FAK activity in cancer‐associated fibroblasts is a prognostic marker and a druggable key metastatic player in pancreatic cancer

Abstract Cancer‐associated fibroblasts (CAFs) are considered the most abundant type of stromal cells in pancreatic ductal adenocarcinoma (PDAC), playing a critical role in tumour progression and chemoresistance; however, a druggable target on CAFs has not yet been identified. Here we report that focal adhesion kinase (FAK) activity (evaluated based on 397 tyrosine phosphorylation level) in CAFs is highly increased compared to its activity in fibroblasts from healthy pancreas. Fibroblastic FAK activity is an independent prognostic marker for disease‐free and overall survival of PDAC patients (cohort of 120 PDAC samples). Genetic inactivation of FAK within fibroblasts (FAK kinase‐dead, KD) reduces fibrosis and immunosuppressive cell number within primary tumours and dramatically decreases tumour spread. FAK pharmacologic or genetic inactivation reduces fibroblast migration/invasion, decreases extracellular matrix (ECM) expression and deposition by CAFs, modifies ECM track generation and negatively impacts M2 macrophage polarization and migration. Thus, FAK activity within CAFs appears as an independent PDAC prognostic marker and a druggable driver of tumour cell invasion.

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EMBO Molecular Medicine has a "scooping protection" policy, whereby similar findings that are published by others during review or revision are not a criterion for rejection. Should you decide to submit a revised version, I do ask that you get in touch after three months if you have not completed it, to update us on the status. I look forward to receiving your revised manuscript.  This manuscript shows some exiting data demonstrating a tumour promoting function of high FAK activity in cancer associated fibroblasts (CAFs). The manuscript is structured into two parts. In the first part the authors explore whether the level of FAK activation in cancer associated fibroblasts (CAFs) could be used as a prognostic marker for resected PDAC patients. Therefore, the analysis of two different tissue arrays (TMA) is performed (Figs 1-2). To assess FAK activation, both sets of TMAs were stained using an antibody specific for FAK phosphorylation at tyrosine 397 (pY397), a marker for FAK activation, and fibroblasts were identified based on their morphology using manual scoring or automated image analysis. The authors observe that CAFs show increased FAK activation compared to fibroblasts in tumour free pancreas and that in resected PDAC patients' high levels of FAK activation in CAFs correlates with reduced disease-free survival and reduced overall survival. The analysis is overall well performed and comprehensive. In the second part, the authors aim to better understand how FAK activation in CAFs promotes PDAC progression by using a variety of in vivo and in vitro experiments. First, the authors use a syngeneic mouse model mouse, whereby KPC derived cancer cells are orthotopically co-injected into the pancreas together with immortalised mouse embryonic fibroblast (MEF) cell lines initially generated from WT and FAK kinase dead (FAK-KD) mutant mice (Fig. 3). This model reveals that tumours containing FAK KD fibroblasts show a reduction in extracellular matrix deposition and a change in tumour associated macrophage polarization towards a pro-inflammatory M1-like phenotype compared to animals co-injected with FAK WT fibroblasts. Most strikingly, coimplantation of FAK KD fibroblasts significantly reduces lung metastasis compared to animals coimplanted with FAK WT fibroblasts. The authors further describe that FAK KD fibroblasts reduce the migration and invasion of tumour cells in vitro (Fig 4) and that pharmacological inhibition of FAK reduces ECM production and deposition of fibroblasts in vitro (Fig 5). In Fig 6, the authors explore the effect of fibroblast conditioned media (CM) collected from fibroblast treated or not with a FAK inhibitor on macrophage activation and migration. They observe that macrophages cultured in CM collected from FAK inhibitor treated fibroblasts show less expression of the M2 marker CD206 and that treatment of fibroblasts with a FAK inhibitor is less potent to attract M2-like macrophages compared ton CM of untreated fibroblasts.
Strength: t These are all interesting observations describing that high FAK activity in fibroblasts i. correlates with poor prognosis in PDAC patients, ii. promotes ECM production/deposition by fibroblasts, iii. increases cancer cell invasion/metastasis and iv. attracts M2-like macrophages to the site of tumour formation. These findings are novel since the role of FAK in PDAC progression has mainly been studied with a focus on cancer cells and not on the surrounding stroma compartment. The manuscript is well structured and the data is well presented.
Main concerns: Fig 2B-E: Please explain in more detail how the survival curves displayed in Fig2B-E were generated. The method section describes that patients were scored based on neg/low/med/high pY397 FAK levels as shown in Fig2A. However, the graphs in panel B-E show only two groups (low/high), with n=60 patients each. Were there no patients with medium pY397 FAK levels? Or, do the graphs as displayed compare the upper half versus the lower half of pY397 FAK levels based on the median? Please explain. Fig 3: Characterization of in vivo model: FAK KD reduces collagen production. Does the altered stroma also affect the architecture of the tumour vascularization which could also impact metastatic spreading? Include stain for vessel density/vessel maturation and hypoxia. Along these lines, since FAK deficient CAFs reduce tumour cell invasion as reported in Fig 4J, please also check for changes in cancer cell invasion at the invasive front of these tumours. Fig 4: The described reduced directional migration of tumour cells in the presence of FAK KD fibroblasts compared to tumour cells co-culture with FAK WT fibroblasts is very interesting, but the underlying molecular mechanisms remain largely elusive. As mentioned by the authors, fibroblasts provide invasive tracks made of ECM deposition for tumour cells. Thus, the authors could explore in more detail the molecular composition of these tracks and how FAK activity changes their deposition and ultimately the migration capacity of tumour cells (different integrins involved). The analysis of ECM production is largely based on IF staining's. The authors should use an additional method to confirm the observed changes in ECM production. The manuscript would benefit of a more comprehensive analysis beside the selected proteins. For example, the authors could apply a proteomic based secretome or ECM deposition analysis to thoroughly assess the function of FAK in ECM protein secretion and or deposition. The authors should assess additional M1/M2-like markers to better characterise changes in their activation. Also, as written it appears that murine macrophages were used to assess the effect of human CAF conditioned media on macrophage activation. Some of the secreted factors might not conserve their biological activity across the different species. Also, a more detailed analysis of the fibroblast secretome (+/-FAK inhibitor) using a quantitative proteomic analysis would strengthen this manuscript and provide some molecular insights about fibroblast-derived factors involved in the reported macrophage activation/polarization.
In summary, the reported findings are very interesting and the studies are well performed. However, in the current format, the molecular mechanisms how FAK activity in fibroblasts impacts tumour cell invasion and/or macrophage polarization remains largely unknown. Thus, an unbiased proteomic based approach to identify some key factors regulating the described changes in tumour cell and/or macrophage functions would clearly strengthen this manuscript.
Referee #2 (Remarks for Author): Zaghdoudi et al. address the relevance of FAK in PDAC biology and clinical behavior. The topic is relevant since PDAC is associated with a very strong desmoplastic reaction. While the study of the relevance of FAK in cancer, and in PDAC specifically, has been addressed by other investigators, this work focuses mainly on its role in fibroblasts, providing significantly novel information. Overall, the work is well performed and represents an important addition to the field, although I have a few comments.
Specific comments 1. The specificity of the antibody against p-FAK used is crucial to the interpretation of the findings, therefore the authors should provide convincing evidence using both immunohistochemistry and western blotting. This is particularly important re: the immunostainings in Figure 1 in normal tissue, where it appears that there is staining pretty much all over in normal pancreas.
2. The clinical associations are interesting and are emphasized by the authors. However, they should tone down some of the conclusions since the HR for high pFAK scores are very modest (in the range of 2) and probably of little clinical applicability.
3. Do the authors have any information re: pFAK expression in the stroma of chronic pancreatitis and of PDAC metastatic lesions? this information is not essential for the paper but it would be useful to place the findings in context. 4. The results on the effect of FAK inactivation in fibroblasts on the tumor microenvironment (macrophages) are interesting but some of the changes are modest. In this regard, I find that the data presented in Figure 6 might fit better immediately after Figure 3. Using more than one marker to define M1 and M2 macrophages would be valuable. Figure 5: it is essential that the analysis of matrisome markers presented in panel A is substantiated by western blotting, for two reasons: the differences are small and the data will be more quantitative. 6. The authors should try to put together a model of how FAK inhibitors would impact, in an integrated manner, on the various tumor and microenvironmental components to affect tumor progression/metastasis. The way the data is presented is too fragmentary.

