PTEN deficiency exposes a requirement for an ARF GTPase module for integrin‐dependent invasion in ovarian cancer

  1. Konstantina Nikolatou
  2. Emma Sandilands
  3. Alvaro Román‐Fernández
  4. Erin M Cumming
  5. Eva Freckmann
  6. Sergio Lilla
  7. Lori Buetow
  8. Lynn McGarry
  9. Matthew Neilson
  10. Robin Shaw
  11. David Strachan
  12. Crispin Miller
  13. Danny T Huang
  14. Iain A McNeish
  15. James C Norman
  16. Sara Zanivan
  17. David M Bryant

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Abstract

Dysregulation of the PI3K/AKT pathway is a common occurrence in high‐grade serous ovarian carcinoma (HGSOC), with the loss of the tumour suppressor PTEN in HGSOC being associated with poor prognosis. The cellular mechanisms of how PTEN loss contributes to HGSOC are largely unknown. We here utilise time‐lapse imaging of HGSOC spheroids coupled to a machine learning approach to classify the phenotype of PTEN loss. PTEN deficiency induces PI(3,4,5)P 3 ‐rich and ‐dependent membrane protrusions into the extracellular matrix (ECM), resulting in a collective invasion phenotype. We identify the small GTPase ARF6 as a crucial vulnerability of HGSOC cells upon PTEN loss. Through a functional proteomic CRISPR screen of ARF6 interactors, we identify the ARF GTPase‐activating protein (GAP) AGAP1 and the ECM receptor β1‐integrin (ITGB1) as key ARF6 interactors in HGSOC regulating PTEN loss‐associated invasion. ARF6 functions to promote invasion by controlling the recycling of internalised, active β1‐integrin to maintain invasive activity into the ECM. The expression of the CYTH2‐ARF6‐AGAP1 complex in HGSOC patients is inversely associated with outcome, allowing the identification of patient groups with improved versus poor outcome. ARF6 may represent a therapeutic vulnerability in PTEN‐depleted HGSOC.

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  1. Version published to 10.15252/embj.2023113987
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    Reply to the reviewers

    Manuscript number: RC-2022-01776

    Corresponding author(s): David Bryant

    1. General Statements [optional]

    We describe an ARF6 GTPase module that controls integrin recycling to drive invasion in PTEN-null Ovarian Cancer (OC). We used high-throughput, time-lapse imaging and machine learning to characterise spheroid behaviours from a series of cell lines modelling common genetic lesions in OC patients. We identified that PTEN loss was associated with increased invasion, the formation of invasive protrusions enriched for the PTEN substrate PI(3,4,5)P3, and enhanced recycling of integrins in an ARF6-dependent matter. We utilised Mass Spectrometry proteomics and unbiased labelling to investigate the interactome of ARF6, identifying a single ARF GAP (AGAP1) and a single ARF GEF (CYTH2). Importantly, this ARF6-AGAP1-CYTH2 modality was associated with poor clinical outcome in patients.

    We thank all Reviewers for their highly complementary assessment of our manuscript, describing our paper as a "very impressive study, very well done and controlled with rigorous statistical analyses that uses sophisticated methods", a study that is "stunning in its thoroughness and depth and breadth of its molecular analysis", with "experiments are properly designed, and the data are well presented. The conclusions are appropriate and supported by the data". Finally, we would like to thank the reviewers for appreciating that our results are "of significance for both scientific discovery and clinical application, which will interest the broad audience in both basic and clinical research".

    2. Description of the planned revisions

    Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

    Reviewer comments in bold. Our response in non-bold.

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

    __ This paper by Konstantinou et al aims at deciphering the mechanisms by which PTEN loss could be driving poorer prognosis in patients. The authors use their great high-throughput 3D screening method coupled to an unbiased proteomic method and a CRISPR screen to uncover a new pro-invasive axis driving collective invasion of high-grade serous ovarian carcinoma (HGSOC) cells. Overall, this is a very impressive study, very well done and controlled with rigorous statistical analyses that uses sophisticated methods to convincingly show that the CYTH2-ARF6-AGAP1-ITGA6/ITGB1 module is required for the pro-invasive effect of PTEN depletion and discriminates patients with poorest prognosis. __

    __

    MAJOR COMMENTS __

    __Below are listed all the claims that, in my opinion, are not adequately supported by the data. __

    __

    1. Choice of the cell line: More justification on the use of the ID8 cell line and on the p53 deletion is needed. The authors need to clearly state that most p53 mutations in ovarian cancer are missense mutations that lead to a strong accumulation of a p53 protein devoid of transcriptional activity. Nevertheless, it seems that p53 mutations are not associated to differences in patient survival. Hence the choice of studying PTEN loss in the complete absence of p53, a situation that does not mirror the clinical situation, needs to be explained. Moreover, the in vivo experiments already performed in the literature mentioned in the discussion should be mentioned in the introduction to provide more context and physiological relevance to this study (especially regarding the special focus on the p53 null/ dKO cells throughout the study). __

    We will update the manuscript with a detailed explanation of the cell line of choice. Briefly, while indeed Tp53 is found mutated in HGSOC, approximately 30-35 % of these are classified as null mutations (PMID: 21552211), making models with null Trp53 representative of the clinical situation. Further, there is no difference in patient outcome in HGSOC by Tp53 mutation type (PMID: 20229506), while gene expression data from TCGA suggest that HGSC is marked by loss of wild-type P53 signalling regardless of Tp53 mutation type (PMID 25109877). Thus, we believe our choice of model can faithfully mirror the clinical situation.

    __

    1. "Therefore, PTEN loss in ovarian cancer, particularly at the protein level, occurs in the tumour epithelium and is associated with upregulated AKT signalling and poor overall survival". This claim is an over-interpretation and over-generalisation of the data presented. I appreciate the honesty of the authors in showing all the ovarian datasets that are available and highlight the discrepancies in expression of the proteins they study in stroma and epithelium. I think the way to present these data in the text without over-interpreting and generalizing would be to show that there is a clear epithelial-specific downregulation of PTEN at the mRNA level. Most likely due to the contribution to other cell types in the stroma, only 3 out of 5 bulk tumour mRNA datasets show a tumour specific downregulation of PTEN and no association with survival based on a median split of PTEN mRNA expression. Nevertheless, although there is no direct correlation between PTEN mRNA and protein levels, patients with low PTEN protein levels have poorer survival that is associated to an upregulation of Akt signalling. This allows to have a clearer conclusion, based solely on the protein data presented and no over-generalisation using the mRNA data. This, to me, makes a stronger case for studying PTEN loss in ovarian cancer and is fully supported by the data presented. __

    We will incorporate this reviewer suggestion into the modified manuscript.

