Primary cell cultures are objects of intense research due to their high impact on molecular biology and oncology advancement. Initially, the patient-derived specimen must be subjected to dissociation and isolation. Techniques for tumour dissociation are usually reliant on the organisation of connecting tissue. The most common methods include enzymatic digestion , chemical treatment (with ethylene diamine tetraacetic acid and ethylene glycol tetraacetic acid), or mechanical disaggregation to obtain a uniform cell population. Cells isolated from the tissue specimen are cultured as a monolayer or three-dimensional culture, in the form of multicellular spheroids, scaffold-based cultures , or matrix-embedded cultures. Every primary cell line must be characterised to identify its origin, purity, and significant features. The process of characterisation should include different assays utilising specific (extra- and intracellular) markers. The most frequently used approaches comprise immunohistochemistry, immunocytochemistry, western blot, flow cytometry, real-time polymerase chain reaction, karyotyping, confocal microscopy, and next-generation sequencing. The growing body of evidence indicates the validity of the usage of primary cancer cell lines in the formulation of novel anti-cancer treatments and their contribution to drug development.Primary cancer cell lines are  Cell culture development significantly changed the area of life sciences and contributed to great advancements in medicine. Research with the use of cell lines is an essential procedure for modelling diseases, stem cell and cancer investigation, and the establishment of therapies . The firCell lines, as we know them today, play a remarkable role in cancer research. The application of cancer cell lines enabled a better understanding of tumour biology and improved drug development . The exaThe main aim of this review is to present the most recent data referring to primary cell lines isolation and characterisation processes. This article highlights the variety of isolation methods depending on the cells’ source and its next implementation. Moreover, we aimed to introduce different molecular methods of isolated cells’ characterisation. This review is a summary of the available knowledge in the field of primary cell lines culture.ex vivo cell populations deriving directly from resected tissue samples, most commonly from core biopsies, fine-needle aspirates, pleural effusions, resections, or autopsy specimens , whole organs, saliva, urine, faeces, and other body liquids . Those are gathered for different purposes: diagnosis, clinical treatment, or research . LaboratThe Clinical Trials Regulation EU No 536/2014 (CTR) modulates research on biological materials of human origin. According to those directions, obtaining biological samples must be held only with the “donors” written informed consent. Alternatively, an authorisation may be given on behalf of the donor by an eligible person or representative authority. Written informed consent should enable the right to withdraw the approval and alter the scope of the agreement. The person concerned can stipulate the use of his/her biological material and personal data at any time. However, when gathering “patients” information, it is hard to foresee the purpose and scope of personal data processing for future research. Thus, it should be allowed to consent to certain areas of study, depending on the “donors” wishes. Alternatively, it is advised to compose an additional article of the consent, stating that the donor permits to use his/her data outside of the clinical trial protocol.1.Research should commence only after an independent evaluation of its scientific value, including the significance of the aim and confirmation of ethical standards by the appropriate committee [Biospecimens are usually collected during surgery or examination . For somStaphylococcus aureus, bacteria, and fungi , methicillin-resistant nd fungi . For thend fungi . Authorsnd fungi . As far nd fungi . Also, and fungi .Techniques for tumour dissociation are usually reliant on the organisation of connecting tissue. To obtain a uniform cell population, the most common methods include enzymatic digestion, chemical treatment, or mechanical dissociation .2. The procedure of cell isolation with collagenase and hyaluronidase is also successful in breast cancer  is effective in breast and ovarian cancer . DNase gc cancer . Dispasec cancer . This en enzymes  may be u enzymes . Isolati enzymes . The use enzymes .2+ and Mg2+, that provide integrity for the cell surface and intracellular structural matrix. The use of chemical agents enables removing those ions, which causes the loosening of intercellular bonds in, i.e., epithelial cells. The best chemicals used for sequestration are ethylene diamine tetraacetic acid (EDTA), ethylene glycol tetraacetic acid (EGTA), and tetraphenyl boron complexes with potassium ions  and free flow of cytokines and growth factors throughout the tissue-like gel. Hydrogels can be natural and synthetic, and natural ones are made with polymers, such as collagen, fibrinogen, Matrigel, gelatin, or alginate. Artificial models usually use synthetic polymers made up of polyethene glycol, polylactic acid, and poly(vinyl acetate). Polymeric scaffolds are replicating the structure of the ECM very accurately, thus they are an essential tool to study cell-to-ECM interactions. Hydrophilic glass fibres