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

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)P3‐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.

PTEN gene deletion can be found in a number of cancers, particularly high-grade serous ovarian carcinoma (HGSOC) and prostate cancers (Taylor et al, 2010;Patch et al, 2015).Mutation of PTEN also occurs at a modest level in most cancers, with glioblastoma and uterine cancers presenting frequent PTEN mutation (The Cancer Genome Atlas [TCGA], cBioPortal; Cerami et al, 2012;Gao et al, 2013).Mutation of the PIP 3 -producing PIK3CA, in contrast, is a frequent event in a number of cancers (Lawrence et al, 2014).This emphasises that dysregulation of the PI3K-PTEN axis is a common event in several cancer types (Hammond & Balla, 2015).Despite this, exactly how these lipid kinase and phosphatases enact the cellular changes that contribute to tumorigenesis remains largely unclear.For instance, given the polarised nature of these lipids, does the loss of PTEN allow for enhanced signalling function at the normal site of PIP 3 in the cell (the basolateral domain) or is PIP 3 produced at ectopic sites, allowing for de novo functions?Clarifying such fundamental questions may inform whether targeting classical downstream targets of PI3K-PIP 3 signalling versus potential dependencies that manifest particularly when PTEN is lost, show therapeutic viability.
The spatial distribution of PIP species has been revealed by the use of domains of proteins that show preferential PIP affinity fused to fluorescent proteins as indirect reporters for PIP location (Watt et al, 2002;Kutateladze, 2010;Shewan et al, 2011;Hammond & Balla, 2015).For example, fusion to fluorescent proteins (e.g.GFP) of the pleckstrin homology (PH) domain from the cytohesin (CYTH) family of GTP exchange factors (GEFs) for ARF GTPases, such as ARNO/CYTH2, (e.g.GFP-PH-CYTH2) can be an exquisite sensor for PIP 3 location.Splicing of these PH-CYTH domains alters their lipid specificity, wherein a di-glycine splice variant of the PH domain (PH-CYTH2 2G ) preferentially binds PIP 3 , while a tri-glycine splice variant (PH-CYTH2 3G ) associates with PI(4,5)P 2 (Klarlund et al, 2000).This illustrates how using lipid-preferential binding domains in such reporters allows the detection of PIP distribution.
Although the PH domains of CYTH-type ARF GEFs have been extensively used as probes for PIP 3 localisation, the extent to which they are required to enact PIP 3 downstream signalling has mostly been neglected.Recent work identifies that the PIP 3 -specific variant of CYTH1 is required for signalling from c-Met to induce migration (Ratcliffe et al, 2019).Moreover, both PI4-and PI5-kinases are effectors of ARF GTPases themselves (Brown et al, 1993;Cockcroft et al, 1994;Honda et al, 1999;Tsai et al, 2017), highlighting that ARF GTPases are intimately involved in maintaining and effecting PIP homeostasis.ARF GTPases are evolutionarily conserved membrane trafficking regulators, controlling many aspects of this process, such as turnover and recycling of receptor tyrosine kinases, cell-cell and cell-matrix adhesion proteins (Palacios et al, 2002;Powelka et al, 2004;D'Souza-Schorey & Chavrier, 2006;Loskutov et al, 2015).ARF GTPases are therefore well-placed to respond to changes in phospholipid metabolism that occur frequently in cancer and enact the cellular alterations that lead to invasive activity.
Here, we used a murine-derived model of HGSOC (ID8 cells) to examine the cellular consequences of Pten loss on collective cancer cell behaviour, using machine learning to detect phenotypic changes across multiday time-lapse spheroid imaging.We identify that Pten loss induces PIP 3 -rich and -driven invasive protrusions into the extracellular matrix (ECM), which leads to invasive activity.We uncover that ARF6 is essential for this process.Through CRISPRmediated ARF6 interactor screening, we identify that ARF6 acts in concert with the ARFGAP protein AGAP1 to promote recycling of active integrin in protrusions and drive invasion.Levels of this ARF6 module predict clinical outcome in ovarian cancer patients.Our approach therefore uncovers an ARF6 vulnerability upon PTEN loss in collective cancer cell behaviour in ovarian cancer.

PTEN loss in the tumour epithelium and association with poor patient survival
To understand how PTEN expression levels are altered in ovarian cancer (OC), we examined PTEN mRNA in tumour epithelium and stroma.In three independent data sets of laser capture microdissected (LCM), ovarian tumours separated into epithelium and stroma (Bowen et al, 2009a;Data ref: Bowen et al, 2009b;Lili et al, 2013a;Data ref: Lili et al, 2013b;Yeung et al, 2013a;Data ref: Yeung et al, 2013b).PTEN mRNA was significantly decreased in Tumour versus Normal ovarian epithelium, whereas stromal PTEN levels were inconsistently altered (Fig 1A -C).As such, an epithelialspecific downregulation of PTEN at the mRNA level was evident in those data sets.Across bulk Ovarian Cancer tumour data sets, which included epithelium and stroma, three of six independent data sets showed decreased PTEN mRNA in Tumour versus Normal samples, with nonsignificant data sets all possessing a low number of normal samples (n = 4-6; Fig 1D; Wu et al, 2007a;Data ref: Wu et al, 2007b;Bonome et al, 2008a;Data ref: Bonome et al, 2008b;King et al, 2011a; Data ref: King et al, 2011b;Elgaaen et al, 2012a;Data ref: Elgaaen et al, 2012b;Hill et al, 2014a;Data ref: Hill et al, 2014b;Yamamoto et al, 2016a;Data ref: Yamamoto et al, 2016b).In The Cancer Genome Atlas (TCGA) Ovarian Cancer data set (Cerami et al, 2012;Gao et al, 2013), 73% of samples possessed TP53 mutation and consequently PTEN alteration occurred frequently with TP53 alteration.Low PTEN mRNA was poorly associated with PTEN copy number changes and modestly associated with low PTEN protein levels (Fig 1E).Comparing high levels of PTEN mRNA (Quartile 4, Q4) to lower (Q1 + 2 + 3) levels did not distinguish overall survival in ovarian cancer patients (Fig 1F).Yet, an 11-month (P = 0.0019) increase in survival was observed in high (Q4) versus not (Q1 + 2 + 3) PTEN protein levels (Fig 1G).Accordingly, while low PTEN mRNA patients (Q1 vs. Q4) displayed significant, but modest AKT activation (pT308, pS473) (Fig 1H), similar comparisons using PTEN protein levels revealed a significant and robust PI3K-AKT signalling signature in low PTEN protein patients (Fig 1I).Therefore, low PTEN protein levels in ovarian cancer are associated with upregulated AKT signalling and poor overall survival.

Pten loss induces modest effects in 2D culture
We aimed to model how PTEN loss in the epithelium affects tumour cell behaviour.A mutant TP53 is a defining feature of HGSOC and is therefore an almost universal characteristic of the disease.An approximate 30-35% of the observed TP53 mutations are classified as null (Yemelyanova et al, 2011) with loss of wild-type P53 signalling observed regardless of mutation type (Hoadley et al, 2014).As such TP53-null models of HGSOC constitute good representations of the clinical situation.Based on the above and the fact that patient outcome is not stratified based on TP53 mutation type (Ahmed et al, 2010), we utilised ID8 ovarian cancer cells knocked out (KO) for Trp53 and Pten, alone or in combination (Fig EV1A; including multiple clones of the double KO, dKO; Walton et al, 2016Walton et al, , 2017)).As a control, we made use of a wild-type (WT) ID8 cell line, derived from Parental ID8 cells upon treatment with CRISPR plasmids containing the sgRNA sequence that produced the Trp53 À/À subline but had in this specific case failed to introduce Trp53 KO (Walton et al, 2016).While Pten KO alone resulted in a trend towards increased AKT activation (pS473), this was not statistically significant.However, Pten KO, in combination with Trp53 KO, significantly increased AKT activation (Fig EV1B -H), indicating synergy between Pten and Trp53 depletion in stabilising pAKT.By examining pS473-Akt staining on cells segmented into membrane, cytoplasm and perinuclear regions, PTEN loss was observed to result in the activation of pAKT at the cell cortex in cells grown in two-dimensional (2D) contexts (Fig EV1I and J,arrowheads).We noted that ID8 cells in 2D displayed a mixed morphology that could be classified into three categories: Cobblestone, Round and Elongated.Trp53 KO alone did not significantly affect cell shape compared with parental (WT) cells.In contrast, Pten co-depletion decreased the frequency of being Round and induced a general elevation in both other classes without a consistent increase in one or the other (Fig EV1K and L).Examination of proliferation or apoptosis, using puromycin treatment as a control for cell death, revealed that neither Trp53 À/À or Trp53 À/À ;Pten À/À dKO affected global growth or death in 2D culture (Fig EV1M and N).Together, this revealed that despite a robust activation of pAKT, p53 and PTEN loss do not manifest in major phenotypes in the examined conditions in cells in 2D culture.