    __3) PTEN loss induces modest effects in 2D culture. The authors make claims regarding the fact that some of the phenotypes they look at happen after PTEN depletion alone or in combination with p53 loss and are more prominent in 3D vs 2D. Many of these are insufficiently backed up by data. A few key experiments are also only performed in 2D and should be done in 3D. Finally, some clarifications if the role of PTEN is most prominent on either collective, ECM-induced or 3D-dependent invasion. __

    __some clarifications if the role of PTEN is most prominent on either collective, ECM-induced or 3D-dependent invasion __

    We believe that the reviewer may be confused. Both of our models, either spheroids or invading monolayers, are events occurring inside gels of ECM. Therefore, these are all are 3D, ECM-induced, collective invasion. We have not performed 2D migration assays. We apologise that the this was not clearer in the first submission. We will correct this in the updated manuscript.

    __First, the authors claim that PTEN loss alone (i.e. without p53 deletion) leads to changes in Akt signalling. Supp fig 1H clearly shows that there is no significant increase in Akt activation, although there seems to be one in the Western Blot (WB) presented in supp fig 1G. There is a clear, significant increase in the Akt activation in all the PTEN KO clones when in association with p53 loss though. This claim is hence not backed up by data and the conclusion seems to be that the effect on Akt signalling requires both deletion of p53 and PTEN. __

    The reviewer is correct: that the increase to pAKT levels upon PTEN KO is more robust with co-KO of TP53, thereby indicating synergy with p53. We will update the manuscript to note this, accordingly.

    __ It will be interesting to see a quantification of the pS473-Akt staining (supp fig S1J), as it seems from these images that pAkt is preferentially found on rounded cells. It should also be performed in 3D conditions to see if there is an enrichment at invasive tips and back-up the invasion data.__

    This observation made us realise that the images we had included were giving the wrong impression (that pAkt levels would be highest in round cells). Based on the quantitation in Fig. S1M, PTEN KO cells (which have elevated pAkt levels), show a marked depletion of rounded cells. Therefore, pAkt elevated is not associated with being enriched in rounded cells. We will replace this image with cells mirroring the phenotypes quantified in Fig S1M.

    We used 2D for quantitation of pAKT staining, as we perform a like for like comparison. We cannot compare pAkt in 3D protrusions accurately between genotypes because of the frequency of protrusions: in p53 KO protrusion are rare. In 3D, therefore, it is not a situation where protrusions are present in both genotypes and we compare enrichment or depletion in a stable structure. Rather, what we can provide is whether when protrusions form, there is clear pAkt labelling in a protrusion. We will include for the revision a representative image of each phenotype in 3D, including a 3D Trp53-/-;Pten-/- spheroid stained for pAKT S473.

    __ Arf6 is recruited to the invasive tips of cells invading a 2D wound (fig4D). How do the authors reconcile the fact that all the machinery required for 3D invasion is present but that PTEN loss has a modest effect on cells in 2D? If the wound assay was done on glass, it should be done again on ECM coated glass to see if it recapitulates the effects seen in 3D. This experiment will help deconvolute if the effect of PTEN loss is more linked to collective behaviour than 3D organization or presence of ECM. __

    We again apologise for not being clearer in our description. Both the wound assays and the IF of invading monolayer were performed with cell monolayers invading into Matrigel. Monolayers are grown on top of Matrigel, wounded, and then overlayed with Matrigel. Therefore, this is orthogonal to our spheroid assay, and completely 3D. We will address this comment by changing the text in the results section to highlight the 3D nature of the method.

    __ The recycling assays are all done in 2D, condition under which the authors claim that the PTEN phenotype is weakest. Although I understand that it is not possible to do this assay in 3D, its contribution to elucidating the mechanism by which integrins participate in the PTEN loss invasive phenotype is not clear. The requirement of integrins relies on the data showing that ITGB1 KO results in no collagen4-positive basement membrane of the cysts and greatly impaired invasion. Experiments looking at the integrin localisation would be helpful: can an enrichment at the invasive tips can be seen? Are ITGA6 and/or ITGB1 repartitions homogeneous between the cysts membranes and the invasive tips? In my opinion the Src/FAK data is not enough to draw the conclusions of fig7I schematic. __

    We will endeavour to include images of 3D spheroids of Trp53-/-;Pten-/- cells and stained for β1 integrin (total and active) and α5 integrin to interrogate localisation at the tips.

    __

    1. Expression of AGAP1 isoforms do not alter ARF6 levels. Data in fig 6C, D show a significant downregulation of Arf6 and Akt signalling after expression of AGAP1S. Can the authors clarify what they mean?__

    We thank the reviewer for picking up that discrepancy between the results and the text. We will change the relevant text to highlight that expression of AGAP1S is associated with a statistically significant reduction of roughly 30% in ARF6 levels and 10% in p:t AKT. We do not know why AGAP1s may enact such an effect.

    __

    1. Arf6 is not modulated in the different cell lines: data in fig4B (far right graph) and supp fig 4B, J seem to indicate otherwise. Can the authors clarify what they mean? __

    It is not clear exactly what the reviewer is referring to here. If the reviewer is referring to Supplementary Figure 4B, this is an experiment examining the levels of ARF5 or ARF6 upon knockdown, so levels would be expected to vary. Fig S4B does not correspond to the experiment performed in S4J. Our interpretation is that loss of p53 alone or in combination with Pten does not seem to be consistently be accompanied with an increase in either the levels of total or bulk GTP-bound ARF6 that could explain the dependency of Trp53-/-;Pten-/- on the GTPase for the invasive phenotype. We will make our interpretation clearer in the text

    __

    1. Immunofluorescence panels without quantifications: Quantifications for the different stainings shown in fig3A; 4D, E; 5H; 7B and supp fig S1L, J; S3 need to be included to fully back the conclusions of the authors. Indeed, these images are used to draw conclusions and not only as illustrations. __

    It is not possible to do a direct comparison between protrusion vs no protrusion (see our response above). We will include a line scan to show clear enrichment at the end of the tip for image shown. Quantitation for Figure S1L is already included (S1K and M), quantitation for Figure S1J is presented in Fig S1I and for Fig 5H quantitation of the phenotype is present in Fig 5I.

