Low Cell Bioenergetic Metabolism Characterizes Chronic Lymphocytic Leukemia Patients with Unfavorable Genetic Factors and with a Better Response to BTK Inhibition

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Assessment of Low Cell Metabolism Characterizes CLL Patients with Unfavorable Genetic Factors and with a Better Response to BTK Inhibition by Mirabili et al.

The aim of this work was to assess the prognostic value of bioenergetic metabolic profiles of CLL patients’ cells. The question addressed in this paper is interesting and a relevant research question in the field of CLL. The metabolism of CLL cells remains an aspect of the disease that may provide novel therapeutic approaches. However, the paper in its current form does not permit to clearly assess the value of bioenergetics relative to other prognostic markers. The paper design, clinical information, and experimental data are not presented in sufficient details to appreciate the relationships between metabolic profiles and clinical features and support the conclusions. Hence, in its current form, this paper is not acceptable for publication and needs to be revised exhaustively to be re-considered for publication.

Please find below my detailed comments that would, in my opinion, considerably enhance the reach and significance of the findings.

Main comments: 

Material and Methods

-Section 2.1: Critical information regarding CLL patients are lacking. This reviewer suggests that the authors use papers similar in scope as examples to build appropriate tables reporting patient information (example: references 5, 8, 31 of the manuscript)

Please state from where (hospital, clinic?) patients were recruited. A summary table of clinicopathological and molecular features (age, sex, rai or binet stage, IgHV status, mutational status, etc) should be provided in the core of the paper. Per patient clinicopathological data, molecular features, prior treatments (no/yes-which ones), WBC count, etc to be provided in a supplementary table for all cases. If I understand well, experiments were conducted on freshly isolated B-CLL cells, a highly valuable aspect that should be stated in the M&M.

-Lines 92-94: Specify the % purity of B-CLL cells (% CD19+ cells).

-Section 2.3: Cytogenetic and molecular analysis data should be included in the summary table and supplementary table requested above.

-Line 211: define NGS 

Results:

-Figure legends are often minimal and do not let the reader get a good grasp of the presented data. See details below for examples. Authors should pay much attention to revise each legend to provide sufficient details for the readers. 

-Section 3.1: There is much emphasis on the clustering, with a large figure 1 which lacks much details in the legend to be understood and that is secondary to the main question addressed in the paper. Authors need to provide examples of metabolic profiles (ECAR and OCR data) of normal B cells and B-CLL cells (low and high). This will help readers understand on which basis the clustering was then conducted. My suggestion is to present a composite new figure 1 that includes examples of metabolic profiles for each cell category (normal, low and high B-cLL), a small graph of the clustering (current figures 1 and 2), then graphs detailing the data described in lines 177-185, including a description of the source of the data (how was it generated, are these data based on the basal respiratory and oxphos measures?). This will then provide the reader with sufficient information to get a better grasp of the experimental basis of the study.

Regarding current Fig.1 (that in my opinion should be a panel in a more complete figure 1): I could not understand the exact meaning and its relevance to the main research question. What is an "adequate" number of clusters and a "reasonable segregation"? These are soft/suggestive terms, what was the threshold? Could authors explain the criteria applied to define the adequate and reasonable? Define n (number of clusters). The figure legend does not explain much more. Revise the legend with an appropriate level of details. Example of a more telling title: Clustering of CLL patients’ B-cells using rates of basal glycolysis (ECAR) and oxphos (OCR). State in the legend that it is based on the metabolic features assessed in B-cells of CLL patients (n=35) and normal B-cells (n=6). Not clear what is “the most frequent result”.

-Fig.3 and lines 187-195: I recommend that Fig. 3 includes a few panels, first with example of OCR profiles for each cell type (normal, low, high). Then current fig.3 box plots that summarize data for all patients. Show datapoints in the boxplots. Define colors, meaning of bar (mean or median?) and whiskers. Provide statistics: any of this data significantly different between groups? Were ECAR features also tested?

-Section 3.2: Correlations of ECAR and OCR with clinicopathological characteristics should be presented graphically, and would include results of lines 200-202 as well as others, even if not significant. Currently, it is difficult to get a good grasp at the relationship existing between metabolic measures, clusters and well-established molecular characteristics of CLL patients, despite the fact that this was the main objective of the study.

-Table 1: Provide % of total cases in each group. Statistical analysis should be performed to define whether enrichment is statistically significant.

-Section 3.3: How was the mutation status of BTK and PLCG2 defined?

Explain on which basis the 13 patients were selected for treatment.

Were they in the low or high group? According to the numbering of patients in table 2 and Fig.2, they seem to all be in the low group. But this is not what is stated in lines 218-219.

-Table 2: define n.v., NGS

-Lines 219-225: Were these measures made during ibrutinib treatment? At which time points?

Again, please present data in graphs, for example OCR and ECAR values before and after treatment.

-Lines 227-229: Again, as stated above, first comment, this information should be provided for each of the 35 cases, and identify the subgroup of patients treated with ibrutinib.

-Lines 230-240: Again, presenting metabolic groups vs clinical features in graphs with statistical significance would permit a better appreciation of the relationships. Each patient should be represented individually within boxplots. Table 3 does not permit to get a fair appreciation. Minimally, if authors also want to present this table, it should include % of patients in each group and statistical significance.

-Fig.4: A more detailed legend is needed. Define PD, CR, PR. What is the meaning of the bold lines? Did all patients receive a smiliar dose of ibrutinib?

-Lines 247-250: Treatment of patients, assessment of response, by whom, on which basis, is not detailed in the M&M. What are the clinical parameters to define the patient's response as PR, PD, CR? Detail how the followup of patients was conducted, frequency, etc. Again, authors are urged to examine high quality papers similar in scope to identify and provide a sufficient amount of information for the reader to appreciate the significance of the results. 

Discussion:

-As stated above, the data generated in this study are not presented in sufficient details to appreciate the prognostic value of metabolic measures (lines 285-290), and compare to what has been previously reported, as for example in ref #31 (lines 271-273).

-Similarly, details are lacking to get a grasp of what is discussed in lines 307-309, or line 341-342.

-What is claimed in lines 311- 312 is not shown in the presented graphs.

-A ref is needed for the statement in lines 336-338.

-Lines 373-379, Conclusions:

Unfortunately, the data, as currently presented, does not permit to reach this level of understanding and conclusion. Authors have to revise thoroughly the text, tables and figures to present rigorously the data in line with clinical features to support the discussion and conclusions.

Minor comments

Introduction: Could be shortened and more focused on bioenergetic metabolism, which is the main aspect covered in this work. Central paragraphs could be summarized in a few sentences. The relevance of AKT, NF-kB, MEK, TIGAR, ATM, PPP, OGDH, CPT1, is low relative to the presented work. After reading the introduction, the main focus of the paper was still unclear to this reviewer, even after reading lines 83-85 that are general statements. Minimally, I suggest that authors use the term bioenergetic metabolism given that glycolysis and oxidative phosphorylation were measured, and no other metabolic pathways.

This reviewer could not access the graphical abstract.

Author Response

Reviewer 1

Assessment of Low Cell Metabolism Characterizes CLL Patients with Unfavorable Genetic Factors and with a Better Response to BTK Inhibition by Mirabili et al.

The aim of this work was to assess the prognostic value of bioenergetic metabolic profiles of CLL patients’ cells. The question addressed in this paper is interesting and a relevant research question in the field of CLL. The metabolism of CLL cells remains an aspect of the disease that may provide novel therapeutic approaches. However, the paper in its current form does not permit to clearly assess the value of bioenergetics relative to other prognostic markers. The paper design, clinical information, and experimental data are not presented in sufficient details to appreciate the relationships between metabolic profiles and clinical features and support the conclusions. Hence, in its current form, this paper is not acceptable for publication and needs to be revised exhaustively to be re-considered for publication.

Please find below my detailed comments that would, in my opinion, considerably enhance the reach and significance of the findings.

Main comments: 

Material and Methods

Q-Section 2.1: Critical information regarding CLL patients are lacking. This reviewer suggests that the authors use papers similar in scope as examples to build appropriate tables reporting patient information (example: references 5, 8, 31 of the manuscript)

A - We thank the reviewer for the meaningful suggestion made to improve the quality of the manuscript. We’ve included all information requested and all the modifications, as explained below in the point-by-point reply.

Q - Please state from where (hospital, clinic?) patients were recruited. A summary table of clinicopathological and molecular features (age, sex, rai or binet stage, IgHV status, mutational status, etc) should be provided in the core of the paper. Per patient clinicopathological data, molecular features, prior treatments (no/yes-which ones), WBC count, etc to be provided in a supplementary table for all cases. If I understand well, experiments were conducted on freshly isolated B-CLL cells, a highly valuable aspect that should be stated in the M&M.

A - We’ve updated the info on CLL patients and included two tables (Table 1 and supplemental Table 1) as requested. Yes, analyses were conducted in freshly enriched CLL cells, as already stated in materials and methods.

Q - Lines 92-94: Specify the % purity of B-CLL cells (% CD19+ cells).

A – percentage of CD19+ cells ranged from more than 81% to 87% according to flow cytometry in lower and higher WBC count, respectively (further enriched by ficoll-hypaque).

Q - Section 2.3: Cytogenetic and molecular analysis data should be included in the summary table and supplementary table requested above.

A - As requested and reported above, we’ve added information about cytogenetic and molecular analysis in table 1 and supplTab1.

Q - Line 211: define NGS

A – NGS is in line 111 instead of 211, and is now as suggested explicited.

 

Results:

Figure legends are often minimal and do not let the reader get a good grasp of the presented data. See details below for examples. Authors should pay much attention to revise each legend to provide sufficient details for the readers.

Q - Section 3.1: There is much emphasis on the clustering, with a large figure 1 which lacks much details in the legend to be understood and that is secondary to the main question addressed in the paper. Authors need to provide examples of metabolic profiles (ECAR and OCR data) of normal B cells and B-CLL cells (low and high). This will help readers understand on which basis the clustering was then conducted. My suggestion is to present a composite new figure 1 that includes examples of metabolic profiles for each cell category (normal, low and high B-cLL), a small graph of the clustering (current figures 1 and 2), then graphs detailing the data described in lines 177-185, including a description of the source of the data (how was it generated, are these data based on the basal respiratory and oxphos measures?). This will then provide the reader with sufficient information to get a better grasp of the experimental basis of the study. Regarding current Fig.1 (that in my opinion should be a panel in a more complete figure 1): I could not understand the exact meaning and its relevance to the main research question. What is an "adequate" number of clusters and a "reasonable segregation"? These are soft/suggestive terms, what was the threshold? Could authors explain the criteria applied to define the adequate and reasonable? Define n (number of clusters). The figure legend does not explain much more. Revise the legend with an appropriate level of details. Example of a more telling title: Clustering of CLL patients’ B-cells using rates of basal glycolysis (ECAR) and oxphos (OCR). State in the legend that it is based on the metabolic features assessed in B-cells of CLL patients (n=35) and normal B-cells (n=6). Not clear what is “the most frequent result”.

A – We thank the reviewer for suggestions. We’ve updated figure 1, which now is composed by a panel of figures in the attempt to clarify what our workflow was. We’ve added more information in figure legends. In addition, we’ve now modified the explanation of the clustering analysis in the materials and methods section in order to avoid misunderstandable terms and to be as clear as possible.

Q - Fig.3 and lines 187-195: I recommend that Fig. 3 includes a few panels, first with example of OCR profiles for each cell type (normal, low, high). Then current fig.3 box plots that summarize data for all patients. Show datapoints in the boxplots. Define colors, meaning of bar (mean or median?) and whiskers. Provide statistics: any of this data significantly different between groups? Were ECAR features also tested?