PTEN loss induces ECM invasion
We next examined whether PTEN loss phenotypes may involve altered collective morphogenesis using multiday time-lapse imaging of single cells plated in ECM gels that developed into threedimensional spheroids (Fig 2A).While parental ID8 spheroids (WT) underwent proliferation and organisation into spherical multicellular objects with infrequent protrusive activity into the ECM (Fig 2B,
To develop a quantitative measure of altered morphogenesis, we used a CellProfiler and CellProfiler Analyst-based Fast Gentle Boosting machine learning pipeline.Upon imaging, this pipeline could classify hundreds-to-thousands of spheroids per condition into Spherical and Hyper-protrusive (Freckmann et al, 2022).The steps involved were as follows: (i) phase-contrast images of segmented spheroids were measured for texture, granularity, shape, size and movement features in tracked objects over multiple days; (ii) a high-accuracy classifier was applied to determine in-focus objects; (iii) out-of-focus objects were removed; (iv) a second high-accuracy classification into Spherical and Hyper-protrusive spheroids was applied; and (v) the frequency of phenotypes over time across different manipulations were calculated (Fig 2C).
We used bubble heatmaps for size (Area) of the spheroids and the proportion of objects across genotypes classified as either Spherical or Hyper-protrusive.This allows simultaneous presentation of (i) the magnitude of change in phenotypes, (ii) the statistical significance of each comparison and (iii) whether the magnitude of the effect was reproducible across independent experiments (Freckmann et al, 2022).In the bubble heatmaps, experiments are presented in 6h time chunks, representing the average value of phenotype proportion during each interval.The control condition is presented as zscored normalised values at each time period (Fig 2D).For treatments, compared with the control condition, the colour of the circle corresponds to Log 2 fold-change to control and the circle is scaled according to the statistical significance of the comparison (Cochran-Mantel Haenszel test with Bonferroni adjustment), with larger circle sizes corresponding to smaller P-values.Additionally, the presence of a black dot in the centre corresponds to effect magnitude, demonstrating homogeneity across biological replicates as determined by a nonsignificant P-value using the Breslow-Day statistical test (with Bonferroni adjustment).Application of this approach revealed that KO of Pten, whether in combination with Trp53 loss (Fig 2D and E Pten loss-induced invasion is associated with PIP 3 enrichment at invasive protrusion tips Class I PI3-kinases (PI3Ks) add a 3-phosphate group to PI(4,5)P 2 , generating PIP 3 .PTEN reverses PI3K activity by removing this 3phosphate group.We thus examined how Pten loss controls PI(4,5) P 2 and PIP 3 distribution in 3D contexts.In poorly protrusive Trp53 À/À spheroids, probes for PI(4,5)P 2 (mNeonGreen [mNG]tagged PH-PLCd1) and PIP 3 (mNG-PH-CYTH3 2G ) localised cortically, as well as in the nucleus in the case of PIP 3 (Fig 3A).In wounded invasive monolayers of Trp53 À/À cells, PI(4,5)P 2 and PIP 3 were not obviously enriched at protrusion tips arrowheads).However, in Trp53 À/À ;Pten À/À dKO cells, a pool of PIP 3 was prominently located to the tips of protrusions in both spheroids and invasive monolayers (Figs 3A and B,and EV3C and D,arrowheads).Accordingly, the tips of the invasive protrusions in the Trp53 À/À ; Pten À/À spheroids were highly enriched for the PIP 3 effector pAKT (S473), prior to F-actin enrichment (Fig EV3E and F,arrowheads).This suggest that the elevated protrusive activity upon PTEN loss is associated with an elevation of PIP 3 and pAKT (S473) at the tip of protrusions.
As A-C PTEN mRNA levels in LCM normal ovarian surface epithelium versus high-grade serous ovarian cancer (HGSOC) epithelium or normal ovarian stroma versus ovarian cancer-associated stroma.).Interestingly, PI3Kd inhibition resulted in a transient elevation of spheroid size that nonetheless did not change the Hyper-protrusive behaviour, suggesting an uncoupling between proliferation and invasiveness under these conditions.Notably, PI3Kb was found localised at the tips of the invasive protrusions (white/black arrowheads), prior to F-actin (yellow arrowheads), in Trp53 À/À ;Pten À/À spheroids (Fig 3K and L).This mirrors pAKT (S473) and PIP 3 localisation to the immediate tip of protrusions (Figs 3A and B,and EV3E and F).
Therefore, in this system, and similar to Ovarian Cancer patients with low PTEN (Fig 1I ), loss of Pten is associated with PI3Kb-AKT signalling, which is localised to the tip of, and required for, invasive protrusions.

Identification of ARF6-proximal protein networks
We examined how ARF6 is a vulnerability in Pten-null cells.We observed no consistent alteration in global levels of Arf6 mRNA (Fig EV4H),protein (Fig EV4I), or GTP-loading (Fig EV4J) in Trp53 À/À or Trp53 À/À ;Pten À/À cells compared with parental cell (WT), including multiple clones of the latter genotype.We therefore examined whether, rather than ARF6 activation or levels being altered, ARF6 interaction partners may change upon Trp53 and Pten loss.
We identified ARF6-proximal proteins through ARF6 fusion to the promiscuous biotin ligase TurboID (Branon et al, 2018) (Figs 4H and EV4K), in WT, Trp53 À/À and Trp53 À/À ;Pten À/À cells, including three clones of the latter genotype and across four independently repeated experiments.This allowed robust statistical support of identified ARF6-proximal proteins by mass spectrometry (MS) proteomic analysis.ARF6-TurboID localisation mirrored that of ARF6-mNG, occurring at cell-cell and cell-ECM contacts in 2D cells (Fig EV4L,black and green arrowheads,respectively) and allowed rapid labelling of ARF6-proximal proteins upon biotin addition (Fig EV4M).Gene Ontology Cell Compartment (GOCC) analysis of ARF6-proximal proteins in Trp53 À/À ;Pten À/À cells compared with TurboID alone in the same cells, identified significant enrichment for proteins involved in cell projections, filopodia and ECM interactions (Fig EV4N).Cytoscape and STRING database analysis identified a highly interconnected network of ARF6-proximal proteins (Fig 4I -L), including a singular ARF GEF, the PIP 3 -regulated CYTH2/ARNO protein, and a singular ARF GAP, AGAP1 (Nie et al, 2002).In addition, networks centred around proteins with known functions of Rho GTPases, cell-ECM adhesion, cell-cell adhesion, endocytosis and endosomal system, and cytoskeleton and migration, as well as others with less reported connections.
A Schema, imaging of ID8 spheroids in three-dimensional (3D) culture over time.Single cell suspensions were seeded onto and overlaid with ECM diluted in medium and then live-imaged.B Time series, showing a representative spheroid for each genotype, 12 h intervals.Arrowheads, protrusions into ECM.Scale bar, 20 lm.Right, cartoon of phenotype.C Schema, analysis pathway to classify ID8 3D phenotypes.(1) Phase contrast images were segmented using CellProfiler.Shape, size, movement, texture, granularity and brightness measurements were extracted for each object.
(2) Based on the measurements obtained from live imaging for each individual spheroid, we used CellProfiler Analyst and user-supervised machine learning (FastGentle Boosting algorithm) to construct rules based on which the objects would be classified as "In Focus" or "Out-of-focus".
(3) The later were filtered out of the data set.(4) Additional machine learning was used to classify remaining 'In-focus' objects as "Hyperprotrusive" or "Spherical," with high accuracy.( 5) Data analysis pipeline was used to quantify the log 2 fold-change of each phenotype relative to control for each subline.D Frequency of Spherical and Hyper-protrusive phenotypes in ID8 sublines, 6 h time intervals over 72 h.Heatmap (grayscale)-phenotype proportion (z-score) in control (Wild-type [WT]).Heatmap (blue-red)-log 2 fold change from control.P-values, bubble size (Cochran-Mantel-Haenszel test with Bonferroni adjustment).Black dot, homogenous effect across independent experiments (Breslow-Day test, Bonferroni adjustment, non-significant).N = 3 independent experiments, 3-5 technical replicates/experiment.Total spheroid number per condition, Table EV1.E Representative phase contrast images of spheroids described in (D).Outlines pseudocoloured for classification (Spherical, green; Hyper-protrusive, blue   (Fig 4K and L), such as CYTH2 interaction increasing upon Trp53 loss irrespective of Pten status, or a5-integrin/Itga5 interaction specifically induced upon Pten loss.This suggests that rather than large-scale alteration to ARF6 networks, loss of Pten may change a small number of key network members or render cells dependent on constitutive ARF6 network members.