    __

    1. Quantifications of invasion show that WT cysts become hyper-protrusive at around the half experiment mark (around 30-40hrs). Nevertheless, all movies or galleries show spherical cysts, which does not seem representative. Can the authors change this or explain why these images/movies were chosen? __

    We present the fold change at each time point because that is intuitively easier to understand rather than the raw number. The quantitation does not show that the cysts necessarily become hyper-protrusive at the specific timepoint, but rather that the proportion of hyper-protrusive cysts observed in this genotype peaks at the specific timepoint. This phenotype may still be in the minority of behaviours. As an example, something that occurs 5% of the time in the control, with a two-fold increase in behaviours, might still only be 10% of the population. Therefore, adding in a picture that may be representative of a small proportion of the population may not be a realistic depiction of what is happening across the entire population. We will provide the reviewer with the exact percentage of spheroids that are classified as hyper-protrusive at the specific cell line across timepoints, to make this clearer.

    __8) Since it seems that the main effect of PTEN is to drive the localisation and intensity of recycling of Arf6 cargoes, it will be helpful to confirm that all the proteins involved in the Arf6 module be shown to be accumulated/present at the pro-invasive tips. Immunofluorescence stainings showing the presence of AGAP1 (could be done with the AGAP1S isoform that is mNeon-tagged), pS473-Akt, ITGB1 (active integrin if possible, otherwise total integrin), ITGA5, PI3K should be included if possible. A quantification comparing signal in the cysts and in the invasive tips should also be included to see if there is an accumulation to PIP3-enriched areas. __

    We will endeavour to include the requested images.

    __

    1. Data in fig5I convincingly show that PTEN loss induces a fragmented collagen4-positive basement membrane. The authors use this data to claim that this is one of the ways that PTEN could be driving invasion but no correlation between these structures and the hyper-protrusive phenotype is made. This experiment needs to be done to support this claim. __

    This comment made us realise that in an attempt to make images simpler (displayed nuclei and COL4 only), we omitted a staining for where protrusions were moving through gaps in the ECM. We will update these times to demonstrate such events.

    __

    MINOR COMMENTS __

    __1) Data visualization: I think that the heatmap representation is overkill when only 2 or 3 conditions are presented. A graph showing the evolution of area or spherical/Hyper-protrusive phenotype proportions across time would be easier to read and more impactful: each genotype could be presented with a colour and the spherical/hyper-protrusive phenotypes as either plain or dashed lanes across time. I understand that this representation allows for the stats to be done at each time points but they are generally pretty clear (especially for the PTEN KO or dKO phenotypes) and do not need to be done for each time point in my opinion. These heatmaps could be put in supplementary figures if the authors feel strongly about putting stats for each time points. __

    We thank their reviewer for their suggestion. We believe that our approach, while complex, is the best visualisation to reflect both the changes across time but also between conditions while allowing appreciation of the statistical significance. This visualisation has been optimised by our lab over years of working with this type of data and we would prefer that they remain consistent with the accepted standard of our other publications. We are, however, happy to expand the explanation in the text on how to interpret the bubble heatmaps.

    __ Fig supp S1M, fig 5I should be presented as a stacked histogram to improve readability and merged with fig supp S1K. __

    We will merge Figures S1M and S1K. We believe that Figure 5I is easier to read as is.

    __Displaying fold change as antilog rather than log values would be easier for the reader to realise the magnitude of the differences. __

    We disagree with the reviewer.

    __A bar graph would be easier to read than the matrix representation for fig 6B. __

    We disagree with the reviewer as we feel it makes it easier to directly compare each lipid between the two cell lines.

    __The way Area data is presented throughout to me makes it very difficult to understand what is going on. Could the authors at least give some explanations in figure legends. A curve graph displaying the evolution of the area across time would be easier to read and see the differences between conditions. __

    Please see our response to Minor point 1

    __2) It is confusing that, in fig supp S1M, there is a significant decrease of the rounded phenotype after PTEN loss that is not associated to a significant change in another of the categories. Could the authors explain how? __

    This can be simply explained from our data: while the rounded phenotype was reduced in a consistent way across replicate experiments (therefore resulting in significance), the effect on the other two phenotypes was not consistent (not set in magnitude and directionality). This therefore does not lead to a significant (i.e. consistent) effect on the latter two phenotypes. PTEN loss therefore seems to allow cells to undergo – at the expense of being round - a range of shape changes, rather than a set phenotype.

    __

    1. One of the big differences of the PTEN KO cells seems their ability to invade through the matrigel bed and migration on the glass below (supp movie S2). From what I gather, these cysts would be considered out of focus and excluded from the analysis. Would it be possible that this would minimize some of the results? Would it be possible to include a quantification of this particular phenotype to confirm it is specific to PTEN KO cells? __

    __In the same spirit, could the authors provide the percentage of non-classified cysts, to make sure that the same proportion of cysts is quantified across all different genotypes. __

    Indeed, we cannot exclude that we under-estimate the magnitude of the effect on the PTEN null. We will include this point in the discussion. We can include a reviewer-only figure showing the proportion of cysts and levels of the ‘OutOfFocus’ objects across cell lines.

    __

    1. Can the authors clarify how a 0 fold change (in log value) in fig 2D can be highly significant? __

    We believe that the reviewer is equating statistical significance with something being biologically meaningful. Statistical analysis does not indicate a priori whether something is biologically meaningful. Rather, it assesses the likelihood that an observed result is occurring by chance (or not). For instance, if a small change (e.g 0.04 in a log2 fold change) occurred repeatedly across experimental replicates this is unlikely to be a result of chance, and therefore could be statistically significant. Yet, such a small magnitude of effect is probably biologically minor. This is why our heatmaps provide both statistical significance, fold change, and consistency in magnitude of effect.

    __5) Delta isoform of PI3K seems to have an effect on area in the middle of the experiment, but has no effect at all on invasion. Could the authors comment? Are these smaller cysts still as invasive? There might be an interesting uncoupling between proliferation and invasion there. __

    The cysts are actually slightly larger with PI3Kδ inhibition and there is no change in invasion. We will expand our comments in text as well to account for this observation.

