IL34 (IL-34 gene) and CSF1 (M-CSF/CSF1 gene) which both signal through CSF1R. PDAC CTC also had high levels of RNA expression for CXCL8, the gene encoding chemokine interleukin-8 (IL-8) which can attract myeloid cells through their CXCR2 receptors. FACS-isolated portal PDAC CTC and M-FB co-cultured ex vivo had increased CTC proliferation, motility, and cluster formation compared to CTC cultured alone. CSF1R and CXCR2 cell surface expression were found on PDAC portal blood CTC and M-FB, suggesting that both cell types may respond to M-CSF, IL-34, and IL-8-mediated signaling. Portal PDAC CTC displayed enhanced RNA expression of CSF1 and IL34, while CTC+M-FB+ clusters formed in vivo had increased RNA expression of CSF2 and IL34. Portal M-FB were found to have high CSF1R RNA expression. CTC isolated from ex vivo 7-day cultures of PDAC patient portal blood mononuclear cells  expressed elevated CSF1, IL34, and IL8 RNA, and CSF1 expression was elevated in M-FB. Treatment with rabbit anti-CSF1R antibodies decreased CTC proliferation. Treatment of PortalBMC cultures with humanized anti-CSF1R, humanized anti-IL-8, or anti-IL-34 antibodies disrupted CTC cluster formation and increased CTC apoptosis. U937 myeloid precursor cell line cultures treated with conditioned media from PortalBMC ex vivo cultures without treatment or treated with anti-IL-8 and/or anti-CSF1R did not prevent myeloid differentiation in the myeloid precursor cell line U937 to macrophage, dendritic cell, MDSC, and M-FB phenotypes; whereas, U937 cultures treated with conditioned media from PortalBMC ex vivo cultures exposed to anti-IL-34 were significantly inhibited in their myeloid differentiation to all but the M-FB phenotype. PDAC patient T cells that were found phenotypically anergic (CD3+CD25+CTLA4+PD1L1+) in PortalBMC could be re-activated (CD3+CD25+CTLA4-PD1L1-), and displayed increased interferon gamma (IFNγ) production when PortalBMC ex vivo cultures were treated with anti-CSF1R, anti-IL-8, and anti-IL-34 antibodies alone or in combination. These findings suggest that PDAC CTC have the potential to influence myeloid differentiation and/or antigen presenting cell activation in the PDAC portal blood microenvironment, and that disruption of CTC/M-FB interactions may be potential targets for reversing the immunosuppression supporting CTC survival in the portal blood.The portal venous circulation provides a conduit for pancreatic ductal adenocarcinoma (PDAC) tumor cells to the liver parenchyma sinusoids, a frequent site of metastasis. Turbulent flow in the portal circulation promotes retention of PDAC shed circulating tumor cells (CTC) and myeloid-derived immunosuppressor cells (MDSC). Excessive colony stimulating factor-1 receptor (CSF1R) signaling can induce myeloid differentiation to MDSC and transformation of MDSC to myeloid-derived fibroblasts (M-FB). Interactions between PDAC CTC and M-FB in the portal blood promotes the formation of immunoresistant clusters that enhance CTC proliferation, migration, and survival. Analysis of portal and peripheral blood samples collected intraoperatively from 30 PDAC patients undergoing pancreatico-duodenectomy showed that PDAC patient plasma contained high levels of macrophage colony stimulating factor (M-CSF/CSF1), granulocyte-macrophage colony stimulating factor (GM-CSF/CSF2), interleukin-8 (IL-8), and interleukin-34 (IL-34) compared to healthy control levels. Moreover, the level of M-CSF in portal blood was significantly higher than that detected in the peripheral blood of PDAC patients. PDAC CTC aseptically isolated by fluorescence activated cell sorting (FACS) out of freshly collected patient portal blood mononuclear cells  had elevated RNA expression of  Pancreatic ductal adenocarcinomas (PDAC) are among the most morbid human cancers . Their dA major contributor to PDAC-related mortality is the propensity for rapid development of distant metastasis . In PDACIn the portal blood, PDAC circulating tumor cells (CTC) can attract and influence myeloid precursor cells which can aid in CTC survival, motility, and extravasation to surrounding tissues , 9–12. MPortal blood myeloid cells in contact with prolonged exposure to M-CSF or tumor cells can be influenced to differentiate toward fibroblast-like phenotypes with tumor support and immunosuppressive functions. These myeloid-derived suppressor cells (MDSC) and their derivative myeloid-derived fibroblasts (M-FB) can act as shields against immune attack, source of factors, provide metabolic support, motility enhancement for extravasation, and scaffolding for metastatic establishment once out into surrounding tissue , 18, 19.In this study, we examined how portal blood PDAC CTC can attract and influence circulating myeloid cells into M-FB differentiation to aid in CTC survival, proliferation, and motility.Portal and peripheral venous blood samples were collected during pancreatico-duodenectomy performed with curative intent in 30 patients diagnosed with pancreatic head ductal adenocarcinoma . Written patient informed consent was obtained prior to surgical treatment, and participation in the study was performed under AHCI IRB approved protocols 946704/507397/562565. Eligible patients for this study were adults ≥ 18yr of age diagnosed with localized PDAC who were deemed candidates for surgical treatment.The median age for the patients at surgery was 62 years . The patient population was 13% Hispanic and 97% Caucasian. Twenty-one patients received chemotherapy prior to surgery . Eleven of these patients also received preoperative external beam radiation therapy. Patient tumor staging data were collected from post-surgical specimen pathological diagnosis after analyses were completed. Pathological staging included tumors of 9 patients staged as T1N0, 1 patient as T1N+, 6 patients as T2N0, 8 patients as T2N+, 3 patients as T3N0, and 3 patients as T3N+. With the exception of CTC numbers in portal blood as previously reported , neitherin vivo-formed clusters out of ficoll histopaque (Sigma-Aldrich) isolated PortalBMC using a 10-parameter/8-color Beckman Coulter MoFlo XDP FACS instrument fitted with a 100 micron nozzle , among the differentially expressed genes in PDAC clones. For confirmatory analyses of these candidate genes’ expression, FACS-isolated single clones of CTC from 12 patients with PDAC were individually extracted and yielded limited RNA samples for quantitative RT-PCR analysis. GAPDH RNA expression in clonal cell isolates (data not shown) was used for RNA copy number calculations of KRASmut, oncogene RET1 [S2], CSF2, CSF2R, and CSF1R expression per clone. B) Graph depicts results from analysis of CTC SCS from 12 PDAC patients using 3 CTC SCS clones per patient, confirming high expression of KRASmut, RET1, and CSF1R RNA with variability seen within each patient’s clonal set. Two CTC clones from a single patient showed high expression of CSF2R as well. C) Quantitative RT-PCR analysis of RNA from SCS CTC clones of 5 individual PDAC patient portal blood samples showed high variability of RAN oncogene expression while its substrate target gene, SPP1, remained relatively stable in its expression across clones. Sample values are shown by open circles, horizontal bar indicates mean of values and error bars depict standard error of the mean. References for (TIF)Click here for additional data file.S3 Fig. Among the 16 PDAC patient samples depicted, 13 patients received neoadjuvant treatment with chemotherapy with or without radiation therapy prior to resection. The remaining 3 patients that did not undergo neoadjuvant therapy had higher CTC counts overall after ex vivo culture, but otherwise changes in CTC proliferation in the defined isolated cell ex vivo cultures were not significantly different from that seen in those of patients receiving neoadjuvant treatment. Bars indicate mean of values and error bars depict standard error of the mean.Data depicted are derived from (TIF)Click here for additional data file.S4 Fig ELISA (Boster Scientific) analysis of matched portal (white bars) and peripheral (grey bars) plasma of 19 patients with adequate volume for extended analysis, with 16 who received neo adjuvant therapy (chemotherapy ± radiation therapy) prior to resection were compared for concentrations of PGE2, GM-CSF, M-CSF, IL-34, IL-6, IL-8, IFNγ, IL1α, and IL1β. (A) The three patients in this data set that did not receive neoadjuvant had high levels of M-CSF, IL-6, and IL1β in their portal blood plasma but little or no detected in their peripheral blood plasma. With so few untreated patient samples represented, no reliable statistical analysis was possible for comparison. For the 16 patients with neoadjuvant treatment (B), statistical differences were seen in increased M-CSF, IL-6, and IL1β concentrations in portal blood compared with peripheral blood plasma. Red lines indicate literature values for concentrations in non-malignant human plasma; p values in red indicate significant differences found in PDAC portal and peripheral blood plasma samples used in this study compared to control values cited in the literature. Sample bar indicates mean of values and error bars depict standard error of the mean, with * indicating p<0.05, ** p<0.01, ns = no significant difference seen, and *** indicate where samples with undetectable analyte levels precluded statistical comparison.Data depicted are derived from (TIF)Click here for additional data file.S1 File(HTML)Click here for additional data file.S2 File(HTML)Click here for additional data file.S1 Data(XLSX)Click here for additional data file. 13 Dec 2021
 PONE-D-21-29413

 Pancreatic Ductal Adenocarcinoma (PDAC) Circulating Tumor Cells Influence Myeloid Cell Differentiation to Support their Survival and Immunoresistance in Portal Vein Circulation

 PLOS ONEDear Dr. Litherland,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.==plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by Jan 27 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at
