Metastatic colorectal cancer (CRC) continues to be a major health problem, and current treatments are primarily for disease control and palliation of symptoms. In this study, we developed a precision medicine strategy to discover novel therapeutics for patients with CRC.Six matched low-passage cell lines and patient-derived xenografts (PDX) were established from CRC patients undergoing resection of their cancer. High-throughput drug screens using a 119 FDA-approved oncology drug library were performed on these cell lines, which were then validated in vivo in matched PDXs. RNA-Seq analysis was then performed to identify predictors of response.Our study revealed marked differences in response to standard-of-care agents across patients and pinpointed druggable pathways to treat CRC. Among these pathways co-targeting of fibroblast growth factor receptor (FGFR), SRC, platelet derived growth factor receptor (PDGFR), or vascular endothelial growth factor receptor (VEGFR) signaling was found to be an effective strategy. Molecular analyses revealed potential predictors of response to these druggable pathways.Our data suggests that the use of matched low-passage cell lines and PDXs is a promising strategy to identify new therapies and pathways to treat metastatic CRC. Colorectal cancer (CRC) continues to be a major public health problem, both in the United States and worldwide; it is the third most common cancer in the United States with approximately 150,000 new cases per year . MetastaOver the past decade, targeting molecular pathways of tumor growth/proliferation has become a major focus of anti-cancer treatments to develop new and novel drugs in CRC. For example, agents like bevacizumab, which targets the vascular endothelial growth factor (VEGF) pathway, or cetuximab and panitumumab, which target the epidermal growth factor receptor (EGFR) pathway, have become standard-of-care therapies. However, once patients have completed treatment or become resistant to these currently-available treatments, there are no effective options left for patients. Unfortunately, new drugs for the treatment of metastatic CRC have been limited, and over the past few years, only two drugs, regorafenib and lonsurf, have been approved in the refractory setting for the treatment of metastatic CRC.Like most other cancers, the failure rate for new cancer drugs is more than 80% in Phase II and 50% in Phase III , 5, and In the current study, we developed a precision medicine strategy for patients with metastatic CRC. Specifically, we developed a series of patient-matched cell lines and PDXs. The cell lines were first used to perform high throughput drug screens to identify potential therapeutic targets, and the matched PDXs were then used to validate these findings. Using this approach, we observed patient-specific heterogeneity in response to both standard-of-care agents and targeted therapies. Among the targeted therapies, ponatinib and trametinib were the most efficacious for different patient-derived models. Further mechanistic studies of ponatinib’s downstream targets demonstrated potential antitumor activity by co-targeting the fibroblast growth factor receptor (FGFR), SRC, platelet derived growth factor receptor (PDGFR) or vascular endothelial growth factor receptor (VEGFR) signaling. Consistent with these observations mining of next-generation RNA sequencing (RNA-Seq) data identified mutations in these pathways as potential molecular predictors of response. Together, our results support the use of a precision medicine pipeline to identify personalized therapies and predictive biomarkers for the treatment of metastatic CRC.Patient derived CRC tumor tissue samples were collected under a Duke Institutional Review Board (IRB) approved protocol (Pro00002435). All participants provided written informed consent to participate in the study. PDX models of CRC were then generated as described previously , 12, andMatched PDX cell lines were generated from the PDXs as follows. Once the PDX tumors reached a size of > 1000 mm3, tumors were harvested, homogenized and grown in 10 cm2 tissue culture-treated dishes in cell culture media , 10 U/ml penicillin and streptomycin) at 37 °C and 5% CO2. Clonal populations of each cell line were then obtained by isolating a single clone using trypsinization of the clone sealed off from the dish by an O ring. The following matched CRC PDXs and cell lines were generated and used in this study; CRC119, CRC057, CRC240, CRC247, 16–159 and 15–496. Cell lines were authenticated using the Duke University DNA Analysis Facility Human cell line authentication (CLA) service by analyzing DNA samples from each individual cell line for polymorphic short tandem repeat (STR) markers using the GenePrint 10 kit from Promega .https://dtp.cancer.gov/). CRC119, CRC057, CRC240, CRC247, 16–159 and 15–496 cell lines were plated in these drug pre-coated plates in a range of 500 and 1000 cells/well. Cell viabilities were assessed via CellTiter-Glo Luminescent Cell Viability Assay  72 h after cell plating. Percent killing was quantified using the following formula: 100*[1-].Automated liquid handling was provided by the Echo Acoustic Dispenser (Labcyte) for drug addition or Well mate (Thermo Fisher) for cell plating, and assays were performed using a Clarioscan plate reader (BMG Labtech). Immediately prior to cell plating, 384 well plates were stamped with 119 FDA-approved drug compounds at a final concentration of 1 uM. The compound library (Approved Oncology Set VI) was provided by the NCI Developmental Therapeutics Program  was purchased from Selleck Chemicals  and was solubilized in DMSO to a final concentration of 50 mM. Five replicates were used for each drug concentration. Each cell line was exposed to a series of seven different drug concentration (1.6 nM – 25 μM) after 24 h of incubation at 37 °C. Cell viability was measured 72 h following the addition of DMSO or drug via CellTiter-Glo Luminescent Cell Viability Assays , and IC50 values were calculated for each cell line using GraphPad Prism software .3. Tumor volume measurements were performed every other day using calipers, and the following formula was used to calculate tumor size: (length x (width)2)/2. The mice were euthanized by bilateral thoracotomy under CO2 induced anesthesia at the end of the study.To test the sensitivity of CRC119, CRC057 and CRC240 PDXs to ponatinib, oxaliplatin and irinotecan, 150 μl of homogenized PDX tissue-PBS suspensions at 150 mg/ml concentration were subcutaneously injected into the right flanks of 5 female and 5 male mice . Following injection, mice were randomized into control and treatment groups. 5 times a week in the morning oral dosing of ponatinib (30 mg/kg); 5 times a week in the morning intraperitoneal dosing of oxaliplatin (10 mg/kg) and irinotecan (10 mg/kg) were initiated when tumor volumes reached approximately 150 mmWestern blots were performed pre- and 24-h post-treatment with vehicle (DMSO) or ponatinib at IC50 doses of each cell line. A total of 100,000 cells were lysed in radioimmunoprecipitation assay (RIPA) lysis buffer supplemented with protease and phosphatase inhibitor cocktail (company), and a total of 50 μg of RIPA lysate was electrophoretically separated at 200 V on 4–20% sodium dodecyl sulfate polyacrylamide gels using a BioRad MiniProtean Tetra system. Subsequent to transfer onto nitrocellulose membranes at 50 V for 2 h, membranes were blocked in StartingBlock T20 (ThermoFisher) for 1 h at room temperature, followed by incubation in primary antibody diluted in StartingBlock T20 overnight at 4 °C with rocking. Membranes were washed three times for 5 min each in PBS + 0.05% Tween-20, incubated in corresponding Horse Radish Peroxidase (HRP) conjugated secondary antibodies according to the specifications of the manufacturer’s protocols. The Odyssey Infrared Imaging System (LI-COR Biosciences) was used for membrane imaging. The following primary antibodies and dilutions were used: FGFR1 (#9740), FGFR2 (#11835), pFGFR (#3471), pSRC (#2105), pVEGFR (#12599), pERK (#4377), pAkt (#4060), pSTAT5 (#4322), pSTAT3(#9131), β-Actin (#4970) antibodies ; pPDGFR (ab5460) antibody ; and pABL , FGFR3 , FGFR4  . All antibodies were used at 1:1000 dilutions.p-value < 0.05 was considered statistically significant.GraphPad Prism 6 software  was used for in vitro and in vivo data recording and statistical analysis. 2-way ANOVA analysis was used to compare the tumor size between control groups and treatment groups in vivo and drug sensitivity in vitro. A https://software.broadinstitute.org/gatk/). One hundred fifty bp PE reads were first aligned using STAR-2pass method with default parameters. The output SAM files were processed by using Picard (http://broadinstitute.github.io/picard/) subsequently to add read group, sort, mark duplicates and index. GATK tool was used for variant calling, and SnpEff was used to annotated the identified variants. In fusion analysis, STAR-Fusion (https://www.biorxiv.org/content/early/2017/03/24/120295) package developed by the Broad Institute was applied to detect fusion reads in the paired-end RNA-seq data with default parameters.The RNA-seq libraries were prepared and sequenced in Illumina HiSeq 4000 with 150 bp paired-end reads. The reads were aligned to human genome hg19. In variant calling analysis, pipeline of GATK  developeIn order to implement a precision medicine strategy for the treatment of metastatic CRC, a CRC precision medicine pipeline was created. We first developed patient derived models of cancer including low passage cell lines and patient derived xenografts (PDX) for patients undergoing resection of their CRC liver metastasis or primary colon at Duke University under an Institutional Review Board (IRB)- and Institutional Animal Care and Use Committee (IACUC)-approved protocol. For each patient, matching cell lines and PDXs were developed as previously described , 12. CRCAs the first step in identifying potential therapeutic targets, we performed a series of in vitro high-throughput drug screens using our patient-derived cell lines. The drug screen consisted of 119 FDA-approved small molecule inhibitors, and we first analyzed responses to commonly-used cytotoxic chemotherapeutic agents. In general, our CRC cell lines appear to be sensitive to anthracyclines (doxorubicin and epirubicin) and vinca alkaloids (vincristine and vinblastine) and resistant to platinum agents (cisplatin and carboplatin) and alkylating agents (ifosfamide and cyclophosphamide) Fig. a. We nexp = 0.0002) and resistant to oxaliplatin treatment. Similarly, as predicted by our in vitro drug screen CRC240 PDX was resistant to both chemotherapeutic agents  were divided into 2 groups  and treated with oxaliplatin (10 mg/kg) and irinotecan (10 mg/kg) intraperitoneally five times a week. Consistent with our in vitro data, the CRC119 PDX tumor was sensitive to irinotecan   Fig. c. No sigOur personalized medicine pipeline identified ponatinib as a potentially effective agent to treat CRC. Ponatinib is a multi-kinase inhibitor that targets the fibroblast growth factor receptor (FGFR), platelet derived growth factor receptor (PDGFR), vascular endothelial growth factor receptor (VEGFR), SRC, and ABL . As ponaTo identify the common pathway or pathways that drive CRC growth in these patient-derived cell lines, we next screened CRC057, CRC119 and CRC240 with specific inhibitors of ponatinib’s targets, including ABL, FGFR, PDGFR, SRC and VEGFR. However, all three cell lines were found to be resistant to the specific inhibitors  was found to be responsive to an irinotecan therapy and simlary her cell and PDX were also found to be sensitive to irinotecan Fig.  In additDeregulation of the FGFR signaling pathway plays an important role in carcinogenesis . GenomicSimarily, we do realize the limitations of our current precisioin medicine pipeline. As this was our intial proof of concept developoment of our pipeline, our drug screen contained only 119 drugs and the majority of the targeted agents in our screen targeted multiple pathways suggesting that in our screen, combinatorial therapy may be critical to find the optimal therapy in CRC. In addition, our screen which used cell lines limits our in vitro studies that involves anticancer compounds that target the microenvironment. Therefore, this limitation may cause underpredicted in vitro cell line response to these compounds, as in CRC240, which was found to be moderately sensitive to ponatinib in vitro, but quite sensitive in vivo. Despite these challenges, in vitro cell line models have still been widely used for initial pharmacogenomic studies as they allow for simple and low cost biological research but future work will determine if using patient derived organoids which overcome the challenges of cell lines can be used in our precision medicine pipeline.The development of precision medicine strategies for cancer faces numerous challenges, including accessing patient samples, establishing reliable models for testing, and the genetic and non-genetic diversity inherent within the ever-evolving cancer. Here we propose a pipeline and workflow to address several of these challenges. By establishing patient-matched cell lines and PDXs we are able to leverage the speed and flexibility of in vitro systems while simultaneously providing a robust system for in vivo validations that takes into account, at least in part, tumor heterogeneity and contributions of the tumor microenvironment. Indeed, we have previously shown that PDXs faithfully recapitulate patient tumor histology and preserve tumor-associated stroma and coupled with high-throughput screens and genomics, this pipeline represents a useful paradigm to identify and validate new treatments for CRC that can be expanded to other solid tumors.Supplementary FigureOur current work has provided a framework for a precision medicine approach to identify new treatments for patients with metastatic CRC (Additional file 1: Supplementary Figure 1. RNA-Seq data to identify fusion and mutations in FGFR1–4. Supplementary Figure 2. Precision Medicine Strategy for Metastatic Colorectal Cancer. Supplementary Figure 3. Uncropped western blots showing all the bands with the molecular weight markers.Additional file 2: Supplementary Table 1. Mutations in FGFR1–4