    __6) ITGB1 depletion seems to induce a downregulation of Akt protein. Is that right? Does it change Akt localisation? Is there a dose effect whereby there is not enough Akt protein to mediate invasion? __

    The p:t AKT ratio does not change consistently across all gRNAs (Figure 5C) but we can look at Akt (total) protein levels and include this information if needed.

    __

    1. Stats should be added directly on the graphs for the recycling assays, doing a pairwise comparison of the different genotypes for each time points. Can the authors clarify what the t-32min quantification graphs adds (fig7E, supp fig S8G-I)? I would advise to remove them, as this data is already presented in the recycling assay graphs. __

    We don't include these because although they are technical replicates, they are demonstrative of a single experiment. What we include instead is the quantitation across independent biological experiments (which each have their own internal multiple technical replicates), where it is appropriate to include statistical analysis.

    __8) There is a substantial amount of typos and erroneous references to figures. I listed below the ones that I spotted and I encourage the authors to carefully check. __

    1. __ there are some mistakes in referencing the number of cysts in supp table 1. There is for example no cysts experiments in Figure 1 but yet there are some references to figure 1 in supp table 1. Please correct it. I think it will be easier for the reader if the number of cysts quantified for each conditions was also indicated in the figure legends. Supp table 1 can still be included for readers that want additional details. __
    2. __ comma missing page 3 __
    3. __ page 3 and 4: PI(3,4)2 means PI(3,4)P2? Can be shorten to PIP2 for ease of read and specify if it is another PIP2 specie otherwise __
    4. __ define CYTH abbreviation: I suppose this is for cytohesin?__
    5. __ fig1F-I: don't understand why TCGA.OV is specified on some but not all the graphs. It seems to me that all the data are from TCGA.OV? Makes it seems it is nit the case __
    6. __ legend of fig1H, I: y axis is -Log10 values in 1I, not Log10 values __
    7. __ page 6: dKO abbreviation is already specified above and should be used to avoid repetition and for ease of read __
    8. __ supp fig S1D: missing legend for the second bar (after Wild Type) __
    9. __ supp fig S1N: legend of the X-axis should be below the axis __
    10. __ supp fig S1O: the numerotation of the X-axis needs to be below the line of the axis for ease of read, not above it __
    11. __ legend of S2A: clones 1.12 and 1.15 are p53-/-;PTEN-/- and not PTEN-/- __
    12. __ supp figS2C can the authors specify the different stages of matrigel (liquid or gel) that are used for the invasion assay, to make it easier for the non-specialist to understand what is going on. Please confirm that the 50% GFR matrigel makes a gel on top of the cells and fill in the wound to produce the 3D invasion assay setup. __
    13. __ page 7: no parental cells are used in S3A, B only p53 null and p53 null and dKO. Please also specify what cells are being compared in the text __
    14. __ description of arrow heads and colours need to be moved to figure legends and not in main text (page 7) __
    15. __ fig 2D: the signification of the dot in the circles needs to be in the legends (since it is its first apparition in the manuscript). It only appears later on, in supp2A legend. Additional description of the matrices is necessary, as they contain a lot of information to digest to understand fully what is going on __
    16. __ legend of fig3: error in figure reference: area data is D and not E, protrusive phenotypes are E and not F __
    17. __ arrow missing in fig3B __
    18. __ fig 3D,E, G, H: please indicate the cell line studied __
    19. __ fig 3I: the different genotypes need to be stated on the galleries for clarity __
    20. __ page 8: define Arf6-mNG in the text __
    21. __ page 9: "We thank the reviewer for their careful examination of the manuscript. We will go through all above points and make the corresponding careful adjustments to the manuscript.

    __OPTIONAL SUGGESTIONS __

    __1) Choice of cell line: There is a high number of patients (around 9% according to (Cole et al. 2016)) that present the R248Q gain-of-function mutation. A recent study has shown that this mutant p53 protein is associated to an activation of Akt signalling and an increase of the intercellular trafficking of EGFR (Lai et al. 2021). Given that EGFR was also a hit in this screen, that is seems to have a central role in Arf6 cargoes (fig 4G), I think it would be a great addition to this study. It could hence cooperate with PTEN loss to drive strong, robust invasion. __

    This is an excellent observation and one we will likely follow-up in an independent study.

    __

    1. Are MAPK involved in the PTEN KO pro-invasive phenotype? In particular Erk1/2, since EGFR is one of the PTEN loss induced Arf6 cargoes. __

    This is an excellent observation and one we will likely follow-up in an independent study.

    __

    REFERENCE Cole, Alexander J., Trisha Dwight, Anthony J. Gill, Kristie-Ann Dickson, Ying Zhu, Adele Clarkson, Gregory B. Gard, et al. 2016. « Assessing Mutant P53 in Primary High-Grade Serous Ovarian Cancer Using Immunohistochemistry and Massively Parallel Sequencing ». Scientific Reports 6 (1): 26191. ____https://doi.org/10.1038/srep26191____.

    Lai, Zih-Yin, Kai-Yun Tsai, Shing-Jyh Chang, et Yung-Jen Chuang. 2021. « Gain-of-Function Mutant TP53 R248Q Overexpressed in Epithelial Ovarian Carcinoma Alters AKT-Dependent Regulation of Intercellular Trafficking in Responses to EGFR/MDM2 Inhibitor ». International Journal of Molecular Sciences 22 (16): 8784. ____https://doi.org/10.3390/ijms22168784____. __

    __Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

    __The authors have conducted a study of the molecular requirements for cancer invasion that is stunning in its thoroughness and depth and breadth of its molecular analysis. The writing is exceptionally precise though also very dense (see below). The molecular model proposed is that PTEN loss (in a p53 null background) leads to reliance upon ARF6 for invasion, with regulation through interactions with AGAP1 and beta1-integrin and it is convincingly demonstrated. They focus on interpreting the consequences of genetic and pharmacologic manipulations in a cell line, using a series of 2D and 3D assays. The phenotypes are more prominent in 3D assays. __

    __Concerns and Suggestions: __

    • __ There is a disconnect between the essentially complete loss of protrusions and invasion in 3D (e.g. 4A) and the reduction in magnitude of protrusive invasion but the continued presence of elongated cells with protrusions in 2D (e.g. S4C). This discrepancy is present in a couple of comparisons and is glossed over in quick callouts to many figure panels. __

    We thank the reviewer for mentioning this as this comment was very helpful in determining that we needed to clarify our description of the role of ARF6 to protrusion formation vs maturation. In the Trp53-/- genotype, protrusions can form, but they rapidly retract, failing to mature into structures that drive invasion through ECM (e.g. Figure S2E). This protrusion maturation occurs upon PTEN KO. When ARF6 depleted, PTEN-null cells can form protrusions, but now again lack the ability to mature into invasion-inducing structures.

