The morphology of breast cancer cells is often used as an indicator of tumor severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastasize. However, the exact regulatory systems that define these changes remain poorly characterized. In this study, we used a network-systems approach to integrate imaging data and RNA-seq expression data. Our workflow allowed the discovery of unbiased and context-specific gene expression signatures and cell signaling subnetworks relevant to the regulation of cell shape, rather than focusing on the identification of previously known, but not always representative, pathways. By constructing a cell-shape signaling network from shape-correlated gene expression modules and their upstream regulators, we found central roles for developmental pathways such as WNT and Notch, as well as evidence for the fine control of NF-kB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates a gene expression module enriched in the RAP1 signaling pathway as a mediator between the sensing of mechanical stimuli and regulation of NF-kB activity, with specific relevance to cell shape in breast cancer. The study of cancer has long been associated with changes in cell shape as morphology can be a reliable way to subtype cancer and predict patient prognosis . Recent Breast cancer is the most common cancer among women and, in most cases, is treatable, with a survival rate of 99% among patients with a locally contained tumor. However, among those patients presenting with a metastatic tumor, this rate drops to 27% . During Signaling and transcriptomic programs are known to be modulated by external physical cues in the contexts of embryonic development , stem-ceHere we have developed a powerful network-based approach to bridge the gap between widely available and cheap expression data, signaling events, and large-scale biological phenotypes such as cell shape A. Our stn = 75,653). Of 102 GEMs , 34 were significantly correlated   with one of eight cell-shape features  that are relevant to the regulation of cell shape. To this end, we used weighted gene correlation network analysis (WGCNA)  on bulk features B. A fullSupplemental Table S3). Modules that are most correlated with all features are the “ARNT KO” module, “ARRDC3−AS1” module, and the “ECM organization” module . Modules that could not be annotated with informative terms were designated as module “non annotated” (NA) 1, 2, 3, etc.We used Enrichr and their suite of gene set libraries  to functP < 0.1; Fisher's exact test) in our modules . We therefore consider these TFs as potentially relevant for the regulation of cell-shape features and their activity levels as a read-out of cell signaling activity in these cells. These TFs include the EMT antagonist FOXA1  we then used Enrichr . The only exceptions were one association with “signaling by NOTCH” and modules associated with “signal transduction,” a spurious pathway containing the complete intracellular signaling system .To extend this further, we sought to investigate the pathways responsible for regulating the identified TFs and, by extension, the GEMs. For this analysis, we also include ENCODE and ChEA Consensus TFs from ChIP-X , DNA bin Enrichr  to perfo Enrichr  enrichme Enrichr C. To ensSupplemental Fig. S2A). Cluster A is more heterogeneous in its morphology, containing the nontumorigenic mammary epithelial cell line MCF-10A as well as cell lines from both luminal and basal breast cancer subtypes. Clusters B and C are more distinctly shaped, roughly composed of luminal and basal cell lines, respectively, except for HCC1954, which was clustered morphologically with luminal subtypes, while being characterized as basal. The basal-like cluster is most morphologically distinct from cluster A but also differs from the luminal-like cluster in that it has a lower nuclear/cytoplasmic area (0.133 ± 0.05 [mean ± SD]), higher ruffliness (0.235 ± 0.12), and lower neighbor fraction (0.258 ± 0.22). The luminal-like cluster had a higher nuclear/cytoplasmic area , lower ruffliness , and a higher neighbor fraction . The neighbor fraction feature corresponds to the fraction of the cell membrane that is in contact with neighboring cells. The lower number of cell–cell contacts in basal-like breast cancer cell lines is indicative of more mesenchymal features associated with worse prognosis owing to metastasis. Increased cell–cell contacts in both the luminal-like cluster and the more heterogeneous cluster A correspond to “cobblestone” epithelial morphology. We found that these groups are closely aligned with the expression of the cell adhesion protein cadherin 2  (https://datadryad.org/stash/dataset/doi:10.5061/dryad.tc5g4).To understand key differences in expression patterns and gene regulation between morphologically distinct breast cancer cell lines, we clustered them based on 10 morphological features including area, ruffliness, protrusion area, and neighbor frequency and performed differential expression analysis between the identified clusters (adherin) A, the exadherin) . RepreseSupplemental Table S7, with gene set enrichment analysis showing up-regulation of genes involved in the extracellular matrix, collagens, integrins, and angiogenesis in the basal-like cluster. Significantly enriched terms (P < 0.05) in down-regulated genes include “fatty acid and beta-oxidation” and “ERBB network pathway.” In the genes up-regulated in the luminal-like cluster, we observed enrichment of terms such as “hallmark-oxidative phosphorylation,” down-regulated genes were enriched in “integrin-1 pathway,” “core matrisome,” and genes linked to “hallmark epithelial–mesenchymal transition and migration.” For the remaining B/L group, the term with the highest normalized enrichment score was “targets of the transcription factor MYC” followed by terms associated with ribosomal RNA processing. Down-regulated terms include “cadherin signaling pathway” .Using the identified groups of cell lines in the previous step, differential expression analysis and TF activity analysis were used to study gene regulation signatures specific to cell line morphological clusters. The results are shown in ARNT/KO module , and ECM organization module  .We also calculated the differential expression for the WGCNA GEMs and found distinct patterns of expression between luminal-like and basal-like clusters of cell lines B. Among Supplemental Fig. S2B,C) to cell signaling, we used the tool DOROTHEA .To link the observed gene expression differences  algorithm . This isP < 0.001) .The resulting network of 691 nodes included 97.11% of the genes identified by our analysis. The new proteins that were included by the PCSF algorithm to maximize prize collection showed gene set enrichment of common terms (Pathways from PANTHER)  relativeSupplemental Fig. S3B–D). The proteins in the network can be ranked by betweenness centrality to disseminate them based on network importance. Nodes with high centrality lie between many paths and can control information flow. Proteins with the highest centrality are primarily prizes  , indicating that the PCSF solution was not achieved by the inclusion of new hub proteins that are not of interest to our analysis. Nevertheless, a small minority of high centrality nodes were not in the original prizes, implicating them as mediating the cross talk between pathways identified in Studying the network properties of our PCSF-derived regulatory network, we found that the degree distribution is typical for a biological network  small-molecule kinase inhibitor data set . Here, tn = 37, Wald test P < 0.05) deviation from the control between drug treatments targeting kinases within the predicted network and those targeting other proteins for cytoplasmic area, cytoplasmic perimeter, nucleus area, nucleus length, nucleus width, and nucleus perimeter . This difference is insignificant for features that were not correlated with gene expression modules in our initial analysis , indicating that our network is phenotype-specific to the features used in network generation. We also repeated this analysis in other cell lines  with results with limited statistical significance . We additionally used a positive control in which the control cells had been treated with TNF-related apoptosis-inducing ligand (TRAIL) to ensure that the observed morphological effects were not caused by apoptotic factors .We combined this with data from a target affinity assay  describiSupplemental Figure S4, A and B. We hypothesized that it was the network properties of kinases within our network that dictated their effect on morphological features, with some targets being on the periphery of our predicted network and therefore having limited influence over the regulation of cell shape. To test this, we studied the extent to which the effect of a kinase inhibitor was correlated with the combined centrality of its targets as defined by our network. For this we used the centrality algorithm PageRank  was moderate but significant for cytoplasm area, cytoplasm perimeter, nucleus area, nucleus length, nucleus half-width, and nucleus perimeter . This correlation in change in morphological features with the centrality of the targeted kinases illustrates the relevance of our constructed network in regulating cell shape. For variables that were not correlated to any GEM, we see visibly lower correlation coefficients and insignificant associations . These results illustrate that the topology of our network explains some of the variation in the effect of kinase inhibitors tested in a manner that is feature specific to the ones that were used to construct the network model.Supplemental Table S9; As TF activity remains the most reliable indicator of signaling that can be extracted from transcriptomics data , we applSupplemental Fig. S5A,B), we can see that the source of this module's probability is mainly from the TFs JARID2 and RUNX2 in luminal-like cell lines and from JARID2 for basal-like. However, the TFs KLF5 and ESRRA in both morphological subtypes also contribute to the ranking of RAP1 signaling via GSK3B and DVL1  and is enriched in members of the mechanosensitive RAP1 signaling pathway. By performing RWR diffusions on each of the seed nodes separately , as well as transcriptional coactivator PPARGC1A and CREBBP.Supplemental Fig. S6; P < 0.01, FDR adjusted) with the