High-dimensional immunotyping of tumors grown in obese and non-obese mice

  1. Cara E. Wogsland
  2. Hilde E. Lien
  3. Line Pedersen
  4. Pahul Hanjra
  5. Sturla M. Grondal
  6. Rolf A. Brekken
  7. James B. Lorens
  8. Nils Halberg

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Abstract

Obesity is a disease characterized by chronic low-grade systemic inflammation and has been causally linked to the development of 13 cancer types. Several studies have been undertaken to determine whether tumors evolving in obese environments adapt differential interactions with immune cells and whether this can be connected to disease outcome. Most of these studies have been limited to single-cell lines and tumor models and analysis of limited immune cell populations. Given the multicellular complexity of the immune system and its dysregulation in obesity, we applied high-dimensional suspension mass cytometry to investigate how obesity affects tumor immunity. We used a 36-marker immune-focused mass cytometry panel to interrogate the immune landscape of orthotopic syngeneic mouse models of pancreatic and breast cancer. Unanchored batch correction was implemented to enable simultaneous analysis of tumor cohorts to uncover the immunotypes of each cancer model and reveal remarkably model-specific immune regulation. In the E0771 breast cancer model, we demonstrate an important link to obesity with an increase in two T-cell-suppressive cell types and a decrease in CD8 T cells.

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  1. Version published to 10.1242/dmm.048977
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    Reply to the reviewers

    Reviewer #1 (Evidence, reproducibility and clarity (Required)):

    **Summary:**

    This study of Nils Halberg and colleagues aims to characterize tumor-associated immune cell

    infiltrates in a mouse model of diet-induced obesity. Authors compared different syngeneic

    tumor cell lines for mammary adenocarcinoma and pancreatic ductal adenocarcinoma. Tumor

    infiltrating leukocytes were analyzed by a 36-parametric mass cytometry protocol. The authors

    put a lot of efforts in the generation of high-quality data by applying state-of-the-art methods for

    sample barcoding and batch analyses, removal of batch-specific variations and in the

    subsequent pipeline of data analysis. The clinical relevance of the topic addressed is well

    documented in several studies, showing a clear association between obesity and the

    development of several tumors, including those tumors investigated in this study.

    Main findings of this study can be summarized that in the model system used tumor-dependent

    differences in the qualitative and quantitative composition of immune cell infiltrates were

    observed. Unfortunately, the mouse model system used obviously did not reveal convincing

    data whether obesity may modulate the process of tumor infiltration.

    The manuscript is well written, quantity of figures is appropriately and of excellent quality and

    prior studies were referenced appropriately.

    In conclusion, authors made tremendous methodological and technical efforts to generate

    robust and high-quality mass cytometry data, but the overall outcome of the study remains

    limited in respect to shedding some new light how obesity is possibly involved in the qualitative

    and quantitative modulation of tumor-related immune cell infiltration.

    Authors’ Response: We thank the reviewer for their constructive and positive feedback as well

    as appreciation of our rigorous approach. We would however argue that our data significantly

    contributes to the understanding of how obesity affects tumor immunity. We believe that our

    systemic approach across multiple tumor systems highlights that i) it matters what model you

    choose, as each model have a separate response to the obesity challenge ii) for one model, the

    E0771 model, our data reflect obesity-dependent alterations to the CD8+ T-cells population.

    This was corroborated by a parallel publication by Rigel et al., 2020 as highlighted by the 2nd

    reviewer. That being said, we too, were surprised that the pro-inflammatory obese environment

    did not have more pronounced effects on the tumor immune infiltrates across the five models.

    **Major comments:**

    Due to the limited data really showing an association between obesity and immune cell

    infiltration of tumors investigated I would suggest that authors should change emphasis of their

    results more closely related to the findings of tumor-dependent immune cell infiltrations than

    obesity-related associations. So, the title of the study should be appropriately changed since

    "High dimensional immunotyping of the obese tumor micro-environment" rather implies

    analyses of spatial relationships of immune, tumor and fat cells by immunohistological analyses,

    which would indeed help to strengthen the outcome of this mass cytometry study.

    Authors’ Response: We appreciate the constructive suggestion. We did not intend the title to

    infer immunohistochemical analysis and apologize that was the case. We have therefore

    changed the title to “High dimensional immunotyping of tumors grown in obese and non-obese

    mice” in the revised version (line 1).