UBC, UBB and RPS27A, in MKN45 compared to 23132/87. However, while the total Ub protein content and the distribution of Ub between the conjugated and free pools were similar in these two GC cell lines, the proteasome activity was higher in MKN45. Ub gene expression was not affected upon YY1, HSF1 or SP1 small interfering RNA (siRNA) transfection, in both 23132/87 and MKN45 cell lines. Interestingly, the simultaneous knockdown of UBB and UBC mRNAs reduced the Ub content in both cell lines, but was more critical in the primary GC cell line 23132/87, causing a reduction in cell viability due to apoptosis induction and a decrease in the oncoprotein and metastatization marker β-catenin levels. Our results identify UBB and UBC as pro-survival genes in primary gastric adenocarcinoma 23132/87 cells.Gastric cancer (GC) is one of the most common and lethal cancers. Alterations in the ubiquitin (Ub) system play key roles in the carcinogenetic process and in metastasis development. Overexpression of transcription factors YY1, HSF1 and SP1, known to regulate Ub gene expression, is a predictor of poor prognosis and shorter survival in several cancers. In this study, we compared a primary (23132/87) and a metastatic (MKN45) GC cell line. We found a statistically significant higher expression of three out of four Ub coding genes,  Ubiquitin (Ub) is a highly conserved 76 amino-acid protein that is covalently conjugated to target proteins by the consecutive actions of three enzymes E1, E2, E3) in a process known as ubiquitylation  into a colored formazan product that is soluble in culture medium. This conversion is accomplished by NADPH or NADH produced by dehydrogenase enzymes in metabolically active cells. The quantity of formazan product, measured by absorbance at 490 nm, is directly proportional to the number of living cells in the culture. The results were expressed as a percentage of residual cell viability compared to control cells treated with 20 nM siGFP (set at 100% cell viability) for both 23132/87 and MKN45 cell lines.The effect of siRNA transfections (performed in 96-well plates as detailed above) on cell viability was evaluated by using the CellTiter 96 AQueous One Solution Cell Proliferation Assay . This assay is based on the reduction of the MTS reagent [3--5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)2N-succinyl-Leu-Leu-Val-Tyr-7-amido-4- methylcoumarin  as previously described [2 and 10 mM KCl and the reaction was initiated by addition of 0.2 mM fluorogenic substrate. The breakdown of the peptide was monitored using a fluorescence microplate reader  with an excitation wavelength of 355 nm and an emission wavelength of 460 nm. Proteasome activity in each sample, expressed as fluorimetric units/min/mg, was calculated by submitting data to linear regression analysis (R2 > 0.99).The chymotrypsin-like activity of the 20S proteasome was measured in 23132/87 and MKN45 GC cell lysates using the fluorogenic substrate escribed . Briefly2 and 1% Nonidet P40, supplemented with a cocktail of protease inhibitors. After 20 min incubation on ice, cell extracts were cleared by centrifugation and protein content was determined by the Bradford assay. Twenty micrograms of extract were incubated at 37 °C for 90 min in a water bath with 0.5 μg of recombinant Usp2 protein or without Usp2 addition (for the undigested control), in a final volume of 40 μL. The digestions were stopped by adding an equal volume of SDS-PAGE sample buffer and boiling. The effective deconjugation of Ub after Usp2 treatment was always verified by running undigested and digested extracts in parallel. To quantify total Ub protein levels, different amounts of Usp2-treated extracts were run on the same gel, in parallel with different amounts of purified ubiquitin (Sigma-Aldrich) used as the reference standard, and submitted to western immunoblotting analysis with an antibody against Ub [2) for ubiquitin standard curves was always in the range 0.98–0.99.23132/87 and MKN45 cells were washed with ice cold PBS and lysed in a buffer consisting of 20 mM Hepes/KOH pH 7.9, 25% glycerol, 0.42 M NaCl, 1.5 mM MgClainst Ub . The adjt-test performed with GraphPad Prism Software version 3.06  was used for statistical analysis of the data; differences between groups were considered statistically significant when p < 0.05. The actual p-values have been reported in the text.The data were expressed as mean ± SD from at least three independent experiments. Student’s


Berberine is multifunctional natural product with potential to treat diverse pathological conditions. Its broad-spectrum anticancer effect through direct effect on cancer cell growth and metastasis have been established both in vitro and in vivo. The cellular targets that account to the anticancer effect of berberine are incredibly large and range from kinases (protein kinase B (Akt), mitogen activated protein kinases (MAPKs), cell cycle checkpoint kinases, etc.) and transcription factors to genes and protein regulators of cell survival, motility and death. The direct effect of berberine in cancer cells is however relatively weak and occur at moderate concentration range (10–100 µM) in most cancer cells. The poor pharmacokinetics profile resulting from poor absorption, efflux by permeability-glycoprotein (P-gc) and extensive metabolism in intestinal and hepatic cells are other dimensions of berberine’s limitation as anticancer agent. This communication addresses the research efforts during the last two decades that were devoted to enhancing the anticancer potential of berberine. Strategies highlighted include using berberine in combination with other chemotherapeutic agents either to reduce toxic side effects or enhance their anticancer effects; the various novel formulation approaches which by order of magnitude improved the pharmacokinetics of berberine; and semisynthetic approaches that enhanced potency by up to 100-fold. While increasing sirtulin-1 (SIRT1) expression, berberine further downregulated p66Shc in doxorubicin intoxicated cells. The study by Xiong et al. [2+]i), attenuated [Ca2+]m and restored the loss of ΔΨm in vitro. In vivo, berberine could also suppress the serum creatine kinase, creatine kinase isoenzyme (CK-MB) and MDA levels while increasing SOD and CAT levels. The histopathological injury amelioration by berberine was a further evidence of its potential to ameliorate the side effect of doxorubicin . T. T15, 10line 17,  and berb (50 μM) . In addiIn view of berberine possessing photosensitive characteristics, its combination with photodynamic therapy in renal carcinoma cell lines was evaluated . The incIt is worth reiterating that poor intestinal absorption, extensive efflux by the action of P-gc and extensive degradation account to the rather poor bioavailability/pharmacokinetics profile of berberine. The various formulation approaches specifically designed to tackle this problem and improve berberine’s anticancer efficacy are shown in O-lipophilic derivatives synthesised by Liu et al. dioxolo isoquino isoquinolin-7-ylium bromide) was the most active against HeLa and HL-60 cell growth in vitro. With IC50 values of these compounds range from 0.7 to 16.7 µM for HL-60 cells and 36 to > 200 µM for HeLa cells (48 h), the prototype compound 23 was shown to induce cell cycle arrest at G2/M and S phases with 30-fold superior antiproliferative activity  and 6-fold higher apoptosis-inducing activity in HL-60 leukaemia cells than berberine. Of the 9-O-berberine derivatives are also compounds 25 and 26 . This was based on the known overexpression level of IDO1 in cancer cells and modulatory role in cancer development and malignancies. Those with more pronounced activity than berberine were compounds -donating compounds were also synthesised to improve the anticancer potential of berberine. The experiment by Chen et al. . O. OO-subsO-berberine , the role of ROS as mediators of anticancer effect of 50 was evident. Another example and rather simple 9-O-alkyl berberine derivative is a sesquiterpene ether prepared by Lo et al. . T. TO- and the highest selectivity index (SI > 20). Since the compound could induce cytoplasmic vacuolation which was not observed for berberine, other novel targets may be involved in the observed anticancer effect.The 9-s 53–59,  and thei62), NAX103 (60), NAX111 (63), and NAX114 (64)) on the viability of breast cancer cell lines. The most active compound was NAX060 (62) with cytotoxicity in the most sensitive cell line, HER-2 overexpressing SK-BR-3 cells, associated with a build-up of sub-G1 population while reducing the G0/G1 and G2/M phase cells. The compounds also act against HER-2 negative tumour cells, such as the human triple-negative MDA-MB-231 cells. In another study by Pierpaoli et al. . I. IO-derrnalogues  at the tplex DNA . A rathen et al. . In a raN-Mannich base berberine derivatives (75–87). In radical scavenging (DPPH and ABTS) assay, (79), (77) and (87) were active while cytotoxicity activity in HeLa cell was evident for (75) and (81). Compound 87 was the most active in CaSki cell line, but weak in SK-OV-3 cell line. On the other hand, compound 75 was more cytotoxic than (79) in Caki-2 cell line. Another synthesis approach by Mistry et al. [N-Mannich bases derivatives of berberine linking piperazine structural moieties at C-12 position. In cervical cancer HeLa cells, 88–97 showed of IC50s < 6 μg/mL, and SI higher than > 30 as IC50s > 125 μg/mL was obtained in Malin-Darby canine kidney (MDCK) cell line. Moreover, the radical scavenging potential of 88–97 was found excellent with IC50s, < 13 μg/mL and < 8 μg/mL in DPPH and ABTS assay, respectively.Mistry et al.  synthesiivatives  with a sy et al.  resulted98–101 could induce cytotoxic activity in seven human cancer cell lines , with IC₅₀ values of 0.02 ± 0.01–13.58 ± 2.84 μM. The most active compound was 13-n-octyl palmatine (101) with an IC₅₀ of 0.02 ± 0.01 μM for SMMC7721. Given the level of potency displayed by these 13-n-alkyl berberine analogues (98–101), the study also included in vivo studies in mice that bear S180 sarcoma xenografted in vivo. The activity via i.p. dosage showed that 99 and 101 were more active than 98 or 100 with compound 99 showing the most tumour inhibitory rate of 59.86% at a dose of 2.5 mg/kg. Unfortunately, compounds 98, 99 and 101 displayed higher toxicity than berberine. The cytotoxicity of synthetic 13-n-alkyl berberine and palmatine analogues  were ass102)  have been obtained. With improvement in pharmacokinetics by up to 30-fold, the anticancer potential of berberine both in vitro and in vivo have been shown to be increased by using novel formulations . The other productive area of research in improving the anticancer efficacy of berberine was through a derivatization approach. Numerous berberine derivatives of 9-, 12- and 13-derivatives as with others have been prepared. Incredibly, over 100-fold improvement in efficacy has been shown for some berberine analogues. The overall research approach of improving the anticancer effect of berberine in recent years is depicted in


