RAS wild-type CRC cell lines with different cetuximab sensitivities. CRC cell lines with different sensitivities to cetuximab showed rather low EGFR expression but high HER2 and HER3 expression. These results were in line with the The Cancer Genome Atlas (TCGA) data from CRC patients, where higher mRNA levels of HER2 and HER3 were also detected compared to EGFR. Therefore, the targets of afatinib were indeed expressed on the CRC cell lines used in this study and in CRC patients. Furthermore, cetuximab resistance had no significant influence on the expression levels of HER receptors in CRC cell lines (p ≥ 0.652). This study also demonstrated that afatinib was able to induce a concentration-dependent cytotoxic effect in RAS wild-type CRC cell lines with different cetuximab sensitivities. Neither cetuximab resistance (p = 0.233) nor hypoxia (p = 0.157) significantly influenced afatinib’s cytotoxic effect. In conclusion, our preclinical data support the hypothesis that treatment with afatinib might be a promising novel therapeutic strategy for CRC patients experiencing intrinsic and acquired cetuximab resistance. The epidermal growth factor receptor (EGFR) is an important therapeutic target in colorectal cancer (CRC). After the initial promising results of EGFR-targeted therapies such as cetuximab, therapeutic resistance poses a challenging problem and limits the success of effective anti-EGFR cancer therapies in the clinic. In order to overcome resistance to these EGFR-targeted therapies, new treatment options are necessary. The objective of this study was to investigate the expression of human epidermal growth factor (HER) receptors and the efficacy of afatinib, a second-generation irreversible EGFR-tyrosine kinase inhibitor, in  RAS wild-type metastatic CRC (mCRC) when given in combination with FOLFIRI  and irinotecan) and with FOLFOX , respectively [RAS patients [RAS wild-type mCRC, the addition of cetuximab to FOLFIRI and panitumumab to FOLFOX resulted in a median overall survival of 23.5 months and 25.8 months versus 19.5 months and 20.2 months with chemotherapy alone, respectively [RAS wild-type disease, 40–60% of patients fail to respond, possibly due to mechanisms that can compensate for reduced EGFR signaling or mechanisms that can modulate EGFR-dependent signaling [Increased or sustained signaling of the epidermal growth factor receptor (EGFR) plays an integral role in the tumorigenesis of many cancer types, including colorectal cancer (CRC), making it a compelling drug target. Inhibition of EGFR signaling has been a focus of research over the last decades and this has led to the development of multiple EGFR-targeted agents. The EGFR-targeted monoclonal antibodies (mAbs) cetuximab and panitumumab have already demonstrated a significant survival improvement in patients with ectively ,4,5,6,7.patients . In RAS ectively . Neverthignaling ,15,16,17The precise mechanisms of intrinsic and acquired resistance to EGFR inhibitors remain unclear. Since EGFR signaling is prominent in CRC, the inhibition of this EGFR pathway is still considered as an important therapeutic strategy. Extensive dimerization among the different human epidermal growth factor (HER) receptor tyrosine kinases suggests that blocking signaling from more than one family member may be essential to effectively treat CRC and limit drug resistance . In contOur previous preclinical research showed not only that afatinib displays a cytotoxic effect in CRC, but also demonstrates effective cytotoxic activity in intrinsic and acquired cetuximab-resistant head and neck squamous cell carcinoma (HNSCC) cell lines ,24. HoweRAS wild-type CRC cell lines that are either sensitive or have intrinsic/acquired resistance to cetuximab. With this in mind, we decided to: (1) examine the expression of HER receptors in CRC in order to determine the presence of afatinib’s targets, (2) test the influence of cetuximab resistance on the expression of HER receptors in RAS wild-type CRC cell lines, (3) determine the cytotoxic effect of afatinib in these RAS wild-type CRC cell lines with different cetuximab sensitivities, (4) study the efficacy of afatinib under both normal and reduced oxygen conditions, as CRC is often characterized by regions with reduced oxygen levels and as there is a link between hypoxia and EGFR signaling [This study aims to provide preclinical data concerning the expression of HER receptors and the potential of afatinib in a panel of ignaling , (5) exaRAS wild-type CRC cell lines (50) values, two out of four CRC cell lines  were considered as intrinsically resistant to cetuximab, as the percentage of viable cells in these cell lines did not decrease below 50%. CaCo2 and Lim1215 were identified as cetuximab-sensitive, as the IC50 values  are considered as clinically relevant [Sensitivity to cetuximab therapy was investigated in a panel of ll lines A. Based relevant . Next, cAs afatinib binds to multiple members of the HER receptor family, the basal cellular membrane protein expression level of these HER family members was determined in our panel of CRC cell lines with different sensitivities to cetuximab. p ≥ 0.652) and ΔMFI of receptor-positive cells (p ≥ 0.695) were observed between cetuximab-sensitive, intrinsically-resistant, and acquired cetuximab-resistant CRC cell lines. HER4 expression was barely observed in any of the CRC cell lines tested and when detected, HER4 expression levels were very low (data not shown).EGFR expression was observed in all the CRC cell lines used in this study. However, the percentage of EGFR-positive cells ranged between 11% and 34% A. Moreovp < 0.001 and p < 0.001, respectively). This corresponds with our flow cytometric findings in CRC cell lines. Moreover, CRC patients also demonstrated very low HER4 mRNA expression, which is in line with our flow cytometric data in CRC cell lines (data not shown). Next, RNA sequencing data from the The Cancer Genome Atlas (TCGA) dataset of CRC patients  was used to compare our in vitro results ,28. The Overall, these results demonstrate that CRC cell lines with different sensitivities to cetuximab show rather low EGFR expression but high HER2 and HER3 expression. These results were in line with the TCGA data from CRC patients, as also in patient material, whereby higher mRNA levels of HER2 and HER3 were detected compared to EGFR. Furthermore, cetuximab resistance had no influence on the expression levels of HER receptors in CRC cell lines. We therefore concluded that the targets of afatinib were indeed expressed on the CRC cell lines used in this study and in CRC patients.50 values for afatinib under normoxia ranged from 0.007 ± 0.002 μM to 2.379 ± 0.869 μM . Furthermore, no statistical difference in the cytotoxic effect of afatinib was observed when cells were treated under hypoxic conditions (p = 0.157).The cytotoxic effect of the irreversible HER family blocker afatinib was studied in cetuximab-sensitive, intrinsically/acquired-resistant, and PBS-treated control CRC cell lines under both normoxic and hypoxic conditions. A clear concentration-dependent cytotoxic effect of afatinib (0–10 μM) after 72 hours of treatment was observed in all CRC cell lines . The IC50.869 μM . IntrinsOverall, afatinib showed a clear concentration-dependent cytotoxic effect in cetuximab-sensitive and intrinsically-resistant CRC cell lines and CRC cell lines with acquired resistance. Our results demonstrated that neither cetuximab resistance nor exposure to hypoxia provoked therapeutic resistance to afatinib.0/G1 cells  and a decrease in the percentage of S phase cells . A significant decrease in the percentage of S cells was also found in CaCo2-R (p ≤ 0.033). In contrast, a significant increase in the percentage of G2/M cells was observed in CaCo2 (p ≤ 0.028), CaCo2-PBS (p = 0.039) and Lim1215-R (p = 0.047) after treatment with high dose of afatinib (IC60 and IC80). Overall, afatinib did not have a pronounced effect on the cell cycle distribution of CRC cells, independently of their sensitivity to cetuximab. We studied the effect of afatinib on the distribution of CRC cells in the different phases of the cell cycle was investigated . SW48 an80 /propidium iodide (PI) staining . This te80 .20 and IC40 values specific for each cell line. The dose–response curves after treatment with both sequential combination regimens are shown in 50 values ranging from 0.09 ± 0.02 μM to 40.07 ± 4.59 μM, not including CaCo2-PBS and CaCo2-R , except in the HT29 cell line (p = 0.023). Furthermore, the combination index (CI) ranged from 0.81 ± 0.10 to 1.12 ± 0.11, indicating an additive interaction. Similarly, treatment with irinotecan followed by afatinib showed no significant decrease in IC50 value (p ≥ 0.116) and CI ranged from 0.72 ± 0.13 to 1.22 ± 0.32, revealing additive to subadditive effects. Thus, sequential exposure to afatinib followed by irinotecan or the reverse regimen  revealed additive, yet no synergistic interactions.In order to investigate the potential interaction between afatinib and irinotecan, CRC cells were incubated with fixed doses of afatinib for 72 hours, followed or preceded by treatment with 0–50 μM irinotecan for 24 h. The fixed afatinib concentrations were based on the outcome of the monotherapy experiments and corresponded to the IC CaCo2-R . ConcernRAS wild-type CRC cell lines was determined. Afterwards, the cytotoxicity of afatinib, as a single agent and in combination with irinotecan, was examined in this panel of RAS wild-type CRC cell lines that are either sensitive or have intrinsic/acquired resistance to cetuximab.Targeted therapies are the key for the personalized treatment of cancer patients. After initial promising results of EGFR-targeted therapies such as cetuximab, therapeutic resistance poses a challenging problem and limits the success of effective anti-EGFR cancer therapies in the clinic. As