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Reviewer #1: Thank you for inviting me to evaluate the article titled“Pancreatic Ductal Adenocarcinoma (PDAC) Circulating Tumor Cells Influence Myeloid Cell Differentiation to Support their Survival and Immunoresistance in Portal Vein Circulation.” In this paper, the authors want to show that Circulating tumor cells (CTC) shed from primary PDAC tumors interact with myeloid cells in the portal blood and can sway myeloid differentiation toward immunosuppressive MDSC and tumor supportive myeloid derived fibroblasts (M-FB) through activation of CSF1R and CXCR2 signaling pathways mediated by M-CSF/IL-34 and IL-8, respectively. However, there are some problems in expression and defects in the results of the experiment.The author used CD147、CD47、EPCAM and Cytokeratin to characterize circulating tumor cells, but according to I know, high expression interstitial marker CTC is more likely to be transferred. Why does the author do not add an interstitial marker?If the above problem can be solved, this article is worth receiving.Reviewer #2: Summary of the research and overall impressionThis work is focused on the study of the circulating tumor cells isolated from the portal blood. Most studies have focused on CTC detection and counting in peripheral blood samples but fewer reports have focused in capturing and detecting CTCs in vessels closer to the tumor, especially in the main veins that drain blood from the organ invaded by the cancer (‘close-to-the-tumor liquid biopsy’). I consider this a very positive aspect of this approach. In this study portal vein CTCs were cultured ex vivo together with myeloids cells to study proliferation, motility and cluster formation compared to CTC alone. The interaction between these two cell types causes myeloid cells to differentiate into an immunosuppressive phenotype mediated by the release of M-CFS/IL-34 and IL-8.Blocking this interaction would prevent this phenotype change in myeloid cells and is postulated as a possible treatment for pancreatic cancer, which is one of the most aggressive solid tumors for which there is little chance of success with current treatments.The purpose of the research is important and of interest to potential readers, however, I have some doubts and problems in understanding the tests performed. My biggest doubt is related to the number of samples used in each assay, I don't quite understand the diversity in the samples used, especially in assays that do not depend on the existence of CTCs in the patient's blood. And also, the fact of assuming that BMC are M-FB, when in fact they are a heterogeneous mixture of different cell types.The text is a bit difficult to follow as there are no clearly delimited sections, for example with a subtitle. The images in general could be improved in relation to the quality of the microscopy images and the size of the axes of the graphs which is sometimes difficult to see. Some images are poorly explained in the text.Material and Methods:CTC isolation method is based on the expression of epithelial cell markers, such as CK19 or EpCAM, couldn´t you have missed CTCs undergoing EMT?Sample collection: The description of the patients indicates that 18 patients had received chemotherapy prior to surgery (Folfirinox or Gemcitabine/Abraxane) and 8 of them had even received radiation. It is described that the treatments will influence the release and the characteristics of CTC into the blood. This factor could be introducing noise in the results. I understand that the number of patients is low but it would be convenient for the authors to analyze the results separately for patients who have received therapy and those who have not. This may even give information on whether therapy affects the interaction of myeloid cells with CTCs.I understand that patients will be in stage I or II disease if they are amenable to surgery but it might be useful to make this clear in the text. Inclusion criteria could be better described.FACS: I have not checked all the antibody references but for example, for CD44 the references indicated correspond to CD34 and CD81 (line 110). Likewise, I don't know if the authors meant to refer to CD47 since CD44 is not among the markers used for cytometry of CTCs or M-FB.RNA: Any assessment of RNA quality?Results:In Figure 1, I would suggest rearranging the panels in the order in which they are cited in the text. It is a bit odd to have to look at figure 1c first. This should be 1a if it is the first one being discussed.Figures 1C-H how have they been taken? Why do we know they are CTC or M-FB? There are supposed to be two dyes but the image quality does not allow to see anything.I find figure 1a rather incomprehensible. The authors claim that CTC-MFB co-culture induces CTC proliferation. I don't understand this result in the graph. I don't understand this result in the graph.Of the 16 patients used in this trial, how many had received therapy? Does that affect the outcome?Figure 2A is not referred in the text. I understand that for trials with CTCs the number of patients is not the total, but to analyze the levels of cytokines and growth factors in blood, why not all patients? What are the red stripes in figure 2A? Are they normal reference values? A bit unusual way to put them.According to the text: “GM-CSF levels in PDAC patient portal blood plasma were highly elevated compared to normal peripheral blood (p=0.0305) while M-CSF levels were moderately elevated over 247 normal (p=0.0383)” Why do you compare portal vein levels with normal peripheral blood levels? You would have to compare portal blood GM-CSF levels with normal levels there and peripheral blood levels with normal levels there. Or are they the same?Figure 2c would benefit from a legend stating that the white bars are portal blood and the gray bars are peripheral blood. Significant differences are marked between the two blood types but are they different from the reference levels in each compartment? Now, however, 24 patients are used for this figure. The criteria for this should be explained.Here as before, treatments may have influenced the release of these factors into the blood.Figure 3: The lettering on panels B-E is virtually unnoticeable.One doubt that arises, perhaps because I have not understood it well, is that the authors refer to M-FB populations and blood mononuclear cells (BMC) indistinctly. But when isolated by FICOL BMC, this is a heterogeneous population of cells. In the test corresponding to Figure 3, they take BMC to study the interaction between CTC-MFB and in reality, it cannot be assumed that what is there is only M-FB.Figure 3F is not clearly visible. Perhaps it could be divided into several graphs, one for each chemokine. Similarly, if all comparisons are made with the first graph, this can be indicated in the figure caption and there is no need to put so many dashes above the figure, you can put the asterisks indicating statistical significance only above each bar. This applies to other figures in the manuscript.Figure 3M and 3N are vaguely explained in the figure legend. Annexin V plot should be supplemented.Figure 4 is not explained in the text. It is very difficult to understand because it makes a statement and refers to the entire figure, panels A to D, without further explanation of what the different images show. What does zero mean, in the second bar of the graphs? For me, figure 4 does not contribute anything because I do not understand it.Figure 5 is not explained either. This causes the reader's attention to be lost because it is very difficult to draw conclusions when they do not explain what they see in the graphs. It requires extra effort on the part of the reader.Figure 6 focuses on gene expression changes in CTCs and M-FBs. In the materials and methods, the whole process of sequencing is described but in reality the only thing that is described is the expression of the same markers that were previously studied. The whole paragraph in materials and methods referring to the identification of differentially expressed genes does not make much sense with the results that are being presented in this article.Figure 7A does not seem to have significant differences, but in the text the impression conveyed is that the treatment reduces the state of anergy . Perhaps this statement or the graph should be explained better.Throughout the text there are a number of typos in the wording that could easily be corrected:For example, in line 140 it says cells cells; 147 it says cluster clusters. Line 154 it does not have a verb (like were used)Line 267, there is a dot after IL-8 that should be a colon.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). 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Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, AttachmentPancreatic Ductal Adenocarcinoma (PDAC) Circulating Tumor Cells Influence Myeloid Cell Differentiation to Support their Survival and Immunoresistance in Portal Vein Circulation .pdfSubmitted filename: Click here for additional data file. 14 Feb 2022Arnoletti et al-Response to ReviewersWe the authors sincerely thank the reviewers for their insightful comments for the improvement of our manuscript. We have taken their comments to heart and have done a thorough review and revision of the entire manuscript including updating all figures and adding 4 supplemental figures in the text here for our responses to answer the reviewers’ clarification requests. We leave it to the reviewers and editorial board to decide if any of these supplemental figures should be included in the final publication. Again, thank you for supporting our efforts to present our data accurately and clearly and improving the manuscript by your review. Below, we re-print the review comments sent to us in italics and answer the reviewers’ queries and comments point by point . “Reviewer #1: Thank you for inviting me to evaluate the article titled “Pancreatic Ductal Adenocarcinoma (PDAC) Circulating Tumor Cells Influence Myeloid Cell Differentiation to Support their Survival and Immunoresistance in Portal Vein Circulation.” In this paper, the authors want to show that Circulating tumor cells (CTC) shed from primary PDAC tumors interact with myeloid cells in the portal blood and can sway myeloid differentiation toward immunosuppressive MDSC and tumor supportive myeloid derived fibroblasts (M-FB) through activation of CSF1R and CXCR2 signaling pathways mediated by M-CSF/IL-34 and IL-8, respectively. However, there are some problems in expression and defects in the results of the experiment.”We thank the reviewer for their kind comments. We agree that the original manuscript as uploaded had defects in presentation and organization that we have hopefully rectified in our thorough review of the entire document, updating data analyses, and revision of the text and figures.