Minor comments
The paper would benefit from some English editing. This manuscript describes elevated levels of phosphorylated FAK in fibroblasts in pancreatic cancer, which is determined to be an independent marker of poor prognosis. Using an orthotopic, syngeneic model of pancreatic cancer, where tumor cells and fibroblasts are injected orthotopically, FAK activity in fibroblasts is implicated in metastasis of the tumor cells. The authors show that FAK in fibroblasts is required for efficient migration and invasion. Further impairing FAK activity in fibroblasts reduces tumor cell migration. They also demonstrate that pharmacological inhibitions of FAK reduces deposition of extracellular matrix proteins and that FAK activity is required for fibroblast contractility. The authors also suggest that FAK activity in tumor-associated fibroblasts alters the macrophage type present in tumors, and that factors secreted from fibroblasts in the presence of FAK activity promote the polarization of macrophages into M2 macrophages and promote the migration of macrophages. Overall, the authors conclude that FAK activity in fibroblasts in pancreatic cancer provides a useful prognostic marker, and inhibition of FAK activity might be an effective therapeutic strategy to prevent pancreatic cancer metastasis.
Overall, the study addresses the important problem of prostate cancer and makes the interesting correlation of phosphorylation of FAK in CAFs as a prognostic marker and that FAK activity in fibroblasts is required for metastasis in an orthotopic tumor model. The authors explore mechanism and suggest that very well established functions of FAK are responsible for their observations, i.e. regulation of cell migration and extracellular matrix deposition/organization. These studies are correlative and incremental. An exciting idea is the role of FAK in fibroblasts in promoting polarization of macrophages and recruitment into tumors. This data is a little less convincing, the effect is weak and there is no attempt to define the mechanism. As the authors point out, there are a number of clinical trials utilizing FAK inhibitors in pancreatic cancers. Therefore, this manuscript may have little impact upon treatment of the disease.

Specific critiques include:
1) The authors should describe controls to validate the authenticity of their IHC. E.g., describe the controls to validate the pY397 FAK is detecting phosphorylated FAK.
2) Some additional controls are required. E.g., in figure 3, do orthotopic tumors (without co-injected fibroblasts) metastasize? In figure 6, do macrophages migrate in serum free medium?
3) Some experimental details are missing. E.g., how is the area of metastasis calculated (Fig 3)? How are collagen I and collagen III quantified and what does the scale mean (Fig 3)? How is directionality calculated (Fig 4)? Is distance migrated the displacement from initial point to final point or does it measure the length of the path traveled?