a result, new treatment options are needed to overcome drug resistance. Due to the intensive interactions between HER receptors, inhibition of one HER receptor can be compensated by other HER family members, which therefore must be targeted by new therapeutic regimens. In contrast to the first-generation EGFR-inhibitors, afatinib irreversibly blocks EGFR, HER2, and HER4. Consequently, we hypothesized that treatment with afatinib might result in a more pronounced therapeutic benefit, even in patients who experience resistance to first-generation EGFR inhibitors. To investigate this hypothesis, we first examined the expression of HER receptors in CRC in order to determine the presence of afatinib’s targets. In addition, the influence of cetuximab resistance on the expression of HER receptors in RAS wild-type CRC cell lines with different cetuximab sensitivities and compared these results with RNA sequencing data from the TCGA dataset of CRC patients. According to the target expression of EGFR and HER2, the CRC cell lines used in this study are valid candidates for treatment with afatinib. These in vitro results were in line with the TCGA data from CRC patients, who also express EGFR and HER2, indicating the presence of afatinib’s targets in CRC. Interestingly, both CRC cell lines and patients demonstrated higher HER2 and HER3 expression compared to EGFR expression. It has already been suggested that HER2 expression might play an important role in response to afatinib treatment in CRC [In the first part of this study, we determined the expression of HER receptors in t in CRC ,30. Conft in CRC ,32, resut in CRC . In addit in CRC ,35,36,37Resistance to cetuximab has been associated with extensive dimerization among the different HER receptor tyrosine kinases . In the KRAS wild-type CRC patients was predictive for efficacy of panitumumab [Several studies have demonstrated a correlation between HER2 amplification and acquired resistance to cetuximab in CRC ,40. Furtitumumab . Althougitumumab . Neverth50 values of SW48, HT29, and CaCo2-R cell lines are higher than this maximum afatinib plasma concentration. In the future, it would be highly interesting to further investigate the efficacy of afatinib in long-term in vitro assays. For example, in the past, Herr et al. have already determined the long-term effect of afatinib treatment on the HT29 CRC cell line in a clonogenic assay  [In the second part of this study, we demonstrated that afatinib has the potential to overcome intrinsic and acquired cetuximab resistance, as it was able to establish a cytotoxic effect in CRC cell lines with different sensitivities to cetuximab. Camidge et al. have recently demonstrated in a phase Ib trial that the maximum plasma concentration of afatinib at its maximum tolerated dose was 313 ng/mL, which corresponds to 0.644 μM [14 days) . They re0/G1 cell cycle arrest and induced apoptotic cell death in different cancer cell lines, including CRC cell lines [0/G1 cell cycle arrest or induction of apoptosis. Therefore, analyzing caspase activity and markers of autophagy and other types of cell death after afatinib treatment, would be highly interesting to study more in depth. Thus, additional research is necessary to further unravel the mode of action of afatinib in CRC. Importantly, exposure to hypoxia did not provoke therapeutic resistance to afatinib in CRC cell lines. This is an interesting finding as oxygen deficiency is a common characteristic of CRC and these hypoxic tumor regions often contain viable cells that are more resistant to conventional therapies [Concerning the molecular mechanisms underlying afatinib’s cytotoxic effect in CRC, previous studies demonstrated that afatinib caused a Gll lines ,46,47,48herapies .50 values compared to cetuximab-sensitive CRC cell lines, thus suggesting the possibility of cross-resistance between cetuximab and afatinib. Previous studies in HNSCC have indicated that afatinib is more effective in patients whose tumors are cetuximab-naïve [Despite the fact that statistical analysis did not reveal a significant effect of cetuximab resistance on the cytotoxicity of afatinib, we noticed that intrinsically and acquired cetuximab-resistant CRC cell lines tended to show higher ICab-naïve ,50,51. CKRAS wild-type CRC patients [KRAS-mutated CRC cell lines, no clinical benefit of afatinib was observed in KRAS mutated patients in this phase II study. Previous treatment history might have had an impact on the lack of clinical benefit observed in the study. Recently, interest in afatinib as monotherapy for the treatment of CRC is diminished, indicated by the low number of novel clinical trials in CRC patients found on ClinicalTrials.gov. However, afatinib did demonstrate promising clinical efficacy in other solid tumors, including HNSCC and non-small-cell lung carcinoma (NSCLC) [In the past few years, afatinib has been investigated in various clinical trials. In phase I studies, some promising signs of antitumor activity after afatinib treatment were observed in patients with advanced solid tumors, including CRC. After these promising phase I dose-escalation studies, afatinib was further investigated in several phase II studies. Hickish et al. reported that afatinib (40 mg/day) demonstrated inferior response and survival compared to cetuximab in patients . Despite (NSCLC) ,53,54.As most adjuvant cancer treatments are combinations of chemotherapeutic agents and/or radiotherapy, we are convinced that new EGFR-targeted agents, such as afatinib, will achieve their greatest efficacy in combination with traditional cytotoxic agents and/or radiotherapy. As cetuximab has been approved for the treatment of mCRC in combination with the irinotecan containing chemo-regimen FOLFIRI, we investigated the combination of afatinib with irinotecan in CRC cell lines that are sensitive and have intrinsic/acquired resistance to cetuximab. In clinical studies, it has already been reported that simultaneous treatment with afatinib and standard chemotherapy is associated with increased frequency of side effects ,56. TherBesides combining afatinib with standard chemotherapy and/or radiotherapy, combinations with other targeted agents such as vascular endothelial growth factor inhibitors have been investigated in clinical studies, with limited success in CRC patients ,58. In aAs mentioned above, interest in afatinib for the treatment of CRC has diminished, and currently no novel studies have been initiated to investigate combinations of afatinib with other drugs for the treatment of CRC patients . However, as our and other preclinical studies have demonstrated the potential of afatinib in CRC cell lines, its limited clinical benefit in CRC patients could be explained by the lack of predictive biomarkers necessary for optimal patient selection. We foresee that liquid biopsies can play a key role in the progress needed for improving the efficacy of targeted therapies in CRC. Liquid biopsies will help to identify and monitor the biomarkers for both response and resistance to EGFR-targeted therapies in CRC as well as speed up the process of patient selection . As suchRAS wild-type. SW48, HT29, and CaCo2 were cultured in DMEM medium, while Lim1215 was grown in RPMI medium . Each medium was supplemented with 10% fetal bovine serum (FBS), 1% penicillin/streptomycin and 1% L-glutamine . Sodium pyruvate was additionally added to RPMI medium. Cells were grown as monolayers and maintained in exponential growth in 5% CO2/95% air in a humidified incubator at 37 °C. Authenticity of cell lines was verified by establishing short tandem repeat profile. All cell lines were confirmed free of mycoplasma infection through regular testing .The human CRC cell lines SW48, HT29, and CaCo2 were purchased from the American Type Culture Collection . Lim1215 was obtained from CellBank Australia . All cell lines were Afatinib for 72 h immediately followed by irinotecan for 24 h;Irinotecan for 24 h immediately followed by afatinib for 72 h.Cell survival was determined using the colorimetric sulforhodamine B (SRB) assay, as previously described ,61. This50 values  were calculated using WinNonlin software . Possible synergism between afatinib and irinotecan was determined by calculation of the combination index (CI) using the ‘Additive Model’ as described by others [The pharmaceuticals, cetuximab  and irinotecan  were diluted in sterile PBS. Afatinib  was diluted in DMSO  and further dilutions were made in cell culture medium. Survival rates were calculated by:  of treated cells/mean OD of control cells) × 100%. ICy others ,63,64. C2) was achieved in a Bactron IV anaerobic chamber , as described previously [2.Hypoxia . After 10 dose doublings, dose–response studies were determined for each resistant cell line (suffix R). In order to examine whether acquired resistance was a transient or permanent effect, dose-response curves of cetuximab were re-assessed in the resistant cell lines after 6 weeks in culture without cetuximab.Generation of cell clones with acquired resistance was performed as described previously ,66,67. A6 cells, 25μg antibody in 1ml, R&D Systems, Minneapolis, MN, USA). Corresponding isotype controls  were included for all samples and served as negative controls. Dead cells were excluded from the analysis by staining with 7-AAD . All samples were measured on a FACScan flow cytometer (BD Biosciences). Flow cytometric data were analyzed using FlowJo v10.1 . The percentages of EGFR, HER2, HER3, and HER4-positive cells (Overton) were determined in comparison with the corresponding isotype control using the Overton method. Furthermore, the signal for aspecific binding was subtracted from the MFI (=ΔMFI). This parameter indicates the amount of extracellular expression of EGFR, HER2, HER3, and HER4 on individual cells. The baseline extracellular protein expression level