O-tetradecanoylphorbol-13-acetate (TPA)—and in vitro—in cell lines representative of different stages of mouse-skin chemical carcinogenesis, as well as in human squamous carcinoma cell (SCC) lines. Moreover, we identify CD44v10 in the mouse-skin carcinogenesis model as the only CD44 variant isoform expressed in highly aggressive spindle carcinoma cell lines together with CD44s and podoplanin. We also characterized CD44v3-10, CD44v6-10 and CD44v8-10 as the major variant isoforms co-expressed with CD44s and podoplanin in human SCC cell lines. Immunofluorescence confocal microscopy experiments show that these CD44v isoforms colocalize with podoplanin at plasma membrane protrusions and cell–cell contacts of SCC cells, as previously reported for CD44s. Furthermore, CD44v isoforms colocalize with podoplanin in chemically induced mouse-skin SCCs in vivo. Co-immunoprecipitation experiments indicate that podoplanin physically binds to CD44v3-10, CD44v6-10 and CD44v8-10 isoforms, as well as to CD44s. Podoplanin–CD44 interaction is mediated by the transmembrane and cytosolic regions and is negatively modulated by glycosylation of the extracellular domain. These results point to a functional interplay of podoplanin with both CD44v and CD44s isoforms in SCCs and give insight into the regulation of the podoplanin–CD44 association.Podoplanin and CD44 are transmembrane glycoproteins involved in inflammation and cancer. In this paper, we report that podoplanin is coordinately expressed with the CD44 standard (CD44s) and variant (CD44v) isoforms in vivo—in hyperplastic skin after a pro-inflammatory stimulus with 12- Squamous cell carcinoma (SCC) is one of the most frequent cancers worldwide. It arises in cells of stratified epithelia such as the epidermis, esophagus and mucosal linings of the upper airways, lips, mouth, salivary glands, throat and larynx . Cutaneous SCC is the second most common non-melanoma skin cancer—and although most of them are easily eradicated by surgical excision, a subset of these tumors become highly aggressive and show higher recurrence and metastatic rates, causing the death of patients . On the Podoplanin  is a Type I transmembrane mucin-like sialoglycoprotein whose expression is upregulated in a variety of cancers, including SCCs ,6,7 PodoThe podoplanin molecule lacks obvious enzymatic motifs, hence, it must exert its biologic and pathologic functions through protein–protein interactions. A number of partners, such as the C-type lectin-like receptor 2 (CLEC-2), ezrin and moesin members of the ERM  protein family, CD9 tetraspanin, and the standard isoform of the hyaluronan receptor CD44s, among others, have been found to interact with podoplanin in different cell types and contexts ,17,18. PCD44 is a highly polymorphic Type I transmembrane glycoprotein which is ubiquitously expressed in the body . It is tIn this article, we have characterized the expression of CD44v isoforms in human SCC cell lines and report the coordinate expression of podoplanin with CD44s and CD44v isoforms in vivo and in vitro. Colocalization and co-immunoprecipitation experiments suggest that podoplanin interacts with both CD44s and CD44v isoforms in SCC cells through the transmembrane (TM) domain. The podoplanin–CD44 interaction is modulated positively by the CT tail and negatively by glycosylation of the EC domain.All the experimental procedures with mice were approved by the internal ethical research and animal welfare committee  and by the local authorities . They complied with the European Union (Directive 2010/63/UE) and Spanish Government guidelines . Seven to nine weeks old C57Bl/6 mice were shaved 24 h before the initiation of the treatment. For skin TPA treatment, 200 μL of 62.5 μg/mL TPA solution in acetone was applied to the shaved skin of each animal. Control animals were treated with the same volume of acetone. Animals were killed at 12, 24, 48, 72, 96, 120 and 144 h posttreatment and the skin was harvested, divided in two pieces and immediately frozen or fixed in 10% formaldehyde and embedded in paraffin for histological analysis.Skin tumors were induced in FVB/N mice by initiation with 32 µg of DMBA in acetone and promotion once per week for 14 weeks with 62.5 μg/mL TPA, as previously described . A total2 humidified atmosphere.The origin, characteristics and cell culture conditions of murine epidermal cell lines and human SCC cell lines are described in CD44s and CD44v constructs tagged with enhanced green fluorescent protein (eGFP) or hemagglutinin (Ha) were obtained by PCR amplification using primers that carry convenient restriction sites to facilitate subcloning into pEGFP-N1 or pcDNA3-Ha vectors, as described in Transient transfections in HN5 and HEK-293T cells were performed with 1 μg of cDNAs, using either FuGENE 6  or Lipofectamine 2000 , following the manufacturer indications. After 24 h, cells were either lysed for immunoprecipitation and Western blot experiments or seeded on glass coverslips for immunofluorescence analysis.Reverse transcription was performed as previously described . The priHN5 cells co-transfected with PDPN-mCherry and CD44 isoforms fused to eGFP were seeded on glass coverslips, fixed with 3.7% formaldehyde for 20 min and permeabilized with 0.05% Triton X-100/1x PBS for 10 min. Confocal images were acquired on a spectral LSM/10 laser-scanning microscope (Zeiss), using objectives Plan-APOCHROMAT 40× and 63×. Images were assembled using the FIJI program.Double-label immunofluorescence detection of podoplanin and CD44v isoforms in mouse-skin tumors was performed in deparaffinized sections, after antigen retrieval with 1 mM EDTA, using a rat monoclonal antibody  specifically recognizing mouse podoplanin  and rabbP-40, 150 mM NaCl, 50 mM Tris-HCl pH 8.0) and a cocktail of protease inhibitors  and phosphatase inhibitor (500 mM NaF). Samples containing the same amount of protein (20–30 μg) were run on 10% SDS-PAGE and transferred to Immobilon-P PVDF membranes . Filters were then immunoblotted with the following Abs: anti-Ha (Merck), anti-mouse podoplanin , anti-hFor co-immunoprecipitation experiments, HEK293T cells were transiently co-transfected with expression vectors as indicated above. After 24 h, cells were lysed in IP buffer  supplemented with a cocktail of protease and phosphatase inhibitors. Tagged podoplanin-FLAG protein was immunoprecipitated from about 750 μg of cell lysate using anti-FLAG  mAb and protein G Dynabeads . After incubation overnight at 4 °C, beads were washed 4 times with lysis buffer and eluted in Laemmli buffer. The co-immunoprecipitated products were detected in a Western blot using the rabbit anti-Ha Ab (Merck).O-tetradecanoylphorbol-13-acetate (TPA) [We have previously reported that podoplanin expression is induced in mouse-skin upon a pro-inflammatory stimulus with 12-te (TPA) . In ordeA coordinate upregulation of podoplanin with CD44s and CD44v is also observed in vivo during malignant progression of mouse-skin chemical carcinogenesis . Yet, whWe also analyzed the expression of podoplanin and CD44 in cell lines representative of different stages of mouse-skin carcinogenesis: immortalized non-tumorigenic keratinocytes (MCA3D), benign papilloma (PB), well to moderately differentiated SCCs  and poorly differentiated spindle cell carcinomas . The profile of RT–PCR expression showed a switch from CD44v transcripts (500–900 pb) to CD44s transcripts (~300 pb) in SpCC cells when compared with the other cell lines A. This CNext, we studied the expression of podoplanin and CD44 by RT–PCR in a panel of human SCC cell lines from different origins: pharynx , tongue (HN5), submandibular (A253), facial epidermis (SCC13) and non-tumorigenic immortalized skin keratinocytes HaCaT. We used first the pair of primers hs5/hs3 aimed to amplify all CD44 transcripts at once A. All ceWestern-blot analysis revealed the presence of at least five polypeptide bands in the cell lines F. The baWe have previously reported that CD44s and podoplanin colocalize at the plasma membrane of HN5 cells . TherefoConsequently, we studied the interaction of CD44v3-10, CD44v6-10, CD44v8-10, CD44s and CD44sC9 with podoplanin by co-immunoprecipitation analysis. To this end, we engineered constructs encoding CD44 isoforms tagged with Ha epitope at the C-terminus. Western-blot analysis of these constructs expressed in HEK293T cells confirmed the expected molecular sizes for CD44 isoforms A; comparTo analyze the regions of the podoplanin molecule involved in the interaction with CD44, we performed immunoprecipitation experiments with HEK293T cells co-expressing CD44s-Ha and several constructs encoding podoplanin mutants tagged with FLAG A. These Because the mutant PDPN-S/Tm had no effect in the binding of podoplanin to CD44, we reasoned that glycosylated Ser and Thr impairing podoplanin–CD44 interaction should be clustered into specific regions of the EC domain. Based in the available data from the literature ,24,50,52We also investigated the role of the TM domain by using other heterologous transmembrane podoplanin mutants in addition to PDPN-TMCD45: PDPN-TMSYN, PDPN-TMGPA and PDPN-TMERBB2, in which the TM domain of podoplanin was substituted by that of synaptobrevin, glycophorin A and the tyrosine kinase receptor ERBB2 , respectively B. The coIn summary, this extensive immunoprecipitation analysis suggests a main role of the TM and CT domains in podoplanin–CD44 interaction, which is hampered by glycosylation of the EC domains.In a previous report, we demonstrated the physical cooperation of podoplanin and CD44s in promoting directional migration of SCC cells . In the Podoplanin expression is coordinately upregulated with both CD44v and CD44s isoforms in the skin upon a proinflammatory stimulus with TPA, and the expression of these proteins is coincidentally associated with a skin hyperplasia. In addition to stimulation with TPA, the expression of podoplanin is induced in basal epidermal keratinocytes and dermal fibroblast-like cells during wound healing and psoriasis ,53,54,55A switch in CD44 isoform expression from CD44v to CD44s has been associated with EMT of epithelial cell lines ,58. ThisIn this work, we also characterized CD44v3-10, CD44v6-10 and CD44v8-10 as the main CD44v isoforms co-expressed with CD44s in human SCC cell lines. In addition, truncated CD44v and CD44s isoforms with shorter cytoplasmic tails (CD44vC9 and CD44sC9) were found to be expressed in SCC cells. These truncated CD44 isoforms have been described in the literature , but theThe primary structural region involved in podoplanin–CD44 interaction seems to be the TM domain, since substitution of this podoplanin region by that of synaptobrevin or glycophorin A largely inhibited or completely abolished this interaction, while heterologous podoplanin molecules with TM regions from CD45 and ERBB2 bound CD44 with similar efficiencies as wild-type podoplanin. These results strongly suggest that there are structural motifs within the TM domain of podoplanin shared by ERBB2 and CD45—but not by synaptobrevin and glycophorin A, which are involved in podoplanin–CD44 interaction. In support of this, ERBB2 has been found to bind CD44 ,71, althIn addition to the TM domain, the CT tails appear to be relevant for podoplanin–CD44 interaction. This is based in the following observations: first, the podoplanin binding efficiency of CD44sC9, containing a truncated cytoplasmic domain, was highly decreased with respect to the full-length isoform CD44s; and second, deletion of the podoplanin CT tail (PDPN-ΔCT mutant) largely inhibited podoplanin–CD44 interaction. Members of the ERM protein family bind a cluster of basic amino acids in the CT tails of both podoplanin ,17 and CIn summary, here we present evidence of a physical interaction of podoplanin with CD44s and CD44v isoforms expressed in human SCC cells, suggesting a functional interplay between podoplanin and CD44 likely associated with cancer cell invasion. A background for this assumption is our demonstration that podoplanin cooperates with CD44s in SCC cells to promote directional migration . Since bIn the present study, we report that podoplanin interacts with both CD44s and CD44v  isoforms expressed in SCC cell lines. This interaction appears to be mediated by the podoplanin TM domain with a requirement of the CT tail to stabilize it and is negatively modulated by glycosylation of Ser and Thr residues located between the PLAG3 and PLAG4 motifs and downstream PLAG4 in the EC domain. These findings are important to better understand the biologic function of the interplay between podoplanin and CD44, given the involvement of both glycoproteins in critical pathophysiological processes, such as the immune response, inflammation and cancer invasion and metastasis. Further studies remain to be conducted to assess the functional significance of podoplanin–CD44 interaction.


Resistance to endocrine therapy is a major clinical challenge in the management of oestrogen receptor (ER)-positive breast cancer. In this setting, p53 is frequently wildtype and its activity may be suppressed via upregulation of its key regulator MDM2. This underlies our rationale to evaluate MDM2 inhibition as a therapeutic strategy in treatment-resistant ER-positive breast cancer.We used the MDM2 inhibitor NVP-CGM097 to treat in vitro and in vivo models alone and in combination with fulvestrant or palbociclib. We perform cell viability, cell cycle, apoptosis and senescence assays to evaluate anti-tumour effects in p53 wildtype and p53 mutant ER-positive cell lines  and MCF-7 lines resistant to endocrine therapy and to CDK4/6 inhibition. We further assess the drug effects in patient-derived xenograft (PDX) models of endocrine-sensitive and endocrine-resistant ER-positive breast cancer.We demonstrate that MDM2 inhibition results in cell cycle arrest and increased apoptosis in p53-wildtype in vitro and in vivo breast cancer models, leading to potent anti-tumour activity. We find that endocrine therapy or CDK4/6 inhibition synergises with MDM2 inhibition but does not further enhance apoptosis. Instead, combination treatments result in profound regulation of cell cycle-related transcriptional programmes, with synergy achieved through increased antagonism of cell cycle progression. Combination therapy pushes cell lines resistant to fulvestrant or palbociclib to become senescent and significantly reduces tumour growth in a fulvestrant-resistant patient-derived xenograft model.We conclude that MDM2 inhibitors in combination with ER degraders or CDK4/6 inhibitors represent a rational strategy for treating advanced, endocrine-resistant ER-positive breast cancer, operating through synergistic activation of cell cycle co-regulatory programmes. Oestrogen receptor (ER)-positive breast cancer accounts for ~ 70% of breast cancer diagnoses. For many patients, the disease is treated effectively by surgery and adjuvant endocrine therapy. Unfortunately, ~ 35% of patients receiving endocrine therapy relapse with resistant disease, either through inherent resistance to treatment or the emergence of acquired endocrine resistance , 2. ResiWhen functioning normally, p53 is activated in response to diverse genotoxic stimuli and initiates transcriptional programmes that lead to cell cycle inhibition, DNA repair and, in the case of irreversible damage, apoptosis. In ER-positive breast cancer and liposarcoma, the incidence of p53 mutation is relatively low . MDM2 anMDM2 is relatively low, increased abundance of MDM2 protein occurs in ~ 38% of all breast cancers and is more frequent among ER-positive than in ER-negative tumours . Fluorescent signals were visualised using the Odyssey CLx Imaging System (LI-COR). Anti-p21 12D1 (2947) and anti-p53 (9282) were sourced from Cell Signalling, and anti-MDM2 2A10 (ab16895) was sourced from Abcam.2(C.I.) < 0 indicates synergy.Cells were seeded into 96-well plates at a density of 3000 cells in 100 μl media per well. Treatment was delivered via replacement of the media in each well with fresh media containing the indicated concentrations of drugs the following day. Mitochondrial activity (as an indication of cell viability) was assessed using the alamar blue assay (Thermo Fisher) according to the manufacturer’s instructions 2 days after treatment. Experiments were performed in three independent replicates. Growth inhibition was calculated using the formula  Talalay . CombinaSenescence was assayed by visualising β-galactosidase activity using Senescence β-galactosidase staining kit  according to the manufacturer’s instructions. Three fields of approximately 100 cells were scored for each of three independent replicates for each treatment condition.Flow cytometry was performed on a Becton Dickinson CantoII flow cytometer using BD FACSDIVA software, and the results were analysed using Flowjo software (Tree Star Inc.). Cells were treated with 10 nM Bromo-deoxyuridine (BrdU) for 2 h prior to harvest by addition of trypsin. BrdU was detected using FITC-conjugated anti-BrdU (BD Bioscience) according to the manufacturer’s protocol. Total DNA was detected by co-staining with propidium iodide (PI). For quantification of apoptosis, cells were assessed using the APC AnnexinV Apoptosis Detection Kit with PI  as per the manufacturer’s instructions. Between 5000 and 10,000 cells were analysed per sample and each experiment was conducted in triplicate.3 sections of tumour tissue were implanted into the 4th inguinal mammary gland of 6–8-week-old female NOD-SCID-IL2γR−/− mice  [3 (using the formula: width2 × length × 0.5) using an online randomisation tool . One hundred milligrammes/kilogramme NVP-CGM097 (20 mg/ml in 2% DMSO) was administered by oral gavage 5 days a week. Five milligrammes/body fulvestrant (50 mg/ml in peanut oil—Sigma P2144) was delivered by sub-cutaneous injection once per week. Mice were treated for 60 days or until tumour volume reached 1000 mm3.All in vivo experiments, procedures and endpoints were approved by the Garvan Institute of Medical Research Animal Ethics Committee (protocol 15/25). KCC_P_3837 (HREC/13/RPA/187) was derived from an untreated grade 3, ER-positive, PR-positive, HER2-negative primary invasive ductal carcinoma. Gar15-13  was resected from a breast cancer liver metastasis of an endocrine-resistant ER-positive, PR-negative, HER2-negative metastatic breast cancer [ty Ltd.) . For KCCTumour tissue was harvested and immediately fixed in 10% neutral buffered formalin at 4 °C overnight before dehydration and paraffin embedding. Antibodies used for IHC were anti-ER  and anti-Ki67 . Primary antibodies were detected using biotinylated IgG secondary antibodies , using streptavidin-HRP (Agilent) for amplification of signal followed by the addition of 3,3′-diaminobenzidine (Sigma) substrate. Images were scanned using Leica Aperio Slide Scanner (Leica Biosystems) and analysed using QuPath software to differentiate tumour tissue from stroma and necrosis, and to quantify Ki-67 positivity in tumour tissue.χ2 test for cell cycle phase analysis). * = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001, ns = p > = 0.05.Statistical analyses were performed in GraphPad Prism  or in Microsoft Excel . Gene set enrichment analysis (GSEA) and pathway annotation were attained using cluterProfiler [http://software.broadinstitute.org). Pathway enrichment was analysed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.genome.jp/kegg/pathway.html).Total RNA was extracted from breast cancer cell lines and tissue samples using RNeasy Mini Kit (Qiagen) following the manufacturer’s instructions. For tumour tissue, snap-frozen sections were first disrupted in lysis buffer in a TissueLyser II (QIAGEN). Sequencing data was processed on the Illumina HiSeq 2500 v4.0 System. Sequences were trimmed using Trim Galore (version 0.4.4) and genome-guided alignment to a human reference (HG38) was performed using STAR software (version 2.5). Feature counts were computed in R (version 3.4.3) using the package Rsubread . Differed [edgeR , 24 and Profiler  in R. GSTP53 mutation (~ 20%) [TP53 mutation or deletion across public cohorts in the cBio Cancer Genomics Portal   database (http://p53.iarc.fr). In the same clinical datasets, no instances of MDM2 mutation or deletion were detected (data not shown). Thus, the inhibition of MDM2 to increase p53wt activity could be a viable strategy in both primary and metastatic ER-positive breast cancer settings.ER-positive primary breast cancer has a relatively low incidence of rg) Fig. a. The inTP53wt-dependent manner [TP53wt MCF-7 and TP53wt ZR75-1 responded to 1 μM NVP-CGM097, exhibiting a loss of normal morphology with the adoption of stellate morphology and greater separation between cells  T-47D, however, showed no morphological changes after 48 h treatment with 1 μM NVP-CGM097   or 1 μM NVP-CGM097 for 48 h , CCND1 (cyclin D1), SFN (14-3-3-σ) and GADD45A (Gadd45), which supports the activation of molecular mechanisms involved in cell cycle arrest. MDM2 inhibition also led to increased mRNA levels of apoptosis markers, including FAS, BAX (Bax), BID (Bid) and BBC3 (PUMA). These findings confirm NVP-CGM097-mediated p53 activation of downstream targets at the molecular level in MCF-7 cell lines.We next performed RNA Sequencing (RNA Seq) on MCF-7 cell to interrogate effects on key p53 targets that are associated with cell cycle progression and programmed cell death Fig. e. Treatmχ2p < 0.001) with concomitant decrease in the proportion of cells in S phase and an increase in the proportion of cells in G2/M   or on the distribution of cells in the cell cycle (χ2p = 0.78) after treatment with NVP-CGM097. We quantified the effect of NVP-CGM097 on apoptosis in MCF-7, ZR75-1 and T-47D by staining for phosphatidylserine on the cell surface using fluorescent AnnexinV. Treatment with 1 μM NVP-CGM097 for 96 h significantly increased the proportion of apoptotic cells compared to vehicle-treated populations in both MCF-7 and ZR75-1   over a 2-h pulse  model of endocrine-sensitive ER-positive breast cancer derived from a surgically resected primary treatment-naïve ER-positive breast cancer (KCC_P_3837). Tumours were implanted concurrently with estradiol pellets and once tumour volume reached 100–150 mm3 were treated with vehicle  or NVP-CGM097 (100 mg/kg by daily gavage) for 60 days. NVP-CGM097 significantly inhibited tumour growth  on viability when combining the drugs . By contrast, the inclusion of 1 nM fulvestrant yielded no significant additional effect, suggesting that the synergy observed in the viability assays does not occur via enhancement of p53-mediated apoptosis.Fulvestrant is a standard therapeutic option in the treatment of women with advanced ER-positive breast cancer. We therefore combined NVP-CGM097 with fulvestrant and measured viability of parental MCF-7 cells to test for an enhanced effect through drug additivity or synergy Fig. a. We calugs Fig. b. We theugs Fig. c. As preugs Fig. f, treatmp value < 0.05 and 2-fold change), 576 genes were significantly regulated by NVP-CGM097  that control proliferative activities and tumour growth alongside downregulation of the anti-senescence markers FOXM1 and E2F1 [2/M cell cycle genes, including the levels of its known mitotic targets CCNB1 and CDC20 [CDKN1A (p21) and CDKN2B (p15)—master effectors of multiple tumour suppressor pathways—and pro-apoptotic markers, such as BBC3, BAX, BID and BAK1, also contributes to the regulation of cell homeostasis, although our in vitro data of combination therapy indicate that synergy between treatments is not dependent on increased apoptosis.Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differential gene expression induced by combination therapy revealed a significant negative regulation of and E2F1 . FOXM1 and CDC20 , 32. The2/M Checkpoint, in combination treated cells compared to either drug alone ) (Table Sp = 4.45 × 10−7).Gene set enrichment analysis (GSEA) revealed enhanced downregulation of key hallmarks involved in cell proliferation and cell cycle regulation, including E2F Targets and Gone Fig. e. To qua Tables S–3 as fol) Table S. Using tχ2p < 0.001) resulting in a decrease in the proportion of cells in S phase, consistent with our findings from parental MCF-7 cells, and no significant difference in distribution was observed with the addition of 100 nM fulvestrant   cell lines. MCF-7 FasR were generated by the continuous administration of 100 nM fulvestrant for 12 months to MCF-7 cells. Resistance was confirmed by examination of relative proliferation of the parental MCF-7 cells and MCF-7 FasR cells using an Incucyte confluence assay Fig. SA. We firant Fig. b. Increaant Fig. B. Treatmant Fig. d. Conseqnt) Fig. e.Fig. 4wt PDX model of endocrine-resistant ER-positive breast cancer, Gar15-13 [p < 0.01; vehicle vs combination p = 0.011, all other pairwise comparisons not significant at α = 0.05)   Fig. SC, demonsp < 0.05) induced by NVP-CGM097 (n = 141), fulvestrant (n = 411) and combination therapy (n = 484)  treated tumours showed differential genes  gene expression changes induced by NVP-CGM097 (n = 572), palbociclib (n = 392) and combination therapy (n = 714) (S17–19), compared to vehicle (0.01% DMSO). Combination therapy encompassed about 90% of the changes promoted by either agent alone . Combination of NVP-CGM097 with palbociclib further downregulates the senescence gatekeepers MYBL2, FOXM1, CCNB1 and E2F1 −8 M (95% C.I. 6.7 × 10−8 M to 1.1 × 10−7 M), as determined by alamar blue assay  in distribution from vehicle  following treatment for 48 h with 1 μM NVP-CGM097  or 500 nM palbociclib-treated cells (p < 0.001) but the combination treatment yielded no significant increase compared to NVP-CGM097 alone . MCF-7 PalbR were generated by the continuous administration of 500 nM palbociclib for 6 months to MCF-7 cells. Resistance was confirmed by examination of relative growth inhibition of the parental MCF-7 cells and MCF-7 PalbR cells to a range of doses of palbociclib   for 48 h. Treatment with either single-agent NVP-CGM097 or the combination with palbociclib increased expression of the senescence marker p21   [The treatment of patients with endocrine-resistant ER-positive breast cancer is extremely challenging, as in this setting, disease leads inevitably to death. Numerous mechanisms of treatment resistance have been identified. While some of these pathways are targetable, for instance, the PI3K pathway and the cyclinD-CDK4/6-Rb axis; the optimal next-line treatment strategies are unclear. In this context, MDM2 inhibition to reactivate p53 is particularly attractive in ER-positive breast cancer as it has a relatively low incidence of  (~ 20%) , meaningA number of small molecule MDM2 inhibitors have been designed and are at various stages of clinical development . NVP-CGM2/M checkpoint signalling and induction of senescence, rather than via a general upregulation of p53-dependent apoptotic pathways above the level of MDM2 inhibition alone. Moreover, we find these same pathways are specifically targeted in response to combined inhibition of both MDM2 and the CDK4/6 pathway.In this study, we show that reactivation of wildtype p53 using NVP-CGM097 is an effective inhibitor of tumour growth in multiple models of endocrine-naïve, endocrine-resistant and CDK4/6 inhibitor-resistant ER-positive breast cancer in vitro and in vivo. We identify the key mechanisms of the synergistic interactions between NVP-CGM097 and endocrine therapy, which occurs through the inhibition of E2F targets and G1 and G2, in TP53wt ER-positive breast cancer cell line (MCF-7 and ZR75-1) models. The only setting in which we did not observe anti-tumour effect was in the case of the TP53mut T-47D cell line model which supports our conclusion that activity of NVP-CGM097 requires wildtype p53. MDM2 inhibition was characterised by very high expression of the key cell cycle regulator p21—which is a p53 target and principal component of cell cycle arrest in G1 and G2 and in the induction and maintenance of senescence [Consistent with activation of p53, NVP-CGM097 induced apoptosis, and cell cycle arrest in Gnescence .1. Our data suggest that combining MDM2 inhibition with endocrine therapy leads to a profound arrest of cell cycle processes and our in vitro analysis of the MCF-7 FasR cell line supports this finding, consistent with an increase in senescence as a possible mechanism.Our data from ER-positive PDX models shows that NVP-CGM097 is effective as a single agent, and it combines with fulvestrant to be highly effective in the fulvestrant-resistant Gar15-13 PDX model. In the only other breast cancer PDX experiments so far reported, another MDM2 inhibitor MI-77301 was effective at reducing tumour growth of two fulvestrant-resistant PDX models . Similarwt is effective in a model of CDK4/6 inhibitor-resistant breast cancer. Interestingly, we have demonstrated that p53-induced cell cycle inhibition in palbociclib-resistant MCF-7 is predominantly in the G2 phase. CDK4/6 inhibition acts to prevent G1 cell cycle entry by preventing the phosphorylation of Rb [1/S checkpoint [1 checkpoints are still susceptible to G2 arrest. The combination of MDM2 inhibition and CDK4/6 inhibition engage pathways that remain intact in palbociclib and endocrine-resistant cells, unlike the G1/S checkpoint which is frequently disabled in these resistance contexts.CDK4/6 inhibitors in combination with endocrine therapy are the standard of care for advanced breast cancer in the first- and second-line treatment settings. However, resistance to combination therapy will inevitably develop [on of Rb –42, and 1 phase or G2/M phase [2/M accumulation along with the downregulation of FOXM1 and other senescence-related transcripts appears to be sufficient to trigger entry into senescence. CDK4/6 inhibitors are known to increase senescence entry and MDM2 can counteract this [We also show that rechallenging palbociclib-resistant cells with palbociclib in combination with MDM2 inhibition increases B-galactosidase staining and p21 expression, both indicative of senescence. Likewise, fulvestrant-resistant cells treated with combination MDM2 inhibition and fulvestrant become senescent. Senescence entry occurs from either G/M phase . Thus, iact this .Collectively, our results suggest that although ER has been shown to be a negative regulator of p53 , 45, 46,Additional file 1: Table S1. Differential expression analysis of MCF7 cell lines following 48 hours treatment with NVP-CGM097 versus vehicle (0.01% DMSO). Table S2: Differential expression analysis of MCF7 cell lines following 48 hours treatment with Fulvestrant versus vehicle (0.01% DMSO). Table S3. Differential expression analysis of MCF7 cell lines following 48 hours treatment with combination therapy (NVP-CGM097 + fulvestrant) versus vehicle (0.01% DMSO). Table S4. Differential genes induced by combination therapy (NVP-CGM097 + fulvestrant) versus vehicle (0.01% DMSO). Table S5. KEGG PATHWAY enrichment analysis of differentially expressed genes following treatment with combination of NVP-CGM097 and fulvestrant versus vehicle (0.01% DMSO). Table S6. Combination effect quantified for all genes following treatment with NVP-CGM097, fulvestrant and combination across cell lines. Table S7. Differential expression analysis of the PDX breast cancer model treated with NVP-CGM097 versus vehicle. Table S8. Differential expression analysis of the PDX breast cancer model treated with fulvestrant versus vehicle. Table S9. Differential expression analysis of the PDX breast cancer model treated with combination therapy versus vehicle. Table S10. Gene set enrichment analysis of DE genes induced by NVP-CGM097 in MCF-7 cell lines. Table S11. Gene set enrichment analysis of DE genes induced by fulvestrant in MCF-7 cell lines. Table S12. Gene set enrichment analysis of DE genes induced by combination therapy in MCF-7 cell lines. Table S13. Gene set enrichment analysis of DE genes induced by NVP-CGM097 in the PDX treated model. Table S14. Gene set enrichment analysis of DE genes induced by fulvestrant in the PDX treated model. Table S15. Gene set enrichment analysis of DE genes induced by combination therapy in the PDX treated model. Table S16. Combination effect quantified for all genes following treatment with NVP-CGM097, fulvestrant and combination in the PDX model. Table S17. Differential expression analysis of MCF7 cell lines following 48 hours treatment with NVP-CGM097 versus vehicle (0.01% DMSO). Table S18. Differential expression analysis of MCF7 cell lines following 48 hours treatment with palbociclib versus vehicle (0.01% DMSO). Table S19. Differential expression analysis of MCF7 cell lines following 48 hours treatment with combination therapy  versus vehicle (0.01% DMSO). Table S20. Combination effect quantified for all genes following treatment with NVP-CGM097, palbociclib and combination across cell lines.Additional file 2: Fig. S1 MDM2 inhibition activates p53 and reduces tumour proliferation in vitro and in vivo. A. Full gel and blot scans for the Western blots shown in Fig. B. Analysis of cell cycle phase using flow cytometry to quantify propidium iodide staining of genomic DNA shows significant alterations to cell cycle phase distribution in p53wt models consistent with arrest in both G1 and G2 after incubation for 48 hours with 1μM NVP-CGM097. Red = G1 (bottom), blue = S (middle), green = G2/M (top). Statistical significance from χ2 test using the vehicle treated profile as the expected value is indicated. C. NVP-CGM097  significantly inhibited tumour volumes compared to vehicle  at endpoint. Final tumour volumes were compared using two-tailed T test to determine significance. D. Representative images of Ki-67 quantification of endpoint tumours in Qupath software showing the classification of different tissue compartments: tumour (red and blue), stroma (green), and necrosis (black); and detection of Ki-67 negative and positive tumour cells. A single classifier was applied to all tumour sections. Fig. S2. NVP-CGM097 treatment causes gene expression changes in cell cycle and p53 pathways in vitro. A. Multidimensional scaling (MDS) plot showing the level of sample similarity between MCF-7 cell lines treated with vehicle, NVP-CGM097, fulvestrant and combination therapy (NVP-CGM097 plus fulvestrant). B. Venn diagram showing the overlap between differentially expressed genes  induced by treatment in MCF-7 cell lines. C. KEGG pathway analysis using RNA-Seq transcriptomic data shows a significant negative enrichment of Cell Cycle regulation in MCF-7 cell lines following 48 hours of treatment with 1μM NVP-CGM097 and positive enrichment of p53 Signalling Pathway. D. KEGG enrichment analysis showing p53 Signalling Pathway activation in MCF-7 cell lines following treatment with NVP-CGM097. Fig. S3 Enrichment analysis of significant differentially expressed genes against curated gene sets. Fig. S4 MDM2 inhibition induces changes in gene and protein expression associated with the cell cycle in endocrine resistant models in vitro and in vivo. A. Incucyte analysis of cell numbers of parental MCF-7 cells (blue) and MCF-7 FasR cells (red) over time after treatment with 100 nM fulvestrant. Vehicle treated parental MCF-7 cell growth (grey) is shown for comparison. B. Full gel images for the Western blots shown in Fig. C. Quantification of ER staining in the nucleus and cytoplasm of three independent replicates per arm from PDX model Gar15-13 following 10 days of treatment with vehicle , NVP-CGM097 , fulvestrant  or the combination of fulvestrant and NVP-CGM097 (purple). Tumours treated with fulvestrant alone or in combination had significantly reduced ER staining in both cellular compartments compared to tumours not treated with fulvestrant. Tumour cell identification and ER quantification were performed in QuPath software. D. Heat map of significant downregulated genes from the E2F Targets and G2/M Checkpoint gene sets in the Gar1513 PDX model. E. Normalised log2 expression levels of significant p53 target genes in in the Gar1513 PDX model induced by NVP-CGM097 (red), fulvestrant (blue) and the combination of NVP-CGM097 and fulvestrant (purple), averaged by condition, associated with cell cycle, apoptosis and senescence. Fig. S5. MDM2 inhibition in combination with CDK4/6 inhibition synergistically downregulates cell cycle markers in vitro. A. MDS plot of MCF-7 cell lines treated for 48 h with Vehicle (0.01% DMSO), 1 μM NVP-CGM097, 500 nM palbociclib, or the combination of NVP-CGM097 and palbociclib. B. Venn diagram showing the overlap between differentially expressed genes  induced by treatment in MCF-7 cell lines. C. Viability of parental MCF-7 (grey) and MCF-7 PalbR (orange) after treatment with the indicated dose of palbociclib for 72 h. Values are shown relative to vehicle treated cells. D. Full gel images for the Western blots shown in Fig.