activity of 19 TFs. Among these are RUNX2 (consistent with the results from our network propagation), TEAD1 (TF mediating the function of YAP1/TAZ), and NFKB1. We also correlated TF activity using the same method on tumor samples extracted from The Cancer Genome Atlas . Using this publicly available data set, we studied 1090 BRCA tumors and performed differential expression on each sample. We found 40 TFs significantly correlated  with RAP1 GEM  in 78 BRCA cell lines. This enabled the correlation of its combined activity with the activity of known TFs predicted by DOROTHEA  than the most frequently assigned morphological subtype . This subtype is a common and homogenous breast cancer grouping characterized by its failure to exhibit morphological features that might allow it to be classified as anything more specific  terms enriched within RAP1 GEM D. This rSupplemental Fig. S6A).NF-kB has been previously linked to the regulation of cell shape in breast cancer. To explore the interface of RAP1 GEM with NF-kB in terms of intracellular signaling, we identified a subnetwork of our network responsible for mediating “information flow” between those two nodes using the algorithm maximum flow E. By stuWe present a method that uses transcriptomics and phenotypic data to derive a concise subnetwork describing the signaling involved in the regulation of cell shape. This analysis recovered known processes like “adherens junction proteins,” “cadherin” , and “inImportantly, this analysis also sheds light on processes with less-characterized associations with cell shape in cancer. We found that a GEM enriched in RAP1 signaling is significantly correlated with cell shape and is the most differentially expressed module between luminal-like and basal-like cell line clusters. We found that it was up-regulated in basal-like cell lines and down-regulated in luminal, consistent with its negative correlation with neighbor fraction, a cell-shape feature most contributing to the “cobblestone”-like features of an epithelial and nonmetastatic cell type. This GEM was also an important node in our identified signaling network, being at the network confluence of multiple activated TFs. We also showed this GEM to be expressed in patient data, with its activity being correlated with known developmental and morphologically related TFs, as well as those used to identify it in the network propagation analysis. In this way, our methodology uses cell line data for network construction and validation, but through our network approach, we focus on more general effects that can be tested and successfully validated in a wider breast cancer clinical context. Hence, we believe these results to be relevant in more general breast cancer applications but are also reflecting the inherent context specificity that exists in biology.The namesake of our identified module, RAP1, is a small GTPase in the RAS-related protein family that has been shown to be involved in the regulation of cell adhesion and migration . SpecifiThe RAP1 signaling GEM identified in the network analysis represents a subset of the transcriptome observable among our analyzed cell lines. Although it is enriched in RAP1 signaling, it is important to note that it represents a collection of latent biological processes rather than a single pathway assigned to it by gene set enrichment. From our network analysis, we hypothesize that it is able to interact with intracellular signaling pathways in order to modulate TF activity and consequently cell shape. Other pathways enriched in the expression module include the HIF1 signaling pathway, which is known to be activated by RAP1 in melanoma , but thiWe also observed that our GEM of interest is significantly correlated with NF-kB in both clinical samples and cell culture. Other investigators have flagged the direct effect of RAP1 on the cytoskeleton and NF-kB , but herARNT KO module being significantly up-regulated in basal-like cell shapes relative to luminal. We also found this GEM to have the highest total correlation with all of the morphological features, indicating a strong association with cell shape. By studying terms enriched in this module from the Enrichr library, we found “TNF-alpha signaling via NF-kB” to be enriched as well as genes down-regulated during AHR nuclear translocator (ARNT) shRNA KO. Signaling by TNF is able to activate NF-kB, a TF known to control the expression of many EMT related genes   . Previouor (AHR) . Our finB . These Aside from the biological findings of this study, we illustrate an approach for network analysis of a specific course-grained phenotype through expression, a notoriously poor (if cheap and widely available) proxy for gauging intracellular signaling . In contThe interoperability of this approach is obvious, with any number of continuous variables measured with gene expression able to be correlated with module eigengenes using WGCNA. Here, we used OmniPath as a base network, but other network-based representations of the cellular environment can be used based on the appropriate context. Thus, our method represents a data-driven, network-based approach compatible with many different multiscale phenotypes that are driven by intracellular signaling. Overall, our unbiased network-based method highlights potential “missing links” between sensing extracellular cues and transcriptional programs that help maintain the cancer stem cell niche and ultimately push breast cancer cells into EMT and metastasis. These represent starting points for further experimental studies to understand and therapeutically target the links between cell shape, cell signaling, and gene regulation in the context of breast cancer.https://datadryad.org/stash/dataset/doi:10.5061/dryad.tc5g4). Cell imaging segmentation was performed using Acapella software (PerkinElmer) with an automated spinning disk confocal microscope. The presented images  of nine. We translated this adjacency matrix , and all showed a high degree of conservation between resampled runs.We created a signed, weighted adjacency matrix using logP-values for given correlations. Multiple hypothesis testing was performed using a permutation based procedure, whereby we recalculated the correlation matrix 1000 times with resampled data. We then generated null distributions for each ranked correlation statistic in our matrix and compared them with our real data of the same rank. We include in Supplemental Table S1 confidence intervals of our permutation-based multiple-correction procedure. For the modules that correlated with morphological features (Pearson correlation coefficient 0.5 and Student P < 0.05), we identified enriched signaling pathways using the R package Enrichr (P < 0.1) with the GEM contents. This was performed separately for inhibitory and activatory expression regulons for each TF, with regulatory relationships of unknown sign being used in the significance calculations for both.We took morphological variables referring to breast cancer cell lines from  Enrichr  and the  Enrichr . Reactom Enrichr , we idenSupplemental Table S3). As some clusters were very obscure, we used the entire Enrichr list of libraries (https://maayanlab.cloud/Enrichr/#stats for full list) with precedence going to the signaling databases of KEGG, Reactome, PANTHER, and Wikipathways   . Some mok-means algorithm, we classified the 14 breast cancer cell lines by the median values of each of their shape features (k = 3) . We performed differential expression analysis using the R package DESeq2 , the differentially activated TFs identified by DOROTHEA (P < 0.1), and the signaling proteins included in the REACTOME pathways that were enriched in TFs identified (P < 0.05). We identified these pathways by using the TRRUST TFs identified in the previous steps, as well as ENCODE and ChEA Consensus TFs from ChIP-X  were given a P-value of one. Results of this were corrected for multiple-hypothesis testing using FDR correction.Using a PCSF algorithm, we generated a cell-shape regulatory network implemented through the R package PCSF . For them ChIP-X , DNA binm ChIP-X , TF protm ChIP-X . Using Em ChIP-X . This waThe “costs” associated with each edge in the regulatory network were the inverse of the number of sources linked to each regulatory connection scaled between one and zero, such that the more the number of citations for an edge, the lower the cost. For the base network used by the algorithm, we used the comprehensive biological prior knowledge database, OmniPath  , extractP < 0.1) with the gene content of the module in question. We represented the respective cell-shape phenotypes as nodes in a similar fashion by including undirected edges from expression modules and phenotypes in which there was significant correlation (|PCC| > 0.5 and P < 0.05) between them. To account for expression modules’ effect on upstream signaling, we added edges from the WGCNA modules back up to proteins that were themselves included within the modules. We set the edge weight of these to 1, such that any predicted activity of the GEM would be translated directly into its constituent signaling proteins and thus account for feedback between cell-shape signaling networks and the context-specific expression modules identified in the first step. We identified enriched terms in the network using the 2016 release of the database PANTHER (We included the WGCNA modules themselves as supernodes in the network by adding incoming edges from the TFs contained within the regulatory network whose regulomes (as described in TRRUST v2)  signific PANTHER  and GSE  PANTHER .We examined the potential effect of significantly activated (FDR < 0.05) and deactivated TFs in different cell line clusters using network propagation in our generated network. We replaced edge weight with the Resnik best-match average (BMA) semantic similarity  between P < 0.05) as seeds for a RWR algorithm using the R package diffuseR (available at https://github.com/dirmeier/diffusr). We judged a node to be significantly ranked if its affinity score relative to the inputted seeds was greater than the same node's affinity score with a random walk simulation performed with randomized seeds. We performed this randomized simulation 10,000 times, from which a P-value was determined to judge significance (P < 0.1). We performed this propagation by RWR for