A – We’ve added the example in figures 1a and 1b. Figure 2 (former figure 3) now shows datapoints and all the info requested. Statistics are provided in the main text. ECAR Features were not taken into account as only the mito stress test has been performed, and no specific test for glycolysis, so we can’t show any parameter other than basal glycolysis.

Q - Section 3.2: Correlations of ECAR and OCR with clinicopathological characteristics should be presented graphically, and would include results of lines 200-202 as well as others, even if not significant. Currently, it is difficult to get a good grasp at the relationship existing between metabolic measures, clusters and well-established molecular characteristics of CLL patients, despite the fact that this was the main objective of the study.

A - We have added a table and a supplementary table with the info requested.

-Table 1: Provide % of total cases in each group. Statistical analysis should be performed to define whether enrichment is statistically significant.

A – We’ve updated the table 1 (now table 2) to include the info requested.

Q - Section 3.3: How was the mutation status of BTK and PLCG2 defined

A – The mutational status of BTK and PLCG2 has been defined by next generation sequencing. Appropriate explanation has been added in the materials and methods section.

Explain on which basis the 13 patients were selected for treatment.

Were they in the low or high group? According to the numbering of patients in table 2 and Fig.2, they seem to all be in the low group. But this is not what is stated in lines 218-219.

A – The 13 patients were selected for treatment according to clinical and prognostic genetic criteria, as now stated in the main text. Additional explanation has been added in the text to clarify this point. We’ve updated patient numbering to be as clear as possible and to show to which cluster each patient belongs.

-Table 2: define n.v., NGS

A – We’ve modified the table, now it does not say NGS but Mutations.

-Lines 219-225: Were these measures made during ibrutinib treatment? At which time points? Again, please present data in graphs, for example OCR and ECAR values before and after treatment.

A – Bioenergetic metabolic measurements are the same performed for clustering analysis before ibrutinib treatment. We’ve added additional explanation in the text to clarify this point.  

-Lines 227-229: Again, as stated above, first comment, this information should be provided for each of the 35 cases, and identify the subgroup of patients treated with ibrutinib.

-Lines 230-240: Again, presenting metabolic groups vs clinical features in graphs with statistical significance would permit a better appreciation of the relationships. Each patient should be represented individually within boxplots. Table 3 does not permit to get a fair appreciation. Minimally, if authors also want to present this table, it should include % of patients in each group and statistical significance.

A – Regarding lines 227-229 and lines 230-240, we’ve updated tables and main text to include the requested information.

-Fig.4: A more detailed legend is needed. Define PD, CR, PR. What is the meaning of the bold lines? Did all patients receive a smiliar dose of ibrutinib?

A – We’ve now improved the legend, defining the acronyms. Yes, all patients received similar dose of ibrutinib as now stated in section 2.1 of materials and methods.

-Lines 247-250: Treatment of patients, assessment of response, by whom, on which basis, is not detailed in the M&M. What are the clinical parameters to define the patient's response as PR, PD, CR? Detail how the followup of patients was conducted, frequency, etc. Again, authors are urged to examine high quality papers similar in scope to identify and provide a sufficient amount of information for the reader to appreciate the significance of the results. 

A – All patients received the standard dose of 420 mg/die of ibrutinib. Treatment response was defined according to the criteria proposed by iwCLL (Hallek, 2018).  According to Hallek the parameters to define patient's response are divided in group A (Lymph nodes, liver and spleen size, constitutional symptoms and circulating lympho-cytes counts) and B (platelet count, hemoglobin, marrow). The response is defined as reported in the legend of table 4 from Hallek [CR, complete remission (all of the criteria have to be met); PD, progressive disease (at least 1 of the criteria of group A or group B has to be met); PR, partial remission (for a PR, at least 2 of the parameters of group A and 1 parameter of group B need to improve if previously abnormal; if only 1 parameter of both groups A and B is abnormal before therapy, only 1 needs to improve); SD, stable disease (all of the criteria have to be met; constitutional symptoms alone do not define PD)]. The follow-up was at least monthly. We’ve now added this explanation in section 2.1 of materials and methods.