“The author used CD147、CD47、EPCAM and Cytokeratin to characterize circulating tumor cells, but according to I know, high expression interstitial marker CTC is more likely to be transferred. Why does the author do not add an interstitial marker? If the above problem can be solved, this article is worth receiving.”We apologize for the typographical error in the Methods section of the manuscript (now corrected on line 131 in the revised manuscript) that misrepresented the biomarker signature we used in flow cytometric and FACS analyses used in the study. The correct profile used for positive detection and FACS isolation of CTC was CD44+CD147+EPCAM+/CK+. We have added references 22 & 23 describing the use of the CD147 and CD44 biomarkers. We agree with the reviewer that use of interstitial biomarkers such as Twist or Vimentin would be useful to identify potential CTC clonal subsets more specifically if possible. However, as seen in published studies [references added: 43-47] using these biomarkers rely on fixed, permeabilized cell fluorescent analyses (IHC fluorescence microscopy and intracellular flow cytometry), which is not possible to use in our studies requiring viable patient cell isolation for ex vivo culture growth to test responses to myeloid cell interactions and live cell signal transduction in the presence of inhibitory antibody treatments. We acknowledge and address this limitation in the Discussion section of the revised manuscript (lines 742-755).“Reviewer #2: Summary of the research and overall impressionThis work is focused on the study of the circulating tumor cells isolated from the portal blood. Most studies have focused on CTC detection and counting in peripheral blood samples but fewer reports have focused in capturing and detecting CTCs in vessels closer to the tumor, especially in the main veins that drain blood from the organ invaded by the cancer (‘close-to-the-tumor liquid biopsy’). I consider this a very positive aspect of this approach. In this study portal vein CTCs were cultured ex vivo together with myeloid cells to study proliferation, motility and cluster formation compared to CTC alone. The interaction between these two cell types causes myeloid cells to differentiate into an immunosuppressive phenotype mediated by the release of M-CFS/IL-34 and IL-8. Blocking this interaction would prevent this phenotype change in myeloid cells and is postulated as a possible treatment for pancreatic cancer, which is one of the most aggressive solid tumors for which there is little chance of success with current treatments.The purpose of the research is important and of interest to potential readers, however, I have some doubts and problems in understanding the tests performed. My biggest doubt is related to the number of samples used in each assay, I don't quite understand the diversity in the samples used, especially in assays that do not depend on the existence of CTCs in the patient's blood.” We truly appreciate the reviewer’s grasp of our findings despite the presentation errors and need for clarification found. We have done an end-to-end review of the manuscript to re-organize it and endeavored to clarify the points in the data that the reviewer observed.We agree that the variations in sample numbers in each analyses was not ideal though we strived to maximize the number of analyses preformed on each sample. However, the inherent variation in patient samples and limitations of collecting and working with viable, minimally processed CTC samples from surgical patients preclude the use of every sample in all analyses. Patient samples were collected during surgery and analyzed blind prior to final pathological analyses and chart review, so we were unable to select samples based on staging or prior treatment. Samples were processed as close to collection as possible so that minimal time/manipulation could affect CTC biology prior to ex vivo analyses, with the clinical/surgical treatment of patients was prioritized above research sample collection in all instances. Patient sample variations in plasma quality/available volume, CTC collectable numbers, and technical limitations sometimes did not allow for equal plasma or cell samples to be available from each participant for this study. Analyses dependent on uncultured FACS isolation of CTC  were limited in available viable cells and extracted RNA for these analyses as well as the available PortalBMC sample remaining for subsequent culture-based analyses. In addition, two patients’ surgery scheduling and technical limitations precluded pre-culture assessment of CTC/MFB from PortalBMC so that their samples could not be included in these data sets. Financial constraints limited the number of samples that could be analyzed by BioLegend Legendplex analysis and RNA SEQ. Portal plasma samples were available from all 30 patients but available volume and reagent costs limited their use in soluble factor  and U937 conditioned media studies . “And also, the fact of assuming that BMC are M-FB, when in fact they are a heterogeneous mixture of different cell types.”We thank the reviewer for pointing out the need to clarify these terms in the manuscript and to better describe which analyses were done using CTC and M-FB FACS isolated prior to culture  and which other analyses were done using unsorted portal blood mononuclear cell  in ex vivo cultures with CTC and M-FB subpopulations characterized and isolated after culture . We have added more detail throughout the revised manuscript text and in all the Figure legends to more clearly delineate the distinction between when uncultured FACS-isolated CTC and M-FB were used and when unsorted PortalBMC were used for the analyses.Supplemental Fig. 1 . Comparison of CTC Numbers ex vivo and after ex vivo Cluster Cultures in Pre-Surgically Treated Patient Samples to Samples from Patients without Pre-Surgical Treatment. A) CTC were aseptically isolated from freshly ficoll gradient separated, uncultured portal blood mononuclear cells  using high-speed FACS. CD44+CD147+EPCAM+/CK+CD45- CTC numbers were compared between 20 PDAC patients that received pre-operative treatment (Neo adjuvant chemotherapy ± radiation) and 8 patients who had no pre-operative therapy. As previously reported , PDAC patients receiving neo adjuvant chemotherapy with or without radiation had lower portal blood CTC counts than untreated patients . Sample values are shown by open circles, horizontal bars indicate mean of values and error bars depict standard error of the mean. B) Comparison of PDAC patient portal CTC numbers in uncultured PortalBMC sub-grouped by tumor stages (T1-3) with (N+) or without (N0) lymph node involvement. TN staging was derived from the post-operative resected tumor tissue pathological review for PDAC patients verified after analysis of portal blood samples from the same patients. PDAC Patients receiving pre-operative treatments are depicted by white circles. PDAC patients without neoadjuvant treatment are represented by black dots. Horizontal bar indicates mean of values and error bars depict standard error of the mean. No statistical difference in portal blood CTC counts was seen between tumor stage with or without lymph node involvement. C) A correlation analysis of the 8 PDAC patients without neoadjuvant therapy showed a weakly linear but statistically significant negative relationship between portal blood CTC numbers and tumor (T) staging, with more CTC detected in lower stage tumors than in higher stage tumors . D) Correlation analysis showed no relationship between portal blood CTC numbers and tumor (T) staging in 20 PDAC patients receiving neo adjuvant treatment. Two PDAC patients  in the study did not have pre-culture FACS isolation of CTC performed; and therefore, are not included in this set of analyses. Supplemental Fig. 2 . RNA Expression Analysis of Single Cell Sorted PDAC individual CTC clones. A) FACS isolated single clones (SCS) of CTC from 2 PDAC patients’ uncultured PortalBMC were collected directly into RNALater and shipped frozen to GeneWiz for RNA extraction and analysis using RNA Microsequencing . Analysis of the top 100 genes expressed by individual clonal CTC (SCS) and oligoclonal CTC FACS isolations (OLIGO) for each patient were compared. A) Heatmaps depict the top 30 genes found by GeneWiz analyses for 3 SCS clones and 1 sample of oligo CTC isolates for the 2 patients as examples of variability among clones of each patient. Further bioinformatic analyses of the expression patterns within each patients’ clonal variants revealed some clones with elevated expression of myeloid cell growth factor signaling genes and increased expression of the oncogene RAN and its regulatory target gene SPP1, which encodes for the tumor motility promoting protein osteopontin [21], among the differentially expressed genes in PDAC clones. For confirmatory analyses of these candidate genes’ expression, FACS-isolated single clones of CTC from 12 patients with PDAC were individually extracted and yielded limited RNA samples for quantitative RT-PCR analysis. GAPDH RNA expression in clonal cell isolates (data not shown) was used for RNA copy number calculations of KRASmut, oncogene RET1, CSF2, CSF2R, and CSF1R expression per clone. B) Graph depicts results from analysis of CTC SCS from 12 PDAC patients using 3 CTC SCS clones per patient, confirming high expression of KRASmut, RET1, and CSF1R RNA with variability seen within each patient’s clonal set. Two CTC clones from a single patient showed high expression of CSF2R as well. C) Quantitative RT-PCR analysis of RNA from SCS CTC clones of 5 individual PDAC patient portal blood samples showed high variability of RAN oncogene expression while its substrate target gene, SPP1, remained relatively stable in its expression across clones. Sample values are shown by open circles, horizontal bar indicates mean of values and error bars depict standard error of the mean.“The text is a bit difficult to follow as there are no clearly delimited sections, for example with a subtitle.”We re-organized the text to hopefully improve its clarity and have incorporated subtitles as the reviewer has suggested.