Therefore, CLC-Pred 2.0 is a significant extension of the capabilities of the initial web application.In vitro cell-line cytotoxicity is widely used in the experimental studies of potential antineoplastic agents and evaluation of safety in drug discovery. In silico estimation of cytotoxicity against hundreds of tumor cell lines and dozens of normal cell lines considerably reduces the time and costs of drug development and the assessment of new pharmaceutical agent perspectives. In 2018, we developed the first freely available web application (CLC-Pred) for the qualitative prediction of cytotoxicity against 278 tumor and 27 normal cell lines based on structural formulas of 59,882 compounds. Here, we present a new version of this web application: CLC-Pred 2.0. It also employs the PASS (Prediction of Activity Spectra for Substance) approach based on substructural atom centric MNA descriptors and a Bayesian algorithm. CLC-Pred 2.0 provides three types of qualitative prediction: (1) cytotoxicity against 391 tumor and 47 normal human cell lines based on ChEMBL and PubChem data  with a mean accuracy of prediction (AUC), calculated by the leave-one-out (LOO CV) and the 20-fold cross-validation (20F CV) procedures, of 0.925 and 0.923, respectively; (2) cytotoxicity against an NCI60 tumor cell-line panel based on the Developmental Therapeutics Program’s NCI60 data  with different thresholds of IG Cancer is a major public health problem worldwide  and the https://www.way2drug.com/cell-line/, accessed on 1 January 2023), for the in silico prediction of cytotoxic effect of compounds in 278 cancer and 27 normal cell lines based on their structural formula [50 data  against NCI60 cell lines based on the data from the Developmental Therapeutics Program (DTP) NCI60 [A number of studies aimed at creating computational tools for the prediction of cell-line cytotoxicity based on structural formulae of compounds, but most of them either were limited by the narrow chemical space due to the small number of compounds used ,9 or mad formula . It was  formula ,14,15 an formula ,17, natu formula ,19,20, a formula ,22. In tThe training set with 128,545 unique structures of compounds tested against 1162 cell lines was created based on the experimental data  related to cytotoxic studies from the ChEMBL and PubChem databases. After PASS training and the creation of classification models of “structure–cytotoxicity” relationships, 438 human cell lines  with an accuracy of prediction (AUC) higher than 0.8 were selected . Most ofThe average accuracy of prediction (AUC) calculated by the leave-one-out (LOO CV) and 20-fold cross-validation (20-fold CV) procedures was 0.925 and 0.923, respectively. It displays the robustness of the cytotoxicity prediction for the selected cell lines.50 values . The average AUC calculated by the LOO CV and 20-fold CV procedures was (1) 0.870 and 0.869 for 100 nM; (2) 0.898 and 0.897 for 10 nM; (3) 0.945 and 0.942 for a 1 nM threshold, respectively. The accuracy of the cytotoxicity prediction against all NCI60 cell lines was higher than 0.80. In contrast to the previous training set based on ChEMBL and PubChem data, in these training sets, the differentiation between active and inactive compounds was made according to the nanomolar thresholds of GI50 values. The number of cell lines and the average accuracy of prediction  according to the origin are shown in The training sets based on the DTP’s NCI60 data included 22,726 unique structures tested against 60 cell lines with 1,262,878 experimental values. These training sets were created based on different thresholds of IGThe training set to predict the molecular mechanisms of action related to the interaction of compounds with human proteins was created based on ChEMBL and PubChem data. The training set included 656,011 unique structures and 957,545 records describing experimental results related to 2876 molecular mechanisms of action. After completing the training procedure and selection of activities with the accuracy of prediction over 0.8, 2170 molecular mechanisms of action were selected. The average accuracy of the prediction calculated by the LOO CV and the 20-fold CV procedures was 0.979 and 0.978, respectively. The distribution of the mechanisms of action based on the ChEMBL family classification of proteins is shown in https://www.way2drug.com/CLC-pred, accessed on 1 January 2023) was developed based on the created SAR models for the prediction of human cell lines’ cytotoxicity and molecular mechanisms of actions related to human proteins. The web application allows one to use SMILES, the names of approved drugs, MDL Molfile or Marvin JS drawn structures as an input. The prediction results consist of three tabs—“Cell-line”, ”TDP NCI-60 (1 nM)”, ”TDP NCI-60 (10 nM)”, ”TDP NCI-60 (100 nM)” and ”Target”. Inside each tab, the user can see the results of the prediction with selectable rows. The results of the prediction contain three main characteristics, the names of activities, Pa (the probability that the compound will be active) and Pi (the probability that the compound will be inactive). In addition to these characteristics, one can see the detailed description of the activity. The prediction results are downloadable as PDF, CSV, Excel files and can be copied to the clipboard. In the section “Training set”, the user sees the total list of the predicted activities with the accuracy of prediction calculated by the LOO CV and 20-fold CV procedures. The interface of CLC-Pred 2.0 with prediction results for the EGFR inhibitor erlotinib is shown on The CLC-Pred 2.0 web application (Pa = 0.255). Pa = 0.267). Such results also coincide with the prediction of the medium probability to reveal cytotoxicity in the kidney sarcoma cell line OS-RC-2  and structural formulas related to cell-line cytotoxicity studies were extracted from the PubChem (February 2022) and ChEMBL (version 29) databases. The compounds were considered active if their IG50 or IC50 values were less than 10,000 nM or if the percent of inhibition exceeded 50%. All compounds were considered as inactive for the appropriate cell line if they were not active for the cell line according to the above-mentioned criteria. The names of cell lines were standardized. Cell lines’ descriptions, tissue specificity and tumor types were used according to ChEMBL, Cellosaurus and ATCC (American Type Culture Collection) data.The experimental data (IG50) at 48 h using the sulforhodamine B assay [50 value was equal or over 9 (1 nM), 8 (10 nM) or 7 (100 nM) for the appropriate training sets. Additional data related with the characteristics of NCI60 cell lines were given from the table with the metadata on the NCI60 panel of cancer cell lines that is available on CellMiner.The initial data from CellMiner  on cytot B assay  were usei, IC50, Kact and % inhibition values) and structural formulas related to the action on human proteins were extracted from the PubChem (February 2022) and ChEMBL (version 29) databases. The compounds were considered active if their Ki or IC50 values were less than 10,000 nM or if the percent of inhibition was higher than 50%. All compounds were considered inactive for the appropriate mechanism of action if they were not active according to the above-mentioned criteria. The canonical names of proteins from the UniProt database were used to create the names of the mechanisms of action. The data for protein targets from PubChem and ChEMBL were coupled based on UniProt IDs.The experimental data (KThe PASS (Prediction of Activity Spectra for Substances) software tool simultaneously predicts many types of biological activities (activity spectra) for compounds based on their structures ,13,14,15The algorithm for revealing structure–activity relationships is based on the naive Bayes approach with some significant enhancements ,15.The leave-one-out and 20-fold cross-validation procedures for all predictable types of biological activity and all substances are used during the training procedure. The invariant accuracy of prediction (IAP) is calculated for accuracy estimations. IAP values are numerically equal to ROC AUC (the area under the ROC curve) values. IAP is the estimation of the probability that positive and negative examples (active and inactive compounds) that are arbitrarily chosen from a validation set may be classified correctly by the prediction.Pa and “to be inactive” Pi. The list of predicted activities is arranged in a descending order according to Pa–Pi values. Thus, the more probable activity types are put at the top of the list. If the user chooses a higher Pa value as a cutoff for the selection of probable activities, the chance to confirm the predicted activities by the experiment is also high; however, many existing activities will be lost. For instance, if Pa > 0.5 is used as a threshold, about a half of the real activities will be lost; for Pa > 0.7, the portion of lost activities is 70%, etc. Moreover, a high Pa value shows the similarity of the queried compound structure and the typical compound structures of similar activity. Thus, experimental studies are mostly likely to confirm such an activity. The Pa value being smaller but still higher than the Pi value means that the chance to confirm the activity is lower. However, if the activity is discovered in the experiment, the compounds may be a new chemical entity for it.The prediction results of PASS are given as a list of activities with probabilities “to be active”


I read with great interest the recent article of Cavalcante et al.For instance, growth inhibition is seen in breast cancer cell lines following administration of caffeic acid phenethyl ester (CAPE) derived from propolis. CAPE modulates intra-tumor angiogenesis as well causes tumoral apoptosis thus decreasing tumor growth in breast carcinomasSimilarly, methanolic extracts of Mexican propolis have recently been shown to exert cytotoxic effects against pancreatic cancer cell lines. These anti-proliferative effects in pancreatic cells are primarily exerted by two new phenylallylflavanones, -6-[1-(4’-hydroxy-3’–methoxyphenyl)prop-2-en-1-yl]pinobanksin (1) and -6-[1-(4’-hydroxy-3’-methoxyphenyl)prop-2-en-1-yl] pinobanksin 3-acetate (2)Similarly, water extracts of Turkish propolis decrease cell viability to almost 18% when it is administered to prostate carcinoma cell linesSimilarly, Portuguese propolis exhibits cytotoxic effects against renal cell carcinoma cells. Similarly, red propolis inhibits growth in leukemia cell lines. The apoptotic capacity of red propolis is almost the same as gleevec. In addition, green propolis exerts anti-leukemic effects though it is not as potent as red propolis.The above examples clearly illustrate the anti-neoplastic effects of propolis and the need for further studies in this regard.


Signaling profiles are characterized by a high variability across the analyzed T-ALL cell lines. Hierarchical clustering analysis documents that higher intrinsic phosphorylation of Erk1/2, Lck, ZAP70, and Akt, together with ZAP70 phosphorylation induced by H2O2, identifies Jurkat cells. In contrast, CEM are characterized by higher intrinsic phosphorylation of JNK and Rb and higher responsiveness of Akt to external stimuli. MOLT4 cells are characterized by higher basal STAT3 phosphorylation. These data document that phospho-specific flow cytometry reveals a high variability in intrinsic as well as modulated signaling networks across different T-ALL cell lines. Characterizing signaling network profiles across individual leukemia could provide the basis to identify molecular targets for personalized T-ALL therapy.Several signaling pathways are aberrantly activated in T-ALL due to genetic alterations of their components and in response to external microenvironmental cues. To functionally characterize elements of the signaling network in T-ALL, here we analyzed ten signaling proteins that are frequently altered in T-ALL -namely Akt, Erk1/2, JNK, Lck, NF-κB p65, p38, STAT3, STAT5, ZAP70, Rb- in Jurkat, CEM and MOLT4 cell lines, using phospho-specific flow cytometry. Phosphorylation statuses of signaling proteins were measured in the basal condition or under modulation with H T-cell acute lymphoblastic leukemia (T-ALL) is a malignancy of immature T cells at defined stages of intrathymic T-cell differentiation . T-ALL aImmunophenotyping allows the classification of T-ALL in different subgroups based on the corresponding stage of T-cell ontogenesis . The proCXCL12 is a homeostatic chemokine secreted by stromal cells, fibroblasts and epithelial cells within tissue microenvironments, including lymph nodes and bone marrow (BM) ,20,21. CIL7 is a cytokine produced by stromal cells in the BM, thymus and other tissues. The IL7 receptor (IL7R) is expressed mainly by lymphoid cells . The IL7To gain overall picture of intrinsic and extrinsic signaling in T-ALL cells, in this study we investigated signaling pathways in three T-ALL cell lines, namely CEM, MOLT4 and Jurkat, using phospho-specific flow cytometry that simultaneously determines protein expression and protein post-translational modifications  at a single cell level ,40,41,422. Peripheral mononuclear cells (PBMC) were obtained from heparin-anticoagulated leukocyte concentrate samples collected as from healthy donors (HDs) at the Transfusion Medicine Unit, Azienda Ospedaliera Universitaria Integrata (AOUI), Verona, Italy. In accordance with the Declaration of Helsinki, all blood donors provided written informed consent for the anonymous collection and use of their leukocyte concentrate samples—as byproduct of the manufacturing of blood donation- for research purposes. The cryopreserved T-ALL cell lines CEM, MOLT4 and Jurkat  were cultured in medium RPMI-1640  with 10% fetal bovine serum  at 37 °C in a humidified atmosphere containing 5% COPBMCs were isolated by Ficoll-hypaque centrifugation  and cryopreserved in liquid nitrogen. Thawing of T-ALL cell lines and PBMC from HDs was performed at 37 °C and transferred in a 1:1 solution of RPMI-1640 complete medium and heat inactivated FBS. Cells were pelleted at 400 g for 5 min and resuspended in prewarmed (37 °C) complete RPMI-1640 medium. Before use, viability of the cells was over 90%, as assessed by flow cytometry analysis after staining with 7-amino-actinomycin D .Immunophenotype characterization of CEM, MOLT4, and Jurkat cell lines was performed as previously described . T-ALL c6/mL and stained with the reactive dye in the dark at room temperature for 30 min. After two washes with PBS, cell lines and PBMC were resuspended in RPMI-1640 with 10% fetal bovine serum at 2 × 106/mL and 4 × 106/mL, respectively; divided in 5 tubes and rested at 37 °C in a humidified atmosphere containing 5% CO2 for 1 h. Each tube was then treated with a different modulator for 15 min  according to the manufacturer’s instructions. Briefly, cells were collected, washed, counted, resuspended in PBS at 1 × 10r 15 min  for phosPhospho-specific flow cytometry was performed as previously described . Briefly2Fold change [log2(RMFI modulated/RMFI unmodulated)], as previously described [2 value equal to 0 corresponds to ‘no change’ with respect to the unstimulated. The response to external modulators was considered positive when log2Fold change > 0. We used the term “node” to refer to a proteomic readout in the presence or absence of a specific modulator. For example, the response to CXCL12 modulation can be measured using pErk1/2 as a readout. That node is designated “CXCL12-pErk1/2” [Flow cytometry data were processed, analyzed, and graphed using FlowJo software . Dead cells, debris and doublets were excluded based on forward-scatter, side-scatter, and LIVE/DEAD signals. All analyses were performed on gated T cells, identified as CD45/CD3 co-expressing cells in HD samples and as cCD3 or CD7 positive in T-ALL cell lines. Median fluorescence intensity (MFI) was normalized with respect to MFI of isotype control (RMFI) and, when indicated, transformed into logBasal phosphorylation statuses of signaling proteins were considered activated when RMFI >1. The response of cells to treatments was calculated as logescribed . A log2 pErk1/2” .https://biit.cs.ut.ee/clustvis/; accessed on 21 September 2021) [p value was corrected for multiple comparisons using the Dunn’s test. Differences were considered statistically significant when p ≤ 0. Graphing and statistical analyses were performed using GraphPad Prism software .The unsupervised hierarchical cluster analysis (HCA) was performed using webtool ClustVis  , calcula2O2, phorbol 12-myristate 13-acetate (PMA), CXCL12, or IL7.The phosphorylation levels of ten signaling proteins that are frequently altered in T-ALL , lymphocyte-specific protein tyrosine kinase (Lck), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) p65, p38, STAT3, STAT5, zeta-chain-associated protein kinase 70 (ZAP70), and retinoblastoma protein (Rb)) were analyzed at the single-cell level in the T-ALL cell lines CEM, MOLT4, and Jurkat, using phospho-specific flow cytometry . Based o2 across the phosphoproteins was 2.7 for CEM, 6.0 for MOLT4, 20.7 for Jurkat; Y319, with higher levels of Erk1/2, Lck, and ZAP70Y319 in Jurkat cells. JNK was phosphorylated in CEM and Jurkat cells. Rb was constitutively phosphorylated in CEM and MOLT4. Basal phosphorylation of Akt was detected in CEM and Jurkat cells, with higher levels in Jurkat. ZAP70Y292 was constitutively phosphorylated in Jurkat cells. Pp38 and pSTAT5 levels were undetectable in all the analyzed T-ALL cell populations .Then, we investigated the responsiveness of T-ALL cell lines to Htivation . ResponsNext, we stimulated T-ALL cell lines with PMA, which activates T cells through protein kinase C (PKC) . As showTaken together, these data reveal high signaling variability across T-ALL cell lines with distinct phosphorylation profiles for each cell population and specific signaling sensitivity to phosphatase inhibition and to PKC activation.2Fold change in phosphorylation between modulated and unmodulated conditions , with T-ALL cell lines showing higher levels than HD T cells, used as control . Moreovenditions . CXCL12 nditions A,B. Enganditions C,D. In cnditions C,D.2 RMFI—and responses to external stimuli—measured by the log2Fold change in phosphorylation between modulated and unmodulated conditions—were subjected to unsupervised hierarchical clustering analysis (HCA) within the three T-ALL cell populations. Unsupervised HCA of all the analyzed nodes (n = 55) identified CEM/MOLT4 cells and Jurkat as separate clusters . Remarkably, this clustering preserved the classification obtained analyzing all nodes and allowed us to identify significant nodes characterizing the different T-ALL cell lines , thus enabling amplification of signaling events. Indeed, previous studies have shown that exposing cancer cells to potentiating inputs, such as H2O2, could allow us to discern cancer network profiles that correlate with disease outcome [Here, we apply phosphospecific flow cytometry to functionally characterize elements of the T-ALL signaling network. We measured signaling properties in the T-ALL cell lines CEM, MOLT4, and Jurkat in the basal condition and following cell modulation. Indeed, we and others have previously shown that signaling readouts in response to external modulators, rather than the basal levels of protein phosphorylation alone, are of clinical relevance in leukemia ,41,47. B outcome ,49. Our Y319, and pSTAT3 in all T-ALL cell lines, with a higher activation of Erk1/2, Lck, and ZAP70Y319 in Jurkat and higher levels of basal pSTAT3 in MOLT4 cells. Interestingly, in the presence of tyrosine-phosphatase inhibition as well as following PMA treatment, pErk1/2 further increases in CEM and Jurkat cells while pSTAT3 increases in all cell lines, with a higher response in CEM cells that in contrast exhibit low pSTAT3 in the basal condition. Moreover, inhibition of tyrosine phosphatase by H2O2 induces augmentation of pNF-κB p65 in MOLT4.Analysis of the phosphorylation status of signaling proteins in the unstimulated condition—which captures intrinsic signaling alterations—documents constitutive phosphorylation of pErk1/2, pNF-κB p65, pLck, ZAP70Erks are members of the MAPK family that play an important role in both regulation of T-cell receptor (TCR) signaling  and funcNOTCH1 have been identified in over 60% of T-ALL cases [PTEN inactivating mutations as well as NOTCH1 mutations have been documented in all the T-ALL cell lines analyzed in this study [NF-κB family transcription factors are a common downstream target for inducible transcription mediated by many different cell-surface receptors, especially those receptors involved in inflammation and adaptive immunity . In T ceLL cases . Consistis study .Y319, with higher phosphorylation levels observed in Jurkat cells. Interestingly, inhibition of tyrosine phosphatases further increases pZAP70Y319 in Jurkat. Lck is a central kinase in T-cell precursors for the transition of CD4/CD8 double negative to double positive thymocytes and proliferation of early T-cell precursors [Y319 may sustain Lck localization and the catalytic activities of both kinases, thereby providing positive feedback [Y292, which is increased following treatment with H2O2. The Y292 residue of Zap70 is known to mediate inactivation of the TCR signaling [T-ALL cell lines also exhibit the concurrent phosphorylation of Lck and ZAP70ecursors . