Lung cancer is a major cause of cancer-related deaths. Alectinib is the first line of treatment for patients with ALK-positive lung cancer, but the survival rate beyond 2–3 years is low. Co-targeting secondary oncogenic drivers such as SHP2 is a potential strategy for improving drug efficacy. This is because SHP2 is expressed ubiquitously, but ALK expression is largely restricted to cancer cells. Thus, the combination of ALK and SHP2 inhibitors may provide a way to restrict synergistic cytotoxicity to cancer cells only, by reducing the dose of SHP2 inhibitors required for anticancer action and minimising SHP2-dependent systemic toxicity. The objective of this study was to investigate whether the combination of a SHP2 inhibitor (SHP099) with alectinib would synergistically suppress the growth of ALK-positive lung cancer cells. Our results demonstrated that the drug combination significantly and synergistically decreased cell viability at relatively low concentrations in ALK-positive H3122 and H2228 cells, due to G1 cell cycle arrest and increased apoptosis because of suppressed downstream RAS/MAPK signalling. The drug combination also induced the expression of mediators of the intrinsic apoptotic pathway, Bim and cleaved caspase-3, and modulated the expression of cell cycle mediators cyclin D1, cyclin B1, and phosphorylated CDK1. These targets are often responsive to drug monotherapy, however, adding to the list of druggable targets in NSCLC are secondary drivers; targets that have the most therapeutic potential when inhibited in combination with primary oncogenic drivers (such as those listed above)3.Substantial advances have been made in recent years in the survival of patients with non-small cell lung cancer (NSCLC) expressing druggable receptor tyrosine kinase (RTK) mutations. The greatest success has been achieved with drugs selectively targeting over-expressed and/or mutated forms of epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase receptor (ALK), ROS proto-oncogene 1 (ROS1), and KRAS proto-oncogene (KRAS)4. SHP2 acts downstream of numerous RTKs and mediates cell signalling, including the important mediator of cell proliferation and survival, RAS-ERK5. SHP2 was not considered to be druggable until 2012 when it was demonstrated that the SHP2 inhibitor II-B08, enhanced the efficacy of phosphoinositide 3-kinase (PI3K) inhibition in models of myeloproliferative disease4. SHP2 can now be targeted using a range of compounds, including several that have entered clinical trials6. Of these, SHP099 is an orally bioavailable small molecule allosteric inhibitor that promotes the inactive state of the protein7.The non-receptor protein tyrosine phosphatase, Src homology region 2-containing protein tyrosine phosphatase (SHP2), has been identified as one such potential target8, as SHP2 is expressed ubiquitously throughout the body9 the toxicity of SHP2 inhibitors poses potential risks10. A strategy that has proven to be effective for reducing toxicity for some drug combinations is to reduce the minimum effective dose (MED) of a drug by combining it with another drug/s to produce synergistic efficacy11. If this can be targeted to cancer cells, then this should not also cause synergistic toxicity. Using SHP2 inhibitors in combination with drugs targeting a primary oncogenic driver might be a way to harness the anti-cancer properties of SHP2 inhibitors whilst minimising toxicity. Given that SHP2 intermediates the growth and survival pathways of RTKs, oncogenic RTKs emerge as a potential co-treatment partner with SHP2 inhibitors12.Although SHP099 has little activity against other specific enzyme systems that are most commonly associated with toxicity13. EML4-ALK is a chromosomal rearrangement caused by fusion between echinoderm microtubule-associated protein-like 4 (EML4) and ALK, creating constitutively active ALK14. ALK-positive (ALK+) lung cancer accounts for ~ 6% of all NSCLC cases15. As ALK is not widely expressed after embryonic development is complete, ALK inhibitor off-target systemic toxicity is low16. ALK inhibitors beginning with crizotinib were first approved by the FDA in 201117, with clinical trials quickly demonstrating the superiority of ALK inhibitors over chemotherapy for ALK+ NSCLC treatment18. Currently, the first-line ALK inhibitor is alectinib, which has superior blood–brain barrier penetration compared to crizotinib15. However, patient outcomes are limited by tumour resistance21.ALK is a promising candidate for co-targeting alongside SHP2 in the treatment of NSCLC+ NSCLC at sub-toxic doses of SHP2 inhibition. Therefore, this study aimed to test the hypothesis that combining the SHP2 inhibitor, SHP099, with alectinib will result in synergistic ALK+ cell growth suppression in two different ALK+ NSCLC cell lines. The drug combination was then used to investigate the cellular mechanisms driving synergistic cell growth suppression.If SHP2 inhibition combines synergistically with ALK inhibition to reduce tumour cell growth, then SHP2 inhibitors might improve outcomes with alectinib in ALKAlectinib and SHP099 were purchased from LC Laboratories  and MedChem Express , respectively. Roswell Park Memorial Institute (RPMI) 1640 medium and TrypLE were purchased from Life Technologies . Penicillin/streptomycin, sulforhodamine B (SRB), trichloroacetic acid (TCA), and dimethylsulfoxide (DMSO) were obtained from Sigma-Aldrich . Fetal bovine serum (FBS) was purchased from Sigma-Aldrich . Bovine serum albumin (BSA) was obtained from Life Technologies . Bicinchoninic acid (BCA) protein assay reagent A was purchased from ThermoFisher Scientific (USA). SuperSignal West Pico Chemiluminescent and CL-XPosure film were obtained from ThermoFisher Scientific . Acrylamide and precision plus protein kaleidoscope standard were purchased from Bio-Rad Laboratories . FxCycle PI/RNase was purchased from Life Technologies .Primary antibodies against ALK , pALK , SHP2 (3752S), pSHP2 , pERK , Bcl2 (3498S), Bim (2189S), Bax (5023S), cleaved caspase-3 (9664 T), p27 (2552S), cyclin D1 (2978S) and CDK4 (12790S) were purchased from Cell Signaling Technology . pCDK1  and pCDK4  were purchased from Invitrogen . The ERK1/2 (9102S), CDK1 (HPA003387), and β-tubulin (T5293) antibodies were purchased from Sigma-Aldrich . The secondary antibody horseradish peroxidase-conjugated goat anti-rabbit was purchased from Calbiochem  and horseradish peroxidase-conjugated goat anti-mouse antibody was purchased from EMD Millipore Corp (USA).+ NSCLC cell line harbouring the EML4-ALK variant one fusion gene was gifted by Professor Daniel Costa, Harvard University. Variant one of the EML4-ALK fusion is the most prevalent variant at 33% of EML4-ALK NSCLC cases22. The H2228 human adenocarcinoma ALK+ NSCLC cell line harbouring the EML4-ALK variant three fusion gene was purchased from ATCC . A549 human lung adenocarcinoma cell line harbouring a KRAS gene codon 12-point mutation were gifted from Dr Gregory Giles, University of Otago. All cells were stored in a humidified incubator with 5% CO2 at 37 °C. Cell lines were maintained in RPMI 1640 medium supplemented with 1% penicillin/streptomycin and either 2%, 5%, or 10% FBS for A549, H3122, and H2228 cells, respectively.The H3122 human adenocarcinoma ALK3, 2.5 × 104, and 4.0 × 103 cells/well, respectively, in 96-well plates for 24 h then treated with the respective drug treatment concentrations for a further 72 h. Cells were fixed in 10% TCA for 30 min, and cell viability was assessed using the SRB cell viability assay according to23. Results are presented as a percentage of the vehicle control, and the EC50 was determined using non-linear regression ((inhibitor) vs. response variable slope (four parameters)) in GraphPad Prism v8.0 from three independent experiments repeated in technical triplicate24.H3122, H2228, and A549 cells were seeded at 7.0 × 105 and 5.0 × 105 cells/well, respectively, in 6 well plates for 24 h. Cells were treated with the EC50 values for the respective cell lines for an additional 24 h. H3122 cells were treated with the vehicle control (0.002% DMSO), alectinib (0.018 μM), SHP099 (11.8 μM), or a combination of both. H2228 cells were treated with the vehicle control (0.003% DMSO), alectinib (0.033 μM), SHP099 (15.9 μM), or the combination.H3122 and H2228 cells were seeded at 3.0 × 10g for 4 min, followed by fixation in 70% ethanol for at least 24 h. Cells were then washed with PBS, centrifuged at 449 × g for 10 min, resuspended in FxCycle PI/RNase staining solution, and incubated in the dark for 30 min. Samples were analysed using the FACs Canto II flow cytometer for H3122 and BD LSRFortessa flow cytometer for H2228 cells, and data were analysed using FlowJo LLC software (v10). Results were presented as a percentage of cells in each phase of the cell cycle from three independent experiments25.For cell cycle analysis, after the treatment period, the cells were washed with PBS, trypsinised, and centrifuged at 797 × 6 and 2.5 × 106 cells per petri dish, respectively, and incubated for 24 h. Cells were treated with the EC50 values for the respective cell lines for an additional 24 h. H3122 cells were treated with the vehicle control (0.002% DMSO), alectinib (0.018 μM), SHP099 (11.8 μM), or a combination of both. H2228 cells were treated with the vehicle control (0.001% DMSO), alectinib (0.033 μM), SHP099 (15.9 μM), or a combination of both. Following the 24-h drug treatment, cells were lysed with lysis buffer (50 mM Tris base (pH–7.5), 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 0.5% NP-40, and 0.5% SDS) containing protease inhibitors  and stored at − 80 °C overnight. Cells were then sonicated, centrifuged at 20,854 × g for 8 min at 4 °C. 20 μg of samples were then loaded onto SDS-PAGE using a Mini Protean 3 system  at 120 V for 90 min. Proteins were transferred to an activated PVDF membrane at 100 V for 90 min. Membranes were blocked in 2% to 5% BSA for 1 h and then incubated overnight at 4 °C in the respective primary antibodies. The primary antibody dilutions used were; ALK rabbit monoclonal antibody (mAb) (1:1000), pALK rabbit antibody (1:1000), β-tubulin mouse mAb (1:2000), SHP2 rabbit polyclonal antibody (pAb) (1:1000), pSHP2 rabbit pAb (1:1000), ERK1/2 rabbit pAb (1:1000), pERK1/2 (T202/Y204) rabbit pAb (1:1000), Bim rabbit antibody (1:1000), Bcl2 rabbit mAb (1:1000), Bax rabbit mAb (1:1000), cyclin D1 rabbit mAb (1:1000), cyclin B1 rabbit mAb (1:1000), CDK1 rabbit pAb (1:1000), pCDK1 rabbit pAb (1:1000), CDK4 rabbit mAb (1:1000), pCDK4 (Thr172) rabbit pAb (1:1000), cleaved caspase-3 rabbit mAb (1:1000), p27 rabbit mAb (1:1000).H3122 and H2228 cells were seeded at 1.0 × 1025.Following incubation, the membranes were probed with secondary antibodies; anti-mouse (1:1000) for β-tubulin and anti-rabbit (1:1000) for all other antibodies. SuperSignal West Pico Chemiluminescent substrate with CL-XPosure film was used to visualise the proteins of interest. A GS-710 calibrated imaging densitometer (Bio-Rad) with Quantity One software (v4.6.7) was used to quantify the density of the bands where the background of each band was subtracted, and the proteins normalised to the housekeeper protein β-tubulin. For each antibody, three biological replicates were performedp < 0.0526.Cell viability and Western blotting data were analysed using a one-way ANOVA followed by a Bonferroni post hoc test. Data are expressed as mean ± SEM. Data analyses were carried out using GraphPad Prism v8.0 , where a significant difference was reported when 27 was carried out on the cytotoxicity data to derive a combination index (CI)  is the product of the effect of alectinib and SHP099. EAS is the degree of the effect of alectinib and SHP099 when applied in combination. A CI of more than one indicates