“ The images in general could be improved in relation to the quality of the microscopy images and the size of the axes of the graphs which is sometimes difficult to see.”Where possible, we have enlarged and worked to improve the images and graphic representations of our data in all the figures in the revised manuscript.“ Some images are poorly explained in the text.”We have added more detail in the text of the Figure legends on the images used in the figures of the revised manuscript.“Material and Methods:CTC isolation method is based on the expression of epithelial cell markers, such as CK19 or EpCAM, couldn´t you have missed CTCs undergoing EMT?”The reviewer has made a valid point of the limitations on assessing EMT status of CTC in our studies. We have addressed this issue by clarifying the CTC biomarkers that were identified and how the CTC subpopulations were used in these studies in the Method section (lines 130-137), and how they could be used in future studies in the Discussion section (lines 736-740) in the revised manuscript.“Sample collection: The description of the patients indicates that 18 patients had received chemotherapy prior to surgery (Folfirinox or Gemcitabine/Abraxane) and 8 of them had even received radiation. It is described that the treatments will influence the release and the characteristics of CTC into the blood. This factor could be introducing noise in the results. I understand that the number of patients is low but it would be convenient for the authors to analyze the results separately for patients who have received therapy and those who have not. This may even give information on whether therapy affects the interaction of myeloid cells with CTCs.”We agree with the reviewer and have updated the proportions of neoadjuvant treated and untreated patients with new data added in the revised manuscript and re-analyzed the data of the study considering these groups. The results of these subgroup analyses are included in 4 Supplemental Figures 1-4 included here in the text of our responses. Beyond the increase in portal blood CTC numbers seen in uncultured  and cultured CTC similar to previously reported in references 7 &11, we did not see any change in statistical analyses in the data of these current studies by the breakout of untreated and neoadjuvant treated sample subpopulations. With further separation of chemotherapy type and radiation therapy subgroups, the data subsets were too small to accurately analyze differences between treatment types statistically. We have described the updated proportions of patient treated and untreated subgroups in the Methods section (lines 112-128). We defer to the reviewers and editors of the journal to judge whether any of the supplemental figures depicted here need to be included with the final publication of the paper. Supplemental Fig. 3. Comparison of Proliferation of FACS isolated cells in Defined ex vivo Cluster Cultures in Pre-Surgically Treated Patient Samples to Samples from Patients without Pre-Surgical Treatment. Data depicted are derived from Fig 1 panel E. Among the 16 PDAC patient samples depicted, 13 patients received neoadjuvant treatment with chemotherapy with or without radiation therapy prior to resection. The remaining 3 patients that did not undergo neoadjuvant therapy had higher CTC counts overall after ex vivo culture, but otherwise changes in CTC proliferation in the defined isolated cell ex vivo cultures were not significantly different from that seen in those of patients receiving neoadjuvant treatment. Bars indicate mean of values and error bars depict standard error of the mean.Supplemental Fig. 4. Comparison of Cytokine and Growth Factor Levels in Portal and Peripheral Blood of Pre-Surgically Treated Patient Plasma Samples to Samples from Patients without Pre-Surgical Treatment. Data depicted are derived from Fig. 2 Panel C. ELISA (Boster Scientific) analysis of matched portal (white bars) and peripheral (grey bars) plasma of 19 patients with adequate volume for extended analysis, with 16 who received neo adjuvant therapy (chemotherapy ± radiation therapy) prior to resection were compared for concentrations of PGE2, GM-CSF, M-CSF, IL-34, IL-6, IL-8, IFNγ, IL1α, and IL1β. (A) The three patients in this data set that did not receive neoadjuvant had high levels of M-CSF, IL-6, and IL1β in their portal blood plasma but little or no detected in their peripheral blood plasma. With so few untreated patient samples represented, no reliable statistical analysis was possible for comparison. For the 16 patients with neoadjuvant treatment (B), statistical differences were seen in increased M-CSF, IL-6, and IL1β concentrations in portal blood compared with peripheral blood plasma. Red lines indicate literature values for concentrations in non-malignant human plasma; p values in red indicate significant differences found in PDAC portal and peripheral blood plasma samples used in this study compared to control values cited in the literature. Sample bar indicates mean of values and error bars depict standard error of the mean, with * indicating p<0.05, ** p<0.01, ns = no significant difference seen, and *** indicate where samples with undetectable analyte levels precluded statistical comparison. “I understand that patients will be in stage I or II disease if they are amenable to surgery but it might be useful to make this clear in the text. Inclusion criteria could be better described.”The tumor stage information (lines 120-128) and inclusion criteria (lines 116-117) for the patients participating in the study have been added in the Methods Section. Analysis of uncultured portal blood CTC numbers considering final pathological diagnosis of tumor stage is included in Supplemental Figure 1B-D shown above in the response text. No statistically significant differences were seen between patients sub-grouped by TN staging . A weakly linear statistically significant correlation of CTC numbers with tumor T staging was seen in the patient subpopulation that did not receive pre-surgical chemotherapy ± radiation therapy ; however, no comparable correlation was seen in the neoadjuvant treated patient subpopulation . These tumor stage comparison results are summarized in the Methods section lines 125-128 in the revised manuscript.“FACS: I have not checked all the antibody references but for example, for CD44 the references indicated correspond to CD34 and CD81 (line 110).”We thank the reviewer for finding this source error  in the submitted manuscript and we have followed their advice to review all of antibody references mentioned in the paper. We have updated all references in the revision to reflect our most up-to-date sources for the reagents used in the studies.“Likewise, I don't know if the authors meant to refer to CD47 since CD44 is not among the markers used for cytometry of CTCs or M-FB.”We apologize for the typographical error in the Methods Section that erroneously referred to the biomarker CD47. The correct antibody reagent used was anti-CD44. The correct CTC identification biomarker panel has been listed on line 131 in the Methods section of the revised manuscript.“RNA: Any assessment of RNA quality?”The assessment of 260/280 ratio was used in evaluating RNA quality and quantity in the samples used in the study. This has been annotated in the revised manuscript Methods section lines 268-270.“Results:In Figure 1, I would suggest rearranging the panels in the order in which they are cited in the text. It is a bit odd to have to look at figure 1c first. This should be 1a if it is the first one being discussed.”We agree and have rearranged Figure 1 as the reviewer has suggested in the revision of the manuscript.“Figures 1C-H how have they been taken? Why do we know they are CTC or M-FB? There are supposed to be two dyes but the image quality does not allow to see anything.”We have re-arranged the images in Figure 1 and enlarged them from the original images to hopefully improve their clarity. We have also added more detail on how the images were taken in the Figure 1 Legend (Results lines 334-361) and added more detailed explanation of the methodology used in these analyses and their limitations in their interpretation in the Methods section (lines 199-209) and Results section (lines 320-332). The identification of CTC and M-FB was based on the FACS-isolation biomarker panel used in their separation from PortalBMC prior to defined cell co-cultures .“I find figure 1a rather incomprehensible. The authors claim that CTC-MFB co-culture induces CTC proliferation. I don't understand this result in the graph.”We thank the reviewer for letting us know more detail on the methods used in these studies was needed to help with their presentation. In the re-organization of Figure 1 suggested by the reviewer, this figure panel is now 1E in the revised manuscript. We agree with the reviewer that the findings can be interpreted better as CTC/M-FB interactions enhancing or supporting CTC proliferation rather than inducing it. We have added more detail information on the pre-culture FACS isolation used in these defined cell type co-culture studies in the Methods section  and in the Results section and Figure 1 Legend (Results lines 334-361). Based on statistical analysis, CTC numbers in cultures of CTC and M-FB co-cultured after FACS isolation were not statistically different than that seen in unsorted PortalBMC but were increased compared to FACS-isolated CTC cultured alone or added back to cultures of negatively selected cell populations out of PortalBMC lacking CTC and M-FB sorted populations (-CTC-MFB+CTC), suggesting the combination of CTC and M-FB cells is needed for cluster formation and enhanced CTC proliferation.“Of the 16 patients used in this trial, how many had received therapy? Does that affect the outcome?”Thirteen patients in this data set  had received pre-operative (neo adjuvant) treatment, 3 did not. Due to the low numbers of the untreated patients in the data set, no statistical analyses were possible to accurately compare the 2 subgroups. Graphs of the separated data from the 2 subgroups are depicted in Supplemental Figure 3 in response text above.“Figure 2A is not referred in the text.”We apologize for this typographical error. The Fig.2A was cited but not bolded as the other figure references were in the original manuscript text. We have corrected this in the revised manuscript .