    Although all the efforts made in mass cytometry data generation are quite commendable in this

    study, basic statistical issues are not clearly addressed regarding the number of biological

    replicates. How many mice were treated per tumor cell line? According to figure 1B nine chow

    and eight HFD animals were used: does this mean that only one or two mice were analyzed per

    cell line, respectively? Please explain how many animals belong to each of the seven mouse

    cohorts.

    Authors’ Response: We agree that this was not clearly defined in the manuscript. We have

    updated Figure 1A and the corresponding legend to make it clearer. The mouse numbers,

    referred to as tumors, are also located in Table 3. In total 69 mice were used, distributed as:

    E0771_1 consists of 4 chow fed mice and 4 HFD mice (N=4/4, where N=chow/HFD, for a total

    of 8 mice)

    E0771_2 is N=5/4. Wnt1 is N=6/6. TeLi is N=5/5. C11_1 is N=5/4. C11_2 is N=5/5. UN-KC is N=5/6.

    Figure 1B shows representative mouse weights only. The female mice are from breast cancer

    cohort E0771_1 and the male mice are from pancreatic cancer cohort C11_1. We chose to only

    show representative data since diet-induced obesity is well established in the C57Bl/6 strain.

    Obviously, cell lines E07771 and C11 were analyzed as duplicates only. Regarding E0771,

    tumor growth was 31 and 23 days, respectively. So, large inter-individual differences in tumor

    growth were obvious and how this is reflected at the level of tumor infiltration? Therefore, please

    explain which criteria were used to decide when the tumors had to be removed. Furthermore,

    please indicate weight, viability and absolute cell number of each tumor sample in a

    supplementary table to get an impression about variability in tumor growth.

    Authors’ Response: The reviewer brings up an important point. The E0771 and C11 cohorts are

    included in the paper as combined cohorts. The individual C11 cohorts had too few tumors

    remaining after removal of samples with too low viability (as discussed below) to analyze

    separately. The E0771 cohorts are presented together as a representation of that tumor model.

    Data analysis for the E0771 cohorts separately shows comparable population abundance

    differences and obesity-dependent changes between chow and HFD tumors. The metacluster

    fold change for non-obese and obese tumors between E0771_1 and E0771_2 correlated with a

    R2 = 0.8586. Presenting the data combined provides a more concise view of the model.

    Removal of E0771 and C11 tumors in each individual cohort were time matched. E0771 tumors

    were continuously measured by caliper and removed before they reached 1 cm3 according to the

    local ethical guidelines. The E0771_2 cohort tumors had to be removed sooner as one tumor

    reached 1 cm3 earlier. We have reported the tumor mass in Figure 1C as that is a more accurate

    measurement of final tumor burden. Pancreatic tumors were removed based on optical imaging

    of luciferase expressing cancer cells and careful monitoring of mouse distress based on the

    grimace scale. The material and methods section has been updated to reflect this (line 556-558).

    Only pancreatic tumors had viability poor enough that they had to be excluded from analysis. A

    cutoff of CD45+ 5000 cells was set and applied to cells remaining following the gating strategy

    shown in Figure 1D. Therefore, CyTOF data for tumors with fewer than 5000 CD45+/Cisplatin

    negative cells were excluded from analysis as indicated with an X in Figure1C. As requested, we

    have included tumor weights and available viability measurements in new Table 5.

    **Minor comments:**

    The generation of orthotopic pancreatic cancer mouse models is technically challenging, and

    needs more complex imaging methods to monitor the growth of the implanted tumor cells.

    Furthermore, orthotopic implantation of tumor cells into the pancreas by surgery can also inflict

    significant physical trauma to the recipient animals. How authors have monitored tumor cell

    implantation?

    Authors’ Response: We agree that tracking tumor growth in the orthotopic pancreas cancer

    model is challenging. As mentioned above, these cells were engineered to express luciferase

    and optical imaging was used to monitor growth of the implanted cells. We did not report these

    numbers as we were unable to convincingly correct for possible light absorption by the

    enhanced adipose tissue mass in the high fat group. As such, these scans were used to

    estimate the end point.

    The number of CD45-positive cells per tumor sample is not given in the manuscript, but this

    information would be important to know, because it can be expected that most of the samples

    showed less than 20.000 cells. This relatively low number of total leukocytes would not allow a

    statistically significant profiling of rare cell subsets, such as DC's or MDSC's. This limitation

    should at least clearly addressed in the discussion section.