are crucial models for designing 3D tumours and tracking cell migration, however, this model is still being explored. Organoids mimic the microenvironment of organs and are very useful for modelling human diseases by pluripotent stem cells derived from patients .The multicellular spheroids model includes techniques such as hanging drop microplates, magnetic levitation, and spheroid microplates. The last approach is especially unique due to its ability to grow freely without any scaffold. Spheroid microplates enable the growth of multicellular cultures because of the large volume. Moreover, cell growth has significant differences between cells grown as multicellular spheres and cultured in 2D culture. The cells from 3D culture showed multidrug resistance, displayed stem-like behaviours, and their motility was increased. The hanging drop method uses gravitation force to induce self-aggregation of cells that further make up spheroids. This approach offers high replicability of results. Magnetic levitation is performed by injecting magnetic nanoparticles into cells that induce cell aggregation to spheroids when exposed to magnetic force. This generates a concentrated environment where ECM can be synthesised, and further biochemical assays may be performed, i.e., western blotting .Cell lines’ characterisation is crucial to identifying cell line origin, purity, and features. The process of characterisation and authentication should include different assays related to specific (extra- and intracellular) markers. The researchers should confirm whether obtained cell lines meet their expectations because, during multiple passages, the cell line may lose its key characteristic features . Additioin vivo state as much as possible, thus fixation is the most significant step. Then the sample is processed in paraffin wax, which provides a sort of supporting media. After that, tissue fragments of 4 μm thickness are dissected and placed in a water bath at the temperature of 45°C. The specimen is laid on a microscope slide and dried at 37°C overnight for better adherence of tissue to the slide. The sample is rewaxed before immunohistochemical staining (Immunohistochemistry (IHC) is a leading method for cancer diagnosis in clinical pathology. IHC uses monoclonal and polyclonal antibodies to identify specific antigens in specimens . There astaining . IHC is staining . IHC maystaining .INSM-1 that regulates the expression of neuroendocrine markers (Western blot (WB) is a commonly used method to separate and identify proteins. Initially, a mixture of proteins is separated during electrophoresis. Then, the proteins are transferred to the membrane, which is incubated with specific antibodies. Incubation is followed by washing which causes a rinse of unbounded antibodies. Antibodies bounded to proteins are detected and visualised on the membrane. The thickness of each band depends on the amount of protein . WB is u markers .Flow cytometry (FC) is a prominent method used in immunology, molecular biology, and cancer research. This approach utilises single or multiple laser light to analyse cells and particles suspended in buffered solution. Each particle undergoes analysis under visible light scatter and fluorescent assessment. Visible light scatters evaluated particles in the forward direction, which displays the comparative cell size and at 90°, which shows the cell’s complexity and granularity. Fluorescent assessment includes cell transfection and fluorescent proteins’ expression, staining with fluorescent dyes, or fluorescently conjugated antibodies. Visible light scatters independent of fluorescence measurement. The most common purpose of FC is immunophenotyping. It enables analysis of cell culture for various parameters, such as T and B cell markers, monocyte markers (CD14 and CD11b), NK cell markers (CD56 and CD161), activation markers (CD69 and CD25), memory markers (CD45RO and CD27), cytokines presence , and antigen-specific markers (MHC). Cell line’s features can be analysed in terms of proliferation (markers Ki67 and CFSE) and apoptosis (the activity of caspases) . Like otImmunocytochemistry (ICC) utilises labelling cells with antibodies to determine population homogeneity and molecular profile. This method visualises individual cells and enables evaluating the distribution of molecular markers in the cell culture. Immunocytochemistry includes cell morphology and exposes subcellular localisation of a particular antigen. The experiment should consist of the negative control (without primary antibody) and positive control (with a cell known to express given antigen) to determine staining propriety. Immunocytochemistry often uses fluorescently tagged secondary antibodies to reveal the primary antibody bounded to an epitope on the investigating molecule. A secondary antibody can detect the heavy chain of the primary antibody. This procedure utilises prolonged incubation for the primary antibody, followed by a few washes to remove the remaining antibodies. Then shorter incubation is applied for a secondary antibody with subsequent washing. Afterwards, the sample is processed under microscopy . The immImmunofluorescence (IF) is a method of choice while working with adherent cell lines. It simplifies the isolation of the individual signal and cell lines usually are not vulnerable to damages, due to similar auto-fluorescence as they exhibit from tissue sections . The immA confocal fluorescence