“I understand that for trials with CTCs the number of patients is not the total, but to analyze the levels of cytokines and growth factors in blood, why not all patients?” Financial limitation prevented samples beyond the first 11 available from being analyzed by Legendplex multiple cytokine cytometry assay. Plasma samples were also used in U937 differentiation analyses and other studies not included in this report, leaving 24 out of 30 plasma samples available for analysis in ELISA . The other 6 samples did not have adequate volumes available for analysis. After all of the above analyses, 19 patient samples had enough plasma available for matched peripheral and portal plasma analyses  in expanded parallel ELISA analyses of M-CSF, IL-6, IL-1beta, GM-CSF, IL-34, IL-8, IL-1alpha, and IFNgamma.“What are the red stripes in figure 2A? Are they normal reference values? A bit unusual way to put them.”We appreciate the reviewer pointing out the need for clarification on the graph presentation. The horizontal red lines in the A, B, and C panel graphs of Figure 2 in the revised manuscript and in Supplemental Figure 4 depicted in response text above are indicators of published healthy control blood mean or median concentrations of the factors tested. The dotted vertical lines in Figure 2A indicate the standard deviation from the mean of these control levels given by the BioLegend Legendplex flow cytometry reagent kit. We have revised the Results text (lines 363-381) and Figure legends  to better describe these indicators on the graphs. In the revision of the figures we have also corrected the positioning of these indicators that appeared to have shifted in the conversion of illustrations to eps and pdf formats required for upload submission.“According to the text: “GM-CSF levels in PDAC patient portal blood plasma were highly elevated compared to normal peripheral blood (p=0.0305) while M-CSF levels were moderately elevated over normal (p=0.0383)” Why do you compare portal vein levels with normal peripheral blood levels? You would have to compare portal blood GM-CSF levels with normal levels there and peripheral blood levels with normal levels there. Or are they the same?”We agree with the reviewer that direct comparison with healthy portal blood would have been better but because portal blood collection is only possible via surgical or invasive endoscopic intervention, we could not ethically collect healthy control portal blood from non-surgical patients or volunteers. The approved IRB protocols covering sample collections in this study limited portal or peripheral blood collection to pancreatic/GI oncological surgery patients. Therefore, we used published or assay manufacturer provided concentration levels for healthy peripheral blood comparisons in these analyses. We have included this explanation in the Methods (lines 244-250) in the revised manuscript.“Figure 2c would benefit from a legend stating that the white bars are portal blood and the gray bars are peripheral blood.”We have added the descriptions suggested by the reviewer to the Figure Legends of Figure 2 (Results lines 383-404) and Supplemental Figure 4 in the response text above.“Significant differences are marked between the two blood types but are they different from the reference levels in each compartment?”We have added the control level lines and statistically significant differences to the graphs in Figure 2C and Supplemental Figure 4 and included a description of these in their Figure Legends Figure 2 (Results lines 383-404) and Supplemental Figure 4 (in response text above).“Now, however, 24 patients are used for this figure. The criteria for this should be explained.”As mentioned above, the main limitation on samples used in these analyses was the volume of plasma available for matched peripheral and portal plasma sample comparisons. We have added an explanation of this limitation to the Figure Legends for Figure 2 (Results lines 383-404) and Supplemental Figure 4 (response text above). “Here as before, treatments may have influenced the release of these factors into the blood.”We have included the requested analyses separating neo adjuvant treated and untreated patients in Supplemental Figure 4 (response text above). No significant changes in statistical analyses were seen though the limited number of untreated samples tested and the undetectable levels of some analytes precluded statistical analyses in the untreated patient sample set.“Figure 3: The lettering on panels B-E is virtually unnoticeable.”We agree with the reviewer that the uploaded pdf version of these images were not as clear as needed. We have re-done this Figure as recommended by the reviewer in the revised manuscript.“One doubt that arises, perhaps because I have not understood it well, is that the authors refer to M-FB populations and blood mononuclear cells (BMC) indistinctly. But when isolated by FICOL BMC, this is a heterogeneous population of cells. In the test corresponding to Figure 3, they take BMC to study the interaction between CTC-MFB and in reality, it cannot be assumed that what is there is only M-FB.”We thank the reviewer for bringing this clarity issue to our attention. We have revised the text of the Methods section  and throughout the manuscript to include a better description of when CTC and M-FB were isolated by FACS prior to culture for defined co-culture experiments , from when analyses were done with ficoll-separated PortalBMC first grown as a unsorted mixed culture and then CTC and M-FB were FACS isolated after culture for analyses . Both types of studies looked at CTC/M-FB interactions but the former looked at these in isolation; whereas, the latter analyzed them out of PortalBMC mixed cell culture.“Figure 3F is not clearly visible. Perhaps it could be divided into several graphs, one for each chemokine.”We have replaced the graph in Figure 3F with multiple graphs for each treatment series as requested by the reviewer and updated the Figure 3 Legend accordingly (Results lines 432-462) in the revision.“ Similarly, if all comparisons are made with the first graph, this can be indicated in the figure caption and there is no need to put so many dashes above the figure, you can put the asterisks indicating statistical significance only above each bar. This applies to other figures in the manuscript.”As the reviewer has suggested, we have simplified the notations on figures where possible in the revision. We have left brackets on some figures where comparisons are between specific groups within multiple ones depicted. “Figure 3M and 3N are vaguely explained in the figure legend. Annexin V plot should be supplemented.”We have added more detail to the Legend of Figure 3 (Results lines 432-462) and Results text to better describe panels M & N (Results lines 427-430) and changed the axis title on the graph in panel M to denote that it depicts the AnnexinV binding data.“Figure 4 is not explained in the text. It is very difficult to understand because it makes a statement and refers to the entire figure, panels A to D, without further explanation of what the different images show.”We apologize for this oversight. A portion of the Methods section text describing the U937 cell line myeloid differentiation assays depicted in Figure 4 was inadvertently deleted in the upload of the original submission. It has been added back in the revision (Methods lines 252-263) and the Results section (lines 464-494). Figure 4 & its Legend (Results lines 496-519) have been revised to simplify the figure and include more detailed explanation of these results in its legend.“ What does zero mean, in the second bar of the graphs?”The ‘zero’ labels in Figure 4 denote U937 cultures that are supplemented with conditioned media from PortalBMC cultures that were not treated with any of the inhibitory antibodies tested. This label was used in Figure 4 to differentiate the conditioned media treated cultures from the control U937 culture grown without any conditioned media added as ‘no treatment (no trt)’ control for the assay. We have included a clearer definition of these labels in the revised Figure 4 Legend (Results lines 496-519).“ For me, figure 4 does not contribute anything because I do not understand it.”We hope that the revision of this figure and its legend (Results lines 496-519), additional explanatory text in the Results (lines 464-494), and the description of the U937 myeloid differentiation assay added back to the Methods (lines 252-263) helps the reviewer in their understanding of its relevance to the findings. These data show the suppression of U937 differentiation to all but M-FB when conditioned media from PortalBMC cultures treated with anti-IL-34 antibodies were used to supplement the U937 cultures. These results suggests that IL-34 in the portal PDAC PortalBMC cultures can act as a major component in the conditioned media promoting skewing myeloid differentiation away from APC and toward M-FB. “Figure 5 is not explained either. This causes the reader's attention to be lost because it is very difficult to draw conclusions when they do not explain what they see in the graphs. It requires extra effort on the part of the reader.”We agree with the reviewer that this figure as originally presented was too complex to easily interpret and have removed some of the graphs depicting negative results in the revision and to make the data presentation less complicated. The purpose of presenting these data was to show the product level phenotype changes seen when cultures were treated with the inhibitory antibodies. We removed panels Figure 5 A-D in original manuscript that depicted the CSF1R and CXCR2 protein expression was unchanged on CTC and M-FB with the antibody treatments tested. We have now described these negative results in the Results text (lines 520-527). The new Figure 5  shows the marked changes in MCSF, IL-8, and IL-34 levels found in cultures supernatants with the treatments and shows that GM-CSF remains unchanged with the antibody treatments. We have revised the new Figure Legends to better detail these phenotypic changes observed with the inhibitory antibody treatments .