    This concept of needing ARF6 for protrusion maturation and maintenance is underpinned by our model of ARF6 regulating recycling of active integrin back to the protrusion front. Indeed, we have observed ARF6 being required not for protrusion initiation, but rather ensuring protrusions are not retracted in other contexts (i.e. upon loss of the ARF6 GEF protein IQSEC1 in invading 3D culture of PC3 cells; PMID: 33712589).

    We also note that, as responded to Reviewer 1, the assay is a 3D invasion rather than 2D migration assay, with cells sandwiched between Matrigel.

    We will update the relevant sections of the results and discussion with the point above.

    __ Once a journal has been identified, it would be wise for the editor to allow some flexibility in word limit to enable some very dense sections to be expanded slightly to guide the reader through the experiments and results more clearly. For example, in the section "ARF6 regulates active integrin pools...", there are callouts like (Fig. 7C,E; S8A-C; G-I) and then (Fig. 7D,E; Fig. S8E-F, H-I). It takes a lot of time to unpack these different experimental claims based on a single sentence. __

    We greatly appreciate the refreshing comments of this reviewer to advocate for actions to improve clarity in our reporting. We would take glad advantage of such a possibility.

    __ The patient data on CYTH2 and its relationship to survival is modestly convincing.__

    In Ovarian Cancer, effects on survival are often minor. This is not a disease where one often sees large shifts in survival, which is why we are so excited about the large shifts that we do see with the ARF GTPase module we identified. However, we concede that the effects on CYTH2, although significant, are not vast changes. We will point this out and tone down our language.

    __ Very minor- search on %- there are a few inconsistencies in terms of spaces and commas vs. periods. The Methods also have some inconsistencies in terms of spaces between numbers and units or numbers and degrees Celsius. References are also in a different font. Overall it was extremely carefully written though (just dense). __

    We thank the reviewer for their careful inspection of our manuscript. We will carefully go over the sections flagged before resubmission

    __Reviewer #2 (Significance (Required)): __

    __One limitation of the experimental design is that the depth of molecular analysis in vitro comes at the expense of any in vivo validation, which the authors acknowledge in the Discussion. They attempt to make similar points using analysis of patient survival data from public databases but these analyses generally yielded small magnitude differences. The main audience for this study is likely to be cell biologists interested in cell migration, cell-ECM adhesion, cancer invasion, and GTPases. I don't see any need for new experiments- what can be done has been done and then some. I do think that it would benefit readers if the text could be made less dense. __

    __Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

    __Summary: Using a murine HGSOC 3D cell model, in combination with analysis of human ovarian cancer datasets, the authors uncover a CYTH2-ARF6-AGAP1 signaling module regulated by PTEN and identify a biomarker for tumor invasion and targeted therapy. __

    __Major comments: __

    __The findings of this study are significant as they reveal a critical signaling module that controls tumor invasion by mediating tumor cell interaction with the extracellular matrix. The experiments are properly designed, and the data are well presented. The conclusions are appropriate and supported by the data. The limitation of the study has also been discussed properly.

    One suggestion regarding the survival analysis in Fig. 6 and 7. __

    __The authors noted that the CYTH2-ARF6-AGAP1 module is not specifically or only induced in Pten-null contexts, but rather that Pten-null cells become more dependent on the module for enacting the invasive phenotype. Based on this, it would be interesting to evaluate how the PTEN status impacts the survival difference by integrating the PTEN genomic status (WT versus mutation) or its expression level (protein or mRNA) into the survival analysis of patient cohorts in Fig. 6 and Fig. 7. __

    We thank the reviewer for this excellent point. We will include such analysis, where possible. One consideration will be that extensive division of patients based on these molecular characteristics may results in patient numbers too low to draw conclusions of significance.

    **Referees cross-commenting**

    __Gene deletion and mutation may elicit different functional outcomes. I therefore agree with Reviewer #1 that "the choice of studying PTEN loss in the complete absence of p53, a situation that does not mirror the clinical situation, needs to be explained". __

    We will make our reasons for this choice clear in the text before submission. Please refer to response to Reviewer 1, Major comment 1.

    __Reviewer #3 (Significance (Required)): __

    __The model used and data presented in this study are of significance for both scientific discovery and clinical application, which will interest the broad audience in both basic and clinical research. __

    3. Description of the revisions that have already been incorporated in the transferred manuscript

    4. Description of analyses that authors prefer not to carry out

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    Referee #3

    Evidence, reproducibility and clarity

    Summary:

    Using a murine HGSOC 3D cell model, in combination with analysis of human ovarian cancer datasets, the authors uncover a CYTH2-ARF6-AGAP1 signaling module regulated by PTEN and identify a biomarker for tumor invasion and targeted therapy.

    Major comments:

    The findings of this study are significant as they reveal a critical signaling module that controls tumor invasion by mediating tumor cell interaction with the extracellular matrix. The experiments are properly designed, and the data are well presented. The conclusions are appropriate and supported by the data. The limitation of the study has also been discussed properly.

    One suggestion regarding the survival analysis in Fig. 6 and 7. The authors noted that the CYTH2-ARF6-AGAP1 module is not specifically or only induced in Pten-null contexts, but rather that Pten-null cells become more dependent on the module for enacting the invasive phenotype. Based on this, it would be interesting to evaluate how the PTEN status impacts the survival difference by integrating the PTEN genomic status (WT versus mutation) or its expression level (protein or mRNA) into the survival analysis of patient cohorts in Fig. 6 and Fig. 7.

    Referees cross-commenting

    Gene deletion and mutation may elicit different functional outcomes. I therefore agree with Reviewer #1 that "the choice of studying PTEN loss in the complete absence of p53, a situation that does not mirror the clinical situation, needs to be explained".

    Significance

    The model used and data presented in this study are of significance for both scientific discovery and clinical application, which will interest the broad audience in both basic and clinical research.