Cytohesin-2 function in invasion and contribution to ovarian cancer
The majority of known ARF GEFs were expressed in ID8 cells, and their expression was not consistently altered upon Trp53 or Pten loss (Fig EV5A).However, only a single ARF GEF, Cytohesin-2 (CYTH2), was identified as interacting with ARF6 (Fig 4K).We therefore investigated chemical inhibition of Cytohesin-class GEFs using SecinH3 (Benabdi et al, 2017).SecinH3 treatment of Trp53 À/ À ;Pten À/À cells resulted in modestly smaller spheroids (Fig EV5B) that displayed less protrusive activity (Fig EV5C and D,arrowheads,Movie EV8).Accordingly, multiple leader cell formation was strongly reduced upon SecinH3 treatment (Fig EV5E, arrowheads)  and consequently invasive activity and invasion distance (Fig EV5F   and G).This suggests that CYTH2 may function with ARF6 to regulate invasion.
In ovarian cancer patients, CYTH2 mRNA was increased in the tumour compared with normal epithelium in both independent data sets of LCM tumours, whereas stromal CYTH2 levels were inconsistent across data sets (Fig EV5H and I et al, 2008b;Bowen et al, 2009b;King et al, 2011b;Elgaaen et al, 2012b;Lili et al, 2013b;Yeung et al, 2013b;Hill et al, 2014b;Yamamoto et al, 2016b).Comparison of CYTH2 mRNA levels, based on median split comparing high (M2) versus low (M1), showed no significant difference in survival (Fig EV5Q).CYTH2, however, can be produced as two alternate transcripts based on alternate inclusion of exon 9.1, which encodes for a single additional glycine residue in the PH domain.Exclusion of exon 9.1 results in the CYTH2 2G isoform, which is preferential for PIP 3 binding, whereas inclusion of exon 9.1 results in the PI(4,5)P 2 -binding CYTH2 3G isoform (Klarlund et al, 2000;Cronin et al, 2004;Oh & Santy, 2012).Therefore, the exon 9.1 Percentage Spliced In (Ex9.1 PSI) ratio can be used to distinguish such alternate PIP-associating CYTH2 isoforms.A modest but significant (3-month, P = 0.0262) decrease in overall survival was observed in patients displaying low Ex9.1 PSI (e.g.predominantly the PIP 3 -associating CYTH2 2G isoform; Fig EV5R).Combination of CYTH2 expression and splicing with ARF6 expression levels revealed a significant (7-9 month) decrease in overall survival in ARF6 HI /CYTH2 HI patient subgroups (M2/M2) (Fig EV5S ), which become more pronounced (13 months) only when CYTH2 Ex9.1 PSI was low (i.e. when PIP 3 -binding CYTH2 2G is predominant;

Identification of ARF6 interactors required for invasive activity
To identify additional ARF6 network proteins required for invasion, we performed a functional proteomic screen of 26 select interactors that represented constitutive ARF6 network members or those altered upon Trp53 and Pten KO compared to WT (Fig 5A).In this approach, Trp53 À/À ;Pten À/À ID8 cells were transduced with a lentiviral pool of 5× sgRNAs/gene and Cas9, for each of the 26 interactors.Each transduced and selected cell pool was then plated as 3D cultures, and Spherical and Hyper-protrusive phenotypes calculated from multiday time-lapse imaging.To ensure accuracy of plating in 3D culture, sgRNAs were broken into four iterations containing distinct gene targets (Screen Iteration 1-4) with a control (sgNontargeting, sgNT) per iteration (Fig 5A).Each iteration contained multiple technical replicates and was performed three independent times.The effect of each pooled sgRNA was calculated as foldchange to control classification.All pooled sgRNAs decreased Hyper-protrusiveness and increased Spherical phenotype to varying degrees, except for 14-3-3theta/Ywhaq, which showed a modest increase in Hyper-protrusive activity ( Consistently, neither Itga5 nor Itgb1 mRNA levels, or those of their ligand, Fibronectin (Fn1) changed across the ID8 sublines (Appendix Fig S1M).This suggests that the total expression of these integrins alone does not stratify patient survival.
To test whether altered interaction with the ECM underpins the Pten-null invasive phenotype, we examined basement membrane formation around spheroids by staining for Collagen IV (COL4), as the expression levels of Col4 did not change upon loss of Trp53 or Pten (Appendix Fig S1M).The pattern of Collagen IV surrounding ID8 spheroids could be classified as Fragmented, Defined or Absent (Fig 5H and I).In Trp53 À/À spheroids, Collagen IV staining was well-defined (92.16% of spheroids, green arrowheads).By contrast, in Trp53 À/À ;Pten À/À spheroids (expressing a nontargeting sgRNA), the majority of spheroids (73.1%) displayed a fragmented basement membrane, representing clear regions of presence (green arrowheads) and absence (yellow arrowheads) of Collagen IV.Continuous basement membrane formation could be restored in Trp53 À/À ; Pten À/À spheroids by KO of Agap1 (85.5%).Notably, basement membrane was largely absent upon Itgb1 KO (80.1%).This suggests that disrupted basement membrane is associated with invasion and may contribute to hyperprotrusive activity upon Pten loss, but that invasion requires b1integrin-dependent function in conjunction with the ARF6 interactor, AGAP1.
◀ Figure 4.The small GTPase ARF6 is required for Pten-loss mediated ECM invasion.
In five of seven bulk tumour data sets, AGAP1 mRNA expression was elevated in tumour compared with normal ovarian tissue, which occurred in the epithelium in independent LCM tumour data sets, but not consistently in the stroma.In contrast, ARF6 showed a less consistent alteration across data sets, with ARF6 mRNA elevated in only three of seven bulk tumour data sets, and ARF6 mRNA elevation occurring in the stroma in LCM data sets ( In both cases, a difference of 10-month survival was observed.Combining ARF6 and AGAP1 mRNA levels, but not AGAP1 Exon 14 PSI, even further separated overall survival, with a robust 17-month increase in overall survival of ARF6 LO -AGAP1 LO patients (red line), compared with the poor survival of ARF6 HI -AGAP1 HI patients (blue line) (Fig 6N).This same effect could not be found when examining splicing of AGAP1 at Exon 14 (Fig 6O).Together, these data indicate that AGAP1 is required for invasion in Pten-null cells and that ovarian cancer patients with high ARF6 and AGAP1 levels, irrespective of the isoform of the AGAP1, have a poor clinical outlook.

ARF6 regulates active integrin pools to produce invasive protrusions
Our data thus far indicate that a CYTH2-ARF6-AGAP1 module is required for invasion in Pten-null cells and that a5-integrin and b1integrin are two ARF6-promixal proteins essential for this phenotype.Although the mRNA levels of these two integrins are not altered in normal versus tumour epithelium, nor do they stratify patient survival based on a medium split (Appendix Fig S1K and L), we explored whether the ARF6 module may act by regulating distribution of ECM-adhesion complexes to the tips of protrusions to drive invasion.In addition to cortical localisation, both a5-integrin and b1-integrin showed localisation to the extreme tips of invasive protrusions in Trp53 À/À ;Pten À/À spheroids, occurring prior to Factin enrichment and mirroring the localisation observed for both pAKT (S473) and PIP 3 (Fig 7A and B).Two markers of ECMsignalling hubs, pY397-FAK and pY416-Src family kinases (SFK), also localised prominently to the tips of protrusions (Fig 7C and D), in addition to the cell-ECM interface.This observation suggests a pool of integrin signalling complexes localise to protrusion tips.
In ovarian cancer patients with low levels of PTEN protein, pY416-SFK protein levels were elevated (Fig 1I).To interrogate whether loss of PTEN may be associated with alterations in membrane trafficking of these integrins, we used a captured-based ELISA approach (Roberts et al, 2001).In Trp53 À/À ;Pten À/À cells, the recycling of internalised total a5or b1-integrin, the active form of b1-integrin, or a control cargo of Transferrin Receptor (TfnR), was increased at all time points examined compared with Trp53 À/À cells.This increase reached statistical significance (P < 0.05) at t = 32 min for active b1 integrin (Fig 7E and F To determine whether ARF6 regulates this enhanced recycling in Trp53 À/À ;Pten À/À cells, we depleted Arf6.Arf6 depletion in Trp53 À/À ;Pten À/À cells specifically blunted recycling of active form of b1-integrin, but not of total a5-integrin, b1-integrin or TfnR (Fig 7G and H;.This suggests that the CYTH2-ARF6-AGAP1 module specifically regulates recycling of the active b1-integrin, while trafficking of inactive b1-integrins and TfnR is controlled by other signalling modules downstream of PIP 3 .
Combined analysis of CYTH2-ARF6-AGAP1 module mRNA levels in ovarian cancer patients indicated that high levels of all three components (blue line; upper grouping based on median split of each gene's expression, M2) showed a significant, 17-month decrease in survival compared to low levels (red line, M1; P = 3.264e-3; Collectively, this indicates a role for the potentially PIP 3 -responsive CYTH2-ARF6-AGAP1 module in regulating survival in ovarian cancer through controlling recycling of a5b1-integrin complexes to invasive protrusion tips.