of EGFR, HER2, HER3, and HER4 was assessed using flow cytometry, as previously described ,67. CellThe obtained results were compared using the RNA sequencing data from the TCGA dataset of CRC patients . RNASeqV2 from TCGA was processed and normalized using the software package RSEM (RNA-Seq by Expectation Maximization) to generate transcripts per million. This dataset was downloaded from cBioportal.20, IC40, IC60, and IC80 values specific for each cell line. Cell cycle distribution was determined using the CycleTESTTM PLUS DNA reagent kit (BD Biosciences). Induction of apoptotic cell death was investigated flow cytometrically using the AnnV-FITC/PI assay (BD Biosciences). Both assays were performed on a FACScan flow cytometer and analyzed with FlowJo v10.1. After overnight incubation, cells were treated for 72 h with afatinib. Since the sensitivity to afatinib strongly varied between the cell lines, afatinib concentrations were based on the outcome of the monotherapy experiments and corresponded with the ICAll experiments were performed at least three times independently, unless otherwise stated. In cytotoxicity experiments, each condition was tested in triplicate in each of the three independent experiments. Flow cytometry experiments were independently performed three times with one sample for each condition. Results are presented as mean  ± standard deviation. The effects of various conditions and treatments were studied using linear regression or linear mixed models in case of non-independent observations. All models were fitted using a stepwise backward strategy, starting from a model with all fixed effects and their interactions. If the interaction term was not significant, a model with only the main effects was fitted. If the treatment effect was significant, a Dunnet posthoc analysis was performed. The effect of cetuximab resistance on the expression of HER receptors was analyzed using a linear mixed model with cetuximab resistance status as fixed effect. A random intercept for cell line was added, in order to account for the dependence between observations within the same cell line. Significant differences in HER receptor mRNA expression of CRC patients (RNASeqV2 TCGA data) were assessed using one-way ANOVA with Bonferroni posthoc analysis.50 values in the combination experiments were investigated using one-way ANOVA with Dunnet posthoc analysis.Effects of oxygen and resistance status on afatinib’s cytotoxic effect were modeled using a linear mixed model with oxygen status, resistance status, and their interaction as fixed effects. A random intercept for cell line was added to account for the dependence between observations within the same cell line. Significant differences in the cell cycle distribution, induction of apoptotic cell death and ICp-values below 0.050 were considered significant.GraphPad Prism 7 was used for data comparison and artwork. All statistical analyses were performed in JMP Pro 13 and SPSS v24. RAS wild-type CRC cell lines with different sensitivities to cetuximab show rather low EGFR expression but high HER2 and HER3 expression. These results were in line with the TCGA data from CRC patients, where higher mRNA levels of HER2 and HER3 were also detected compared to EGFR. Consequently, the targets of afatinib are expressed on the CRC cell lines used in this study and in CRC patients. Cetuximab resistance had no influence on the expression levels of HER receptors in CRC cell lines. The present study also demonstrated that afatinib was able to establish a concentration-dependent cytotoxic effect in RAS wild-type CRC cell lines that are either sensitive or have intrinsic/acquired resistance to cetuximab. Neither cetuximab resistance nor hypoxia significantly influenced afatinib’s cytotoxic effect. Treatment with afatinib had little effect on the cell cycle distribution and the induction of apoptosis. Sequential combinations of afatinib with irinotecan demonstrated additive effects, yet no synergistic interactions. Although these preclinical data support the hypothesis that afatinib might be a promising novel therapeutic strategy for the treatment of RAS wild-type CRC patients experiencing cetuximab resistance, to date no survival benefit has been observed in clinical trials. Therefore, additional studies with biomarker-driven patient recruitment are required to further explore the efficacy of afatinib in CRC patients.This study demonstrated that


It is therefore necessary to design robust scalable methods to enable efficient expansion of bona fide CSCs in vitro. Here, we evaluated the applicability of computer-controlled bioreactors combined with 3D aggregate culture and microcarrier technology, widely used in stem cell bioprocessing, for the expansion and enrichment of CSCs isolated from different types of solid tumors—colorectal cancer (CRC) and non-small-cell lung cancer (NSCLC) from two patients. Results show that these culture strategies improved cell expansion and CSC enrichment. Both patient-derived CSC lines were able to grow on microcarriers, the best results being achieved for PPlus 102-L, Pro-F 102-L, Fact 102-L, and CGEN 102-L beads . As for 3D aggregate culture strategy, the cell proliferation profile was donor dependent. NSCLC cells were the only cells able to form aggregates and proliferate, and the flat-bottom bioreactor vessel equipped with a trapezoid-shaped paddle impeller was the most efficient configuration for cell growth (21-fold increase in cell concentration achieved in 8 days). Serum-free medium promotes CSC enrichment in both 3D aggregate and microcarrier cultures. The protocols developed herein for CSC expansion have the potential to be transferred to clinical and industrial settings, providing key insights to guide bioprocess design towards the production of enriched CSC cultures in higher quantity and improved quality.Cancer stem cells (CSCs) have recently raised great interest as a promising biological system for designing effective cancer therapies. The scarcity of CSCs  Cancer stem cells (CSCs) represent a promising target for effective anticancer therapies , 2 as thIt has been reported that CSCs exist within almost every solid tumor , 6 at a In this work, computer-controlled stirred tank bioreactors combined with 3D cell aggregate cultures as well as microcarrier technology were applied for the first time to expand and enrich CSCs from two different patient-derived cell lines—non-small-cell lung cancer (NSCLC) and colorectal cancer (CRC). The findings reported herein provide novel knowledge to guide cell bioprocess design towards the production of CSC in higher quantity and improved quality, which are key requisites for their application in drug discovery and in the development of new cancer therapeutics.CSC lines were established in Merck Biopharma, ImmunoOncology, following a proprietary protocol. Tumor cells were derived from lung and colorectal cancer patients and purchased from Indivumed . Classification of the tumors was large-cell carcinoma, NOS, and colorectal carcinoma. CSC lines (CRC and NSCLC) were routinely propagated in collagen I-coated T-flasks as described in supplemental online data.6 cell/mL and cultured during 8 days in two different bioreactor configurations—round-bottom bioreactor vessel equipped with a pitched 4-bladde impeller (BR-R/P4b) and flat-bottom bioreactor vessel equipped with a trapezoid-shaped paddle impeller (BR-F/T) . Two culture medium formulations (serum-containing medium (SCM) and serum-free medium (SFM)) and four cell aggregate dissociation protocols were tested .CSC lines were inoculated as single cells in computer-controlled stirred tank bioreactors at a concentration of 0.25 × 104 cell/cm2) in ultra-low-attachment plates and cultured for 6 days at 37°C in a humidified atmosphere of 5% CO2 using SCM or SFM. Eight commercially available microcarriers were tested: Cytodex1™, Cytodex3™, PPlus 102-L, Pro-F 102-L, Fact 102-L, CGEN 102-L, Cytopore2™, and CultiSpher®-S as described in the supplemental online data.CSC lines were inoculated as single cells with empty microcarriers  (data not shown). For their expansion, different bioreactor configurations and aggregate dissociation protocols were evaluated. In addition, microcarrier-based cultures were investigated; eight different microcarriers  and non-small-cell lung cancer (NSCLC) from two patients, when routinely cultured in static adherent culture systems, show percentage of ALDHem cells , 11, supμm) [+ cells (>70%) , contrasting to SCM cultures where no increase was attained  (NSCLC cells were able to grow as aggregates in computer-controlled stirred tank bioreactors independently of the medium used (SCM or SFM) Figures . Importaμm) . Notewors (>70%) . Culture/T; SCM) .+ subpopulations (74-83%)   were obtained in less culture time (6 days) when compared to aggregate culture in bioreactors, the exception being Cytopore2™ beads . In addinoculum) .+ subpopulations in relation to the inoculum were observed for cultures using SFM (6 cells/mL) tested  was observed for PPlus 102-L, Fact 102-L, CGEN 102, and Pro-F 102-L beads . The culsing SFM . The twosing SFM , Table 1This work describes, for the first time, the successful application of computer-controlled stirred tank bioreactors combined with 3D aggregate cultures as well as microcarrier technology to expand and enrich human CSCs. Despite the fact that there is no universal culture strategy capable of embracing different types/patient-derived CSCs, the protocols developed herein for CSC expansion can be easily screened prior to their transfer to clinical and industrial settings. This study also provides key insights to guide bioprocess design towards scalable production of patient-derived CSCs with improved quality. This will potentiate their application in drug discovery and for the development of new cancer therapeutics.


