Background: It is estimated that one in five people worldwide faces a diagnosis of a malignant neoplasm during their lifetime. Carvacrol and its isomer, thymol, are natural compounds that act against several diseases, including cancer. Thus, this systematic review aimed to examine and synthesize the knowledge on the antitumor effects of carvacrol and thymol.Methods: A systematic literature search was carried out in the PubMed, Web of Science, Scopus and Lilacs databases in April 2020 (updated in March 2021) based on the PRISMA 2020 guidelines. The following combination of health descriptors, MeSH terms and their synonyms were used: carvacrol, thymol, antitumor, antineoplastic, anticancer, cytotoxicity, apoptosis, cell proliferation, in vitro and in vivo. To assess the risk of bias in in vivo studies, the SYRCLE Risk of Bias tool was used, and for in vitro studies, a modified version was used.Results: A total of 1,170 records were identified, with 77 meeting the established criteria. The studies were published between 2003 and 2021, with 69 being in vitro and 10 in vivo. Forty-three used carvacrol, 19 thymol, and 15 studies tested both monoterpenes. It was attested that carvacrol and thymol induced apoptosis, cytotoxicity, cell cycle arrest, antimetastatic activity, and also displayed different antiproliferative effects and inhibition of signaling pathways (MAPKs and PI3K/AKT/mTOR).Conclusions: Carvacrol and thymol exhibited antitumor and antiproliferative activity through several signaling pathways. In vitro, carvacrol appears to be more potent than thymol. However, further in vivo studies with robust methodology are required to define a standard and safe dose, determine their toxic or side effects, and clarify its exact mechanisms of action.https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=176736.This systematic review was registered in the PROSPERO database (CRD42020176736) and the protocol is available at  It is estimated that one in five people worldwide faces the diagnosis of some malignant neoplasm during their lifetime, and the number of people with cancer is forecast to double by the year 2040 . In factTaxus brevifolia Nutt. species . There are many medicinal plants whose pharmacological properties have already been described and scientifically proven . However species . More re species .Origanum vulgare L.), thyme (Thymus vulgaris L.) and “alecrim-da-chapada” (Lippia gracilis)  and its thymol isomer (2-isopropyl-5-methylphenol), classified as natural multi-target compounds, deserve attention. Both are monoterpenoid phenols, the main components present in essential oils obtained from several plant species of the Lamiaceae and Verbenaceae families, such as oregano (racilis) , which hracilis) , includiracilis) . In addiracilis)  and antiracilis)  activitiracilis) . Hence, in vitro) or in animal models of cancer? The review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (The purpose of this systematic review was to answer the following question: Do carvacrol and thymol exhibit an anti-tumor effect on cancer cells ((PRISMA) .in vitro and in vivo.A PICOS strategy  was used based on: P: Animals with cancer or tumor cells; I: Treatment with carvacrol or thymol; C: No treatment, healthy cells or placebo (vehicle); O: Cytotoxic and antitumor effects, induction of apoptosis and inhibition of proliferation; S: Pre-clinical studies in vitro, in vivo, carvacrol or 5-isopropyl-2-methylphenol and thymol or 5-methyl-2-propan-2-ylphenol  using a combination of health descriptors, MeSH terms and their synonyms, such as antitumor, antineoplastic, anticancer, cytotoxicity, apoptosis, cell proliferation, in vitro studies of cancer cell lines, in vivo study of animals with cancer; cytotoxic effect, antitumor effect, inhibition of proliferation and apoptosis and experimental studies (in vitro and in vivo). The exclusion criteria were: experiments with derivatives of the carvacrol or thymol, association of the two compounds with other substances or in mixtures composing essential oils and extracts, animals with other diseases in addition to cancer, review articles, meta-analyses, abstracts, conference articles, editorials/letters and case reports. A manual search of the reference lists of all selected studies was also conducted, in order to identify additional primary studies for inclusion.Two independent reviewers (L.A.S. and L.T.S.P.) analyzed the research results and selected potentially relevant studies after reading their title and abstract, using the systematic review application, Rayyan . We usedWe extracted the following data from the included articles: author, year, country, data about the monoterpene , concentration and/or dose, type of animal or cell line, results , proposed mechanisms involved in the antitumor effect and conclusion. The authors of the included studies were contacted when necessary (whenever any data or article was not available).in vivo experimental studies  (ICnit (μM)  and wereκ = 0.813) among the reviewers, after selecting the titles and abstracts.The initial search resulted in 1,170 records, of which 594, 169, 194, and 213 were found in PubMed, Web of Science, Scopus and Lilacs, respectively. Of these, 388 were excluded due to duplication. After screening the title and abstract, 706 reports were excluded, and 1 report was sought for retrieval, as it met the criteria after reading the full text, resulting in 77 studies. Of these, three were excluded after reading the full text (two for presenting the same results and one that tested the anti-cancer effect on bacteria) and four for not having access to the full text, resulting in 70 articles. In addition, 9 studies were identified after a manual search of the references and two studies obtained from other sources, but only seven studies were added , finally resulting in 77 included studies . There wn = 14), China (n = 14), Turkey (n = 13), the Republic of Korea (n = 5), Slovakia (n = 5), Iran (n = 5), Morocco (n = 2), Brazil (n = 2), Greece (n = 2), Iraq (n = 2), The United States of America (n = 2), Canada (n = 2), Egypt (n = 2), Croatia (n = 1), Lithuania (n = 1), Italy (n = 1), Spain (n = 1), The United Kingdom (n = 1), Peru (n = 1), Belgium (n = 1). Asia (51.9%) was the continent with the largest number of publications on the subject, followed by Europe (33.7%), the Americas (9.3%) and Africa (5.1%), with a greater trend of publications on this subject in the last three years (in vitro experiments and 10 (12.9%) were in vivo studies. Likewise, 43, 19, and 15 publications tested only carvacrol, thymol and both compounds, respectively. They were mostly obtained commercially, provided by Sigma Aldrich (n = 52), Fluka (n = 5), Aldrich Chemical (n = 2), Western Chemical (n = 1), Agolin SA (n = 1), Alfa Aesar (n = 1). In only five studies were the compounds isolated from essential oils. Remarkably, ten studies did not report the source of the tested compounds. A detailed description of the included studies is shown in in vitro and in vivo studies.The selected studies were carried out in different countries: India  was able to inhibit the viability and proliferation of lung cancer cells (A549 cell line), in addition to inducing early apoptotic characteristics  and reduIn relation to hepatocarcinomas (HepG2 cell line), carvacrol exhibited anticancer effects, provoking cell death and antiproliferative effects in a concentration-dependent manner . The inhColorectal cancer (Caco-2 cell line) also exhibited reduced cell viability and a significant increase of early apoptotic cells after carvacrol incubation (115 μM) . There wIn respect of breast cancer, treatment with carvacrol decreases MDA-MB231  and MCF-It was also observed that the administration of carvacrol provoked cytotoxic and apoptotic effects on HeLa and SiHa cervical cell lines . In factCarvacrol also induced cytotoxicity and apoptosis (via caspase-3 and reactive oxygen species—ROS) of human oral squamous cell carcinoma (OC2 cell line) in a concentration-dependent manner . In tongIn a prostate cancer cell line (DU 145), carvacrol showed a significant reduction of cell viability and proliferation in a concentration and time dependent manner, marked by a cell cycle arrest, resulting in the accumulation of cells in the G0/G1 phase, and apoptosis, related to the increased activity of caspase-3, production of ROS and loss of mitochondrial membrane potential . PC-3 ceRegarding human choriocarcinoma (JAR and JEG3 cell lines), carvacrol was able to inhibit proliferation and induce cell cycle arrest. The results showed that carvacrol reduced cell proliferation and provoked apoptosis mediated by mitochondrial membrane potential depolarization, increased mitochondrial calcium and activation of Bax and Cytochrome C expression. In addition, treatment with this monoterpene promoted an accumulation of cells in the sub-G1 phase, indicating that changes in intracellular calcium and ROS generation are related to the antiproliferative effects observed. Additionally, there was a marked phosphorylation of ERK1/2 and also inhibition of the PI3K/AKT signaling pathway, indicating that carvacrol regulates signaling pathways by inhibiting MAPK and PI3K .In murine B16-F10 and A375 melanoma cell lines, carvacrol reduced cell viability and induced cytotoxicity . The ant50 of 421 and 229.68 μM in 48 and 72 h, respectively (50 = 60.01 μg/mL) concentrations and antitumor effects (apoptosis and inhibition of cell proliferation) at higher concentrations (>IC50 = 60.01 μg/mL)  . ElshafiThymol also showed concentration-dependent cytotoxic effects and reduced the proliferation of Caco-2 cells . In contBreast cancer cells (MDA-MB 231 cell line) also exhibited a reduction in cell viability after thymol treatment . The inhThe administration of thymol to bladder cancer  provoked inhibition of cell proliferation and decreased the cell viability in a concentration and time dependent manner, along with marked cell cycle arrest in the G2/M phase and induction of apoptosis through the intrinsic pathway, together with the activation of caspase-3 and -9, JNK and p38, release of cytochrome C, negative regulation of Bcl-2 family proteins and production of ROS. In addition, a considerable decrease in the expression of cyclin A, B1 and CDK2, as well as an increase in the expression of p21 were observed after treatment with thymol, suggesting that its antitumor effect occurs by inhibiting the PI3K/Akt signaling pathway, via MAPKs, and generation of ROS . In humaHuman glioblastoma cells (DBTRG-05MG) showed reduced viability in a concentration-dependent manner when treated with carvacrol (200–600 μM), induced apoptosis and necrosis by ROS production and caspase-3 activity . In a raRegarding thymol, treatment at concentrations of 100 and 200 µM induced a significant reduction in cell viability and inhibited the migration of glioma cells (C6 cell line) through phosphorylation of PKCα and ERK1/2, that resulted in decreased expression of MMP-9 and MMP-2 . In addiTreatment with carvacrol in leiomyosarcoma cells exhibited antiproliferative effects in a concentration dependent manner and also inhibition of cell growth . In addiIn relation to thymol, there were concentration-dependent cytotoxic effects and interruption of the cell cycle progression in the G0/G1 phase in P-815 cells . In humaCarvacrol showed cytotoxic effects against human myeloid leukemia cells (K-562 cell line)  and agai2O2, and Bax protein levels; and activation of caspase-8, -9, -3 and PARP  that at a concentration of 300 µM of carvacrol the KG1 cell lines were more sensitive compared to the HL60 cell line, and at 400 µM the K-562 cell line showed resistance after 48 h of treatment. Regarding thymol (50 µM), the KG1 cell line was also more sensitive when compared to the other two and at the 100 µM dose, thymol was able to induce complete cell death in the KG1 and HL60 cell lines (It was observed by Bouhtit ll lines .When using mouse myoblast cells (CO25 cell line) transformed with human N-RAS oncogene, 50 (μM) values determined 24 h after the incubation of the studied cells with carvacrol or thymol. It was possible to verify that, in general, carvacrol  is more potent than thymol , with difference between means of 103.8 (±106.9). The lowest IC50 values for carvacrol were against prostate carcinoma , vascular endothelial growth factor (VEGF) and decreased expression of the gamma glutamyl transferase (GGT) gene  gene .Carvacrol supplementation significantly improved the weight gain and growth rate of animals with colon cancer induced by 1,2-dimethylhydrazine (DMH), exhibiting a lower incidence of tumors and pre-neoplastic lesions, along with a reduction in oxidative stress damage , suggesting that carvacrol presents chemopreventive effects .in vivo models to investigate the effect of thymol on cancer progression. For the colorectal cancer model, HCT116 xenograft was injected (i.p.) into BALB/c mice, which after 7–10 days (when the tumors grew to approximately 100 mm3) were treated with thymol (75 or 150 mg/kg every other day). A significant reduction in cancer growth, a greater number of necrotic lesions and a lower level of Ki-67 expression were observed, which reflects cell proliferation. As for the lung metastasis model, HCT116 cells were injected into the tail vein of each mouse and then received treatment with thymol (75 or 150 mg/kg every other day). After 6 weeks, they found that the average number of tumor nodules on the lung surface of the two treatment groups was significantly lower, revealing an anti-metastatic effect, probably due to the inhibition of the Wnt/β-catenin signaling pathway  of the studies recorded reported randomness when taking photos of selected areas regarding apoptotic activity assays. However, 97% of the studies applied the same conditions  for in vitro assays, ensuring a low risk of bias  record random allocation of the animals in the study  were classified as uncertain due to the lack of information in the articles. In contrast, 30%  reported that carvacrol decreased CDK4 protein expression in human tongue squamous cell carcinoma (Tca-8113 cells) (n = 10) of in vitro studies also represents a promising target of cancer treatment, since natural compounds can act as modulators, interrupting the cell cycle and, therefore, killing cancer cells (n = 38) of the studies reported induction of apoptosis after treatment with carvacrol or thymol in their in vitro studies and only 40% (n = 4) in in vivo studies.Neoplasia can be described as a disease of unchecked cell division, and its progression is related to abnormal activity of cell cycle regulators. The cell cycle consists of four discrete phases in which the cell increases in size and cellular content is duplicated (gap 1 or G1 phase), DNA is replicated , it prepares to divide , and then divides, creating two identical daughter cells . As a cell moves through each phase, stimulated by growth and transcription factors, it passes through several checkpoints, which ensure that mitosis occurs only when the cellular genome has been precisely replicated, avoiding mutations and generation of transformed cells . The cel3 cells)  and in b3 cells)  and only3 cells) . CDK inh3 cells) , such as3 cells)  and brea3 cells) . In addier cells . The uncer cells , that iser cells   and can interrupt DNA replication and cell division through cell cycle arrest (G1/S checkpoint), in order to restore genetic integrity, thereby preventing genetically transformed cell proliferation . When rein vitro studies (n = 17) and 10% of in vivo studies showed an increase in Bax levels after treatment with carvacrol or thymol. Additionally, 26% (n = 18) of in vitro studies reported negative regulation of the anti-apoptotic protein Bcl-2 (n = 23) of in vitro studies reported an increase of caspase activity, more specifically caspase-3  of the studies that tested carvacrol or thymol showed that they were able to increase in vitro JNK phosphorylation in colon cancer (n = 3) of the studies in vitro induced phosphorylation of p38 in hepatocarcinoma cells  of in vitro studies using thymol in human promyelocytic leukemia cell lines (n = 5) of in vitro studies that used carvacrol in colon cancer  (in vivo study (10%) reported a decrease in the levels of MMP-2 and MMP-9 in liver cancer after treatment with carvacrol (n = 6) were in vitro studies. It is important to note that carvacrol prevented metastasis in vivo, demonstrated by tumors less likely to spread through intrahepatic veins  . On the ression) , colorecression) , ovarianression) , and wasression) . In addiression)  serving ression) . The incression) . The decression) . Togetheression) . High MMression)  and brearession) , oral (Lression) , bladderression) , skin (Fression) , larynx ression)  cancer. arvacrol , the majic veins  and previc veins , and thy pathway . HoweverIn vitro, carvacrol and thymol increased the generation of reactive oxygen species in 24.63% (n = 17) of the studies, a fact that is also observed in chemotherapeutics such as doxorubicin , these are a group of molecules that contain reduced forms of oxygen with short life and that are more energetically reactive than molecular oxygen . The genorubicin , cisplatorubicin  and bleoorubicin , which an cancer  and hepan cancer  increasen cancer . Similarn cancer . Howevern cancer . In thisn cancer . FurtherPost-treatment with carvacrol also promoted an increase in the expression of the DAPK1 gene and a decrease in the enzyme PPP2R2A . DAPK1 iin vitro studies, we found that carvacrol proves to be more potent than thymol, and appears to have a greater cytotoxic effect for some cell lines, such as carcinomas (prostate and stomach)  . Thymol in vivo studies, on the other hand, the effect of carvacrol has been predominantly studied against hepatocarcinoma, with few studies for breast and colon cancer. In a way, it can be seen that the studies in this area do not seem to evolve toward a specific target, with low complementarity of the screenings carried out in vitro for the studies developed with experimental animals. This is a great barrier advances in this area of knowledge and helps to explain the discrepancy in the number of studies published in vitro (n = 69) and in vivo (n = 10) over the last two decades, as well as the absence of clinical trials. Thus, it is necessary to consider the studies already carried out for these compounds before conducting new primary studies on this topic, in order to evolve the research stages to the next level of drug development: in vivo studies and clinical trials.In 50 of 138.1 ± 8.7 μM of carvacrol   (Seven studies used a positive control to compare the antitumor effect of carvacrol  and/or t/100 µL) , 5-fluor/100 µL) , doxorub/100 µL)  (1.0 μg//100 µL)  and (2 μ/100 µL) , camptot/100 µL)  and in v2 mg/kg) , were thin vivo studies, particularly in respect of thymol, are needed. Further studies are required to help unravel and define the mechanisms of action of the two compounds against cancer cells, as well as studies that further explore their chemopreventive and anti-metastatic effects. It is of note that several studies suggested that signaling pathways (PI3K/AKT/mTOR and MAPKs) may be the main mechanism of action of these monoterpenoids, but more advanced studies are needed to elucidate this issue. There is also still a wide variation in the doses used, requiring the establishment of a consensus in respect of defining potentially effective and safe doses. In addition, further studies are also needed to clarify the effects of these two compounds on normal/healthy cells in vitro, and in appropriate in vivo models.In summary, we observed that more in vitro studies that carvacrol seems more potent than thymol, and was shown to have a greater cytotoxic effect for some cell lines. Moreover, further animal studies should be encouraged, as they have as yet made limited progress, and most of the current results are based on in vitro studies.The knowledge obtained through the reviewed studies provides strong evidence of the antitumor and antiproliferative activity promoted by carvacrol and thymol. However, there are still gaps regarding the standard or ideal dose, the exact mechanism of action and safety of the two compounds, revealing challenges for future studies. In addition, it was found through


TP53 mutated ultra high-risk neuroblastoma (NB) cell lines, SK-N-DZ, Kelly, SK-N-AS, SK-N-FI, and BE(2)-C. Following treatment, a dose-dependent accumulation in either S- and/or G2/M-phase was observed. This coincided with a heterogeneous increase of cell cycle checkpoint proteins, i.e., phos-ATM, phos-CHK1, phos-CHK2, Wee1, p21Cip1/Waf1, and p27Kip among the cell lines. Combination treatment with doxo and a small-molecule inhibitor of ATM showed a delay in regrowth in SK-N-DZ, of CHK1 in BE(2)-C, of Wee1 in SK-N-FI and BE(2)-C, and of p21 in Kelly and BE(2)-C. Further investigation revealed, in all tested cell lines, a subset of cells arrested in mitosis, indicating independence on the intra-S- and/or G2/M-checkpoints. Taken together, we mapped distinct cell cycle checkpoints in ultra high-risk NB cell lines and identified checkpoint dependent and independent druggable targets.Most chemotherapeutics target DNA integrity and thereby trigger tumour cell death through activation of DNA damage responses that are tightly coupled to the cell cycle. Disturbances in cell cycle regulation can therefore lead to treatment resistance. Here, a comprehensive analysis of cell cycle checkpoint activation following doxorubicin (doxo) treatment was performed using flow cytometry, immunofluorescence and live-cell imaging in a panel of  Neuroblastoma (NB) is the most common extracranial solid tumor in early childhood. It is a heterogenous neoplasia with clinical presentation ranging from spontaneous regression of metastatic disease to a rapidly progressive course . Fifty pSmall-molecule inhibitors of anaplastic lymphoma kinase (ALK), antibodies against the NB antigen GD2 and radiolabelled somatostatin analogues ,7 represCip1/Waf1 (p21), which in turn inhibit the catalytic activity of cyclin-dependent kinases [DNA damage leads to the activation of cell cycle checkpoint surveillance mechanisms causing cells to either arrest in order to ensure DNA repair  or to un kinases ,14. Ther kinases . Further kinases .TP53 mutations (mut) at diagnosis are rare, occurring in <2% of cases independent of tumour stage [TP53 mut, are identified in 49% of NB following relapse, indicating a mechanistic relevance in the development of therapy resistance [TP53 mut NB.While checkpoint proteins maintain genomic intregitry, dysregulation might be associated with therapy resistance . Among cur stage . Insteadsistance . In addisistance . TherefoTP53 mut NB cell lines, SK-N-DZ, Kelly, SK-N-AS, SK-N-FI, and BE(2)-C, to study cell cycle checkpoints and regrowth capacity following doxo treatment. The aim is to increase the understanding of conditions leading to treatment resistance and relapse, and to identify targetable vulnerabilities. Here, we utilized a panel of five ultra high-risk We demonstrate heterogeneity in cell cycle distribution among the tested cell lines following doxo treatment. Furthermore, a fraction of cells showed activation of checkpoint proteins and sensitivity to chemical inhibition of these proteins. However, a subset of cells were able to progress though the S-phase and arrest in mitosis, indicating independence of the intra-S- and G2/M-checkpoints. These results demonstrate heterogeneity in cell cycle regulation with the presence of both checkpoint-dependent and independent subpopulations of tumour cells in all tested cell lines. Identification of CT escape mechanisms does not only increase the knowledge on chemoresistance–but helps to identify specific vulnerabilities that can be targeted in conjunction with conventional therapy.TP53 mut cell lines, we measured DNA fragmentation (sub-G1) after 48 h of treatment with doxo serially diluted at 0.01 µM, 0.1 µM and 1 µM, using flow cytometry. Importantly, the highest dose tested, 1 µM, corresponds to clinically relevant plasma peak level in patients [The cytotoxic effect of doxo has been shown to be highly varying among p53 inactive NB cell lines, with IC50 values ranging from 0.06 to 0.2 µM . To invepatients ,26.p < 0.01 and from 0.5 to 7.4%, p < 0.05, respectively). However, SK-N-FI and BE(2)-C showed no change in sub-G1 fractions after any of the tested concentrations. Thus, the data indicated a high resistance to doxo-induced apoptosis, except in SK-N-DZ, Kelly, and SK-N-AS cell lines, where full resistance was not observed.As shown in Since doxo-induced DNA damage is suggested to trigger cell cycle arrest, we investigated cell cycle distribution following doxo exposure using flow cytometry. Forty-eight hours following treatment with the lowest tested dose (0.01 µM doxo), all cell lines maintained the bulk of cells in G0/G1 (46–66%), similar to the results after mock-treatment (51–66%).p < 0.0001), with a decreased G0/G1 population  Treatment with the intermediate dosing (0.1 µM doxo), resulted in accumulation of G2/M-phases in all cell lines as compared to mock treatment  presented, similar to 0.1 µM, decreased levels of diploid cells (G0/G1) in all cell lines. Notably, all cell lines, except BE(2)-C, presented S-phase accumulation, suggesting an intra-S-phase arrest. Especially two cell lines, Kelly and SK-N-AS, showed a high preference for S-phase accumulation reaching levels of approximately 70% compared to 30% when mock-treated To determine the long-term effect of doxo treatment, regrowth assays were conducted. Regrowth (confluency) was mapped during a period of 44 days following a pulse treatment of mock or doxo at 0.01 µM, 0.1 µM or 1 µM, for 48 h. Two cell lines, SK-N-DZ and Kelly, showed detachment from the plates once reaching high confluency following mock treatment, most likely due to crowding effects. Following the lowest tested dose (0.01 µM doxo) regrowth followed the same growth pattern as with mock treatment. Moreover, for all cell lines, except BE(2)-C, a dose dependency with a lag period of approximately 20 days following 0.1 µM doxo, and 24 days following 1 µM doxo was observed before an increase in confluency (regrowth). However, BE(2)-C cells showed regrowth at day 6 following 0.1 µM and at day 14 following 1 µM doxo, indicating a less prominent lag period compared to the other four cell lines TP53 mut cell line in their cell cycle response upon doxo treatment. This is especially seen after 1 µM doxo treatment, where SK-N-AS and Kelly accumulated in S-phase, SK-N-DZ and SK-N-FI in both S- and G2/M-phase and BE(2)-C in G2/M-phase. Furthermore, all cell lines showed regrowth capacity following all tested concentrations of doxo, albeit with different kinetics indicating the presence of resistant cells.The combined data illustrate heterogeneity among the Kip1 in single-cells using immunofluorescence. This was done to correlate doxo-induced alterations in the cell cycle distribution with intra-S- and/or G2/M-checkpoint activation.Next, the activation of checkpoint proteins was investigated following doxo treatment by determining the expression of ATM-phospho S1981 (pATM), CHK1-phospho S296 (pCHK1), CHK2-phospho T68 (pCHK2), Wee1, p21, and p27Kip1 positive cells following treatment Forty-eight hours following 1 µM doxo treatment a robust increase in the number of pATM positive cells was observed for all cell lines, except SK-N-AS. The fraction of positive cells increased for pCHK1 in Kelly and BE(2)-C, and for pCHK2, a downstream target of ATM, for all cell lines, except SK-N-AS Kip1 were generally low, the exception was pATM which was strongly increased for all cell lines, except SK-N-AS.These results illustrate that the doxo-induced frequencies of cells positive for cell cycle checkpoint proteins, pCHK1, pCHK2, Wee1, p21 and p27An increase in the number of positive cells for one or several cell cycle checkpoint proteins was shown after doxo treatment for all NB cell lines, indicating their role in doxo-induced cell cycle arrest.Kip1.To validate the functional role of cell cycle checkpoint proteins we employed pharmacological inhibitors. Small molecular inhibitors of ATMi (KU 60019), CHK1i (PF-477736), CHK1/2i (AZD 7762), Wee1i (Adavosertib), and p21i (UC2288) were tested in order to assess the effect of the inhibitors on NB viability and growth. A small molecular inhibitor was not commercially available for p27The ability of each drug was tested as a single-agent, using a 4-log dose–response cell viability assay with a 72-h time point. Inhibition of ATM or p21 did not give a decrease in cell viability post-treatment for any of the cell lines. However, all tested NB cell lines were sensitive to single-agent inhibitions of CHK1, CHK1/2, or Wee1, compared to mock treatment. Kelly and SK-N-AS showed the most sensitivity, followed by SK-N-DZ, SK-N-FI, and BE(2)-C Current developmental strategies for inhibitors of cell cycle checkpoint proteins have been focused on their chemosensitizing properties. Here, a sub-sequential assessment of the ability of the molecular inhibitors to potentiate the CT doxo in the panel of NB cell lines was made. Each inhibitor was combined at a concentration of 10 µM for ATMi and p21i or the corresponding IC50 value with 1 µM doxo and cellular growth (confluency) was measured for 44 days.p < 0.05–0.0001). Kelly showed delay in growth following combination treatment with doxo and p21i (p < 0.05) and BE(2)-C showed delay in growth following combination treatment with doxo and either CHK1i (p < 0.05–0.0001), Wee1i (p < 0.05–0.0001), or p21i (p < 0.05–0.0001). SK-N-FI showed an initial delay in growth  following combination treatment with doxo and Wee1i however, this was not observed from experimental day 8 and later. None of the combination treatments showed a delay in growth compared with a single doxo treatment in the SK-N-AS cell line A significant delay in growth was observed in the SK-N-DZ cell line after combination treatment with doxo and ATMi compared to single doxo treatment  was observed in all cell lines, except for Kelly. SK-N-FI and BE(2)-C displayed the highest fraction of cells in mitotic arrest following doxo treatment reaching 20% and 15% PH3 positive cells respectively A significant reduction of EdU positive cells was observed in all tested cell lines after exposure to 1 µM doxo, as expected from doxo-induced cell cycle arrest. The most dramatic reductions of EdU positive cells, indicating abundant arrest, were noted for Kelly and SK-N-FI . Despite statistically significant reductions, SK-N-DZ, SK-N-AS, and BE(2)-C, maintained relatively high levels, ranging 64–81%, of EdU positive cells following doxo treatment A small, but significant, increase in PH3+/EdU+ cells (between 4.7–9.3%) was detected in SK-N-DZ, SK-N-AS, SK-N-FI, and BE(2)-C, but not in Kelly, which showed a reduction to 0.8% PH3+/EdU+ cells Furthermore, all tested cell lines showed an increase in PH3+/EdU- cells Considering the longstanding clinical experience of enhanced anti-tumour effect from repeated CT we investigated cell cycle behaviour after repeated doxo treatment, were 1 µM doxo was added for 48 h followed by medium change and a second dose of 1 µM doxo in combination with EdU for another 48 h.In all tested cell lines, after adding doxo (1 + 1 µM), resilient cells could be shown to mostly reside in cellular arrest, as demonstrated by either increased expression of PH3 and/or failure to incorporate EdU Furthermore, a small fraction of PH3+/EdU+ cells (0.8–3.5%) were detected in all cell lines, except Kelly These data illustrate the presence of cells in mitotic arrest (PH3 positive) following both single and repeated doxo treatment in all tested cell lines. Moreover, the subset of cells arrested in mitosis can be further subdivided depending on their cell cycle behavior, indicated by cells either with or without progression through an S-phase prior to the arrest.To investigate the consequence of replicating cells during treatment, we pulse labelled with EdU during the single 1 µM doxo treatment and examined regrowth capacity (colonies). Three cell lines, Kelly, SK-N-FI, and BE(2)-C, were selected as they displayed a range of cell cycle behaviours following doxo treatment, with Kelly preferentially accumulating in S-phase and having a low fraction of EdU positive cells (20%), whereas SK-N-FI showed accumulation in both S- and G2/M-phase and had a low fraction of EdU positive cells (27%) and BE(2)-C showed accumulation in G2/M-phase and had a higher fraction of EdU positive cells (64%) p < 0.05, N = 3, Student’s t-test) but not compared to SK-N-FI . The consequence of these EdU positive cells remains to be investigated.In the Kelly cell line, EdU positive cells persisted in 2/45 newly formed colonies, in SK-N-FI EdU positive cells persisted in 12/36 newly formed colonies and in BE(2)-C EdU positive cells persisted in 37/40 newly formed colonies. Besides observing EdU positive cells in colonies, there was also a fraction of EdU positive cells not directly adjacent to colonies Taken together, the results indicate heterogeneity among the NB cell lines regarding the fraction of regrowth colonies with descendants from tumor cells completing replication during the doxo challenge.Based on our findings, and due to having the highest fraction of EdU positive colonies, the BE(2)-C cell line was selected for additional regrowth tracing using EdU in combination with repeated (1 + 1 µM) doxo treatment. BE(2)-C cells were pulse labelled with EdU during the second dose of doxo and colonies were investigated for the presence of EdU positive cells. However, no EdU positive cells were detected in 63/63 identified colonies. This suggests that cells responsible for regrowth following repeated doxo treatments have not progressed through an S-phase, at least during the second doxo dose. p < 0.001), similar to that found following a single doxo treatment. The fraction of pCHK1 or pCHK2 positive cells was reduced, reaching levels similar to the mock treatment. However, there was a persistent increase in the fraction of cells positive for Wee1 , and p21 , supporting the notion of sustained checkpoint activation. Furthermore, there was also an increase in the fraction of p27Kip1 expressing cells  indicating that a subset of cells was arrested for an extended period of time.In fact, investigating the fraction of BE(2)-C cells positive for cell cycle checkpoint proteins following repeated doxo These data illustrate that cell progression through the S-phase during single doxo treatment is able to regrow. However, this is not the case when doxo is given repeatedly. Instead, cells that are most likely already arrested following the first doxo treatment, are capable of regrowth.The challenge of ultra high-risk and relapse tumours remains a major hurdle in the successful treatment of NB. Cell cycle checkpoint activation following therapy has been linked to tumour cell survival and although exploiting these checkpoints has been a successful treatment strategy in several adult tumours , the potTP53 mut NB cell lines. The flow cytometry data showed, for all cell lines, high, but not full, resistance to doxo induced apoptosis Here, the cell cycle checkpoint activation following treatments with doxo was investigated in a panel of five in vitro ultra high-risk MYCN amplification contributes to driving proliferation but it also transcriptionally upregulates CHK1 [MYCN amplification, and two of these upregulated CHK1 following doxo treatment, whereas only BE(2)-C showed a reduction in growth following combination treatment with CHK1i. Moreover, BE(2)-C cells also responded with delay in growth following combination treatment with Wee1i and p21i, in agreement with an active G2/M-checkpoint. On the other hand, SK-N-DZ, which following doxo treatment displayed the highest fraction of pATM expressing cells (89%) among the tested cell lines, showed a delay in growth following combination treatment with ATMi, indicating its sensitivity. Synthetic lethality has been suggested between inhibitors of CHK1 and ATM, thus this combination might be an alternative in MYCN-amplified NB cells [Among common genetic alterations in NB, tes CHK1 . Three oNB cells .Moreover, the SK-N-AS cell line, which harbours the loss of one 11q copy rendering the cell line with impaired functional ATM and CHK1 due to haploinsufficiency , showed Moreover, when using a single-cell approach we were able to detect heterogeneity in the endogenous expression of cell cycle checkpoint proteins among the different NB cell lines. Both Kelly and SK-N-FI displayed a high fraction of p21 expressing cells  in the mock-treated samples, both also showed increased S-phase accumulation and few EdU positive cells  following doxo treatment. This cell cycle behaviour might be correlated to a p53 independent expression of p21 since endogenous expression of p21 has been linked to cell cycle behaviour with high expression leading to a senescence fate . FurtherOur results, even though they illustrate a heterogeneic response among NB cell lines summarized in Kip1 in the BE(2)-C cell line following repeated doxo treatment, indicating maintained arrest and/or quiescence.Furthermore, co-labeling with PH3 and EdU revealed that a subset of cells in mitotic arrest had progressed through the S-phase during the single doxo treatment. These cells seem to have bypassed the intra-S- and the G2/M-checkpoint, a phenomenon known as checkpoint adaptation . AlthougTP53 mutations are more frequently occurring. Furthermore, these in vitro findings may have an impact on the development of new treatment strategies through that doxo today is commonly included in first-line and salvage therapy protocols. The presence of a subset of resistant cells that are able to actively replicate during the treatment adds complexity when it comes to sensitizing tumour cells to DNA damaging drugs. One alternative treatment comes from a study where a CHK1/2 inhibitor revealed strong synergy with the antimetabolite gemcitabine, which targets tumour cell progression in S-phase . It is a2 with high humidity.BE(2)-C and Kelly were obtained from ATCC . SK-N-AS, SK-N-FI and SK-N-DZ were a kind gift from professor Per Kogner at Karolinska Institutet. STR analysis  confirmed the authenticity of the cell lines (data not shown). Basic characteristics of the NB cell lines are described in Doxorubicin (doxo)  was diluted in 1X PBS and administered to the culture medium. Doxo was added for 48 h at concentrations of 0.01 µM, 0.1 µM or 1 µM, to cells in logarithmic growth. For the repeated (1 + 1 µM) doxo treatment 1 µM doxo was added for 48 h followed by medium change and a second dose of 1 µM doxo for another 48 h. For mock treatment, 1X PBS was added to the culture medium.® . The data were analysed and quantified by using NovoExpressTM software . Each condition was run in triplicate. Statistics were performed on the proportion of cells within a specific gate.All five NB cell lines, in logarithmic growth, were treated with mock (1X PBS), or 0.01 µM, 0.1 µM, 1 µM doxo. Forty-eight hours post-treatment the cells were fixated with 70% EtOH before incubation with Hoechst 33343  according to the manufacturer’s protocol and run on a FACS Novocyte3 in 1X PBS overnight at +4 °C. Following washing, cells were incubated with 2’ab diluted in TNB buffer, for 2 h at room temperature. The cells were washed and mounted with Prolong Gold antifade with DAPI  to visualize the nuclei. Stained cells were analysed using a Metafer® Slide Scanning Platform . A list of the antibodies and kits used is presented in Following fixation for 15 min in ice-cold freshly prepared 4% paraformaldehyde , cells were incubated in TNB Buffer: 0.5g of blocking reagent  to 100 mL TBS buffer (Tris/NaCl pH 7.4) for 30 min at room temperature. Cells were then incubated with the 1’ab diluted in 0.3% Triton X-100, 0.1% NaN® AQueous One Solution Assay  was added per 100 μL media. The plates were then incubated for 3 h and analysed in the microplate reader FLUOstar Omega . The absorbance at 490 nm and 690 nm was measured to assess the cell viability post-treatment. Each treatment was completed in triplicates. DMSO was used as a negative control.Cells, in logarithmic growth, were treated with a titration series of 10 to 0.01 μM ATM inhibitor (ATMi) , CHK1 inhibitor (CHK1i) , CHK1/2 inhibitor (CHK1/2i) , Wee1 (Wee1i) , or p21 (p21i)  diluted in dimethyl sulfoxide (DMSO). After 72 h a 3--5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay was performed where 20 μL CellTiter 96® Cell Analysis System  every 48 h for 44 days. Fresh medium was added every 4 days. The proliferation was determined by measuring the cell confluency. Each treatment was done in five replicates and the data are presented as mean ± Standard Error of the Mean (SEM).Each cell line, in logarithmic growth, was treated at a concentration of 10 µM for ATMi and p21i or the corresponding IC50 value for each drug. The appropriate drugs either as a single agent ATMi, CHK1i, CHK1/2i, Wee1i, p21i, or as a combination with 1 µM doxo were added for 48 h followed by medium change. An amount of 1 μM doxo, 1X PBS (mock), or DMSO (mock) was used as negative controls. The plates were scanned by an IncuCyte S3 LiveThe thymidine analogue 5-Ethynyl-2′-deoxyuridine (EdU)  was added at a concentration of 10 µM 48 h before fixation. EdU labelling was performed according to the manufacturer’s protocol.® Slide Scanning Platform. Colony formation, with a threshold of >10 cells in proximity set for the definition of a colony, was assayed by ocular inspections.Kelly, SK-N-FI, and BE(2)-C cells, in logarithmic growth, were treated with 1 µM doxo in combination with 10 µM EdU for 48 h followed by medium change. Cells were allowed to grow, with fresh medium added every 4 days, until colonies were visible. The cells were fixed for 15 min in ice-cold freshly prepared 4% paraformaldehyde, EdU labelling was performed according to the manufacturer’s protocol. Cell density was visualised by DAPI staining and the number of EdU positive cells was deciphered as cells per scanned image (10× image) using a Metafert-test and two-way analysis of variance (ANOVA). Dunnett or Bonferroni post-hoc test was applied to adjust for multiple testing as indicated in the text. The software GraphPad Prism 7.03  was used for testing the normal distribution and generation of graphs. The immunofluorescence data were analysed in RStudio  and the percentage of positive cells followed by the mean ± Standard Deviation (SD) were calculated for each combination.Data were analysed using Student’s


The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses. Importantly, the genomic and transcriptomic profiles of all cancer cell lines employed in GDSC were extensively characterized as a part of the COSMIC cell line project . These resources have the potential to link anti-cancer drug sensitivity to detailed genomic information and facilitate the discovery of relevant molecular biomarkers when coupled with powerful analytical tools to cope with the high-dimensionality and complexity of these datasets.Recent significant advances in investigating drug sensitivity have been driven by advances in high-throughput technologies that can generate large amounts of biological data at low cost. Pioneers of such datasets include the NCI-60 database , GenomicA variety of approaches have been proposed for investigating drug sensitivity in cancer cell lines. One of the first models was developed by Staunton et al., which employed a weighted voting classification model for anti-cancer drug sensitivity based on NCI-60 gene-expression data . Recent http://www.hprd.org, accessed on 9 December 2021). [In the present study, we demonstrate that cell-line and drug clustering prior to machine learning modeling can significantly improve the accuracy of cell-line drug-sensitivity prediction. We first represented genomic profiles of cell lines and chemical features of drugs in the form of separate feature networks. Several network-based papers for drug-sensitivity prediction have been previously published. For instance, Wang et al. proposed a heterogeneous network model of cell lines, drugs, and targets ,18. Zhanr 2021). . This rer 2021). ,24,25. Tr 2021). ,26. It wIn summary, the heterogeneity of pan-cancer cell lines and structurally diverse drugs in large-scale pharmacogenomic databases makes prediction of drug sensitivity challenging. In this work, we propose a novel computation method for clustering cell lines and drugs in an unsupervised way, followed by a supervised prediction-modeling of drug responses. Our results demonstrate that modeling on homogeneous data significantly improves the prediction accuracy. Moreover, clustering increases the focus for understanding potential biomarkers and mechanisms of drug sensitivity.Hierarchical clustering of the cell lines resulted in six clusters with the highest average silhouette score. The numbers of cell lines in each cluster were 149, 113, 130, 174, 208, and 141, respectively, labeled clusters 1 through 6. 2) values were computed for the predicted and observed log(IC50) values. 2 of the predicted and observed log(IC50) values in the 30 paired clusters of cell lines and drugs.For each of the 30 paired clusters (six clusters for cell lines and five clusters for drugs), random forest regression models were trained and validated, using 635 genes and 165 cheminformatic features. A three-fold cross-validation approach was employed, such that in each cross validation, 2/3 of the data were used for training, and 1/3 of the data were used for validation of the model. After performing the three-fold cross validation in each paired cluster, correlation (R) and coefficient of determination  obtained via random forest on the whole dataset  using a three-fold cross validation scheme. Further, R and R2 in the best and worst paired clusters with respect to prediction accuracies were (R = 0.96 and R2 = 0.93) and (R = 0.79 and R2 = 0.62), respectively  values for all the 30 clusters and then calculated R and R2 . For comectively . The celectively . In addiAfter applying the modeling pipeline, we investigated the prediction accuracy for individual cell lines and drugs. t-test, differentially expressed genes were further identified. For example, we investigated a paired cluster: cell-line cluster 4 and drug cluster 1, which is one of the highest performing cluster pairs. Initially, the top 200 genes were selected based on the importance score in random forest modeling, and 70 out of the 200 genes met a Bonferroni corrected p-value < 0.05. For these 70 genes, gene ontology enrichment analysis was performed using MetaCore software to discover significant biological correlates. −20. The hypergeometric distribution was used to compute unadjusted p-values. For further insight, a protein–protein interaction (PPI) network with direct connections among the set of 70 gene products was constructed as shown in To identify significant genes, we employed a two-step approach: (1) the importance score for each gene was derived based on its contribution to the random forest accuracy  and (2) In this study, we developed a network-based method for predicting the drug sensitivity of pan-cancer cell lines in the GDSC database. Several studies have proposed network-based methods for drug response prediction on single omics data ,21, wherBCL6 gene, was initially discovered as an oncogene in B-cell lymphomas, driving a malignant phenotype via the repression of DNA damage and proliferation checkpoints [BCL6 has also been implicated in an expanding spectrum of solid and hematologic tumors, including leukemia, breast cancer, and NSCLC [BCL6 expression has been implicated in the modulation of apoptotic responses of malignant cells to chemotherapeutic reagents, suggesting the development of BCL6 inhibitors as a potential therapeutic option [In the application of machine-learning techniques to biology, interpretability is very important. Clustered cell lines and drugs, and the resulting random forest models in individual paired clusters, can be deeply interrogated to gain further insights into the determinants of cell-line drug effectiveness. We have demonstrated that post-modeling analysis using bioinformatics techniques enables the identification of plausible biological correlates. For example, we investigated the pair of cell-line cluster 4 and drug cluster 1 see A. Cell-lckpoints . BCL6 hand NSCLC . Additioc option .A limitation of this study is that using IC50 as the measure of drug sensitivity may be biased, due to different growth rates of cancer cells growing in culture . MoreoveWe used the anti-cancer drug-response data from the GDSC database. GDSC is a publicly available large-scale pharmacogenomic database that includes drug-screening data for more than a thousand human pan-cancer cell lines. The dataset consists of 265 compounds, including cytotoxic chemotherapeutics as well as targeted therapeutics. GDSC drug responses are given as log-transformed IC50 values  and the area under the curve (AUC) for a fitted model. We used log(IC50) as the degree of drug responses. Genomic mRNA expression profiles (Affymetrix Human Genome U219 Array) of the cell lines within GDSC were obtained from the CCLP database. A protein–protein interaction (PPI) network was obtained from the HPRD database. Cell lines with missing data for more than 80% of the drugs were removed, leaving 915 cell lines .http://oncokb.org/, accessed on 9 December 2021) that consists of 1019 genes. Among those 1019 genes, 796 genes were common to both CCLP and HPRD. In the HPRD network with those 796 genes, the largest connected network component consisted of 635 genes, which we focused on in this study  and downloaded the SMILES (Simplified Molecular Input Line Entry Specification) string of 241 drugs, for which the PubChem ID was provided in the GDSC database. Two-hundred drugs had response values for more than half of the cell lines, resulting in 183,000 cell-line drug pairs . The descriptors included functional groups, fragment counts, and estimated chemical properties as well as simple atomic descriptors. The overview of data analysis is shown in The CCLP and HPRD datasets had 8483 genes in common. Even though our method is applicable to large scale data, in this study, we wanted to focus on genes of known relevance in cancer. Thus, we used a smaller set of genes from the OncoKB (Precision Oncology Knowledge Base) database (gej) . By normIf we let this stochastic signal-passing process proceed from an initial state based on gene expressions, in repeated steps according to these probabilities , it can be shown that the system reaches a stationary distribution, implying that the system is invariant under a right multiplication by e., πp=π . SolvingWe initially extracted molecular descriptors of the 200 drugs from Dragon software. The following descriptors were removed: descriptors that are constant or near constant and descriptors with missing values, yielding 1500 features. We further removed many highly correlated features via unsupervised clustering, using the Spearman’s correlation between the features. We selected a representative feature from each cluster, which had the highest average correlation to all other intra-cluster features. This further reduced the number of features to 500. We then constructed a network of these cheminformatic features via the graphical LASSO, which suppressed unimportant feature connections to build a sparse network (see the W1 Wasserstein distance (EMD) metric  metric . OMT is The resultant pair-wise Wasserstein distance matrix was input to a hierarchical agglomerative clustering method, resulting in a set of clusters of cell lines or drugs. To find the optimal number of clusters, the silhouette score was used . The silmtry parameter was set to p/3, where p is the number of features [Predictive modeling of drug responses was conducted in each cell-line drug cluster, employing random forest regression on the associated gene-expression profiles and cheminformatic features. We chose the number of decision trees to be 100. For all other parameters, the default settings were used: the minimum number of samples in the terminal nodes was set to 5, and the features . Figure features , which ifeatures . We asseThis study proposed a novel network-based clustering method based on OMT theory for drug response prediction. Clustering was performed for cell lines and drugs using gene-expression profiles and cheminformatic drug features, respectively, represented in the form of data networks. Random forest modeling was then performed for each cell-line drug cluster pair. Prediction modeling on clustered homogeneous data is likely to improve the prediction accuracy for drug sensitivity, as well as enhance the biological interpretability compared to modeling using all the data together. We plan to apply the proposed approach to several biomedical problems with multi-modal data, including genomics and medical imaging.


In vivo and in vitro gene functional studies showed that LPCAT1 contributed to the pathogenesis of gefitinib resistance in LUAD, where an LPCAT1-EGFR positive feedback loop formed and then regulated its downstream signaling molecules of the EGFR/PI3K/AKT signaling pathway. The results provided novel insights into the acquired resistance mechanism of EGFR TKI from the perspective of phospholipid metabolism. These findings suggest LPCAT1 may serve as a potential therapeutic target for patients with EGFR TKI-resistant NSCLC.Currently, the mechanisms of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) resistance have been a focus of clinical research. Despite that most of the mechanisms of acquired EGFR TKI resistance have been revealed, about 30% of non-small-cell lung cancer (NSCLC) cases have not been fully elucidated, especially for lung adenocarcinoma (LUAD). Recently, LPCAT1, an important enzyme of phospholipid metabolism, has been found to bridge the gap between the oncogene and metabolic reprogramming. In NSCLC, LPCAT1 has been shown to participate in progression and metastasis. However, little is known about the role of LPCAT1 in acquired EGFR TKI resistance. In this study, elevated LPCAT1 expressions were observed in an EGFR TKI-resistant cell line (PC-9R) relative to a corresponding EGFR TKI-sensitive cell line (PC-9).  Lung cancer is the leading cause of cancer mortality in China, with 714,699 deaths from the disease recorded in 2020 Besides the classical EGFR-T790M mutation and transformation to a small-cell lung cancer (SCLC) phenotype, aberrant activations of bypass signaling pathways responsible for EGFR TKI resistance have been identified, such as c-MET amplification and abnormal activation of the PI3K/AKT pathway As is well known, metabolic reprogramming is becoming a hotspot in the field of cancer research. Cancer cells can alter their capability to metabolize carbohydrates, lipids, and proteins in an effort to strive for cell proliferation and survival, including lung cancer cells in vitro and in vivo.In the present report, three cell lines  representing normal human embryonic lung cells, gefitinib-sensitive cells harboring an EGFR exon 19 deletion mutation, and gefitinib-resistant cells, respectively, were selected. Gefitinib was chosen as the research agent due to the wide usage of EGFR TKI in China. We first explored the baseline level of LPCAT1 among three cell lines, and our data revealed that LPCAT1 expression was significantly higher in PC-9R cells than in PC-9 or MRC-5 cells. Then, LPCAT1 knockout was performed and found to be strongly associated with restoring the sensitivity to gefitinib in the PC-9R cell line. Finally, the PI3K/AKT pathway was confirmed to positively participate in the development of LPCAT1-mediated gefitinib resistance both PC-9 and PC-9R cell lines were purchased from Beinabiology Co., Ltd. , and the MRC-5 cell line was purchased from Procell Life Science & Technology Co., Ltd. . Five-week-old male BALB/c nude mice were obtained from Hunan SJA Laboratory Animal Co., Ltd. . Gefitinib (CAS: 184475-35-2) and the inhibitor of PI3K/AKT pathway, LY294002 (CAS: 154447-36-6), were purchased from Shanghai Yuanye Bio-Technology Co., Ltd.  and MedChemExpress , respectively.2 and 95% atmosphere, 37 °C). The medium was changed every three days and cells were passaged with 0.25% trypsin for digestion when the cell density reached 80% to 90% confluence.PC-9, PC-9R, and MRC-5 cell lines were cultured in Roswell Park Memorial Institute 1640 medium  containing 10% fetal bovine serum and 1% double antibiotics  and then maintained in standard conditions  according to the instruction manual. PC-9 and PC-9R cell lines stably expressing LPCAT1-specific short hairpin RNA (shRNA) or scrambled control-shRNA were constructed using a lentiviral shRNA technique from GeneChem Company . The LPCAT1 shRNA was designed based on the small interfering RNA (siRNA) sequence (GenBank identification no.: 79888).The primer used to obtain the overexpression sequence of LPCAT1 is as follows: F: 5'-CCCTGTGACCATGACGATGT-3', R: 5'-TGAATGCACCAGGTTTGAAGGT-3'. Meanwhile, the primer used for the overexpression negative control (NC) of LPCAT1 is as follows: F: 5'-UUCUCCGAACGUGUCACGUTT-3'; R: 5'-ACGUGACACGUUCGGAGAATT-3.Separately, the employed LPCAT1 shRNA sequences are as follows: (1) F: 5'-GGACAG AUACUCAGAAAGATT-3', R: 5'-UCUUUCUGAGUAUCUGUCCTT-3'; (2) 5'-CCAUUG ACCAAGAGGAGAATT-3', R: 5'-UUCUCCUCUUGGUCAAUGGTT-3'; and (3) F: 5'-CUGAGGAGGAGAAGAGGAATT-3', R: 5'-UUCCUCUUCUCCUCCUCAGTT-3'. Finally, the sequence of carboxyfluorescein (FAM)-labeled NC siRNA is as follows: F: 5'-UUCUCCGAACGUGUCACGUTT-3'; R: 5'-ACGUGACACGUUCGGAGAATT-3'. All primer pairs were synthesized by Beyotime .4 cells/well, and then treated with different concentrations of gefitinib  for 48 hours. The cells were subsequently incubated with a 10% solution of CCK-8 at 37 °C for two hours. Finally, the absorbance at 450 nm was measured using a multifunctional enzyme-linked analyzer . Each experiment was repeated at least three times.A cell counting kit 8 (CCK-8) assay was conducted using a CCK-8 kit from KeyGEN Biotechnology . The PC-9 and PC-9R cell lines were plated in 96-well plates at 1.0 × 106 cells from various groups were collected, washed with phosphate-buffered saline (PBS) and suspended in 500 μL of binding buffer. Then, the cells were stained with an annexin V-fluorescein isothiocyanate/propidium iodide apoptosis detection kit  at room temperature for 10 minutes according to the manufacturer's instructions. Cell apoptosis was analyzed by flow cytometry.A total of 1 × 10LPCAT1 gene. Total RNA was extracted using the Ultrapure RNA kit ; then, reverse transcription was conducted using the HiScript® II Q RT SuperMix for qPCR . qRT-PCR was performed with 2× SYBR Green PCR Master Mix  on a real-time fluorescent quantitative PCR meter . The β-actin gene was used as an internal reference gene, and the data were analyzed using the 2-ΔΔct method. PCR primers were ordered from Beyotime . All experiments were conducted according to the manufacturer's protocols. Experiments were performed at least in triplicate.Quantitative real-time transcription-PCR (qRT-PCR) was used to detect the messenger RNA (mRNA) expression of the The PCR primer sequences used are as follows: LPCAT1 (F) 5'-CCCTGTGACCATGACGATGT-3, (R) 5'-TGAATGCACCAGGTTTGAAGGT-3' and β-actin (F) 5'-TGGCACCCAGCACAATGAA-3', (R) 5'-CTAAGTCATAGTCCGCCTAGAAGCA-3'.The cells collected from various groups were lysed in radioimmunoprecipitation assay lysis buffer . Subsequently, the cells were centrifuged for 10 minutes at 12,000 rpm. Then, the supernatants were taken, and total protein was determined via bicinchoninic acid assay (CoWin Biosciences). An equivalent amount of protein was separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred to polyvinylidene fluoride membranes . Then, the membranes were blocked with 5% bovine serum albumin (Solarbio) and subsequently probed using respective primary antibodies overnight at 4 °C. Membranes were then incubated with the secondary antibody conjugated with horseradish peroxidase  for two hours at room temperature. Finally, the target proteins were measured by enhanced chemiluminescence  and Chemi Doc™ XRS+ System (Bio-Rad Laboratories). Glyceraldehyde 3-phosphate dehydrogenase (GADPH)  served as the internal control. The information of primary antibodies were listed as followed: anti-LPCAT1 , anti-EGFR , anti-p-EGFR, anti-PI3K , p-AKT (S473) , t-AKT, and horseradish peroxidase (HRP) conjugated anti-rabbit secondary antibody .6 cells in 200 μL of serum-free PBS, then subcutaneously inoculating them into the right rear flanks of five-week-old female BALB/c nude mice. Gefitinib was prepared by dissolving a 250-mg clinical-grade tablet in 150 μL of sterile water of 0.05% Tween-80 . When the volume of the transplantation tumor reached 100 mm3 , the mice were treated with different drugs as indicated below (n = 3 per group). Xenograft tumor volumes were measured every three days when they were palpable.PC-9R cells were prepared by suspending 1 × 10Mice were treated with (1) gefitinib (10 mg/kg) once daily via oral gavage, (2) LY294002 (2.5 mg/kg) every three days via intraperitoneal injection, (3) gefitinib and LY294002 co-treatment at the same doses, or (4) an