    Authors’ Response: The reviewer raises a great point. Since the cells were live cryopreserved

    and thawed before measuring CD45, we did not determine the total immune cell infiltrate. After

    thawing, the CD45+ cells accounted for roughly 1-12% of the total events collected across all

    batches leading to a total number of CD45+ cells ranging between 54,317 and 1,102,767 per

    batch. Numbers for each batch can be found in Table 3. After gating and exclusion of tumors

    with less than 5000 CD45+ cells, the remaining tumor data were equally sampled and 5206

    CD45+ cells were included in further analysis from each tumor. Overall, we were focused on

    broad phenotyping of the immune infiltrate and not on rare subsets. Some subsets had low

    abundance in some tumors and high abundance in others. Because the analysis was performed

    altogether, the overall phenotyping and clustering did not find any truly rare subsets. DCs and

    MDSC were not rare when assessed across the datasets. While we cannot characterize the

    subsets that are small in a specific tumor type, we can be confident in the characterization

    provided by the streamlined analysis of the data as a whole.

    According to table 2 authors have used 36 immune cell-related antigens including casp3, which

    was only used to exclude apoptotic cells from downstream analyses. But as written in the

    results section only 26 phenotyping markers were used to generate the viSNE map shown in

    Figure 3. In Figure 3C-F 30 markers were shown. Please explain this obvious inconsistency of

    markers used.

    Authors’ Response: Thank you for this question. Our goal here was to generate a viSNE map

    that best separated out immune cells by phenotype. Lineage markers and well-established

    phenotyping markers were therefore included to create the well separated viSNE map. It follows

    that some markers were not included: i) Markers that were used to gate the population of

    interest (CD45 and c-Cas3) were excluded from the viSNE input parameters.; ii) Markers that

    had relatively low signal were also excluded such as MHC-1 and CD117. Including negative

    markers is computationally costly, provides limited biological insight, and can produce a worse

    viSNE map by reducing cell separation due to shared lack of signal (Diggins et al., 2015); iii)

    Activation/ exhaustion markers were excluded from the viSNE analysis because the focus of the

    phenotyping was on major cell subsets and not on activation states. The hope was to observe

    differences in exhaustion marker expression between chow and HFD; and iv) CD5 was

    excluded because having two bright T cell markers skewed the map towards a more T cell

    dominant view. Markers with meaningful expression were reintroduced in the MEM analysis

    after the viSNE map was made. Exclusion of markers from viSNE analysis is a generally

    accepted practice and has been applied previously (Wogsland et al., 2017, Cheng et al., 2016,

    Huse et al., 2019, Leelatian et al., 2020, Doxie et al., 2018, Okamato et al., 2021, Henderson et

    al., 2020). The reasoning behind using the 26 phenotyping markers have been included in the

    revised manuscript (line 754 – 757)

    How viability of tumor samples was determined?

    Authors’ Response: Viability was measured at three points using membrane exclusion assays.

    Viability was first measured upon tumor dissociation using trypan blue and a Countess cell

    counter on the single cell suspension before freezing. Values were used to guide cell aliquoting

    for cryopreservation. Viability was again measured with trypan blue upon thaw in order to

    barcode and stain 3 million live cells per sample. Before fixation, cells were again stained for

    viability, this time with cisplatin, to exclude dead cells after data collection with gating. This has

    been added to the methods section (line 559-562)

    Cells were additionally stained for cleaved-Cas3 as an indicator of cells undergoing apoptosis.

    Only pancreatic tumors had viability poor enough that they had to be excluded form analysis.

    Tumors with fewer than 5000 CD45+ Cisplatin negative cells were excluded from analysis as

    indicated with an open X in Figure1C. The tumor count in parentheses in Table 3 indicates the

    tumors that were not excluded.

    Please indicate cell loss caused by cryopreservation of dispersed tumor tissue samples.

    Authors state that mainly neutrophilic granulocytes will be lost during cryopreservation, and that

    this would help to the "definitive identification and characterization of G-MDSC". But there are

    several reports showing that MDSC-subsets also behave very sensitive during cryopreservation

    and that it is recommended to analyze fresh samples if MDSC's are of particular interest (DOI:

    10.1177/1753425912463618; DOI: 10.1177/1753425912463618). This possible limitation

    should be discussed in the manuscript and not only highlighted as advantage on the way to

    identify MDSC-subsets.