Breast cancer is a highly heterogeneous disease characterized by multiple histologic and molecular subtypes. While a myriad of breast cancer cell lines have been developed over the past 60 years, estrogen receptor alpha (ER)+ disease and some mutations associated with this subtype remain underrepresented. Here we describe six breast cancer cell lines derived from patient-derived xenografts (PDX) and their general characteristics.Established breast cancer PDX were processed into cell suspensions and placed into standard 2D cell culture; six emerged into long-term passageable cell lines. Cell lines were assessed for protein expression of common luminal, basal, and mesenchymal markers, growth assessed in response to estrogens and endocrine therapies, and RNA-seq and oncogenomics testing performed to compare relative transcript levels and identify putative oncogenic drivers.−/low cell line has mucinous features, a rare histologic type of breast cancer. The three other lines are ER− and represent two basal-like and a mixed ductal/lobular breast cancer. The cell lines show varied responses to tamoxifen and fulvestrant, and three were demonstrated to regrow tumors in vivo. RNA sequencing confirms all cell lines are human and epithelial. Targeted oncogenomics testing confirmed the noted ESR1 mutation in addition to other mutations  and amplifications  frequently found in breast cancers.Three cell lines express ER and two are also progesterone receptor (PR) positive; PAM50 subtyping identified one line as luminal A. One of the ER+PR+ lines harbors a D538G mutation in the gene for ER (ESR1), providing a natural model that contains this endocrine-resistant genotype. The third ER+PRThese new generation breast cancer cell lines add to the existing repository of breast cancer models, increase the number of ER+ lines, and provide a resource that can be genetically modified for studying several important clinical breast cancer features. Breast cancer comprises a complex and diverse group of neoplasms that are stratified into various subtypes to guide treatment . The thrIn vitro propagating cell lines are the most long-standing and heavily used models for conducting basic breast cancer research. The first breast cancer cell line was generated from a primary breast IDC by Lasfargues et al. over 60 years ago named BT-20, a basal-like TNBC . In the We previously reported the generation of a collection of PDX with an emphasis on ER+ models . These pHormones and drugs 17β-estradiol (E2), progesterone (P4), dihydrotestosterone (DHT), 4-hydroxy-Tamoxifen (4-OH-Tam), and ICI 118,551 (Fulvestrant (Fulv)) were purchased from Sigma-Aldrich .2 in Dulbecco’s modified minimum essential medium (DMEM)/F12 containing L-glutamine (365 mg/L) buffered with sodium bicarbonate (1200 mg/L) and HEPES (3575 mg/L) with 10% fetal bovine serum, cholera toxin (100 ng/mL), hydrocortisone (1 μg/mL), insulin (10−9 M), and pen strep. Cells were fed every 3–4 days until they emerged from crisis into passageable cell lines (6–18 months). Control breast cancer cell lines T47D and MDA-MB-468 were obtained from the University of Colorado Cancer Center Tissue Culture core. Short tandem repeat (STR) profiling and analysis was performed by the University of Arizona Genetics Core . PDX-derived cell lines were compared to their PDX of origin and other cell lines in the database. Cell lines were routinely tested for mycoplasma using the MycoAlert detection kit .Derivation and propagation of PDX was described previously , 24. Gen5 cells were seeded onto glass coverslips in 6-well plates in regular media. For assessing PR, cells were treated with 10 nM E2 for 48 h prior to collection. Cells were washed twice with PBS and fixed with ice-cold 70% acetone/30% methanol for 5 min. Fixed cells were blocked with 10% normal goat serum  in 0.05% TBS-T for 30 min. Primary antibodies were as follows: ERα , PR , 1–2 × 104, 1:50, , CK5 , 10 nM E2, 100 nM P4, E2 plus P4, or 10 nM DHT for 48 h. Cell lysates were harvested in lysis buffer  plus protease/phosphatase inhibitors. Primary antibodies were as follows: ERα , 1:1000), PR , AR , and α-tubulin . Secondary antibodies were as follows: IRDye 800CW Goat-Anti-Mouse IgG and IRDye 680RD Goat-Anti-Rabbit IgG . The Odyssey CLx Infrared Imaging System and Image Studio 5.2 (Li-Cor Biosciences) were used to capture images.Lentiviral particles were produced by co-transfecting HEK293FT cells with the structural plasmids VSV-G, d8.9, and pLJMGFP-GFP-3xNLS-puromycin constructs using TransIT-LT1. Media containing viral particles was harvested after 48 h. UCD cell lines were plated at ∼ 60,000 cells per 6 cm dish in 5 mL of complete medium. Approximately 24 h post plating, the medium was replaced with medium containing lentiviral particles. Polybrene was added at a final concentration of 8 mg/mL. Cells were transduced for 48 h, medium replaced with fresh complete medium and allowed to recover for 24 h, and selection performed over 7–10 days.Real-time imaging  was used to measure proliferation of nuclear-GFP labeled cells at × 10 magnification. UCD4 cells were plated at 15,000 cells/well, UCD65 at 20,000 cells/well, and UCD12, UCD46, UCD115, and UCD178 cells at 10,000 cells/well, all in 96-well plates. For treatments, cells were given vehicle, 10 nM E2, 100 nM 4-OH-Tam, or 10 nM Fulv on day one. Green count was taken immediately after treatment and subsequently taken every 4 h for 6 days. Counts were calculated as fold change. Significance was assessed by one-way ANOVA/Tukey at the final time point.6 cells in 90% Cultrex Basement Membrane Extract  into the fourth mammary fad pad of ovariectomized female NOD-scid IL2Rgammanull (NSG). Silastic pellets containing 17β-estradiol (1 mg) were implanted subcutaneously at time of tumor cell injection. Tumors were measured weekly using a digital caliper, and tumor volume estimated using the formula (lw2)/2.Animal experiments were performed under an approved University of Colorado Institutional Animal Care and Use Committee protocol. Tumor xenografts were developed by injecting 1 × 10Cell lines were plated in 60 mm plates in regular media. When cells were ~ 70% confluent, cells were washed with PBS and lysed with 0.7 mL Qiazol  immediately. RNA was prepared using miRNeasy mini columns (Qiagen) and treated with RNase-free DNase. RNA concentration was measured using a Nanodrop 2000 (Thermo-Fisher), and integrity analyzed using an Agilent 2100 Bioanalyzer and the RNA 6000 Nano kit. Libraries were prepared using the Illumina TruSeq Stranded mRNA Library Prep kit and samples sequenced using the Illumina Novaseq System. Paired-end 150 nt reads were aligned to the human genome version GRCh38.p13 using STAR 2.6. The downstream expression analysis was done using Cufflinks 2.2.1. PAM50 scores were assigned from the expression values (TPM) using the R package genefu 2.18.1 .https://assets.thermofisher.com/TFS-Assets/LSG/brochures/oncomine-comprehensive-assay-v3-flyer.pdf). Data were annotated by the MDL according to the ASCO/AMP guidelines. All samples were confirmed to be human in origin.Samples of each of the six cell lines were submitted to the Virginia Commonwealth University Molecular Diagnostics Laboratory (MDL) for testing using the Oncomine Comprehensive Assay v3 panel (Thermo-Fisher). This assay uses an NGS platform to detect relevant SNVs, CNVs, gene fusions, and indels from 161 unique cancer driver genes  by immunoblot in the presence of vehicle, E2, P4, E2+P4, or DHT Fig. . FurtherGrowth of PDX UCD4, UCD12, and UCD65 has been previously described , 24. UCDGrowth of all three ER− lines  was unaffected by E2 treatment. However, UCD46 and UCD178 showed a very modest reduction in growth in the presence of E2+4-OH-Tam vs vehicle or E2 alone. Cell population doubling times in the absence and presence of E2 are summarized in Table To test if cell lines are tumorigenic in vivo, we inoculated UCD12, UCD46, and UCD65 cells into the #4 mammary fat pads of female NSG mice supplemented with estrogen , AR, CK5 (KRT5), and CK8/18 (KRT8/KRT18) are generally reflective of their protein levels. By RNA-seq, UCD115 has comparatively high expression of vimentin (VIM), N-cadherin (CDH2), and Slug (SNAIL2), for example, and low expression of all cytokeratin genes  and E-cadherin (CDH1). The three ER− cell lines all contain vimentin-positive cells , consistent with the original tumor diagnosis. All cell lines were absent for CD45 (PTPRC), a marker of hematopoietic cell lineage, suggesting none of the cell lines were derived from lymphoma. We next applied the intrinsic molecular subtype algorithm described by Parker et al. [To measure and compare gene expression patterns across the six cell lines, RNA-seq was performed on cells grown in regular media in the absence of hormone supplementation. Normalized expression of transcripts is summarized in Supplemental Table r et al.  to the cTo assess common oncogenic alterations in the six cell lines, we utilized a targeted genomic testing platform that covers SNVs, CNVs, and gene fusions for 161 cancer driver genes Table . All of The innate intertumoral heterogeneity among breast cancers and an increasing emphasis on individualizing therapies necessitate that we continue to generate research models to meet this challenge. Advances in measuring CTCs and circulating tumor DNA further facilitate real-time monitoring of disease progression and personalized care. Our group and others have derived collections of breast cancer PDX that can be utilized for pre-clinical drug testing . HoweverOne of our primary goals was to increase the number of workable ER+ breast cancer cell lines, which are relatively underrepresented compared to their clinical predominance. The primary “workhorse” ER+ breast cancer models include IDC subtypes  with several ER+ ILC cell lines seeing increased use  , 30. Sevcbioportal.org), even though some were derived from metastatic patients, prospectively because these patients were either untreated or treated prior to standard use of AIs in the 2000s. To functionally study the mutant ERs, laboratories have used exogenous expression or generated CRISPR knock-in models of the D538G and Y537S ESR1 mutations, or forced mutations by long-term endocrine treatment of ER+ breast cancer cells [It is now recognized that up to 30% of advanced breast cancer patients contain somatic genetic anomalies in the ER gene (ESR1), prospectively driven by long-term estrogen deprivation with aromatase inhibitors (AIs) . Existiner cells –34. Someer cells . To our er cells , 36, 37.The three ER−PR− cell lines (by our limits of detection) were derived from PDX which each contained rare ER+ cells . Supplemental Figure 2. Nuclear-GFP labeled UCD cell lines for proliferation assays. Representative images of UCD cell lines during Incucyte analysis. Individual wells were captured at 6-18 h post plating. Mag bars,300 microns. Supplemental Fig. 3. Growth of UCD cell lines in vivo. One million cells were inoculated billaterally into the #4 mammary fat pads of NSG mice. Animals were supplemented with slow release estrogen pellets and tumors measure weekly. Mean plus SEM are indicated. N=6 tumors UCD12 and UCD47 and N = 3 tumors forUCD65. Supplemental Figure 4. Expressison profiles of UCD cell lines show moderate to strong positive correlation to their PDX tumors of origin. The RNA-seq expression profiles (FPKM) of UCD cell lines and UCD PDX were compared using the r package ggpubr. The r2 correlation and p values were plotted for each cell line and thecorresponding PDX. Supplementary Figure 5. Vimentin expression in ER negative UCD cell lines. UCD46, UCD115, and UCD178 cells were plated on glass slides in regular media. Cells were fixed and stained by immunocytochemistry with an antibody to vimentin with DAPI counterstain. Scale bar, 50 μM.Additional file 2: Supplementary Table 1. RNA-seq of UCD cell lines.