an inhibitory effect of the drugs, and a CI of less than one indicates positive synergy between the two drugs in combination. A CI of one indicates additivity with separate drug targets when in combination.To determine if the combination of alectinib and SHP099 exhibited synergy, the Bliss model(CI) Eq.  as descrBliss combination equation for calculation of synergy when alectinib and SHP099 are applied in combination.p < 0.05.Flow cytometry data for both cell cycle and apoptosis were analysed using a two-way ANOVA followed by a Bonferroni post hoc test. Data are expressed as mean ± SEM. Data were gathered using FlowJo LLC software v10 and analysis was carried out using GraphPad Prism v8.0 . Significant differences were reported when + lung adenocarcinoma cells; H3122 (ALK+ variant 1) and H2228 (ALK+ variant 3) cells were highly sensitive to alectinib, with EC50 values of 0.018 μM and 0.033 μM, respectively , we were unable to obtain EC50 values for alectinib as test concentrations were constrained by drug solubility   Fig. . These v50, 0.5 × EC50, and 1 × EC50 concentrations of both alectinib and SHP099 decreased cell viability in combination significantly more than when applied alone  significantly decreasing the cell viability in the combination compared to either drug alone  of ALK in either cell line tested  reduced the growth of ALKp < 0.001) in pALK/ALK levels of 0.88- and 0.97-fold respectively compared to the control (p < 0.05) but no further decrease in pALK/ALK levels with the combination compared to alectinib alone (p > 0.05). There was no change in protein levels with individual SHP099 treatment alone  decrease in protein levels following SHP099 treatment compared to the control, with a similar decrease following alectinib treatment  and a very strong decrease following treatment with the combination compared with the control .For H3122 SHP2 activation (pSHP2/SHP2 expression) there was a significant  with there being no significant differences between any of the monotherapies and combination , SHP099 (11.5%), and the combination (7.8%) , we measured changes in expression of the anti-apoptotic proteins, Bcl2 and Bax, and the pro-apoptotic protein, Bim, along with the marker of the intrinsic apoptosis pathway protease cleaved caspase-3. In both the H3122 and H2228 cell lines, there were no significant changes in either Bcl2 or Bax in any of the treatment groups  and late (ERK) signalling steps for the major ALK pathway, with no combination effect on ALK activation. The combination effect is therefore an intermediary step between cytosolic ALK signalling and mediators of cell survival and proliferation, specifically in the regulation of the cell cycle and apoptosis; reflected in a continued, strong, combination/interaction effect for cell cycle mediators and mediators of the intrinsic apoptosis pathway.In this study, the effect of combining the SHP2 inhibitor, SHP099, with alectinib, an ALK inhibitor, was investigated in two different ALK29 (such that the systemic toxicity of ALK inhibitors is low).We may conclude that the synergy of cancer cell growth suppression is apparent, from the impact of the potency of the combination of alectinib and SHP099 from the beginning of the ALK signalling pathway through multiple steps that encompass the regulation of the cell cycle and apoptosis. This suggests that patient trials co-titrating SHP2 inhibition with ALK inhibition may be a way to produce a specific inhibitory effect on cancer cell growth, whilst minimising toxicity in ALK-negative cells in the body. ALK may be therefore particularly suited for co-targeting with SHP2 in the treatment of NSCLC, as it is not widely expressed after embryonic development is complete30. WD40 is responsible for the organisation of mitotic spindles and hence mitotic progression31. Therefore, this could explain the differences in the cell cycle occurring between the cell lines, especially concerning the G2/M phase.Interestingly, although the effects of the drugs on cell viability were similar for H3122 and H2228 cell lines, the biochemical responses of the two cell lines were different in several ways, especially concerning the G2/M phase. Specifically, in H3122 cells, there was a significant decrease in cyclin B1 levels for alectinib alone compared to the combination, an effect not observed in the H2228 cells. This was similar for pCDK1, where in the H3122 cells there was a marked increase in the combination group compared to either drug alone but not in the H2228 cells. H3122 and H2228 cells harbour the EML4-ALK variant 1 and 3, respectively. Unlike variant 3, variant 1 retains the tandem atypical β-propeller (TAPE) in EML4. The TAPE domain has been reported to show greater sensitivity to ALK inhibitors, which could explain the differences in drug responses between the cell lines. In addition, H2228 cells have a significantly smaller proportion of WD40 repeats within TAPE in the EML4 domain compared to the H3122 cells32. However, this current study is the first to demonstrate and measure the synergy of a strong and sustained combination effect from ALK through intracellular signalling to cell death. Our results support the hypothesis that the combination of SHP2 inhibition with ALK inhibition may have therapeutic potential in the treatment of ALK+ NSCLC by specifically targeting cancer cells. This is reflected in a strong, combination/interaction effect for cell cycle mediators and mediators of the intrinsic apoptosis pathway. In conclusion, these results suggest that SHP2 inhibition may be a way to improve outcomes with alectinib at sub-toxic doses by targeting the cancer cells while minimising toxicity in the rest of the body.Previous work has demonstrated promising results for SHP099 in combination with ceritinib in resistant patient-derived cell lines, both in vitro and in vivoSupplementary Information.


Neuroblastoma (NB) is a childhood tumor that originates in the peripheral sympathetic nervous system and is responsible for 15% of cancer-related deaths in the pediatric population. Despite intensive multimodal treatment, many patients with high-risk NB relapse and develop a therapy-resistant tumor. One of the phenomena related to therapeutic resistance is intratumor heterogeneity resulting from the adaptation of tumor cells in response to different selective environmental pressures. The transcriptional and epigenetic profiling of NB tissue has recently revealed the existence of two distinct cellular identities in the NB, termed adrenergic (ADRN) and mesenchymal (MES), which can spontaneously interconvert through epigenetic regulation. This phenomenon, known as tumor plasticity, has a major impact on cancer pathogenesis. The aim of this review is to describe the peculiarities of these two cell states, and how their plasticity affects the response to current therapeutic treatments, with special focus on the immunogenic potential of MES cells. Furthermore, we will discuss the opportunity to combine immunotherapy with chemotherapy to counteract NB phenotypic interconversion. PatientNB ITH originally demonstrated in isogenic tumor-derived cell lines consisted on the presence of multiple cell types that differed in morphology, tumorigenic properties, and biochemical markers , 6. BaseRecent gene expression and epigenetic profiling of 33 different NB cell lines have provided insight into the details of NB ITH. van Groningen and colleagues identified two predominant cell identities, named adrenergic (ADRN) and mesenchymal (MES), that can spontaneously interconvert through epigenetic regulation –9. The pThe potential for interconversion between different cell states, is known as tumour plasticity and has recently been proposed as a new ‘emerging hallmark of cancer’ . Indeed,In this review, we focus on the peculiarities of ADRN and MES cell states, and how their plasticity may influence the response to current therapeutic standards. Furthermore, we discuss how the combination of chemotherapy and immunotherapy has the potential to successfully target the dual nature of NB, possibly improving the prognosis of these young patients.2in vitro by acting on lineage-specific core regulatory circuits (CRCs). In 2017, Van Groningen and colleagues showed that the over-expression of the Paired related homeobox protein 1 (PRRX1) gene, a MES-specific CRC transcription factor (TF), is able of reprogramming the transitional and epigenetic landscape of ADRN cells towards a MES state  gene has been described to genetically determine NB fate by promoting the ADRN cell state  analyses did not identify MES-like cells in primary tumours (-) and MES (CD44+) components of different cell lines with a mixed phenotype  to drive NB cells towards ADRN differentiation. However, this is not sufficient to suppress the plasticity potential of the cells, which still retain the ability to transdifferentiate in the MES direction when isolated from the xenograft and cultured in vitro  developed the Rapid COJEC regimen as an induction chemotherapy step , 30. TheIn vitro, MES NB cell lines, such as SH-EP, do not undergo differentiation in response to all-trans retinoic acid (ATRA) treatment compared to their ADRN counterparts (SH-SY5Y and SK-N-BE (2)-C) -C) . ATRA inE (2)-C)  -C) . It has E (2)-C) , suggestE (2)-C) . They shE (2)-C) .When analysing GD2 expression and publicly available RNA-seq data for 23 NB cell lines, Mabe and colleagues found that GD2-high and GD2-low expression cell lines were strongly correlated with ADRN and MES signatures, respectively  Figure. They prThe MES-like phenotype has been recently associated with resistance to ALK inhibitors (ALKi) due to the absence of ALK expression even in presence of tumour-driving ALK mutations, suggesting a role of ADRN-to-MES conversion in relapse . ALK-mutTaken together, this evidence reinforces the role of cell identity and plasticity as resistance factors and supports the evidence that the MES phenotype contributes to resistance to conventional chemotherapy and the onset of relapse.4Although heterogeneous, NB has been widely described as a ‘cold tumor’, i.e., characterized by a lack of T-cell infiltration and therefore unable to trigger a strong immune response , 37. NB Another study further investigated the immunological aspect of NB by analyzing RNA-seq data from 498 well-annotated primary human NB tumors and scRNA-seq data from a total of 40 tumors . The ana+ T cell- and NK cell-mediated killing  when administered at low doses . ICD is Based on this, we recently proposed a chemo-immunotherapy approach combining low-dose mitoxantrone (MTX) with PD-1 and TGFβ blockade . This co6NB cells represent a dynamic entity strongly influenced by the TME and therapeutic treatments. Their plasticity underlines the ability to optimize their survival by acquiring phenotypic traits that become favorable under specific conditions. It has been shown that in both mouse and human models the TME tends to force NB cells towards a predominantly ADRN identity . HoweverGiven the great plasticity of NB cells, a targeted approach to ADRN/MES duality may not be sufficient for a lasting therapeutic response. Regardless of the most promising therapeutic approach, new studies are needed to better understand the different transcriptional configurations that NB cells can acquire.SD’A: Conceptualization, Writing – original draft, Writing – review & editing. PT: Writing – review & editing, Investigation. PG: Writing – review & editing, Data curation. KK: Writing – review & editing, Data curation. OM: Writing – review & editing, Data curation. MD: Writing – review & editing, Data curation. FL: Writing – review & editing, Supervision. DF: Funding acquisition, Writing – original draft, Writing – review & editing, Conceptualization.