“Figure 6 focuses on gene expression changes in CTCs and M-FBs. In the materials and methods, the whole process of sequencing is described but in reality the only thing that is described is the expression of the same markers that were previously studied. The whole paragraph in materials and methods referring to the identification of differentially expressed genes does not make much sense with the results that are being presented in this article.”We agree with the reviewer and have revised the presentation of these analyses and their results in the revision of the manuscript, including clarifying the RNA analyses in the Methods section (lines 148-161 & 265-283) and separating the data from uncultured, FACS-isolated CTC, in vivo formed CTC clusters and M-FB in new Figure 6  and data from CTC and M-FB isolated from PortalBMC after culture in new Figure 7 . We have added the reviewer requested data from RNA SEQ pilot analyses in Supplemental Figure 2 (in response text above). RNA SEQ data were used to look at intra-sample clonal variability which was apparent in pilot oligo clonal/SCS comparisons of 2 PDAC patients tested by RNA SEQ and in confirmatory RT-PCR tests with 12 patients’ SCS CTC RNA samples. The revised Legend for the Figure 6 is found in Results Section lines 570-582, and for the new Figure 7 in Results lines 602-621. We revised the Methods section to include an explanation of the findings of the RNA SEQ (Methods lines 148-161) and how they were used to design the RT-PCR studies depicted in Figures 6 & 7 in the revised manuscript. We have revised the text in the Results to be more conservative and accurate in our interpretation of these data (Results lines 556-645).“Figure 7A does not seem to have significant differences, but in the text the impression conveyed is that the treatment reduces the state of anergy . Perhaps this statement or the graph should be explained better.”The reviewer is correct, there are not enough data from this limited data set to draw any statistically significant conclusions, only to show a trend toward decreased T cell anergy with the treatments depicted. We have revised the text and Figure Legend  in the revised manuscript  to clarify this limited interpretation of the data.“Throughout the text there are a number of typos in the wording that could easily be corrected:For example, in line 140 it says cells cells; 147 it says cluster clusters. Line 154 it does not have a verb (like were used) Line 267, there is a dot after IL-8 that should be a colon.”We apologize for the confusion these errors caused in the review. We have done an end-to-end editorial review of the manuscript and incorporated corrections to the items the reviewer listed as well as others found in the review process. We have also re-organized the manuscript to hopefully smooth the flow of the narrative throughout. We thank the reviewer for their detailed review comments that have helped us to improve the manuscript for re-submission.AttachmentResponse to Reviewers with supplemental figures.docxSubmitted filename: Click here for additional data file. 8 Mar 2022Pancreatic Ductal Adenocarcinoma (PDAC) Circulating Tumor Cells Influence Myeloid Cell Differentiation to Support their Survival and Immunoresistance in Portal Vein CirculationPONE-D-21-29413R1Dear Dr. Litherland,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. 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HLA-DQB1 significantly associated with asthma risk  previously associated with asthma and broad allergic phenotype. Subsequent fine-mapping analyses of classical HLA alleles revealed a novel allele significantly associated with asthma protection  that had been linked to infectious and autoimmune diseases, and peanut allergy. HLA haplotype analyses revealed a novel haplotype DQA1*01:02-DQB1*06:04 conferring asthma protection .Despite asthma has a considerable genetic component, an important proportion of genetic risks remain unknown, especially for non-European populations. Canary Islanders have the largest African genetic ancestry observed among Southwestern Europeans and the highest asthma prevalence in Spain. Here we examined broad chromosomal regions previously associated with an excess of African genetic ancestry in Canary Islanders, with the aim of identifying novel risk variants associated with asthma susceptibility. In a two-stage cases-control study, we revealed a variant within  Asthma has an increasing prevalence, affecting more than 300 million people worldwide3, which represents a high health care cost. In Europe, the economic burden of asthma in adults is estimated to account for over €19 billion per year4. This global health problem is partly because of the heterogeneity of the disease that makes both its clinical management and research challenging. In this sense, asthma and the allergic traits are strongly conditioned by genetic factors, with heritability estimates ranging between 35 and 95%5. Several genome-wide association studies (GWAS) have revealed several asthma risk genes, including those encoding the ORMDL Sphingolipid Biosynthesis Regulator 3 (ORMDL3), Gasdermin B (GSDMB), and Cadherin Related Family Member 3 (CDHR3), as well as a number of genes related to innate immunity and immunoregulation6.Asthma is a complex respiratory disease characterized by reversible airflow obstruction and chronic inflammation of the lower respiratory tract, usually linked to allergic and atopic manifestations7. Among other reasons, this could be because most of the previous genetic association studies have focused on patients of European ancestry10. Therefore, other genetic risks that are more frequent in other ethnicities could remain unknown. The key implications of ancestry in asthma risks have been evidenced in previous studies in non-European admixed populations that revealed novel gene risks for asthma in African American and Latino populations14. However, although in recent years the number of genetic studies in admixed populations has increased considerably, the statistical power in many of them has not been sufficient due to the large sample sizes required by GWAS to overcome the stringent significance penalties, making screening for asthma-related genes in admixed populations still a challenge10.Despite the efforts made by large consortia, less than 5% of asthma variability can be explained by the genetic risk variants revealed to date19, given that they attain better study power using more limited sample sizes20. Briefly, the genome of admixed individuals is constituted by chromosomal segments from their parental populations. The average proportion with which each parental group contributes to the genome of an admixed individual is known as global genetic ancestry, whereas the local genetic ancestry is defined as the ancestry proportion of each particular locus. Studies such as admixture mapping studies allow to reveal genomic regions where local ancestry correlates with disease risk, and the main challenge remains in the subsequent fine mapping studies of these regions to identify the causal variants21.In this sense, studies leveraging local genetic ancestry analyses constitute an alternative to further disentangle the genetics underlying asthma in recently-admixed populations22. Additionally, we identified five genomic regions showing an excess of African ancestry in this population within chromosomes 2, 3 (two regions), 6, and 13. These regions were enriched in genes linked to respiratory diseases, including asthma22, which is not surprising since the Canary Islanders have the highest prevalence of asthma in Spain24. As a matter of fact, we recently performed an admixture mapping of asthma in this population, revealing a novel locus associated with asthma risk16.We previously assessed the local ancestry estimates in an admixed southwestern European population, the Canary Islands population (Spain), revealing the largest African genetic ancestry in Europe, with average proportions of 22.0% of North African (NAF) ancestry and 3.0% of Sub-Saharan African (SSA)Based on the previous evidence, we hypothesized that the targeted screening of the five genomic regions from the current Canary Islander population that were found to be enriched in African alleles could reveal novel asthma risks. To test this possibility, we conducted a two-stage association study focused on the regions of interest in Canary Islanders, followed by a fine-mapping study of the highly variable human leukocyte antigen (HLA) region.p = 1.30 × 10–7) (Table HLA-DQB1)  has been associated with asthma   and broad allergic phenotype26 before. No significant variants were identified in the other regions of interest on chromosomes 2, 3, and 13.After quality controls, 930 Canary Islanders from stage 1 (313 cases and 617 controls) and 557 from stage 2 (251 cases and 306 controls) remained in the study. Detailed information of these participants can be found in Table 29, providing additional information that can be used to assess the role of this region in asthma physiopathology. We fine mapped this region using a specific method to impute a total of 172 common HLA classical alleles from 3 class I genes  and four class II genes  and assessed their association with asthma susceptibility (Supplementary Table p < 0.05) . The association with asthma protection of HLA-DQA1*01:02 reached study-wise significance in the meta-analysis results from stage 1 and stage 2   after setting a Bonferroni threshold at p = 2.17 × 10–3  and amino acid polymorphisms from SNP genotyping dataHLA-DQA1*01:02 sequence translation to the amino acid sequence revealed a specific missense substitution within exon 2 at position 57  . However, a few of its linkage disequilibrium (LD) proxies (r2 > 0.77 in Europeans) were nominally significant in the meta-analysis . Bioinformatic tools for variant prioritization  showed that the top-ranked proxy for rs10093 was rs9271588, an intergenic variant located between HLA-DQA1 and HLA-DRB1, which was predicted to have the higher confidence of regulatory impact , HLA-DQB1, and HLA-DRB1 were downregulated in patients with severe asthma compared to healthy controls  have been associated with asthma in large genetic association studies34. Interestingly, other studies in populations with African ancestry have allowed to link classical HLA class II gene alleles to the total serum IgE levels in patients with asthma36.Several studies support that non-European populations would carry the greatest burden of asthma37, and this interpretation becomes even more complicated when the prioritized variant is in the highly polymorphic HLA region. Because of that, classical HLA alleles are more biologically informative and can be related to stronger effects than individual