Discussion
Here, we propose a model of how loss of Pten can drive invasive behaviours, central to which is PTEN's function as a phosphatase controlling PIP 3 levels and localisation (Fig 7M).In Pten-expressing cells, PIP 3 localises to cell-cell contacts.In Pten KO cells, while cellcell contact PIP 3 is not lost, a prominent pool of PIP 3 appears in ECM-invading protrusion tips.The small GTPase ARF6 likely acts directly in PIP 3 -elevated areas through activation by the PIP 3associating variant of its cognate GEF, CYTH2 2G , and via its GAP AGAP1.This ARF6 module functions in the recycling of internalised active pools of integrin, thereby driving invasive protrusions enriched for the adhesion signal-transducing FAK and SFKs.This suggests a model wherein PTEN loss elevates recycling of the invasion-promoting cargoes a5b1-integrins.The cellular consequence of this is altered interaction with the ECM.It is notable that this CYTH2-ARF6-AGAP1 module was not specifically and only induced in Pten-null contexts, but rather that Ptennull cells became dependent on the module for enacting the invasive phenotype.Indeed, with the exception of a5-integrin, the majority of ARF6-proximal protein network was largely unchanged across Trp53 or Pten knockout cells.This suggests that ARF6 and interactors likely have a steady-state function in recycling active integrins.It may be that this function is enhanced in Pten KO cells, as in our functional proteomic CRISPR screen of ARF6-proximal proteins we identified KINDLIN-2/FERMT2, a PIP 3 -binding regulator of integrin activation.When PIP 3 levels are high, it is possible that, in addition to selectively supporting recycling of previously internalised active integrin cargoes, ARF6 may collaborate with KINDLIN-2 to promote or maintain activation of recycled integrins at the plasma membrane, although this remains to be tested.In addition, a number of additional hits in the screen, such as EGFR, FMNL3, LAMTOR5 and ITGA6, gave strong reductions in Hyper-protrusiveness and may act as additional ARF6 cargoes or effectors in regulating collective invasion.
It should be highlighted that our observations herein and the model we are suggesting do not imply that ARF6 is required for invasion initiation, but rather for invasion maturation and persistence.Indeed, upon ARF6 depletion, PTEN-null spheroids often exhibited the formation of fine, transient protrusions.In most cases, however, these were not enough to lead to the formation of a stable invasion structure.Similarly, ECM-embedded Trp53 À/À ;Pten À/À ARF6 KD monolayers were still able to invade and eventually close the monolayer wound albeit with reduced efficiency compared with their ARF6-proficient counterparts.This mirrored the behaviour of both WT and Trp53 À/À spheroids and monolayers.We reported a similar function of ARF6 regulating protrusion maturation rather than initiation in conjunction with the ARF GEF protein IQSEC1 in invading 3D cultures of prostate cancer cells (PC3) (Nacke et al, 2021).
It is notable that the effects of Pten or Trp53 loss were most prominent in 3D culture.This suggests that the phenotypes of loss of these central tumour suppressors may only manifest when cells are embedded in extracellular matrices and/or when multicellularity is considered.Indeed, the basement membrane around Trp53 À/À ; ◀ Figure 5.A functional proteomic CRISPR screen for ARF6-proximal proteins controlling collective invasion.

A
Schema, (1) CRISPR screen.26 ARF6-proximal proteins from TurboID studies were investigated for their contribution to ARF6-mediated invasion of ID8 Trp53 À/À ; Pten À/À spheroids.(2) For each interactor, 5 sgRNAs were cloned into lentiviral CRISPR vectors.(3) A pooled approach was used, generating a KO cell line with all 5 sgRNAs (4) Live imaging performed.( 5 Representative confocal images of Trp53 À/À and Trp53 À/À ;Pten À/À clone 1.15 spheroids expressing sgNT, sgAgap1 (sg3) or sgItgb1 (sg4), stained for collagen IV (grayscale) and F-Actin (magenta).Boxed areas, basement membrane region in higher magnification.Arrowheads, Collagen IV labelling that is: well-  The EMBO Journal Pten À/À spheroids was fragmented.This may explain how Pten loss resulted in the hyperactivation of leader-cell function in most cells at the ECM interface, rather than co-ordination of follower cells behind a singular leader cell.The tumour suppressor function of PTEN therefore may normally function to co-ordinate polarisation and cellular position in multicellularity by controlling basement membrane assembly through integrins, structurally influencing where invasive protrusions can occur.
The application of machine-learning approaches to live imaging allowed us to classify hundreds-to-thousands of spheroids tracked over time into robustly statistically supported categories, Spherical and Hyper-protrusive.While these labels were pivotal in identifying molecular perturbation that essentially turn on or off invasive behaviours, they are broad categories.It may be that subtle and important differences occur between perturbations, which could be further segregated with additional phenotype classifications.Indeed, while Hyper-protrusive Trp53 À/À ;Pten À/À spheroids have fragmented basement membranes, this could be reversed to a fully defined basement membrane upon Agap1 KO, thereby preventing protrusions.Itgb1 KO spheroids, however, largely lacked an assembled basement membrane but also the ability to interact with the ECM to form protrusions.Both Agap1 and Itgb1 KO in Trp53 À/À ; Pten À/À spheroids lack invasive protrusions, suggesting different alterations can result in similar morphogenetic consequences.
For consistency and clarity, we excluded from our analyses objects that were either out of focus during imaging or had completely invaded to the bottom of the dish.This ensured that objects that could not be imaged properly would not be improperly segmented and thus erroneously classified as either Spherical or Hyper-protrusive.Furthermore, the highly invasive cells that sometimes invaded from early time points were almost exclusively found in the Trp53 À/À ;Pten À/À spheroids (across all clones).While we cannot exclude the possibility that this exclusion may lead to some underestimation on the magnitude of the effect of PTEN loss, we felt that since the effect was clear even upon exclusion, this was a more honest way of performing our analyses as it allowed for more accurate classification of 3D structures.More refined subcategorisations may help to detect additional phenotypic variations.
Pten knockout alone was sufficient to drive in vitro invasion in the absence of Trp53 depletion.The exact contribution of Trp53 loss to the invasive phenotypes we examined is unclear.We observed modest alterations to phenotypes upon Trp53 KO alone, such as increased protrusive tip formation or invasive capacity, but not sufficiently outside the range of normal variation to reach significance.Dissecting the role of Trp53 is likely more complicated than we have examined as although TP53 alteration is nearuniversal in ovarian cancer, many of these are distinct mutation, including some hotspots.Intraperitoneal injection of Trp53-null ID8 cells increases tumour growth rate and decreases survival compared with parental cells (Walton et al, 2016), showing that Trp53 loss alone does cause in vivo functional differences to tumorigenesis.This tumorigenesis effect is accelerated in vivo by Pten co-knockout (Walton et al, 2017).Whether Trp53 mutation versus loss differently contributes to Pten-depleted phenotypes remains to be examined.
The intraperitoneal injection of ID8 cells is an excellent system for in vivo examination of tumorigenesis in an immune-competent host.However, the rapid progression to clinical endpoint due to excess ascites production and spread of cells within the peritoneal fluid, rather than bona fide invasion, renders it poorly suited to determine contributions to metastasis, particularly in the case of Pten-null tumours due to rapid progression (Trp53 À/À ;Pten À/À , 34 days; Trp53 À/À , 47 days; Parental ID8, ~100 days) (Walton et al, 2016(Walton et al, , 2017)).Validation of the in vivo contribution of PTEN loss to metastasis requires the use of approaches that allow metastasis to occur before clinical endpoint is reached.Introduction of such additional models is beyond the mechanistic cell biological studies provided here.