Drug resistance is a challenge in anticancer therapy, particularly with targeted therapeutics and cytotoxic compounds. In many cases, cancers can be resistant to the drug prior to exposure, i.e., possess intrinsic drug resistance. However, we lack target-independent methods to anticipate resistance in cancer cell lines or characterize intrinsic drug resistance without a priori knowledge of its cause. We hypothesized that cell morphology could provide an unbiased readout of drug sensitivity prior to treatment. We therefore isolated clonal cell lines that were either sensitive or resistant to bortezomib, a well-characterized proteasome inhibitor and anticancer drug to which many cancer cells possess intrinsic resistance. We then measured high-dimensional single-cell morphology profiles using Cell Painting, a high-content microscopy assay. Our imaging- and computation-based profiling pipeline identified morphological features typically different between resistant and sensitive clones. These features were compiled to generate a morphological signature of bortezomib resistance, which correctly predicted the bortezomib treatment response in seven of ten cell lines not included in the training dataset. This signature of resistance was specific to bortezomib over other drugs targeting the ubiquitin-proteasome system. Our results provide evidence that intrinsic morphological features of drug resistance exist and establish a framework for their identification. Targeted cancer therapies are often thwarted by drug resistance . ResistaBortezomib is an anticancer drug commonly used to treat multiple myeloma and nearly half of multiple myeloma patients show no initial response to bortezomib therapy, indicating intrinsic resistance . MalignaBortezomib resistance can be attributed to targeted mechanisms such as mutations in the bortezomib-binding pocket of the targeted proteasome subunit (PSMB5) and overexpression of proteasome subunits  as well A growing literature suggests that specific genetic alterations, treatment response, and prognosis can be predicted from conventional hematoxylin and eosin tissue slides using machine learning , indicatHere, we used Cell Painting , a multiWe first isolated and characterized drug-resistant cell lines . We baseTo determine the appropriate drug concentrations to use in order to produce only a few surviving drug-resistant cells, we performed proliferation assays on HCT116 parental cells with our drugs of interest: bortezomib (proteasome inhibitor), ixazomib (proteasome inhibitor), or CB-5083 (p97 inhibitor) –D. AlthoTo screen for multidrug resistance, which might convolute any specific signature of bortezomib resistance, we measured proliferation of our clones in the presence of drugs that target disparate pathways: bortezomib (a proteasome inhibitor), taxol (a microtubule poison), and mitoxantrone (a topoisomerase inhibitor) . As multWhen we treated our cell lines with bortezomib, only the cell lines isolated following high-dose bortezomib treatment (BZ01–10 and BZ clones A and E) had reduced bortezomib sensitivity, with LD50s ranging ~2.8- to ~9-fold that of the wild-type parental cell line . In contWe next applied the Cell Painting assay to these drug-sensitive and -resistant cell lines. Cell Painting captures signal in five imaging channels from six fluorescent dyes that stain cells for eight cellular components including mitochondria, actin, Golgi, plasma membrane, cytoplasmic RNA, nucleoli, endoplasmic reticulum, and DNA  Bray et. With thWe next applied the Cell Painting assay to these drug-sensitive and -resistant cell lines. Cell Painting captures signal in five imaging channels from six fluorescent dyes that stain cells for eight cellular components including mitochondria, actin, Golgi, plasma membrane, cytoplasmic RNA, nucleoli, endoplasmic reticulum, and DNA  Bray et. With thWe first examined whether there were any clear qualitative morphological differences between wild-type and bortezomib-resistant cell lines and chose the wild-type polyclonal parental cell line, wild-type clones WT01-WT05, and bortezomib-resistant clones A, E, and BZ01-BZ05 for these initial studies. We treated cells with 0.1% DMSO  and performed Cell Painting, staining fixed HCT116 cells and imaging as per the published protocol . ImagingWe then pre-processed profiles to remove low-variance and highly correlated features, and population-averaged single cell measurements at the well level (see Methods). The morphological profiles of wild-type and bortezomib-resistant cells did not cleanly distinguish cell lines based on bortezomib sensitivity . This waEach observed morphological measurement results from a combination of both technical and biological variables. It is therefore important to control and test for technical variables as these can confound subtle signatures that would otherwise dominate the morphological profiles of the cells being analyzed. Using wild-type clones WT01–05 and bortezomib-resistant clones BZ01–05 to quantify and reduce the impact of technical variables, we fit a linear model to each morphological feature adjusting for technical variables  and biological variables (resistance status) (see Methods). We then discarded morphological features with variances that correlated with experimental run (batch), incubation time (4 or 13 hours with 0.1% DMSO), cell density, or those features that varied between two or more pairs of wild-type clones (clone ID) –E. Of thAnticipating well location as a possible technical artifact, we plated our cells in a repeating serpentine pattern, ensuring that each cell line would be imaged in multiple locations across each plate . We founTo evaluate the performance of our Bortezomib Signature, we used machine learning best practices, separating our data into training, validation, test, and holdout datasets  or CB-5083 (a p97 inhibitor that acts upstream of the proteasome). If our Bortezomib Signature was a general signature of UPS-targeting drug resistance, we would expect our signature to perform equally well at characterizing the drug sensitivity of bortezomib-, ixazomib-, and CB-5083-resistant cell lines. Our Bortezomib Signature performed better than chance at identifying ixazomib-resistant and CB-5083-resistant cell lines , correctTo test whether our Bortezomib Signature could correctly characterize the bortezomib sensitivity of cell lines not included in the training dataset, we imaged an entirely new set of wild-type  and bortezomib-resistant clones (BZ06-BZ10) using the Cell Painting protocol. Overall, the Bortezomib Signature performed well, with an AUROC of 0.75, compared to 0.55 for the shuffled data . Our BorWe used Cell Painting, a high-throughput and high-content image acquisition and analysis assay, as a target-independent method to capture the morphological profiles of untreated cells that were either sensitive or resistant to the ubiquitin-proteasome system (UPS)-targeting anticancer drug, bortezomib. After processing profiles to reduce the impact of technical variables, we generated a signature of bortezomib resistance and characterized the performance of this signature using machine learning best practices. This Bortezomib Signature correctly predicted the bortezomib treatment response of seven out of ten cell lines not included in the training dataset and was specific to the drug under investigation, in this case bortezomib, even as compared to cells that were resistant to other drugs targeting the UPS. Our work demonstrates that there are intrinsic morphological features of drug resistance in cells that can be identified using Cell Painting and provides a reproducible pipeline for generating morphological signatures of drug resistance.The Bortezomib Signature’s performance was not perfect; it mischaracterized three clones not included in the training dataset. Interestingly, one of the misidentified clones (BZ06) had reduced sensitivity to mitoxantrone as well as bortezomib. Given the considerable genetic heterogeneity in this mismatch repair-deficient HCT116 cell line , it is pOur Bortezomib Signature performed better at identifying bortezomib-resistant cell lines compared to ixazomib-resistant cell lines, and better at identifying ixazomib-resistant cell lines compared to CB-5083-resistant cell lines. All three drugs broadly target the UPS, however bortezomib and ixazomib both target the same subunit of the proteasome, albeit with different potentially non-overlapping spectrums of off-targets . Our datThis work has shown potential for morphological profiling with Cell Painting to characterize drug sensitivity in untreated cells, having generated a robust signature of resistance to bortezomib, a drug with a high failure rate in treating cancer. Our results indicate that different mechanisms of bortezomib resistance may be generating distinct morphological profiles; with larger and broader training data, it may be possible to identify signatures for multiple mechanisms of bortezomib resistance as well as signatures of resistance to other drugs. An important step will be determining whether this method can be extended to patient samples where identifying intrinsic drug resistance in cells prior to treatment has the potential to improve targeted cancer therapy. We expect that further refinement might develop Cell Painting as a tool for identifying drug-resistant cells, perhaps even guiding therapeutic strategies to overcome intrinsic resistance.HCT116 cells , also referred to as HCT116 wild-type parental cells, were maintained in McCoy’s 5A Medium (Gibco) supplemented with 10% (v/v) FBS (Sigma) and cultured at 5% CO2 and 37°C. Bortezomib-resistant, ixazomib-resistant, and CB-5083-resistant clonal cell lines were isolated as previously described . BrieflyCell proliferation was evaluated using an Alamar Blue assay . Briefly3 cells/mL in 96-well glass-bottomed tissue culture dishes (Greiner Bio-One) and allowed to adhere for 48–72 hours prior to fixation. At either 4 or 13 hours prior to fixation, cells were treated with either 0.1% DMSO or 7 nM bortezomib and 30 min prior to fixation cells were treated with MitoTracker Deep Red . 16 % paraformaldehyde (EMS) was added to each well for a final concentration of 3.2% and cells were fixed in the dark at room temperature for 20 minutes. Wells were washed with HBSS (Invitrogen), permeabilized with 0.1% Triton-X for 15 minutes, and then washed twice with HBSS before incubating with staining solution  in the dark for 30 minutes. Wells were then washed twice with HBSS and imaged using an ImageXpress high-content imaging system (Molecular Devices) with a 20× 0.45 NA S Plan Fluor ELWD objective (Nikon) and captured with a Zyla 5.5 sCMOS detector (Andor Technology). Each well was imaged at 12–17 non-overlapping sites in five channels using Semrock filters .High-throughput imaging was performed according to the published Cell Painting protocol . HCT116 https://cellprofiler-manual.s3.amazonaws.com/CellProfiler-3.1.9/help/output_measurements.html).We used CellProfiler versions 3.1.8 and 3.1.9  to perfohttps://github.com/broadinstitute/profiling-resistance-mechanisms  to create similarity matrix heatmaps.After processing all images and forming normalized and feature selected profiles per well, we performed several additional analyses to explore the results and discover a morphology profile of bortezomib resistance. We performed initial comparisons of morphological profiles using Morpheus  post hoc test per model to determine which specific categorical covariate comparison contributed to a significant finding and to control for within-covariate-group multiple comparisons through a family-wise error rate (FWER) adjustment that accounts for different within-group sizes .