LRRK2 protein is expressed prominently in immune cells, cell types whose contribution to LRRK2-associated genetic Parkinson’s disease (PD) is increasingly being recognised. We investigated the effect of inflammatory stimuli using RAW264.7 murine macrophage cells as model systems. A detailed time course of TLR2 and TLR4 stimulation was investigated through measuring LRRK2 phosphorylation at its specific phospho-sites, and Rab8 and Rab10 phosphorylation together with cytokine release following treatment with LPS and zymosan. LRRK2 phosphorylation at Ser935, Ser955 and Ser973 was increased significantly over untreated conditions at 4–24h in both WT-LRRK2 and T1348N-LRRK2 cell lines to similar extents although levels of Ser910 phosphorylation were maintained at higher levels throughout. Importantly we demonstrate that LPS stimulation significantly decreased phospho-Rab10 but not phospho-Rab8 levels over 4–24h in both WT-LRRK2 and T1348N-LRRK2 cell lines. The dephosphorylation of Rab10 was not attributed to its specific phosphatase, PPM1H as the levels remained unaltered with LPS treatment. MAPK phosphorylation occurred prior to LRRK2 phosphorylation which was validated by blocking TLR4 and TLR2 receptors with TAK242 or Sparstolonin B respectively. A significant decrease in basal level of TNFα release was noted in both T1348N-LRRK2 and KO-LRRK2 cell lines at 48h compared to WT-LRRK2 cell line, however LPS and zymosan treatment did not cause any significant alteration in the TNFα and IL-6 release between the three cell lines. In contrast, LPS and zymosan caused significantly lower IL-10 release in T1348N-LRRK2 and KO-LRRK2 cell lines. A significant decrease in phospho-Rab10 levels was also confirmed in human IPS-derived macrophages with TLR4 activation. Our data demonstrates for the first time that LRRK2-dependent Rab10 phosphorylation is modulated by LPS stimulation, and that cytokine release may be influenced by the status of LRRK2. These data provide further insights into the function of LRRK2 in immune response, and has relevance for understanding cellular dysfunctions when developing LRRK2-based inhibitors for clinical treatment. However, loss of dopaminergic neurons in the substantia nigra is a common link amongst all LRRK2 mutation carriers  and kinase domain at its core. The enzymatic core is flanked by several proteinprotein interaction domains thought to play roles in signalling and scaffolding functions . Pathoge to date  and is a to date . In vitrtoxicity . Notablytoxicity . The GTPtoxicity . Familiatoxicity . The T13toxicity . Additiotoxicity . As the LRRK2 locus have been associated with heightened risk of developing Inflammatory Bowel Disease, an autoimmune disorder, and multibacillary leprosy  (LRRK2 protein has a number of phospho-sites that can either be autophosphorylated or constitutively phosphorylated by other kinases . Activat Rab7L1) . Importa Rab7L1) , a link In this study we have investigated the phospho-regulation of LRRK2 and its substrates following TLR stimulation using murine RAW264.7 cells as a model for LRRK2, taking advantage of genome edited RAW264.7 cells lacking LRRK2 or carrying a mutation T1348N that ablates LRRK2 GTP binding. Additionally we have verified part of the data in human IPS-Macrophage (iPS-Mac) cells. The specificity of TLR activation was investigated using antagonists/inhibitors of TLR2 (Sparstolonin B) and TLR4 (TAK242), and finally we measured cytokine release, namely TNFα, IL-6 and IL-10 in the above-mentioned cell lines with TLR activation.22.12) at 37°C and were grown in Dulbecco’s Modified Eagle Medium (DMEM) (Life Technologies #41965–039)  supplemented with 10% heat-inactivated foetal bovine serum (FBS) (Thermofisher #10500) and 1% penicillin, streptomycin and Amphotericin B (Thermofisher #15240062). The culture medium was refreshed every 2–3 days of culture. For treatments, 200,000 cells were seeded in each 6 well plates. Following treatments, cells were collected in modified RIPA buffer . Protein lysates were collected and centrifuged at 14,000×g for 15min to pellet cell debris and supernatants collected and stored on ice or at −20 °C for further use.Three RAW264.7 murine macrophage cell lines were obtained from American Type Culture Collection: wild-type (#SC-6003), T1348N-LRRK2 (#SC-6005), and KO-LRRK2 (#SC-6004). Both the T1348N and KO-LRRK2 RAW264.7 cell lines are homozygous. Cells were incubated in a 100% relative humidified incubator  and zymosan 200 μg/ml (Sigma Z4250) were dissolved in cell media prior to experimentation.2.4TAK242 was obtained from Tocris (cat no: 243984-11-4) and Sparstolonin B was obtained from Sigma (Cat no: SML1767). Stock solutions were dissolved in DMSO and the required concentrations of 1 μM for TAK242 and 50 μM Sparstolonin B were added to the required medium. The doses of TAK242 and Sparstolonin B were chosen based on their use in previously published works . Small a2.5Frozen cell lysates extracted with RIPA buffer were thawed on ice and 20 μg/μl of protein sample was loaded on Criterion TGX Precast 18-well 4%–20% Midi protein gel (Bio-Rad). Standard protocol was used for immunoblotting and has been described in details by our group in a previous publication . The ant2.6RNA was extracted using T RIzol reagent using manufacturer’ s instructions (Thermo Fisher).To determine the RNA concentration and purity, a Nanodrop spectrophotometer (Thermoscientific) was used. 2 μg of RNA was then reverse transcribed using SuperscriptIV (Thermo Fisher) using random hexamers. Power SYBR green master mix (Thermo Fisher) was used for QRT-PCR using Mx3000P System (Agilent). Data were generated using the ΔΔCT methods. Results were normalised to the reference genes GAPDH and B2M and presented relative to one control wild-type sample . Details   GAPDH:   F:   5’-GCATCTTCTTGTGCAGTGCC,   R:   5’-TCACACCCATCACAAACATG   B2M:   F:   5’-CACTGAATTCACCCCACT,   R:   5’-TGTCTCCATCCCAGTAGAC   TLR2:   F:   5’-TGTAGGTGATCTTGTTGAAA,   R:   5’-TCAGACAAAGCGTCAAAT   TLR4:   F:   5’-AATGAGAATGATGAAGGAA,   R:   5’-CTGAATGACAAGACTACA2.7Cells at a density of 20,000 were grown on glass coverslips in 24-well plates and were treated with the above-mentioned doses of LPS or zymosan for 24h. To determine the number of live and dead cells following TLR4 or TLR2 stimulation, cellular nuclei were stained with propidium iodide  to give the number of dead cells, and Hoechst 33342  for 15 min at 37 °C for the total number of cells. Cells were viewed using a Zeiss Axioskop 2 fluorescence microscope with 20x Neofluor objective . Five fields were randomly chosen to image per treatment per experiment at 20x magnification and all the living and dead cells in those fields were counted. The data was then represented in histograms as the percentage of dead cells.2.8The three cell lines, WT-LRRK2, T1348N-LRRK2 and KO-LRRK2 RAW264.7 were treated with LPS and zymosan for 24 and 48 h. Media supernatants were then collected after 24 and 48 h which were then used to carry out ELISA assays. ELISA assays were performed using mouse tumor-necrosis factor alpha (TNFα), interleukin-6 (IL-6) and Interleukin-10 (IL-10) Quantikine ELISA kits (R&D Systems) according to the manufacturer’s instructions. Samples, run in triplicates, were normalised against ELISA kit controls. The values were then normalised over concentrations of total protein.2.9One-way analysis of variance (ANOVA) with Tukey’s post-hoc test was used to compare the difference between non-stimulated cells and cells stimulated with LPS or zymosan between the means of three or more independent groups, e.g. when determining any significant difference between the three cell lines, phospho-sites and time-points. Twotailed student’ s T-test was used to determine the significant difference between the means of two groups. All immunoblot data display replicates from one experiment. Statistics has been performed using all the data points for all experiments.33.1We investigated baseline levels of phosphorylation of LRRK2 Ser910, Ser935, Ser955 and Ser973, Rab8 T72 and Rab10 T73 in the three cell lines as these are readouts of LRRK2 kinase activity. This was accomplished using immunoblot analysis with phosphorylation specific antibodies . The T13There was no LRRK2 or phospho-LRRK2  expression in the KO-LRRK2 cell line. Comparison of phosphorylation levels of Ser935 between WT-LRRK2 and T1348N-LRRK2 cells revealed no significant alteration in T1348N-LRRK2 compared with WT-LRRK2 cells –E. Total3.2LRRK2 phosphorylation at Ser910, Ser935, and Ser955 and Ser973 phospho-residues were assessed following LPS  and zymosan  at 2h, 4h, 8h and 24h time points in WT-LRRK2  and T134To investigate how zymosan affects LRRK2 phosphorylation at Ser910, Ser935, Ser955 and Ser973 residues of LRRK2, both cell lines were treated with 200 μg/ml of zymosan for 2h, 4h, 8h and 24h. Similar to that observed with LPS treatment, basal phosphorylation levels for Ser910 residue remained constant with no further upregulation seen with zymosan treatment at all time-points tested in both WT-LRRK2  and T1343.3In order to investigate whether Rab8 or Rab10 phosphorylation was affected by LPS or zymosan treatment in the WT-LRRK2 and T1348N-LRRK2 cell lines, we treated cell lines with LPS (100 ng/ml) and zymosan (200 μg/ml) and examined the phosphorylation of Rab8 and Rab10 with phospho-specific antibodies at 2h-24h time points as before. Total Rab8 and Rab10 levels did not change with LPS treatment ,E. There3.4In order to establish whether the levels of protein-phosphatse 1H (PPM1H) are altered as a result of LPS treatment on RAW264.7 cells, we treated the two cell lines with LPS for 2hr, 4hr, 8hr and 24hr after which cell extracts were subjected to immunoblots with PPM1H and β-actin. There was no alteration in the levels of PPM1H at any of the treatment time-points .3.5TLR stimulation leads to activation of the MAPK pathway resulting in phosphorylation of MAPK .Therefor3.6In order to confirm the specificity of the effects of LPS and zymosan on TLR4 and TLR2 respectively, we pre-treated cells with TAK242 (TLR4 antagonist) at 1 μM or Sparstolonin B (TLR2 antagonist) at 50 μM for 45min prior to treatment with LPS (100 ng/ml) or Zymosan (200 μg/ml). Cells were collected at 30min and 4h to measure phosphorylated MAPK (pMAPK) and phosphorylated Ser935 (pSer935). Treatment with TAK242 in WT-LRRK2 and T1348N-LRRK2 cells alone caused suppression of pSer935 and pMAPK at both 30min and 4h ,D. Spars3.7In order to examine whether the decrease in Rab10 phosphorylation also occurred in human immune cells, we quantitated the effects of LPS treatment on LRRK2 and Rab10 phosphorylation in iPS-Macs. Firstly we showed a significant increase in Phospho-LRRK2 Ser935 with LPS treatment at 4hs with a concomitant significant decrease in phospho-Rab10 levels compared with non-LPS treated IPS-Macs . Zymosan3.8In order to assess whether the expression levels of TLR4 and TLR2 are similar between the three cell lines, we measured mRNA levels of TLR4 and TLR2 levels by qRT-PCR. Our results demonstrate that there was a small but significant decrease in TLR2 mRNA levels in T1348N-LRRK2 compared with WT-LRRK2 cells , whilst 3.9In order to test whether the LPS and zymosan doses used in the cell culture experiments are not affecting cell viability, we performed the live/dead assay after treating WT-LRRK2 RAW264.7 cells for 24h. Our results indicate that there were no significant differences in the percentage of live cells between any of the three cell lines, WT-, T1348N- and KO-LRRK2 RAW264.7 cell lines either at basal or following stimulation with LPS or zymosan at the doses used for the