C
Western blots of ID8 Trp53 À/À ;Pten À/À 1.15 cells expressing either sgNT or sgAgap1 (sg3) and either mNeonGreen (mNG) or CRISPR-resistant mNG-Agap1_S or -L isoforms.Blotted for ARF6, pS473-AKT, AKT, mNG, and VCL.VCL is loading control for AKT, pS473-AKT and ARF6 and sample integrity control for others.n = 3 independent lysate preparations.D Quantitation of (C).Data, mean AE SD for ARF6 and pS473/AKT band intensity ratio, normalised to sgNT.P-values, unpaired two-tailed t-test, annotated when significant.E, F Quantitation of ID8 Trp53 À/À ;Pten  In ovarian cancer patient cohorts, PTEN loss is frequent and PTEN protein loss is associated with poor outcome.ARF6 mRNA levels themselves were not consistently altered across independent data sets, making ARF6 mRNA alone a likely unsuitable potential biomarker of poor outcome.Both the CYTH2 GEF and AGAP1 GAP mRNAs were elevated in tumour tissue in a number of data sets; however, CYTH2 contribution is complicated by poor outcome being specifically conferred by the PIP 3 -associating CYTH2 2G isoform.While the effect of CYTH2 2G (PIP 3 -binding variant) on survival is modest; (3-month decrease when CYTH2 2G is high), combining this with high ARF6 levels allows identification of a 10month decrease in survival.Due to this isoform lacking a single amino acid difference to the PI(4,5)P 2 -binding CYTH2 3G isoform, this complexity renders CYTH2 analysis alone a poor biomarker candidate.AGAP1 mRNA levels, in contrast, strongly stratified patient outcome.Combined high versus low mRNA levels of CYTH2-ARF6-AGAP1 provided the most robust 17-month different in survival of ovarian cancer patients, which occurred in patients with an PI3K-AKT signature.This emphasises the capacity of in vitro 3D cell biology to identify mechanistic insight into tumour suppressor contribution to cancer that can be used to clinically stratify poor and superior patient survival groups.
Source data are available online for this figure.
Ó 2023 The Authors The EMBO Journal 42: e113987 | 2023 imaged/well).Images were extracted and aligned using the Fiji plugin "Image stabiliser" and a custom-made Fiji macro.Custom pipelines in CellProfiler (v4.2.0) identified and tracked individual spheroids at each time point, while extracting information on their size, shape, movement and brightness variation (Freckmann et al, 2022).The generated data set was used in CellProfiler Analyst (v2.2.0) to apply user-supervised machine learning (FastGentle Boosting algorithm) and classify spheroids as "Out of Focus" or "In Focus" (accuracy > 80% according to confusion matrix).The shape, size and movement measurements of only "In Focus" spheroids were used again in CellProfiler Analyst to construct rules (Table EV5) and classify them based on their morphology as "Hyper-protrusive" or "Spherical" (Accuracy 92% according to confusion matrix).These rules were exported as .txtfiles and incorporated in a CellProfiler pipeline that would perform prospective classification of new data sets without the need for retraining.A custom KNIME Data Analytics Platform (v3.3.1)pipeline was used to collate data, log 2 transform and normalise the proportion of phenotypes across conditions and time points, perform statistical analyses and generate heatmaps.Statistical tests are described in figure legends, and P-values are annotated on figures.Heatmaps were generated using ggplot2 (v3.3.0;Wicham, 2009) in the R environment (v3.6.2).Statistical comparison was performed in R using the Cochran-Mantel-Haenszel test wherein a comparison is only statistically significant where the effect was present across all biological replicates.Using the DescTools (v0.99.31;Andri et al, 2022) R package, the Breslow-Day statistic was used to test the assumption that the magnitude of effect of a condition is homogeneous across all strata (biological replicates): a nonsignificant P-value indicates homogeneity.In both statistical tests, a Bonferroni adjustment was applied to correct for multiple testing.

Cloning
Molecular cloning was performed using either classical ligation or In-Fusion technology.Restriction reactions were performed using High-Fidelity Restriction enzymes from New England Biolabs (NEB), by incubating 2 lg of DNA with 2 U of each enzyme in the presence of 10X NEB CutSmart buffer, diluted to the appropriate concentration in nuclease-free water.The restriction reaction was performed at 37°C (or other appropriate incubation temperatures) for 1 h.The digested products were stained with 6× DNA loading dye and resolved at 110 V for 1 h in 1% agarose in TAE buffer supplemented with Midori green (Nippon Genetics, MG04).The desired DNA was purified using the QIAquick Gel Extraction Kit (Qiagen, 28706X24) as per the manufacturer's instructions.For ligations using the Rapid DNA Ligation Kit (Roche, 11635379001), vector and insert were mixed in a 1:3 molar ratio, supplemented with 1× Dilution buffer, 1× Ligation buffer and 1 ll Ligase in a total volume of 10 ll, and incubated for 5 min at RT.For ligation reactions using the T4 DNA Ligase (NEB, M0202), the same molar ratio was used, supplemented with 2 ll of 10× T4 DNA Ligase buffer, 1 ll T4 DNA in a total of 20 ll.The reaction was performed at RT for 10 min and the ligase was subsequently heat-inactivated at 65°C for a further 10 min.For In-Fusion Cloning, a 1:3 vector to insert molar ratio was combined with 2 ll of 5× In-Fusion Reagent in a total volume of 10 ll.In-Fusion reaction was performed at 50°C for 15 min.Bacterial transformation was performed using either Stbl3 (Thermo Fisher Scientific, C737303) or Stellar (Takara, 636766) chemically competent cells using the bacteria:DNA ratio as per the manufacturer's instructions.A 10-min incubation on ice was followed by heat-shock of 45 s at 42°C.Transformed bacteria were plated on suitable agar plates and incubated overnight at 37°C.
Only those with high on-target potential and low off-target risk were retained.(all sgRNA sequences available in Table EV2).The pLenti-CRISPRv2 Neo vector was used as a backbone and the cloning procedure followed the steps as described by the Zhang Lab (Sanjana et al, 2014;Shalem et al, 2014).From each oligo pair, 2 ll were combined with 1 ll 10× T4 Ligation Buffer (NEB, M0202), 6.5 ll Nuclease-free H2O and 0.5 ll T4 Polynucleotide Kinase (PNK) (NEB, M0201).The oligos were annealed in a thermocycler with gradual T reduction from 95 to 25°C at a rate of 5°C/min and subsequently diluted 1:20 into Nuclease-free water (Thermo Fisher Scientific, AM9938).The pLentiCRISPRv2 plasmid was digested for 1 h at 55°C with 1 U per lg of DNA BsmBI-v2 (NEB, R0580), in 5 ll Buffer 3.1 and diluted to a final volume of 50 ll.The digested backbone was dephosphorylated with 1 U/mg FastAP Thermosensitive Alkaline Phosphatase (Thermo Fisher Scientific, EF0651) for 10 min at 37°C.FastAP was inactivated at 75°C for 5 min.For ligation, 50 ng of digested plasmid was combined with 1 ll diluted oligo duplex, 1× Rapid DNA Ligation buffer, 1× Dilution buffer, nuclease-free water to a final volume of 10 ll and 1 ll Ligase (Roche, 11635379001).
The mixture was incubated at RT for 5 min.Bacterial transformation was performed as described above.
The screen was performed in two phases.The first phase was performed in four iterations.The 4-5 gRNAs targeting each gene were pooled together and used to transfect HEK-293FT cells as described above.The viruses produced were then used to transduce Trp53 À/À ;Pten À/À 1.15 cells and generate a single, stable cell line (Pooled KO) for each gene.The Pooled KO cell lines were imaged with the IncuCyte system as described above and compared with a Pooled sgNT cell line.Processing of images and data analysis was performed independently for each iteration as described above.The results are presented as fold change to the iteration's sgNT cell line and each iteration has been colour-coded to allow for easier comparison.Heatmaps were generated using ggplot2 (v3.3.0;Wicham, 2009) in the R environment (v3.6.2).Statistical comparison was performed in R using the Cochran-Mantel-Haenszel test wherein a comparison is only statistically significant where the effect was present across all biological replicates.Using the DescTools (0.99.31?) (Andri et al, 2022) R package, the Breslow-Day statistic was used to test the assumption that the magnitude of effect of a condition is homogeneous across all strata (biological replicates): a nonsignificant P-value indicates homogeneity.In both statistical tests, a Bonferroni adjustment was applied to correct for multiple testing.Select interactors were deconvoluted in Phase 2, where 4-5 distinct KO cell lines were generated using each individual sgRNA and compared against a single sgNT cell line.
Fixed 3D and 2D imaging and analysis For 2D samples, ID8 cells were seeded on a black-bottom 96-well plate (Greiner, 655090) with 2,000 cells per well and incubated for 24 h at 37°C.For 3D samples, ID8 spheroids were set up in eightwell chamber slides coated with 60 ll of 50% GFRM.4,000 cells were seeded per well as single-cell suspensions supplemented with 2% GFRM and then incubated for 48 h.Spheroids or cells were washed once with PBS and fixed with 4% PFA for 15 min at RT. Blocking was achieved with PFS (0.7% fish skin gelatine and 0.025% saponin in PBS).The following antibodies were added at 1:200 dilution in PFS and incubated overnight at 4°C with gentle shaking: Collagen IV (Abcam, ab19808), pAKT pS473 (CST, 4060, D9E), pFAK pY397 (CST, 3283), pSRC Family pY416 (CST, 2101), V5-Tag (ABM, G189), PI3Kb (Proteintech, 21739-1-AP), AGAP1 (TFS, 50542), ITGB1 (Merck, MAB1997), ITGA5 (BD Bioscience, 553319).Following three PFS washes, secondary antibodies Alexa Fluor 488 Donkey Anti-Mouse IgG (H + L) and/or Alexa Fluor 647 Donkey Anti-Mouse IgG (H + L) (Thermo Fisher Scientific, A21202 and A31571, respectively) were added in PFS (1:1,000) together with Alexa Fluorâ 568 Phalloidin (Thermo Fisher Scientific, A12380, 1:200 dilution), HCS CellMask Deep Red Stain (1:50,000) and Hoechst 34580 (Thermo Fisher Scientific, H21486) (1:1,000) and incubated at RT for 45 min.Samples were further washed with PFS (twice) and with PBS (thrice).Invading monolayers and spheroids were imaged using a Zeiss 880 Laser Scanning Microscope with Airyscan using either confocal or super resolution functions.Images taken in super resolution mode were processed using the Zeiss proprietary ZEN 3.2 software, exported as TIFF files and processed in Fiji.Line scan intensity analysis on tips of invading protrusions was performed using Fiji.Invading monolayers and cells on 96-well plates were also imaged using an Opera Phenix high content analysis system (×20 or ×63) and the Columbus High-Content Imaging and Analysis Software (PerkinElmer, Version 2.9.1) was used to generate custom pipelines and perform object segmentation, intensity measurements and machine learning.For 2D morphology assays, cells were identified based on nuclear staining (Hoechst) and the shape of each cell was defined by CellMask staining.Machine learning and manual training was used to classify cells as either "elongated," "cobblestone" or "round."Each cell was imaged in 1 plane.Cells in contact with the image border were excluded.For measurement of pAKT enrichment, cells were identified based on nuclear staining (Hoechst), and the total cell area was defined by CellMask staining and cells in contact with the image border were excluded.The cell area was split into three Regions: Ring Region, or "Perinuclear," resized to 35% Outer Border Shift (OBS) and 50% Inner Border Shift (IBS), "Membrane," resized to À10% OBS and 10% IBS and "Cytoplasm," resized to 10% OBS and 35% IBS.The cells were imaged in three planes with 1 lm distance between planes and processed as a maximum projection.The staining intensity of pAKT was measured in each cell, for each individual area and was expressed as a proportion of the total (sum of all areas).The log 2 -transformed values were plotted using a custom R pipeline.Due to the large number of values measured, only the means of each experimental replicate are shown as dot-plots overlaid on violin plots depicting the distribution of the normalised pAKT intensity values of all cells measured.Statistical tests are described in figure legends, and P-values are annotated on figures.

