Review Reports - Comparative Analysis of Digital Transcriptomics Between Pre- and Post-Treatment Samples of Patients with Locally Advanced Cervical Cancer: A Preliminary Study

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The researchers applied digital gene expression analysis to comprehensively quantify immune-related genes in matching FFPE cervical cancer samples, and how these molecules affect different cellular processes.

The novelty of the manuscript contributes to this field; however, there are some limitations:

BioMaSOTA contains rules on collecting biological samples, but does it also include ethical approval?

The two main types are mentioned in the manuscript: squamous cell carcinoma and adenocarcinoma. Why did the authors choose samples from both types due to the altered origin?

The description of the methodology seems to me to be incomplete, especially in the steps for the selection of DEGs. Did the authors filter the DEGs directly for genes involved in immune processes using the R program? Have the authors used other databases e.g. STRING or Reactome for network analyses?

The authors mention that more studies would be needed. I agree, I also think it would be recommended to use the GEPIA database to investigate further the genes they highlighted and to validate their results by performing a qRT-PCR reaction.

Figure 5 could be sharper and in a larger font.

Author Response

Comment 1: BioMaSOTA contains rules on collecting biological samples, but does it also include ethical approval?

Response 1: Thank you for your comment. The retrospective analysis of FFPE samples was approved from the local ethic committee for the patients, who signed informed consent of BioMaSOTA at initial diagnosis, which is routine procedure at the University Hospital of Cologne and 22-1475 is the protocol number of our own project. Based on your suggestion, we modified the sentence.

Comment 2: The two main types are mentioned in the manuscript: squamous cell carcinoma and adenocarcinoma. Why did the authors choose samples from both types due to the altered origin?

Response 2: Thank you very much for your keen remarks. Based on your comment, we made a supplementary table to explain how we end up examining limited number of patients with heterogeneity (Table S3). As shown in Table S3, after selection of patients and FFPE samples, especially for those who have matching pre- and post-treatment samples in our biobank archives, there were only six patients remaining who met our study design criteria. The main reason for poor availability of matching samples is attributed to chemoradiation as standard of care for patients with locally advanced cervical cancer, since after the therapy for responder no further biopsy is needed. Due to limited number of available samples, it could not be avoided, to examine both squamous cell carcinoma and adenocarcinoma. We are hoping for further multicenter investigation in larger cohort to enable more robust statistical analysis.

Comment 3: The description of the methodology seems to me to be incomplete, especially in the steps for the selection of DEGs. Did the authors filter the DEGs directly for genes involved in immune processes using the R program? Have the authors used other databases e.g. STRING or Reactome for network analyses?

Response 3: Thank you for your careful revision of our manuscript. As stated in the methods section (p.4 l.125-131), we performed gene set enrichment analysis. Therefore, DE genes were determined statistically as described above and the fold-changes between the two groups were calculated. We used the permutation-based tool WebGestalt to investigate molecular pathways/networks. The used reference database was KEGG.

Comment 4: The authors mention that more studies would be needed. I agree, I also think it would be recommended to use the GEPIA database to investigate further the genes they highlighted and to validate their results by performing a qRT-PCR reaction.

Response 4: Thank you for your valuable input. Based on your suggestion, we used the TCGA dataset to investigate this in a larger cervical cancer cohort (Fig. S1B-F). Our analysis revealed that C7, EGR2, and S100A7 align with our argument. Although IL17RB showed a slightly higher median expression in the responder group, the expression of IL17RA, a paralog of IL17RB, was higher in the non-responder group, indirectly supporting our argument. However, it is important to note that these findings were statistically insignificant in the TCGA cohort. This may be attributed to the unmatched nature of the patient cohort and the small number of non-responders included in the dataset.

We also appreciate your suggestion to conduct qRT-PCR validation for the candidate genes identified through our Nanostring analysis. However, we believe that the Nanostring data already provides robust, accurate, and reliable results due to its high sensitivity and specificity. Studies have demonstrated a high degree of correlation between Nanostring data and qRT-PCR results, suggesting that further validation through qRT-PCR would be redundant [1,2]. In addition, due to our sample limitations, we unfortunately do not have enough material left for a qRT-PCR validation.

While we acknowledge your concerns, we believe that the current Nanostring data offers sufficient validation for our findings. As per your suggestion above, we have investigated the targets in a larger cohort and hope that offers sufficient validation for our findings.

Comment 5: Figure 5 could be sharper and in a larger font.

Response 5: Thank you for your comment. We have improved it based upon your suggestion.