duration of 24h . This in3.10In order to test if cytokine release was affected in the three RAW264.7 cell lines at both basal levels and following LPS or zymosan stimulation, we examined the release of TNFα, IL-6 and IL-10 using specific ELISAs. TNFα and IL-6 levels were significantly increased with both LPS and zymosan treatments in the three cell lines at both 24h and 48h with the effect of zymosan seeming to be greater than LPS . The basIn contrast, a significant increase in IL-10 release was observed with zymosan treatment in WT-LRRK2 and T1348N-LRRK2 cell lines only at both 24h and 48h. LPS treatment only caused a significant increase in IL-10 in WT-LRRK2 at 48h but not at the earlier time point of 24h. Interestingly, there was no significant release of IL-10 in KO-LRRK2 with either LPS and zymosan at both time points . Basal I4Evidence from recent research supports an important role for LRRK2 in immune cell function . In thisThe steady state phosphorylation of four phospho-sites in LRRK2  and Rab8 and Rab10 phosphorylation levels revealed no detectable LRRK2 or any of the phospho-LRRK2, pRab8 and pRab10 proteins in KO-LRRK2 RAW264.7 cell line. This authenticates the homozygous KO-LRRK2 RAW264.7 cell line and lends further evidence to the notion that Rab8 and Rab10 are indeed bonafide substrates of LRRK2 kinase activity , 2017. TOur data shows that LPS and zymosan treatments affect the phosphorylation of LRRK2 at Ser935 residue to similar extent and is sustained until 24h at least which is similar to the effect previously observed  in both α, IKKβ, IKKε, TBK1  pathway is via TRAF6, which recruits receptor-interacting protein kinase (RIPK) proteins leading to phosphorylation of MAPK  which isIn our study, we used TAK242  and Sparstolonin B (TLR2 antagonist) to study the specific effects of LPS/TLR4 and zymosan/TLR2 on MAPK and LRRK2 Ser935 phopshorylation. We demonstrate that TAK242 causes a significant downregulation of pMAPK and LRRK2 pSer935 in both WT-LRRK2 and T1348N-LRRK2 cell lines. In contrast, Sparstolonin B did not show downregulation effect to the same extent. Therefore, we have shown that the effect of TAK242 is more sustained on MAPK and LRRK2 in RAW264.7 cell lines. However we also noted that TAK242 and Sparstolonin B treatment on its own demonstrated significant downregulation of MAPK and Ser935LRRK2 phosphorylation which is most likely owing to trace contamination of endotoxin levels in heat-inactivated FBS (as stated in Thermofisher product documentation). TAK242 binds to the intracellular domain of TLR4 which inhibits TLR4 signalling by disrupting the interaction of TLR4 with its downstream adaptor molecules . These dLRRK2 plays distinct roles in microglia and macrophages and LRRK2 expression is stringently regulated in both peripheral and innate immunity . It has In animal models, loss of LRRK2 decreases pro-inflammatory myeloid cells in brains of rats and decreases neurodegenerative responses to LPS and α-synuclein .Although LRRK2 knockdown or kinase inhibition in primary microglia has shown a decrease in the production of pro-inflammatory cytokines TNFα and IL-1β , there aWe appreciate that there are some general issues concerning established cell lines that include comparability between various laboratories, their characteristics and stability over time. However, it has been demonstrated that the phenotypic and functional characteristics of RAW264.7 cell line remain stable for up to 30 passages . We haveIn summary, the key and novel finding from our study is that Rab10 phosphorylation is sensitive to TLR4 stimulation and is similarly decreased in both the WT- and T1348N-RAW264.7 cell lines  and corrFig S1-S7


It is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites.Blood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to detect and identify metabolites present in serum, but to also establish a method capable of measure their uptake and secretion by different cell lines.We develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the ‘exometabolome’ or metabolic footprint).Our method measures some 4000–5000 metabolic features in both positive and negative electrospray ionisation modes. We show that the metabolic footprints of different cell lines differ greatly from each other.Our new, 15-min untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum. This will enable future research to study these differences in multiple cell lines that will relate this to transporter expression, thereby advancing our knowledge of transporter substrates, both natural and xenobiotic compounds.The online version of this article (10.1007/s11306-020-01725-8) contains supplementary material, which is available to authorized users. One of the great unsolved problems of modern biology concerns the substrates of membrane transporters  of small molecules in biological matrices  and ATP-binding cassette (ABC), transporters. Around 500 known SLC transporters are known which mediate the uptake of such compounds whereas ABC transporters are involved in efflux of these, K562 cell line was a kind gift from Dr Philip J. Day , SAOS2 and U2OS cell lines were a kind gift from Prof. Peter Gardner .A549, K562, SAOS2 and U2OS cell lines were cultured in RPMI-1640  culture media supplemented with 10% fetal bovine serum  and 2 mM glutamine  without antibiotics. Cell cultures were maintained in T225 culture flasks  kept in a 5% COCells from adherent cell lines were harvested by removing growth media and washing twice with 5 mL of pre-warmed Dulbecco’s Phosphate Buffered Saline (PBS) without calcium or magnesium , then incubated in 3 mL of Gibco™ TrypLE™ Express Enzyme (1X), no phenol red (Gibco Cat No. 12604013) for 2–5 min at 37 °C. At the end of incubation cells were resuspended in 5–7 mL of respective media when cells appeared detached to dilute TrypLE treatment. The cell suspension was transferred to 50 mL centrifuge tubes and immediately centrifuged at 300 × g for 5 min. Suspended cell lines were centrifuged directly from cultures in 50 mL centrifuge tubes and washed with PBS as above. The cell pellets were resuspended in 10–15 mL media and cell count and viability was determined using a Countess II FL Automated Cell Counter (ThermoFisher Scientific) set for Trypan Blue membrane exclusion method. Cells with > 95% viability were used for serum incubation experiments.The procedure for incubation of cells in serum is described pictorially in Fig. 4 , 225 µM; L-lysine-d4 , 112.5 µM; L-Tryptophan-(indole-d5) , 5.625 µM; stearic acid-d35 , 225 µM; succinic acid-d4 , 112.5 µM; 13C6-carbamazepine , 2.25 µM; Leucine-d10 , 22.5 µM and methionine-d4 , 22.5 µM.An internal standard stock mixture was prepared using the following compounds and concentrations: citric acid-dFresh or spent serum samples were thawed at room temperature and maintained on ice throughout the sample preparation process. Samples were prepared by addition of 100 µL sample to a 2 mL Eppendorf containing 330 µL Methanol (LC-MS grade) and 20 µL of internal standards mix (ISTDs). The mixture of methanol and ISTDs was previously cooled at − 80 °C and maintained on dry ice when adding serum. The mixture of serum, methanol and ISTD was vortexed vigorously followed by centrifugation at 13,300 rpm for 15 min at 4 °C to pellet proteins. Multiple 75 µL aliquots (for extraction replicates) of the resulting supernatant dried in a vacuum centrifuge  with no temperature application and stored at − 80 °C until required for LC-MS/MS analysis.Quality controls (QC) and conditioning QC samples were also prepared in this way using pooled human serum.Extraction blanks were prepared in the same way as spent serum samples replacing serum and internal standard mix with 120 µL of water (LC-MS grade). Evaluation/system suitability samples were also prepared by replacing serum with water.Prior to analysis, samples were resuspended in 40 µL water (LC-MS), centrifuged at 13,300 rpm for 15 min at 4 °C to remove any particulates and transferred to glass sample vials.Untargeted HPLC-MS/MS data acquisition was performed following methodologies and guidelines in  were exported to Compound Discoverer 3.1 (CD3.1) for deconvolution, alignment, and annotation (full workflow and settings are provided in supplementary information 2). Peak areas from CD3.1 were subsequently exported as a .csv file and QC-based LOESS signal correction performed in R (version 4.0.2) as discussed in . Within this workflow, Principal Components Analysis was used to visualise trends. Subsequently, simple univariate statistical analyses were carried out on logFor all data acquired, annotation and identification criteria were according to . Of these, 1156 matched mass libraries in ChemSpider (level 3) on our searches against BioCyc, HMDB, KEGG, MassBank and NIST. In addition, around 700 compounds were matched against mass libraries provided by CD3.1 software along with our own imported ones . Some of these can be seen in Supplementary Information 3, where 57 of 211 annotated compounds were duplicated in this way. As an example, we found Arachidonic acid eluting at 4 different retention times yet spectral matching of all these with mzCloud was > 90% in confidence. Another such example was Ecgonine, eluting at two different retention times yet both having excellent spectral matching to mzCloud (> 90% match). Further investigation may reveal this to be due to  isomers and/or some compounds simply binding to the column and eluting over different times. This is of lesser importance for present purposes where we aim to find unique markers of transporter substrates.Taking into account that these results are illustrative of a QC run, results in Table One of the main purposes for developing the above untargeted LC-MS/MS methodology is to apply this to measure the uptake and excretion of serum compounds by mammalian cell lines. As a proof of principle, we assessed this by analysis in ESI+ of spent serum samples from 4 cell lines  incubated in serum at two different densities (2 and 4 million) at two timepoints (0 and 20 minutes incubation).A summary table of the number of compounds detected and identified with various levels of confidence can be found in Supplementary Table S1. The results obtained are comparable to those shown in Table PCA reveals differences in samples Fig. : the difTo confirm that the separation of cell lines as well as the effect of incubation time and cell density are the result of differences in the uptake and excretion of serum components, we performed simple univariate analyses. The volcano plots in Fig. Fuller results will be reported elsewhere for a much larger panel of cell lines; however, we provide examples of a compound exclusively consumed by one cell line . Fragmentation data matching against mzCloud suggest some possible substructures of this compound match fragments for an environmental compound on the NORMAN suspect list  whereas in other cell lines the changes were below our threshold (log2 fold change > 0.5 and P < 0.05). The identification of this compound is at a reasonable level 2, with 90.2% match to this compound in mzCloud, and low mass error (− 0.00029 Da or − 1.06 ppm) as can be seen in Supplementary Fig. 3B.As can be seen in Fig. 2 fold change 0.55, P = 0.0010 at 2 million density and log2 fold change 0.98, P = 0.002 at 4 million density) yet consumed by K562  and U2OS  cell lines at 4 million density and A549 cells at 2 million density only .Nicotinamide  was found to be secreted by SAOS2 cell lines in a density dependent manner Supplementary file2 (XLSX 203 kb)Supplementary file3 (XLSX 118 kb)Below is the link to the electronic supplementary material.