microscope serves as a great tool for assessments in cells and tissues. Confocal microscopes generate high-contrast images through optical sectioning. It is achieved by a high-resolution objective lens which produces optical sections thinner than 1 μm without slicing the specimen. A single confocal image, also called a slice, is enough for quantification, although it is possible to use a series of confocal images to generate a 3D dataset. The 3D model enables the reconstruction and quantification of the whole sample volume. The use of fluorescent molecules is advantageous while assessing molecules’ localisation from various cellular compartments . In pracGene expression measurement in cultured cells by reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) is an essential technique used in molecular biology and medical research . This toKaryotype analysis includes pairing and ordering all organisms’ chromosomes, resulting in the genome-wide projection of the individual chromosomes. The preparation of karyotypes consists of staining, enabling exposure to chromosomes’ structural characteristics . KaryotyThere is an increasing demand for high throughput technologies for gene expression analysis of cell lines . In thisPrimary cell lines are pivotal for cancer research and have significantly contributed to understanding tumour biology, molecular processes, oncogene activation, and gene expression of individual patients. Primary cancer cultures are crucial for conceptualising therapeutic targets that may serve for future drug development or personalised cancer therapy. Patient-derived samples are currently extensively used for the improvement of existing treatments in personalised medicine. Moreover, the combination of primary cancer cell lines with advanced techniques (such as NGS) is a prospective cancer research tool that leaves the door open to identify novel biomarkers and oncogenes. Knowing the limitations and benefits of primary cell lines, scientists should pay more attention to the choice of model they will work on. It is important to also use established cell lines according to the purpose of the planned experiment. Nevertheless, the culture of primary cell lines is a key element in research towards personalised medicine, which is the future of cancer treatment.MR and OP wrote the manuscript with support from MM, IP, WMS, and TT. MR and OP declare an equal co-authorship. MM and IP participate in drafting the article and helped supervised the article. WMS and TT make substantial contributions to the conception and design of the article and give final approval of the version to be submitted and any revised version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


BRCA1 or BRCA2 that result in deficient homologous recombination repair (HRR). Use of olaparib, niraparib or rucaparib for the treatment of high-grade serous ovarian cancer, including in the maintenance setting, has extended both progression free and overall survival for women with this malignancy. While different PARP inhibitors (PARPis) are mechanistically similar, differences are apparent in their chemical structures, toxicity profiles, PARP trapping abilities and polypharmacological landscapes. We have treated ovarian cancer cell line models of known BRCA status, including the paired cell lines PEO1 and PEO4, and UWB1.289 and UWB1.289+BRCA1, with five PARPis  and observed differences between PARPis in both cell viability and cell survival. A cell line model of acquired resistance to veliparib showed increased resistance to the other four PARPis tested, suggesting that acquired resistance to one PARPi may not be able to be rescued by another. Lastly, as a proof of principle, HRR proficient ovarian cancer cells were sensitised to PARPis by depletion of BRCA1. In the future, guidelines will need to emerge to assist clinicians in matching specific PARPis to specific patients and tumours.Several poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitors are now in clinical use for tumours with defects in BReast CAncer genes  BRCA1 and BRCA2 mutated tumours, with clinical benefits seen in patients with mutations in these DNA repair genes such as ovarian [BRCA1 methylated tumours are also sensitive to PARP inhibition [RAD51C, RAD51D, ATM and PALB2, where tumours are described as having a “BRCAness” phenotype [The advent of pharmacological inhibitors of the DNA repair enzyme poly (adenosine diphosphate-ribose) polymerase (PARP) has heralded major therapeutic advances for malignancies that have defects in components of homologous recombination repair (HRR) pathways ,2,3. The ovarian , breast  ovarian , prostat ovarian  and panc ovarian . BRCA1 mhibition , as are henotype ,9,10. Wihenotype ,12,13.BRCA mutations. By inhibiting PARP function in cells with deficient HRR, cells lose their ability to choose the default BER pathway to repair DNA damage, creating synthetic lethality that leads to cell death [PARP family members function in a number of cellular processes including the regulation of gene transcription, chromatin remodelling and DNA repair ,15. PARPll death ,19,20.+) in a process known as PARylation. While PARP1 is reported to conduct more than 90% of PARylation associated with DNA damage, PARPs 2, 3, 4, 5a and 5b also demonstrate PARylation activity [PARP1, PARP2, PARP3, PARP4  and tankyrases 1 and 2 (PARP5a and PARP5b) are amongst the most studied members of the PARP family . Catalytactivity ,22. Inhiactivity ,24.