Reference:

Geiss, G.K.; Bumgarner, R.E.; Birditt, B.; Dahl, T.; Dowidar, N.; Dunaway, D.L.; Fell, H.P.; Ferree, S.; George, R.D.; Grogan, T.; et al. Direct Multiplexed Measurement of Gene Expression with Color-Coded Probe Pairs. Nat Biotechnol 2008, 26, 317–325, doi:10.1038/nbt1385.
Malkov, V.A.; Serikawa, K.A.; Balantac, N.; Watters, J.; Geiss, G.; Mashadi-Hossein, A.; Fare, T. Multiplexed Measurements of Gene Signatures in Different Analytes Using the Nanostring nCounter Assay System. BMC Res Notes 2009, 2, 80, doi:10.1186/1756-0500-2-80.

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Author, thank you for the opportunity to review this manuscript.

Methods: Why Did you include only six women?

How Were the patients be treated?

Discussion: What are the implications of your results in the clinical practice?

Suggested citation:

Mereu, L.; Pecorino, B.; Ferrara, M.; Tomaselli, V.; Scibilia, G.; Scollo, P. Neoadjuvant Chemotherapy plus Radical Surgery in Locally Advanced Cervical Cancer: Retrospective Single-Center Study.Cancers2023,15,5207.

Comments on the Quality of English Language

Minor editing of English language required.

Author Response

Comment 1: Methods: Why Did you include only six women?

Response 1: Thank you very much for your comment, all authors are aware of low patients’ number as main limitation of the present study. Based on your remark, we made a supplementary table 3 to explain, why only six patients were examined: after selection of patients and FFPE samples, especially for those who have matching pre- and post-treatment samples in our biobank archives, there were unfortunately only six patients remaining, who met our study design criteria. The main reason for poor availability of matching samples is attributed to chemoradiation as standard of care for patients with locally advanced cervical cancer, since after the therapy no further biopsy is needed for responder.

We are hoping for further multicenter investigation in larger cohort to enable more robust statistical analysis.

Comment 2: How Were the patients be treated?

Response 2: Thank you very much for your clinically meaningful observation. Based on your comment, we made a supplementary table (Table S4) showing the timeline of each patient along with therapy from initial diagnosis until obtaining post-treatment samples. For non-responder further therapies were carried out, however, due to the complexity and irrelevance for understanding the present study, the documentation of further therapies for non-responders was omitted. We hope, with this supplementary information based on your suggestion, it will be of more interest to its readership.

Comment 3: Discussion: What are the implications of your results in the clinical practice?

Response 3: Thank you again for your clinically meaningful comment. The observed trends of our study comparing with TCGA dataset in the expression of C7, EGR2 and IL17RB between non-responders and responders suggest the potential biomarkers of these genes for therapeutic outcomes. Especially in our study, two responders were treated with neoadjuvant chemotherapy with excellent PFS over more than five years, while one patient (C1), who was treated with same therapy regimen, developed rapid progression of disease. As you mentioned, neoadjuvant chemotherapy (NACT) followed by radical surgery has been considered an alternative approach to improve disease control and reduce toxicity. Although many studies have demonstrated feasible outcomes for NACT and surgery regarding response rates and toxicity, its impact on overall survival remains still unproven [1–3]. Especially for the patients, who do not have access to chemoradiation [4] or wish to preserve fertility [5], neoadjuvant chemotherapy followed by tailored surgical intervention can be an alternative if they present corresponding biomarker. Finding significant transcriptomic changes between those patients with same therapy regimens can help to determine the individualized therapy strategy in each patient and may lead to therapy (de)-escalation minimizing side effect without jeopardizing the prognosis. While the present study focused on the transcriptomic changes in response to therapy, further analysis in longitudinal timeline in relation to clinical outcome can be conceptualized after validation of methods and confining DEGs. In this way, precision medicine approaches could be developed to stratify patients by their likelihood of treatment response, allowing for individualized therapy plans that improve efficacy and minimize side effects. Additionally, these results could support the exploration of adjunct therapies, such as immunotherapy, which may enhance treatment effectiveness for certain molecular profiles.

Reference:

Gupta, S.; Maheshwari, A.; Parab, P.; Mahantshetty, U.; Hawaldar, R.; Sastri (Chopra), S.; Kerkar, R.; Engineer, R.; Tongaonkar, H.; Ghosh, J.; et al. Neoadjuvant Chemotherapy Followed by Radical Surgery Versus Concomitant Chemotherapy and Radiotherapy in Patients With Stage IB2, IIA, or IIB Squamous Cervical Cancer: A Randomized Controlled Trial. JCO 2018, 36, 1548–1555, doi:10.1200/JCO.2017.75.9985.
Huang, H.-J.; Chang, T.-C.; Hong, J.-H.; Tseng, C.-J.; Chou, H.-H.; Huang, K.-G.; Lai, C.-H. Prognostic Value of Age and Histologic Type in Neoadjuvant Chemotherapy plus Radical Surgery for Bulky (>/=4 Cm) Stage IB and IIA Cervical Carcinoma. Int J Gynecol Cancer 2003, 13, 204–211, doi:10.1046/j.1525-1438.2003.13004.x.
Mereu, L.; Pecorino, B.; Ferrara, M.; Tomaselli, V.; Scibilia, G.; Scollo, P. Neoadjuvant Chemotherapy plus Radical Surgery in Locally Advanced Cervical Cancer: Retrospective Single-Center Study. Cancers 2023, 15, 5207, doi:10.3390/cancers15215207.
Zubizarreta, E.H.; Fidarova, E.; Healy, B.; Rosenblatt, E. Need for Radiotherapy in Low and Middle Income Countries – The Silent Crisis Continues. Clinical Oncology 2015, 27, 107–114, doi:10.1016/j.clon.2014.10.006.
Buda, A.; Borghese, M.; Puppo, A.; Perotto, S.; Novelli, A.; Borghi, C.; Olearo, E.; Tripodi, E.; Surace, A.; Bar, E.; et al. Neoadjuvant Chemotherapy Prior Fertility-Sparing Surgery in Women with FIGO 2018 Stage IB2 Cervical Cancer: A Systematic Review. Cancers (Basel) 2022, 14, 797, doi:10.3390/cancers14030797.

Reviewer 3 Report

Comments and Suggestions for Authors

· · First, it is a quite interesting article focusing upon the comparative analysis of digital transcriptomics between pre-and post-treatment samples of patients with locally advanced cervical cancer.

· In the title should be added; A preliminary study, since the authors claim that the present study is the first to utilize digital gene expression analysis for comprehensive quantification of immune-relate genes in comprehensive quantification of immune-related genes in matching FFPE cervical cancer samples along with pre-and post-therapeutic changes.

· The meaning of FFPE tissue (Formalin-Fixed-Embedded-Paraffine-tissue) should be added in parenthesis as the other abbreviations, C7, BGR2, SAA, S100A7, and IL17RB.

· In the abstract, the results of gene SAA aren’t mentioned, while the function of SAA in the discussion section is reported.

· In Material and Methods part, the number of recruited patients (6) is very small, since four patients despite of treatment showed poor outcome with progression of the disease within 2 years (non-responders), and only two showed excellent outcome without progression or metastases (responders).

· In Material and Methods part, references 14 and 16, aren’t related to the text.

· In Discussion, figure S1 in parenthesis, is missing.

· Figures S1 and S2 should be bigger to be readable.

· Finally, in conclusion, how the only 2 responders can support the findings that C7, BGR2, and IL17RB, may serve as biomarkers for predicting therapeutic outcomes.

Author Response

Comment 1: In the title should be added; A preliminary study, since the authors claim that the present study is the first to utilize digital gene expression analysis for comprehensive quantification of immune-relate genes in comprehensive quantification of immune-related genes in matching FFPE cervical cancer samples along with pre-and post-therapeutic changes.

Response 1: Thank you for your comment. We have added it in the title accordingly.

Comment 2: The meaning of FFPE tissue (Formalin-Fixed-Embedded-Paraffine-tissue) should be added in parenthesis as the other abbreviations, C7, BGR2, SAA, S100A7, and IL17RB.

Response 2: Thank you for your remark. We added it accordingly.

Comment 3: In the abstract, the results of gene SAA aren’t mentioned, while the function of SAA in the discussion section is reported.

Response 3: Thank you for your keen observation. The results of gene SAA1 has been added in the abstract.

Comment 4: In Material and Methods part, the number of recruited patients (6) is very small, since four patients despite of treatment showed poor outcome with progression of the disease within 2 years (non-responders), and only two showed excellent outcome without progression or metastases (responders).

Response 4: Thank you very much for your comment, all authors are aware of low patients’ number as main limitation of the present study. Based on your remark, we made a supplementary table 3 to explain, why only six patients were examined: after selection of patients and FFPE samples, especially for those who have matching pre- and post-treatment samples in our biobank archives, there were unfortunately only six patients remaining, who met our study design criteria. The main reason for poor availability of matching samples is attributed to chemoradiation as standard of care for patients with locally advanced cervical cancer, since after the therapy no further biopsy is needed for responder.

We are hoping for further multicenter investigation in larger cohort to enable more robust statistical analysis.

Comment 5: In Material and Methods part, references 14 and 16, aren’t related to the text.

Response 5: Thank you for your careful review. Previous reference 14 (now reference 17) has been changed to “Integrative Analysis of Pleomorphic Dermal Sarcomas Reveals Fibroblastic Differentiation and Susceptibility to Immunotherapy. Clin Cancer Res (2020) 26 (21): 5638–5645.” Previous reference 16 was mistakenly cited and has been removed.