Discussion:

Q - As stated above, the data generated in this study are not presented in sufficient details to appreciate the prognostic value of metabolic measures (lines 285-290), and compare to what has been previously reported, as for example in ref #31 (lines 271-273).

-Similarly, details are lacking to get a grasp of what is discussed in lines 307-309, or line 341-342.

-What is claimed in lines 311- 312 is not shown in the presented graphs.

A – As requested, we’ve now updated tables and main text in order to address these issues.

- A ref is needed for the statement in lines 336-338.

Q – Reference has been added

-Lines 373-379, Conclusions:

Unfortunately, the data, as currently presented, does not permit to reach this level of understanding and conclusion. Authors have to revise thoroughly the text, tables and figures to present rigorously the data in line with clinical features to support the discussion and conclusions.

A – We hope that with the on-point revisions suggested by the reviewer our conclusion are now more supported by our data.

Minor comments

Q - Introduction: Could be shortened and more focused on bioenergetic metabolism, which is the main aspect covered in this work. Central paragraphs could be summarized in a few sentences. The relevance of AKT, NF-kB, MEK, TIGAR, ATM, PPP, OGDH, CPT1, is low relative to the presented work. After reading the introduction, the main focus of the paper was still unclear to this reviewer, even after reading lines 83-85 that are general statements. Minimally, I suggest that authors use the term bioenergetic metabolism given that glycolysis and oxidative phosphorylation were measured, and no other metabolic pathways.

A – As per reviewer suggestions, we’ve shortened the introduction and applied the term bioenergetic metabolism where possible. We included the term in the title also.

This reviewer could not access the graphical abstract.

A – we’ve uploaded the graphical abstract together with the main text. For reviewer’s convenience, we paste the image in this text.

Reviewer 2 Report

Comments and Suggestions for Authors

This is a very interesting and timely manuscript addressing the question if bioenergetic heterogeneity in primary CLL specimens from ibrutinib treated and untreated patients is associated with clinical parameters. However, in spite of the relatively small size of their cohort, the authors can attempt to extract more information from the data as well increase the impact of the work. Some paper cited are not discussed in vis-à-vis the results described in the manuscript while key manuscripts in the area are not references.

Please find below my comments/suggestions:

·       In figure 2, the values are to be expressed by cell number. The authors must  base the clustering based on the bioenergetic profile of the CLL samples by potting the estimated ATP production rates by Ox/Phos (y-axis) and glycolysis (x-axis) In this configuration the upper left quadrant represents ox/phos cells, the lower right glycolytic cells, the upper right active cells and the lower left quiescent cells. (Formulas for calculations: https://www.agilent.com/cs/library/whitepaper/public/whitepaper-quantify-atp-production-rate-cell-analysis-5991-9303en-agilent.pdf)

·        In this context, the authors can calculate estimate the contribution to ATP synthesis by Ox/Phos and glycolytic ATP in each sample for clustering or differences between clusters by bioenergetic profile as described above.

·       It would be informative that the authors repeat their analysis looking at differences between the OCR/ECAR Ratios in CLL patients, including in the analysis the ratios for normal B cells and use these ratios for correlation with clinical parameters.

·       The seahorse data should be provided in the supplementary data in a table format. It would be informative that the authors provide the data for uncoupled respiration, spare respiration and glycolytic capacities in the different groups regardless of statistical significance.

·       Could it be possible that the there are actually 3 clusters with high, low and intermediate? Could the authors analyze the data excluding the “intermediate” group (i.e.., 7/9/10/12/20/26/21)?

·       It would be great if the authors assess the effect of ibrutinib on OCR and ECAR on CLL samples of selected patients from each cluster and perhaps a glutaminase inhibitor.

·       Reference 13 by Chowdhury et al (Markers in Chronic Lymphocytic Leukemia and Is Normalized by Ibrutinib Treatment), is cited but not commented or discussed.

·       Page 2 lines 58-60 “Although a certain degree of activity is present at the level of the glycolytic pathway, this does not seem to play a fundamental role in the metabolism of CLL, unlike other lymphoproliferative neoplasms characterized by a more pronounced proliferation”.

·       Reference 8 by Galicia-Vazquez (“Ibrutinib resistance is reduced by an inhibitor of fatty acid oxidation in primary CLL lymphocytes”). Is important to note that these samples were from patients not clinically treated with ibrutinib. Page 2 lines 53-55“Given the key role of the BCR signaling in supporting the growth and survival of CLL cells, the signaling pathways activated by this receptor represent elective targets for molecular therapies”.

·       To support the correlation between chromosomal aberration and metabolic rewiring in CLL, in addition to reference 16, the authors can reference the manuscript by Kluckova et al. (“B-cell Receptor Signaling Induced Metabolic Alterations in Chronic Lymphocytic Leukemia Can Be Partially Bypassed by TP53 Abnormalities”),  

 

Comments on the Quality of English Language

  NA

 

Author Response

Reviewer 2

 

This is a very interesting and timely manuscript addressing the question if bioenergetic heterogeneity in primary CLL specimens from ibrutinib treated and untreated patients is associated with clinical parameters. However, in spite of the relatively small size of their cohort, the authors can attempt to extract more information from the data as well increase the impact of the work. Some paper cited are not discussed in vis-à-vis the results described in the manuscript while key manuscripts in the area are not references.

A — We thank the reviewer for his suggestions, in the point-by-point reply we answered his questions in the attempt to improve the manuscript 

Please find below my comments/suggestions:

Q -  In figure 2, the values are to be expressed by cell number. The authors must  base the clustering based on the bioenergetic profile of the CLL samples by potting the estimated ATP production rates by Ox/Phos (y-axis) and glycolysis (x-axis) In this configuration the upper left quadrant represents ox/phos cells, the lower right glycolytic cells, the upper right active cells and the lower left quiescent cells. (Formulas for calculations: https://www.agilent.com/cs/library/whitepaper/public/whitepaper-quantify-atp-production-rate-cell-analysis-5991-9303en-agilent.pdf)  In this context, the authors can calculate estimate the contribution to ATP synthesis by Ox/Phos and glycolytic ATP in each sample for clustering or differences between clusters by bioenergetic profile as described above.

A - We thank the reviewer for the interesting observations. Certainly, it could be very informative to perform such analysis. However, as stated in the materials and methods section, we’ve only performed a mito stress test and not a glycolysis one. As such, we have limited information on glycolysis and we cannot estimate ATP production rate from glycolysis. Moreover, as stated in materials and methods, all analysis were performed with 500k cells in each well.

Q -  It would be informative that the authors repeat their analysis looking at differences between the OCR/ECAR Ratios in CLL patients, including in the analysis the ratios for normal B cells and use these ratios for correlation with clinical parameters.

A – It is the ratio between ECAR and OCR which defines the two clusters in our analysis (statistics section of materials and methods), and as such the correlation have been made upon this very information. 

Q - The seahorse data should be provided in the supplementary data in a table format. It would be informative that the authors provide the data for uncoupled respiration, spare respiration and glycolytic capacities in the different groups regardless of statistical significance.

A – As suggested, we’ve now added a table in the supplementary material that indicate values for each sample, as well as its affiliation to a cluster.

Q -  Could it be possible that the there are actually 3 clusters with high, low and intermediate? Could the authors analyze the data excluding the “intermediate” group (i.e.., 7/9/10/12/20/26/21)?

A – It’s our statistical analysis which indicated the number of clusters, as it shows that the most fitting number of clusters is 2 (figure 1c, former figure 1), and that 3 is a way less appropriate number of clusters to describe our data, occurring 5 times in 48 runs, as compared to the 21 times for n=2. A detailed explanation has been added in the main text.

Q - It would be great if the authors assess the effect of ibrutinib on OCR and ECAR on CLL samples of selected patients from each cluster and perhaps a glutaminase inhibitor. 