−6).Separately, we fit another linear model on continuous features to adjust for features that were significantly impacted by well confluence We next applied a specific exclusion criterion to specifically isolate features that contributed to resistance status. We excluded features that were significantly different across incubation times, batches, and cell counts. We also excluded features that were different within clone type (features varying between two or more wild-type clones) to reduce the contribution of features that may mark generic inter-cell line differences nonspecific to resistance status. This procedure resulted in a total of 45 features that were significantly different by resistance status and not significantly impacted by any of the technical covariates we considered. Of the 45 features, 14 had higher values in resistant clones and 31 had lower values in resistant clones .singscore method  and calculates an internal rank per profile of how highly ranked and lowly ranked each of the up and down features are, respectively. The method then adds the up and down rank scores to form a total singscore per sample, which ranges between −1 and 1 and represents a rank-based normalized concordance score that can be directly compared across profiles that may have been normalized differently. Therefore, the score is robust to outliers and different normalization procedures. In addition to calculating the singscore per sample, we also calculated singscore with 1,000 random permutations, in which we randomly shuffled feature rankings to derive a range in which a sample may be scored by chance.We used the e method  to charaWe used several metrics to evaluate signature quality across five different evaluation scenarios . Because we are measuring a binary decision in a balanced dataset (roughly the same amount of positive as negative classes), we used accuracy  to quantify performance. We also calculated mean average precision using sci-kit learn, averaging over samples along the precision recall curve , which iSupplement 1


Introduction:Cinnamomi ramulus (CR) is one of the most widely used traditional Chinese medicine (TCM) with anti-cancer effects. Analyzing transcriptomic responses of different human cell lines to TCM treatment is a promising approach to understand the unbiased mechanism of TCM.Methods: This study treated ten cancer cell lines with different CR concentrations, followed by mRNA sequencing. Differential expression (DE) analysis and gene set enrichment analysis (GSEA) were utilized to analyze transcriptomic data. Finally, the in silico screening results were verified by in vitro experiments.Results: Both DE and GSEA analysis suggested the Cell cycle pathway was the most perturbated pathway by CR across these cell lines. By analyzing the clinical significance and prognosis of G2/M related genes  in various cancer tissues, we found that they were up-regulated in most cancer types, and their down-regulation showed better overall survival rates in cancer patients. Finally, in vitro experiments validation on A549, Hep G2, and HeLa cells suggested that CR can inhibit cell growth by suppressing the PLK1/CDK1/ Cyclin B axis.Discussion: This is the first study to apply transcriptomic analysis to investigate the cancer cell growth inhibition of CR on various human cancer cell lines. The core effect of CR on ten cancer cell lines is to induce G2/M arrest by inhibiting the PLK1/CDK1/Cyclin B axis. Cinnamomi ramulus (CR) or “Guizhi” is dried twigs of Cinnamomum cassia (L.) Presl, which has been used in clinics frequently for thousands of years, with the property of relieving exterior syndrome by diaphoresis, warming and smoothing the meridian, reinforcing Yang to transform Qi  , and the2 in an incubator . Cells were incubated at 37°C and 5% COncubator .−4, 1 × 10−3, 3.3 × 10−3, 1 × 10−2, 3.3 × 10−2, 1 × 10−1, 3.3 × 10−1, and 1 g/mL) of CR for 24 h. After incubation, 110 μL fresh medium, including 10 μL CCK8 solution, was added to each well. The plates were incubated for an additional 2 hours at 37°C. Then the absorbance was measured at 450 nm wavelength by a microplate reader (BioTek).CR granules were bought from E-FANG pharmaceutical  and dissolved in their corresponding culture medium. Then we performed CCK8 assay to detect the cell viability after CR treatment . Cells whttps://old.tcmsp-e.com/tcmsp.php), SymMap (http://www.symmap.org), ETCM(http://www.tcmip.cn/ETCM/), TCM-ID (https://bidd.group/TCMID/), BATMAN (http://bionet.ncpsb.org.cn/batman-tcm/), HERB (http://herb.ac.cn/), and literature. Putative gene targets were collected from BioAssay Results from Pubchem. Moreover, we only collected active interactions between compounds and human genes. Finally, 857 compound-genes interactions from the 83 compounds and 341 distinct targets were obtained , ETCM , HERB   following the manufacturer’s protocol. We sequenced pair-end reads of 100 bp (PE100) on the BGISEQ-500 platform for subsequent data analysis .http://ge-lab.org/idep/), integrated Differential Expression and Pathway analysis, was used to perform read counts normalization and differential expression analysis  with the parameter of “–genome hg38 –gencode” to obtain the read count matrix for all samples. iDEP  wain Atlas  and Oncoin Atlas  were usein Atlas . cBioporin Atlas  was usedQPCR was used to analyze the mRNA expression of CDK1, PLK1, CCNB1, CCNB2, and CDC25C in cancer cell samples. Total RNA extraction was performed with the Direct-zol RNA miniprep kit according to the manufacturer’s instructions. cDNA was generated using SuperScript III Reverse Transcriptase (Invitrogen). The total cDNA was mixed with SYBR Green and master mix, then loaded into QuantStudio™ 6 Flex Real-Time PCR System (Applied Biosystems) for amplification and detection . The priWestern blot was performed as previously described . The equCell cycle assay was performed using a Cell Cycles and Apoptosis Analysis Kit  . Cells wp < 0.05 were considered statistical significance.All experiment data were presented as mean ± SD and analyzed using GraphPad Prism nine software. Statistical analysis of data was performed with Student’s t-test. Results with We first performed UPLC-MS/MS to identify the characteristic compounds in CR granules. Cinnamaldehyde, 2-hydroxycinnamic acid, coumarin, and 2-methoxycinnamic acid were identified from CR granules . To inve50 of the CR in different cell lines. Then, we treated ten cancer cell lines with different concentrations of CR as L , M  and H  group (http://bioinformatics.sdstate.edu/idep93/) to process the sequencing data, and hierarchical clustering results showed that the heatmap could be clustered into ten different cell lines  and performed CCK8 assay to detect the IC0) group . After mll lines . Then well lines  for eachll lines  and furtll lines  across 1ll lines .n ≥ 5) were included for further analysis.  from Pentose phosphate pathway and 16 genes from Cell cycle pathway . We remoSeveral databases and web servers were used to evaluate hub genes’ clinical significance and prognosis value, including Timer 2.0, GEPIA, Oncomine, HPA, and cBioPortal. Using Timer 2.0, we found that PLK1, CDK1, CCNB1, and CCNB2 were upregulated in different cancer types compared with normal samples from the TCGA data set . And thein vitro experiments. qPCR results showed that all the hub genes were significantly downregulated , IC30 (M), and IC50 (H) to treat the chosen cell lines. From our results, ten cancer cell lines showed decreased cell viability. After CR treatment, Hep G2, A549, and HeLa showed significant cell shrinkage and less adhesion.In recent years, TCM has attracted global attention for its promise in treating diseases, especially complex diseases like diabetes, cardiovascular disease, and cancers . Many Chp values of Cell cycle-related pathways are much larger than that of the Pentose phosphate pathway  combined with experiments to clarify the TCM function. Therefore, gene expression profiles in responses to TCM will significantly accelerate the modernization of TCM and provide clues for treating complex diseases.


Glioblastoma multiform (GBM) is an incurable heterogenous brain cancer with few clinical target options. IQGAP1 is a scaffold oncoprotein involved in GBM with unclear mechanism. Here we report that the antipsychotic drug Haldol differentially alters IQGAP1 signaling and inhibits GBM cell proliferation, thus providing novel molecular signatures for GBM classification and potential targeted therapy in personalized medicine. Glioblastoma multiform (GBM) is an incurable primary brain cancer of heterogenous cellular origin that lacks effective treatment and personalized clinical biomarkersHere, using cell saturation density assays, we identified Haloperidol  as an inhibitor of cell proliferation in the three different GBM cell lines U87, LN18, and U118 . HaldolIn control (vehicle-treated) U87 cell lines, the known IQGAP1 partner Rho GTPase was identified. Rho GTPase is a member of the Ras superfamily of the small GTPases known for its regulation of the cytoskeleton dynamics and cell migrationTreatment with Haldol revealed a different set of IQGAP1 interactions in the two GBM cell lines , suggesIn the Haldol-treated LN18 GMB cell lines, the Zinc finger protein 502 (ZN502) was identified as an IQGAP1-binding partner . As lisThe results also highlighted the influence of Haldol on cell mass, as gleaned from comparing the protein mass in the untreated and treated cell lines . This fIn summary, using Haldol as a pharmacologic agent, we identified new IQGAP1 partners  potentiCell Culture:2and used at log phase (~ 80% confluency). Cells were discarded after 5 passages and replaced with earlier generations from frozen stocksThe glioblastoma U87, LN18 and U118 cell lines were purchased from ATCC and grown in DMEM media supplemented with 1% penicillin-streptomycin and 10% fetal bovine serum (FBS) per ATCC instructions. The cells were incubated at 37°C in a sterile humidified incubator supplied with 5% COCell Proliferation Assay6 in triplicatesand incubated overnight to attach. Next morning, the media were replaced by media containing 10 μM Haldol or DMSO vehicle as control. The cells were counted every other day for 6 days, using a hemocytometer or the Countess II (Invitrogen) cell counter.The classic saturation density assay was used to measure cell proliferation capacity as previouslyImmunoprecipitation (IP):3VO4. The lysates were cleared by centrifugation and protein concentration was determined by bicinchoninic acid assay . Four hundred to 1 mL of lysates were pre-cleared with 15 μl of NP40-equilibrated 50% slurry of protein A beads for 1 hr. and used for the immunoprecipitation (IP) reactions. 5 μL primary IQGAP1-specific antibody or IgG as control were added to the cleared cell lysates for IP. The samples were rotated back-to-back for three hours at 4°C followed by the addition of 15 μl agarose bead slurry and the samples were rotated for another three hours and centrifuged at 10,000 rpm for one minute. The supernatant was aspirated and the pellets were washed 3X by adding 1 mL of NP-40 and repeating the centrifugation, and 40 μl of the buffer was added to resuspend the beads. The samples were heated at 100°C for seven minutes and incubated on ice for three minutes before being centrifuged for two minutes and resolved by SDS-PAGE and the gels were Coomassie stained.Immune complexes from IQGAP1-specific antibodies vs. IgG controls were analyzed by mass spectrometry to identify IQGAP1-interacting proteins in each condition. Cells growing at ~ 80% confluency were treated with 10μM Haldol or DMSO as a drug vehicle control and incubated in cell culture incubator for 48 hrs. Total cell lysate was prepared by rinsing the cells with ice-cold PBS followed by scraping into ice-cold NP40 lysis buffer  supplemented with protease inhibitors  and 3 mmol/L NaMass Spectrometry:2pieces via clean scalpel blades and individually placed in Eppendorf tubes and covered with 25 mM NH4HCO3. Samples were vortexed for ten minutes and the supernatant discarded. 25 μl of 10 mM Dithiothreitol was added to each tube, and the samples were vortexed for five minutes followed by supernatant removal, and an hour resting period. Thereafter, 25 μl of 55 mM Iodoacetamide was added, the samples were vortexed, the supernatant removed, and the samples left to rest for 45 minutes. The gels were then dehydrated with 100 μl of 25 mM NH4HCO3in 50% Acetonitrile (ACN), vortexed for five minutes, and rested for 20 minutes. Trypsin (5-25 μl) was added to cover the gel pieces and the samples placed on ice, spun, and incubated overnight. Finally, 1 μl of each sample was loaded onto the MALDI-TOF (Bruker Ultraflextreme) and the corresponding mass to charge (m/z) ratios, spectra, and intensity values were recorded and used for data analyses.Mass spectrometric peptide analyses was performed using the Coomassie stained in-gel digest with trypsin per Bruker Daltonik instruction manual that was modified from Shevchenko et al, 1996 protocol. Briefly, the gel lanes were carefully cut into 1mmData AnalysesTo identify the samples by mass-spectrometry, the MS-Spectrum of each sample was compared to known protein mass-spectra using the Mascot Search Engine. Analyses of the samples were done using the free online Mascot Program within the Protein Pilot Software (Version 5.0.2). Furthermore, UniProt (2022_05 release) and NIH-NCBI Blast Program were used to identify and describe the corresponding proteins identified from each sample.Statistical Analysespvalues less than 0.05 were considered statistically significant.Data are representative of at least three independent experiments with 2-3 replicas each. Statistical analyses were performed using Graph Pad Prism 6.0 , and the algorithms in Microsoft Excel software (Version 2018) were also used to compare levels between different groups. All statistical tests were two-sided, andReagents Information


The present study explores the resemblance of GBM cell lines to primary tumors regarding membranome expression. Gene expression data was retrieved from Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA). Membranomic genes were annotated and tumor purity was accounted for when correlating tumors and cell lines. The results suggest some commonly used cell lines, including AM38 and U87MG, display relatively little resemblance to tumors membranome. Differential gene expression analysis and subsequent gene set enrichment showed numerous genes related to neurexin/neuroligin, ion homeostasis, and synaptic signaling were downregulated in cell lines’ membranomes compared to that of GBM tumors. The findings suggest that the membranome of GBM cell lines exhibit pronounced changes in gene expression compared to primary tumors and may not be completely representative of the disease process.Genes encoding for proteins associated with the plasma membrane, referred to as the membranome, have long been recognized to play an important role in the development and maintenance of glioblastoma multiforme (GBM). GBM cell lines are commonly used to mimic tumors for  Patientmembranome. The membranome is particularly relevant to oncology as it composes many potential targets for drug therapies, as evident in monoclonal antibody-based therapies [Proteins associated with the plasma membrane have long been recognized to play an central role in the development and maintenance of cancer by mediating interactions between tumor cells and their surrounding environment, including cell-to-cell signaling, molecular transport, cell adhesion, and interaction with the extracellular matrix . The genherapies , 7. Corrherapies . While iherapies , 9.Despite the prevalent use of cell lines to establish drug therapies for GBM –13, the 2.2.1https://portals.broadinstitute.org/ccle/home). Level 3 released RSEM gene abundances for RNASeqV2 for GBM tumor samples were retrieved from the Broad GDAC portal (http://gdac.broadinstitute.org/), which hosts the TCGA project. The CCLE and TCGA databases were chosen for this analysis due to their open-source availability and comprehensive analysis of the genomic landscape of glioblastoma multiforme. In total, gene expression data from 142 primary GBM tumors and 31 GBM cell lines was analyzed. Tumor purity was calculated (defined broadly as the proportion of non-immune cells in the tissue sample) for tumor and cell line samples using the ESTIMATE algorithm [Gene expression data from RNA-sequencing and accompanying annotation file for cell lines were downloaded from CCLE . Then the top 3000 genes, ranked by interquartile range across primary tumor samples, were selected to calculate rank-based Spearman correlations between GBM cell lines and primary tumors, as these genes have the highest likelihood to be biologically informative.In the present study, the membranome was defined as the set of all human genes coding for proteins integrated within or covalently associated with the cell plasma membrane. All genes shared between the CCLE and TCGA datasets were screened using a combined analysis of previously established gene ontology annotations  plus the2.3DESeq2 v.3.1  was used2.4All relevant analyses were conducted with programming language R . Biocond3.The observed median correlations between the membranome of GBM cell lines and GBM primary tumors ranged from 0.63 to 0.75 . Cell li4.With increasing number of GBM cell lines being developed , choosinA certain number of these underexpressed genes were associated with synaptic signaling. Indeed, recent evidence suggests that gliomas engage in synaptic communication and this neural integration may be crucial for glioma progression , 30. It Additionally, genes related to neurexins and neuroligins exhibited a similar pattern of decreased expression in cell lines. Neuroligins are synaptic cell-adhesion molecules (CAMs) that interact with presynaptic neurexins to mediate transsynaptic signaling –34. NeurIt is a limitation of the current study that only transcriptomic data was used to analyze the membranome of GBM cell lines and tumor samples. Future examinations should use gene expression data in conjunction with other molecular profiling assays such as metabolomics and proteomics. Nonetheless, these initial findings suggest changes in the membranome of GBM cell lines compared to primary tumors that may not be representative of the disease process. Previous literature examining the membranome of other cancer cell types and primary tumors demonstrate significance in membranome genes and the tumor phenotype, but analysis at the protein level would be appropriate to validate our conclusions at the level of transcription . CorrespTable S1Table S1: Significant differentially expressed membranome genes between GBM cell lines and primary tumors. Tumor_downregulated: Membranome genes downregulated in GBM primary tumors compared to cell lines. Tumor_upregulated: Membranome genes upregulated in GBM primary tumors compared to cell lines.


Vimentin, hyaluronan synthase-3, S100 calcium binding protein A6, TGFB1, CD44, CD55, and CD109 in European American and African American aggressive variant prostate cancer (AVPC) subtypes—mCRPC, neuroendocrine variant (NEPC), and taxane-resistant. The taxane-resistant gene FSCN1 was also expressed highly in single-cell subclonal populations in mCRPC. Furthermore, metronomic-topotecan single agent and combinations with DTX downregulated these EMT markers as well as CD44+ and CD44+/CD133+ “stem-like” cell populations. A microfluidic chip-based cell invasion assay revealed that METRO-TOPO treatment as a single agent or in combination with DTX was potentially effective against invasive prostate cancer spread. Our RNA-seq and scRNA-seq analysis were supported by in silico and in vitro studies, suggesting METRO-TOPO combined with DTX may inhibit oncogenic progression by reducing cancer stemness in AVPC through the inhibition of EMT markers and multiple oncogenic factors/pathways.Prostate cancer is the second leading cause of noncutaneous cancer-related deaths in American men. Androgen deprivation therapy (ADT), radical prostatectomy, and radiotherapy remain the primary treatment for patients with early-stage prostate cancer (castration-sensitive prostate cancer). Following ADT, many patients ultimately develop metastatic castration-resistant prostate cancer (mCRPC). Standard chemotherapy options for CRPC are docetaxel (DTX) and cabazitaxel, which increase median survival, although the development of resistance is common. Cancer stem-like cells possess mesenchymal phenotypes [epithelial-to-mesenchymal transition (EMT)] and play crucial roles in tumor initiation and progression of mCRPC. We have shown that low-dose continuous administration of topotecan (METRO-TOPO) inhibits prostate cancer growth by interfering with key cancer pathway genes. This study utilized bulk and single-cell or whole-transcriptome analysis [(RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq)], and we observed greater expression of several EMT markers, including The utilization of metronomic-like dosing regimens of topotecan alone and in combination with DTX resulted in the suppression of makers associated with EMT and stem-like cell populations in AVPC models. The identification of molecular signatures and their potential to serve as novel biomarkers for monitoring treatment efficacy and disease progression response to treatment efficacy and disease progression were achieved using bulk RNA-seq and single-cell-omics methodologies. The androgen receptor (AR) signaling pathway plays a pivotal role in prostate development and homeostasis, as well as in the progression of prostate cancer  disease  in tumors displaying mesenchymal phenotypes  and CD44of mCRPC . Furtherof mCRPC . TherefoP < 0.05) compared with conventional topotecan (CONV-TOPO) treatment in prostate cancer cell lines (P ≤ 0.05) smaller tumor volume (65.4% ± 11.2%) compared with control (136% ± 14%) and CONV-TOPO–treated animals (138% ± 10%) in an aggressive xenograft tumor model of human prostate cancer implanted in male NCr athymic mice (P ≤ 0.05) smaller tumor volumes (54.8% ± 16.5%) than animals receiving CONV-TOPO dosing (144% ± 11%) or the control group  smaller tumor volumes compared with ALZET control animals with no observable toxicities (Low-dose continuous drug exposure using metronomic-like (METRO) chemotherapy involves frequent administration of chemotherapeutic agents at low or