Annona crassiflora, Miconia albicans, Miconia chamissois, Stryphnodendron adstringens, Tapirira guianensis, Xylopia aromatica, and Achyrocline alata showed half-maximal inhibitory concentration (IC50) values < 30 μg/mL for at least one CCC. A. crassiflora and S. adstringens extracts were selective for CCC. Mass spectrometry (Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometer (ESI FT-ICR MS)) of A. crassiflora identified fatty acids and flavonols as secondary compounds. One of the A. crassiflora fractions, 7C24 (from chloroform partition), increased H2AX phosphorylation (suggesting DNA damage), PARP cleavage, and cell cycle arrest in CCC. Kaempferol-3-O-rhamnoside and oleic acid were bioactive molecules identified in 7C24 fraction. These findings emphasize the importance of investigating bioactive molecules from natural sources for developing new anti-cancer drugs.Cervical cancer is the third most common in Brazilian women. The chemotherapy used for the treatment of this disease can cause many side effects; then, to overcome this problem, new treatment options are necessary. Natural compounds represent one of the most promising sources for the development of new drugs. In this study, 13 different species of 6 families from the Brazilian Cerrado vegetation biome were screened against human cervical cancer cell lines (CCC). Some of these species were also evaluated in one normal keratinocyte cell line (HaCaT). The effect of crude extracts on cell viability was evaluated by a colorimetric method (MTS assay). Extracts from  Among several cancer types with a higher incidence in Brazil, cancer of the uterine cervix is the third most common in women, excluding non-melanoma skin cancer (NMSC) . In 2018Natural compounds represent one of the most promising sources for new drug discovery. From 1940 to 2014, 175 small molecules were approved for cancer treatment, including 131 (75%) of non-synthetic origin and 85 (49%) being natural products or their derivatives . The cheThese metabolites also have beneficial effects on human health. For this reason, some Cerrado plants are traditionally used to treat issues such as bacterial, fungal, or parasitic infections, gastrointestinal disorders, arthritis, respiratory disorders, wound healing, and cancer-related conditions ,16,17.Annona crassiflora was selected for the partitioning process, using four different solvents with increasing polarity: alcohol, hexane, chloroform, and ethyl acetate. The chloroform partition of A. crassiflora was fractionated, and the 7C24 fraction was evaluated for proteins related to DNA damage, apoptosis, and cell cycle regulation.We evaluated the in vitro effect of different Brazilian Cerrado plant species against cervical cancer, which has a high incidence rate in Brazil. For this, 13 crude extracts from the leaves of plants from 6 families  were screened in human cervical cancer cell (CCC) lines. A. crassiflora with biological activity and revealed cytotoxic potential in extracts from Cerrado vegetation that can contribute to the development of new anti-cancer drugs.Our findings identified compounds in 50) values ranging from 8.24 to 169.50 μg/mL, when all extracts were considered for all cancer cell types in the main panel . In this study, four species showed IC50 values < 30 μg/mL in at least one of the lines in the central panel: Annona crassiflora (7), Miconia albicans (18), Miconia chamissois (19), and Stryphnodendron adstringens (21-I). Three species showed lower IC50 values in the cancer cell lines listed in Tapirira guianensis (1), Xylopia aromatica (3), and Achyrocline alata (10).The National Cancer Institute’s screening program indicates that crude extracts yielding IC50 values in all main panel cell lines were Annona crassiflora (7) (which ranged from 8.24 to 42.44 μg/mL) and Stryphnodendron adstringens (21-I) (from 21.12 to 47.01 μg/mL).The species that presented the lowest IC50 value) of a normal human skin keratinocyte cell line, HaCaT. HaCat cell line is commonly used to represent in vitro human papillomavirus (HPV) infection-induced carcinogenesis [A. crassiflora and S. adstringens in CaSki and HeLa cell lines (A. crassiflora) to proceed with the partitioning process).We next evaluated the selectivity index (SI) for these species based on the response  [The ICin panel . It was . (2018) .A. crassiflora chloroform partition indicated the presence of fatty acids and flavonols (A. crassiflora chloroform partition are shown in Electrospray Ionization Fourier Transform Ion Cyclotron Resonance (ESI FT-ICR) mass spectrometry was performed to characterize the constituents of the natural compounds. The profile of the lavonols . The conPartition 7C was identified as one of the most cytotoxic for CCCs and was chosen for additional viability assays and evaluation to identify the possible mechanism of action.50 values lower than 30 µg/mL in HeLa and C4-I cell lines; 7C25 in C4-I; 7C28 in CaSki, HeLa and C4-I; 7C57 in C4-I; and 7C60 in HeLa and C4-I  and cisplatin . After 24 h, Western blotting showed histone H2AX phosphorylation and PARP cleavage in SiHa, and an increase in these events in HeLa cells, suggesting 7C24 modulated the DNA damage and cell death pathways.Among the fractions that reduced viability in all cell lines, 7C24 had one of the lowest ICThe 7C24 fraction induced a slight increase in p21 expression in SiHa cells and a decrease in HeLa cells. Cisplatin induced a slight increase in PARP cleavage and H2AX activity and decreased p21 expression in both cell lines a. ChangeESI FT-ICR mass spectrometry showed the chloroform fraction of 7C24 contained oleic acid and kaempferol-3-O-rhamnoside .50 values lower than 30 µg/mL after treatment with Annona crassiflora, Miconia albicans, Miconia chamissois, Stryphnodendron adstringens, Tapirira guianensis, Xylopia aromatica and Achyrocline alata. A. crassiflora and S. adstringens were tested and showed selectivity for cancer cells. The bioassay-guided fractionation of A. crassiflora revealed the effect of one fraction, 7C24, on pathways in DNA damage and apoptosis, and caused cell cycle arrest.Brazilian flora represents a vast resource for the discovery of substances with biological properties, and studies have shown that extracts from a variety of plants have cytotoxic effects on human tumor cell lines ,19,25,26Annona is the second largest genus of this family, corresponding to approximately 166 species [Annona species, in vitro antitumor potential has been demonstrated for many compounds isolated from the essential oils of leaves of A. vepretorum and A. sylvatica [A. squamosa, A. muricata, A. coriacea, and A. cacans plants carry acetogenin compounds which exhibit activity against human cancer cell lines [Annona sp., and they are well known as potent inhibitors of mitochondrial respiratory chain complex I and nicotinamide adenine dinucleotide (NADH) oxidase [The Annonaceae family includes 135 genera and 2500 species distributed mainly in tropical zones . Annona  species . Among Aylvatica . Differell lines ,30. Thes oxidase .A. crassiflora possess antitumor properties (methanolic and hexane extract), antimalarial effects , antimicrobial and antioxidant activities (flavonoids), and chemopreventive and anti-inflammatory effects (methanolic extract) [50) ranged from 2.49 to 44.83 µg/mL, and only one cell line was resistant to the highest tested concentration. In the same study, the crude extracts from seeds were even more promising, with GI50 values of 0.01 to 8.90 µg/mL [50 values of 6.00 to 14.90 µg/mL in colon and brain cancer, melanoma, and leukemia cell lines [Extracts and compounds isolated from extract) . Formagi90 µg/mL . Mesquitll lines . We recell lines .Schima wallichii  Korth. (Theaceae