    Authors’ Response: We thank the reviewer for this insightful comment. We agree that we likely

    lost some MDSC during the cryopreservation process as shown in the reference above. But

    since no neutrophils survive standard cryopreservation (Graham-Pole et al., 1977), the Ly6G

    positive cells in our analysis are G-MDSC and not neutrophils. We assume that any cell death

    related to cryopreservation would be consistent across samples, so although cell totals may be

    lower than in the tumor, abundance differences and phenotype can still be evaluated. We have

    included a discussion of this in the revised manuscript

    (line 408 – 410).

    In the Figure 1D X-axis named by "193Ir-NA" should be replaced by "193Ir-DNA".

    Authors’ Response: NA is shorthand for nucleic acid since the iridium intercalates into DNA

    and RNA. The figure legend has been updated to make this clear.

    Furthermore, please explain "(T)" in the figure legend. Percentages in the last two dot plots

    related to "all previous gates" are confusing: 20,44% of all DNA-containing single cells were

    finally intact, living CD45+ cells, i.e. almost 80% of cells were excluded because they were dead

    or apoptotic and this corresponds to 57,06% of intact, living CD45-positive cells related to all

    CD45-positive cells? How these percentages are related to the "Percent of CD45/total raw

    events" in the last column of Table 3?

    Authors’ Response: These are great points. Thank you for bringing them to our attention. This

    confusing notation has been removed since Figure 1D is a representative gating strategy. “All

    previous gates” means that the previous gates were all applied to the population showing in that

    plot. CyTOF data requires thorough gating to remove the events that are not representative of

    actual cells so yes, many events were removed before analysis. Even more cells were excluded

    here since our focus was on the CD45+ cells and not the cancer cells. The CD45+ cells

    indicated in Table 3 and visualized in Figure 1E can be calculated by summing the total gated

    CD45+ cells per Figure 1D for each batch and dividing that by the total number of events

    collected per batch. The summed CD45+ values and the total collected events are also in Table

    Authors claimed that "155Gd_IRF4" was changed to "155Gd", but it is not clear why to mention

    that IRF4 has been NOT used throughout the study? Please provide only those technical

    details, which are necessary to understand what has been done.

    Authors’ Response: We apologize for any confusion. This change was mentioned because

    most cohorts included the IRF4 channel while a two (C11_2 and Wnt_1) did not. The FCS files

    were changed to allow for simultaneous analysis. The IRF4 antibody did not work so there

    shouldn’t be any bleed into other channels in the samples that were stained with IRF4.

    According to general practice, we believe that it is important to make note of any manipulation to

    the FCS files.

    Re Figure 6: please explain the abbreviation "TNBC".

    Authors’ Response: We apologize for not explaining this abbreviation. TNBC is short for triple

    negative breast cancer. This has been corrected in the resubmitted version.

    Experiments done with TKO mice are not described in the Materials and Methods section. In

    particular, it would be important to know the number of replicates and the number of tumors

    grown in this model. It should be also discussed that the growth kinetics of tumors in chow and

    HFD TKO mice seem to be much faster as compared to wild type mice. Principally, the TKO

    model used here is only of limited value to clarify especially the role of CD8 cells since all other

    T- and B- cell subsets including NK cells are also absent in this knockout model and indirect

    effects caused by these cells cannot be excluded.

    Authors’ Response: We deeply apologize that the TKO experiments were not included in the

    Triple knockout (Rag2-/-::CD47-/-::Il2rg-/-; TKO) mice were purchased from Jackson Laboratories (Stock No: 025730).

    We agree with the reviewer it is an important point that the E0771 tumors overall grew faster in the TKO model. Ringel et al. 2020 saw similar results when depleting CD8 T cells in their MC38 model. Comparably, the most striking difference observed was that the tumor growth between obese and non-obese mice disappeared in the TKO mice.

    We have modified the results section to include these points (line 309-310). Reviewer #1 (Significance (Required)):

    material and methods section.

    experiment was performed with N=5/5. The description of the TKO model has been added to

    Orthotopic implantation and

    monitoring of E0771 and C11 cells were performed as with the wild type C57BL/6 mice. Each

    the methods section (line 520- 533) and number of mice used has been added to the figure

    legend.

    did not observe any major growth changes (overall growth rate and growth differences between

    obese and non-obese mice) in the TKO mice compared to the wild type mice.