fractionated doses at close intervals over prolonged periods of time . METRO ill lines . We thenmic mice . This ov; ref. 3 . ALZET pxicities . Moleculxicities . Severalxicities . Howeverin vitro model systems of treatment-refractory and treatment-emergent AVPC, we demonstrated that the METRO-TOPO showed efficacy as a single agent, as shown previously. We showed synergistic activity in combination with conventional taxanes (CONV-DTX) and METRO-TOPO. A microfluidic chip-based confined cell invasion assay was performed. The assay recapitulates the dimensionality of pores and longitudinal channel-like tracks encountered by cancer cells during migration to investigate the effect of METRO-TOPO on cancer cell invasion and stemness. Flow cytometry and cell sorting with prostate cancer stemness-specific antibodies  were accomplished to identify differential percentages of stem-like cell populations  in all prostate cancer subtypes, including EA and AA, to evaluate the impact of METRO-TOPO in eroding “stem-like” (CD44+/CD133+) cell subpopulations. The effect of METRO-TOPO treatment on EMT gene expression (GE) and protein expression was determined, and a comparative analysis with whole-genome transcriptomics data and reverse matching DEGs from patients with prostate cancer were used to examine the potential clinical significance of METRO-TOPO treatments.Therefore, we performed pretreatment versus posttreatment bulk and single-cell RNA sequencing (scRNA-seq) to identify differentially expressed genes (DEG) for prostate cancer subtypes of EA and AA origin and potential molecular pathways associated with METRO-TOPO activity in aggressive variant prostate cancer  at the tumor and subclonal levels and to gain molecular insights into METRO-TOPO activity. Furthermore, our RNA-seq and scRNA-seq data showed that METRO-TOPO treatment was potentially effective against the development of prostate cancer subclones with a reduction of treatment-resistant stem-like genes. Using in vitro studies, and patient databases , we conclude that METRO-TOPO has the potential to improve the clinical outcome in AVPC chemotherapy by enhancing the therapeutic efficacy of standard-of-care drugs and abrogating the possibilities of development of drug resistance. Such an evidence-based approach promises to minimize the chances of trial failures and improve the probability of clinical success.Therefore, using an innovative approach that integrated advanced molecular techniques [next-generation mRNA sequencing (mRNA-seq) and single-cell-omics technologies] with microfluidics, flow cytometry, MoFlo XPD Flow high speed-based cell sorting, cytotoxicity profiling, immunoblotting as Chemicals and Reagents, details are provided in the Low mCRPC/NEPC , ARHigh EA mCSPC , ARHigh EA mCRPC (C4-2B), ARHigh AA mCSPC (MDA-Pca-2b), and normal prostate  cell lines were obtained from ATCC. AA-ARHigh CSPC  and AA-ARLow mCRPC (RC43T) were obtained from our collaborator's lab . Cells were subcultured when they reached approximately 80%–90% confluence. Cell lines were used until passage 30. After removing from the cryo-storage (passage 2–3), cells were cultured and passaged for 2–3 weeks before beginning studies.ARThree-dimensional (3D) spheroids were harvested and transferred into a 96-well plate in 100 μL of recommended media. After 2 days of acclimation, 100 μL of media ± drug/well were added to each well containing a spheroid. On days 3 and 5, 100 μL of media/well were removed and replaced with 100 μL of fresh media with/without (±) drug/well. On off-media exchange days, 10 μL of media/well was removed and replaced with 10 μL of media ± drug. METRO-TOPO dosing was simulated by using 20x TOPO directly spiked into the wells at 10 μL in 190 μL of media. The CONV-TOPO was added as a bolus dose on day 0. METRO/extended exposure (EE)-TOPO treatment was given daily as a fractionated dose at 1/7th the CONV-TOPO. Total topotecan dosing was 100 nmol/L during each week of therapy. Control and treated spheroid were collected and stored in RNAlaterer (Qiagen) for RNA-seq. Fresh/live samples were used for scRNA-seq.Low mCRPC/NEPC PC-3 tumor model (3D spheroid) were assessed using RNA-seq. Pre- and post-drug exposure, as described above, tumor cells were harvested, and high-quality RNA was extracted using QIAshredder and RNeasy kit (Qiagen). RNA concentration and integrity were assessed using a Nanodrop-8000 spectrophotometer and Agilent 2100 Bioanalyzer. An RNA integrity number threshold >8 was applied, and RNA-seq libraries were constructed using Illumina TruSeq RNA Sample Preparation kit v2. Libraries were then size-selected to generate inserts of approximately 200 bp. RNA-seq was performed on Illumina's NovaSeq platform using a 150 bp paired-end protocol with a depth of >20 million reads per sample. Average quality scores were above Q30 for all libraries in both R1 and R2  was assessed by RNA-seq. Furthermore, the effects of METRO-TOPO and CONV-TOPO exposure for 6 weeks on AR1 and R2 . AA cell1 and R2 . LibraryP < 0.05 were considered the threshold for reporting significant DEGs. Heat maps were generated using unsupervised hierarchical clustering (HC) analysis based on top DEGs. Sequencing data on AA cell lines were compiled as FastQ files for downstream analysis  and Partek Flow software. Briefly, reads were preprocessed and mapped to the hg38 human genome build using the STAR Aligner tool. Next, mapped read counts were counts per million (CPM) normalized, and differential Gene Expression Profile (GEP) analysis was performed. Genes with mean fold change >|1| and analysis .scRNA-seq analysis was assessed for prostate cancer cell lines at baseline and TOPO-METRO  in the PC-3 tumor model (3D spheroid). Automated single-cell capture and cDNA synthesis were performed using the 10X Genomics Chromium platform. scRNA-seq–based GE analysis was performed on an Illumina HiSeq 2500 next-generation sequencing (NGS) platform by paired-end sequencing technique at 2*125 bp and 100 cycles using v3 chemistry.scRNA-seq datasets were obtained as matrices in the Hierarchical Data Format (HDF5 or H5). CellRanger, Seurat, and Partek Flow software packages were used to preprocess the data analysis. Highly variable genes were selected for clustering analysis based on a graph-based cluster approach. The visualization of cell populations was performed by t-distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) for dimension reduction analysis for biomarker-based identification of subclones representing CSC-EMT, taxane-resistant cells, potential METRO-TOPO targeted subclones, and METRO-TOPO treatment-induced erosion of these subclones.Ingenuity Pathway Analysis  was performed using top DEGs to reveal molecular pathways/mechanisms, upstream regulator molecules, downstream effects, biological processes, and predicted causal networks governing prostate cancer subtypes and METRO-TOPO functions in AVPC .http://ualcan.path.uab.edu/index.html), a web-based tool for analyzing TCGA RNA-seq and clinical data, was used to evaluate the association of GE and patient survival in 31 types of cancers, including prostate cancer (http://gepia.cancer-pku.cn/), an interactive web-based tool for survival analysis based on GE, was also used. According to the characteristics of gene normalization, GEPIA allowed two different genes to be input at the same time for survival analysis  were used to compare transcriptome data on target candidate pathway genes with tumor metastasis and patient survival from the prostate expression data matrix . GE profe cancer . Furtheranalysis .+, CD133+, and double-positive cells using a Beckman Coulter Analytical Flow Cytometer—a CytoFLEX LX flow cytometer at 50,000 events/measurement. “Stem-like” cell populations were measured by assessing the shift in the mean fluorescence intensity by flow cytometry. Data were analyzed (gates were set), normalizing to unstained cells of each prostate cancer subtype. Ghost Dye Red 780 was used to detect live cells in each cell population. Furthermore, ARLow cells were seeded in 6-well plates and exposed to CONV-TOPO, METRO-TOPO, and CONV-DTX+METRO-TOPO as combination treatments calculated on the basis of our earlier studies  for flow cytometry. For combination, prostate cancer cell lines were treated with estimated MTT IC50, IC50/2 for CONV-DTX and MTT IC50, IC50/2 for METRO-TOPO .Prostate cancer cells were labeled with a binding buffer containing stemness markers CD44, CD133, or both antibodies. Cells were collected and quantified as CD44 studies . We havell lines . Plated TRO-TOPO . After 7In vitro cytotoxicity assays were performed using the MTT assay. Briefly, cells were plated in a 96-well culture plate at 2 × 103 cells/well and incubated for 24 hours at 37°C with 5% CO2. We have determined IC50 for MERO-TOPO, CONV-TOPO, and CONV-DTX for 48 and 72 hours for all prostate cancer cell lines (50 and IC50/2 are provided in 50 and IC50/2 estimated by MTT for each treatment protocol (CONV and METRO) for single agent  relative to untreated controls was calculated at each drug concentration.ll lines . The spele agent . For comCell death by apoptosis was measured using a Caspase-Glo 3/7 kit. Briefly, cells were seeded and treated according to the MTT protocol  in 6-well plates and exposed to TOPO-CONV, TOPO-METRO, CONV-DTX as a single agent and in combination with CONV-DTX+METRO-TOPO according to MTT protocol  in a double-blind manner.Cellular morphology was assessed after cells were seeded at 2.5 × 104 prostate cancer cells were harvested and resuspended in Aldefluor assay buffer containing the ALDH substrate, BODIPY-amino acetaldehyde (BAAA). Negative control samples were treated with diethylamino-benzaldehyde (DEAB), an inhibitor of ALDH1 enzymatic activity. Cells were incubated at 37°C and suspended in Aldefluor assay buffer. The brightly fluorescent ALDH+ cells were detected by BD LSR II flow cytometry.Aldehyde dehydrogenase (ALDH1) activity was assessed using an Aldefluor assay kit. Briefly, 1 × 10https://imagej.nih.gov/ij/).Pretreatment and Posttreatment cells were lysed in RIPA Lysis and Extraction Buffer (Thermo Fisher Scientific). Briefly, cells were seeded and treated according to the MTT protocol  width × (H) height = 3 × 10 μm2] or partially confining (W × H = 10 × 10 μm2) microchannels. The devices were placed on an automated Nikon Ti2 Inverted Microscope equipped with a Tokai stage-top incubator. Cell entry into the channels was recorded via time-lapse microscopy. Images were recorded every 10 minutes for 8 hours with a 10x/0.45 NA Ph1 objective. The percentage of cell entry into microchannels was calculated by measuring the number of cells entering the channels divided by the total number of cells seeded adjacent to the microchannel entrances.The fabrication of the polydimethylsiloxane (PDMS)-based microchannel device using standard multilayer photolithography and replica molding has been reported earlier . Briefly4 cells (mL/well), incubated overnight, and treated with CONV-TOPO, METRO-TOPO, CONV-DTX as a single agent, and CONV-DTX+METRO-TOPO in combination according to the MTT protocol , sorted using CD44P values <0.05 were considered significant. ANOVA was performed for continuous outcomes, and the Benjamini–Hochberg multiple testing methods were used as a post hoc test. The two-group t test was used to perform differential GE analysis between groups and detect the DEGs. Genes