®) was the first PARPi endorsed by the Food and Drug Administration (FDA) for the treatment of advanced germline BRCA-mutated ovarian cancer in 2014, followed by rucaparib (Rubraca®) for use to treat the same indication in 2016 [®) was endorsed in 2020 by the FDA as maintenance for advanced epithelial ovarian, fallopian tube or primary peritoneal cancer where patients have had complete or partial response to first-line platinum-based chemotherapy [®) became licensed by the FDA in 2018 for the treatment of BRCA-mutated HER2-negative breast cancers [BRCA mutations or mutations in other HRR-related genes such as PALB2 or RAD51C, loss of BRCA1 methylation that re-establishes HRR proficiency, the increase in expression of drug efflux pumps such as the MDR1 (p-glycoprotein) gene, aberrant replication fork protection and down-regulation of PARP proteins themselves, possibly as a result of PARP trapping [Olaparib  have defects in genes that function in HRR . We use BRCA2 mutant PEO1 cells were responsive to olaparib, niraparib and talazoparib over the dose curve compared with wild-type (WT) PEO4 cells, but not to rucaparib and veliparib in the context of cell viability  in PEO1 and PEO4 A, as weliability . Velipar2 status .BRCA1 mutant UWB1.289 cells were more responsive to all five PARPis tested based on LC50 data, compared to the paired cell line UWB1.289+BRCA1  [The human HGSOC cell lines UWB1.289, UWB1.289+BRCA1  and OVCAWT BRCA2 ,55. Thisstralia) ,52. Cleastralia) . Furtherstralia) . The A272 atmosphere.All cell lines were grown in RPMI 1640  supplemented with 10% FBS , with the exception of the UWB1.289 and UWB1.289 + BRCA1 cell lines which were maintained in 50% RPMI 1640 , 50% MEGM , supplemented with 3% FBS at 37 °C in a humidified 5% COTM Mycoplasma Detection Kit .Cell line authentication was performed by the Australian Genome Research Facility (AGRF) Melbourne, Australia, by short tandem repeat (STR) profiling using the GenePrint-10 System which co-amplifies ten loci, including the ASN-0002 loci  as well as D21S11. All cell lines tested negative for mycoplasma with the MycoAlertBRCA1 siRNA #13 , Chadstone, VIC, Australia), BRCA1 siRNA #14  or a non-silencing negative control  using HiPerfect transfection reagent (Qiagen). After 18 h, siRNA transfected cells were re-seeded for MTS or clonogenic assays. RNA was extracted 48 h post transfection using the RNeasy Mini kit  and 500 ng converted to cDNA using the SuperScript™ IV First-Strand Synthesis System . Quantitative real-time PCR (qRT-PCR) was performed using the TaqMan Fast Advanced Master Mix Kit  with Taqman assays BRCA1  and hydroxymethylbilane synthase (HMBS) endogenous control  on the QuantStudio 12K Flex Real-Time PCR System (Thermo Fisher Scientific). Each experiment was performed in triplicate and repeated at least three times, with data reported as the mean ± S.E.M.Cells were seeded into 6-well plates  for 24 h followed by transfection with 20 nM Cells were seeded into 96-well plates  and treated with niraparib, olaparib, rucaparib, talazoparib  or veliparib  for 5 days before being assessed for cell viability using the CellTiter 96 Aqueous One Solution Cell Proliferation Assay . This assay measures cellular metabolic activity and is a surrogate for cell viability. All results using this assay are described in the context of cell viability. Each experiment was performed in triplicate and repeated four times, with data reported as the mean ± SEM.Relative lethal concentration 50 (LC50), the concentration required to bring the dose curve halfway between the top and bottom plateau of the curve, was calculated using GraphPad Prism 9. To address the issue that some of the drugs tested did not achieve total loss of cell viability at high levels, data were normalised to vehicle alone (100%) and the highest drug concentration where a plateau was observed (0%). A non-linear regression curve was fitted to the data and LC50 concentrations extrapolated .w/v crystal violet in 25% v/v methanol for 5 min [Clonogenic cell survival assays measure the ability of a single cell to grow into a colony post an intervention, in this case treatment with a PARP inhibitor or down-regulation of a gene. All results using this assay are described in the context of cell survival. Cells were seeded into 6-well plates at a density of 1000 cells/well for UWB1.289+BRCA1 and OVCAR-3, 2000 cells/well for UWB1.289, 750 cells/well for PEO1, 1500 cells/well for PEO4, 200 cells/well for A2780 and 180 cells/well for A2780veliR. Cells were then treated with niraparib, olaparib, rucaparib, talazoparib or veliparib for 8–21 days. Cells were fixed with 100% methanol for 20 min, rinsed briefly with water and stained with 0.5% or 5 min . Colonieor 5 min . A Jupytor 5 min .post hoc test was used to test for multiple comparisons between cell lines at discrete drug dosages. For all analyses, p < 0.05 was considered statistically significant.IBM SPSS software version 27.0  was used for statistical analyses. All results are expressed as the mean ± SEM from at least three independent experiments unless otherwise stated. One-sample t-tests were used to assess the efficacy of gene down-regulation. Independent samples T tests were used to compare plating efficiency in cell survival assays. Two-way ANOVA were used to compare paired cell lines over dose courses for different PARPis. One-way ANOVA with Tukey’s


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.