Invasion assay
Cell invasion was examined using the Scratch Wound assay method on the IncuCyte System (Zoom 1 or S3, Satorius).The wells of a 96well IncuCyte Image Lock plate were coated with 20 ll of 1% GFRM (Corning,354230) overnight and incubated at 37°C.The GFRM was removed and 6.5 × 10 4 cells were added per well and incubated at 37°C for 4 h to facilitate attachment.The IncuCyte Scratch Wound Tool was used as per the manufacturer's instructions to create the wound.PBS was used to wash cell debris from the wells and 50 ll of 50% GFRM diluted in cell culture medium was placed on top of the cells and then incubated for 1 h at 37°C.If inhibitors /drugs were used; an appropriate volume was added in the GFRM to achieve the desired concentration.After incubation, 100 ll of cell culture medium (supplemented with inhibitors/drugs when required) was placed on top of the GFRM and the plate was imaged using the Scratch Wound module.Images were taken every 1 h using the ×10 objective and from a single field per well.Any wells where the wound did not form properly were not included in the analysis.Images were analysed using the dedicated IncuCyte analysis tool.For each time point, the relative wound density (RWD) was measured.Statistical analyses were performed, and graphs were generated using Microsoft Excel and RStudio (v1.4.1717).Data are presented for t = 1/2 max of Control condition as bee swarm "super-plots" (Lord et al, 2020).Statistical tests are annotated on figure legends, and P-values on Figs A similar approach was used for tracking of the leader cells, using the ×20 objective.A 1:2,000 dilution of IncuCyte NucLight Red dye (Sartorius, 4717) was added to stain nuclei and images were obtained every 15 min.Produced stacks were aligned using the Fiji plugin "Image stabiliser" and a custom Fiji macro.Leader cell tracking was performed using the MTrackJ plugin on Fiji.Spider plots were generated using RStudio (v1.4.1717).For scratch wound assays, fixed and stained for immunofluorescence, the same procedure was followed to set up cells as monolayers on black-bottom 96-well plates (Greiner, 655090) and a 20 ll pipette tip was used to manually form the wound.Following an incubation period of 19 h at 37°C, the invading monolayers were fixed and stained as described above.

RNA extraction and sequencing
RNA extractions were performed using the RNeasy kit (Qiagen, 74106) and the QIAshredder spin columns (Qiagen, 79656).For 2D samples, cells at 70-80% confluence were washed twice with PBS and lysed in 600 ll of buffer RLT with 6 ll b-mercaptoethanol for 2 min.Cells were scraped and homogenised using a QIAshredder spin column, centrifuged for 2 min at > 8,000 g.A 1:1 ratio of flowthrough to 70% EtOH was mixed well and transferred onto a RNeasy Mini spin and the RNA isolated following the manufacturer's instructions.The eluted RNA was stored at À80°C.For 3D spheroids, ID8 cells were passaged so they were sparse.The next day, six-well plates (Falcon, 353046) were coated using 180 ll of 50% GFRM per well and left to set for 60-75 min in an incubator at 37°C.Cells were washed, trypsinised, centrifuged, resuspended in fresh media, counted and adjusted to 8 × 10 4 cells/ml.In each well, 1.6 ml of cell suspension supplemented with 2% GFRM was added per well, and spheroids allowed to develop for 2 days in an incubator at 37°C with 5% CO 2 .For RNA extraction, cells were washed twice and the protocol for lysis was as described above for 2D samples.Adjustments were made to support the disruption of the ECM, by passing the lysates through a 25-27G needle slowly 10× before homogenisation using a QiaShredder.Lysis was performed using 350 ll of RLT buffer per well.For subsequent RNA sequencing of both 2D and 3D samples, extracted RNA underwent DNase treatment.An aliquot corresponding to 1.3 lg of RNA was obtained and combined with 1 ll 10× DNase I Reaction Buffer and 1.3 ll DNase 1 (1 U/ll) (Thermo Fisher Scientific, 18068015) to a final volume of 10 ll with RNase-free water.The RNA/DNase mix was incubated at RT for 15 min and the reaction was stopped with addition of 10% v/v EDTA and heat-inactivation at 65°C for 10 min.The DNase treated RNA was placed on ice.300 ng of RNA was taken and diluted to 50-100 ng/ll and used for TapeStation quality control of samples with a RNAIntegrity Score (RIN) of > 6 considered acceptable.The leftover 1 lg of RNA was brought to 50 ll volume with RNase-free water.