The observation that some aspects of amoeba-fungal interactions resemble animal phagocytic cell-fungal interactions, together with the finding that amoeba passage can enhance the virulence of some pathogenic fungi, has stimulated interest in the amoeba as a model system for the study of fungal virulence. Amoeba provide a relatively easy and cheap model system where multiple variables can be controlled for the study of fungi-protozoal (amoeba) interactions. Consequently, there have been significant efforts to study fungal–amoeba interactions in the laboratory, which have already provided new insights into the origin of fungal virulence as well as suggested new avenues for experimentation. In this essay we review the available literature, which highlights the varied nature of amoeba-fungal interactions and suggests some unsolved questions that are potential areas for future investigation. Overall, results from multiple independent groups support the ‘amoeboid predator–fungal animal virulence hypothesis’, which posits that fungal cell predation by amoeba can select for traits that also function during animal infection to promote their survival and thus contribute to virulence. Cryptococcus neoformans in macrophages and the free-living amoeba Acanthamoeba castellani revealed remarkable similarities leading to the proposal that its capacity for virulence emerged accidentally from selection by amoeboid predators in soils [Aspergillus spp., Histoplasma capsulatum, Blastomyces dermatitides, Sporothrix schenkii, Cryptococcus gattii and entomogenous fungi [Fungi are major pathogens of plants, insects and ectothermic vertebrates but there are relatively few that are able to cause disease in mammals. The remarkable resistance of mammals to fungal diseases is attributed to a combination of endothermy, which creates a thermal restriction zone for survival of most fungal species and adaptive immunity ,2,3. Howin soils . Subsequus fungi ,7,8,9. Aus fungi ,11.In the past two decades, the concept that amoeba serve as selection mechanism leading to the emergence of virulence in different microbes has gained credence ,13,14 anCryptococcus spp. [A. castellanii was shown to feed on C. neoformans [Torula famata and Candida parapsilosis [C. parapsilosis was much faster than growth on the filamentous T. famata since the protozoa had difficulty ingesting hyphae [C. neoformans with amoeba and established that the protozoa preyed and devoured encapsulated cells with the emergence of hyphal cells as resistant forms, which were hypovirulent in mice [C. neoformans in the environment [A. castellanii was shown to rapidly kill cultures of S. cerevisiae, reducing more than half of the fungal cells after 90 min of co-incubation [The interaction of amoeba with animal pathogenic fungi has been studied for decades. In 1931, Castellani reported an amoeba growing in cultures of a cus spp. , providioformans  and subspsilosis . Interesg hyphae . Beginni in mice ,25. Furtironment , establicubation .Alternaria, Curvularia, Helminthosporium were internalized by Thecamoeba spp. and later egested in a viable condition, in a closer resemblance of nonlytic exocytosis [The focus of this review is on animal pathogenic fungi with particular emphasis on those that cause disease in mammals, but we note that other investigators have also explored the interaction of soil amoeba with other groups of soil fungi, including yeast and filamentous fungi acting in nature as saprophytes, entomopathogens and even as phytopathogens ,30,31,32ocytosis . DespiteC. neoformans is the most extensively studied species with regards to interactions with protozoa , a process discovered in macrophages [A. castellanii [D. discoideum [C. neoformans cells produce 3-hydroxy fatty acids, which inhibit amoeba phagocytosis and could have a similar role during infection for macrophages [In addition to the correspondence between animal and amoeba survival for selected fungal cells expressing certain virulence factors, there are also remarkable similarities in several cellular processes. response . The capresponse . The progal cell . Hence, gal cell , incapabrophages ,49, was tellanii  and D. dscoideum , providiscoideum . Anotherscoideum , which hscoideum  and to iscoideum . C. neofrophages .C. neoformans virulence comes from passage experiments of an attenuated cryptococcal strain with D. discoideum, which showed a rapid increase in virulence [D. discoideum mutant defective in myosin VII synthesis was susceptible to a highly attenuated acapsular C. neoformans strain [C. neoformans are Paramecium spp, which readily ingested and killed cryptococcal cells [More direct evidence for the ability of amoeba to affect irulence . This wos strain . Anotheral cells .C. neoformans, there is circumstantial evidence that amoeba and Aspergillus spp. interact in the environment. Analysis of the environmental microflora in moisture-damaged buildings revealed the co-habitation of molds and amoeba [Aspergillus spp. benefitted from the presence of the protozoa while amoeba were indifferent to the presence of molds [C. neoformans, the interaction of Aspergillus fumigatus conidia with A. castellanii was found to have remarkable similarities to their interactions with avian macrophages [A. fumigatus with another amoeba, Vermamoeba (Hartmannella) vermiformis, promoted fungal filamentation and growth of and the major reduction of amoeba viability, while in the same conditions the interaction of this fungus with A. castellanii had no influence in fungal growth [A. fumigatus with the social amoeba D. discoideum revealed concordance between virulence factors for animals and those needed for fungal survival and killing of amoeba [D. discoideum thus linking the production of mycotoxins to predator control and fungal environmental survival [Entamoeba histolytica [Aspergillus spp. have been shown to have toxic effects on Naegleria gruberi and to mediate anti-phagocytic effects on human neutrophils and macrophages [Aspergillus spp. DHN melanin, which inhibits phagolysosomal acidification also in macrophages interferes with phagocytosis by amoeba, suggesting that this virulence determinant has different functions for fungal cell survival with confronted by animal and protozoal phagocytic cells [Like d amoeba . Laboratof molds . In pararophages . In addil growth . The autf amoeba . Specifisurvival . Anothertolytica , and on tolytica . Substanrophages ,66. Aspeic cells .Candida spp. interactions. Although commensal Candida spp. limited to mammalian hosts would not be expected to be under predation by ameba in soils, there are some body sites that are co-inhabited by fungal and protozoal cells where they could potentially interact. For example, amoeba are present in the human oral cavity [Candida spp. are also commonly found in a commensal state. Steenbergen et al. reported that A. castellanii preyed on C. albicans and efficiently reduced colony-forming units consistent with fungal killing [V. vermiformis was shown to be able to internalize yeast cells of C. albicans, C. glabrata and C. parapsilosis and promote their proliferation in tap water with or without saliva traces [Candida spp. and S. cerevisiae with D. discoideum and other non-axenic social amoeba was studied, including mutant strains of both the fungal and protozoal spp. [Relatively little work has been done to explore amoeba-l cavity  where Ca killing . In anota traces . Recentloal spp. . This stoal spp. .B. dermatitides, H. capsulatum and S. schenkii (A. castellanii and reported that each of these fungal species was able to grow in the presence of the amoeba [Fusarium spp. with amoeba is of interest given that both are involved in corneal infections. Amoeba have been proposed to be major biotic control factors for Fusarium oxysporum in soils [Fusarium with two different types of amoeba, A. castellanii and V. vermiformis, showed that although fungal cells were ingested the interaction resulted in enhanced fungal growth [Fusarium spp. with A. castellanii can enhance the virulence of both organisms [Fusarium spp. and A. castellanii has been reported with the suggestion that this condition should be suspected in situations of refractory infection [One study has investigated the interaction of schenkii  with A. e amoeba . The intin soils . Given tin soils ,73. Incul growth . Moreovenfection .Candida parapsilosis [C. neoformans lawns resulted in the emergence of pseudohyphal forms, which were resistant to amoeba but less virulent in mice [B. dermatitides, H. capsulatum and S. schenkii yeast cells with A. castellani at 37 °C led to the rapid emergence of filamentous forms for each fungal species even at a temperature where the yeast form is preferred [A. fumigatus conidia with the free living amoeba V. vermiformis was reported to promote fungal filamentation and growth [C. albicans forms hyphae inside or outside D. discoideum, but no hyphae formation without amoeba [C. neoformans hyphae, pseudohyphae and yeast forms showed that the elongated forms were resistant to phagocytosis