Disseminating cells in human oral tumours possess an EMT cancer stem cell marker profile that is predictive of metastasis in image-based machine learning
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eLife assessment
This is a valuable study that convincingly demonstrates that quantification of EpCAM+/CD24+/Vimentin+ cells in the stroma of human oral cancers followed by machine learning algorithms can be used as a prognostic indicator of metastasis.
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Abstract
Cancer stem cells (CSCs) undergo epithelial-mesenchymal transition (EMT) to drive metastatic dissemination in experimental cancer models. However, tumour cells undergoing EMT have not been observed disseminating into the tissue surrounding human tumour specimens, leaving the relevance to human cancer uncertain. We have previously identified both EpCAM and CD24 as CSC markers that, alongside the mesenchymal marker Vimentin, identify EMT CSCs in human oral cancer cell lines. This afforded the opportunity to investigate whether the combination of these three markers can identify disseminating EMT CSCs in actual human tumours. Examining disseminating tumour cells in over 12,000 imaging fields from 74 human oral tumours, we see a significant enrichment of EpCAM, CD24 and Vimentin co-stained cells disseminating beyond the tumour body in metastatic specimens. Through training an artificial neural network, these predict metastasis with high accuracy (cross-validated accuracy of 87–89%). In this study, we have observed single disseminating EMT CSCs in human oral cancer specimens, and these are highly predictive of metastatic disease.
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Author Response
Reviewer #3 (Public Review):
Youssef et al. have used a range of markers to identify cancer stem cells (CSCs) in patients with oral cancers. CSCs were identified in lab conditions and were often linked to the invasiveness of cancers. The authors found a combination of markers convincingly liked to known biology and found cells expressing them in the invading cancers.
The major weakness of the paper is in the technical side. There isn't enough description as to how they discriminated between CSCs inside the tumour and those invading its surroundings. Similarly, the way the information is presented it is not clear why artificial intelligence was needed to enhance the accuracy of the method linking CSCs to cancer invasion (and ultimately deadly metastasis to other organs).
The method for applying tumour mask is …
Author Response
Reviewer #3 (Public Review):
Youssef et al. have used a range of markers to identify cancer stem cells (CSCs) in patients with oral cancers. CSCs were identified in lab conditions and were often linked to the invasiveness of cancers. The authors found a combination of markers convincingly liked to known biology and found cells expressing them in the invading cancers.
The major weakness of the paper is in the technical side. There isn't enough description as to how they discriminated between CSCs inside the tumour and those invading its surroundings. Similarly, the way the information is presented it is not clear why artificial intelligence was needed to enhance the accuracy of the method linking CSCs to cancer invasion (and ultimately deadly metastasis to other organs).
The method for applying tumour mask is displayed in Figure 2E for cohort 1 and Figure 2 figure supplement 3 for cohort 2. Briefly, in the image analysis pipeline, dense areas of EpCAM+ (cohort 1) or Vimentin+ (cohort 2) cells are merged to specify tumour/stroma regions. Thus, CSCs inside tumours (in the EpCAM dense tumour region) can be discriminated from CSCs invading the surroundings (in the Vimentin dense stromal region).
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Reviewer #3 (Public Review):
Youssef et al. have used a range of markers to identify cancer stem cells (CSCs) in patients with oral cancers. CSCs were identified in lab conditions and were often linked to the invasiveness of cancers. The authors found a combination of markers convincingly liked to known biology and found cells expressing them in the invading cancers.
The major weakness of the paper is in the technical side. There isn't enough description as to how they discriminated between CSCs inside the tumour and those invading its surroundings. Similarly, the way the information is presented it is not clear why artificial intelligence was needed to enhance the accuracy of the method linking CSCs to cancer invasion (and ultimately deadly metastasis to other organs). -
eLife assessment
This is a valuable study that convincingly demonstrates that quantification of EpCAM+/CD24+/Vimentin+ cells in the stroma of human oral cancers followed by machine learning algorithms can be used as a prognostic indicator of metastasis.
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Reviewer #1 (Public Review):
This is a valuable study that convincingly demonstrates that quantification of EpCAM+/CD24+/Vimentin+ cells in the stroma of human oral cancers followed by machine learning algorithms can be used as a prognostic indicator of metastasis.
This manuscript explores the utility of detecting a population of EpCAM+/CD24+/Vimentin+ cells in the stroma of human oral cancers as a prognostic indicator of metastasis. This follows work from the group showing that these cells manifest EMT plasticity. The authors used standard analyses and then machine learning algorithms on a test cohort of 24 patients and then a validation cohort of 60. Overall the staining seems clean, and the presence of these cells does seem to be predictive in a cohort of oral cancer patients.