with mean fold changes>|1| and P < 0.05 were considered as the threshold for reporting significant differential GE. Finally, heat maps were generated using unsupervised HC analysis based on the top DEGs. Kaplan–Meier curves were generated using survival time and censored data to plot survival against high versus low expression of significantly associated genes. All statistical analyses were performed using R v4.1.0 and GraphPad Prism v9.0.All tests were two sided. Differences with https://www.ncbi.nlm.nih.gov/geo/), accession number GSE233341 or from the corresponding author upon reasonable request.The datasets generated during or analyzed during the current study are available at the NCBI Gene Expression Omnibus , ARLow/mCRPC/NEPC  and normal prostate cell lines  DEGs for ARLow/mCRPC/NEPC cell lines were the majority of EMT markers (upregulated) such as Vimentin (VIM), hyaluronan synthase-3 (HAS3), Laminin subunit beta-3 (LAMB3), S100 calcium binding protein A6 (S100A6), Serpin-E2 (SERPINE2), insulin-like growth factor binding protein-4 (IGFBP4), Annexin A2 (ANXA2), Annexin A2 pseudogene-2 (ANXA2P2), G protein-coupled receptor class C-5A (GPRC5A), and CD55, whereas downregulated genes were included endothelial transcription factor GATA2, nerve growth factor inducible VGF/VGF, insulin-like growth factor binding protein-2 (IGFBP2), solute carrier family 44-A4 (SLC44A4), guanylate cyclase-1 alpha-1 (GUCY1A1), NK3 homeobox-1 (NKX3-1), kinesin-1A (KIF1A), prune homolog-2 with BCH domain (PRUNE2), transmembrane serine protease-2 (TMPRSS2), and acid phosphatase-3  and ARHigh/mCSPC-AA  showed discrete DEGs  of prostate cancer subtypes (Low/mCRPC/NEPC prostate cancer (P < 9.43E-06) and EMT (P < 5.63E-06) as key pathways associated with prostate cancer development in AA  associated with the development of more aggressive forms  and its taxane-resistant version ARLow/mCRPC/NEPCtaxane-resistant (PC-3TXR and DUTXR). GEP data from baseline DUTXR and PC3-TXR indicated 3,289 and 2,215 genes were uniquely expressed in PC-3TXR and DUTXR, respectively. Furthermore, GEP also identified 2,968 commonly expressed genes in both sensitive and TXR cell lines , and “stem-like” subclonal cell populations in ARLow/mCRPC/NEPC  compared with ARHigh/mCSPC  prostate cancer cell lines  in ARLow/mCRPC/NEPC  compared with ARHigh/mCSPC  prostate cancer cell lines was also noted  and the clonally derived acquired taxane-resistant mCRPC cell line DUTXR. Some common overlap in GE was observed between the DUTXR and DU145 subclones, as expected . Howeverve DU145 . While tth DUTXR . OverallTop genes representing single-cell clusters that were shared and which were unique between DU145 and DUTXR were further investigated using IPA pathway analysis and upstream prediction. The following clusters were unique to DU145: Cluster 1, Cluster 3, Cluster 5, and Cluster 7. Cluster 8 was shared between both the lines . On the VIM, AHNAK2, ANXA1, ANXA2, CD109, CD44, CD59, GPRC5A, HAS, S100A6, TGFBR2. Moreover, the taxane-resistant gene FSCN1 was also found to be a common gene in both cohorts of prostate cancer cell line models , IGF2R (receptor for both insulin-like growth factor 2), IL11RA (cytokine), SEMG2 (semenogelin proteins), CCR2 (chemokine receptor type 2), TRAF3IP3 , GGTI-2154 (inhibitor of geranylgeranyltransferase I), L778123 (inhibitor of FPTase and GGPTase-I), TGFB1, and CXCL5 , Kaplan–Meier curves (P = 0.00051). Low expression of FSCN1 was associated with longer DFS. In contrast, high expression of FSCN1 was associated with poor DFS. Another top EMT marker, TGFB1, showed similar trends. Low expression was associated with longer DFS, whereas high expression was associated with poor DFS  for “stem-like” cells in prostate cancer  using immunoblotting in all prostate cancer cell lines. These data indicated that the protein expression of these top EMT markers was concurrently higher in ARLow/mCRPC/NEPC  compared with ARHigh/mCSPC  and ARHigh/mCSPC  cells , and lower than ARLow/mCRPC/NEPC prostate cancer  and taxane-resistant ARLow/mCRPC/NEPC  cells . EMT proLow/mCRPC/NEPC  cell lines, were assessed by MTT assay at their estimated IC50 and IC50/2 drug concentrations  was more potent at reducing cell growth compared with CONV (high dose) treatments at IC50 and IC50/2 (Low/mCRPC/NEPC (DURXR) prostate cancer subtypes. Results showed similar trends, greater potency was observed in METRO-TOPO and combination treatments.Previously we reported the cytotoxicity of CONV-TOPO, CONV-DTX, and METRO-TOPO for all cell lines . In thisd IC50/2 . Furtherd IC50/2 . The effThe effect of METRO-TOPO on cell density and cellular morphology was determined. In agreement with MTT assays, micrographs of prostate cancer cells exposed to all dosing regimens showed decreases in cell density compared with control cells . Treatme50 and IC50/2  and acquired taxane-resistant ARLow/mCRPC/NEPC (DUTXR). A significant increase in caspase 3/7 activity following each treatment compared with controls (50 and IC50/2 dose for metronomic topotecan) compared with CONV-TOPO and DTX . The greatest treatment-induced apoptosis was observed in combination with CONV-DTX+METRO-TOPO  in all cell lines  activity. The level of apoptosis was estimated at the ICcontrols . A greatll lines . The relll lines .S100A9, ESRP1, ASPM, EPCAM, CLDN7, CDH1, INPP4B, CD70, FN1, CDH11) and drug metabolism or resistance markers  compared with CONV-TOPO treatment  compared with CONV-TOPO (69%–84%) treatment  representing control (no drug treatment), 6-week EE-TOPO (extended exposure of topotecan), and 6-week CONV-TOPO treatment . Furtherr trends . Taxane-reatment . RNA-seqreatment .Low/mCRPC/NEPC  were treated with CONV-TOPO, METRO-TOPO, and combination CONV-DTX+METRO-TOPO treatments. Pretreated and posttreated prostate cancer cell lines were stained with stemness markers CD44, CD133, and both CD44/133 antibodies. All cell lines showed a greater reduction in the CD44high (“stem-like”) cell population following METRO-TOPO treatment compared with CONV-TOPO treatment. Furthermore, combination treatment reduced the highest percent of the CD44high cell population  downregulated following METRO-TOPO compared with CONV-TOPO treatment in PC-3M. Combination treatment exhibited the greatest downregulation (17%–56%) of all EMT proteins compared to other treatments  microchannels was greater in PC-3M compared with PC-3, suggesting that PC3-M cells are more invasive into mechanically challenging microenvironments. For confined (W × H = 10 × 10 μm2) microchannels, cell entry was greater for PC-3M compared with PC-3 but the difference was not significant.This experiment allowed us to study the effect of drug and dosing schedule as a single agent and in combination on tumor cell motility through microchannels of dimensions that mimic the size of confining pores and channel-like tracks encountered by migrating cells in vivo . Figure in vivo  showed tThe effect of drug treatment on PC-3M migration was accessed. PC3-M entry into confining microchannels was markedly suppressed upon TOPO-METRO single-agent and TOPO-METRO+CONV-DTX combination treatment. Of note, combination therapy resulted in a higher reduction in the invasiveness of PC-3M compared with other treatments .high (“stem-like”) cell populations by METRO-TOPO (81.0%) compared with CONV-TOPO treatments (71.5%). Furthermore, combination treatment showed the greatest reduction (63.4%) of the “stem-like” CD44high cell populations , and analyzed by flow cytometry. These results showed a greater reduction in CD44ulations ; Table 2Low/mCSPC/NEPCtaxane-resistant (DUTXR) cell lines. Combination treatment (CONV-DTX+METRO-TOPO) exhibited the greatest downregulation (26%–61%) of EMT proteins compared with other treatments  treatments. A marked suppression in cell survival was observed in the combination treatment compared with other groups  and combination treatment (3.2%) compared with CONV-TOPO (54%) and CONV-DTX (31%) treatments  treatment effect on CD44eatments .Next, we evaluated the potential effect of CONV-TOPO, CONV-DTX, METRO-TOPO, and combination (METRO-TOPO+CONV-DTX) treatment on the proliferative capacity of the DUTXR cells using the colony-forming assay. TOPO-METRO treatment alone significantly reduced colony number (16.4%) as well as colony size when compared with control or CONV-TOPO (67.0%) or CONV-DTX (33.5%) treatment. Furthermore, colony-forming assay results also showed the combination of METRO-TOPO+CONV-DTX treatments further reduced the colony numbers (9.53%) and size .VIM, AHNAK2, ANXA1, ANXA2, CD109, CD44, CD59, GPRC5A, HAS, S100A6, TGFBR2 in both cohorts. Importantly, the taxane-resistant gene FSCN1 was also identified as a common gene in both cohorts of prostate cancer cell lines.Drug development for aggressive, lethal treatment-resistant prostate cancer poses a significant challenge with few therapeutic successes . We used+ and CD44+/CD133+) in aggressive metastatic prostate cancer subtypes, which was confirmed by the RNA-seq and subclonal overexpression of several EMT markers . We also identified top DEGs and subclonal populations between the taxane-sensitive and taxane-resistant prostate cancer subtypes. These data provide greater insights into the acquisition of drug resistance and possibly novel treatment targets.We identified “stem-like” cell populations  is an important constituent of the stem cell niche (HAS3 (4.8-fold upregulation) resulted in the secretion of large amounts of HA bound to CD44+ in the “stem-like” cell population and plays a critical role in the development CSCs by regulating cell adhesion, migration, proliferation, differentiation, cancer metastasis, and multidrug-resistant  of EMP1 in our aggressive prostate cancer subtypes which regulates the expression of CD44 to promote stemness (TGFB1-induced EMT (TGFB1 in aggressive prostate cancer (3.41-fold in GE and ∼2-fold protein). Furthermore, we identified a higher percentage of TGFB1 overexpressed subclonal population (scRNA-seq) in aggressive taxane-resistant DUTXR compared with taxane-sensitive DU145 prostate cancer subtypes. We reported that METRO-TOPO (6-week EE) treatment reduces TGFB1 expression in subclonal populations. On the basis of evaluation of TCGA database, the downregulation of TGFB1 is beneficial and associated with increased patient survival.Importantly, we identified ll niche . CD44 isesistant . In our stemness . Furtheruced EMT , and our+ cells (96%) frequency and protein expression were greatest in taxane-resistant mCRPC cell lines. Furthermore, we identified a higher percentage of CD44-overexpressed subclonal population (scRNA-seq) in aggressive taxane-resistant DUTXR compared with taxane-sensitive DU145 prostate cancer subtypes. The ability to reduce stemness offers a potential strategy to treat aggressive and resistant prostate cancer. In our study, METRO-TOPO and combination (METRO-TOPO+CONV-DTX) treatment downregulated CD44+ (71.7% and 63.4%) cells along with protein expression (55% and 29%). We also observed high percentages of CD44+ cell populations in mCRPC cell lines and METRO-TOPO following single agent and in combination therapy with DTX reduced the amount of CD44+ cells significantly after treatment. Furthermore, METRO-TOPO treatment significantly decreased cell invasion and colony formation in aggressive and taxane-resistant forms of prostate cancer. Further studies employing CRISPR knockdown