Comment 6: In Discussion, figure S1 in parenthesis, is missing.

Response 6: Thank you for your valuable comment. We have added the following sentence in line 89: “As there were no differences in tumor purities between the two groups, no further correction was performed (Figure S1A).” As per the previous Figure S1B, it was irrelevant to this study, so we have withdrawn it.

Comment 7: Figures S1 and S2 should be bigger to be readable.

Response 7: We have made the changes accordingly and hope that now the figures are easier to read.

Comment 8: Finally, in conclusion, how the only 2 responders can support the findings that C7, BGR2, and IL17RB, may serve as biomarkers for predicting therapeutic outcomes.

Response 8: We appreciate the reviewer's comment regarding the potential of C7, EGR2, and IL17RB as biomarkers for predicting therapeutic outcomes. To address this, we utilized the TCGA dataset to investigate a larger cohort of cervical cancer patients (Fig. S1 B-F).

Our analysis demonstrated that the expression of C7, EGR2, and S100A7 aligns with our hypothesis. Furthermore, IL17RB showed a slightly higher median expression in the responder group. Interestingly, while IL17RA, a paralog of IL17RB, exhibited higher expression in the non-responder group, this finding indirectly supports our argument. It is important to note that these observations were not statistically significant in the TCGA cohort, likely due to the small number of non-responders (N = 5) and the unmatched nature of the patient cohort.

In conclusion, while the small sample size and lack of statistical significance in the TCGA dataset limit the strength of our findings, the observed trends in the expression of C7, EGR2, and IL17RB in responders suggest that these genes may serve as potential biomarkers for predicting therapeutic outcomes. Further studies with larger, matched cohorts are warranted to validate these preliminary findings.

Reviewer 4 Report

Comments and Suggestions for Authors

The research article “Comparative Analysis of Digital Transcriptomics between Pre and Post-treatment Samples of Patients with Locally Advanced Cervical Cancer” may promise to enhance our understanding of therapeutic responses and inform the development of targeted biomarkers for improved patient management after a few minor corrections that I mentioned bellow:

1. The limited number of participants (just six patients) raises questions about the statistical strength and applicability of the results. I suggest that the authors openly address this limitation and evaluate its potential influence on the trustworthiness of their conclusions.

2. In this study findings could be strengthened, and a more comprehensive view of the transcriptomic changes could be provided by including a comparative analysis with controls.

3. The authors need to address the possible confounding variables that could affect the findings, including variations in treatment plans, Tumor properties, or any other medical factors that could influence gene expression and patient results.

4. In the section's conclusion, considering future research directions, it is important to address any limitations of the study. This could involve explicitly recognizing factors.

5. I suggest discussing how these findings might affect current treatment strategies, including any implications for precision medicine approaches in cervical cancer management.

Comments for author File: Comments.pdf

Author Response

Comment 1: The limited number of participants (just six patients) raises questions about the statistical strength and applicability of the results. I suggest that the authors openly address this limitation and evaluate its potential influence on the trustworthiness of their conclusions.

Response 1: We appreciate the reviewer’s insight regarding weakness of statistical strength and applicability of the results mainly because of the small patient number. Indeed, all authors recognize that the limited patient number and preliminary nature of the study may impact the overall trustworthiness of our conclusion and reduce the generalizability of our findings.

Nevertheless, we have taken steps to mitigate the limitations and weakness of the study. First, we made a supplementary table (Table S3) to explain, why only six patients were examined: after selection of patients and FFPE samples, especially for those who have matching pre- and post-treatment samples in our biobank archives, there were unfortunately only six patients remaining, who met our study design criteria. The main reason for poor availability of matching samples is attributed to chemoradiation as standard of care for patients with locally advanced cervical cancer, since after the therapy no further biopsy is needed for responder. Furthermore, to overcome this limitation and to validate our candidate genes, we utilized the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma dataset (N = 310) available at The Cancer Genome Atlas Program (TCGA) database, which shows the observed trends in the expression of C7, EGR2, and IL17RB along with the therapy. Despite investigating the TCGA dataset, we were only able to observe the similar patterns that were observed using our cohort without stastical significance. While this limitation suggests that caution should be applied when interpreting our results, we believe our study provides an important preliminary insight into the transcriptomic changes in response to therapy in patients with locally advanced cervical cancer.

We are hoping for further multicenter investigation in larger cohort to enable more robust statistical analysis. Based on your comment, we will address those limitations to provide more transparency of the manuscript.

Comment 2: In this study findings could be strengthened, and a more comprehensive view of the transcriptomic changes could be provided by including a comparative analysis with controls.

Response 2: Thank you for your thoughtful suggestion. We have revised Figure 5B in a way where it is easier to identify the transcriptomic changes that occurred between the pre- and post-treatment samples for each patient of the candidate genes. We hope this would provide a more comprehensive view for the readers.

Comment 3: The authors need to address the possible confounding variables that could affect the findings, including variations in treatment plans, Tumor properties, or any other medical factors that could influence gene expression and patient results.

Response 3: Thank you for highlighting the potential influence of confounding variables. We acknowledge that tumor properties such as histological subtype, grade, or stage, could influence baseline and post-treatment transcriptomic profiles and would impact gene expression changes and patient outcomes. Furthermore, variations in treatment protocols and each patient’s character, including medical history, may contribute to variability in our data, limiting the extent to which our findings can be generalized. Therefore, while we believe our study provides important preliminary insights, we also recognize the need for future research in larger, more comprehensive cohorts. This will help to more clearly elucidate the relationship between treatment and transcriptomic changes in patients with locally advanced cervical cancer.

Based on your comment, we will address those confounding factors to provide more transparency of the manuscript.

Comment 4: In the section's conclusion, considering future research directions, it is important to address any limitations of the study. This could involve explicitly recognizing factors.

Response 4: Thank you for your clinically meaningful input. Future studies should be carried out in larger patient cohorts, which will help to mitigate the effects of sample size limitations and enable more robust statistical analysis. By stratifying patients based on their clinical and biological factors such as tumor stage, histological subtype, and treatment protocol, patients and tumor heterogeneity should be controlled to assess the impact of these variables on gene expression more precisely and to determine whether the observed transcriptomic changes are generally associated with therapeutic response or vary across patient subgroups. Furthermore, a longitudinal study design that includes multiple time points would allow for a more detailed analysis of transcriptomic changes as they evolve. This would also enable the identification of early biomarkers of response or resistance, as well as long-term changes associated with sustained remission or recurrence. Future studies could integrate data from public databases such as The Cancer Genome Atlas (TCGA), as we did in this study, to validate findings in larger and more diverse cohorts. Additionally, collaborating with other research centers would enable us to access and analyze larger, well-matched cohorts, further reducing the impact of variability in treatment protocols and patient populations.

We summarized the perspective regarding future research direction in conclusion based on your suggestion.

Comment 5: I suggest discussing how these findings might affect current treatment strategies, including any implications for precision medicine approaches in cervical cancer management.

Resopnse 5: Thank you again for your clinically meaningful comment. Despite limitations, we believe that the observed trends of our study comparing with TCGA dataset in the expression of C7, EGR2 and IL17RB between non-responders and responders suggest the potential biomarkers of these genes for therapeutic outcomes. For example, neoadjuvant chemotherapy (NACT) followed by radical surgery has been considered an alternative approach to improve disease control and reduce toxicity. Although many studies have demonstrated feasible outcomes for NACT and surgery regarding response rates and toxicity, its impact on overall survival remains still unproven [1–3]. Indeed, in our study two responders were treated with neoadjuvant chemotherapy with excellent PFS over more than five years, while one patient (C1), who was treated with same therapy regimen, developed rapid progression of disease. Those patients showed distinguished transcriptomic profiles, which should be validated in larger homogeneous cohorts. Especially for the patients, who do not have access to chemoradiation [4] or wish to preserve fertility [5], neoadjuvant chemotherapy followed by tailored surgical intervention can be an alternative if they present corresponding biomarker.

Reference:

Gupta, S.; Maheshwari, A.; Parab, P.; Mahantshetty, U.; Hawaldar, R.; Sastri (Chopra), S.; Kerkar, R.; Engineer, R.; Tongaonkar, H.; Ghosh, J.; et al. Neoadjuvant Chemotherapy Followed by Radical Surgery Versus Concomitant Chemotherapy and Radiotherapy in Patients With Stage IB2, IIA, or IIB Squamous Cervical Cancer: A Randomized Controlled Trial. JCO 2018, 36, 1548–1555, doi:10.1200/JCO.2017.75.9985.
Huang, H.-J.; Chang, T.-C.; Hong, J.-H.; Tseng, C.-J.; Chou, H.-H.; Huang, K.-G.; Lai, C.-H. Prognostic Value of Age and Histologic Type in Neoadjuvant Chemotherapy plus Radical Surgery for Bulky (>/=4 Cm) Stage IB and IIA Cervical Carcinoma. Int J Gynecol Cancer 2003, 13, 204–211, doi:10.1046/j.1525-1438.2003.13004.x.
Mereu, L.; Pecorino, B.; Ferrara, M.; Tomaselli, V.; Scibilia, G.; Scollo, P. Neoadjuvant Chemotherapy plus Radical Surgery in Locally Advanced Cervical Cancer: Retrospective Single-Center Study. Cancers 2023, 15, 5207, doi:10.3390/cancers15215207.
Zubizarreta, E.H.; Fidarova, E.; Healy, B.; Rosenblatt, E. Need for Radiotherapy in Low and Middle Income Countries – The Silent Crisis Continues. Clinical Oncology 2015, 27, 107–114, doi:10.1016/j.clon.2014.10.006.
Buda, A.; Borghese, M.; Puppo, A.; Perotto, S.; Novelli, A.; Borghi, C.; Olearo, E.; Tripodi, E.; Surace, A.; Bar, E.; et al. Neoadjuvant Chemotherapy Prior Fertility-Sparing Surgery in Women with FIGO 2018 Stage IB2 Cervical Cancer: A Systematic Review. Cancers (Basel) 2022, 14, 797, doi:10.3390/cancers14030797.

