS" was not our original intention. We only included TLS that exceeded the threshold for statistical analysis to avoid bias. Since all the TLS divisions are completed manually, the area threshold can eliminate the subjective decisions made during the TLS division process.

Comments 7. The statistical analysis may not adequately account for the non-independence of TLS observations within the same patient. Given that multiple TLS were analyzed per patient, statistical approaches that account for intra-patient clustering (e.g., mixed-effects models) should be considered.

Reply 7:

Thank you for raising this important question. The cells within each patient's TLS share the same genetic background, while the TLS of different patients vary. We decided not to combine the results of the two spatial transcriptome analyses. Regarding the research on the pathological features of TLS (the TLS density, TLS area ratio), our analysis process also separately conducted statistics for TLS in each case. For example, we calculated the total number of TLS and area for each individual case, and standardized them to the tumor bed area of the corresponding case. Our imprecise woding might have caused confusion. We will carefully revise the wording to clarify this. 

Minor:

Comments 1: The manuscript repeatedly uses "responsible group" and "non-responsible group." The correct terminology should be "responder" and "non-responder" groups throughout the manuscript.

Comments 2: Numerous grammatical errors and awkward expressions are present.

Reply1&2:

Thank you for your criticism. We will carefully consider and extensively revise the text. All the changes and modifications will be highlighted.

Comments 3: More details regarding patient recruitment, inclusion/exclusion criteria, treatment regimens, and follow-up duration should be provided.

Reply 3：

Thank you for this critical methodological concern. We will add a Summary Table of Patient Characteristics in the supplementary materials.

However, due to the retrospective nature of our pathological archive, detailed records for smoking history, PD-L1 status, treatment regimens, and precise pathological staging were not uniformly available for all cases. With only age, pathological classification, and sex accessible, a robust multivariate logistic regression is not statistically feasible for our current dataset.

To address this, we have removed causal claims from our manuscript and have redefined our study as a descriptive spatial molecular characterization in both the Abstract and Discussion. We have also highlighted the lack of these clinical covariates as a limitation. We believe this honest reframing appropriately aligns our conclusions with the scope of the available data.

Comments 4: Several spatial transcriptomic and heatmap figures contain small labels and low-resolution annotations, making interpretation difficult.

Reply4:

Thank you for your criticism. After proofreading, we discovered an error in the scale bars or annotation sizes of the figures. Besides, we will improve the figures and labels in the revised submission

Comments 5: Key study limitations, including the limited spatial transcriptomic sample size, retrospective design, and lack of functional validation, are not adequately discussed and should be acknowledged.

Reply 5：

Thank you for your suggestion. We will address these limitations in the Discussion.

1. Barmpoutis P, Di Capite M, Kayhanian H, et al. Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer. PLoS One. 2021;16(9):e0256907. doi:10.1371/journal.pone.0256907
2. Vanhersecke L, Brunet M, Guégan JP, et al. Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression. Nat Cancer. 2021;2(8):794-802. doi:10.1038/s43018-021-00232-6

Reviewer 3 Report

Comments and Suggestions for Authors

This article addresses Spatial Transcriptomics of Immune Cell Distribution in NonSmall Cell Lung Cancer identiffes Tertiary Lymphoid Structure and its density and area fraction were associated with neoadjuvant Therapy Response, this study investigated the Spatial transcriptomics reveals immune cell heterogeneity, and distribution patterns in NSCLC, with activated B and T cells localized inside and plasma cells/macrophages outside. Antigen presenting machinery (APM) may be activated in TLS. mTLS have a larger area by mainly containing more B cells. The responder group had signiffcantly higher (mature) TLS density and larger (mature) TLS area proportion compared with the non-responder group, suggesting their potential function in antitumor effect in neoadjuvant treatment. This is an interesting and timely research that could attract a broad readership for the journal.

Several comments are provided below to improve the current manuscript:

1.Not so many references about this topic, from this perspective, the innovation and importance of this article.

2.In Statistical analysis section, I think should add p-value < 0.05 was considered statistically significant.

3. Please add a Abbreviations list.
4. I know this is a groundbreaking study, but is it okay to select only one sample per group.
5. Are there any differences of Spatial Transcriptomics of Immune Cell Distribution between LUSC and LUAD?

6.Is the Spatial Transcriptomics of Immune Cell Distribution related with the types of neoadjuvant Therapy (different drug).

7. Was confirmed that Spatial Transcriptomics of Immune Cell Distribution directly with the neoadjuvant Therapy Response? Were which Immune Cell mostly reveal the neoadjuvant Therapy Response(T B or MDSC).
8. Very interesting topic, will you verify your conclusion through tumor mice?

Author Response

Dear reviewer:

Thank you for taking your precious time to review our manuscript (Manuscript ID: cancers-4383527, Title: Spatial Transcriptomics of Immune Cell Distribution in Non-Small Cell Lung Cancer identifies Tertiary Lymphoid Structure and its density and area fraction were associated with neoadjuvant Therapy Response), and for offering us very valuable suggestions on our article. Below, we provide our point-by-point responses to each comment. In the revised manuscript, all changes will be highlighted.

Comments1：Not so many references about this topic, from this perspective, the innovation and importance of this article.

Reply 1:

We truly appreciate your positive comments on the novelty of our work.

Comments2: In Statistical analysis section, I think should add p-value < 0.05 was considered statistically significant.

Reply 2:

Thank you for your suggestion. We fully agree with you. We will provide clearer annotations in the figure legend. If you are referring to the violin plot, the P-values obtained by the Wilcoxon rank-sum test are all < 0.001, so we did not separately indicate significance at the 0.05 level.

Comments3: Please add a Abbreviations list.

Reply3:

 Thank you for your suggestion. An abbreviations list has been added. Also, we will clearly indicate the full name at first mention of each abbreviation in the text.

Comments4: I know this is a groundbreaking study, but is it okay to select only one sample per group.

Reply 4:

Thank you for raising this important question.

We had indeed attempted to perform spatial transcriptome sequencing on more samples from the non-responder group within an independent cohort. However, the resulting data quality was poor: the median number of genes detected per spot was only 1,669, which is substantially lower than that of the LUSC (3,700) and LUAD (5,898) samples. Including such low-quality data would introduce substantial bias, so we excluded it from the manuscript. Furthermore, due to budget limitations, we were unable to sequence additional samples at this stage. Hence, only the two samples (LUSC and LUAD from responders) were included. We hope the editor and reviewers can understand this limitation.

Comments5: Are there any differences of Spatial Transcriptomics of Immune Cell Distribution between LUSC and LUAD?

Reply5:

          Thank you for your question. In our spatial transcriptomic data, the major immune cell subsets (B cells, T cells, macrophages, and plasma cells) and the formation of TLS showed highly comparable distribution patterns between LUSC and LUAD. However, due to the spot-based resolution of the Visium platform, we are unable to separate the subtypes of cells or to extract precise intercellular physical distances. Higher-resolution platforms would be required to explore potential subtype-specific spatial differences. We will include this limitation in the Discussion section.

Comments6: Is the Spatial Transcriptomics of Immune Cell Distribution related with the types of neoadjuvant Therapy (different drug).Reply 6:

Thank you for your question. Since the density, area proportion, and maturity of TLS are related to the treatment outcome, it is an interesting research topic whether different treatments will lead to different distributions of immune cells and thereby affect these characteristics of TLS. However, patients in our cohort have the same treatment background. I'm unable to directly answer your question based on our current data. We agree that this would be an extremely valuable direction for future investigation.

Comments7: Was confirmed that Spatial Transcriptomics of Immune Cell Distribution directly with the neoadjuvant Therapy Response? Were which Immune Cell mostly reveal the neoadjuvant Therapy Response(T B or MDSC).

Reply7:

          Due to the sample size limitations, we are not able to confirm neoadjuvant therapy response directly withspatial transcriptomic data in immune cell distribution. With pathological quantification of TLS, our retrospective study results showed a significantly higher density of TLS‑resident B cells in responder group than non-responder group.

Comments8: Very interesting topic, will you verify your conclusion through tumor mice?

Reply 8:

          Thank you for your appreciation for our article. Establishing a TLS model specifically for NSCLC in mice remains a challenge, and to our knowledge, no such model has been reported.

          In order to solidify our conclusion, we have now performed in silico analyses to explore the potential upstream regulators and downstream effectors of APM. Specifically, we compared the expression levels of NLRC5 and CD69 inside and outside the TLS. The former gene activates the expression of HLA, while the latter gene is a marker for the early activation of T cells after the interaction between MHC class I molecules and the T‑cell receptor.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Dear Editor and Authors,

I have re-evaluated and re-read the revised manuscript submitted. The authors have substantially improved the manuscript and have addressed adequately most of the comments that were raised. 

The study as previously mentioned is interesting and has a number of strengths which include: spatial transcriptomics, pathological / histological validation, multiplex IHC and quantitative TLS assessment. In addition the question examined has clinical relevance.

It is limitted by the fact that only two ST samples were analyzed and that there is no ST in nonresponders as well as its retrospective nature but these issues have been acknowledged by the authors and are acceptable.

Therefore, I am now happy to recommend the publication of the work. Kind regards to all.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have adequately addressed all concerns, and the manuscript is now acceptable for publication.