7)
In some cases data is normalized and in some cases it is not. How do you account for variability in the control sample when the data is normalized to 100%? 8) Additional editing would improve the manuscript. ***** Reviewer's comments ***** Referee #1 (Remarks for Author): This manuscript shows some exiting data demonstrating a tumour promoting function of high FAK activity in cancer associated fibroblasts (CAFs). The manuscript is structured into two parts. In the first part the authors explore whether the level of FAK activation in cancer associated fibroblasts (CAFs) could be used as a prognostic marker for resected PDAC patients. Therefore, the analysis of two different tissue arrays (TMA) is performed (Figs 1-2). To assess FAK activation, both sets of TMAs were stained using an antibody specific for FAK phosphorylation at tyrosine 397 (pY397), a marker for FAK activation, and fibroblasts were identified based on their morphology using manual scoring or automated image analysis. The authors observe that CAFs show increased FAK activation compared to fibroblasts in tumour free pancreas and that in resected PDAC patients' high levels of FAK activation in CAFs correlates with reduced disease-free survival and reduced overall survival. The analysis is overall well performed and comprehensive. In the second part, the authors aim to better understand how FAK activation in CAFs promotes PDAC progression by using a variety of in vivo and in vitro experiments. First, the authors use a syngeneic mouse model mouse, whereby KPC derived cancer cells are orthotopically co-injected into the pancreas together with immortalised mouse embryonic fibroblast (MEF) cell lines initially generated from WT and FAK kinase dead (FAK-KD) mutant mice (Fig. 3). This model reveals that tumours containing FAK KD fibroblasts show a reduction in extracellular matrix deposition and a change in tumour associated macrophage polarization towards a pro-inflammatory M1-like phenotype compared to animals coinjected with FAK WT fibroblasts. Most strikingly, co-implantation of FAK KD fibroblasts significantly reduces lung metastasis compared to animals co-implanted with FAK WT fibroblasts. The authors further describe that FAK KD fibroblasts reduce the migration and invasion of tumour cells in vitro (Fig 4) and that pharmacological inhibition of FAK reduces ECM production and deposition of fibroblasts in vitro ( Fig  5). In Fig 6, the authors explore the effect of fibroblast conditioned media (CM) collected from fibroblast treated or not with a FAK inhibitor on macrophage activation and migration. They observe that macrophages cultured in CM collected from FAK inhibitor treated fibroblasts show less expression of the M2 marker CD206 and that treatment of fibroblasts with a FAK inhibitor is less potent to attract M2-like macrophages compared ton CM of untreated fibroblasts.
Strength: t These are all interesting observations describing that high FAK activity in fibroblasts i. correlates with poor prognosis in PDAC patients, ii. promotes ECM production/deposition by fibroblasts, iii. increases cancer cell invasion/metastasis and iv. attracts M2-like macrophages to the site of tumour formation. These findings are novel since the role of FAK in PDAC progression has mainly been studied with a focus on cancer cells and not on the surrounding stroma compartment. The manuscript is well structured and the data is well presented. We agree with reviewer 1 and apologize for these missing information. As suggested by the reviewer, we have now included details regarding how the two groups were generated in the Materials and Methods section -Patient Samples: "Statistical analyses: Percentage of cells expressing low, medium and high pY397 FAK staining were quantified using Definiens Tissue Studio® (Imag'IN core Institut Universitaire du Cancer, Toulouse, France) in stromal and tumour areas. pY397 FAK scoring (h-score) was calculated as followed: 1X%low pY397 FAK cells + 2X%medium pY397 FAK cells +3X%high pY397 FAK cells. Patients were separated in half (60/60) based on their pY397 FAK h-score (either in CAFs or in tumour cells depending on the performed analysis), generating pY397 FAK low group (lower half based on the median) and pY397 FAK high group (upper half) of 60 patients each. The outcome variables were disease free survival (DFS) and overall survival (OS)". As suggested, we have performed IHC analyses in order to better characterize the impact of fibroblastic FAK inactivation on tumour vessels and hypoxia. Staining and quantification of CD31 IHC are now included in the manuscript (figure 3D). To deeper analyze the vasculature, we planned to quantify vessels coverage with pericytes by performing co-IF of pericyte marker (NG2) and endothelial cell marker (MECA32). Briefly, paraffin-embedded tumour slices were stained with anti-NG2 (neuron-glial antigen 2) and anti-MECA32 (plasmalemma vesicle-associated protein) antibodies and DAPI. Unfortunately, as shown in figure bellow, pericytes and MEFs express NG2 at similar levels, making impossible the pericyte coverage quantification specifically in pericyte/vessels. As other markers such as SMA and PDGFR for examples are also expressed by MEFs, we were not able to deeper analyze the tumor vasculature maturation, and apologize for that.
In order to analyze hypoxia, we performed IHC staining of hypoxia inducible factor alpha (HIF1a) and its target CAIX ( figure 3E). Quantification shows a slight decrease of hypoxia markers in the FAK-KD fib. tumour group (but not significant).
We really want to thank reviewer 1 for his question about changes in cancer cell invasion at invasive front as the results (now included in figure 6A and EV5) have strengthen the manuscript. Indeed, we identify a drastic difference between the two groups: " Figure 6A shows that, in the FAK-WT fibroblast tumours, CAFs are oriented toward the adjacent pancreatic tissue, thus with a radial arrangement (parallel to the tumour radius but aligned perpendicularly to the tumour edge), but FAK-KD CAFS are organized perpendicular to the tumour radius (parallel to the tumour edge). Interestingly, ECM (Sirius Red) and CAFs have the same orientation ( Figures 6A, EV5A). "   Fig 4: The described reduced directional migration of tumour cells in the presence of FAK KD fibroblasts compared to tumour cells co-culture with FAK WT fibroblasts is very interesting, but the underlying molecular mechanisms remain largely elusive. As mentioned by the authors, fibroblasts provide invasive tracks made of ECM deposition for tumour cells. Thus, the authors could explore in more detail the molecular composition of these tracks and how FAK activity changes their deposition and ultimately the migration capacity of tumour cells (different integrins involved).
Based on reviewer comment, we first characterized the impact of FAK activity in ECM organization. To do so, we first performed the scratch wound healing assay using activated fibroblasts (FAK-WT or FAK-KD). Upon wound closure, decellularization was performed and ECM stained using NHS-alexa 488 ( Figure  EV5B). Analysis of deposited ECM during fibroblast migration shows that FAK activity is required for the production of ECM tracks as activated FAK-WT fibroblasts generate dense and abundant ECM fibers, oriented through the wound, whereas FAK-KD fibroblasts do not deposit ECM tracks but ECM heaps ( Figure 6B). In order to better characterize the difference in ECM composition, we performed matrisome analyses of deposited matrix from human primary CAFs treated or not by FAK inhibitor (see below, point Fig 5, and figure 6 F to J and EV5 H to J). Finally, we also analyzed the impact of FAK-I induced matrix modifications on tumour cell integrin beta 1 activation. As now shown in Figure 6L and EV5K, β1 integrin activation from tumor cells is dramatically decreased upon adhesion to ECM deposited from FAK-I treated hCAFs compared to non-treated hCAFs ( Figures 6L, EV5K), while β1 integrin expression is not changed. Fig 5: The analysis of ECM production is largely based on IF staining's. The authors should use an additional method to confirm the observed changes in ECM production. The manuscript would benefit of a more comprehensive analysis beside the selected proteins. For example, the authors could apply a proteomic based secretome or ECM deposition analysis to thoroughly assess the function of FAK in ECM protein secretion and or deposition.
As suggested, we performed an unbiased proteomic based approach named "matrisome" on 3 different human primary CAFs. Data are available via ProteomeXchange with identifier PXD018899 (Username: reviewer60110@ebi.ac.uk Password: Xg5PyR2a). Figure 6 (F-K) and EV5 (H-J) clearly validate our claim that FAK inactivation in CAFs downregulates expression of core matrisome proteins. Indeed "This analysis shows that FAK inhibition strongly decreases the expression of proteins belonging to the "core matrisome" (structural and fibrillar extracellular components, glycoproteins and proteoglycans) which drops from 23% to 16% of whole matrisome content. In contrast, "matrisome associated proteins" (ECM regulators, ECM affiliated proteins and ECM-secreted factors) are globally not impacted by FAK inhibition ( Figure 6F). Figures 6I-L show that, among the 3 hCAFs tested, two present, under FAK inactivation, a strong decrease of collagens (10 different collagens were detected, Figure 6G), proteoglycans (3 detected, Figure 6H) and glycoproteins (40 proteins detected, Figure 6I,J). Importantly, as opposed to CAF1 and 2, basal level of collagens, proteoglycans and glycoproteins produced by CAF3 are very low and are not impacted by FAK inhibition (Figures 6G-I). Such differences in term of response to treatment are in accordance with our IF results ( Figure EV5F) and were expected as recent publications have reported CAF heterogeneity in PDAC tumours (36). Regarding "matrisome associated proteins" that are globally not modified by FAK inhibition (Figure 6K), ECM regulators (32 detected) are downregulated in all 3 CAFs whereas ECM affiliated proteins (affiliated either structurally or physically with the core matrisome, 14 detected) and secreted factors (such as growth factors and cytokines, known or suspected to bind to ECM, 17 detected) show no modification ( Figures 6K, EV5H-J)." In summary, the reported findings are very interesting and the studies are well performed. However, in the current format, the molecular mechanisms how FAK activity in fibroblasts impacts tumour cell invasion and/or macrophage polarization remains largely unknown. Thus, an unbiased proteomic based approach to identify some key factors regulating the described changes in tumour cell and/or macrophage functions would clearly strengthen this manuscript.

Referee #2 (Remarks for Author):
Zaghdoudi et al. address the relevance of FAK in PDAC biology and clinical behavior. The topic is relevant since PDAC is associated with a very strong desmoplastic reaction. While the study of the relevance of FAK in cancer, and in PDAC specifically, has been addressed by other investigators, this work focuses mainly on its role in fibroblasts, providing significantly novel information. Overall, the work is well performed and represents an important addition to the field, although I have a few comments.
Specific comments 1. The specificity of the antibody against p-FAK used is crucial to the interpretation of the findings, therefore the authors should provide convincing evidence using both immunohistochemistry and western blotting. This is particularly important re: the immunostainings in Figure 1 in normal tissue, where it appears that there is staining pretty much all over in normal pancreas.
We agree on reviewer comment and performed experiment to validate the specificity of the antibody against p-Y397 FAK. Data are now included in the manuscript in Appendix figure S1. We used two approaches: one uses lambda phosphatase to show that the antibody is phosphospecific, and the other uses a FAK phosphopeptide to show that the antibody is specific to the sequence that it's supposed to recognize. Indeed, as the antibody recognizes the 390 -401aa of Uniprot# Q05397, we used a blocking phopshospecific peptide from abcam (ab40145). Both the blocking phopshospecific peptide and the lambda phosphatase totally abrogate the IHC staining as well as induce the disappearance of the 125 kDa band in western blot, altogether validating the specificity of the antibody used. Data are now included in the revised version in Appendix figure 1 2. The clinical associations are interesting and are emphasized by the authors. However, they should tone down some of the conclusions since the HR for high pFAK scores are very modest (in the range of 2) and probably of little clinical applicability.
We respectfully disagree with reviewer, as for example CA19-9 serum levels and lymph node ratio (LNR), classically used in clinic and shown to be correlated with PDAC patient prognosis, have HR in a range of 2. Indeed, regarding CA19-9, Hammad N et al. reported, in a clinical study analyzing 111 patients with pancreas cancer, that "lower baseline CA19-9 levels were positively associated with OS (median 9.1 vs 6.1 months, P = 0.0057) and TTP (Time to progression) (median 6.4 vs 4.2 months, P = 0.0044).The covariate adjusted hazard ratio (HR) for progression among patients with baseline CA19-9 >or= 1000 ng/mL was HR = 1.94 (95% CI 1.24-3.02), with P = 0.0035. The covariate adjusted risk of death among patients with baseline CA19-9 >or= 1000 ng/ml was similarly elevated: HR = 1.90 (95% CI 1.23-2.94), with P = 0. . While we agree that analyzing FAK activity specifically in fibroblasts could be challenging, we want to point out that there is now software available allowing a reproducible and sensitive quantification of IHC staining (free software : QuPath; commercial software: Definiens). Altogether, we believe that an HR around 2 is not modest in PDAC and highlights the potential benefit of evaluating FAK activity within CAFs to identify patients with high risk of early relapse and patients that will benefit from personalized protocol using FAK inhibitor treatment.
3. Do the authors have any information re: pFAK expression in the stroma of chronic pancreatitis and of PDAC metastatic lesions? this information is not essential for the paper but it would be useful to place the findings in context. We thank reviewer for such question but apologize as we have no answer so far. Access to patient metastatic tissue is difficult since metastatic patients are not operated. Cytopunction material is not sufficiently informative to perform reliable IHC. Patient pancreatitis tissue samples could inform on whether FAK is activated in non-cancerous inflammatory lesions where fibroblasts are already activated into myofibroblasts, and this may be tested in a future study. In mice, However, Hong Jiang et al. publication (Nat Med 2016) Figure 1F shows IHC staining of p-FAK1 in pancreatic tissue from the p48-Cre/LSL-KrasG12D/p53Flox/+ (KPC) mouse model, where FAK activity seems not induced in fibroblasts around late Panin lesions.
4. The results on the effect of FAK inactivation in fibroblasts on the tumor microenvironment (macrophages) are interesting but some of the changes are modest. In this regard, I find that the data presented in Figure 6 might fit better immediately after Figure 3. Using more than one marker to define M1 and M2 macrophages would be valuable.
We thank reviewer for this comment. All data regarding immune cells are now included in Figure 4 and EV3. We also repeated the CM-induced macrophage polarization experiment using mouse BMDM and CM from mouse fibroblasts. We analyzed for a total of 8 different macrophage markers (CD163, CD206, F4/80, CD11b, CD80, CD86, dectin, MHCII), and show that CM from CAFs favors M2 macrophage polarization dependently on FAK activity. Figure 4D shows that Dectin and CD206 markers are decreased when macrophages are stimulated with CM from FAK-KD activated fibroblasts. This two markers are mainly expressed by the M2a macrophage sub-group, which is of interest as M2a macrophages are known to be profibrotic. Figure 5: it is essential that the analysis of matrisome markers presented in panel A is substantiated by western blotting, for two reasons: the differences are small and the data will be more quantitative.

5.
In order to answer reviewer comment, western blots on primary human CAFs and on tumour lysates were carried out. We confirm coll I, III, and periostin decreases upon FAK inactivation in both in vitro CAFs and in tumors, whereas coll IV and OPN decreases are more evident in tumors (representative WB are shown in figure EV5G).
In addition, we performed an unbiased proteomic based approach named "matrisome" on 3 different human primary CAFs allowing a quantitative analysis of the matrix. Data are available via ProteomeXchange with identifier PXD018899 (Username: reviewer60110@ebi.ac.uk Password: Xg5PyR2a). Figure 6 (F-K) and EV5 (H-J) clearly validate our claim that FAK inactivation in CAFs downregulates expression of core matrisome proteins. Indeed "This analysis shows that FAK inhibition strongly decreases the expression of proteins belonging to the "core matrisome" (structural and fibrillar extracellular components, glycoproteins and proteoglycans) which drops from 23% to 16% of whole matrisome content. In contrast, "matrisome associated proteins" (ECM regulators, ECM affiliated proteins and ECM-secreted factors) are globally not impacted by FAK inhibition ( Figure 6F). Figures 6I-L show that, among the 3 hCAFs tested, two present, under FAK inactivation, a strong decrease of collagens (10 different collagens were detected, Figure 6G), proteoglycans (3 detected, Figure 6H) and glycoproteins (40 proteins detected, Figure 6I,J). Importantly, as opposed to CAF1 and 2, basal level of collagens, proteoglycans and glycoproteins produced by CAF3 are very low and are not impacted by FAK inhibition (Figures 6G-I). Such differences in term of response to treatment are in accordance with our IF results ( Figure EV5F) and were expected as recent publications have reported CAF heterogeneity in PDAC tumours (Neuzillet et al. The Journal of pathology. 2019;248(1):51-65) which is also observed for fibroblastic pY397 FAK in different patients. Regarding "matrisome associated proteins" that are globally not modified by FAK inhibition (Figure 6K), ECM regulators (32 detected) are downregulated in all 3 CAFs whereas ECM affiliated proteins (affiliated either structurally or physically with the core matrisome, 14 detected) and secreted factors (such as growth factors and cytokines, known or suspected to bind to ECM, 17 detected) show no modification ( Figures 6K, EV5H-J)." 6. The authors should try to put together a model of how FAK inhibitors would impact, in an integrated manner, on the various tumor and microenvironmental components to affect tumor progression/metastasis. The way the data is presented is too fragmentary.
As asked by reviewer, we have added a model of how fibroblastic FAK activity impacts tumour microenvironment (ECM, secreted factors and immune cells) leading to tumour progression. It is now included in the "the paper explained" section.

Minor comments
The paper would benefit from some English editing. English editing has been performed by English-native researcher.

EMM-2020-12010 Fibroblastic FAK Activity is a Prognostic Marker and a Druggable Key Player in Pancreatic Cancer Sonia Zaghdoudi et al.
This manuscript describes elevated levels of phosphorylated FAK in fibroblasts in pancreatic cancer, which is determined to be an independent marker of poor prognosis. Using an orthotopic, syngeneic model of pancreatic cancer, where tumor cells and fibroblasts are injected orthotopically, FAK activity in fibroblasts is implicated in metastasis of the tumor cells. The authors show that FAK in fibroblasts is required for efficient migration and invasion. Further impairing FAK activity in fibroblasts reduces tumor cell migration. They also demonstrate that pharmacological inhibitions of FAK reduces deposition of extracellular matrix proteins and that FAK activity is required for fibroblast contractility. The authors also suggest that FAK activity in tumor-associated fibroblasts alters the macrophage type present in tumors, and that factors secreted from fibroblasts in the presence of FAK activity promote the polarization of macrophages into M2 macrophages and promote the migration of macrophages. Overall, the authors conclude that FAK activity in fibroblasts in pancreatic cancer provides a useful prognostic marker, and inhibition of FAK activity might be an effective therapeutic strategy to prevent pancreatic cancer metastasis.
Overall, the study addresses the important problem of prostate cancer and makes the interesting correlation of phosphorylation of FAK in CAFs as a prognostic marker and that FAK activity in fibroblasts is required for metastasis in an orthotopic tumor model. The authors explore mechanism and suggest that very well established functions of FAK are responsible for their observations, i.e. regulation of cell migration and extracellular matrix deposition/organization. These studies are correlative and incremental. An exciting idea is the role of FAK in fibroblasts in promoting polarization of macrophages and recruitment into tumors. This data is a little less convincing, the effect is weak and there is no attempt to define the mechanism. As the authors point out, there are a number of clinical trials utilizing FAK inhibitors in pancreatic cancers. Therefore, this manuscript may have little impact upon treatment of the disease.

Specific critiques include:
1) The authors should describe controls to validate the authenticity of their IHC. E.g., describe the controls to validate the pY397 FAK is detecting phosphorylated FAK.
We agree on reviewer comment and performed experiment to validate the specificity of the antibody against p-Y397 FAK. Data are now included in the manuscript in Appendix figure S1. We used two approaches: one uses lambda phosphatase to show that the antibody is phosphospecific, and the other uses a FAK phosphopeptide to show that the antibody is specific to the sequence that it's supposed to recognize. Indeed, as the antibody recognizes the 390 -401aa of Uniprot# Q05397, we used a blocking phopshospecific peptide from abcam (ab40145).
Both the blocking phopshospecific peptide and the lambda phosphatase totally abrogate the IHC staining as well as induce the disappearance of the 125 kDa band in western blot, altogether validating the specificity of the antibody used. Data are now included in the revised version in Appendix figure 1 2) Some additional controls are required. E.g., in figure 3, do orthotopic tumors (without co-injected fibroblasts) metastasize?
According to reviewer comment, Figure 3G now encompasses metastasis quantification in mouse grafted with tumour cells alone (as well as, in Figure 3A, tumor mass), which show that co-grafting tumor cells with FAK-WT fibtoblasts, but not FAK-KD significantly increases metastasis number and area. Regarding tumor mass, we show that, compared to tumor cells alone, the co-grafting of tumor cells and fibroblasts (either FAK-WT or FAK-KD) increase tumor mass (but not significantly).
In figure 6, do macrophages migrate in serum free medium? Figure EV3G shows quantification of M1 and M2 macrophages in DMEM/F12 + 0.5% FBS (identified "M" for medium): 2% of M1 macrophages and 7% of M2 are able to migrate through the transwell membrane, this migration being significantly increased for both M1 and M2 macrophages by MC from CAFs, but not from CAFs pre-treated with FAK-I.
3) Some experimental details are missing. E.g., how is the area of metastasis calculated (Fig 3)? How are collagen I and collagen III quantified and what does the scale mean (Fig 3)? How is directionality calculated (Fig 4)? Is distance migrated the displacement from initial point to final point or does it measure the length of the path traveled?
We apologize for such omission. Details are now included in the material and method section or in figure legend: Material and method: -"Areas of lung metastasis were quantified based on CK19 staining allowing tumour cell identification. Percentage of metastasis area is the ratio of CK19 positive area divided by total lung area * 100." -"According to manufacturer's instructions (Abcam, ab150681), areas of yellow-orange birefringent fibers corresponding to type I (thick fibers) collagen, and green birefringent fibers corresponding to type III (Thin fibers) collagen, were quantified using ImageJ." -"Distance of migration (

length of the path traveled), directionality (calculated by comparing the Euclidian distance to the accumulated distance, represents a measurement of the directness of cell trajectories) and migration velocity (rapidity of cell motion, calculated on moving cells) were analyzed using Chemotaxis and Migration Tool software (free software tool for data analysis from time stack chemotaxis experiments, based on the National Institute of Health's (NIH) ImageJ image processing system)." Scale bars are now included in figure legends
4) In Fig 1, quantification of FAK is scored on a scale of 0 to 6, but the Materials and Methods describing scoring on a scale of 1 to 3. Please clarify.
Clarification has been added in the materials and Methods: "Two spots per patient were double blinded quantified based on pY397 FAK staining intensity and positive cell number (a total of 4 quantifications were performed per patient, and mean calculated). pY397 FAK cytoplasmic and nuclear staining were both scored on a scale of 6 : 0 for no staining and 6 for high positive staining, taking into account staining intensity and positive cell quantity. pY397 FAK CAF staining is the mean of cytoplasmic and nuclear score." 5) Standard deviation should be used instead of SEM According to Embo Molecular Medecine guidelines regarding statistical analysis: "Descriptive statistics should include a clearly labelled measure of centre (such as the mean or the median), and a clearly labelled measure of variability (such as standard deviation or range). [...] Authors must state whether a number that follows the ± sign is a standard error (s.e.m.) or a standard deviation (s.d.) Authors must justify the use of a particular test and explain whether their data conform to the assumptions of the tests.", we have thus decided to keep the SEM. However, if there is a scientific reason to ask for SD instead of SEM that we don't know, please let us know as we will reconsider our choice. Fig 3? From Fig 3D, it appears that increases and decreases could be expected and a two-tailed test might be more appropriate.

6) Why is a one-tailed Student's t-test performed on the immunophenotyping data in
We agree on reviewer comment. However, we can explain our choice of using one-tailed test (that we changed for a two-tailed test). Indeed, at the time when we performed the immunophenotyping experiment, we already knew that hight fibroblastic pY397 score in PDAC patient samples correlated with CD206+ tumour-associated macrophage abundance ( Figure 4H), and thus, we expected a decrease of the M2 macrophage number in tumours with FAK-KD fibroblasts. As immunophenotyping comes first in the manuscript ( Figure 4A), we have repeated the experiment to increase mice number, in order to verify the significance of our results using two-tailed test. As shown in Figure 4A, M2 macrophage number within FAK-KD fib. tumours is significantly decreased at early and late time points. 7) In some cases data is normalized and in some cases it is not. How do you account for variability in the control sample when the data is normalized to 100%?
We understand reviewer comment, and performed modification in figure 4D allowing to see the impact of CM from control sample on macrophage polarization. However, regarding figure 6D and EV5F, that encompass up to 11 different hCAFs, we choose to keep results normalized on control samples due to important CAF heterogeneity that complexifies the figure without adding information. We believe that normalizing on control samples facilitates figure reading and understanding, and, importantly, as all raw data (such as MFI of IF staining) will be included, readers would have access to information such as variability in control samples.

8) Additional editing would improve the manuscript.
Additional editing has been done. 1st Revision -Editorial Decision 14th Aug 2020 Dear Dr. Jean, Thank you for the submission of your revised manuscript to EMBO Molecular Medicine, and please accept my apologies for the delay in getting back to you. We have now received the enclosed reports from the two referees who reviewed the new version of your manuscript. As you will see, they are now overall supportive of publication. Still, referee #2 raises a few minor concerns that should be addressed experimentally and in writing. Furthermore, before acceptance, please address the following editorial amendments: 1) Main manuscript text: -Please answer/correct the changes suggested by our data editors in the main manuscript file (in track changes mode). This file will be sent to you in the next couple of days. Please use this file for any further modification. -Please remove the paragraph "Significance". -Author contributions: the contribution of every author must be detailed in a separate section (before the acknowledgments). Please also add Manon Strehaiano in the submission system. -Please note that all corresponding authors are required to supply an ORCID ID. An ORCID identifier is missing for Corinne Bousquet. -Dataset EV legends: the movie files need to be renamed "Movie EV1" etc. and need their legends removed from the manuscript and zipped with the respective movie files. The legends for EV Tables should be removed from the main manuscript.
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Referee #1 (Comments on Novelty/Model System for Author):
This is a revised version of a previous submitted manuscript. The authors added more mechanistical insights on how FAK expression in cancer associated fibroblasts promotes PDAC proregression. The findings are novel and timely.
Referee #1 (Remarks for Author): The authors addressed well each of my previous comments. The quality of the manuscript markedly improved and the findings are novel, interesting and important. Congratulation to the authors for this work.

Referee #2 (Comments on Novelty/Model System for Author):
This is a high quality paper that reports on the effects of fibroblastic FAK on tumor progression; interesting topic given that most people focus on FAK in tumor cells and that there are drugs targeting FAK.
Referee #2 (Remarks for Author): The authors have responded to most of my comments and the paper is now improved. However, I still have some concerns on the response to my previous points 1 and 2. It would be nice if the authors could perform one additional experiment (point 1) and make some minor changes in the text (point 2), following the arguments I provide below.
Specific comments 1. The results shown on the analysis of antibody specificity are convincing in themselves. However, using a phospho-peptide for inhibition is not adequate: if there is a cross-reactive epitope, the cross-reactivity will be inhibited by the peptide. The way to show that the antibody is specific is a knockdown or CRISPR-knockout.
2. I also respectfully disagree with the authors. CA19.9 levels, as a marker of outcome, have a HR of 2... but this conclusion is made after years of use in clinical practice. In fact, CA19.9 is used because there is nothing better so far -but it is not very useful. By contrast, the value of new markers as indicators of outcome sistematically decreases when many studies are performed as follow-up. In biomarker research, the usefulness of a marker essentially never improves as more studies are performed. Therefore, the comparison is unfair and I think that the authors should tone-down their conclusions. I think that this paper merits publication but there is no need to "oversell" anything, for the sake of rigorous science. A HR of 2 is modest and generally of only limited clinical use in decision-making, especially if it refers to selecting a drug for therapy.
The authors have responded to most of my comments and the paper is now improved. However, I still have some concerns on the response to my previous points 1 and 2. It would be nice if the authors could perform one additional experiment (point 1) and make some minor changes in the text (point 2), following the arguments I provide below.
Specific comments 1. The results shown on the analysis of antibody specificity are convincing in themselves. However, using a phospho-peptide for inhibition is not adequate: if there is a cross-reactive epitope, the cross-reactivity will be inhibited by the peptide. The way to show that the antibody is specific is a knockdown or CRISPRknockout.
In order to complete our antibody specificity validation shown in Appendix Figure 1, we performed western blot experiment on FAK deleted cells (MEFs isolated from murine homozygous Fak floxed embryos and treated with an adenovirus expressing the cre-recombinase (adeno-cre)). As now shown in the Appendix Figure 1C, FAK deletion induces the disappearance of the 125 kDa band in western blot, altogether validating the specificity of the antibody used.
2. I also respectfully disagree with the authors. CA19.9 levels, as a marker of outcome, have a HR of 2... but this conclusion is made after years of use in clinical practice. In fact, CA19.9 is used because there is nothing better so far -but it is not very useful. By contrast, the value of new markers as indicators of outcome sistematically decreases when many studies are performed as follow-up. In biomarker research, the usefulness of a marker essentially never improves as more studies are performed. Therefore, the comparison is unfair and I think that the authors should tone-down their conclusions. I think that this paper merits publication but there is no need to "oversell" anything, for the sake of rigorous science. A HR of 2 is modest and generally of only limited clinical use in decision-making, especially if it refers to selecting a drug for therapy.
We would like to thank the Reviewer for this very interesting exchange. As we agree on the fact that taking into account the "systematic decrease" of the value of new prognostic markers, we carefully edited our manuscript to avoid to oversell our results. Thank you for submitting your revised version of the manuscript. I have now looked at everything and all is fine. I am therefore very pleased to accept your manuscript for publication in EMBO Molecular Medicine! It will now be sent to our publisher to be included in the next available issue of EMBO Molecular Medicine.
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a description of the sample collection allowing the reader to understand whether the samples represent technical or biological replicates (including how many animals, litters, cultures, etc.).

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Sample size was kept small regarding the 3R guidelines. Sample sizes are based on previous similar and preliminary experiments that resulted in statistical significant results. graphs include clearly labeled error bars for independent experiments and sample sizes. Unless justified, error bars should not be shown for technical replicates. if n< 5, the individual data points from each experiment should be plotted and any statistical test employed should be justified the exact sample size (n) for each experimental group/condition, given as a number, not a range; Each figure caption should contain the following information, for each panel where they are relevant:

Captions
As in point 1a, sample sizes were based on previous similar and preliminary experiments that resulted in statistical significant results No animals were excluded from the study.
Whenever possible, particularly in immunohistochemistry and immunofluorescence, investigator was blinded to the experimental groups to reduce bias. Staining of pY397 FAK level in CAF from the first cohort (40 human PDAC samples and 10 normal human pancreas) was double blinded quantified.

Manuscript Number: EMM-2020-12010V2
Classical statistical analysis was run using the GraphPad Prism software (GraphPad). Data were tested to confirm the gaussian distribution (Shapiro-Wilk test) and the similarities of variances (Ftest or Chi-square test). Depending on experiments, two-group data were analyzed using unpaired or paired, one or two-tailed Student t-test (gaussian distribution and equal variances) or Mann-Whitney test (non-gaussian distribution). Multi-group data were analyzed using one-way ANOVA followed with the Tukey's multiple comparison post-test (gaussian distribution). All values are mean ± SEM. Differences were considered statistically significant when P< 0.05 (*P<0.05, **P<0.01, ***P<0.001). Survival curve statistical analyses were estimated with the Kaplan-Meier method and compared using log-rank test. We used non-parametric tests (chi2-test) to compare independent groups for categorical data. Multivariate analyses, with backward variable selection, were conducted with the Cox proportional-hazards regression model. Variables up to the 0.05 level in univariate analyses were included in the multivariate model. The level of significance for all tests was defined as P<0.05. Statistical analyses were carried out with PRISM 5 and XLSTAT 2017.
Co-grafted mice included in the immunophenotyping experiment were randomly assigned to Day 21 or Day38.
Yes. Immunchemistry from the first cohort was double blinded quantified by two different researchers. Immunohistochemistry analysis using definiens software (second patient cohort) were reviewed for tumor or stromal attribution by pathologists.
No blinding methods were used to allocate animals into experimental groups.

Data
the data were obtained and processed according to the field's best practice and are presented to reflect the results of the experiments in an accurate and unbiased manner. figure panels include only data points, measurements or observations that can be compared to each other in a scientifically meaningful way.

E-Human Subjects
Murine cells were either isolated from C57bl6J FAK-WT or -KD embryos (for murine fibroblasts), or LSL-K-RasG12D/+;LSL-p53R172H/+;Pdx1-Cre mice (for murine tumor cells). Human CAFs were isolated from human pancreatic tumor tissues using the outgrowth method. Cells were tested for mycoplasma contamination Please, see point 5 Please, see point 5 Please, see point 5 The manufacturer and the catalog number for each antibody used in this study are provided in table EV2.
8-week-old female c57bl/6j were purchased from Charles River, France and kept under specific pathogen free conditions (SPF). All mice are kept in ventilated cages. All mice were housed in a temperature and light controlled environment, with regular 12/12 hour light/dark cycle. Tumor growth was monitored (weekly at the begining and twice a week later) using ultrasound monitoring ( Aixplorer®, Supersonic imagine). Mice were sacrificed if one ethical endpoint was reached (tumor burden > 700 mm3, or body weight loss >20%, or reach of any clinical endpoint defined by our institutional guidelines and European animal protection law).
All experiments were in accordance with institutional guidelines and European animal protection law and approved by the responsible government agency (agreement number 201612011806414).
Animal studies were performed in line with the ARRIVE guidelines.

G-Dual use research of concern
F-Data Accessibility