Reviewer 5 Report

Comments and Suggestions for Authors

The manuscript by Sunhwa Baek et al., titled “Comparative Analysis of Digital Transcriptomics between Pre- and Post-treatment Samples of Patients with Locally Advanced Cervical Cancer,” presents a commendable and insightful investigation into a highly relevant and critical area of oncology. The study effectively highlights the comparative analysis of pre- and post-treatment samples from patients with advanced cervical cancer, revealing significant findings. Notably, the upregulation of C7 and EGR2 in post-treatment samples, alongside the upregulation of IL17RB and S100A7 in pre-treatment samples, suggests enhanced immune activity as a pivotal factor in therapeutic success. While the authors are to be applauded for their efforts in unraveling the complex roles of these biomarkers, a more detailed and nuanced exploration of certain aspects could substantially elevate the scholarly depth and impact of the work. Addressing these key areas with thoughtful revisions would greatly enhance the manuscript’s academic rigor, ensuring its readiness for publication at the highest level.

Detailed Comments:

How do you envision these biomarkers being integrated into clinical practice for predicting therapeutic outcomes or guiding personalized treatment strategies? Is there a roadmap for future clinical trials to evaluate their predictive accuracy and clinical utility?
Can you provide further insights into the digital multiplexed gene expression analysis utilized in this study? Were there any potential limitations or inherent biases in the technique that could have influenced the gene expression results?
What evidence supports the correlation between the pre-treatment upregulation of IL17RB and S100A7 and therapeutic resistance? Furthermore, has there been any functional or clinical validation of these genes as markers of resistance in cervical cancer?
Could you provide a more detailed overview of the pathway enrichment analysis conducted? Which specific immune response and apoptosis pathways were enriched, and how do they relate to the observed post-treatment changes?
While the upregulation of C7 and EGR2 suggests an enhanced immune response, could you elucidate the specific mechanisms by which these genes facilitate treatment success? Additionally, were there any distinct immune cells or signaling pathways identified as pivotal in mediating this response?

Author Response

Comment 1: How do you envision these biomarkers being integrated into clinical practice for predicting therapeutic outcomes or guiding personalized treatment strategies? Is there a roadmap for future clinical trials to evaluate their predictive accuracy and clinical utility?

Response 1: Thank you for your clinically meaningful comment. Despite limitations, we believe that the observed trends of our study comparing with TCGA dataset in the expression of C7, EGR2 and IL17RB between non-responders and responders suggest the potential biomarkers of these genes for therapeutic outcomes. For example, neoadjuvant chemotherapy (NACT) followed by radical surgery has been considered an alternative approach to improve disease control and reduce toxicity. Although many studies have demonstrated feasible outcomes for NACT and surgery regarding response rates and toxicity, its impact on overall survival remains still unproven [1–3]. In our study, two patients treated with NACT achieved excellent PFS, whereas one patient developed rapid disease progression despite receiving the same treatment. Those patients showed distinguished transcriptomic profiles, which should be validated in larger homogeneous cohorts. Especially for the patients, who do not have access to chemoradiation [4] or wish to preserve fertility [5], neoadjuvant chemotherapy followed by tailored surgical intervention can be an alternative if they present corresponding biomarker.

Finding significant transcriptomic changes between those patients with same therapy regimens can help to determine the individualized therapy strategy in each patient and may lead to therapy (de)-escalation minimizing side effect without jeopardizing the prognosis. For that, further studies should be carried out in larger patient cohorts, which will help to mitigate the effects of sample size limitations and enable more robust statistical analysis. By stratifying patients based on their clinical and biological factors such as tumor stage, histological subtype, and treatment protocol, patients and tumor heterogeneity should be controlled to assess the impact of these variables on gene expression more precisely and to determine whether the observed transcriptomic changes are generally associated with therapeutic response or vary across patient subgroups. Furthermore, a longitudinal study design that includes multiple time points would allow for a more detailed analysis of transcriptomic changes as they evolve. This would also enable the identification of early biomarkers of response or resistance, as well as long-term changes associated with sustained remission or recurrence. Future studies could integrate data from public databases such as The Cancer Genome Atlas (TCGA), as we did in this study, to validate findings in larger and more diverse cohorts. Additionally, collaborating with other research centers would enable us to access and analyze larger, well-matched cohorts, further reducing the impact of variability in treatment protocols and patient populations.

By validating these gene expression patterns, precision medicine approaches could be developed to stratify patients by their likelihood of treatment response, allowing for individualized therapy regimens that improve efficacy and minimize side effects. Additionally, these results could support the exploration of adjunct therapies, such as immunotherapy, which may enhance treatment effectiveness for certain molecular profiles.

We summarized the perspective regarding future research direction in conclusion based on your suggestion.

Comment 2: Can you provide further insights into the digital multiplexed gene expression analysis utilized in this study? Were there any potential limitations or inherent biases in the technique that could have influenced the gene expression results?

Response 2: Thank you for your thoughtful comments and questions. We have addressed the potential limitations and inherent biases in the multiplexed gene expression analysis technique in the discussion section (lines 394-401):

“Another potential limitation of our study is the use of digital multiplexed gene expression analysis. The results are dependent on the pre-selected gene panel comprised of 770 genes focusing on specific cancer pathways and related immune regulation. In addition, using this technique may have lead to underestimation of genes expressed at very low levels. Some inherent biases may lie in the normalization procedures as well as technical variability, which can affect the final results. In the panel, there are appropriate controls as well as housekeeping genes employed to mitigate these issues.”

Comment 3: What evidence supports the correlation between the pre-treatment upregulation of IL17RB and S100A7 and therapeutic resistance? Furthermore, has there been any functional or clinical validation of these genes as markers of resistance in cervical cancer?

Response 3: We appreciate the reviewer's comment regarding the correlation between the pre-treatment upregulation of IL17RB and S100A7 and therapeutic resistance. To address this, we investigated a larger cervical cancer cohort using the TCGA dataset (Fig. S1 B-F).

Our analysis showed that while IL17RB exhibited a slightly higher median expression in the responder group, IL17RA, a paralog of IL17RB, had higher expression in the non-responder group, indirectly supporting our argument about IL17RB. Similarly, the expression patterns of S100A7 aligned with our hypothesis. However, it is important to note that these findings were not statistically significant in the TCGA cohort, likely due to the small number of non-responders (N = 5) and the unmatched nature of the patient cohort.

Despite these limitations, the observed trends in the expression of IL17RB and S100A7 suggest a potential correlation with therapeutic resistance. These preliminary findings highlight the need for further investigation with larger, matched cohorts to establish the role of IL17RB and S100A7 in predicting therapeutic outcomes and resistance.

Additionally, it is worth mentioning that S100 family proteins and IL17-related pathways are often involved in inflammation and immune response, which could indirectly contribute to resistance mechanisms in various cancers. Although IL17RB and S100A7 have not been frequently reported as markers of resistance in cervical cancer specifically, related research suggests that immune-modulating biomarkers and cytokine profiles, such as IL6, IL8, and tumor-promoting factors, play significant roles in cervical cancer progression and response to treatment. For instance, studies investigating immune markers in cervical cancer, such as those evaluating responses to therapies like Ipilimumab, indicate that immune activation markers like ICOS, PD-1, and cytokines are closely linked to treatment resistance and survival outcomes.

By integrating these insights, our findings contribute to a broader understanding of the potential mechanisms behind therapeutic resistance and emphasize the importance of immune-related pathways in cancer treatment.

Comment 4: Could you provide a more detailed overview of the pathway enrichment analysis conducted? Which specific immune response and apoptosis pathways were enriched, and how do they relate to the observed post-treatment changes?

Response 4: We appreciate the reviewer's comments. To give a more detailed information about the specific enriched pathways, the involved genes of each pathway, and the respective metrics, we added an additional supplemental file (File S2). We indicated this on the manuscript.

Comment 5: While the upregulation of C7 and EGR2 suggests an enhanced immune response, could you elucidate the specific mechanisms by which these genes facilitate treatment success? Additionally, were there any distinct immune cells or signaling pathways identified as pivotal in mediating this response?

Response 5: We appreciate the reviewer's comment regarding the specific mechanisms by which the upregulation of C7 and EGR2 facilitates treatment success, as well as the identification of distinct immune cells or signaling pathways involved.

C7 is part of the complement system, which enhances the body’s ability to clear pathogens and damaged cells, promoting an inflammatory response. In cervical cancer, the upregulation of C7 suggests increased complement activation, leading to membrane attack complex (MAC) formation and recruitment of immune cells. This activation contributes to tumor cell killing, particularly when combined with immunotherapies. For instance, checkpoint inhibitors can boost T-cell activity, which synergizes with complement-mediated lysis.