by both macrophages and amoeba [C. neoformans cells with hyphal and pseudohyphal morphology were much less virulent in mice and moths, indicating that resistance to one host may be associated with vulnerability in another [C. neoformans during amoeba predation of cryptococcal lawns in agar was considered an ‘escape hatch’ for the survival of some cells [An emerging theme in amoeba-fungal studies is that hyphal forms are more resistant to protozoal predation than yeast forms. This was noted as early as 1964 when Nero et al. reported that a mold was more resistant to amoeba than psilosis . Similar in mice . Analysi in mice , suggestd growth . C. albit amoeba . Comparid amoeba . However another . The difme cells .A. castellanii grown in axenic media. Amoeba that have been selected in laboratory conditions to grow in axenic conditions may exhibit very different physiology than wild amoeba. In general, amoeba recovered from the environment do not grow in culture conditions and thus are difficult to use for fungal–amoeba interaction studies. Given the tremendous variety of amoeba in the environment, the reliance on A. castellanii in laboratory studies raises the concern that insights gained with this protozoal species may not be representative of most fungal–amoeba interactions in nature. Similarly, the relatively few studies of fungal interactions with D. discoideum have also been done with laboratory strains. Hence, the limitations of current systems and the paucity of amoeba strains that have been examined suggest caution in extrapolating laboratory results from environmental conditions. One potential resource to overcome this problem could be the studies of fungal pathogens with mycophagous amoeba. Both A. castellanii and D. discoideum have been shown to be more adapted to have bacterial cells as food sources. Although we can find a few studies about the interaction of amoeba species that in nature feed primarily on fungal cells, there is a general lack of knowledge of the interaction of mycophagous amoeba with animal fungal pathogens [Most of the studies of fungal–amoeba interactions have been conducted with athogens ,80. One C. neoformans and other pathogenic fungi with environmental habitats were reported to be more resistant to A. castellanii than C. albicans or laboratory adapted S. cerevisiae strains [C. neoformans with A. castellanii in phosphate buffered saline result in fungal growth while confrontations in amoeba media result in fungal predation with reduction of cryptococcal colony-forming units [C. neoformans, recent findings suggest that the enhanced protozoal activity could have resulted from divalent cations amoeba media. In this regard, the presence of Ca2+ and Mg2+ have strong effects in potentiating A. castellanii antifungal activity [Which entity prevails in fungal–amoeba confrontations is highly dependent on the experimental parameters. As evident from the review of the available literature, fungi prevailed in some studies while amoeba were ascendant in others. Clearly, there are major differences in the ability of individual fungal species to resist amoeba predation, a finding that dates to early studies . In this strains ,6. Thereng units . Althougactivity , suggestAlthough from our anthropomorphic vantage point we tend to focus on animal and human pathogenic fungi, it is important to note that this is a minute part of fungal interactions with other hosts ranging from those with Protista, Plantae and Animalia. Indirect evidence for the longstanding nature of amoeba-fungal interactions comes from the finding that higher fungi synthesize lectins that are toxic to amoeba, a finding consistent with the development of inducible fungal anti-protozoal defense mechanism . There iCryptococcus sp. and mechanisms for iron acquisition and biofilm formation in Candida spp. Once adapted to the intracellular environment of amoebas, these pathogens could also be protected from the action of biocides and environmentally harsh conditions, making their survival and dissemination more effective. Therefore, the expression of these fungal virulence traits should be also considered in the extent to which amoeba predation could influence susceptibility to antifungal drugs. In this regard, passage of fungi from sites inhabited by birds in amoeba reduced their susceptibility to amphotericin B [C. neoformans triggers transcriptional changes to lipid genes that affect the susceptibility to antifungal agents [Due to the enormous genetic variety of the community of microorganisms in the soil, each possesses a unique combination of characteristics and distinct attributes to avoid the action of phagocytic predators, such as chemical defenses, including the expression of capsule in ericin B . Althougl agents . In a siMetarhizium anisopliae and Beauveria bassiana has also been proposed to involve interactions with soil amoeba selected for traits that allow survival in insect hemophytes, which are host phagocytic defensive cells [The idea that animal pathogenic fungi acquired many of their characteristics for virulence from interactions with soil protozoal is almost two decades old . The orive cells . DespiteC. neoformans as the polysaccharide capsule, melanin synthesis and phospholipase production have protective roles in the environment that are equally adapted to protection against macrophages during animal infection [Aspergillus spp., melanin and mycotoxins tripacidin, gliotoxin and fumagillin serve similar functions during its interaction with amoeba and macrophages [The amoeboid predator-animal virulence hypothesis posits that constant amoeboid predation over eons selected for fungal traits that also facilitate survival in certain animal hosts and thus confer on those fungal species the capacity for virulence. According to this view such virulence factors in nfection  (Figure rophages . AlthougCryptococcus spp., each of which have capsules, found that the capsule of C. neoformans was the most effective in protecting fungal cells against macrophages [A central feature of the amoeboid predator-animal virulence hypothesis is that it posits that the capacity for virulence can emerge independent of the final host. The phenomenon was termed ‘accidental virulence’ in prior essays . The notrophages . Alterna


Culex quinquefasciatus. To elucidate the specific role of each effector in the GPCR regulating pathway, we initially expressed a GPCR, G-protein alpha subunit (Gαs), adenylate cyclase (AC), and protein kinase A (PKA) in insect Spodoptera frugiperda (Sf9) cells and investigated their regulation function on cyclic AMP (cAMP) production and PKA activity. GPCR, Gαs, and AC individually expressed Sf9 cells showed higher cAMP production as the expression of each effector increased. All the effector-expressed cell lines showed increased PKA activity however. Moreover, Sf9 cytochrome P450 gene expression and cell tolerance to permethrin were examined. The relative expression of CYP9A32gene in Sf9 cells tested was significantly increased in all effector-expressed cell lines compared to a control cell line; these effector-expressed cell lines also showed significantly higher tolerance to permethrin. Inhibitor treatments on each effector-expressed cell line revealed that Bupivacaine HCl and H89 2HCl robustly inhibited cAMP production and PKA activity, respectively, resulting in decreased tolerance to permethrin in all cell lines. The synergistic functions of Bupivacaine HCl and H89 2HCl with permethrin were further examined in Culex mosquito larvae, providing a valuable new information for mosquito control strategies.The G-protein-coupled receptor (GPCR) regulated intracellular signaling pathway is known to be involved in the development of insecticide resistance in the mosquito,  G-protein-coupled receptors (GPCRs) are cell surface, membrane-binding proteins that are responsible for signal transmission through extracellular signal binding to activate and regulate intracellular factors. Both the constitutive and spontaneous activities of GPCRs are critically involved in cell signaling responses , providiCulex quinquefasciatus [In the last decade, many studies have confirmed that GPCRs play a crucial role in regulating insect physiological processes such as development, behavior, metabolism, and reproduction. These conserved intracellular pathways are present in several insect species. Because of the importance of functional GPCRs  and theiasciatus ,12,13. Iasciatus .Sf9 cells via baculovirus-mediated insect expression in order to investigate the specific function of each effector in insecticide resistance and the P450-expressed regulation of insect cells, as well as their complex connection via second messenger (cAMP) and PKA activity. The findings of this study are expected to not only lead to exciting new insights into intracellular cascades in insecticide resistance, but also to provide useful information that will support the development of novel strategies and/or insecticides for pest control and resistance management in