family). The compound has shown promising results in the induction of apoptosis in breast and prostate cancer [Nymphaea odorata and exhibited anti-cancer activity against androgen-sensitive LNCaP and androgen-independent PC-3 prostate cancer cells through the inhibition of LIM domain kinase 1, which catalyzes various processes in cancer progression [In this study, we showed that the chloroform fractions reduced cell viability. One of these fractions, 7C24, induced alterations suggestive of DNA damage and apoptosis  and modulated the cell cycle, observed as changes in p21 expression and arrest in the G0/G1 phase in SiHa cells. NMR analysis of the 7C24 fraction revealed different molecules as its constituents. Among them we identified the kaempferol-3-O-rhamnoside and oleic acid. Although it is not possible to affirm the effect is due to those molecules, the role of each one of them was already described in the literature. Kaempferol-3-O-rhamnoside is a glycosyloxyflavone identified in the Indonesian plant, e cancer ,33. Moregression .Annona hypoglauca, from where it presented activities against diseases other than cancer [Oleic acid is a fatty acid already found in seed from n cancer . Regardin cancer . In genen cancer , and redn cancer .50 did not confirm a higher cytotoxic potential than partition. This could be explained by characteristics of secondary metabolites, which can interact, producing synergistic, additive, or antagonistic effects [During the initial screening of natural extracts, the cytotoxic potential can provide a guide to isolating fractions purer than the crude extract, which, in general, are expected to be more bioactive . Despite effects .A. crassiflora, it is noteworthy that the other twelve species tested presented some degree of cytotoxic potential. During the initial screening of natural extracts, this potential can provide a guide to isolating fractions purer than the crude extract, which, in general, are expected to be more bioactive [50 value, Bauhinia variegata, when submitted to fractionation cycles by our group, reduced viability of MDA-MB231 human breast cell line after 24 h, exhibiting an IC50 value of 10.2 µg/mL [Beyond ioactive . Interes.2 µg/mL . This fr.2 µg/mL . The con.2 µg/mL .M. chamissois and showed the effect of the crude extract on cervical cancer cell viability, but this effect was more evident in other cell types, such as breast (data not published) and glioblastoma [We also evaluated blastoma . In glioblastoma . Furtherblastoma , leadingStryphnodendron adstringens containing gallic acid, procyanidin dimer B1, and (−)-epicatechin-3-O-gallate showed antioxidant activity and cytotoxicity toward human breast cancer cell lines [Some species that produced extracts with the ability to inhibit cervical cancer cell viability have been tested in other tumor types. Recently, a leaf fraction of ll lines . A proanll lines .Achyrocline alata are cytotoxic to human hepatocellular carcinoma cell lines but not normal primary human hepatocytes [Ethanolic extracts from the aerial parts and flowers of atocytes .Tapira guainensis and Xylopia aromatica. Woody tissue extracts have been evaluated elsewhere and showed cytotoxic effects at 100 µg/mL against a central nervous system cancer cell line SF-268 (T. guainensis) and a leukemia cancer cell line RPMI-8226 (X. aromatica) [Astronium fraxinifolium extract on cancer cells.Our article represents the first report of the cytotoxic activity of crude leaf extracts of omatica) . This reSiparuna guianensis, which exhibited cytostatic activity against human and mouse cancer cells, including breast cancer [Only one report has described the antitumor potential of leaf extracts from t cancer .A. crassiflora, with fraction 7C24 promoting cell cycle arrest, DNA damage, and cleavage of PARP in cervical cancer cells. These findings contribute to the investigation of bioactive molecules from natural sources and their potential in developing new therapies for cervical cancer.In this study, most screened extracts reduced viability of human cancer cell lines and is the first time that this cytotoxic potential has been reported for some species. Through bioactivity-guided fractionation cycles, some putative bioactive compounds were identified in Thirteen plant species from the Brazilian Cerrado biome were collected in Ituiutaba, MG  or Uberlândia-MG , between November 2007 and 2008. After identification, they were deposited in the Herbarium of the Federal University of Uberlândia (HUFU), the Herbarium of the Federal University of Minas Gerais (BHCB), or the Herbarium of the Federal University of Mato Grosso do Sul, Campo Grande campus, Brazil (CGMS), as indicated in The leaves were washed, air-dried and ground into a powder. Then, 100 g of each specie were extracted in an hydroethanolic solution (7:3), with a ratio of powder plant material/solvent of 1:5. The extracts were filtered and freeze-dried to obtain a crude extract and different yield were obtained, as follows: specie 1 (2.1%), 7 (approximately 10%), 8 (13.2%), 10 (2.5%), 14-I (4.5%), 15-I (6.9%), 16-I (3.7%), species 2, 3, 17-I, 18 and 19 (approximately 15%) and 21-I (17%). Screens were performed with extracts 1, 2, 3, 7, 8, 10, 14-I, 15-I, 16-I, 17, 18, 19, and 21-I . They weAnnona crassiflora (7), was selected for partitioning. This partitioning, already described in Silva et al. [After screening the crude extracts, the most cytotoxic, a et al. , was perOnce the chloroform partition was identified as one of the most cytotoxic for cervical cancer cells, it was fractionated by classical chromatography, resulting in several fractions. All the fractions with enough material available were tested, ten in total, identified as 7C18, 7C22, 7C24, 7C25, 7C28, 7C39, 7C45, 7C52, 7C57, and 7C60.A. crassiflora, they were submitted for analysis in a negative ion-mode Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometer (ESI (−) FT-ICR MS, model 9.4 T Solarix, Bruker Daltonics). The mass spectra were externally calibrated using NaTFA, with the m/z from 200 to 2000). The parameters of the ESI (−) source were: capillary voltage 3–3.5 kV, capillary transfer temperature 250 °C and nebulizer gas pressure of 0.5–1.0 bar. The resolution power was m/Δm50% ≅ 200,000 (where Δm50% is the maximum peak width at peak height m/z ≅ 400) and mass accuracy < 8 ppm. The equation double bond equivalent (DBE) = c − h/2 + n/2 + 1  was used to deduce the degree of unsaturation for each molecule directly from its DBE value. The FT-ICR mass spectrum was acquired and processed using Compass Data Analysis software. The elemental compositions of the present compounds were determined by measuring the m/z ratio. The proposed molecule for each formula was determined using the KNApSAcK database (last update 2020/09/15).To identify the molecules present in the chloroform partition and the fraction 7C24 of The cervical cancer cell lines used throughout this work are described in 3 to 5 × 103 cells per well in a 96-well culture plate. The next day, 10% fetal bovine serum (FBS) medium was replaced with 0.5% FBS medium. After approximately 24 h, the cells were treated with 100 μL medium plus the diluted drugs. Control wells received medium + DMSO (1%). Seventy-two hours after treatment, cell viability was evaluated by a colorimetric assay based on mitochondrial metabolism, Cell Titer 96 Aqueous cell proliferation assay  following the manufacturer’s instructions. The absorbance values at 490 nm were read in a microplate reader Varioskan . The half-maximal inhibitory concentration (IC50) value was determined by nonlinear regression curve using GraphPad Prism version 5.01 for Windows (GraphPad Software).Cells were disaggregated by trypsin, counted, and plated at a density of 3 × 10A. crassiflora, S. adstringens, and cisplatin. The cisplatin IC50 value was determined and those extracts that had at least a two-fold greater effect were considered selective.The SI was determined to evaluate whether the compounds selectively inhibit the viability of cancer cells versus normal cells . This inThe CCC lines were treated with cisplatin to compare the effect of these compounds with treatment used in clinical practice (from 3–250 µg/mL). The stock solution of cisplatin  was diluted in an aqueous solution of 0.9% NaCl and stored at 4 °C.A. crassiflora fraction 7C24 could modulate signaling pathways involved in cell death, DNA damage, and the cell cycle, we evaluated the expression of cleaved-PARP, phosphor-H2AX, and p21 by Western blotting. Cell lines were selected according to their IC50 values, HeLa (15.96 µg/mL) as a low example and SiHa (33.88 µg/mL) as a high one. Cells were seeded into 6-well plates at a density of 0.5 × 106 and treated with an IC50 dose of 7C24. After 24 h, the cells were rinsed in Dulbecco’s phosphate-buffered saline , lysed , and the sample was scraped and prepared for Western blotting. Proteins were separated by SDS-PAGE gel electrophoresis, transferred to a nitrocellulose membrane, and incubated overnight with primary antibodies targeting PARP, phospho-H2AX, p21 (dilution 1:1000) or α-tubulin (dilution 1:2000), used as the loading control. All antibodies were from Cell Signaling Technology . The membranes were incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies (1:5000) and enhanced chemiluminescence Western Blotting Detection Reagents , with detection using an ImageQuant LAS 4000 mini documentation system .To analyze whether 50 value) for 24 h. The cells were harvested, centrifuged, washed in PBS, and stained with the nuclear marker propidium iodide with the kit BD Cycle test Plus DNA kit (BD Biosciences). Data acquisition was performed using the BD Accuri flow cytometer (BD Biosciences) and was analyzed using the BD Accuri program (BD Biosciences).The SiHa cell line was cultured in 6-well plates and treated with 33.88 µg/mL 7C24 .An unpaired t-test was used to compare flow cytometry results for the control and treated groups, with


Predicting patient drug response based on a patient’s molecular profile is one of the key goals of precision medicine in breast cancer (BC). Multiple drug response prediction models have been developed to address this problem. However, most of them were developed to make sensitivity predictions for multiple single drugs within cell lines from various cancer types instead of a single cancer type, do not take into account drug properties, and have not been validated in cancer patient-derived data. Among the multi-omics data, gene expression profiles have been shown to be the most informative data for drug response prediction. However, these models were often developed with individual genes. Therefore, this study aimed to develop a drug response prediction model for BC using multiple data types from both cell lines and drugs.50 values for 220 drugs tested in these cell lines from Genomics of Drug Sensitivity in Cancer (GDSC). Using these data, we developed a multiple-layer cell line-drug response network (ML-CDN2) by integrating a one-layer cell line similarity network based on the pathway activity profiles and a three-layer drug similarity network based on the drug structures, targets, and pan-cancer IC50 profiles. We further used ML-CDN2 to predict the drug response for new BC cell lines or patient-derived samples.We first collected the baseline gene expression profiles of 49 BC cell lines along with IC50 values for all GDSC cell line-drug pairs of 0.873. Also, ML-CDN2 showed a good performance when used to predict drug response in new BC cell lines from the Cancer Cell Line Encyclopedia (CCLE), with a Pearson correlation coefficient of 0.718. Moreover, we found that the cell line-derived ML-CDN2 model could be applied to predict drug response in the BC patient-derived samples from The Cancer Genome Atlas (TCGA).ML-CDN2 demonstrated a good predictive performance, with the Pearson correlation coefficient between the observed and predicted ICThe ML-CDN2 model was built to predict BC drug response using comprehensive information from both cell lines and drugs. Compared with existing methods, it has the potential to predict the drug response for BC patient-derived samples.The online version contains supplementary material available at 10.1186/s12885-021-08359-6. One of the key goals of precision medicine in breast cancer (BC) is to predict how a cancer patient will respond to a particular chemotherapy or targeted therapy, which could help clinicians prescribe the most effective and least toxic therapeutic strategy. To this end, researchers have been developing computational models to predict the anti-cancer drug response of cancer cells based on their molecular profiles  using cell line-derived (in vitro) datasets. Ideally, a drug response prediction model should be first trained using existing patient-derived (in vivo) data and then used to predict the response of new patients to a particular drug. However, currently available in vivo datasets, such as The Cancer Genome Atlas (TCGA) , do not 50) also play important roles in drug response prediction. Thus, computational models combining genomic profiles with information about the drug’s chemical structure would improve drug response prediction in vitro and in vivo [Many computational models have been developed to predict anti-cancer drug response using cell lines from various cancer types and these models are known as pan-cancer prediction models . Most of in vivo , 13. Men in vivo  develope in vivo  proposed in vivo  proposed in vivo . We note in vivo : 1) the More importantly, the models mentioned above were trained only on a per-drug and pan-cancer basis, but they do not take the heterogeneity of cancer types into consideration. Thus, new efforts have been focused on making predictions for drugs for a specific cancer type and are referred to as cancer-specific response prediction models . Some BCIn the present study, inspired by Wei et al.’s dual-layer network model  and the ftp://ftp.sanger.ac.uk/pub4/cancerrxgene/releases/release-7.0) as a benchmark dataset in this study, which consists of 1065 cancer cell lines and 266 tested drugs. We downloaded the gene expression, CNV and mutation profiles for 49 BC cell lines. For gene expression data, we used the Robust Multichip Average (RMA) [50 value (potency).We used the GDSC release 7.0 dataset (ge (RMA)  to normage (RMA)  of Affymge (RMA) , and hav50 to 13 out of the GDSC drugs downloaded from CCLE  were used for model validation in this study. For the RNA-Seq data, we used expression level computed using the RNA-Seq by Expectation-Maximization (RSEM) method [6 to obtain Transcripts Per Million (TPM) [50 values can vary widely depending on experimental conditions. Therefore, for each of the two datasets (GDSC and CCLE), we subtracted the mean IC50 value  from each IC50 value and then divided this value by the standard deviation of IC50 values for all drugs. This normalization process gives different drugs the same baseline value and range across all BC cell lines for the GDSC and CCLE studies.RNA-Seq expression profiles of 28 BC cell lines, as well as their response measured by IC) method  providedon (TPM)  and log2parse.smiles function of the R package rcdk [get.fingerprints function of the R package rcdk. Regarding the drug targets, we extract the interactions between the 220 drugs and 272 target proteins from GDSC.Drug chemical structure data were also curated. In order to reduce 3D drug chemical structure data into a 1D string variable we used the simplified molecular-input line-entry system (SMILES). We extracted the canonical SMILES strings for 220 out of the 266 GDSC small molecules from PubChem , a databhttps://gdac.broadinstitute.org), multiplied by 106 to obtain TPM [For the TCGA BC cohort, the patient-specific RNA-Seq gene expression data computed by the RSEM algorithm  were dowtain TPM  and log2tain TPM . We also, we scored the pathway activities using the gene expression profiles of BC cell lines or patients from GDSC, CCLE or TCGA datasets. Note that we first standardized gene expression within each cohort, and then performed pairwise homogenization procedure before scoring pathway activities as described in other studies [ComBat function from the sva R package [Gene sets of 1329 canonical pathways, which were curated from various pathway databases , were extracted from the Molecular Signatures Database (MSigDB) website . As desc studies , 25 to m package  to adjuscnv, where associations between every two cell lines C and Ci are measured by the Tanimoto correlation ) between their CNV profiles; 2) CSNmut, which connects every cell line pair C and Ci using the Tanimoto correlation ) calculated based on their gene mutation profiles; and 3) CSNpath, which connects every two cell lines C and Ci based on their Pearson correlation ) of pathway activity profiles. Hence, these CNSs are weighted networks. The CSNs were generated using GDSC data and all of them are complete graphs of 49 BC cell lines, where the weights of the interactions between each pair of the cell lines were measured by the correlation coefficients of their respective pathway activity, CNV, and mutation profiles. Each of the three CSNs is a single-layer CSN since each was constructed using a single data type. Next, we used the Similarity Network Fusion (SNF) algorithm in the R SNFtool package [affinityMatrix function with default parameters; 2) the three affinity matrices were fused into a cell line similarity network fusion (CSNF) using the SNF function, which connects every cell line pair C and Ci by the SNF algorithm-derived correlation ). The CSNF is a three-layer CSN since it combined CSNpath, CSNcnv, and CSNmut.We estimated the cell line similarities by constructing three CSNs using CNV, mutation, and pathway activity profiles Fig. a: 1) CSN package  to integstru, which connects every two drugs D and Dj based on the Tanimoto correlation ) of their molecular fingerprint properties; 2) DSNtarg, where associations between every two drugs D and Dj are measured by the Jaccard correlation ) between their target information; and 3) DSNsens, which connects every drug pair D and Dj using the Pearson correlation ) calculated based on their sensitivity profiles with respect to the IC50 values across the 1065 cell lines from all cancer types. Next, we integrated the three single-layer DSNs into a three-layer drug similarity network fusion (DSNF) using the SNFtool package [D and Dj using the SNF correlation ).Three drug data types  were used to construct three DSNs to estimate drug similarities Fig. b: 1) DSN package  by conne50 values). Note that a ML-CDN is not a complete bipartite graph due to some missing values in the GDSC dataset. With the four CSNs and four DSNs constructed in the above two sections, we obtained a total of 16 ML-CDNs: 1) nine dual-layer CDNs were built using each of the three single-layer CSNs  and each of the three single-layer DSNs ; 2) three four-layer CDNs were built using the three-layer CSNF combined with each of the three single-layer DSNs; 3) similarly, another three four-layer CDNs were built using the three-layer DSNF combined with each of the three single-layer CSNs; 4) finally, a six-layer CDN was built using the two integrated similarity networks, CSNF and DSNF. In this study, we referred to the ML-CDN based on CSNF and DSNF as ML-CDN1 and the one based on CSNpath and DSNF as ML-CDN2.We next integrated each of the four CSNs and each of the four DSNs together to develop a multiple-layer cell line-drug response network (ML-CDNs), which predicts drug response in previously tested BC cell lines Fig. c. In thiC, D), we are able to make a prediction of the response of the cell line C to the drug D using Eq. Ω\{} is the set of all other pairs  except , R denotes the observed response of the pair . C, D). The product of w and w reflects the contribution of R to w is the weight function between cell lines C and Ci and w is the weight function between drugs D and Dj, which can be calculated as Eqs. w increases with respect to the cell line similarity correlation ρ, and σ measures the decay rate when ρ decreases. Similarly, the parameter τ measures the decay rate with the decrease of ρ.For a given cell line-drug pair , to be used in the weight function of cell lines and drugs, respectively. The decay parameter pair  was optimized by minimizing the sum of the squared errors for all possible cell line-drug pairs using Eq. R is the observed response value of cell line C to drug D, and C to the drug D. σ and τ are both ranged from 0 to 1 with an increment of 0.01, and the pair  takes all possible combinations.The ML-CDN models contain two decay parameters  while the remaining fold used as the test set for estimating the prediction performance of the models. The performance was evaluated using Pearson correlation coefficient and root mean squared error (RMSE) between the predicted and the observed drug responses for all drugs. RMSE is the square root of the mean squared error. A higher Pearson correlation coefficient and lower RMSE indicate a better prediction performance of a method.To compare the performance of the 16 ML-CDN models, we split all cell line-drug pairs into three folds. Two folds were used as the training set for optimizing the decay parameter pairs (path and DSNF and demonstrated good prediction performance in terms of Pearson correlation and RMSE (See the Results Section), was chosen for predicting the response of a new BC patient- or cell line-derived sample S to a known drug D , we first estimated the similarity between the new sample S and any existing cell line Ci by calculating the Pearson correlation ) in terms of their pathway activity profiles and further obtained the weight ) between S and Ci using Eq. σ optimized from ML-CDN2. In parallel, the similarity between D and any existing drugs Dj was measured using ρDSNF from DSNF and further weighted using Eq. τ optimized from ML-CDN2 to obtain wDSFN. R is the observed response value of existing cell line Ci to existing drug Dj. Thus, R can be predicted by taking advantage of response data from all existing cell lines Ci based on their weighted similarities with the new sample S and all existing drugs Dj based their weighted similarities with D as shown in Eq. Although the ML-CDN models can be used to predict drug response in a new cell line or tumor tissue sample based on the CSN, or cell line response to a new drug based on the DSN, we focused on the former in this study. We therefore expected that the BC cell line-derived ML-CDNs could have good predictive performance in BC patient-derived data. Among the 16 ML-CDN models, ML-CDN2, which was constructed by connecting CSNσ optimized from ML-CDN2. The prediction of the drug response could then be made using Eq. To validate the performance of ML-CDN2, we employed the ML-CDN2 model, which was trained using the GDSC dataset. We then predicted the drug response of the same drugs in new BC cell lines from the CCLE dataset using Eq. σ optimized from ML-CDN2. In the end, the responses of these TCGA BC patients to the five drugs were made with Eq. 50 values in the GDSC study were measured using cell viability, we expected that patients in the Responder group would have a lower predicted IC50 value than patients in the Non-responder group.To further study ML-CDN2’s performance in vivo, we employed the model to predict the drug response of five drugs for patients in the TCGA BC dataset for which drug response was recorded. Five drugs were tested in the GDSC study, including paclitaxel, fluorouracil, tamoxifen, doxorubicin, and docetaxel. For each of the five drugs, the patients were assigned to two groups based on the recorded drug response: Responder (patients showing a “complete response”) and Non-responder . For these patients, we first calculated their pathway activity scores based on their whole-genome gene expression profiles and then measured the similarity between these BC tumors and the GDSC BC cell lines using the Pearson correlations of their pathway activity profiles. The Pearson correlations were further weighted using Eq. 50 values for lapatinib among the four groups. We expected to see that groups with higher HER2 expression levels would demonstrate lower predicted IC50 values. BC patients treated with tamoxifen were divided into two groups (Negative and Positive) based on the IHC status of ER. Then the predicted IC50 values for tamoxifen were compared between the two groups. We expected that the predicted IC50 values for patients in the ER Positive group would be lower than the IC50 values from the patients in the ER Negative group.Using Eq. 50 value (response variable) of the inhibitor and the expression levels of the pathway genes (predictors). We obtained the p-value for each gene and corrected them for multiple comparison, using Bonferroni correction (α = 0.05).In addition, we used Eq. 50 profiles  and each of the four DSNs  to build a total of 16 ML-CDNs and compared their prediction performance  is  for ML-CDN1 and  for ML-CDN2. The IC50 values were predicted for all pairs using the ML-CDN1 and ML-CDN2 models to calculate the Pearson correlation and RMSE. The scatter plots for ML-CDN1  and ML-CDN2 . The ML-CDN2 model obtained a correlation between the predicted and the observed responses of 0.873, while Zhang et al.’s method had a value of 0.670  and compared this to those with responses (available observed). We predicted the missing responses to five EGFR tyrosine kinase inhibitors . Since such EGFR inhibitors are more potent (lower IC50) in individuals with mutations in EGFR, we stratified the data into wild type and mutant groups. The predicted missing median IC50 values of EGFR inhibitors for the EGFR wild type and mutant cell lines are − 0.09 and − 0.32, respectively, and the available observed median IC50 values for the EGFR wild type and mutant groups are 0.10 and − 0.28, respectively , likely because there are only two data points in the mutant group. However, we found that the predicted missing median IC50 of the EGFR-wild type group was significantly higher than the available observed median IC50 of the EGFR-mutant cell lines  . Overall, our results suggest that our ML-CDN2 model can correctly predict the drug responses of missing data in the GDSC dataset.Similar to the EGFR inhibitors, we predicted the missing responses of three mitogen-activated protein kinase inhibitors . These compounds are expected to be more potent and therefore have lower ICF kinase , so we s). The results suggest that the ML-CDN2 model can be used to predict response values of new BC cell lines to the existing drugs. However, the model did not work well with cell-drug pairs with observed IC50 values of 8 μM or higher. In CCLE, drugs were tested in eight doses with 8 μM being the maximum. Thus, some drugs ended up having an IC50 of 8 μM or higher in some cell lines but these are all listed as having an IC50 of 8 μM. The IC50 of 8 μM is 0.55 after normalization, hence all IC50 values of 8 μM or higher from CCLE are represented as the observed normalized value of 0.55 . In the tamoxifen treatment group, patients in the “Responder” group appeared to have a lower median predicted IC50 value than patients in the “Non-responder” group but the results are not statistically significant .Although drug response data in BC cell lines is more widely available and easier to obtain, it is less representative of the disease than human tumor samples. In addition, a goal of models such as ours is to predict successful cancer drug treatment based on BC patient tumor samples. We therefore tested our ML-CDN2 against the TCGA dataset on a subset of BC patients where their drug response was recorded as “Responder” or “Non-responder”. The predicted drug ICp-value = 0.01 by ANOVA). Tamoxifen responses were similarly grouped by ER status. Although tamoxifen appeared to be more sensitive in the ER-positive than in the ER-negative group this result was not statistically significant  and FASLG .We also employed the ML-CDN2 model to predict the drug response of several phosphoinositide 3-kinase (PI3K) / mechanistic target of rapamycin (mTOR) signaling pathway inhibitors for TCGA breast tumor samples. 20 PI3K inhibitors were tested in the GDSC study and we observed statistically significant associations between the level of pathway gene expression and predicted drug responses for each inhibitor. A total of 120 associations were obtained for seven inhibitors  was used, the ML-CDN models derived from CSNpath and CSNF show a higher Pearson correlation and a lower RMSE than the ML-CDN models derived from CSNcnv and CSNmut, implying that pathway activity profiles and CSNF are more informative than the CNV and mutation profiles. The best-preforming model is ML-CDN1, derived from CSNF and DSNF, suggesting that integration of the three types of data from cell lines along with the three types of data from drugs improve predictive performance of the model.All of the 16 ML-CDN models we constructed show good predictive performance, with Pearson correlation coefficients greater than 0.8. When the same CSN was used, the ML-CDN model derived from DSNIt is noteworthy that the 16 ML-CDNs do not differ much in their predictive performance. The Pearson correlation ranges from 0.833 to 0.875 while the RMSE ranges from 0.493 to 0.563. A possible reason for this could be that the ML-CDN models are BC-specific models. The idea behind the ML-CDN modeling method is that similar cell lines exhibit similar drug response. These models were trained on 49 BC cell lines, which show high similarity among each other in terms of pathway activity, CNV, or gene mutation profiles. Therefore, any of the three types of profiles can be used by the ML-CDN model to accurately predict drug response.50 values for the paclitaxel “Responders” than the “Non-responders”  is the most aggressive BC subtype with the lowest survival time and having few effective therapies. Our model was developed with different BC subtypes taken into consideration and was validated using different BC subtypes represented by cell lines in CCLE and patient-derived samples in TCGA. It would be interesting to look at the performance of our model specifically in the TNBC subtype in future work.A limitation of this study was that our model did not take into account cell lines that are known to be resistant to specific chemotherapeutic drugs. Therefore, when we validated our model in the CCLE dataset, it did not work well with cell-drug pairs with observed ICWe developed a BC-specific computational model by integrating multiple cell line and drug data types to predict anticancer drug responses. One of the main contributions is that this BC cell line-derived model has the potential to predict the drug response for BC tissue samples. Such a model may one day be used to predict drug response and influence drug selection for BC treatment.Additional file 1: Table S1. Correlations of cell line pairs with respect to their CNV, mutation and pathway activity profiles, as well as their response to all tested drugs in the GDSC.Additional file 2: Table S2. Associations between the expression level of PI3K pathway genes and PI3K inhibitor responses predicted by ML-CDN2. This table lists the PI3K pathway genes which show significant association between their expression levels with the predicted IC50 values of the PI3K pathway inhibitors.Additional file 3: Figure S1. Correlations of cell line pairs, tumor pairs and cell line-tumor pairs. (a) The pairwise Pearson correlation of the 49 BC cell lines from GDSC was calculated based on their gene expression profiles. (b) The pairwise Pearson correlation of the 1100 BC samples from TCGA was calculated based their gene expression profiles. (c) The pairwise Pearson correlation between the 49 BC cell lines and the 1100 BC samples was calculated based their gene expression profiles. (d) The pairwise Pearson correlation of the 49 BC cell lines was calculated based on their pathway activity profiles. (e) The pairwise Pearson correlation of the 1100 BC samples from TCGA was calculated based their pathway activity profiles. (f) The pairwise Pearson correlation between the 49 BC cell lines and the 1100 BC samples was calculated based their pathway activity profiles.


Glioblastoma multiforme is a malignant primary brain tumor with a poor prognosis and high rates of chemo-radiotherapy failure, mainly due to a small cell fraction with stem-like properties (GSCs). The mechanisms underlying GSC response to radiation need to be elucidated to enhance sensitivity to treatments and to develop new therapeutic strategies. In a previous study, two GSC lines, named line #1 and line #83, responded differently to carbon ions and photon beams, with the differences likely attributable to their own different metabolic fingerprint rather than to radiation type. Data from the literature showed the capability of RHPS4, a G-quadruplex stabilizing ligand, to sensitize the glioblastoma radioresistant U251MG cells to X-rays. The combined metabolic effect of ligand #190, a new RHPS4-derivative showing reduced cardiotoxicity, and a photon beam has been monitored by magnetic resonance (MR) spectroscopy for the two GSC lines, #1 and #83, to reveal whether a synergistic response occurs. MR spectra from both lines were affected by single and combined treatments, but the variations of the analysed metabolites were statistically significant mainly in line #1, without synergistic effects due to combination. The multivariate analysis of ten metabolites shows a separation between control and treated samples in line #1 regardless of treatment type, while separation was not detected in line #83. Glioblastoma (GBM) is the most lethal primary brain tumor and is associated with a median overall survival of 14 months. It inevitably recurs, even after surgical resection followed by intensive chemo-radiation therapy ,3,4. To 8).In a previous paper , it has To gain greater insight into the synergistic effects of radiation and G4-ligands, we performed a metabolomic analysis with MR spectroscopy (MRS) to study changes occurring after GSC treatment with one of the RHPS4 analogues, namely, Ligand #190, and photon beam irradiation. Improved knowledge of the metabolism of brain tumors after chemo-radiotherapeutic treatment may contribute to the discovery of new diagnostic or prognostic biomarkers. In particular, metabolic reprogramming is a hallmark of GSCs  used to GSC lines #1 and #83 have been selected from a panel of patient-derived GSCs on the basis of their biological, metabolic, and genetic characteristics ,32,33,34The synergistic effect of radiation and ligand #190 has been tested through MRS experiments at different times after ligand treatment and photon irradiation at 20 Gy; three different treatment conditions  were compared with the controls. Cell survival in these different conditions was also evaluated.8, namely, ligand #190, was prepared according to the synthetic protocols outlined in 2–6 to improve the global yields of the single steps, as described in the The target compound 8, we performed a primary ammine acylation of 6 and subsequent methylation of 7, following a known protocol not already reported in the literature, on our substrate. In brief, 2-amino-4-nitroaniline is first subjected to a nucleophilic substitution conducted in methanol treated with 9-chloroacridine 1 over 30 min. The mixture was refluxed for 90 min, cooled, and the product was precipitated with diethyl ether. Product 3 was obtained after a nitrosation of 2 with NaNO2 in HCl, and it was subsequently thermalized in Triglyme, reaching compound 4. The methylation of 4 with NaH and dimethyl sulfate yielded 8-methyl-2-nitroquinoacridine 5, which was then reduced to the corresponding amine 6 with stannous chloride in concentrated hydrochloric acid, after which the mixture was stirred at room temperature for 5 days. Then, to obtain the final product, we performed an acylation of 6 with the cyclopropanecarbonyl chloride, gaining the amide 7, which was then heated with an excess of methyl iodide for 4 days at 150 °C in a pressure tube to obtain 8 (ligand #190) with a high yield  was observed late after the different treatments in the spectra of line #1, while this signal intensity showed a fluctuant behaviour in spectra of line #83.Mobile lipid (ML) signals at 1.28 ppm and 0.89 ppm from the spectra of both cell lines are affected by the treatments, even if to differing extents. An increase of 2D A peak intensity  signal is clearly observable in line #1 cell spectra while it is slightly above the detection threshold in cell #83 spectra B,B’. TreThe signals of GSH, Gln, and Glu were influenced in line #1 and line #83 after treatments, though to a different extent . InteresFinally, an unsupervised hierarchical analysis was performed with a metabolomics approach for both lines for the 10 metabolic parameters from 2D spectra Line #83 was only slightly affected by irradiation, as previously observed in Palma et al. . Ligand (ii)Line #1 cells showed a slowdown in growth after the combined treatments with radiation and ligand #190. Cell growth was mainly slowed down by the action of the ligand, while the combined treatment gave only a moderate synergistic effect;(iii)MRS experiments on the two cell lines showed significant effects on several metabolites, mainly on lipid signals.Metabolomics may help in clarifying stem cell fate and reprogramming function ,44. In tLipid metabolism is abnormally regulated in gliomas compared to normal cells. GBM tumors accumulate more fatty acids than the surrounding normal brain ,47. ThesFrom the MR spectra of the two cell lines, we observed that the mobile lipid content of control samples present in cytoplasmic droplets is quite different from the cell lines and is also differently influenced by treatments, i.e., radiation . In factMetabolic alterations have long been regarded as a hallmark of cancer cells; however, contradictory results have been reported for GSCs, suggesting metabolic plasticity, especially concerning mitochondria and energetic metabolism . Recent Glutamine (Gln)-high GSCs metabolize more glutamine to sustain mitochondrial respiration, and the reduction of glutamine can weaken their ability to enhance cell proliferation and self-renewal. Our study on genetic and metabolic profiles distinguished two clusters of GSCs, GSf-like and GSr-like ,34. CellIt is known that GBM tissue shows a higher glutamine level than the surrounding normal brain and that the ability to metabolize glutamine is critical for GBM proliferation and survival . In cellGlu is often synthesized from glutamine by the enzyme glutaminase or from oxidative deamination. Its synthesis and degradation are part of the glutamate/glutamine cycle. Due to its role as a neurotransmitter, Glu is constantly released from neurons and its uptake by astrocytes at the neuronal synapse is essential for maintaining normal brain function ,66. AfteMolecular changes in the GSH antioxidant system and remolding in GSH homeostasis have been implicated in tumor initiation, progression, and treatment response because of both protective and pathogenic roles of GSH. Although in healthy cells it is crucial for the removal and detoxification of carcinogens, elevated GSH levels in tumor cells are associated with tumor progression and increased resistance to chemotherapeutic drugs. Recently, several novel therapies have been developed to target the GSH antioxidant system in tumors .GSH deficiency or a change in the GSH/GSSG ratio increases the vulnerability of cells to oxidative stress, inflammation, and tumor progression. However, elevated GSH levels increase antioxidant capacity and resistance to oxidative stress, as is evident in many tumors . It has As for GSH, signal intensities in spectra from line #83 are not affected by treatments, thus suggesting that GSH is not a key metabolite in protecting cells from treatment; other mechanisms have to be invoked probably due to the high heterogeneity of line #83 and cell lines belonging to the mesenchymal cluster. In addition, GSH levels in line #83 are low and do not seem to be relevant for cell protection.  On the cMyo-I is one of the most abundant metabolites in the human brain, and is located mainly in glial cells and functions as an osmolyte. Myo-I concentration is high in many brain disorders, including Alzheimer’s disease and in a variety of pathological conditions that involve astrocytic proliferation ,71. The As far as NAA is concerned, the literature reports that NAA levels decreased in GBM compared to normal brains and that NAA is a marker of the neuronal phenotype. Different levels of NAA were observed in GSCs associated with different metabolic and genomic signatures ,44. In mAn unsupervised statistical analysis of 10 metabolites , made with a metabolomic approach, showed different groupings for the two cell lines. In line #1, controls are grouped in a different cluster compared to their treated counterparts , while for line #83 cells, no cluster separation was observed.In conclusion, in both cell lines a possible synergistic interaction of combined treatments seems to be excluded. From the metabolic point of view only for a few metabolites was a synergistic effect between ligand treatment and radiation observed, but the complete statistical analysis does not confirm this synergy. These results confirm that the mesenchymal phenotype is more resistant to radiation and treatment with ligand #190 than the proneural phenotype. Specifically tailored therapies should be developed which take into account the high heterogeneity of GSCs as the use of combined treatments, such as those proposed in this paper, seem to fail. On the other hand, single treatment with G-quadruplex stabilizing ligand seems to induce alterations at the metabolic level in the two GSC lines, mainly in the proneural phenotype.® BEH C18 column . The following method was used: flow 1.4 mL/min, isocratic gradient over 2 min 95% of H2O + 0.1% TFA (5% CH3CN), gradually to 40% aqueous solvent over 6 min, then isocratic flow for 4 min (λ = 256 nm). UPLC-MS data were recorded using a surveyor UPLC system  equipped with a BEH Acquity UPLC column (1.7 μm) 2.1 × 50 mm, and an LCQ ADV MAX ion-trap mass spectrometer, with an ESI ion source. Chromatographic purification was conducted using ISOLERA-One, a Biotae Flash Purification system. Recordings of 1H-NMR spectra were made with a Bruker Avance 300 MHz  calibrated to the internal standard TMS or the residual solvent peak. All the products were characterized by ESI-MS and 1H-NMR. The synthetic procedure needed to obtain RHPS4 derivative is reported in detail in the All solvents and reagents were purchased from TCI and Sigma-Aldrich and used without further purification. HPLC analyses were performed using an Agilent system SERIES 1260  with an XBridgeGSCs were obtained from adult GBM patients (WHO grade IV) who had undergone complete or partial surgical resection at the Institute of Neurosurgery, Catholic University School of Medicine, Rome, Italy. Informed consent was obtained from the patients before surgery. The tumor tissues were mechanically dissociated and single-cell suspension cultured in a serum-free medium supplemented with epidermal growth factor and basic fibroblast growth factor, as previously described . The in 4 cells/mL. After 16 h, #190 was added to the cells. ATP levels were measured at different time points as a surrogate of cell viability using CellTiter-Glo™  according to the manufacturer’s instructions. The mean of the raw luminescence values from triplicate wells treated with vehicle alone (mLC) was used as a reference to interpolate percentage viability from wells treated with drugs (VD), using the following formula: VD = (LD/mLC) × 100 [For ligand #190 dose-response experiments, GSCs were mechanically dissociated and plated in 96-well plates at a density of 2 × 10C) × 100 .Maximal inhibitory concentration (IC50) and 25% inhibitory concentration (IC25) values were determined by a non-linear regression curve through the plotted experimental data and represent compound concentrations able to reduce cell proliferation by 50% and 25%, respectively.2 flasks at a density of 10 × 104 cells/mL in 50 mL total volume. Cells were the treated with 0.7 µM (line #1) and 0.75 µM (line #83) ligand #190 concentration (IC 25 for each line) for 48, 72, 96, and 120 h.For MRS and cell viability experiments, GSCs were seeded in T175 cm137Cs) gamma ray source  operating at a dose rate of 0.8 Gy/min (Istituto Superiore Sanità facility). A single radiation dose of 20 Gy was used, comparable to total doses delivered during radiation therapy in a fractionated regimen and to a single high dose used in special therapeutic modalities, such as intraoperative radiotherapy and stereotactic radiosurgery [For combined treatments see , cells wosurgery .2O and 2mM Sodium 3-(trimethylsilyl)propionate-2,2,3,3-d4 (TMSP) as a frequency standard. A 20 μL aliquot of the suspension was transferred into a 1 mm MR tube and centrifuged to obtain a packed cell volume, as previously described [Cells were washed in phospate buffered saline (PBS)  without calcium and magnesium at pH 7.4 and centrifuged at 162 rcf for 3 min. The pellet was resuspended in PBS with 20% Described . All MRS1H MRS experiments were run on a digital Bruker Avance spectrometer  at 400.14 MHz equipped with a 1mm microprobe. Both one-dimensional (1D) and two-dimensional correlation spectroscopy (2D COSY) experiments were performed, at T = 298 K.1H MRS spectra of GSCs and culture media were acquired with a 90° RF pulse, the number of scans (ns) was equal to 1000  for cell spectra. 2D COSY spectra were acquired with a 90°-t1–90°-t2 pulse sequence and ns = 32 for cell or ns = 128 for culture media samples. Spectra were acquired as a matrix of 512 × 128 data points in time domain.1D MRS parameters were obtained in at least three independent experiments and data expressed as mean ± standard deviation (SD) values. WINNMR software  was used to perform 1D signal deconvolution and 2D cross peak integration, as reported in . A macrot-test were performed utilizing Past 4.03 Software, version 2020 [Unsupervised agglomerative hierarchical clustering, principal component analysis and Student’s ion 2020 .