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    Referee #2

    Evidence, reproducibility and clarity

    The authors have conducted a study of the molecular requirements for cancer invasion that is stunning in its thoroughness and depth and breadth of its molecular analysis. The writing is exceptionally precise though also very dense (see below). The molecular model proposed is that PTEN loss (in a p53 null background) leads to reliance upon ARF6 for invasion, with regulation through interactions with AGAP1 and beta1-integrin and it is convincingly demonstrated. They focus on interpreting the consequences of genetic and pharmacologic manipulations in a cell line, using a series of 2D and 3D assays. The phenotypes are more prominent in 3D assays.

    Concerns and Suggestions:

    1. There is a disconnect between the essentially complete loss of protrusions and invasion in 3D (e.g. 4A) and the reduction in magnitude of protrusive invasion but the continued presence of elongated cells with protrusions in 2D (e.g. S4C). This discrepancy is present in a couple of comparisons and is glossed over in quick callouts to many figure panels.
    2. Once a journal has been identified, it would be wise for the editor to allow some flexibility in word limit to enable some very dense sections to be expanded slightly to guide the reader through the experiments and results more clearly. For example, in the section "ARF6 regulates active integrin pools...", there are callouts like (Fig. 7C,E; S8A-C; G-I) and then (Fig. 7D,E; Fig. S8E-F, H-I). It takes a lot of time to unpack these different experimental claims based on a single sentence.
    3. The patient data on CYTH2 and its relationship to survival is modestly convincing.
    4. Very minor- search on %- there are a few inconsistencies in terms of spaces and commas vs. periods. The Methods also have some inconsistencies in terms of spaces between numbers and units or numbers and degrees Celsius. References are also in a different font. Overall it was extremely carefully written though (just dense).

    Significance

    One limitation of the experimental design is that the depth of molecular analysis in vitro comes at the expense of any in vivo validation, which the authors acknowledge in the Discussion. They attempt to make similar points using analysis of patient survival data from public databases but these analyses generally yielded small magnitude differences. The main audience for this study is likely to be cell biologists interested in cell migration, cell-ECM adhesion, cancer invasion, and GTPases. I don't see any need for new experiments- what can be done has been done and then some. I do think that it would benefit readers if the text could be made less dense.

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    Referee #1

    Evidence, reproducibility and clarity

    This paper by Konstantinou et al aims at deciphering the mechanisms by which PTEN loss could be driving poorer prognosis in patients. The authors use their great high-throughput 3D screening method coupled to an unbiased proteomic method and a CRISPR screen to uncover a new pro-invasive axis driving collective invasion of high-grade serous ovarian carcinoma (HGSOC) cells. Overall, this is a very impressive study, very well done and controlled with rigorous statistical analyses that uses sophisticated methods to convincingly show that the CYTH2-ARF6-AGAP1-ITGA6/ITGB1 module is required for the pro-invasive effect of PTEN depletion and discriminates patients with poorest prognosis.

    Major comments

    Below are listed all the claims that, in my opinion, are not adequately supported by the data.

    1. Choice of the cell line: More justification on the use of the ID8 cell line and on the p53 deletion is needed. The authors need to clearly state that most p53 mutations in ovarian cancer are missense mutations that lead to a strong accumulation of a p53 protein devoid of transcriptional activity. Nevertheless, it seems that p53 mutations are not associated to differences in patient survival. Hence the choice of studying PTEN loss in the complete absence of p53, a situation that does not mirror the clinical situation, needs to be explained. Moreover, the in vivo experiments already performed in the literature mentioned in the discussion should be mentioned in the introduction to provide more context and physiological relevance to this study (especially regarding the special focus on the p53 null/ dKO cells throughout the study).
    2. "Therefore, PTEN loss in ovarian cancer, particularly at the protein level, occurs in the tumour epithelium and is associated with upregulated AKT signalling and poor overall survival". This claim is an over-interpretation and over-generalisation of the data presented. I appreciate the honesty of the authors in showing all the ovarian datasets that are available and highlight the discrepancies in expression of the proteins they study in stroma and epithelium. I think the way to present these data in the text without over-interpreting and generalizing would be to show that there is a clear epithelial-specific downregulation of PTEN at the mRNA level. Most likely due to the contribution to other cell types in the stroma, only 3 out of 5 bulk tumour mRNA datasets show a tumour specific downregulation of PTEN and no association with survival based on a median split of PTEN mRNA expression. Nevertheless, although there is no direct correlation between PTEN mRNA and protein levels, patients with low PTEN protein levels have poorer survival that is associated to an upregulation of Akt signalling. This allows to have a clearer conclusion, based solely on the protein data presented and no over-generalisation using the mRNA data. This, to me, makes a stronger case for studying PTEN loss in ovarian cancer and is fully supported by the data presented.
    3. PTEN loss induces modest effects in 2D culture. The authors make claims regarding the fact that some of the phenotypes they look at happen after PTEN depletion alone or in combination with p53 loss and are more prominent in 3D vs 2D. Many of these are insufficiently backed up by data. A few key experiments are also only performed in 2D and should be done in 3D. Finally, some clarifications if the role of PTEN is most prominent on either collective, ECM-induced or 3D-dependent invasion.

    First, the authors claim that PTEN loss alone (i.e. without p53 deletion) leads to changes in Akt signalling. Supp fig 1H clearly shows that there is no significant increase in Akt activation, although there seems to be one in the Western Blot (WB) presented in supp fig 1G. There is a clear, significant increase in the Akt activation in all the PTEN KO clones when in association with p53 loss though. This claim is hence not backed up by data and the conclusion seems to be that the effect on Akt signalling requires both deletion of p53 and PTEN.

    It will be interesting to see a quantification of the pS473-Akt staining (supp fig S1J), as it seems from these images that pAkt is preferentially found on rounded cells. It should also be performed in 3D conditions to see if there is an enrichment at invasive tips and back-up the invasion data.

    Arf6 is recruited to the invasive tips of cells invading a 2D wound (fig4D). How do the authors reconcile the fact that all the machinery required for 3D invasion is present but that PTEN loss has a modest effect on cells in 2D? If the wound assay was done on glass, it should be done again on ECM coated glass to see if it recapitulates the effects seen in 3D. This experiment will help deconvolute if the effect of PTEN loss is more linked to collective behaviour than 3D organization or presence of ECM.