the future.This study focuses on the expression of the mosquito GPCR, Gαs, AC, and PKA in insect Culex mosquitoes [Previous studies have shown that cell signaling effectors of GPCR–Gαs–AC–PKA–P450 link up to form a functional transduction pathway in squitoes ,12,13. TSf9 cells, the PKA activity dramatically increased from 40 U/mL/mg protein at MOI = 0.2 to 60 U/mL/mg protein at MOI = 0.5, and 90 U/mL/mg protein at MOI = 1.0. In the Gαs006458 expression Sf9 cells, the PKA activity significantly increased from 17 to 22 U/mL/mg protein as the MOI ratio rose from 0.2 to 1.0, but there was no significance of PKA activity between MOI ratio of 0.2 and 0.5. In the AC007240 expression Sf9 cells, PKA activity significantly increased from 14 to 22 U/mL/mg protein as the MOI rose from 0.2 to 1.0, however, no significance of PKA activity between MOI ratio of 0.2 and 0.5 was detected. In the PKA018257 expression Sf9 cells, the PKA activity significantly increased from 70 to 95 U/mL/mg protein as the MOI rose from 0.2 to 1, as well as the PKA activity showed similar levels as ~70 U/mL/mg at MOI ratio of 0.2 and 0.5  in CAT expression cells . Followi and 0.5 . In addiCulex mosquitoes [CYP9A30, -A31, -A32, and CYP333B4) could be induced by insecticides in Sf9 cells indicating they may be involved in insecticide resistance [Sf9 cell lines, we detected the relative expression changes of CYP9A30, -A31, -A32, and CYP333B4 genes in GPCR020021, Gαs006458, AC007240, and PKA018257 expression cell lines. The results showed one out of four P450 genes (CYP9A32) was significantly increased expression in all the cell lines in comparison with the control cell line (CAT)  A. The rell lines A. Howevell lines A. Since ll lines B. Howevell lines B. Even all lines B.Sf9 cell lines. There was no difference in the chemical effect between Bupivacaine HCl/acetonitrile and DMSO on Sf9 cell viability among all cell lines, thus, it is not necessary to use DMSO and acetonitrile to treat the cell lines as a negative control. In the control cell line (CAT expression), there was a decrease in the cell survival ratio from 85% to 60% under permethrin treatment (50 to 400 µM) after inhibitor treatment in comparison with cells treated with the Bupivacaine HCl + acetonitrile (control) , followed by measuring the cAMP concentration in each treatment. The results showed that the cAMP concentration significantly decreased in the Bupivacaine HCl-treated cell lines (~1.5- to 2-fold) in comparison with the cell lines that were treated with the solvent  A. The sycontrol) B. In thecontrol) B. In thecontrol) B. The cAcontrol) B.Sf9 cells was examined in specific gene expression cell lines and the CAT expression cell line. In the CAT expression cell line, H89 2HCl-treated cells showed significantly increased susceptibility to permethrin  compared to cells treated with the H89 2HCl + acetonitrile  was utilized to treat the GPCR020021, Gαs006458, AC007240, and PKA018257 expression cell lines, and the CAT gene expression cell line. The H89 2HCl-treated cell lines all showed significantly decreased PKA activity compared to cell lines that were treated with the solvent (DMSO) A. The syonitrile B. The GPonitrile B.G8 were treated with a serial of concentrations of Bupivacaine HCl ranging from 25 to 200 µM and simultaneously exposed to serial concentrations of permethrin ranging from 0.1 to 3 ppm. The HAmCqG8 larvae showed significantly decreased resistance to permethrin at 100 and 200 µM of Bupivacaine HCl, but lower concentrations of Bupivacaine HCl (25 and 50 µM) had no apparent effect, with treated mosquitoes exhibiting the same resistance level to permethrin to the controls exposed to permethrin alone or DMSO plus permethrin  using LR Clonase Enzyme mix . The LR reaction was analyzed by PCR with primers of Polyhedrin forward primer and V5 reverse primer according to the manufacturer’s instructions. Sf9 insect cells were purchased from Invitrogen  and the Sf9 cells were grown and maintained in SF 900 III SFM medium  supplemented with 10% FBS . The cell lines were cultivated either in monolayers or in suspension at 27 °C, shaken at 120 rpm. Transfections with recombinant virus following the required multiplicity of infection (MOI) were performed in suspension at a cell density of 1.0 × 106 cells/mL. Specifically, GPCR, Gαs, AC, and PKA recombinant with P2 MOI of 0.2, 0.5, and 1 were expressed in Sf9 cells for 72 h prior to cell lysis preparation. Chloramphenicol acetyltransferase (CAT) expression cells with the same serial MOI served as controls. Cell harvest was determined at the peak of production (72 h post-infection) for tests of cAMP production, PKA activity, and P450 gene expression.The cDNAs for GPCR020021 (Gene ID: CPIJ020021), Gαs006458 (Gene ID: CPIJ006458), AC007240 (Gene ID: CPIJ007240), and PKA018257 (Gene ID: CPIJ018257) were amplified by polymerase chain reaction (PCR) using forward primers containing 4 nucleotides (CACC) prior to the start codon (ATG) that stabilize the PCR product cloning into the Gateway entry vector pENTR/D-TOPO following the procedure described in the pENTR/D-TOPO Cloning Kit manual . Details of the primer pairs are listed in Sf9 cell lines was examined using the Detect X Direct Cyclic AMP Enzyme Immunoassay Kit (Arbor Assays) following the instructions in the kit manual. 1 × 108 cells/mL were washed by PBS and then directly treated with Sample Diluent for 10 min at room temperature, followed by centrifugation at ~1000 rpm at 4 °C for 15 min, after which the supernatant was assayed. Samples containing cAMP were bound by the cAMP antibody and cAMP-peroxidase conjugate. After 2 h of incubation at room temperature with shaking, the reaction was terminated and the intensity of the generated color at 450 nm was measured via a Cytation 3 imaging reader. Moreover, a cAMP production inhibition test was conducted by treating the recombinant gene expressed cells (MOI = 0.5) with 40 µM Bupivacaine HCl (in DMSO)  [To identify the critical function of cAMP and its connection with each effector in the GPCR regulation pathway, the cAMP production in gene expressed TX, USA)  for 2 h Sf9 cells. CAT expression cells served as the control. The Detect X PKA (Protein Kinase A) Activity Kit  was used to determine the PKA activity following the kit manufacturer’s instructions. Briefly, suspension Sf9 cells were prepared at a cell density of 1.0 × 108 cells/mL after 72 h expression with recombinant gene  then lysed in Activated Cell Lysis Buffer  for 30 min on ice with occasional vortexing. The resulting cell lysate mixture was then centrifuged at 10,000 rpm at 4 °C for 10 min, and the supernatant utilized for PKA assay. Lysate containing PKA was used to phosphorylate the immobilized PKA substrate on a microtiter plate using the kit-provided ATP to produce a phosphor-PKA substrate that could be bound by a specific rabbit antibody and then detected by peroxidase-conjugated Goat-anti-Rabbit. The absorbance of the reaction was determined using a Cytation 3 imaging reader at 450 nm in order to determine the amount of PKA in the samples. To further confirm the involvement of PKA in the GPCR regulatory pathway, recombinant gene-expressed Sf9 cells were treated with a potent PKA inhibitor . The GPCR, Gαs, AC, PKA expression (MOI = 0.5) Sf9 cells were pretreated with 60 µM H89 2HCl (in DMSO) at 27 °C for 1 h avoiding light, then 10 µM forskolin (in DMSO)  [To investigate the relationships linking GPCR–Gαs–AC–PKA, a PKA activity test was performed based on the GPCR, Gαs, AC, and PKA recombinant expression in TX, USA)  and 30 µSf9 cell lines (MOI = 0.5) using the E.Z.N.A. HP Total RNA Kit (Omega). Prior to cDNA synthesis, genomic DNA was removed from the total RNA using a TURBO DNA-free kit . cDNA was synthesized by SuperScript® IV Reverse Transcriptase  following the instructions in the kit manual. qRT-PCR was performed using the All-in-OneTM qPCR Mix  and CFXTM 96 Real Time system. Each qRT-PCR reaction (25 µL) consisted of SYBR Green master mix, specific primer pairs for four Sf9 P450 genes [CT−∆∆ method [Total RNAs were isolated from GPCR020021, Gαs006458, AC007240, PKA018257, and the CAT expression 50 genes  -2,5 diphenyltetrazolium bromide MTT (Sigma) colorimetric assay [Sf9 cells after 48 h infection by a construction baculovirus, either GPCR020021, Gαs006458, AC007240, or PKA018257 (MOI = 0.5), were transferred to a 24-well plate  at a cell density of 1.0 × 106 cells/mL. CAT-infected Sf9 cells served as control. The cells were then treated with permethrin  at a series of concentrations ; cells treated with acetonitrile alone served as solvent controls (CK) in each cell line treatment. The cell survival rate was evaluated by MTT assay after 48 h permethrin treatments. Briefly, the culture medium was removed and 200 µL MTT (5 mg/mL) added to each well. The MTT-treated cells were then incubated avoiding light at 37 °C for 4 h. After incubation, 1 mL of DMSO was added to each well. The absorbance of the resulting solution was determined using a Cytation 3 imaging reader