The authors have addressed previous comments, adding …
Reviewer #1 (Public Review):
This is a valuable study that convincingly demonstrates that quantification of EpCAM+/CD24+/Vimentin+ cells in the stroma of human oral cancers followed by machine learning algorithms can be used as a prognostic indicator of metastasis.
This manuscript explores the utility of detecting a population of EpCAM+/CD24+/Vimentin+ cells in the stroma of human oral cancers as a prognostic indicator of metastasis. This follows work from the group showing that these cells manifest EMT plasticity. The authors used standard analyses and then machine learning algorithms on a test cohort of 24 patients and then a validation cohort of 60. Overall the staining seems clean, and the presence of these cells does seem to be predictive in a cohort of oral cancer patients.
The authors have addressed previous comments, adding additional patients and streamlining the work to focus on one hypothesis.
An additional validation set would enhance the work.
The authors should include clinical data for all samples used.
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Reviewer #2 (Public Review):
It is recommended to use a blind sample test to determine the specimen's status using the AI they developed.
Where these markers promote tumorigenesis or metastasis if tested in vivo?
The article would be very valuable in the future to promote using AI to predict disease status and facilitate cancer screening.
Much more improvement is required for data validation and presentation. -
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###Reviewer #3:
The article by Youssef G et al, focused on developing a Machine Learning system to use immunofluorescence data to detect metastatic cells in tumor stroma, which might be responsible for metastasis in case of OSCC. To detect single cells in the transition of EMT to MET they focused on EMT-Stem cells rather than only EMT phenotypes. They have shown that retention of epithelial marker EpCAM and stem cell marker CD24 and upregulation mesenchymal marker Vimentin can identify disseminating EMT stem cells in the tumor stroma. It is very well presented, well written and has high implication.
Comments to improve:
Strongly recommended to add the distribution of tumor status vs. proposed marker expression pattern. That is to show the distribution of EpCAM, CD24, Vimentin +/- in metastatic vs. other tumor status as mentioned in …
###Reviewer #3:
The article by Youssef G et al, focused on developing a Machine Learning system to use immunofluorescence data to detect metastatic cells in tumor stroma, which might be responsible for metastasis in case of OSCC. To detect single cells in the transition of EMT to MET they focused on EMT-Stem cells rather than only EMT phenotypes. They have shown that retention of epithelial marker EpCAM and stem cell marker CD24 and upregulation mesenchymal marker Vimentin can identify disseminating EMT stem cells in the tumor stroma. It is very well presented, well written and has high implication.
Comments to improve:
Strongly recommended to add the distribution of tumor status vs. proposed marker expression pattern. That is to show the distribution of EpCAM, CD24, Vimentin +/- in metastatic vs. other tumor status as mentioned in Supplementary figure 2. This might help you to establish these markers combination to follow a pattern in disease progression.
In all cell and tissue images add the scale.
For figure 3f, show enlarged picture of the single cell staining on the inset or add a separate panel to show only single cell staining.
Figure 4, the panel name or the font is too small to read, enlarge the font size (a, b, c, d, f).
Same problem with figure 6a, font size too small. In addition, in the heat maps, is it possible to add cluster names horizontally? Also for figure 6c, the cluster names are too small.
The EMT sub-populations are not associated with a spectrum of epithelial/mesenchymal genes expression (supplementary figure 5). The explanation is not very clear.
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###Reviewer #2:
The authors tackle the important and intractable question of the mismatch between the primacy of EMT in cell culture studies versus the rarity with which EMT is morphologically apparent in resected tumour tissues.
The early part of the study is convincing and well conducted, with identification of subpopulations of EMT cells with the ability to undergo MET, and associated marker profiles in flow cytometry.
They then develop an impressive multiplex assay for the identification of cells with the same profile in resected tumour material- a really promising approach bringing molecular findings into the context of primary tumour tissue.
The major issue that I have is in the application of this assay to tissues, and the subsequent AI analysis. Only one example of the putative invading population is shown (Fig 4C) and the …
###Reviewer #2:
The authors tackle the important and intractable question of the mismatch between the primacy of EMT in cell culture studies versus the rarity with which EMT is morphologically apparent in resected tumour tissues.
The early part of the study is convincing and well conducted, with identification of subpopulations of EMT cells with the ability to undergo MET, and associated marker profiles in flow cytometry.