SNPs39. Our results revealed that the classical HLA allele HLA-DQA1*01:02 was significantly associated with asthma protection. HLA-DQA1*01:02 had never been linked to asthma risk before, although it has been related with protection from infectious40 and autoimmune diseases, including type-1 diabetes41, Crohn’s disease42, tubulointerstitial nephritis, and uveitis syndrome43, and risk for peanut allergy44. Additionally, HLA haplotype analyses revealed, for the first time, the association of DQA1*01:02-DQB1*06:04 with asthma protection, which supports that the combination of variants within both genes could influence the pathophysiology of the disease. This is in line with previous studies in asthma where HLA haplotypes were identified to be significantly associated with the disease45. The protein encoded by HLA-DQA1 binds to the protein encoded by HLA-DQB1, constituting a heterodimer that plays a central role in the immune system. Interestingly, our analyses prioritized an LD proxy of the missense change defining the HLA-DQA1*01:02 as a clear lung eQTL for HLA-DQA1 and HLA-DQB1. This evidence agrees with human transcriptomic observations revealing a reduction of the HLA-DQA1 and HLA-DQB1 lung gene expression in patients with severe asthma compared to healthy controls. Thus, a lower activity of the heterodimer among asthma patients would be expected in carriers of this classical HLA allele.Our staged association study revealed the association of rs1049213 with asthma risk, which has been previously linked to asthma, supporting the robustness of our results. Accordingly, it is broadly known that interpreting SNP associations can be problematic, partly due to the difficulty in identifying the causal variant22. Additionally, the use of population controls could bias the results in the sense that it is possible that these individuals could develop asthma or related phenotypes during their lives. However, this type of controls has been widely used in genetic and genomic studies and is the most optimal control group to get the most representative subset of the population, reducing the selection bias46. We did not include other confounders such as environmental factors or responses to asthma treatment because data were lacking for most individuals. However, we validated our results in an independent case–control sample, supporting that our results may not be biased by these variables. Significant differences in sex and age were found among cases and controls from both stages, although results remained significant after including these variables in the models. Finally, in addition to the difficulties inherent to the highly polymorphic HLA region, because of technical limitations , we did not assess all common classical HLA alleles of this population, which could have masked additional asthma associations.We acknowledge some limitations of this study. First, all individuals were selected based on their self-declaration of having two generations of ancestors born in the Canary Islands. Nevertheless, this agrees with the National Institutes of Health (NIH) guidelines, and previous genetic studies in the same population have shown that this selection has no major effect on the findings of the studyHLA-DQA1*01:02) associated with asthma protection. This suggests that not all shared genetic risks between asthma and autoimmune diseases have opposite directions of effect47. Further studies will be needed to validate these results in other populations, as well as to assess the potential of HLA-DQA1*01:02 as an asthma biomarker.A two-stage association study targeting the genomic regions with an excess of African ancestry in the Canary Islands population revealed a novel classical HLA allele  and approved by the Research Ethics Committee from the Hospital Universitario Nuestra Señora de Candelaria. Written informed consents were collected from all subjects or their representatives.22. The stage 1 was performed to prioritize genetic variants within these regions, while the stage 2 allowed us to validate these associations in an independent data set. A meta-analysis combining stage 1 and stage 2 association results was performed to establish the genetic variants associated with asthma in this study.We performed a two-stage case–control study of asthma susceptibility targeting the five genomic regions that showed an excess of African ancestry in the current population of the Canary Islands 48 and were diagnosed according to the Global Initiative for Asthma (GINA) guidelines49. Population controls were obtained from the Cardiovascular, Diabetes, and Cancer (CDC) cohort study from the Canary Islands50. DNA was extracted from peripheral blood following column-based methods. Further details of these studies can be found elsewhere51.All case and control individuals declared at least two generations of ancestors born in the Canary Islands (Spain). The stage 1 consisted of 314 cases of asthma and 674 controls, while the stage 2 comprised a total of 278 asthma patients and 349 controls. All asthma patients were part of the Genetics of Asthma study in the Spanish population (GOA) study52 and PLINK v1.953. Genetic variants with genotype call rates (CR) < 95%, low minor allele frequency (MAF < 5%), or that deviated from Hardy Weinberg equilibrium  were excluded. A total of 403,615 filtered high-quality SNPs were kept for downstream analyses. Additionally, we also excluded individuals with missing clinical information, sex mismatches between records and those inferred from the genotype data, CR < 95%, high degree of kinship with others included in the study (PIHAT > 0.2), or that constituted heterozygosity outliers. A principal component analysis (PCA) was performed using PLINK v1.953 to obtain the leading principal components (PCs) to correct for population stratification during association testing. The PCA was assessed for stages 1 and 2, separately, and individuals were projected on data from The 1000 Genomes Project54  by the Spanish Genotyping Center (CeGen). Variant calling was performed separately in cases and controls from the discovery study and low-quality SNPs and samples were excluded according to the manufacturer’s instructions. Stringent quality controls were subsequently conducted using R programming (v3.2.2)55 selecting SHAPE-IT v2.r790 for chromosome phasing56 and European population data from the Haplotype Reference Consortium release 1.1 as reference panel57. Logistic regressions were performed with EPACTS v3.2.658 using a binary Wald test and assuming an additive inheritance model. We included the first four PCs as covariates. Variants with MAF < 1% and a poor imputation quality (Rsq < 0.3) were excluded from the analysis. The genomic inflation factor (λ) of the results was calculated with the R package “qqman”59 61 and established the meta-analysed significance at p = 1.20 × 10–6 based on Bonferroni correction.Genotyping and statistical analyses of the data from the two stages followed the same procedures. To assess the overall effect size of associated SNPs across the two stages, a meta-analysis was conducted using METASOFT v2.0.1, where effect heterogeneity was assessed with the Cochran's Q test significance27. The association analysis with asthma was also performed with HIBAG using an additive model on those individuals with a high confidence score (probability threshold > 0.5). The four first PCs were included in the models. A meta-analysis was conducted with METASOFT v2.0.160 for the common alleles (MAF ≥ 1%) that showed a nominal association (p < 0.05) and the same direction of effects on both stages. For this study, a meta-analysed significance threshold was declared at p = 4.50 × 10–4 after Bonferroni correction based on the number of alleles tested. A post-hoc sensitivity analysis was also performed to address potential biases of the results including sex, age, and local ancestry estimates as covariables in the HIBAG association models. The estimation of local ancestry was carried out using ELAI62 assuming a three-way admixture as detailed elsewhere 22. HLA haplotype analyses were performed using BIGDAWG63, focusing on those genes that were in the vicinity of the variants that showed significant association. Statistical testing of the haplotype difference was based on a Chi-squared test, using BIGDAWG’s default parameters.A more detailed assessment of the HLA region, residing in chromosome 6, was performed on both stages separately. Classical HLA alleles from three class I genes  and four class II genes  were imputed with HLA Genotype Imputation with Attribute Bagging (HIBAG) v1.4 using imputation models adapted to the Axiom Genome-Wide Human CEU 1 Array and a European reference panel2 > 0.7) by using different in silico tools. Variant prioritisation was based on results obtained with DSNetwork64, RegulomeDB65, and VEP66. Additionally, we assessed the potential regulatory role of the variants using HaploReg v4.167 and RegulomeDB, as well as the existence of long-distance physical interactions with Capture Hi-C Plotter68. We also accessed GTEx69, ExSNP70, and SNPdelScore71 to evaluate tissue-specific local expression quantitative trait loci (eQTLs) and splicing quantitative trait loci (sQTLs). Further details are provided in the Supplement.We used HIBAG to convert the associated P-coded HLA alleles to amino acid sequences, in order to reveal amino acid residues and, hence, those SNPs that may explain the classical HLA allele associations. We explored the potential biological consequences of the SNP predicting the altered codon in the amino acid in the classical HLA allele and its best proxies  (GSE74986)73. The differential expression was assessed with two-sample t-tests and one-way analyses of variance (ANOVA). Further details are provided in the Supplement.In parallel, we accessed the results of public gene expression studies of asthma available in Gene Expression Omnibus (GEO). Differential gene expression between cases with asthma and healthy controls was examined for those genes in the vicinity of the SNPs and classical HLA alleles significantly associated with asthma susceptibility in our study, to provide additional information on the role of these genes in asthma physiopathology. First, we accessed transcriptomic data of bronchial brushing samples from 27 healthy controls and 128 individuals with asthma  (GSE63142)Supplementary Information.