at 540 nm. To test the regulation pathway through cAMP and PKA activity, each effector expression cell line was treated with either 40 µM of the cAMP production inhibitor (Bupivacaine HCl) or 60 µM of the PKA activity inhibitor (H89 2HCl) and simultaneously treated by permethrin, as described above. Inhibitors with acetonitrile was served as control treatment. The cell survival ratio was calculated as  * 100%. Each treatment was repeated at least 3 times with independent infections of the construction baculovirus.The relative number of viable ic assay . TypicalCx. quinquefasciatus, HAmCqG8 and MAmCqG6, were utilized in this study. HAmCqG8 is the eighth generation of permethrin-selection HAmCqG0 offspring that was collected from Mobil County, AL; MAmCqG6 is the sixth generation of permethrin-selection MAmCqG0 offspring that was collected from Madison County, AL. These two counties are located more than 600 km apart. HAmCqG8 and MAmCqG6 have obtained ~2700- and ~590-fold higher levels of resistance than lab-susceptible mosquito strains, respectively. To maintain the resistant levels, HAmCqG8 and MAmCqG6 strains were treated with certain concentrations of permethrin every 6 months [Cx. quinquefasciatus in 6 oz. Sweetheart ice cream cups  containing regular tap water, inhibitors, and 1% permethrin solutions in acetone at four or five concentrations that yielded >0% and <100% mortality. Serial concentrations of the inhibitors were applied simultaneously with permethrin. Control groups received either permethrin alone or DMSO plus permethrin solution. All tests were performed at 25 °C and replicated at least four times on different days. Mortality was assessed after 24 h. Bioassay data were pooled, and a probit analysis was conducted using Abbott’s correction for control mortality.Two insecticide resistant mosquito strains of 6 months . The most-test for all 2-sample comparisons and a one-way analysis of variance (ANOVA) for multiple sample comparisons using Statistical Package for the Social Science (SPSS) software with both least significant difference (LSD) and Tukey tests to analyze the significance of the means; a value of p ≤ 0.05 was considered statistically significant. All significances were present by alphabetic letters .The statistical significances for the cell viability, PKA activity, cAMP production, and gene expression were calculated using a Student’s


Cancer cell lines (CCLs) have been widely used to study of cancer. Recent studies have called into question the reliability of data collected on CCLs. Hence, we set out to determine CCLs that tend to be overly sensitive or resistant to a majority of drugs utilizing a nonlinear mixed-effects (NLME) modeling framework. Using drug response data collected in the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), we determined the optimal functional form for each drug. Then, a NLME model was fit to the drug response data, with the estimated random effects used to determine sensitive or resistant CCLs. Out of the roughly 500 CCLs studies from the CCLE, we found 17 cell lines to be overly sensitive or resistant to the studied drugs. In the GDSC, we found 15 out of the 990 CCLs to be excessively sensitive or resistant. These results can inform researchers in the selection of CCLs to include in drug studies. Additionally, this study illustrates the need for assessing the dose-response functional form and the use of NLME models to achieve more stable estimates of drug response parameters. Moreover, using in vitro CCL models to study cancer pharmacogenomics2 can be helpful to understand the resistance and sensitivity to therapy currently in use in cancer treatment, explore genomic factors associated with drug response, and develop more anticancer drugs3. Recently, two independent large-scale studies, the Cancer Cell Line Encyclopedia (CCLE)4 and the Genomics of Drug Sensitivity in Cancer (GDSC)5, were completed in which drug response information was collected on a number of therapeutic agents in addition to extensive molecular information . The use of large-scale drug studies on CCLs depends on the reliability and reproducibility of drug response assessments.Over the past decades, the cancer cell lines (CCLs) have been widely used to study the biological processes in cancer, as well as 7. Multiple factors are likely to contribute to these observed inconsistencies in the drug-response data, including methodological and analytical challenges due to the differences in assay types, maximum tested drug concentration, range of tested drug concentrations, and drug sensitivity measurements employed by different studies8. In response, the authors of CCLE and GDSC reported a significantly better agreement for the differences between these two pharmacological data by incorporating both analytical and biological considerations9. However, discrepancies in the measured drug sensitivities still persist10. Furthermore, consistency can be achieved when biologically grounded analysis methods are incorporated using the standardization of assay methods and laboratory conditions12.Despite the wide use of CCLs for drug response studies, inconsistency in drug-response data and poor concordance between mutational profiles of CCLs compare to patient tumors have been reported13. Analysis of dose-response data typically focuses on the EC50 14, which is a description of shape of the dose-response curve at its midpoint, by which cell lines are often determined to be either sensitive or resistant. However, the dose-response curves can differ in other aspects, such as the slope of the curve or the area under the curve. Analyzing each cell line individually across a drug combination is highly problematic since the variation exhibited across the cell lines is ignored and this information could be harnessed inform the response profile of any single cell line . Incorporating variation across all the cell lines in dose-response curve fitting can reduce noise and lead to more reliable inferences for any single CCL. Recently, the nonlinear mixed-effects (NLME) model has become an important approach to improve the accuracy of EC50 estimates (or similar parameters), through the borrowing of information across all CCLs16. Such models allow one to account for the repeated measures aspect in the data  through the inclusion of random effects in model, along with fixed effects  as included in traditional nonlinear regression models.Testing of drug sensitivity has been a routine procedure in clinical and laboratory researches. Dose-response data collected on CCLs often are sigmoidal in shape and thus nonlinear logistic models are often used to model the data for each cell line individuallyThe objectives of this study are first to determine difference in model functional form  for drug-response data using data from the CCLE and GDSC. Then, for each cancer type and drug combination, a NLME model was fit to the drug-response data collected on the CCLs that had the same nonlinear functional form. Using the CCL specific estimated random effects from the NLME model, a set of cell lines were determined to be consistently sensitive or resistant to a large number of drugs. These findings can aid cancer researchers in the selection of cell lines to include in their experiments by eliminating CCLs that are always sensitive or resistant to drugs as results may not be generalizable. Moreover, this study illustrated the need for assessing model functional form for drug-response data and the ability to model all cell lines simultaneously using a NLME model to provide more stable estimates of drug response parameters.http://broadinstitute.org/ccle was downloaded (February 2015) and processed. In total, the analysis consisted of in vitro drug response data for 8-point dose were collected on 24 compounds assessed on 504 cancer cell lines. Supplemental Fig. The drug sensitivity data available at 1. Drug sensitivity data included 213,605 drug-response series distributed over 990 cell lines and 265 drugs were downloaded from http://www.cancerrxgene.org (July 2016). The drug-response data were generated over 9 drug concentrations (2-fold dilution series) or 5 drug concentrations (4-fold dilution series). Supplemental Fig. The drug sensitivity data are generated by the Cancer Genome Project at the Wellcome Trust Sanger Institute (WTSI) and the Center for Molecular Therapeutics at Massachusetts General Hospital using a collection of >1000 cell lines6.As both the CCLE and GDSC used existing CCLs, the two studies had a number of cell lines and drugs represented in both studies. In particular, dose-response data across 354 cell lines for 15 drugs were in common between the CCLE and GDSC. However, it should be noted that the two studies used different experimental protocols such as differences in the pharmacological assay types and the range of drug concentrationsin vitro dose response data for each CCL individually for a given drug. The shape of the dose-response curves vary between CCLs for a given drug  or four-parameter (4P) logistic regression13. Although the functional form of the model remains the same for all cell lines , the parameter values vary from cell line to cell line.Typically, an individual dose-response model is fitted to the 17 for repeated measures dose-response data have become popular due to their flexible covariance structure which allows for the joint modeling of multiple in vitro measurements taken off a set of CCLs. An advantage of using the NLME model is that both within-cell line and between-cell line variation are accounted to improve the statistical estimation of parameters. Moreover, the problem of extreme estimates caused by considering a single drug-cell line is reduced by using the NLME model where the information across cell lines is borrowed, with individual cell line parameters shrunk towards the population-level parameters17.Recently, nonlinear mixed-effects (NLME) modelyij represent the jth dose-response at ni drug doses, ith cell line, xij denote the corresponding jth drug dose assayed for the ith cell line. The total number of measurements collected is equal to eij is a random error term reflecting uncertainty in the response, given the ith cell line. In Eq. , and βi4 is the slope of the dose-response curve for the ith cell line18. Collecting the errors for the ith cell line into (ni × 1) vector, eij in Eq.  were fit with models assessed with the Akaike’s information criterion (AIC)22 to account for the complexity of the model . The best fitting model was then determined for each CCL drug combination. Then, for each cancer site and drug combination, the model that fit most of the CCLs the best was selected as the best fitting model.Choice of the 4P logistic regression model is fairly typical in dose-response data analysis, while for some dose-response data, the 4P logistic regression model does not provide an adequate fit. In these cases, often a 3P logistic regression model is used by setting 0 in Eq. . The non0 in Eq. , can be 17. To model the drug response data for all CCLs for a given drug simultaneously, a NLME model is considered where the function form was either 3P or 4P logistic regression model. Although, the form of regression function f is common to all CCL dose-response data for a given drug, the parameter vector βi may vary across CCLs. Variation among different cell lines is accounted for through the cell line regression parameters where a model for the dependence of βi on random components is taken into account. This can be accomplished with a hierarchical model framework by adding the following second-level model to the nonlinear model outlined in Eqs (βi asβ0 is (r × 1) vector of fixed-effects parameters representing the parameter values for the population, bi is a (q × 1) vector of random effects, and Ai (p × r) and Bi (p × q) are the design matrices associated with the fixed-effects and the random effects, respectively. The within-cell variation in Eq.  was used to compare both the estimates of EC50 from the single cell line model and the standardized random effect estimates (SREs) from the NLME. Similar to Haibe-Kains, Benjamin, et al.6, the qualitative descriptions of correlation values are defined as; poor (rs < 0.5), fair (0.5 ≤ rs ≤ 0.6), moderate (0.6 ≤ rs ≤ 0.7), substantial (0.7 ≤ rs ≤ 0.8), and perfect (0.8 ≤ rs).As a different NLME model was fit for all cell lines of a given cancer type and drug, we standardize the estimated cell lines random effects for each drug to enable comparison across drugs. For CCLE and GDSC datasets, the Spearman’s rank correlation coefficient  and a linear model (LM) to the dose-response data for each cell line, with the best fitting model determined based on AIC ; Type II or “moderate” outlier cell line if SREs fall between ; and Type III or “extreme” outlier cell line if SREs fall outside of ±3. CCLs with SREs fall between  are neutral. These outlier types (I-III) are defined based on the boundaries of 95%, 99.4%, and 99.9% confidence intervals, respectively. CCLs with SREs < −1.65 or SREs > 1.65 across greater than or equal to 20% of drugs are sensitive or resistant, respectively, while the rest of CCLs were considered neutral. The second approach we employed to determine problematic CCLs or outliers was based on the distribution of the SREs. CCLs with SREs < 0 or SREs > 0 across greater than or equal to 80% of drugs are sensitive or resistant, respectively, while the rest of CCLs are neutral.To determine the cell lines that are consistently sensitive or resistant to a number of drugs assayed in the CCLE and GCSC, and therefore potential cell lines to remove from future drug studies, the estimated random effects were examined for each cell line and drug combination determined from the NLME model, as outlined in previous sectionNonlinear models with functional forms of 4P and 3P logistic and a linear model (LM) were fit to the 497 CCLs in the CCLE, with best functional form based on AIC for each CCL and drug combination presented in Supplemental Tables Similar to the comparison of model fit statistics for the CCLE, we completed the assessment of functional forms  for the 979 cell lines in the GDSC  which fit the majority of CCLs was used in fitting a NLME model defined in Eqs . As an example, Fig. 26.Similar to analysis completed on the CCLE, we completed the identification of outlier cell lines in the GDSC across 29 out of 54 cancer types with more than 10 cell lines. Similar to the CCLE analyses, all GDSC cell lines were not included in each model. The median number of NLME models that the cell lines in GDSC were included in was 74 drugs out of 242 drugs (range 3 to 153 drugs). Supplemental Figure rs = 0.75; PLX4720 with rs = 0.74), along with moderate correlations for drugs TAE684 (rs = 0.56) and AZD0530 (rs = 0.50)  and Paclitaxel (rs = 0.64) were observed with moderate correlations and drug Sorafenib (rs = 0.52) had fair correlation between estimates  across 354 CCLs and 15 drugs. For each drug, the Spearman’s rank correlation between SREs and nonlinear estimates of EC50 were computed , and two drugs with fair correlations  between studies. The majority of drugs (7 out of 11) yielded poor concordance (rs < 0.50).We then assessed the consistency between drug response data using SREs for EC50 across 196 CCLs and 11 drugs in both GDSC and CCLE, while the two studies used different experimental protocols12 Fig. . We obsein vitro drug response data generated on cell lines and second to determine using a nonlinear mixed-effects model cell lines that appear to be sensitive or resistant to a majority of the drug tested in either the CCLE or the GDSC studies. As the model for curve fitting has a direct impact on the measurement of drug potency , we compared the appropriateness of the 4P logistic, 3P logistic, and linear models for the 497 cell lines and 24 drugs in the CCLE and the 979 cell lines and 265 drugs in the GDSC. We observed that the best fitting dose-response relationships is often a 3 or 4 parameter logistic (3P logistic or 4P logistic) nonlinear model. However for some dose-response data, neither the 4P or 3P logistic models provided an adequate fit to the data. These results illustrate the need to assess the functional form for in vitro studies to ensure the most accurate and precise estimates of drug response, such as the EC50 or the area under the dose response curve. Further work is needed to assess other non-linear models besides the 3P and 4P logistic, such as the Cedergreen-Ritz-Streibig five-parameter model used in Moyer et al.27, for modeling drug-response data in the CCLE and GDSC.The goals of this study were to first determine if there was a common functional form for 28 .The use of NLME model allows the joint modeling of all the CCL drug response data collectively with the ability to model the cell line-to cell line variation along with the within-cell line variation. Moreover, the flexible covariance structure allows both within-cell line and cell line-to-cell line variations are accounted to improve the statistical analysis. Such NLME model uses the information across the cell lines to adjust the extreme estimates caused by considering a single drug-cell line . Therefore, a NLME model was used to detect sensitive or resistant cell lines with the SREs for the EC50 parameter using two approaches, one based on determination of outliers using the boundaries of confidence intervals and one based on the distribution of SREs for a CCL. We found 17 cell lines based on the CCLE data to be overly sensitive and resistant to many drugs (see Table in vitro drug screens are generalizable. Additionally, this study illustrated the need for assessing functional form for the drug-response data and the ability to model all cell lines simultaneously using a NLME model to provide more accurate estimates of drug response parameters.In conclusion, the results from this study can aid basic scientists in the selection of cell lines to include in experiments to ensure that results from the Supplemental InformationSupplemental Tables 1 - 10