Metabolic and personalized interventions in cancer treatment require a better understanding of the relationship between the induction of cell death and metabolism. Consequently, we treated three primary liver cancer cell lines with two anthracyclins (doxorubicin and idarubin) and studied the changes in the lipidome. We found that both anthracyclins in the three cell lines increased the levels of polyunsaturated fatty acids (PUFAs) and alkylacylglycerophosphoethanolamines (etherPEs) with PUFAs. As PUFAs and alkylacylglycerophospholipids with PUFAs are fundamental in lipid peroxidation during ferroptotic cell death, our results suggest supplementation with PUFAs and/or etherPEs with PUFAs as a potential general adjuvant of anthracyclins. In contrast, neither the markers of de novo lipogenesis nor cholesterol lipids presented the same trend in all cell lines and treatments. In agreement with previous research, this suggests that modulation of the metabolism of cholesterol could be considered a specific adjuvant of anthracyclins depending on the type of tumor and the individual. Finally, in agreement with previous research, we found a relationship across the different cell types between: (i) the change in endoplasmic reticulum (ER) stress, and (ii) the imbalance between PUFAs and cholesterol and saturated lipids. In the light of previous research, this imbalance partially explains the sensitivity to anthracyclins of the different cells. In conclusion, our results suggest that the modulation of different lipid metabolic pathways may be considered for generalized and personalized metabochemotherapies. All cancers are characterized by an inherent metabolic reprograming that promotes tumorigenesis by facilitating and enabling proliferation, metastasis, and resistance to therapies ,2. ThereClinicians have used anthracyclins, such as doxorubicin (DOX) and idarubicin (IDA), as chemotherapeutic agents for more than five decades. Anthracyclins intercalate into the nucleus and mitochondrial DNA and subsequently inhibit the synthesis of proteins and affect the redox state of the cell. Anthracyclins also act on the mitochondrial electron transport and convert oxygen into reactive oxygen species that may cause mitochondrial dysfunction, change the redox state, and induce lipid peroxidation . HoweverCurrently, there are few studies about the effect of DOX on the lipidome of cancer cells and even less on the effect of other anthracyclins. In addition, different studies have reported that lipidic modulation affects ER stress, cell death, and cardiotoxicity induced by anthracyclins ,13,14. C® HB-8065™, SNU 449 ATCC® CRL-2234™, and Huh7 from Dilruba Ahmed, Karolinska Institute, Stockholm, Sweden) were cultured at 37 °C with 5% CO2 and 95% humidity within a CO2 incubator. HepG2 and Huh7 were cultured in GlutaMAX™ supplemented, high glucose DMEM  supplemented with 1% antibiotic antimycotic solution  and 10% fetal bovine serum (FBS)  (cell culture media + FBS: CCMFed). SNU449 was cultured in GlutaMAX™ supplemented, RPMI  medium supplemented with 1% antibiotic antimycotic solution and 10% FBS (cell culture media + FBS: CCMFed). Standard culture medium without supplemented FBS was used during starvation (CCMSM). Misidentification of all cell lines was checked at the Register of Misidentified Cell Lines . In. In33]. Here, we show that anthracyclins elicit the upregulation of PUFAs and etherPEs with PUFAs in different primary liver cancer cells. By using DOX and IDA in three different cell lines, we present new evidence that this is a general effect of anthracyclins on the metabolism of cancer cells. We also present evidence that the regulation of cholesterol lipids and de novo lipogenesis by anthracyclins is cell type-dependent for three different cell lines of primary liver cancer. As etherPEs with PUFAs are involved in programmed cell death by ferroptosis  and statLXRs and SREBPs control the expression of the enzymes responsible for the de novo lipogenesis of fatty acids, glycerolipids, and cholesterol . The incConsidering our results and previous research together, we conclude that the up- or downregulation of the LXR/SREBP-mediated de novo lipogenesis of fatty acids, glycerolipids, and cholesterol was not a general effect of anthracyclins. Nevertheless, as we saw different regulations for three different lines of the same type of cancer, it could depend on the cell type, tumor, or even individual genotypes and phenotypes.The synthesis of etherGLs is initiated in the peroxisome and completed in the ER, where the activity of alcohol reductase FAR1 is the limiting step  40,41,4,441,42. The levels of free PUFAs could be increased by three different factors: (i) the increase in their synthesis by elongation and desaturation of essential linoleic and linolenic acids ; (ii) thWe conclude that the observation of the ensemble of free PUFAs and glycerolipids with PUFAs suggests the upregulation of the elongation and desaturation of linoleic and linolenic acids as responsible for the increase in free PUFAs and etherPEs with PUFAs .Regarding cell death by anthracyclins, PUFAs and etherGLs with PUFAs have been associated with lipid peroxidation and ferroptosis ,50,51,52In the context of lipid peroxidation and ferroptosis, first, our analysis about the regulation of PUFAs indicates that their increase was due to the upregulation of elongation and desaturation . In thisSecond, we found a general increase in etherPEs with PUFAs. It is known that: (i) glycerophospholipids of ethanolamine with PUFAs are necessary for ferroptosis ; and  of cell death.In conclusion, the behavior of the lipidome in the three cell lines suggests that: (i) PUFAs or PUFA-containing lipids may potentiate the effect of anthracyclins in a general way, and (ii) statins may potentiate or attenuate the effect of anthracyclins in a cancer/patient-specific way. Further research in animal models and patients is warranted to establish the possibility of transferring these observations from bench to bedside.