Separating antibody-negative neuromyelitis optica spectrum disorders (NMOSD) from multiple sclerosis (MS) in borderline cases is extremely challenging due to lack of biomarkers. Elucidating different pathologies within the likely heterogenous antibody-negative NMOSD/MS overlap syndrome is, therefore, a major unmet need which would help avoid disability from inappropriate treatment.In this study we aimed to identify distinct subgroups within the antibody-negative NMOSD/MS overlap syndrome.Twenty-five relapsing antibody-negative patients with NMOSD features underwent a prospective brain and spinal cord MRI. Subgroups were identified by an unsupervised algorithm based on pre-selected NMOSD/MS discriminators.n = 6) often had central vein sign and cortical lesions . All patients from Group 2  had short-segment myelitis and no MS-like brain lesions. Group 3  had high percentage of bilateral optic neuritis and longitudinally extensive transverse myelitis  and normal brain appearance (100%). Group 4  typically had a history of NMOSD-like brain lesions and LETM. When compared with other groups, Group 4 had significantly decreased fractional anisotropy in non-lesioned tracts  and decreased thalamus volume .Four subgroups were identified. Patients from Group 1 termed “MS-like” (NMOSD/MS cohort contains distinct subgroups likely corresponding to different pathologies and requiring tailored treatment. We propose that non-conventional MRI might help optimise diagnosis in these challenging patients.The online version contains supplementary material available at 10.1007/s00415-021-10619-1. Since the nineteenth century neuromyelitis optica  defined as acute simultaneous bilateral optic neuritis (BON) and transverse myelitis has been regarded as different from typical MS. The uniqueness of NMO has been emphasised by poor visual and motor outcome, and longitudinal extension of the transverse myelitis lesion (termed LETM) . NMO wasSome patients with features of NMOSD have serum antibodies against MOG rather than aquaporin-4 , 6. MOG-The recognition of aquaporin-4-antibody NMOSD and MOGAD as separate diseases sparked interest in the cohort of patients with NMOSD features and red flags for the diagnosis of MS who are negative for both autoantibodies. It is widely accepted that this cohort is highly heterogenous and likely contains distinct diagnoses ranging from atypical multiple sclerosis to NMOSD mediated by yet undiscovered antibodies. Making ultimate diagnosis is very challenging as shown by high disagreement between experts when presented with individual cases . AccordiIn this study we analysed clinical, paraclinical and non-conventional imaging features of 25 antibody-negative patients recruited from the Specialist NMO Clinic in Oxford with recurrent syndromes at the borderline of MS and NMOSD, to identify patient subgroups, propose classification and guide clinicians when diagnosing and treating these challenging patients.Patient inclusion criteria included (1) the presence of at least one NMOSD feature as per 2007 criteria , (2) serAll participants underwent an MRI scan of the brain and cervical spinal cord in the Wellcome Centre for Integrative Neuroimaging in Oxford. The scan was obtained using a 3 T Siemens Prisma. The protocol sequences included T1 MP-RAGE, T2 FLAIR, T2 double inversion recovery (DIR), T2* mapping and diffusion tensor imaging (DTI) for the brain and T1 MP-RAGE and T2 FLAIR for the spinal cord.The identification of cortical lesions on DIR was based on the consensus between two experienced raters . The presence or absence of the central vein sign was assessed according to the guidelines . FLAIR iThe spinal cord was processed and automatically segmented using a deep learning algorithm implemented within the Spinal Cord Toolbox . VertebrEleven NMO/MS discriminators were selected to separate patient subgroupings, including BON, poor residual visual acuity after optic neuritis (6/36 or worse in at least one eye), CSF oligoclonal bands unmatched in the serum, LETM, short-segment transverse myelitis, NMO-like brain lesions , MRI MS brain lesion distribution criteria  , corticak-means clustering (with four centres) was applied using k-means function from R software. As PCA normalises the data and uncorrelates the variables, the clustering was performed on the PCA matrix rather than original data. A scree plot was generated to show the proportion of variance explained by each principal component and to decide the number of principal components included in the matrix . Figure PCA on the obtained data matrix was performed using the prcomp function within the R statistical software. Visualisation of the first two principal components indicated the existence of four separate patient subgroups. To objectively determine which patients form each subgroup, After subgroup identification various non-conventional imaging measures reflecting different aspects of tissue damage were analysed in the four identified subgroups: fractional anisotropy in corticospinal tracts, corpus callosum and optic radiation, R2* relaxometry in the normal-appearing white matter, normalised brain volume, normalised volume of basal ganglia , mean diffusivity in the cortex, cortical thickness and mean cervical cord cross-sectional area). We selected fractional anisotropy rather than mean diffusivity as a measure of structural damage in the normal-appearing white matter , 25. MeaTwenty-five patients were included in the study of whom 15 were female. Mean age at scan was 49 years (range 20–73) and median disease duration was 9.8 years (range 1–28). All patients were relapsing, their median number of attacks was three (range 2–11) and median EDMUS was three (range 0–8). Twelve patients had previous attacks of LETM, five had had BON and six were left with visual acuity at 6/36 or worse in at least one eye. Five patients were reported to have NMO-like brain lesions at some stage of their disease. Thirteen patients had CSF-exclusive oligoclonal bands. At the time of the research scan nine patients were not on any disease-modifying treatment, six were on azathioprine, five on methotrexate, two on mycophenolate mofetil, one on prednisolone alone, one on regular intravenous immunoglobulins and one on fingolimod. The breakdown of NMO- and MS-like features for individual patients is shown in Supplementary Table S1.Eight out of 25 patients had distinct white matter lesions in the brain on FLAIR-weighted images . Five patients had Dawson’s finger(s), three had lesions adjacent to the body of lateral ventricle, two had infratentorial temporal lobe lesions and one had curved juxtacortical lesions. Seven patients fulfilled MS brain lesion distribution criteria . Six hadFour separate subgroups were identified Fig. . Group 1p = 0.003) and decreased thalamus volume . Table When compared with other groups, patients from Group 4 had significantly decreased fractional anisotropy in non-lesioned white matter tracts  “classic NMO-like” patients did not appear to have spinal cord atrophy and had significantly higher mean cervical spinal cord cross-sectional area when compared to “NMO-like with brain involvement” patients , but not in corticospinal tracts  strongly contributed to the generation of “NMO-like with brain involvement” subgroup we analysed whether this difference is attributed to any particular white matter tract. Interestingly, we have found significant differences in fractional anisotropy in the corpus callosum when comparing between Group 4 and each of the three other groups  or in the spinal cord (Group 2), one with optico-spinal “classic NMO-like” presentation (Group 3) and one with predominantly spinal cord and brain NMOSD-like disease (Group 4). We examined differences in non-conventional imaging findings. which were not used for subgroup identification, and found that while there were no significant differences between Groups 1 and 2, Group 4 displayed significantly more damage in the brain and spinal cord as compared with Group 3.Current knowledge of antibody-negative NMOSD and the borderland between MS and NMOSD is very limited, as this group of patients has been largely neglected from previous studies, most likely due to difficulties in the analysis resulting from diagnostic uncertainty. It is understood that this group might include atypical MS, NMOSD mediated by yet undiscovered antibodies and other conditions such as neurosarcoid. The condition can be disabling in its own right and more so if treated inappropriately . Most exThere are several limitations of the study. These include the supervised choice of discriminators, which might bias the generation of clusters. To reduce this bias we included only the most typical NMOSD and MS features see “” combinePatients diagnosed with likely antibody-negative NMO in the clinic were all assigned to Group 3 or 4 by the unsupervised algorithm, and these groups differed significantly between each other. Patients from Group 4 had highly destructive disease of the spinal cord and brain as seen in advanced MS, however, they all had a number of red flags for the diagnosis of MS as well. First, all these patients had a history of LETM, considered a hallmark of NMOSD  and extrAll patients from Group 3 had a normal brain appearance based on standard imaging and significantly less brain atrophy or white matter disintegrity when compared with Group 4. These patients had predominantly optico-spinal presentation but they had less spinal cord atrophy than Group 4 despite a high proportion of previous LETM in both groups. Two out of five had attacks of both BON and LETM, two had recurrent isolated optic neuritis including at least one attack of simultaneous BON and one had LETM followed several months later by unilateral optic neuritis. Two out of five in this group were left with poor residual visual acuity. This group, as opposed to other groups, did not have any evidence of cortical damage, was the least disabled of all groups (average EDMUS 2.4) and appeared to have a milder form of NMOSD with a high proportion of BON, similar to what is observed in MOGAD . We findPatients from Groups 1 and 2, despite being referred to NMO clinic, appear to have forms of MS. Brain lesions in these patients, although there were typically only a few of them, were positive for landmark MS features (Group 1). Those without brain lesions (Group 2) had short-segment lesions in the spinal cord but very rarely had NMOSD features, such as LETM (12.5%), BON (0%), poor visual recovery (12.5%) or NMO brain lesions (0%). Of the patients in this group, 50% had unmatched oligoclonal bands in the CSF. This form of spinal MS may turn out to be pathologically distinct and is worth further research.In conclusion, to our knowledge this is the first study focussing on the heterogenous cohort of antibody-negative NMO/MS patients using non-conventional imaging and unsupervised unbiased clustering algorithms to subclassify these patients and guide treatment decisions. Our results suggest that differential diagnosis in NMOSD/MS patients might benefit from the use of novel imaging techniques not yet widely used in clinical practice, including central vein sign, cortical lesions and the implementation of MS MRI brain lesion distribution criteria. If present these biomarkers might direct clinicians towards the likely diagnosis of MS. We suspect patients who are negative for these features and have a normal brain imaging might still have the diagnosis of MS if they present with spinal short-segment disease either isolated or associated with unilateral optic neuritis. We also believe that patients with a history of LETM have a non-MS disease. Those with LETM who have a history of BON might have NMOSD mediated by yet undiscovered antibodies, while those with MS-atypical brain lesions and significant brain atrophy could possibly have a diagnosis alternative to MS or ‘true’ NMOSD. Further prospective studies in identified subgroups, correlated with clinico-pathological studies and antibody research, will help clarify the underlying processes in each group.Supplementary file1 (DOCX 16 KB)Below is the link to the electronic supplementary material.


Dear Editor,Translational Psychiatry, Takahashi et al.1 reported their analysis of shared polygenic risk scores suggest a shared genetic background for both attention deficit hyperactivity disorder (ADHD) and narcolepsy. By another method, we described a similar association between ADHD and narcolepsy. In a paper delivered at the 1995 American Academy of Neurology2, we reported three families each in two generations with concurrent ADHD, hypersomnia defined by pupillometry3 without sleep log evidence of sleep disorder and both HLA markers DR-2/DQw-1 employed at that time associated with narcolepsy (Fig. In a recent issue of 4. Clinically, we have observed late adolescent ADHD patients who will not drive automobiles without their alerting medications for fear of falling asleep. We have additionally observed an increased frequency of these HLA markers as well as hypersomnia in individuals with dyslexia, both with and without ADHD5. However, either condition may occur independent of these HLA markers.Our subsequent studies have suggested a frequency of HLA markers associated with narcolepsy more than double than anticipated in Caucasians in individuals with behavioral and cognitive evidence of ADHD6.The social significance of such an association if valid is that an unknown percentage of ADHD children/adolescents/adults may require alerting medication to become safe adult drivers, as sleepiness increases the risk of motor vehicle accident by a factor of 2.5