RNA sequencing and analysis
Sequencing was performed at the CRUK Beatson Institute using the Illumina polyAselection (2x36 PE Sequencing) kit without long reads.Quality checks and trimming on the raw fastq RNA-Seq data files were performed using FastQC version 0.11.9 (Andrews, 2010), FastP version 0.20.1 (Chen et al, 2018) and FastQ Screen version 0.14 (Wingett & Andrews, 2018).RNA-Seq paired-end reads were aligned to the GRCm38.101version of the mouse genome and annotation (Yates et al, 2020), using HiSat2 version 2.2.1 (Kim et al, 2019) and sorted using Samtools version 1.7 (Li et al, 2009).Aligned genes were identified using Feature Counts from the SubRead package version 2.0.1 (Liao et al, 2014).Expression levels were determined and statistically analysed using the R environment version 4.0.3(R Core Team, 2020) and utilising packages from the Bioconductor data analysis suite (Huber et al, 2015).Differential gene expression was analysed based on the negative binomial distribution using the DESeq2 package version 1.28.1 (Love et al, 2014) and adaptive shrinkage using Ashr (Stephens et al, 2020).Computational analysis was documented at each stage using MultiQC (Ewels et al, 2016), Jupyter Notebooks (Kluyver et al, 2016) and R Notebooks (RStudio Team, 2019).Log 2 Transformation of counts and heatmap generation was performed using PRISM.

Protein Domain-GST fusion purification
The PH domain sequences corresponding to the two isoforms of AGAP1 were ordered as GeneArt String DNA Fragments (Thermo Fisher Scientific) and cloned by In-Fusion as GST Fusions in pGEX-4T1 vector.Plasmids encoding GST-Control, GST-hAgap1_PH_L and GST-hAgap1_PH_S were transformed into Rosetta 2(DE3)pLysS (Novagen) and proteins were expressed in Luria Broth based autoinduction medium including trace elements (Formedium) at 37°C for 6.5 h followed by 18°C for 12 h.Cells were harvested by centrifugation and the resulting pellets were resuspended in 200 mM NaCl, 50 mM Tris-HCl, pH 7.6, 1 mM DTT, 2 mM PMSF prior to lysis with a microfluidizer at ~15,000 psi.Lysate was clarified by centrifugation, incubated with glutathione agarose resin (Agarose Bead Technologies), washed with resuspension buffer without PMSF and eluted with wash buffer containing 10 mM glutathione and 5 mM DTT.The glutathione agarose eluate was diluted to a concentration of 50 mM NaCl, applied to a 5 ml HiFliQ Q ion exchange FPLC column (Neo Biotech) and eluted with a linear gradient ranging from 50 to 600 mM NaCl in 50 mM Tris-HCl, pH 8.5.Selected fractions were combined and applied to a HiLoad 26/60 Superdex 75 (manufactured by GE Healthcare, now produced by Cytiva Life Sciences) equilibrated in 150 mM NaCl, 25 mM Tris-HCl, pH 7.6, 1 mM DTT.Protein concentration was based on the measured absorbance at 280 nm and calculated molar extinction coefficients (Wilkins et al, 1999) of 44,350, 73,800 and 66,810 M À1 cm À1 for GST-control, GST-hAGAP1_PH_L and GST-hAGAP1_PH_S, respectively.

BioID mass spectrometry proteomics and data analysis
An improved version of the promiscuous ligase BirA* (TurboID; Branon et al, 2018), was fused to the C terminus of ARF6, followed by a V5 Tag, a cleavable T2A peptide and BFP and cloned into a lentiviral vector.The construct was stably expressed in ID8 cells as described above.A construct lacking ARF6 but containing BirA*, V5, T2A and BFP was used as a negative control for nonspecific labelling.Cells at ~70-80% confluence were labelled for 30 min at 37°C by adding 50 lM of Biotin in full medium (Merck, S4501).Cells in Biotin-free medium were used as negative control.Cells were washed five times in ice-cold PBS and lysates were obtained by adding 800 ll of Lysis Buffer (50 mM Tris-HCl pH 7.4, 100 mM NaCl, 5 mM in MS-grade water) supplemented with one each of cOmplete TM , Mini Protease Inhibitor (Roche, 05892970001) and PhosSTOP TM Phosphatase Inhibitor tablets (Roche, 04906837001).The lysates were scraped, incubated on ice for 30 min, sonicated and centrifuged at 13,600 g for 30 min at 4°C.Protein concentration was determined by performing a BCA assay (Pierce TM BCA Protein Assay Kit, Thermo Fisher Scientific, 23225, following the manufacturer's instructions).350 lg of proteins was used per condition.200 ll of streptavidin sepharose beads (Streptavidin Sepharose High Performance, Merck, GHC-17-5113-01) was washed thrice in 50 mM Tris-HCl pH 7.4.All samples were incubated with 25 ll prewashed beads in each at 4°C for 2 h with rotation.The beads were washed four times with 400 ll Washing Buffer (50 mM Tris pH 7.4, 100 mM NaCl, 5 mM EDTA) and each time centrifuged at 1,200 g for 1 min at 4°C.Samples were resuspended in 2 M urea in 100 mM ammonium bicarbonate buffer and stored at À20°C until further processing.On-bead digestion was performed from the supernatants.Quadruplicate biological replicates were digested with Lys-C (Alpha Laboratories) and trypsin (Promega) on beads as previously described (Hubner et al, 2010).Following trypsin digestion, peptides were separated by means of nanoscale C18 reverse-phase Liquid Chromatography (LC) using an EASY-nLC II 1200 (Thermo Fisher Scientific) system directly coupled to a mass spectrometer (Orbitrap Fusion Lumos, Thermo Fisher Scientific).Elution was performed using a 50-cm fused silica emitter (New Objective) packed in-house with ReproSil-Pur C18-AQ, 1.9 lm resin (Dr Maisch, GmbH).Separation was carried out using a 135 min binary gradient at flow rate of 300 nl/min.The packed emitter was maintained at 50°C by means of a column oven (Sonation) integrated into the nanoelectrospray ion source (Thermo Fisher Scientific).Air contaminants signal levels were decreased using an Active Background Ion Reduction Device (ABIRD ESI Source Solutions).Data acquisition was performed using the Xcalibur software.A full scan was acquired over a mass range of 350-1,400 m/z at 60,000 resolution at 200 m/ z.The 15 most intense ions underwent higher energy collisional dissociation fragmentation and the fragments generated were analysed in the Orbitrap (15,000 resolution).MaxQuant 1.6.14.0 was used for data processing.Data were processed with the MaxQuant software (Cox & Mann, 2008;Cox et al, 2011) querying SwissProt (UniProt, 2019) Mus musculus (25,198 entries).First and main searches were performed with precursor mass tolerances of 20 ppm and 4.5 ppm, respectively, and MS/MS tolerance of 20 ppm.The minimum peptide length was set to six amino acids and specificity for trypsin cleavage was required.Cysteine carbamidomethylation was set as fixed modification, whereas Methionine oxidation, Phosphorylation on Serine-Threonine-Tyrosine, and N-terminal acetylation were specified as variable modifications.The peptide, protein and site false discovery rate (FDR) was set to 1%.All MaxQuant outputs were analysed with the Perseus software version 1.6.2.3 (Tyanova et al, 2016).Protein abundance was measured using label-free quantification (LFQ) intensities, which were calculated according to the label-free quantification algorithm available in MaxQuant (Cox et al, 2014), reported in the ProteinGroups.txtfile.Only proteins quantified in all three replicates in at least one group were used for further analysis.Missing values were inputted separately for each column (width 0.3, down shift 1.8), and significantly enriched proteins were selected using a permutation-based t-test with FDR set at 5% and s0 = 0. Processed data were filtered using Microsoft Excel to select the hits likely representing true interactions.Typically, proteins with Student's t-test difference in their LFQ value of > 1.2, when compared to ID8 Trp53 À/À ;Pten À/À 1.15 TurboID, were considered as true interactors.Protein networks were visualised using Cytoscape (v3.9.1) and bubble heatmaps were generated using RStudio (v1.4.1717).

Integrin recycling assay
96-well ELISA plates were coated with 50 ll of integrin antibody at the optimised concentration diluted in 0.05 M Na 2 CO 3 pH 9.6 at 4°C overnight and blocked with 5% BSA in TBS-T.Cells at 80% confluence were washed with cold PBS and surface labelling was achieved with 0.13 mg/ml sulfo-NHS-SS-Biotin for 1 h.For internalisation, cells were washed with cold PBS and treated with 12-14°C cell medium for 30 min at 37°C.Medium was removed, cells were washed with pH 8.6 buffer (50 mM Tris pH7.5, 100 mM NaCl, adjust pH with 10 M NaOH) and MesNa (95 mM of MesNa in pH 8.6 Buffer) was added to achieve thiol reduction at 4°C for 30 min.Cells were washed with PBS and prewarmed medium was added to induce recycling at 37°C for the annotated time points.Cells were washed with PBS and pH 8.6 Buffer, followed by another round of thiol reduction.The reaction was quenched with the addition of 1 ml 20 mM iodoacetamide at 4°C.Cells were lysed using 280 ll of lysis buffer (200 mM NaCl, 75 mM Tris, 15 mM NaF, 1,5 mM Na 3 VO 4 , 7.5 mM EDTA, 7.5 mM EGTA, 1.5% Triton X-100, 0.75% Igepal CA-630, 50 lg/ml leupeptin, 50 lg/ml aprotinin and 1 mM AEBSF).Lysates were scraped and syringed once through a 30 G needle, centrifuged at 13,000 g for 10 min at 4°C, added in the ELISA 96-well plates and incubated overnight at 4°C.The ELISA plate was extensively washed with PBS-T to remove the unbound material.Streptavidin-conjugated horseradish peroxidase in PBS-T (1:6.666)containing 0.1% BSA was added to each well for 1 h at 4°C.The plate was extensively washed with PBS-T and then with PBS to remove the Tween.For detection, 50 ll of Citrate/PO4 buffer (4 mM o-Phenylenediamine dihydrochloride corrected to pH 5.5 with H 2 O 2 ) were added per well until colour in the total pool was well developed.The reaction was stopped with 50 ll of 8 M H 2 SO 4 and absorbance was read at 490 nm.
Proliferation and cell death assays ID8 cells were plated in a 96-well plate (2,000/well), 24 h either alone or in the presence of 1:1,000 dilution Sytox Green (Thermo Fisher Scientific, S7020).Imaging was carried out on IncuCyte ZOOM or S3 every hour for 48 h.Cell area (confluence) and the number of green objects over confluence were measured using the IncuCyte analysis software.
PCR genotyping for AGAP1 KO cell lines AGAP1 KO cell lines (ID8 Trp53 À/À ;Pten À/À 1.15 sgAgap1_2 and sgAgap1_3) and a control cell line (ID8 Trp53 À/À ;Pten À/À sgNT) were allowed to reach 80% confluence.Genomic DNA isolation was performed using 500 ll Lysis Buffer per well of 6-well plate (100 mM Tris-HCl pH 8.5, 5 mM EDTA, 0.2% SDS, 200 mM NaCl supplemented with 10 lg/ml Proteinase K) and overnight incubation at 55°C.Extraction was performed using Phenol:Chloroform: Isoamyl Alcohol (25:24:1 v/v) The upper layer was retained, and the extraction repeated twice on the supernatants using 450 and 400 ll of chloroform.The final 450 ll were precipitated by adding 35 ll of 4 M Sodium Acetate pH 5.2 and 770 ll of 100% EtOH.The precipitate was spun at 14,000 rpm for 1 min, the supernatant removed and the DNA pellet washed twice with 1 ml of 70% EtOH.Following the removal of EtOH, the DNA pellet was left to air-dry for 5 min, resuspended in 250 ll TE buffer and incubated at 55°C with gentle shaking for 5 min to dissolve.An empty pUC19 vector was linearised by PCR (pUC 5 0 : 5 0 -TCTAGAG-GATCCCCGGGTAC-3 0 , pUC3 0 : 5-CTGCAGGCATGCAAGCTTGG-3 0 ).The NCB1 Blastn tool was used to find the Mus musculus Agap1 gene and identify a 500 bp region with the target sequence in the middle for each of the gRNAs.Primers that would amplify the specified genomic regions were designed, including 20 bp complementary edges to the linearised pUC19 backbone.PCR was performed using the Q5â Hot Start Master mix (NEB M0491), supplemented with 10 lΜ of each primer and 20 ng of template in a final volume of 25 ll in the following conditions: initial denaturation: 98°C, 30 s, denaturation: 98°C, 30 s, annealing: 3-5°C lower than the Tm of the least stable primer in the reaction, 20 s, extension: 72°C, 20-30 s per kb; repeat Steps 1-3 for 30 cycle, final extension: 72°C, 2 min.

PIP strips
PIP strips (Tebu-bio, 117P-6001) were used as per the manufacturer's instructions.The membranes were blocked for 1 h in PBS-T (0.1%) with 3% BSA, at RT.Each strip was incubated with 1 lg of purified GST or GST-PH Domain fusion in PBS-T with gentle agitation.Strips were washed thrice in PBS-T for 5 min.Anti-GST antibody (Merck, 06-332) was added diluted 1:1,000 in PBS-T with 3% BSA and incubated with gentle agitation in RT for 1 h.The strips were washed thrice with PBS-T and secondary HRPconjugated antibody was added (1:5,000 in PBS-T 3% BSA) for 1 h at RT. Supersignal West Pico Plus Chemiluminescent Substrate (Thermo Fisher Scientific, 34580) was added for 3 min and the strips were scanned using the Bio-Rad ChemiDoc Imaging system.

Statistical analysis
Sample size was not predetermined, and the data were not randomised prior to analysis.The number of biological and technical replicates are described in the figure legends.Where appropriate, the exact number of objects analysed is provided in Table EV1. Figure1.
) or alone(Fig 2F and G), and across multiple clones(Figs 2D and E  and EV2A), results in the induction of a hyperprotrusive, invasive spheroid phenotype (Fig2H, arrowheads; Movies EV2 and EV3).Confirmation of this increased activity upon Pten KO occurred in orthogonal 3D invasion assays with monolayers plated on ECM, wounded and then further overlaid with more ECM (FigEV2B-D; Movie EV4).Tracking of the directionality of invasive front of the wound edge revealed an increase in additional depth and persistence occurred upon co-loss of Pten compared with Trp53 alone (FigEV2E).Notably, while invasion of parental cells into ECM occurred via infrequent chains of cells following a leader cell, upon Pten KO most cells at the leading edge displayed leader cell behaviours(Fig EV2C and F, arrowheads; Fig EV2G).Therefore, loss of Pten is associated with desynchronised leader cell activity into the ECM, leading to a hyperprotrusive, persistently invasive phenotype.
low PTEN protein patient tumours displayed a PI3K-AKT substrate phosphorylation activation signature (Fig 1I), we examined the requirement for PI3K-AKT signalling in the hyperprotrusive PTEN KO phenotype.PIP 3 can be generated from PI(4,5)P 2 through four Class-I PI3Ks (a, b, c, d) (Fig 3C).Pan inhibition of these PI3Ks (pan-PI3K-i; LY294002) or AKT (AKT-I; AKT Inhibitor II) (Fig 3D) abolished protrusion formation, resulting in smaller spheroids with ◀ Figure 1.Loss of Pten in HGSOC epithelium is associated with poor outcome.
upregulation of the Spherical phenotype and loss of Hyperprotrusive classification (Fig 3E-G; Movie EV5).Deconvolution of class-I PI3K contribution using isoform-preferential inhibitors revealed a major contribution of PI3Kb to invasion and growth across the entire imaging period, and a more modest effect of PI3Ka at earlier timepoints (1-36 h; Fig 3D and H-J; Movie EV6 Figure 2.
Fig 5B).Notably, several constitutive ARF6 interactors (Fig 4J), such as ITGB1 and AGAP1, showed robust reduction in Hyper-protrusive activity when depleted (Fig 5B), while reduction in Hyper-protrusiveness could also be seen for sgRNAs against Trp53 or Pten loss-induced interactors, such as Cyth2 or Itga5, respectively (Figs 4K and L and 5B).Deconvolution of sgRNAs to Itgb1, Agap1, and Itga5 revealed efficient CRISPR editing to each target across multiple independent sgRNAs (Fig 5C and D; Movies EV9 and EV10; Appendix Fig S1A-F).This occurred without consistent alterations to pS473-AKT levels in the Itgb1 and Agap1-depleted cell lines (Fig 5C and D, Appendix Fig S1C and D), suggesting that these effects are downstream of PI3K signalling.Each of Itgb1, Agap1 (Fig 5E-G) and Itga5 (Appendix Fig S1G and H) depletions resulted in spheroids that lacked Hyper-protrusive activity (arrowheads denote protrusions), confirming the pooled screen results (Fig 5B).This revealed that a5b1-integrin may be a major cargo of ARF6 that regulates interactions with the ECM to promote invasion, in conjunction with the GEF, CYTH2, and the GAP, AGAP1.This is particularly notable as although ITGB1 and AGAP1 association occurred across all genotypes (Fig 4J), ARF6 association with ITGA5 increased specifically in Pten-null conditions (Fig 4K).Notably, there was no change in the mRNA levels of either integrins in LCM HGSOC patient samples, while the comparison of either ITGA5 or ITGB1 mRNA levels based on median split comparing high (M2) versus low (M1) showed no significant different in survival (Appendix Fig S1I-L; Data ref: Yeung et al, 2013b).
Source data are available online for this figure.Ó 2023 The Authors The EMBO Journal 42: e113987 | 2023 15 of 25