    The recycling assays are all done in 2D, condition under which the authors claim that the PTEN phenotype is weakest. Although I understand that it is not possible to do this assay in 3D, its contribution to elucidating the mechanism by which integrins participate in the PTEN loss invasive phenotype is not clear. The requirement of integrins relies on the data showing that ITGB1 KO results in no collagen4-positive basement membrane of the cysts and greatly impaired invasion. Experiments looking at the integrin localisation would be helpful: can an enrichment at the invasive tips can be seen? Are ITGA6 and/or ITGB1 repartitions homogeneous between the cysts membranes and the invasive tips? In my opinion the Src/FAK data is not enough to draw the conclusions of fig7I schematic.

    1. Expression of AGAP1 isoforms do not alter ARF6 levels. Data in fig 6C, D show a significant downregulation of Arf6 and Akt signalling after expression of AGAP1S. Can the authors clarify what they mean?
    2. Arf6 is not modulated in the different cell lines: data in fig4B (far right graph) and supp fig 4B, J seem to indicate otherwise. Can the authors clarify what they mean?
    3. Immunofluorescence panels without quantifications: Quantifications for the different stainings shown in fig3A; 4D, E; 5H; 7B and supp fig S1L, J; S3 need to be included to fully back the conclusions of the authors. Indeed, these images are used to draw conclusions and not only as illustrations.
    4. Quantifications of invasion show that WT cysts become hyper-protrusive at around the half experiment mark (around 30-40hrs). Nevertheless, all movies or galleries show spherical cysts, which does not seem representative. Can the authors change this or explain why these images/movies were chosen?
    5. Since it seems that the main effect of PTEN is to drive the localisation and intensity of recycling of Arf6 cargoes, it will be helpful to confirm that all the proteins involved in the Arf6 module be shown to be accumulated/present at the pro-invasive tips. Immunofluorescence stainings showing the presence of AGAP1 (could be done with the AGAP1S isoform that is mNeon-tagged), pS473-Akt, ITGB1 (active integrin if possible, otherwise total integrin), ITGA5, PI3K should be included if possible. A quantification comparing signal in the cysts and in the invasive tips should also be included to see if there is an accumulation to PIP3-enriched areas.
    6. Data in fig5I convincingly show that PTEN loss induces a fragmented collagen4-positive basement membrane. The authors use this data to claim that this is one of the ways that PTEN could be driving invasion but no correlation between these structures and the hyper-protrusive phenotype is made. This experiment needs to be done to support this claim.

    Minor comments

    1. Data visualization: I think that the heatmap representation is overkill when only 2 or 3 conditions are presented. A graph showing the evolution of area or spherical/Hyper-protrusive phenotype proportions across time would be easier to read and more impactful: each genotype could be presented with a colour and the spherical/hyper-protrusive phenotypes as either plain or dashed lanes across time. I understand that this representation allows for the stats to be done at each time points but they are generally pretty clear (especially for the PTEN KO or dKO phenotypes) and do not need to be done for each time point in my opinion. These heatmaps could be put in supplementary figures if the authors feel strongly about putting stats for each time points.

    Fig supp S1M, fig 5I should be presented as a stacked histogram to improve readability and merged with fig supp S1K.

    Displaying fold change as antilog rather than log values would be easier for the reader to realise the magnitude of the differences.

    A bar graph would be easier to read than the matrix representation for fig 6B.

    The way Area data is presented throughout to me makes it very difficult to understand what is going on. Could the authors at least give some explanations in figure legends. A curve graph displaying the evolution of the area across time would be easier to read and see the differences between conditions.

    1. It is confusing that, in fig supp S1M, there is a significant decrease of the rounded phenotype after PTEN loss that is not associated to a significant change in another of the categories. Could the authors explain how?
    2. One of the big differences of the PTEN KO cells seems their ability to invade through the matrigel bed and migration on the glass below (supp movie S2). From what I gather, these cysts would be considered out of focus and excluded from the analysis. Would it be possible that this would minimize some of the results? Would it be possible to include a quantification of this particular phenotype to confirm it is specific to PTEN KO cells?

    In the same spirit, could the authors provide the percentage of non-classified cysts, to make sure that the same proportion of cysts is quantified across all different genotypes.

    1. Can the authors clarify how a 0 fold change (in log value) in fig 2D can be highly significant?
    2. Delta isoform of PI3K seems to have an effect on area in the middle of the experiment, but has no effect at all on invasion. Could the authors comment? Are these smaller cysts still as invasive? There might be an interesting uncoupling between proliferation and invasion there.
    3. ITGB1 depletion seems to induce a downregulation of Akt protein. Is that right? Does it change Akt localisation? Is there a dose effect whereby there is not enough Akt protein to mediate invasion?
    4. Stats should be added directly on the graphs for the recycling assays, doing a pairwise comparison of the different genotypes for each time points. Can the authors clarify what the t-32min quantification graphs adds (fig7E, supp fig S8G-I)? I would advise to remove them, as this data is already presented in the recycling assay graphs.
    5. There is a substantial amount of typos and erroneous references to figures. I listed below the ones that I spotted and I encourage the authors to carefully check.
    • a. there are some mistakes in referencing the number of cysts in supp table 1. There is for example no cysts experiments in Figure 1 but yet there are some references to figure 1 in supp table 1. Please correct it. I think it will be easier for the reader if the number of cysts quantified for each conditions was also indicated in the figure legends. Supp table 1 can still be included for readers that want additional details.
    • b. comma missing page 3
    • c. page 3 and 4: PI(3,4)2 means PI(3,4)P2? Can be shorten to PIP2 for ease of read and specify if it is another PIP2 specie otherwise
    • d. define CYTH abbreviation: I suppose this is for cytohesin?
    • e. fig1F-I: don't understand why TCGA.OV is specified on some but not all the graphs. It seems to me that all the data are from TCGA.OV? Makes it seems it is nit the case
    • f. legend of fig1H, I: y axis is -Log10 values in 1I, not Log10 values
    • g. page 6: dKO abbreviation is already specified above and should be used to avoid repetition and for ease of read
    • h. supp fig S1D: missing legend for the second bar (after Wild Type)
    • i. supp fig S1N: legend of the X-axis should be below the axis
    • j. supp fig S1O: the numerotation of the X-axis needs to be below the line of the axis for ease of read, not above it
    • k. legend of S2A: clones 1.12 and 1.15 are p53-/-;PTEN-/- and not PTEN-/-
    • l. supp figS2C can the authors specify the different stages of matrigel (liquid or gel) that are used for the invasion assay, to make it easier for the non-specialist to understand what is going on. Please confirm that the 50% GFR matrigel makes a gel on top of the cells and fill in the wound to produce the 3D invasion assay setup.
    • m. page 7: no parental cells are used in S3A, B only p53 null and p53 null and dKO. Please also specify what cells are being compared in the text
    • n. description of arrow heads and colours need to be moved to figure legends and not in main text (page 7)
    • o. fig 2D: the signification of the dot in the circles needs to be in the legends (since it is its first apparition in the manuscript). It only appears later on, in supp2A legend. Additional description of the matrices is necessary, as they contain a lot of information to digest to understand fully what is going on
    • p. legend of fig3: error in figure reference: area data is D and not E, protrusive phenotypes are E and not F
    • q. arrow missing in fig3B
    • r. fig 3D,E, G, H: please indicate the cell line studied
    • s. fig 3I: the different genotypes need to be stated on the galleries for clarity
    • t. page 8: define Arf6-mNG in the text
    • u. page 9: "<" symbol should be an alpha symbol
    • v. fig 4A: indicate the cell line used on the figure
    • w. supp fig S4E: why is it specified mouse-specific for the shArf6?
    • x. 4H, I, J: indicate on the figure if these interactors are mostly unchanged, strong interactors or weak interactors for clarity
    • y. legend of fig4H: "coloured spots underneath denote the protein complex that each interactor belongs (in J)" should indicate panel G and not J
    • z. fig4I, J: are you sure of the legend for the fold change coloring? Log2 of 1 is a 0 fold change, i don't see how these could show any significant difference (i.e. some of the pale red circles are significant)
    • aa. page 11: description of the assay (starting with "Machine learning classification of...") is very confusing, please clarify
    • bb. page16: figure 4H should be 4I (PTEN-null specific association of Arf6 with ITGA5)
    • cc. supp fig S5H-P: choose Tumour or cancer to homogeneise naming across the graphs
    • dd. fig 5H: box are difficultly visible in green, change color to yellow or something more visible
    • ee. page 13: Fig6E, F should also refer to 6G
    • ff. LCM abbreviation on page 10 and 12 refers to LCMD? Otherwise please define it.

    Optional suggestions

    1. Choice of cell line: There is a high number of patients (around 9% according to (Cole et al. 2016)) that present the R248Q gain-of-function mutation. A recent study has shown that this mutant p53 protein is associated to an activation of Akt signalling and an increase of the intercellular trafficking of EGFR (Lai et al. 2021). Given that EGFR was also a hit in this screen, that is seems to have a central role in Arf6 cargoes (fig 4G), I think it would be a great addition to this study. It could hence cooperate with PTEN loss to drive strong, robust invasion.
    2. Are MAPK involved in the PTEN KO pro-invasive phenotype? In particular Erk1/2, since EGFR is one of the PTEN loss induced Arf6 cargoes.

    Reference

    Cole, Alexander J., Trisha Dwight, Anthony J. Gill, Kristie-Ann Dickson, Ying Zhu, Adele Clarkson, Gregory B. Gard, et al. 2016. « Assessing Mutant P53 in Primary High-Grade Serous Ovarian Cancer Using Immunohistochemistry and Massively Parallel Sequencing ». Scientific Reports 6 (1): 26191. https://doi.org/10.1038/srep26191.

    Lai, Zih-Yin, Kai-Yun Tsai, Shing-Jyh Chang, et Yung-Jen Chuang. 2021. « Gain-of-Function Mutant TP53 R248Q Overexpressed in Epithelial Ovarian Carcinoma Alters AKT-Dependent Regulation of Intercellular Trafficking in Responses to EGFR/MDM2 Inhibitor ». International Journal of Molecular Sciences 22 (16): 8784. https://doi.org/10.3390/ijms22168784.

    Significance

    It has only been recently appreciated that PTEN loss is a driver in ovarian cancer (Martins et al. 2020) but no studies to data have aimed at understanding the mechanisms. This study is hence the first to propose one and as such provides a very valuable advance for researchers interested in ovarian cancer. The authors also propose that the CYTH2-ARF6-AGAP1 high mRNA be used as a signature of worsen prognosis. This hence paves the way to better understanding and stratify patients with ovarian cancers.

    One of the main difference after PTEN loss is the accumulation of PIP3 in pro-invasive tips that correlates with the recruitment of Arf6 to these tips. The authors have developed a very powerful automated quantification pipeline to follow the behaviour of cysts grown in 3D that they have coupled to an unbiased proteomic method to identify interactors and a CRISPR screen to test their functional relevance. This is clearly the strongest aspect of the paper that allows them to gather very robust data and identify the machinery driving invasion in PTEN KO cells. The authors' model claims that this in turns recruit the Arf6 machinery, composed of CYTH2 2G (the only CYTH2 isoform correlated to a poorer prognosis, preferentially binding PIP3) and AGAP1 that leads to a local increase in active integrin recycling that mediates the more invasive phenotype of PTEN depleted cells. It is rightfully mentioned in the discussion that PTEN depletion only leads to a modest change in Arf6 interactors, and that most likely PTEN loss acts by locally directing the Arf6 machinery to the invasive tips. Indeed, the authors convincingly show that Arf6, AGAP1 and ITGB1 are required for the formation of these invasive protrusions.

    The limitation of this study is the combination of 2D and 3D experiments to drive general conclusions on the mechanism. These are listed in the previous section. Another big limitation, in my opinion, is the choice of the cell model: indeed, nearly all patients present a vast increase in the amount of the p53 protein present due to a very large number of mutations that in most cases prevent its binding to DNA. Throughout this paper the authors have used a p53 null cell line that expresses no p53 protein. This is not compatible with the clinical situation. Moreover, since p53 also present frequent gain-of-function mutations that have been shown to be associated to an increase of Akt signalling and intercellular trafficking of EGFR. Studying the implication of the Arf6 module identified here in a context of p53 WT or mutant protein overexpression would be of great interest.

    Reference

    Martins, Filipe Correia, Dominique-Laurent Couturier, Anna Paterson, Anthony N. Karnezis, Christine Chow, Tayyebeh M. Nazeran, Adekunle Odunsi, et al. 2020. « Clinical and Pathological Associations of PTEN Expression in Ovarian Cancer: A Multicentre Study from the Ovarian Tumour Tissue Analysis Consortium ». British Journal of Cancer 123 (5): 793‑802. https://doi.org/10.1038/s41416-020-0900-0.

  6. Version published to 10.1101/2022.11.29.518198v1 on bioRxiv