equal volume of diluents as control therapy. The mice were humanely euthanized using conventional cervical dislocation after 12 days of treatment. Tumors were excised and dissected for further pathological evaluation. Hematoxylin and eosin staining was performed to evaluate the pathological and morphological changes in the xenograft tumor tissues. Ethical approval of this study was obtained from the Ethics Committee of the Jiujiang University Affiliated Hospital.p < 0.05.The data are presented as mean ± standard deviation values. Intergroup differences between two groups were assessed by the Student's t-test. Differences among multiple groups were assessed by one-way analysis of variance. The comparisons between the groups after the analysis of variance test were confirmed with the Student-Newman-Keuls method. In the present study, the experiments conducted were performed independently at least in triplicate. Significant differences were defined as when Figure 1, the mRNA expression of LPCAT1 in the PC-9R cell line was significantly higher than that in the PC-9 and MRC-5 cell lines. The PC-9 cell line displayed an enhanced LPCAT1 mRNA level relative to that of the MRC-5 cell line . Similarly, the ranking order of LPCAT1 protein levels in descending order of expression was as follows: PC-9R, PC-9, and MRC-5 cell lines .Normal human embryonic lung cells (MRC-5 cells) were used as the control cell line. As shown in Figures  and 2D, the IC50 values of gefitinib in the PC-9 and PC-9R cell lines were 12,653 nM (12.653 μM) and 53,480 nM (53.48 μM), respectively, which further verified the resistance of PC-9R cells to gefitinib relative to that of PC-9 cells. Considering experimental efficiency, we chose the concentrations of 12.653 μM and 53.48 μM as the optimal ones for the following experiments.To explore the sensitivity to gefitinib in PC-9 and PC-9R cell lines, we conducted a cell viability analysis by CCK-8 method. As evidenced in Figure 3, the levels of LPCAT1 mRNA and protein were robustly elevated in groups treated with the LPCAT1 overexpression vector compared to in groups treated with the empty vector and NC among either PC-9 or PC-9R cell lines by qRT-PCR and western blotting . Furthermore, we designed three siRNAs to interfere with LPCAT1 expression in PC-9 and PC-9R cell lines and, as shown in Figure 3, either the PC-9 or PC-9R cell line in siRNAs groups exhibited lower levels of LPCAT1 mRNA and protein than did the empty vector and NC groups. These findings substantiated that the transfection was successful.In this study, we first transfected PC-9 and PC-9R cell lines with the LPCAT1 overexpression vector and empty vector, respectively. As presented in Figures ) (and hereafter refer to this as the “interfere” group).Owing to the highest interference efficiency being present in the PC-9R cell line, we chose siRNA2-LPCAT1 for the subsequent experiments , which strongly indicated that LPCAT1 knockdown restored gefitinib sensitivity and inhibited cell proliferation in the PC-9R cell line.As illustrated in Figures ). However, the interference of LPCAT1 siRNA failed to induce cell apoptosis in PC-9R cell lines. These results suggest that other mechanisms  are involved in the proliferative inhibition of PC-9R cells transfected with LPCAT1 siRNA.In the PC-9 cell line, the cell apoptosis rate was significantly higher in the gefitinib/interfere group compared to in the other three groups  protein expression was observed. As depicted in Figure , compared to in the gefitinib group, gefitinib and LY294002 co-treatment significantly decreased the levels of PI3K and p-AKT proteins. In comparison with gefitinib monotherapy, LPCAT1 overexpression distinctly elevated the expression levels of EGFR, p-EGFR, PI3K, and p-AKT proteins. Furthermore, the addition of LY294002 obviously decreased the levels of EGFR, p-EGFR, PI3K, and p-AKT proteins compared to the group of LPCAT1 overexpression plus gefitinib treatment among PC-9R cells. Similarly, there was no significant change in t-AKT expression. These findings above indicated that LPCAT1 was the important upstream molecule that regulated the EGFR/PI3K/AKT signaling pathway in the PC-9R cell line.As shown in As the preceding results showed that LPCAT1-mediated gefitinib resistance of PC-9R cells is closely associated with the EGFR/PI3K/AKT pathway, we next verified this result by establishing a tumor-bearing mouse model.Figures 7, compared to in the control group, the volumes of xenograft tumors were slightly decreased in the groups of gefitinib and interfere NC plus gefitinib . However, in comparison with gefitinib monotherapy, the treatment of gefitinib plus interfere group markedly diminished the volume of xenograft tumors . Similarly, compared with interfere NC plus gefitinib, the addition of LY294002 substantially triggered tumor shrinkage . In particular, the volume of the xenograft tumor dramatically shrunk in the combination group of interfere, LY294002, and gefitinib compared to that in the other five groups .As described in Figure 8, compared with the control group, gefitinib treatment led to slight cell necrosis and smaller cell nuclei . In comparison with in the gefitinib group, cells in the groups of either interfere plus gefitinib or interfere NC, LY294002 plus gefitinib presented tumor cells with markedly smaller nuclei, greater cell necrosis, and larger gaps between cells Figures B, 8D-8E. Of note, the combination treatment of interfere, LY294002, and gefitinib resulted in massive tumor-cell necrosis, nuclei pyknosis, and lower intratumoral vascularity compared to the other five groups .Furthermore, hematoxylin and eosin staining was conducted to elucidate the pathological and morphological changes in the xenograft tumor tissues. As illustrated in EGFR TKI, especially gefitinib, has widely been used in advanced NSCLC patients with EGFR-sensitive mutations and has dramatically improved the clinical outcome of NSCLC. However, nearly all NSCLC patients will eventually progress to an EGFR TKI-resistant status. The mechanism of EGFR TKI acquired resistance is very complex, including both T790M-positive and T790M-negative resistance, each of which account for about 50% of cases In recent years, metabolic reprogramming, including lipid metabolisms, has been confirmed to contribute to tumor development and metastasis and drug resistance in vivoIn normal lung tissue, LPCAT1 has been found to be highly expressed and to regulate surfactant lipid generation in vitro revealed that LPCAT1 expression was markedly higher in the PC-9 cell line (EGFR 19 del E746_A750) than in the A459 cell line  and H460 cell line (EGFR wild-type) A study in vivo study was performed to observe the effect of LPCAT1 knockout in PC-9R cell tumor-bearing mice. We found that, in the xenograft model, LPCAT1 silencing resulted in tumor shrinkage and restored gefitinib sensitivity in the PC-9R cell line. Further in vivo experiments demonstrated that LY294002 substantially circumvented resistance to gefitinib and promoted gefitinib-induced shrinkage of xenograft tumors on the basis of LPCAT1 knockdown. Of coincidence, the study by Zhou et al. reported that LY294002 exerted the activity of sensitizing EGFR wild-type NSCLC cell lines to erlotinib chemotherapy According to the foregoing literature, the LPCAT1-EGFR positive feedback loop contributes to the tumorigenesis of glioma cells In summary, our study reported that enhanced LPCAT1 was observed in the EGFR TKI-resistant cell line compared to the corresponding EGFR TKI-sensitive cell line. A functional investigation found that LPCAT1 contributed to the pathogenesis of gefitinib resistance in LUAD, the mechanism of which may be at least partly attributed to positive regulation of EGFR/PI3K/AKT signaling cascade. The present study also provided a novel insight into the mechanism underlying acquired resistance to EGFR TKIs from the perspective of phospholipid metabolism. Since high LPCAT1 expression exists in a variety of cancers, LPCAT1 may serve as a potential target of targeted therapy.Of course, there are shortcomings in this study. One is that only a few single cell types were studied; the other is that the association of LPCAT1 with major resistant mutations  was not explored. These questions will need to be further explored in our future research.