EGR2 is a transcription factor involved in the regulation of immune responses, particularly in T-cell development and function. It can modulate cytokine production, influencing immune-mediated tumor suppression. In cervical cancer, upregulation of EGR2 may enhance the anti-tumor immune response by promoting the differentiation of T-cells into a more cytotoxic phenotype, which is crucial for eliminating cancer cells during therapies such as chemoradiation and immunotherapy. EGR2 also plays a role in the regulation of natural killer T-cells and promotes apoptosis through the PTEN-induced pathway.

Our KEGG pathway analysis (Figure S2C) revealed enrichment in complement-related pathways in post-therapeutic samples, suggesting that complement activation is contributing to tumor clearance. This is supported by studies linking complement components like C7 to improved prognosis in cancers such as gastric and prostate cancer. Additionally, the activation of apoptosis pathways post-therapy, highlighted in our pathway enrichment results, supports the role of EGR2 in enhancing immune-mediated destruction of cancer cells.

While our dataset did not specifically resolve immune cell subsets, the increased expression of genes involved in immune cell recruitment and activation, such as CCL13, suggests that immune cell infiltration is likely contributing to the observed response. The pathways identified, such as complement activation and apoptosis regulation, are strongly associated with innate and adaptive immunity, indicating that C7 and EGR2 are key mediators of immune-driven tumor regression in responders to therapy.

Reference:

Gupta, S.; Maheshwari, A.; Parab, P.; Mahantshetty, U.; Hawaldar, R.; Sastri (Chopra), S.; Kerkar, R.; Engineer, R.; Tongaonkar, H.; Ghosh, J.; et al. Neoadjuvant Chemotherapy Followed by Radical Surgery Versus Concomitant Chemotherapy and Radiotherapy in Patients With Stage IB2, IIA, or IIB Squamous Cervical Cancer: A Randomized Controlled Trial. JCO 2018, 36, 1548–1555, doi:10.1200/JCO.2017.75.9985.
Huang, H.-J.; Chang, T.-C.; Hong, J.-H.; Tseng, C.-J.; Chou, H.-H.; Huang, K.-G.; Lai, C.-H. Prognostic Value of Age and Histologic Type in Neoadjuvant Chemotherapy plus Radical Surgery for Bulky (>/=4 Cm) Stage IB and IIA Cervical Carcinoma. Int J Gynecol Cancer 2003, 13, 204–211, doi:10.1046/j.1525-1438.2003.13004.x.
Mereu, L.; Pecorino, B.; Ferrara, M.; Tomaselli, V.; Scibilia, G.; Scollo, P. Neoadjuvant Chemotherapy plus Radical Surgery in Locally Advanced Cervical Cancer: Retrospective Single-Center Study. Cancers 2023, 15, 5207, doi:10.3390/cancers15215207.
Zubizarreta, E.H.; Fidarova, E.; Healy, B.; Rosenblatt, E. Need for Radiotherapy in Low and Middle Income Countries – The Silent Crisis Continues. Clinical Oncology 2015, 27, 107–114, doi:10.1016/j.clon.2014.10.006.
Buda, A.; Borghese, M.; Puppo, A.; Perotto, S.; Novelli, A.; Borghi, C.; Olearo, E.; Tripodi, E.; Surace, A.; Bar, E.; et al. Neoadjuvant Chemotherapy Prior Fertility-Sparing Surgery in Women with FIGO 2018 Stage IB2 Cervical Cancer: A Systematic Review. Cancers (Basel) 2022, 14, 797, doi:10.3390/cancers14030797.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript has been improved, however, I recommend you consider replacing these websites in the reference list.

The supplementary figure A contains a white box, I suggest the revision of the figure.

Author Response

Comment 1: The manuscript has been improved, however, I recommend you consider replacing these websites in the reference list.

Response 1: Thank you for your thoughtful feedback about the citation of websites for cervical cancer statistics. I understand the preference for peer-reviewed publications; however, I have chosen to reference these specific websites (WHO, the American Cancer Society and Krebs - Cervical Cancer) because they provide the most current and authoritative data on cervical cancer. Statistical data, particularly in the field of public health, is frequently updated, and these websites are maintained by highly reputable organizations that regularly revise their information to reflect the latest findings and trends. Given the dynamic nature of cervical cancer statistics, I believe these online sources are valuable for providing readers with the most recent and reliable data, which may not always be available in publications. However, I am open to any further suggestions you may have to ensure that the manuscript meets the highest scholarly standards.

Comment 2: The supplementary figure A contains a white box, I suggest the revision of the figure.

Response 2: Thank you for your keen observation. We improved the figure accordingly.