They then develop an impressive multiplex assay for the identification of cells with the same profile in resected tumour material- a really promising approach bringing molecular findings into the context of primary tumour tissue.
The major issue that I have is in the application of this assay to tissues, and the subsequent AI analysis. Only one example of the putative invading population is shown (Fig 4C) and the stromal 'infiltrative' subpopulation is adjacent to a very flat and 'pushing' tumour/stroma boundary, with no apparent budding into the stroma. This would need to be addressed with several more examples and high-magnification H&E images. Furthermore, this is a major claim- namely that occult infiltrating EMT cells are commonly encountered in peritumoural stroma but can only be differentiated from somatic stroma by multiplex immunofluorescence- and it needs major evidence to back it up. What do these cells look like on H&E? Are they mesenchymal in their appearances on H&E? Can they be conclusively differentiated from other stromal constituents (eg myofibroblasts, plasma cells) immunohistochemically and/or morphologically? It could be that the power to predict metastatic status power is related to somatic stromal factors rather than EMT.
The AI prediction of metastatic status is compelling, but this fundamental point would need to be persuasively addressed in order to support the author's major claims. I do not feel qualified to comment upon the AI strategies used later in the study.
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###Reviewer #1:
This manuscript follows previous studies describing the existence of a subpopulation of mesenchymal-like cells (expressing Vimentin) that also express EpCAM and/or CD24 concomitant with the ability to undergo MET. These subpopulations appear to exist within oral squamous cell carcinoma (OSCC) cell lines and within primary tissues. The paper demonstrates that CD24 expression is requisite for plasticity and suggests that the presence of CD24+/EpCAM+/VIM+ cells in the stroma of OSCC tumors may be indicative of metastasis. Some whole genome transcriptome analysis was also done to determine differences between bulk, EMT restricted and EMT stem populations. Overall, the notion that specific cells have the plasticity needed to move between epithelial and mesenchymal states is intriguing, and the presumption that these cells …
###Reviewer #1:
This manuscript follows previous studies describing the existence of a subpopulation of mesenchymal-like cells (expressing Vimentin) that also express EpCAM and/or CD24 concomitant with the ability to undergo MET. These subpopulations appear to exist within oral squamous cell carcinoma (OSCC) cell lines and within primary tissues. The paper demonstrates that CD24 expression is requisite for plasticity and suggests that the presence of CD24+/EpCAM+/VIM+ cells in the stroma of OSCC tumors may be indicative of metastasis. Some whole genome transcriptome analysis was also done to determine differences between bulk, EMT restricted and EMT stem populations. Overall, the notion that specific cells have the plasticity needed to move between epithelial and mesenchymal states is intriguing, and the presumption that these cells contribute to metastasis seems logical. However, the work is still rather preliminary. Accordingly, it is difficult to make solid conclusions regarding the prognostic utility of this state or of what may regulate it.
Major comments:
The study uses a very small sample size (24 patients) for the test and validation cohorts. The study should be expanded to use a different set of patient samples for test and validation sets. Moreover, the utility of the stem-EMT signature should be tested using multivariate analyses.
In figure 4, it looks like CD24 is positive in the bulk of tumors (regardless of stage) and in skin. Is this specific? Also, there appear to be VIMENTIN/EPCAM/CD24 positive cells in the bulk of non-metastatic tumours. Can this be seen using sequencing? Overall, the images as presented are not overly convincing.
EMT stem versus restricted signatures should be validated using additional models. Also, greater evidence is required to determine how these cell fractions may differ. Are they sitting in different epigenetic states? Can trajectories be detected in human cancers, using single cell sequencing, for example? Finally, do they have different metastatic potentials?
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##Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
###Summary:
This manuscript was reviewed by experts in the areas of cancer stem like cells, EMT events and pathology. Overall, all of the reviewers were intrigued by the concepts underlying this paper. However, it seems that the work is validating the existence of an EMT-stem like population, whilst also attempting to formulate a clinical prognostic application for the existence of these cells. The function of these cells as metastatic drivers requires further exploration. Moreover, the pathological assessments must be …
##Preprint Review
This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.
###Summary:
This manuscript was reviewed by experts in the areas of cancer stem like cells, EMT events and pathology. Overall, all of the reviewers were intrigued by the concepts underlying this paper. However, it seems that the work is validating the existence of an EMT-stem like population, whilst also attempting to formulate a clinical prognostic application for the existence of these cells. The function of these cells as metastatic drivers requires further exploration. Moreover, the pathological assessments must be improved upon. We hope that these comments are helpful.
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