 

A – Undoubtedly, this can be an excellent addition to our paper. We’ve already started these experiments and we saw a progressive decrease in OCR and a heterogeneous response by ECAR, however results are too preliminary to be presented and will be the focus of a following paper.

 

 

Q -  Reference 13 by Chowdhury et al (Markers in Chronic Lymphocytic Leukemia and Is Normalized by Ibrutinib Treatment), is cited but not commented or discussed.

 

A - Reference 13 has been removed

 

Page 2 lines 58-60 “Although a certain degree of activity is present at the level of the glycolytic pathway, this does not seem to play a fundamental role in the metabolism of CLL, unlike other lymphoproliferative neoplasms characterized by a more pronounced proliferation”.

 

Reference 8 by Galicia-Vazquez (“Ibrutinib resistance is reduced by an inhibitor of fatty acid oxidation in primary CLL lymphocytes”). Is important to note that these samples were from patients not clinically treated with ibrutinib. Page 2 lines 53-55“Given the key role of the BCR signaling in supporting the growth and survival of CLL cells, the signaling pathways activated by this receptor represent elective targets for molecular therapies”.

 

A – We agreed with the reviewer and we’ve now removed the reference number 8 from that sentence. We can confirm that our samples also are from patient not treated with ibrutinib.

 

 

 To support the correlation between chromosomal aberration and metabolic rewiring in CLL, in addition to reference 16, the authors can reference the manuscript by Kluckova et al. (“B-cell Receptor Signaling Induced Metabolic Alterations in Chronic Lymphocytic Leukemia Can Be Partially Bypassed by TP53 Abnormalities”),  

 

A – As suggested, we’ve added the reference in the introduction section.

 

 

 

 

 

 

 

 

 

 

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The use of OCR and OX/PHOS as well ECAR and glycolysis are used as synonyms. However, this is not appropriate because basal glycolysis is defined as the increase in ECAR reading by addition of glucose onto a glucose free media. In this context, the presence of pyruvate and glutamine in addition to glucose (as indicated in materials and methods) might contribute to non-glycolytic acidification such as CO₂  generated by mitochondrial oxidation of pyruvate and glutamate. Therefore, the authors needs to use ECAR through the manuscript instead of glycolysis or glycolytic rate.

 

Could the authors clarify why ECAR perturbations to evaluate specific glycolytic contribution to  PPR were not included in the experimental design.

 

Specific comments:

 

Materials and methods:

In Material and methods, it is only indicated that “Bioenergetic rates for oxidative phosphorylation and glycolysis were  measured using a Seahorse XFp Extracellular Flux Analyzer (Agilent Technologies, CA,USA) as previously published”.

 

Results:

Please indicate in the main text how Basal OCR and ECAR were calculated vs ECAR in Fig 1C and Table 2.

 

In Figure 2, the Basal OCR and OCR coupled to OX/Phos (ATP production) values are identical for CLL high but for CLL High. However, for CLL low the mean OCR is 15 and  ATP production is 10,  is the difference due to elevated non-mitochondrial respiration or uncoupled respiration?

 

In Table 2, the authors need to indicate if there are differences in the representation clinically untreated or clinically treated  CLL samples in the of CLL low or high.

 

Discussion:

Important article to be include in this section.  This paper is important because support the authors findings with regard to the impact of IgVH status in metabolic rewiring in CLL. Notably the work was performed in a large cohort of patient’s samples that enable multivariate analysis. In this paper Lu et al. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6717593/) reported that,

 reported that unmutated CLL display higher glycolytic activity than M-CLL and suggested that higher glycolysis activity of CLL cells reduces sensitivity to drugs, while higher respiration activity contributes to increased sensitivity ex vivo. Ex vivo, CLL samples ibrutinib resistance was associated with elevated  OCR. As well Lu et al.  reported that although CLL cells and normal B cells have a similar basal glycolytic activity, CLL cells had a significantly higher glycolytic capacity and glycolytic reserve with respect to non-malignant B-cells.

Author Response

Please see the attachment

Author Response File: Author Response.pdf