    In the C11 model, interestingly, we

    We agree that the combined lack of B- and NK- cells in combination to the lack of T-cells

    exclude a direct conclusion on the effect of obesity-dependent alteration in T-cell phenotypes.

    Altogether, this study is a paragon that a single technology-based study alone, even when well-

    designed, is not sufficient to explore complex tumor microenvironment-immune cell interactions

    and that additional information on spatial relationships of cells and possibly single cell-based

    RNAseq techniques are necessary to shed new light on this ambitious topic. But there is no

    doubt that the potential of mass cytometry has been not fully exploited in this study and that a

    more focused view on particular cell types identified so far, such as macrophages or CD8 cells,

    by using as many immunophenotypic and functionally-related parameters as necessary would

    allow a more in depth-phenotyping of particular immune cell compartments.

    The significance of this subject would have been tremendously increased if human samples will

    be analyzed in a future confirmative study.

    Authors’ Response: Again, these are important insights. To what extend we have taken full

    advantage of the suspension mass cytometry technology is of course debatable. When we set

    out to perform these studies, we were compelled to take a broad approach rather than focusing

    on a single cell type for the following reasons: i) we had noticed extreme variability in immune

    targeted analysis through FACS of murine cancer models. Since we set out to demonstrate

    systemic effects of the obese environment rather than model-specific effects, the broad antibody

    panel made the most sense and ii) tumor immune infiltrates are known to be composed of

    multiple cell types and the effect of the obese state would likely affect multiple of these. To not

    bias ourselves this prompted us to design a rather broad immune panel. With the knowledge

    derived from this study and others (For example Rigel et al, Cell 2020 and Chung et all, Cell

    2020), new and more focused panels could be developed and implemented for future studies.

    We agree that the inclusion of human data would be of great value. We were, however, unable

    to obtain suitable human material that could be used for this suspension mass cytometry

    analysis. This was largely due to large inconsistencies in reported patient BMI and inadequate

    tumor freezing conditions.

    Even when I'm not a specialist in tumor biology, based on my expertise in the fields of chronic

    inflammation and cytometry, I'm convinced that the outlined way of generating

    immunophentypic data by single cell-based mass cytometry is of major interest not only for

    tumor biologists, but will be for sure recognized by a broad scientific community interested in the

    generation of single cell-based immunophenotypic data.

    Authors’ Response: Thank you for your helpful and supportive feedback. It is indeed our hope

    and motivation that the immunophenotyping platform presented herein will be broadly applicable

    to other cancer immunologists and fields.

    Reviewer #2

    (Evidence, reproducibility and clarity (Required)):

    Wogsland et al. apply herein mass cytometry (CyTOF) to investigate how obesity affects tumor

    immune infiltrates. They use several models of murine breast and pancreatic cancers and

    analyse their immune landscape thanks to an extended panel of 36 markers. They notably

    describe a decrease in CD8 T cells in one breast cancer model fed with high fat diet inducing

    obesity which favors tumor development.

    Overall, the report is clearly written and follows a very logical plan. Figures are also clear and

    nicely support the text. The mass cytometry approach appears quite original and could be

    relevant for many readers.

    Authors’ Response: We thank the referee for their constructive and positive comments on our work.

    The referee raises the general criticism that our study is descriptive.

    Nevertheless, some concerns have to be made and would need to be acknowledged by

    authors:

    -First of all, the paper appears very descriptive. Except at the end of the last figures, authors

    only establish of catalog of immune cells in different tumors. Even if the trueness of such

    observations is undisputable, their relevance to improve our understanding of tumor biology is

    clearly questionable.

    Authors’ Response:

    While we agree that the majority of the manuscript is focused on establishing a robust immune

    atlas in multiple tumor models grown in obese and non-obese mice, we believe that such work

    has important merit: i) our immune cell atlas of 5 transplant models will be a valuable resource

    for other cancer researchers interested in the immune-oncology field (as also highlighted by the

    first referee); ii) our findings clearly underscore the critical need to apply multiple cell lines in

    experimental setups when studying the interaction between tumors and immune cells –

    particularly in the obese setting; iii) we have implemented an analysis pipeline that is broadly

    applicable for high dimensional mass cytometry data that will be useful for future high-

    dimensional immunotyping efforts, iv) through our unbiased analysis pipeline we did identify

    obesity-dependent alterations to the CD8 cell population in the E0771 model. This finding was

    corroborated by the studies by Ringel et al., 2020. Collectively, we strongly believe that our

    studies will contribute to the advancement of our understanding of tumor biology.

    -Moreover, the major finding claimed in this study (CD8 T cells decrease in tumors from HFD

    mice) has been very recently published paper also providing mechanistic insights (Ringel et al.,

    Cell 2020). Authors could legitimately be disappointed but the interest of their study is sadly

    severely impacted by this prior publication. This key paper should be at least discussed and

    included in references.

    Authors’ Response: The paper by Ringel et al., was published after we originally submitted our

    manuscript for review and was therefore not referenced or discussed (Ringel et al., 2020). In the

    resubmitted version of the manuscript, we have included a thorough discussion of the paper’s

    findings in terms of consistencies and inconsistencies with our conclusions (line 545-463).

    -Finally, even if the initial strategy of integration of breast and pancreatic cancers was

    indubitably a good one, results reported in figures 5 & 6 clearly show a quite specific

    observation in the E0771 model. So in this context, integrating all these datasets do not improve

    the understanding of this phenotype

    Authors’ Response: We thank the referee for bringing up this point. Regardless of the outcome, we would strongly argue that the integrated approach to be advantageous to individual analysis. The integrated approach did not hinder new discoveries in any of the datasets – if anything, the integrated analysis pipeline developed herein would facilitate new discoveries that would be missed by repeating individual analysis. By integrating the datasets, we enabled the robust identification of more cell subsets. In particular cell types that displayed low abundance in some models. Those cells would have likely been hidden or even missed in a larger subset had the models been analyzed separately. As such, we maintain that the integrated approach is the correct and most biological meaningful to follow when given the possibility.

    Besides these quite general comments, few more specific points:

    -In Fig 2, the F4/80 signal appears very weak in all datasets except one (TeLi) with an almost

    flat curve for all the other ones. It asks the question of the reproducibility of the staining that

    could be only partially corrected with batch correction algorithms.

    Authors’ Response: The F4/80 peak in the TeLi cohort is indicative of a large F4/80

    population rather than a sign of signal intensity differences. TeLi tumors had much higher

    abundance of F4/80+ cells than did the other tumor types as can be seen in Figure 4B. For

    each mass cytometry run, we included a control sample to ensure equal staining patterns

    between the antibodies in each run.

    -Obesity is clearly known to be sex/hormone dependent as confirmed by authors themselves in

    their Fig 1B so again the global integration (both sex and 2 organs) strategy is disputable here.

    It is hard to know if there is no effect in the pancreas because of tissue or sex specificities.

    Authors’ Response: Thank you for the feedback. We specifically tried to show the different

    tumors side by side without making too many comparisons across tumor types because of the

    sex and tissue differences (as was noted in the results section of the manuscript, line 267). Both

    breast and pancreatic tumor models are relevant for studying the obesity cancer connection

    which is why we have worked to develop these models with different cancer types. Even with

    the sex and tissue differences, male and female mice became obese on a high fat diet, and

    tumors from both tissues grew larger on a high fat diet

    . It is our hope that this work will pave the

    way for future studies to interrogate these differences.

    -On Fig 1C, red dots are closed or open but explanation of this is lacking.

    Authors’ Response: Thank you for pointing this out. The figure legend has been updated. The

    X indicates a tumor that had too few live CD45+ cells to be included in the data CyTOF

    analysis. We apologize this was not clear.

    -Authors use 36 markers in their CyTOF panel but use only 26 for the dimension reduction

    without clearly explaining this choice. Should be amended. For example, why excluding CD5?

    Authors’ Response: Thank you for bringing this up. We have addressed these concerns in

    response to Reviewer #1 above.

    Reviewer #2 (Significance (Required)):

    Severly impaired by Ringel et al., Cell 2020

    Authors’ Response:

    It is clear that the study by Ringel et al., demonstrate new and important mechanistic insights

    into the connection between obesity, T-cell biology and tumor behavior. Our studies share many

    of the same conclusions on tumor immune cell infiltrate in obesity – particularly the T-cell finding

    in our E0771 model. However, we stipulate that our experimental approach and scientific

    questions differ. Our approach was to generate high-dimensional immune phenotyping atlas

    across multiple models to identify overarching obesity-dependent effects. The manuscript by

    Ringel et al., has a more mechanistic focus. The field would benefit from the additive insights

    from the two papers combined.

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  3. Review Commons

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    Referee #2

    Evidence, reproducibility and clarity

    Wogsland et al. apply herein mass cytometry (CyTOF) to investigate how obesity affects tumor immune infiltrates. They use several models of murine breast and pancreatic cancers and analyse their immune landscape thanks to an extended panel of 36 markers. They notably describe a decrease in CD8 T cells in one breast cancer model fed with high fat diet inducing obesity which favors tumor development.

    Overall, the report is clearly written and follows a very logical plan. Figures are also clear and nicely support the text. The mass cytometry approach appears quite original and could be relevant for many readers.

    Nevertheless, some concerns have to be made and would need to be acknowledged by authors:

    -First of all, the paper appears very descriptive. Except at the end of the last figures, authors only establish of catalog of immune cells in different tumors. Even if the trueness of such observations is undisputable, their relevance to improve our understanding of tumor biology is clearly questionable.

    -Moreover, the major finding claimed in this study (CD8 T cells decrease in tumors from HFD mice) has been very recently published paper also providing mechanistic insights (Ringel et al., Cell 2020). Authors could legitimately be disappointed but the interest of their study is sadly severely impacted by this prior publication. This key paper should be at least discussed and included in references.

    -Finally, even if the initial strategy of integration of breast and pancreatic cancers was indubitably a good one, results reported in figures 5 & 6 clearly show a quite specific observation in the E0771 model. So in this context, integrating all these datasets do not improve the understanding of this phenotype

    Besides these quite general comments, few more specific points: -In Fig 2, the F4/80 signal appears very weak in all datasets except one (TeLi) with an almost flat curve for all the other ones. It asks the question of the reproducibility of the staining that could be only partially corrected with batch correction algorithms.

    -Obesity is clearly known to be sex/hormone dependent as confirmed by authors themselves in their Fig 1B so again the global integration (both sex and 2 organs) strategy is disputable here. It is hard to know if there is no effect in the pancreas because of tissue or sex specificities.

    -On Fig 1C, red dots are closed or open but explanation of this is lacking.

    -Authors use 36 markers in their CyTOF panel but use only 26 for the dimension reduction without clearly explaining this choice. Should be amended. For example, why excluding CD5?

    Significance

    Severly impaired by Ringel et al., Cell 2020

  4. Review Commons

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    Referee #1

    Evidence, reproducibility and clarity

    Summary:

    This study of Nils Halberg and colleagues aims to characterize tumor-associated immune cell infiltrates in a mouse model of diet-induced obesity. Authors compared different syngeneic tumor cell lines for mammary adenocarcinoma and pancreatic ductal adenocarcinoma. Tumor infiltrating leukocytes were analyzed by a 36-parametric mass cytometry protocol. The authors put a lot of efforts in the generation of high-quality data by applying state-of-the-art methods for sample barcoding and batch analyses, removal of batch‐specific variations and in the subsequent pipeline of data analysis. The clinical relevance of the topic addressed is well documented in several studies, showing a clear association between obesity and the development of several tumors, including those tumors investigated in this study.

    Main findings of this study can be summarized that in the model system used tumor-dependent differences in the qualitative and quantitative composition of immune cell infiltrates were observed. Unfortunately, the mouse model system used obviously did not reveal convincing data whether obesity may modulate the process of tumor infiltration. The manuscript is well written, quantity of figures is appropriately and of excellent quality and prior studies were referenced appropriately. In conclusion, authors made tremendous methodological and technical efforts to generate robust and high-quality mass cytometry data, but the overall outcome of the study remains limited in respect to shedding some new light how obesity is possibly involved in the qualitative and quantitative modulation of tumor-related immune cell infiltration.

    Major comments:

    Due to the limited data really showing an association between obesity and immune cell infiltration of tumors investigated I would suggest that authors should change emphasis of their results more closely related to the findings of tumor-dependent immune cell infiltrations than obesity-related associations. So, the title of the study should be appropriately changed since "High dimensional immunotyping of the obese tumor micro-environment" rather implies analyses of spatial relationships of immune, tumor and fat cells by immunohistological analyses, which would indeed help to strengthen the outcome of this mass cytometry study. Although all the efforts made in mass cytometry data generation are quite commendable in this study, basic statistical issues are not clearly addressed regarding the number of biological replicates. How many mice were treated per tumor cell line? According to figure 1B nine chow and eight HFD animals were used: does this mean that only one or two mice were analyzed per cell line, respectively? Please explain how many animals belong to each of the seven mouse cohorts. Obviously, cell lines E07771 and C11 were analyzed as duplicates only. Regarding E0771, tumor growth was 31 and 23 days, respectively. So, large inter-individual differences in tumor growth were obvious and how this is reflected at the level of tumor infiltration? Therefore, please explain which criteria were used to decide when the tumors had to be removed. Furthermore, please indicate weight, viability and absolute cell number of each tumor sample in a supplementary table to get an impression about variability in tumor growth.

    Minor comments:

    The generation of orthotopic pancreatic cancer mouse models is technically challenging, and needs more complex imaging methods to monitor the growth of the implanted tumor cells. Furthermore, orthotopic implantation of tumor cells into the pancreas by surgery can also inflict significant physical trauma to the recipient animals. How authors have monitored tumor cell implantation? The number of CD45-positive cells per tumor sample is not given in the manuscript, but this information would be important to know, because it can be expected that most of the samples showed less than 20.000 cells. This relatively low number of total leukocytes would not allow a statistically significant profiling of rare cell subsets, such as DC's or MDSC's. This limitation should at least clearly addressed in the discussion section. According to table 2 authors have used 36 immune cell-related antigens including casp3, which was only used to exclude apoptotic cells from downstream analyses. But as written in the results section only 26 phenotyping markers were used to generate the viSNE map shown in Figure 3. In Figure 3C-F 30 markers were shown. Please explain this obvious inconsistency of markers used. How viability of tumor samples was determined? Please indicate cell loss caused by cryopreservation of dispersed tumor tissue samples. Authors state that mainly neutrophilic granulocytes will be lost during cryopreservation, and that this would help to the "definitive identification and characterization of G-MDSC". But there are several reports showing that MDSC-subsets also behave very sensitive during cryopreservation and that it is recommended to analyze fresh samples if MDSC's are of particular interest (DOI: 10.1177/1753425912463618; DOI: 10.1177/1753425912463618). This possible limitation should be discussed in the manuscript and not only highlighted as advantage on the way to identify MDSC-subsets. In the Figure 1D X-axis named by "193Ir-NA" should be replaced by "193Ir-DNA". Furthermore, please explain "(T)" in the figure legend. Percentages in the last two dotplots related to "all previous gates" are confusing: 20,44% of all DNA-containing single cells were finally intact, living CD45+ cells, i.e. almost 80% of cells were excluded because they were dead or apoptotic and this corresponds to 57,06% of intact, living CD45-positive cells related to all CD45-positive cells? How these percentages are related to the "Percent of CD45/total raw events" in the last column of Table 3 ?

    Authors claimed that "155Gd_IRF4" was changed to "155Gd", but it is not clear why to mention that IRF4 has been NOT used throughout the study? Please provide only those technical details, which are necessary to understand what has been done.

    Re Figure 6: please explain the abbreviation "TNBC". Experiments done with TKO mice are not described in the Materials and Methods section. In particular, it would be important to know the number of replicates and the number of tumors grown in this model. It should be also discussed that the growth kinetics of tumors in chow and HFD TKO mice seem to be much faster as compared to wild type mice. Principally, the TKO model used here is only of limited value to clarify especially the role of CD8 cells since all other T- and B- cell subsets including NK cells are also absent in this knockout model and indirect effects caused by these cells cannot be excluded.

    Significance

    Altogether, this study is a paragon that a single technology-based study alone, even when well-designed, is not sufficient to explore complex tumor microenvironment-immune cell interactions and that additional information on spatial relationships of cells and possibly single cell-based RNAseq techniques are necessary to shed new light on this ambitious topic. But there is no doubt that the potential of mass cytometry has been not fully exploited in this study and that a more focused view on particular cell types identified so far, such as macrophages or CD8 cells, by using as many immunophenotypic and functionally-related parameters as necessary would allow a more in depth-phenotyping of particular immune cell compartments. The significance of this subject would have been tremendously increased if human samples will be analyzed in a future confirmative study.

    Even when I'm not a specialist in tumor biology, based on my expertise in the fields of chronic inflammation and cytometry, I'm convinced that the outlined way of generating immunophentypic data by single cell-based mass cytometry is of major interest not only for tumor biologists, but will be for sure recognized by a broad scientific community interested in the generation of single cell-based immunophenotypic data.

  5. Version published to 10.1101/2020.10.14.338806v1 on bioRxiv