of HAS3 (and other top genes) are needed to further explore the significance of these novel mechanisms and their impact on cancer stemness and its association with METRO-TOPO treatment-related efficacy.Previous studies have reported that CD44 expression is greater in prostate cancer, and involved in cancer cell proliferation, invasion, migration, and drug resistance . Some stVIM as a top common EMT marker from RNA-seq and scRNA-seq for aggressive prostate cancer subtypes. Earlier studies also identified its role in invasion and metastasis in prostate cancer  prostate cancer subtypes. Our microfluid-based migration assay showed high cell invasion for these prostate cancer cell lines. Furthermore, METRO-TOPO as a single agent and in combination with DTX significantly reduced cell invasion in the mCRPC cell line model.Furthermore, vimentin . In our e cancer . We obseCD55 also promotes prostate cancer cell survival and metastatic lesion formation  in aggressive mCRPC/NEPC and taxane-resistant mCRPC/NEPC subtypes. We also identified a higher percentage of subclonal cell populations in aggressive taxane resistance prostate cancer subtypes overexpressing CD55 and CD109.Furthermore, ormation . Recent arcinoma . Our scRANXA3, ANXA2, and its pseudogene ANXA2P2 are overexpressed in various cancers, including prostate cancer, and facilitated transcription of the stemness genes Nanog, Sox2, and Oct4  in aggressive mCRPC/NEPC and taxane-resistant mCRPC/NEPC subtypes. We also identified greater protein expression levels of Nanog, Sox2, and Oct4 genes in mCRPC/NEPC and taxane-resistant mCRPC/NEPC prostate cancer subtypes. METRO-TOPO downregulated Oct-4 (64%), Sox2 (60%), and Nanog (39%) in mCRPC/NEPC (PC-3M) prostate cancer cell line model. METRO-TOPO also downregulated Oct-4 (41%), Sox2 (56%), and Nanog (49%) in taxane-resistant mCRPC (DUTXR) prostate cancer cell line model. The combination of METRO-TOPO and DTX exhibited similar results. A downregulation of Oct-4 (57%), Sox2 (47%) and Nanog (17%) in mCRPC/NEPC (PC-3M), and downregulation of Oct-4 (28%), Sox2 (61%), and Nanog (35%) in taxane-resistant mCRPC/NEPC (DUTXR) prostate cancer cell line model was observed after combination treatment.Earlier studies reported that and Oct4 . AHNAK2 with EMT . Our scRALDH1 is another stemness marker in prostate cancer and overexpressed in mCRPC  for taxane resistance in aggressive prostate cancer. Our results also showed that the treatment of METRO-TOPO (6-weeks EE) downregulates FSCN1 expression in subclonal populations in aggressive mCRPC/NEPC 3D tumor models, whereas CONV-TOPO resulted in increased expression (P < 0.05). Furthermore, TCGA patient cohort also revealed lower expression of FSCN1 associated with better DFS (P = 0.00051).Recent studies identified e cancer . We idenC1orf116, TUBB2B are common genes in taxane-resistant prostate cancer subtypes. C1orf116 is a novel EMT biomarker for prostate cancer, and our study showed 17-fold and 24-fold downregulation of this gene in taxane-resistant prostate cancer subtypes DUTXR and PC-3TXR, respectively. Contrastingly, TUBB2B was upregulated in both taxane-resistant prostate cancer cell lines, approximately 12-fold in DUTXR and approximately 43-fold in PC-3TXR. An earlier study revealed downregulation of C1orf116 is associated with poor prognosis in patients with lung and prostate cancer  prostate cancer. Further preclinical validation and single-cell multi-omics strategies using CRISPR-based knockout xenograft models and patient-derived organoids/xenografts will be necessary. Our approach in this study promotes the understanding of subclonal molecular pathways underlying the differential patterns of prostate cancer aggressiveness and drug response among various prostate cancer subtypes. Overall, our study identified novel mechanisms of action that can serve as a pipeline to advance METRO-TOPO as a potent clinical-trial-ready therapeutic option for the management of lethal prostate cancer with stem-like features. These mechanisms will be key in identifying patients with molecular signatures that are sensitive to treatment, and, most importantly, can identify biomarkers that can be used to monitor treatment effectiveness and disease progression.Cell invasion is one of the major characteristics of EMT transition and cancer progression . Our micSupplementary MethodsSupplementary MethodsClick here for additional data file.Supplementary Tables 1-7Supplementary TablesClick here for additional data file.Supplementary Figure 1Supplementary Fig. 1 shows Differentially expressed gene signature (DEGs) based on next gene sequencing (mRNA sequencing) for ARLow /mCRPC/NEPC  vs ARHigh /mCSPC  prostate cancer (PCa) cell lines and normal prostate cell lines RWPE1 and RWPE2. Volcano plots representing mRNA expression for all cell lines. (blue p<0.05 and gray>0.05) HeatMap representing Differentially Expressed Gene Signature (DEGs) for ARLow/mCRPC/NEPC  vs ARHigh/mCSPC  PCa and normal prostate cell lines RWPE1 and RWPE2 (FDR<0.05 and TOP-100).Click here for additional data file.Supplementary Figure 2Supplementary Fig. 2 shows Ingenuity pathway analysis (IPA) predictions for European American/ Caucasian American (EA/CA) ARHigh/mCSPC , ARLow/mCRPC/NEPC  and African American (AA) ARHigh/mCSPC (MDA-Pca-2b) PCa cell lines as well as normal prostate cell lines RWPE1 and RWPE2.   IPA predicted A) Diseases and biological pathways for development of ARLow/mCRPC/NEPC. Major pathways are cell movement, morbidity, mortality, migration, invasion, survival, viability and development of vasculature- vasculogenesis and angiogenesis. B) Causal network pathway for development of ARLow/mCRPC/NEPC, which include oxidative phosphorylation, p70S6K, unfolded response, TREM1 signaling, NF-kB signaling, ERK5 signaling, IL-8 signaling, BAG2 signaling, Integrin signaling and VEGF signaling. C) IPA predicted HIF1α and EMT as key pathways associated with PCa development in African Americans (AA).Click here for additional data file.Supplementary Figure 3Supplementary Fig. 3 shows Differentially Expressed Gene Signatures (DEGs) based on next-gene sequencing (mRNA sequencing) for ARLow/mCRPC/NEPC (PC-3DU145), ARLow/mCSPC/NEPC taxane resistant (DUTXR and PC-3TXR) PCa cell lines. IPA predicted key pathways based on DEGs between Taxane resistant and sensitivity. Fig. 3A. Differentially expressed gene Signature was identified among DEGs for ARLow/mCRPC/NEPC (PC-3DU145), ARLow/mCSPC/NEPC taxane-resistant (DUTXR and PC-3TXR) PCa cell lines. Venn diagrams represent unique and common DEGs for ARLow/mCRPC/NEPC , and ARLow/mCSPC/NEPCtaxane-resistant (DUTXR and PC-3TXR) cell lines. Fig. 3B. IPA predicted key pathways based on DEGs between Taxane resistant and sensitivity. Cluster 2 (IPA predicted activation of PPARA), Cluster 4 , Cluster 6 .Click here for additional data file.Supplementary Figure 4Supplementary Fig. 4 shows Differentially Expressed Gene Signature (DEGs) based on next-gene sequencing (mRNA sequencing) and single cell RNA sequencing for all PCa cell lines. Differentially expressed gene Signature was identified among DEGs for RNS seq and scRNAseq.Click here for additional data file.Supplementary Figure 5Supplementary Fig. 5 shows Ingenuity pathway analysis (IPA) predictions for common DEGs among RNAseq vs. scRNAseq in all PCa cell lines IPA predicted A) Diseases and biological pathways for common DGEs. Major pathways are cell invasion, movement, neoplasia, migration, transformation, metastatic and growth B) Causal network pathway for common DEGS include LY2109761 (TGF-β receptor inhibitor), IGF2R (receptor for both insulin-like growth factor 2), IL11RA (cytokine), SEMG2 (semenogelin proteins), CCR2 (chemokine receptor type 2), TRAF3IP3 , GGTI-2154 (inhibitor of geranylgeranyltransferase I ), L778123 (inhibitor of FPTase and GGPTase-I), TGFB1 and CXCL5. C) upstream regulators for common DEGs were THBF1, RAF1, AGT, ER receptor, OSM, SLC15A4, JUN, and IL1B D) Canonical pathways for common DEGs were s100 family, Hepatic fibrosis, Rho family, RHOGDI and integrin singling pathway.Click here for additional data file.Supplementary Figure 6Supplementary Fig. 6 shows Cytotoxicity Profiling of ARLow/mCRPC/NEPC (PC-3 and PC-3M) and Taxane Resistance ARLow/mCRPC (DUTXR): PC-3, PC-3M and DUTXR cell lines were treated with CONV-TOPO, METRO-TOPO, CONV-DTX and combination (CONV-DTX+METRO-TOPO) treatment, and cell cytotoxicity and caspase3/7 levels ware assed A) Cytotoxicity profiling by MTT showed combination (CONV-DTX+METRO-TOPO) reduces highest cell survival compared with other treatments for all PCa cell lines, CONV-TOPO>CONV-DTX>METRO-TOPO B) Caspase3/7 activity showed combination (CONV-DTX+METRO-TOPO) treatment reduced apoptosis the greatest compared to other treatments in all cell lines. C) Cytation5 images showed treatment effects on the cell lines PC-3M and DUTXR. Results showed significantly higher cell death in METRO compared to CONV treatment for both the cell lines and combination (CONV-DTX+METRO-TOPO) treatment reduced cell death greater for both PCa cell lines. ImageJ analysis showed significant differences in cell density for CONV-TOPO, CONV-DTX, METRO-TOPO, and combination (CONV-DTX+METRO-TOPO) treatment in PC-3, PC-3M, and DUTXR cell lines. (*p ≤ 0.05).Click here for additional data file.Supplementary Figure 7Supplementary Fig. 7 shows Single-Cell transcriptomics; Identifies epithelial to mesenchymal transition (EMT) marker TGFB1 in ARLow/mCRPC/NEPC . Result showed TGFB1 expression decreases after EE compared to CONV treatment, which indicated EE moderated stemness in ARLow/mCRPC/NEPC. Single-cell RNA sequencing used the droplet sequencing method (10X Genomics). Each dot represents a single cell. t-distributed stochastic neighbor embedding (tSNE) plots showing the comparison between the single-cell clusters as shown.Click here for additional data file.Supplementary Figure 8Supplementary Fig. 8 shows Flow Cytometry; Prostate cancer cell lines stained with stemness markers  to determine the percent of CD44+, CD133+ and double positive cells among PCa subtypes.  Treatments with CONV-TOPO, METRO-TOPO and CONV-DTX+METRO-TOPO reduced stemness. METRO-TOPO reduced CD44+ cells to a greater extent compared to CONV-TOPO and CONV-DTX treatments, whereas combination treatment with CONV-DTX+METRO-TOPO reduced stemness (CD44+ cells) the most in ARHigh/mCRPC/NEPC. A) PC-3M, B) DU145 cell lines.Click here for additional data file.Supplementary Figure 9Supplementary Fig. 9 shows Fluorescent-Activated Cell Sorting CD44High: PC-3M cell line stained with stemness markers (CD44) and sorted CD44+ cells, followed by CONV-TOPO, METRO-TOPO, CONV-DTX, and combination (CONV-DTX+METRO-TOPO) therapy and cell cytotoxicity and caspase3/7 levels assessed A) Cytotoxicity profiling by MTT showed combination (CONV-DTX+METRO-TOPO) reduces cell survival compared with other treatments CONV-TOPO>CONV-DTX>METRO-TOPO B) AR-mCRPC- PC-3M showed combination (CONV-DTX+METRO-TOPO) reduces levels the most of apoptosis compared to other treatments (*p<0.05).Click here for additional data file.Supplementary Video 1Supplementary Video 1Click here for additional data file.Supplementary Video 2Supplementary Video 2Click here for additional data file.Supplementary Video 3Supplementary Video 3Click here for additional data file.Supplementary Video 4Supplementary Video 4Click here for additional data file.