both luminal-like and basal-like morphological clusters on significantly activated and deactivated TFs separately in addition to simulations in which each seed was considered in isolation. We implemented these simulations with a restart probability of 0.95. We generated a graphical representation of the network edges and TFs responsible for the ranking of RAP1 signaling by plotting all the shortest paths between RAP1 and the TFs that caused it to have a non-zero affinity score when each TF was considered in isolation.We used the differentially activated TFs identified using DOROTHEA . This data set is derived from the treatment of six cell lines with a panel of 105 small-molecule kinase inhibitors. They measured textural and morphological variables following treatment by high-throughput image analysis . Using this data set, we filtered for only molecule-binding target pairs with a binding class of one (representing a KD < 100 nM affinity). Further to this, we removed molecules that had more than five targets with a KD of 100 nM. Consequently, the remaining kinase inhibitors were relatively narrow spectrum, thus simplifying analysis of their phenotypic effect. We expressed these results as batch-specific log fold changes of 10-µm drug treatment relative to the mean of the control set (untreated and DMSO-treated cells). Spearman's rank correlation was calculated between the drug target's network centrality and the absolute log fold change of the morphological variable. We also used the Kolmogorov–Smirnov statistic to assess significance between cell morphology after treatment with drugs targeting kinases inside versus outside our predicted network. This was also repeated on other breast cancer cell lines and using a TRAIL control .We used single-cell, small-molecule kinase inhibition data from the Harvard Medical School (HMS) LINCS Center , which ianalysis . We combThe morphological data in the kinase inhibition screen were measured using two dyes (DRAQ5 and TMRE), the intensity of which we used to normalize textural features and the measurement of cytoplasmic and nuclear small spots. We reported counts for small-nuclear or cytoplasmic spots as a mean of the individually normalized readings from both dyes. We considered a treatment perturbing our network if at least one of the kinase inhibitors targeted a protein that was represented by a node within the network.We incorporate information from the TAS assay, as well as graph-based properties of kinase inhibitor targets, using the product of the Szymkiewicz–Simpson similarity (measured between the cell-shape network nodes and the drug targets) and the centrality of the targeted nodes in the predicted network with semantic similarity edge weights. The product of these generates, for a given kinase inhibitor, the statisticlgorithm . This celgorithm . We usedhttps://www.cancer.gov/tcga, accessed 01/04/21). For computational efficiency, we used gamma-Poisson models to predict differentially expressed genes from our samples using the package glmGamPoi . Correlation was quantified using the Kendall rank correlation coefficient. Differences in distributions of morphological subtypes was quantified by the Kolmogorov–Smirnov test.For the cell lines, we used RNA-seq data from the ArrayExpress  . This walmGamPoi . We usedFor maximum-flow calculations, we used the Resnik BMA semantic similarity  as the mP-value cut-offs are shown in parentheses after reporting an effect as significant. A weighted Pearson's correlation with t-test for significance was used to correlate eigengenes and cell-shape features using the RNA package WGCNA. We used a one-way ANOVA test for comparing the means of the shape variables among the identified three cell line clusters  and a Tukey honest significant difference test to perform multiple-pairwise comparison among the means of the groups. The same tests were performed on the differences in 10 cell-shape variables when Hs 578T was treated with 37 kinase inhibitors . A Fisher's exact test was used to test significance of overlap between TRRUST regulons and identified GEMs .Statistical tests were performed in base R  unless oP-value of the enrichment with the z-score deviation from the expected rank. The preranked gene set enrichment algorithm FGSEA was used for the identification of enriched terms in the differentially expressed genes, allowing for accurate estimation of arbitrarily low P-values that occur in expression data sets.Enrichment of gene sets was performed by Enrichr, an enrichment library that uses a hypergeometric test to identify significantly enriched terms in a gene list. This tool  according to Equation t-test is not fulfilled.Kolmogorov–Smirnov test was used to measure differences in distributions of clinically assigned tumor morphologies. This was because clinical groupings are mixed , and others are characterized by an absence of features over their presence. This means that the assumption of normality required for a For differential expression analysis, the DESeq2 R package  was usedP < 0.1). Figures were presented using ggplot  10,000 times and selecting only the nonseed nodes that were significantly ranked relative to the randomized simulations .The complete R scripts and data used for this methodology are available as


This demonstrates that pro-oncogenic, ligand-independent EphA2 signaling plays a dominant role in CD90+ cells with higher motility and metastatic activity than EpCAM+ cells. We also showed that an AKT inhibitor reduced the proliferation and survival of CD90+ cells but did not affect those of EpCAM+ cells. Taken together, our results suggest that AKT activation may be a key pro-oncogenic regulator in HCC.Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide. Additionally, the efficacy of targeted molecular therapies with multiple tyrosine kinase inhibitors is limited. In this study, we focused on the cellular signaling pathways common to diverse HCC cells and used quantitative reverse phase protein array (RPPA) and statistical analyses to elucidate the molecular mechanisms determining its malignancy. We examined the heterogeneity of 17 liver cancer cell lines by performing cluster analysis of their expression of CD90 and EpCAM cancer stem cell markers. Gaussian mixture model clustering identified three dominant clusters: CD90-positive and EpCAM-negative (CD90+), EpCAM-positive and CD90-negative (EpCAM+) and EpCAM-negative and CD90-negative . A multivariate analysis by partial least squares revealed that the former two cell populations showed distinct patterns of protein expression and phosphorylation in the EGFR and EphA2 signaling pathways. The CD90+ cells exhibited higher abundance of AKT, EphA2 and its phosphorylated form at Ser Liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer-related deaths worldwide. Hepatocellular carcinoma (HCC) is the most prevalent type of primary liver cancer, accounting for 75–85% of liver cancer cases . Even wiThis limited efficacy of TKIs for HCC most likely stems from tumor heterogeneity. This heterogeneity is proposed to arise from a subset of cancer stem cells (CSCs) with self-renewal and differentiation capabilities . Liver CSeveral signaling pathways have been implicated in the pathogenesis of HCC ,20. The 897). This, in turn, activates Rho GTPase signaling, resulting in cell migration and metastasis. Thus, ligand-independent activation of EphA2 has pro-oncogenic functions. The relative balance between these two types of EphA2 activation may be a key determinant of tumor heterogeneity. However, the mechanisms underlying ligand-dependent and ligand-independent EphA2 signaling have not been fully explored in HCC; hence, their connections to liver CSCs are largely unknown.Ephrin type A receptor 2 (EphA2) is a receptor tyrosine kinase that has been found to be overexpressed in a variety of cancers, including HCC . Increas897 and AKT. We further demonstrated that an AKT inhibitor selectively affected CD90-positive cell proliferation and survival.In this study, we classified 17 liver cancer cell lines based on the expression of EpCAM and CD90 CSC markers. Additionally, we compared the patterns of protein expression and phosphorylation of the EGFR and EphA2 signaling pathways between EpCAM-positive and CD90-positive cells. We found that pro-oncogenic EphA2 signaling was more pronounced in CD90-positive cells, in that they exhibited higher levels of EphA2, pEphA2-SerTo address the heterogeneity of HCC cells in terms of CSCs, we first applied Gaussian mixture model (GMM) clustering to examine the potential clusters in 17 liver cancer cell lines based on EpCAM and CD90 expression, as detected on the RPPA analysis . The clu588 and pEphA2-Ser897. Here, ‘p’ denotes phosphorylated proteins) for 17 liver cancer cell lines. Then, using this data and RPPA data for EpCAM and CD90, we performed hierarchical clustering of those cell lines .We further examined the differential expression of proteins and phosphoproteins among the GMM clusters using partial least squares (PLS) analysis ,34,35. Pp < 0.0001). Using a mean-centered PLS , we foun897, which is consistent with the upregulation of pro-oncogenic, ligand-independent EphA2 signaling. In contrast, EpCAM+ cells exhibited significantly higher levels of ERK, RSK and pRSK, which are involved in MAPK signaling. They also exhibit modest, but not significant, activation of pEphA2-Tyr588, which is reminiscent of anti-oncogenic, ligand-dependent EphA2 signaling. These results suggest that anti-oncogenic EphA2 signaling may occur in EpCAM+ cells, but appears too weak to suppress pro-oncogenic MAPK signaling.Having identified AKT as a potential molecular target specific to the CD90+ cell cluster, we evaluated the effect of the AKT inhibitor, MK2206, on cancer cell proliferation. Using two cell lines from each cell cluster , we first compared cell proliferation among these clusters under low-serum culture conditions A. When wp < 0.0001). This indicates that the inhibitor changed the pattern of proliferation time course from a monotonic increase to an inverted V-shape, making it similar to that of EpCAM+ cells without the inhibitor. Thus, MK2206 suppressed the growth of JHH-6 from day 3 to day 7. In contrast, for JHH-7, two-way ANOVA revealed only a significant main effect of day (p < 0.0001). This suggests that the inhibitor did not affect the proliferative ability of JHH-7 cells. Overall, these results suggest that the effect of AKT inhibition on proliferation is specific to the CD90+ cell cluster.Noting this marked difference in cell proliferation between the CD90+ and EpCAM+ cell clusters, we next examined the effect of AKT inhibition on cell proliferation in the CD90+ cell cluster by comparing it with that in the EpCAM+ cell cluster. Both JHH-6 (CD90+ cell cluster) and JHH-7 (EpCAM+ cell cluster) were treated with MK2206 under the same culture conditions B. For JHWe also examined the effect of AKT inhibitor on cell motility using a transmigration chamber C,D. JHH-The prognosis of HCC remains poor because of its high recurrence rate and drug resistance. Despite recent progress in targeted molecular therapies for advanced HCC, their clinical benefits are still limited. This probably stems from the tumor heterogeneity of HCC, which is linked to liver CSCs. In our study, 17 liver cancer cell lines were classified into clusters  according to their expression of CSC markers, EpCAM and CD90. This classification was congruent with their known phenotypes: EpCAM+ cells show typical epithelial morphology and high tumorigenicity, whereas CD90+ cells show mesenchymal cell morphology and high metastatic activity to distant organs in vivo .897. High levels of AKT expression and pEphA2-Ser897 were consistent with our observation that these cells were able to survive under high cell density conditions. Additionally, pEphA2-Ser897 activates the small GTPase RhoG and enhances cell motility, invasion and metastasis [Furthermore, we found that these CSC-positive cell clusters showed differential protein expression and phosphorylation in the EGFR and EphA2 signaling pathways. In particular, CD90+ cells exhibited higher levels of AKT, EphA2 and pEphA2-Sertastasis  and is atastasis . Thus, CFinally, we demonstrated that the AKT inhibitor, MK2206, blocks the prolonged survival of CD90+ cells and reduces their motility. Previous reports have suggested that the drug inhibits the proliferation of some HCC cell lines ,38,39,401068) (D7A5), phospho-MEK1/2-Ser217/221 (41G9), ERK1/2 (4696), phospho-ERK1/2-Thr202/Tyr204 (13.14.4E), RSK1/2/3 (32D), phospho-RSK1/2/3-Ser380 (D3H11), AKT (40D4), phospho-AKT-Ser437 (D9E), phospho-EphA2 -Tyr588 (D7 × 2L) and phospho-EphA2 -Ser897 (D9A1) were purchased from Cell Signaling Technology . A monoclonal antibody against EphA2 (C-3) was purchased from Santa Cruz Biotechnology . A polyclonal antibody against EpCAM (21050-1-AP) and a monoclonal antibody against CD90 (66766-1-Ig) were purchased from Proteintech . All antibodies were validated for RPPA analysis by western blot analysis , phospho-EGFR  cell lines  and two hepatoblastoma cell lines (HuH-6 clone5 and Hep G2) were obtained from the JCRB Cell Bank . Additionally, KH, Km, Kami41 and MT cell lines were established at Kanazawa University ,43,44. Hg at 4 °C for 20 min; the supernatant was subjected to the following analyses. After adjusting the protein concentration to approximately 1.0 mg/mL according to Bradford protein assay (Bio-Rad), the lysates were manually diluted in two-fold serial dilutions with an extraction buffer. The diluted lysates were boiled with 2% SDS and 2.5% β-mercaptoethanol and printed onto nitrocellulose-coated slides in four replicates  using an Aushon Biosystems 2470 arrayer . After blocking with an ODYSSEY blocking buffer  supplemented with 0.1% Tween-20, the blotted slides were probed with validated primary antibodies, followed by secondary antibodies conjugated to infrared dyes, IRDye 680RD and 800CW (LI-COR Biosciences) (Confluent liver cancer cells were cultured under serum-starved conditions for 24 h and were lysed using T-PER protein extraction reagent (Thermo Fisher Scientific), supplemented with inhibitors  and protease inhibitor cocktails . The lysates were scraped from the culture dish, collected and centrifuged at 15,000× ciences) . A total3 cells/well; TPP Techno Plastic Products AG, Trasadingen, Switzerland) and incubated for 7 days at 37 °C in 5% CO2. Additionally, the JHH6 and JHH7 cell lines were further treated with either DMSO or MK2206 (Sigma) at the indicated concentrations and incubated for 7 days at 37 °C in 5% CO2. The number of living cells was counted using either a hemocytometer or Coulter Counter . Transwell migration assays were performed as described previously [4 cells) in growth medium containing 0.5% FBS was introduced into the upper chamber. The plates were incubated at 37 °C in 5% CO2 for 4 h. After incubation, the cells that had migrated to the lower side were stained with 0.25% crystal violet/20% methanol solution and counted using a light microscope at ×10 magnification. The values obtained represent the averages of the five fields.JHH-6, JHH-7, Kami41, PLC/PRF/5, JHH-7 and HuH-7 cell lines were suspended in serum-free medium containing 0.5% fetal bovine serum, seeded into a 24-well culture plate (5 × 10eviously . BrieflyWe performed Gaussian mixture model (GMM) clustering of 17 liver cancer cell lines based on the RPPA intensities for EpCAM and CD90. We used the R (version 4.0.3) package mclust  to fit tsaliences in the PLS literature. The left singular vectors are basis vectors for the row space of M, representing independent, orthonormal contrasts among the GMM clusters . The right singular vectors are basis vectors for the column space of M, representing independent, orthonormal patterns of protein expression and phosphorylation . LVs are computed by projecting the original data matrix onto these saliences. The diagonal matrix ∑ provides the singular values, each representing the covariance between a pair of cluster and protein saliences.Partial least squares (PLS) analysis ,34,35 isp < 0.05). In addition, the reliability of protein salience was assessed using the bootstrap estimation of standard errors (SEs). This entails random sampling with replacement of each cell line data, keeping the cluster membership fixed for each cell line. Bootstrapped SEs were used to calculate bootstrap ratios . The BSR is approximately equivalent to a z-score and can be used to define a threshold to assess significance . We computed 1000 permutation tests and 1000 bootstrap estimations. We note that no corrections for multiple comparisons are necessary, as PLS analyses are conducted in a single mathematical step on the whole set of protein expression and phosphorylation.The statistical significance of the LVs was assessed using permutation tests. Each permutation involves random reassignment of the order of the GMM clusters to each cell line by sampling without replacement. Each LV was considered significant when the actual singular value was larger than the permutated singular value in more than 95% of all permutations  with Benjamini–Hochberg multiple testing correction, followed by a post-hoc Games–Howell test. Moreover, the effect of incubation day on cell growth was evaluated for each cell line using Welch’s ANOVA with Games–Howell post-hoc test. The effects of incubation day and AKT inhibitor on cell growth were evaluated for each cell line by two-way ANOVA using a heteroscedasticity-corrected coefficient covariance matrix. The effect of the AKT inhibitor on cell migration was evaluated by a one-sample t-test (two-tailed) with Bonferroni correction.897. This indicates that CD90+ cells show enhanced pro-oncogenic, ligand-independent EphA2 signaling. We further demonstrated that the AKT inhibitor MK2206 reduced the proliferation and survival of CD90+ cells. Our findings suggest that AKT activation may be a key pro-oncogenic regulator in HCC.In summary, liver cancer cell lines were distinctively clustered according to their expression of CSC markers, EpCAM and CD90. While EpCAM+ cells exhibited higher levels of ERK, RSK and pRSK, CD90+ cells exhibited higher levels of AKT, EphA2 and pEphA2-Ser


Hyperactivation of the PI3K/AKT/mTOR cell signalling pathway is an important and well-described mechanism of trastuzumab resistance in HER2-positive breast cancer. In cell-line models of acquired trastuzumab resistance generated in our laboratory, we demonstrate this type of activation, which is independent of HER2-mediated regulation. We investigate whether the use of specific mTOR inhibitors, a PI3K/AKT/mTOR pathway effector, could lead to decreased activity of the pathway, influencing trastuzumab resistance. We demonstrate that TAK-228, a mTORC1 and mTORC2 inhibitor, can reverse resistance and increasing response to trastuzumab in models of primary and acquired resistance.The use of anti-HER2 therapies has significantly improved clinical outcome in patients with HER2-positive breast cancer, yet a substantial proportion of patients acquire resistance after a period of treatment. The PI3K/AKT/mTOR pathway is a good target for drug development, due to its involvement in HER2-mediated signalling and in the emergence of resistance to anti-HER2 therapies, such as trastuzumab. This study evaluates the activity of three different PI3K/AKT/mTOR inhibitors, i.e., BEZ235, everolimus and TAK-228 in vitro, in a panel of HER2-positive breast cancer cell lines with primary and acquired resistance to trastuzumab. We assess the antiproliferative effect and PI3K/AKT/mTOR inhibitory capability of BEZ235, everolimus and TAK-228 alone, and in combination with trastuzumab. Dual blockade with trastuzumab and TAK-228 was superior in reversing the acquired resistance in all the cell lines. Subsequently, we analyse the effects of TAK-228 in combination with trastuzumab on the cell cycle and found a significant increase in G0/G1 arrest in most cell lines. Likewise, the combination of both drugs induced a significant increase in apoptosis. Collectively, these experiments support the combination of trastuzumab with PI3K/AKT/mTOR inhibitors as a potential strategy for inhibiting the proliferation of HER2-positive breast cancer cell lines that show resistance to trastuzumab. ERBB2/neu oncogene and/or overexpression of its associated HER2 tyrosine kinase receptor [Despite ongoing advances in understanding diagnosis and treatment, breast cancer continues to place an enormous burden on healthcare systems worldwide and poses a risk to the lives of many patients. Breast cancer is the second leading cause of cancer deaths among women worldwide, representing 30% of all new cancer diagnoses: More than 2.25 million new cases and around 700,000 deaths were estimated in 2020 . Breast receptor ,7. In 19receptor ,9. In spreceptor ,10. Severeceptor , heterodreceptor ,13,14, Sreceptor  and aberreceptor ,17. The receptor ,19. As areceptor , and tarreceptor .Due to the involvement of the PI3K pathway in both HER2-mediated signalling and in the emergence of resistance to trastuzumab, this network becomes a good target for drug development. Because inhibition of the PI3K/AKT/mTOR axis results in enhanced HER2 signalling in HER2-overexpressing breast cancer, especially increased expression of HER2 and HER3 , targeti2. All cell lines were checked for authentication every 6 months, either by using the Cell Line Authentication service at LGC Standards (UK) , or by running a home-made mutational profiling assay.The effects of trastuzumab on cell growth were studied in a panel of eleven HER2-amplified breast cancer cell lines, including four trastuzumab-conditioned cell lines selected for long-term outgrowth in trastuzumab-containing medium. BT-474 (HTB-20) ductal carcinoma, SK-BR-3 (HTB-30) and AU-565 (CRL-2351) adenocarcinoma, as well as HCC1419 (CRL-2326) and HCC1954 (CRL-2338) ductal carcinoma cell lines, were obtained from the American Type Culture Collection. EFM-192A (ACC-258) and JIMT-1 (ACC-589) ductal carcinoma cells were obtained from the German Tissue Repository DSMZ. Trastuzumab-resistant BT-474.rT3, SK-BR-3.rT1, AU-565.rT2 and EFM-192A.rT1 cell lines were generated as previously described . BT-474,The recombinant humanised monoclonal HER2 antibody trastuzumab (a concentration of 15 µg/mL was selected as indicated elsewhere ) (Hercep5 cells for each condition (sensitive and resistant), grown both in the absence and in the presence of trastuzumab for 7 days. The results were processed using the algorithm described by O’Brien, which correlates the rate of growth between the treated and nontreated cells, reflecting the doubling time of the cells [Establishment of drug resistance was confirmed by cell proliferation assay, as determined in P100 plates containing 5 × 10he cells . Once re5 cells per plate and allowed to adhere and enter the growth phase before being treated with or without 15 μg/mL trastuzumab for 7 days in the appropriate culture medium. Cells were then harvested by trypsinisation and counted with trypan blue using the TC20 Automated Cell Counter . The appropriate culture media and trastuzumab were replaced every 3 days. All experiments were repeated three times with readings at least in triplicate for each concentration.Cells were seeded in triplicate in P100 plates at a density of 5 × 10To determine the IC50 of the mTOR inhibitors, a panel of HER2-positive breast cancer cell lines was treated with escalating concentrations of BEZ235, everolimus and TAK-228. Proliferation was measured by counting after 7 days of treatment. Viable cells were counted by trypan blue exclusion. IC50  was calculated using SigmaPlot software. Values are mean IC50 from three independent experiments.g for 10 min at 4 °C, and the supernatant was retained and stored. Protein extracts were quantified using the Pierce BCA protein assay kit , following the manufacturer’s instructions.Cells were washed with 3 mL PBS at RT. Next, cells were scraped in the presence of 150 µL lysis buffer  at 4 °C and transferred to a 1.5-mL tube. Cells were incubated in lysis buffer for 20 min at 4 °C and sonicated afterwards. Then the cell lysate was spun at 13,000× Protein aliquots were prepared at 1 µg/µL in 4× Laemmli loading buffer and boiled at 95 °C for 6 min. Twenty µL of protein extract was loaded in a 10% polyacrylamide gel (SDS-PAGE). Next, proteins were transferred to a nitrocellulose membrane for 1 h at 100 V and 4 °C. The membrane was blocked (5% milk in TBST 1×) for 1 h, washed 3 times for 10 min and then incubated with the primary antibody at RT overnight under agitation. The concentrations used were as follows: HER3 , p-HER3 Tyr1197 (1:1000), HER2 (1:500), p-HER2 Tyr1221/1222 (1:1000), AKT (1:1000), p-AKT Thr308 (1:300), p-AKT Ser473 (1:500), p44/42 MAPK (ERK1/2) (1:1000), p-p44/42 MAPK (ERK1/2) Thr202/Tyr204 (1:1000), 4E-BP1 (1:500); p-4E-BP1 Thr37/46 (1:500); p-4E-BP1 Thr70 (1:500); S6 ribosomal protein (S6) (1:500); p-S6 ribosomal protein (p-S6) Ser235/236 (1:1000)  and GAPDH . All primary antibodies were rabbit, except the anti-HER3, which was mouse; all were monoclonal. Then the membranes were washed 3 × 10 min in TBST and incubated with a secondary antibody (diluted in 2.5% BSA in TBS 1×) at RT for 1 h. ECL-anti-mouse and ECL-anti-rabbit secondary antibodies attached to peroxidase  were used at a concentration of 1:5000. The membranes were washed 3 × 10 min again, and immeserd in the detection reagent  for 1 min, prior to developing on a photographic film. Densitometry and quantification of proteins were carried out using ImageJ software.Before carrying out cell cycle detection and apoptosis, cell lines were synchronised by serum starvation for 24 h. Cell cycle and apoptosis were analysed after treatment with either vehicle  for 24 and 72 h, respectively. For cell cycle arrest analysis, cells were collected after treatment, washed with PBS and fixed with 70% cold ethanol at –20 °C for at least 2 h. Cells were incubated with 0.5 mg/mL RNase (Sigma-Aldrich) at 37 °C for 30 min, and finally stained with propidium iodide  for 10 min. Apoptosis was assessed with the Annexin-V-FITC Apoptosis Detection Kit (BD Biosciences) according to the manufacturer’s instructions. Flow cytometry was performed on a FACS Canto II (BD Biosciences), and data were analysed with FACS Diva software (BD Biosciences).t-test . This work was performed in accordance with the Reporting Recommendations for Tumour Marker Prognostic Studies (REMARK) guidelines [All data are expressed as means ± standard deviations for at least three replicates (unless otherwise indicated). Statistical significance was analysed by a two-tailed Student’s idelines .To test the efficacy of a combination of HER2 blockade with mTOR inhibition as a potential therapeutic strategy to overcome resistance to trastuzumab in HER2-positive breast cancer cell line (BCCL) models, we first developed four different cellular models with acquired resistance to trastuzumab . BrieflyTo determine the effects of BEZ235, everolimus and TAK-228 on the inhibition of the PI3K/AKT/mTOR proliferation axis in HER2-positive cells, the panel of eleven cell lines with varying sensitivity to trastuzumab was treated with increasing inhibitor concentrations. After seven days of treatment, cellular proliferation was measured to determine the IC50 for each drug and cell line . In geneIn view of these results, we considered that combining anti-HER2 therapy with each of these mTOR inhibitors might show a greater antiproliferative effect. For therapeutic studies, the concentration and time of treatments were based on previous reports, and administered as follows: Trastuzumab 15 µg/mL) [ µg/mL 1, everoliIn order to assess the potential synergistic effects of trastuzumab in combination with mTOR inhibitors, we performed viability assays in the four sensitive cell lines, as well as their correspondent resistant models. Overall, the combination of trastuzumab with BEZ235 or everolimus influenced the therapeutic response to a lesser degree than the combination treatment of trastuzumab with TAK-228 because, although it causes a reduction of mTOR activation in the cell lines, cell viability was not affected. In contrast, the combination of trastuzumab with TAK-228 significantly increased the therapeutic response in all cases, suggesting that the decreased mTOR activation status by TAK-228 affects sensitivity to trastuzumab.p-value < 0.01) and TAK-228 . In addition, a significant decrease in proliferation was also observed in trastuzumab with TAK-228 1 nM combination therapy . In the SK-BR-3.rT1 line, the combination of trastuzumab and TAK-228 5 nM decreased growth very significantly (44%) compared to treatment with trastuzumab (96%) and TAK-228 . The same effect was observed in the AU-565.rT2 line, with reduced proliferation in combination therapy . Finally, in EFM-192A.rT1, a significant decrease in proliferation was identified trastuzumab plus TAK-228 5 nM combined therapy compared to individual treatments .The treatment effect of the TAK-228 inhibitor was evaluated using two treatment concentrations (1 nM and 5 nM), as monotherapy and in combination with trastuzumab . A singlp-value < 0.001) and EFM-192A.rT1  compared to control and trastuzumab treatment conditions. Furthermore, in BT-474.rT3, the combined treatment of BEZ235 plus trastuzumab significantly reversed trastuzumab resistance compared to the trastuzumab treatment condition . In sensitive cell lines, trastuzumab combined with BEZ235 20 nM potentiated the effect of trastuzumab individually, with no significant effect.To test the effect of BEZ235 in combination with trastuzumab on cell proliferation, three concentrations of the drug  were selected, all below the IC50 value for all lines. The effect on cell proliferation was assessed in the four trastuzumab-sensitive and trastuzumab-acquired resistance lines . Using ap-value = 0.003) and EFM-192A.rT1  lines, compared to trastuzumab-alone treatment. Furthermore, this treatment combination enhanced the effect of trastuzumab in the four sensitive lines , SK-BR-3 (26%), AU-565 (35%) and EFM-192 A (31%)), decreasing their proliferation compared to the single-treatment conditions, without being statistically significant (Two concentrations of everolimus (0.5 nM and 1 nM) were selected below the IC50 value in all cell lines . Treatmenificant .The effects of drug combinations on BCCLs with primary resistance to trastuzumab were markedly dependent on each particular cell line . In the BEZ235 5 nM plus trastuzumab combination, 16% proliferation was identified compared to BEZ235 5 nM treatment (29%) and trastuzumab treatment (87%), with a significant reduction in proliferation . Finally, 8% proliferation was observed in the BEZ235 20 nM plus trastuzumab combination, compared to the single treatments . JIMT1 cell proliferation was not affected by any of the treatment conditions.Treatment with BEZ235 at any of the three concentrations tested in combination with trastuzumab resulted in a significant decrease in proliferation in the HCC1954 line. A 65% decrease in proliferation was observed in the BEZ235 1 nM plus trastuzumab condition compared to the single-treatment conditions , reversing trastuzumab resistance. In addition, treatment with everolimus 1 nM significantly reduced the proliferation of this line . In the JIMT-1 cell line, treatment with everolimus 0.5 nM, both alone and in combination with trastuzumab, showed no effect on cell proliferation, while treatment with everolimus 1 nM resulted in a significant reduction in cell proliferation .For everolimus, two concentrations (0.5 nM and 1 nM) were selected below the IC50 value in the cell lines, except in JIMT-1. Its effect on cell proliferation was evaluated in the untreated condition, treatment with trastuzumab 15 µg/mL, everolimus 0.5 nM or 1 nM and the combination of both treatments at the two selected everolimus concentrations . Treatmep-value < 0.001) and TAK-228 . Cell proliferation of the JIMT-1 line was not modified by any of the treatment conditions tested.The treatment effect of the TAK-228 inhibitor was evaluated using two treatment concentrations, 1 nM and 5 nM, in monotherapy and in combination with trastuzumab. This resistance reversal effect was also observed in the primary resistant line HCC1954. Combination therapy with trastuzumab and 5 nM TAK-228 significantly reduced cell proliferation compared to trastuzumab , p-4E-BP1 (Thr37/46) and p-4E-BP1 (Thr70) of the mTORC1 complex; and p-AKT (Ser473) of the mTORC2 complex, as well as their total forms; in addition, the analysis of the phosphorylated form of ERK was included. Protein expression profiling was performed after 24 h of treatment with trastuzumab 15 μg/mL, or treatment with TAK-228 5 nM, with TAK-228 50 nM, or the combination of trastuzumab plus TAK-228 at the two concentrations above, as well as the control condition.Combination treatment of trastuzumab with TAK-228 (at either of the two concentrations tested) resulted in a decrease in AKT phosphorylation levels (Ser473) in the BT-474 line, but not in the BT-474.rT3 line A. In botIn SK-BR-3 and SK-BR-3.rT1 lines, combined treatment consisting of TAK-228 50 nM and trastuzumab reduced p-AKT levels (Ser473) compared to baseline and trastuzumab treatment, with no change in total form expression B. In botIn AU-565 and AU-565.rT2 lines, treatment with TAK-228 in combination with trastuzumab resulted in decreased phosphorylation of AKT (Ser473) and S6 (Ser235/236) C. In addSimilarly, in the EFM-192A and EFM-192A.rT1 lines, treatment with TAK-228 at 5 nM and 50 nM and combination with trastuzumab resulted in inhibition of the PI3K/AKT/mTOR pathway D. In botThe molecular effect of TAK-228 on the two lines with primary resistance to trastuzumab  was also studied under the treatment conditions mentioned above. In the presence of combined treatment at both concentrations, the HCC1954 line showed a slight decrease in AKT phosphorylation (Ser473) . It was In summary, the combined treatment decreased the phosphorylation levels of HER2/HER3, diminished PI3K/AKT/mTOR signalling and limited ERK phosphorylation, as a direct consequence of the TAK-228 mechanism of action.p = 0.004, p = 0.009, respectively), and the combination with trastuzumab improved the cell cycle delay . AU-565 and EFM-192A lines showed an increase in G0/G1 arrest with trastuzumab  and TAK-228  treatment alone, but the effect was enhanced with the combined treatment . However, in the corresponding resistant lines AU-565.rT2 and EFM-192A.rT1, only TAK-228  and both treatments  were able to significantly induce cell cycle arrest. No significant changes were detected in the cell lines BT-474 and BT-474.rT3.The results of resistance reversal obtained in cell proliferation assays with the combination of trastuzumab plus TAK-228 led us to investigate whether the treatment would also have an impact on cell cycle control, as well as on apoptosis induction. We firstly checked cell viability at shorter times, after treatment with trastuzumab in combination with different concentrations of TAK-228, to discard a deleterious effect. Cell cycle arrest was analysed after treatment with trastuzumab, TAK-228 and the combination of both for 24 h, in the cell lines SK-BR-3, AU-565 and EFM-192A, as well as in their corresponding resistant lines, SK-BR-3.rT1, AU-565.rT2 and EFM-192A.rT1 A. We obsp = 0.02, p = 0.0001, respectively), but the combination of both drugs  showed a greater rise in cell death. Furthermore, treatment of BT-474.rT3 cells with trastuzumab alone induced a significant increase in the percentage of apoptotic cells (p = 0.03). In SK-BR-3 and SK-BR-3.rT1 cell lines trastuzumab did not significantly affect the percentage of apoptotic cells, though the treatment with TAK-228  or the combination of the two led to a significant increase in cell death.Apoptosis was determined by positive staining with annexin V by flow cytometry, including both early and late apoptosis. We analysed the apoptotic effect of each treatment as a single agent and in combination in the BT-474, BT-474.rT3, SK-BR-3 and SK-BR-3.rT1 cell lines B. In BT4The development of anti-HER2 targeted therapies to treat patients with HER2-positive breast cancer has proved to be effective in survival in both early and advanced settings. For this reason, trastuzumab has been the standard treatment for HER2-positive breast cancer for more than two decades. Despite this advance, almost all patients eventually experience disease progression on trastuzumab-based therapy, due to de novo or acquired resistance. Aside from alterations in the receptor itself, one mechanism that trastuzumab interferes with HER2 signalling is inhibition of the PI3K/AKT/mTOR signalling pathway . As a loHowever, given the importance of this network in the cellular processes of proliferation, differentiation and apoptosis, its inhibition can be expected to be compensated by hyperactivation of alternative molecular pathways, which would offer the tumour cells escape routes to continue oncogenesis and would eventually lead to the therapy failure. Therefore, it seems logical to test different inhibitors of the pathway, from PI3K to AKT to mTOR (both the mTORC1 and mTORC2 complexes) together with trastuzumab, to see which combination is most effective in controlling tumorigenesis and preventing the development of resistance. We decided to test three different inhibitors covering a broad spectrum of effectors in the pathway, from PI3K to the two mTOR complexes, to ensure the effective blockade of the pathway. One strategy has focused on inhibiting the HER2 signalling pathway more effectively with dual blockade approach. The combined use of trastuzumab and mTOR inhibitors has been shown to be more effective to treat HER2-positive breast cancer than single agents . In addiThe availability of four cellular models of acquired resistance to trastuzumab over an extended period of time (as well as models of primary resistance), in which we had observed hyperactivation of PI3K/AKT/mTOR pathway markers, led us to explore whether combined suppression of HER2 and PI3K/AKT/mTOR signalling was necessary to achieve optimal therapeutic efficacy, given that there are few such studies in the literature. BEZ235 is an inhibitor of the PI3K/AKT/mTOR pathway with a dual inhibitory capacity of PI3K and mTOR due to the high similarity of the tyrosine kinase domains of both proteins. The combination of trastuzumab plus BEZ235 targets those cells with alterations in the PI3K/AKT/mTOR signalling pathway, due to loss of PTEN or activating mutations in PI3K, while maintaining therapeutic pressure on other cells in the same heterogeneous population that are still sensitive to HER2-targeted drugs . Our resEverolimus is a rapamycin derivative with mTORC1 complex inhibitory capacity, approved by the FDA to treat postmenopausal patients with ER-positive and HER2-negative metastatic breast cancer. Early phase I trials demonstrated that this drug, in combination with trastuzumab, resulted in decreased cell proliferation in trastuzumab-sensitive cell lines . These rOur most conclusive results in cellular models, however, were obtained with the combination of trastuzumab plus TAK-228. TAK-228 is a competitive inhibitor of the ATP domain of mTOR that can simultaneously block the activity of the mTORC1 and mTORC2 complexes. In the three primary trastuzumab-resistant lines and the four lines with acquired resistance, dual blockade of the HER2 and PI3K pathways significantly increased the therapeutic response. In sensitive lines, the association of TAK-228 with trastuzumab significantly decreased cell proliferation and demonstrated, at the molecular level, an ability to block both mTOR complexes, decreasing phosphorylation of all the effectors analysed. Therefore, TAK-228 potentiates the inhibitory effect of trastuzumab on the PI3K/AKT/mTOR pathway . Our resSensitivity to trastuzumab is related to activating alterations of the PI3K/AKT/mTOR pathway (either by PIK3CA mutations , low/losHere, we demonstrate that dual blockade of HER2 and PI3K/AKT/mTOR signalling is effective in improving the therapeutic response in HER2-positive breast cell lines with long-term induced resistance to trastuzumab. The combination of trastuzumab with TAK-228 significantly increased the therapeutic response in all the cases A, suggesIn summary, our results obtained in models of sensitive breast cancer cell lines, lines with acquired resistance, and lines with primary resistance to trastuzumab, exposed to combination therapy with specific inhibitors of the PI3K/AKT/mTOR signalling pathway plus trastuzumab, suggest that this combination therapy favours the reversal of trastuzumab resistance. Inhibition of the PI3K/AKT/mTOR pathway using the mTORC1 and mTORC2 inhibitor, TAK-228, can reverse acquired resistance to trastuzumab in all models generated and in some primary resistant lines. When combined with trastuzumab, treatment with the inhibitor TAK-228 has been shown to be superior to the other two inhibitors tested, BEZ235 and everolimus, in reversing acquired trastuzumab resistance. However, in the presence of PI3K activating mutations, single and combined treatment with BEZ235 has been shown to be superior to treatment with TAK-228 and everolimus.


Targeted nanomedicine-based approaches that aim at the simultaneous delivery of synergistic drug combinations to multiple cellular populations are of high relevance for tackling heterogeneity on solid tumors. Considering that cancer stem cells (CSC) may originate from non-stem cancer cells, single-drug regimens targeting only one of these cell populations could enable tumors to evade treatments. As such, the identification of a common marker, such as nucleolin, might result in a therapeutic advantage. The results herein generated suggested a transversal role of nucleolin in the internalization of F3 peptide-targeted pegylated pH-sensitive liposomes into bulk ovarian cancer cells, including putative CSC-enriched ovarian cells. The intracellular delivery of a drug combination such as the one tested herein was relevant in the context of cell lines with higher intrinsic resistances to doxorubicin. The enhanced efficacy of the F3 peptide-targeted liposomal combination of doxorubicin/C6-ceramide was supported by the downregulation of the Akt pathway, within a specific range of basal level of expression.high/CD44high). Co-encapsulation of DXR:C6-ceramide into F3 peptide-targeted liposomes improved cytotoxic activity relative to liposomes containing DXR alone, in an extent that depended on the intrinsic resistance to DXR and on the incubation time. The enhanced cytotoxicity of the targeted combination was mechanistically supported by the downregulation of PI3K/Akt pathway by C6-ceramide, only among the nucleolin-overexpressing cancer cells presenting a basal p-Akt/total Akt ratio lower than 1.Targeting multiple cellular populations is of high therapeutic relevance for the tackling of solid tumors heterogeneity. Herein, the ability of pegylated and pH-sensitive liposomes, functionalized with the nucleolin-binding F3 peptide and containing doxorubicin (DXR)/C6-ceramide synergistic combination, to target, in vitro, ovarian cancer, including ovarian cancer stem cells (CSC), was assessed. The underlying molecular mechanism of action of the nucleolin-mediated intracellular delivery of C6-ceramide to cancer cells was also explored. The assessment of overexpression of surface nucleolin expression by flow cytometry was critical to dissipate differences identified by Western blot in membrane/cytoplasm of SKOV-3, OVCAR-3 and TOV-112D ovarian cancer cell lines. The former was in line with the significant extent of uptake into (bulk) ovarian cancer cells, relative to non-targeted and non-specific counterparts. This pattern of uptake was recapitulated with putative CSC-enriched ovarian SKOV-3 and OVCAR-3 sub-population (EpCAM Cancer is one of the leading causes of death worldwide and responsible for an estimated 10 million deaths in 2020. Among them, female breast and ovary represented 6.9% and 2.1%, respectively, of the total of cancer deaths in 2020  (DSPE-PEG2k), 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[maleimide(polyethylene glycol)-2000] , L-α-phosphatidylethanolamine-N-(lissamine rhodamine B sulfonyl) (RhoD-PE), N-hexanoyl-D-erythrosphingosine (C6-ceramide) were acquired from Avanti Polar Lipids  or Lipoid . F3 (KDEPQRRSARLSAKPAPPKPEPKPKKAPAKK) and the nonspecific (NS) peptide were custom synthesized by Genecust . All other chemicals were of analytical grade purity.Doxorubicin hydrochloride (DXR) was obtained from MicroBiopharm . Calcein, 4-(2-hydroxyethyl)piperazine-1-ethanesulfonic acid (HEPES), 2-(® HTB-77™) cancer cell line was cultured in DMEM high glucose . The ovarian cancer cell line TOV-112D (ATCC® CRL-11731™), the triple-negative breast cancer cell line MDA-MB-231 (ATCC® HTB-26™), the A549 (ATCC® CCL-185™) and H1975 (ATCC® CRL-5908™) human lung cancer cell lines and the nucleolin-overexpressing cell line MDA-MB-435S (ATCC® HTB-129™) were cultured in RPMI 1640 , both supplemented with 10% (v/v) of heat-inactivated Fetal Bovine Serum (FBS) , 100 U/mL penicillin, 100 µg/mL streptomycin . The OVCAR-3 (ATCC® HTB-161™) ovarian cancer cell line was cultured in RPMI 1640 , supplemented with 20% (v/v) of heat-inactivated Fetal Bovine Serum (FBS) , 100 U/mL penicillin, 100 mg/mL streptomycin . MCF12A (ATCC® CRL-10782™) cell line was cultured in RPMI 1640  supplemented with 5% (v/v) of heat inactivated FBS , 100 U/mL penicillin, 100 μg/mL streptomycin , 20 ng/mL human Epidermal Growth Factor  and 0.5 µg/mL hydrocortisone . All cell lines were maintained at 37 °C in a 5% CO2 atmosphere up to 1 month  to prevent unwanted mutations. Cells were routinely tested for mycoplasma contamination, following the Center for Neuroscience and Cell Biology (CNC) internal rules, and morphology was assessed by microscopy. Cell lines authentication was performed by short Tandem Repeat Profiling.The ovarian SKOV-3 (ATCC2k:DSPE-PEG2k-F3: C6-ceramide at 2.66:1.34:2:2:0.62:0.18:2 (18 mol% of ceramide) or 4:2:2:2:0.62:0.18:0 molar ratio, respectively, as previously described  , or with the corresponding isotypes IgG2b  and IgG1 K , for 30 min at 4 °C, in PBS-BSA. The cell-associated calcein signal was immediately analyzed by flow cytometry in a BD FACSCalibur system  and a total of 20,000 events were collected. Appropriate controls were used to assure the correct compensation of fluorescence signals in each channel.2. Afterwards, cell culture medium was exchanged for a fresh one and the experiment extended up to 96 (TOV-112D and A549), 120 (SKOV-3 and H1975) or 144 h (OVCAR-3), depending on the cell line used. Cell viability was assessed by the resazurin reduction assay, by monitoring absorbance at 570 nm and 600 nm (background) in a Spectramax Gemini EM . Cell death was calculated from the formula 100 − ((Test570–600 − CtrNeg570–600)/(Ctr570–600 − CtrNeg570–600)) × 100, where Test570–600, Ctr570–600 and CtrNeg570–600 were the corrected absorbance for treated cells, untreated controls and negative control, respectively.The cytotoxic potential of F3 peptide-targeted liposomal combinations of DXR:C6-ceramide against ovarian cancer  and non-small cell lung cancer (A549 and H1975) cell lines was assessed. Additionally, the cytotoxicity of free DXR or C6-ceramide against ovarian cancer  cell lines was also evaluated. Briefly, cancer cells, adherent to 96-well plates, were incubated with serial dilutions of different liposomal formulations containing DXR or free DXR or free C6-ceramide for 1, 4 or 8 h at 37 °C in an atmosphere of 5% CO50 of F3 peptide-targeted liposomal DXR:C6-ceramide combination at a molar ratio of 1:1 ([F3]L-DC611). SKOV-3 cells were incubated with 0.63 or 0.49 µM for 4 and 8 h, respectively, MDA-MB-231 cells were incubated with 25.9 or 6.35 µM for 1 and 4 h, respectively, at 37 °C of F3 peptide-targeted liposomal DXR ([F3]L-D), DXR:C6-ceramide combination at a molar ratio of 1:1 ([F3]L-DC611) and the non-targeted counterpart (L-DC611). DXR and C6-ceramide were quantified after extraction from the cultured cells using 500 µL of 80% of methanol and sonicated in a cup-horn for 2 min with 60% amplitude using cycles of 3 s on and 2 s off. Samples were frozen at −80 °C for 1.5 h and sonication was repeated with the same parameters as the previous step. Each sample (100 µL) was spiked with 10 µL of the internal standard solution (daunorubicin and C8-ceramide) and 10 µL of 50% acetonitrile + 0.1% formic acid followed by centrifugation for 20 min at 14,000× g, and the supernatant was collected for LC-MS/MS analysis. Calibration curves were prepared in cell-matrix by spiking 100 µL of blank cells with 10 µL of standard solution and 10 µL of the internal standards (IS) solution. Eight calibration points with concentration ranges of 0.009–0.597 µM for DXR and 0.001–0.075 µM for C6-ceramide were analyzed to generate the calibration curves. The samples were analyzed on an LC Nexera system  coupled to a hybrid triple quadrupole/linear ion-trap 4000 QTrap mass spectrometer operated by Analyst 1.6.3 . The injector was a CTC-xt . The chromatographic separation was performed using a Gemini C18 column  with a 4 × 2.0 mm C18 guard-column . The flow rate was set at 250 µL/min and mobile phases A and B were 0.1% formic acid in water and 0.1% formic acid in acetonitrile, respectively. The gradient elution was programmed to: 2–30% of B (0.0–0.3 min), 30–60% of B (0.3–3.5 min), 60–99% of B (3.5–4.0 min), 99% of B (4.0–7.9 min), 99–2% of B (7.9–8.0 min) and 2% of B (8.0–8.2 min). The ionization source  was operated in the positive mode set to an ion spray voltage of 5500 V, 40 psi for nebulizer gas 1 (GS1), 30 psi for the nebulizer gas 2 (GS2), 30 psi for the curtain gas (CUR), and the temperature was 600 °C. All molecules were analyzed by Multiple Reaction Monitoring (MRM) setting Q1 and Q3 at unit resolution, the entrance potential (EP) at 10 eV, the collision gas (CAD) at 8 psi, and the dwell time was 75 ms . Afterwards, RNA concentration and quality were determined using a NanoDrop 2000 . Samples presenting a 260/280 ratio under 1.9 were discarded. Samples of total RNA were stored at −80 °C until use. cDNA was obtained using the NZY First-Strand cDNA Synthesis kit  according to the protocol established by the manufacturer, using a Unocycler Thermal Cycler . Using species-specific pairs of primers, AKT1 and S6K1 gene expression was quantified by qRT-PCR using β-ACTIN or HPRT as housekeeping genes for data normalization. The primers were obtained from a primer bank database  and acquired from Integrated DNA Technologies   . NZYSpeeFollowing previous results obtained in breast cancer ,33, and p < 0.05 and ** p < 0.01, respectively), and in contrast with the result obtained with OVCAR-3 cells (p > 0.05). In addition, nucleolin was identified in the cytoplasmic/membrane fraction (lamin B1 negative) of all ovarian cancer cells tested (p < 0.05) and MDA-MB-231 (*** p < 0.001) cells, respectively (p < 0.01), MDA-MB-435S  and the non-stem cancer cell (EpCAMlow/−/CD44low/−) populations, but also putative intermediate cell populations (EpCAMhigh/CD44low/− and EpCAMlow/−/CD44high populations) (p < 0.05) or 7.1-fold (p < 0.001) higher cellular association to EpCAMhigh/CD44high SKOV-3 (low/−/CD44low/− non-stem cancer cell populations (non-SCC). These results supported the capacity of lipid-based nanoparticles functionalized with the nucleolin-binding F3 peptide to target, simultaneously, multiple cancer cell populations, including ovarian CSCs.CD44 and EpCAM markers, suitable for staining in flow cytometry, enabled the establishment of a gating strategy aiming at identifying not only the putative CSC (EpCAMlations) A. Accordlations) B,C. Thish SKOV-3 D or OVCAh SKOV-3 E sub-popTo further reinforce the therapeutic potential of the targeted drug combination, the cytotoxicity was then assessed.50 and IC90 values  was assessed. Herein, a new presentation strategy was adopted , in orde0 values  and faci90 values (90 higher than 50 µM), encapsulating either single (DXR) or drug combinations, targeted or non-targeted, for both 4 or 8 h incubation time. This higher resistance is particularly evident at the IC90. In fact, only the targeted drug combinations, either at a 1:1 ([F3]L-DC611) or 1:2 molar ratio ([F3]L-DC612) enabled a measurable 90% of SKOV-3 cancer cells death , following an 8 h incubation.The analysis of the IC0 values  clearly 90 values, but now moving to the TOV-112D cell line, the samples enabling the greener and smaller circles were still the targeted drug combinations, either at a 1:1 ([F3]L-DC611) or 1:2 molar ratio ([F3]L-DC612), closely followed by the targeted liposomes containing only DXR, for both 1  and 4 h  of incubation. With this cell line, a dependence on the incubation time was evidenced, for all of the tested samples, towards an increase of activity. As we move to the more sensitive cell line (OVCAR-3), differences among the IC90 values tend to dissipate. Nevertheless, the lowest values are still associated with [F3]L-DC611 and [F3]L-DC612, for both incubation times: 2.18 and 2.39 µM for 1 h and 1.11 and 1.77 µM for 4 h of incubation, respectively. Similar trends were observed within the set of IC50 values  in order to provide in the end, a cell density enabling drug quantification.To unravel some of the mechanisms supporting the improved cytotoxicity of the targeted drug combination, the capability of the developed F3 peptide-targeted lipid-based nanoparticle to enable intracellular drug delivery was assessed upon quantifying intracellular DXR and C6-ceramide by mass spectrometry. This experiment was performed with the triple-negative breast cancer MDA-MB-231 or ovarian cancer SKOV-3 cells, as in both, incubation with targeted drug combinations of DXR and C6-ceramide in a molar ratio of 1:1 enabled a 90% cell death, following 4 or 8 h, respectively, a level of cell death not reached by the counterpart containing DXR alone  enabled an intracellular accumulation of DXR that was always significantly higher than the one from the non-targeted counterpart (L-DC611), which instead was similar in the case of C6-ceramide (611) enabled intracellular levels of DXR similar to the ones from F3 peptide-targeted liposomes containing only DXR ([F3]L-D) in SKOV-3 and MDA-MB-231 cells.With this set of cells, an interesting observation arose. Regardless of the cell line or incubation time tested, the targeted formulation  or 24 h (p < 0.01) of incubation, relative to untreated cells. At 64 µM, a 5.4- or 25.8-fold decrease on p-Akt levels were observed following 4 (p < 0.001) or 24 h (p < 0.001), respectively, relative to untreated control  only at 32 or 64 µM of F3 peptide-targeted liposomal C6-ceramide  and their counterpart containing only DXR ([F3]L-D])  .Overall, these results pointed out that C6-Ceramide-mediated improved cytotoxicity of the F3 peptide-targeted drug combination is supported by the downregulation of phosphorylated/active Akt, an effect dependent on the basal levels of p-Akt .6) for 24 h, but without statistical significance , thus mediating their biological activities, such as cell differentiation, adhesion and leukocyte trafficking, inflammation, angiogenesis and tumor development ,41. Nuclp < 0.05) to the nucleolin positive control MDA-MB-435S cells. Interestingly, assessment of cell surface density alone, by flow cytometry, dissipated any previous differences relative to MDA-MB-435S, while evidencing a decrease on OVCAR-3 , with cell surface levels significantly higher than the non-neoplastic cell line tested, in line with previously demonstrated nucleolin overexpression in ovarian cancer SKOV-3 and OVCAR-3 cell lines . Th. Thp > 044low/−) . This is44low/−) ,7,45, be90 level of SKOV-3 cells, where the intrinsic resistance to DXR, relative to OVCAR-3 and TOV-112D , presenhievable .3H-DPPC, by C-26 cells was significantly lower than that of radiolabeled 14C-C6-ceramide at all times tested, suggesting the latter being translocated into the cells in its free form. Importantly, the extent of the transbilayer diffusion associated with short-chain sphingolipids was reduced in the presence of DSPE-PEG2k in the same formulation [Interestingly, similar intracellular levels of C6-ceramide in ovarian cancer SKOV-3 and triple-negative breast cancer MDA-MB-231 cell lines, upon delivery by non-targeted and targeted liposomes, suggested a passive delivery process, in contrast to DXR. This was actually in line with the previously described cellular uptake of short-chain sphingolipids . In factmulation . Moreovemulation , relativMechanistically, the cytotoxic activity may be underlined by two sets of mechanisms. While DXR is a topoisomerase II inhibitor inducing cell necrosis even when actively delivered ,37, the Additionally, basal protein levels of glucosylceramide synthase (GCS), an enzyme involved in ceramide metabolism  has beenThese observations were accompanied by a minimal impact on the mRNA levels of AKT1 and S6K1 , a downsImportantly, data suggested that pathway inhibition is not due to Akt degradation but rather to its deactivation. This is consistent with the proposed interaction of ceramide with PI3KC2β, an upstream effector of Akt pathway, suppressing its activation, and consequently decreasing p-Akt levels in ovarian cancer cells . Moreovehigh/CD44high). Interestingly, while the active intracellular delivery of DXR has been reinforced, C6-ceramide uptake seemed to be associated with a passive process, independent from the presence of the F3 peptide. Intracellular delivery of a drug combination as the one herein tested revealed to be relevant in the context of cell lines with higher intrinsic resistance to DXR, as in the case of ovarian SKOV-3 cells. In fact, the enhanced efficacy of the F3 peptide-targeted liposomal combination of DXR/C6-ceramide was supported by the downregulation of the Akt pathway, within a specific range of basal level of expression.Overall, upon a certain level of surface nucleolin expression as determined by flow cytometry, the results herein generated pointed towards a transversal role of nucleolin in the internalization of F3 peptide-targeted pegylated pH-sensitive liposomes into bulk ovarian cancer cells, including putative CSC-enriched ovarian SKOV-3 and OVCAR-3 cells (EpCAM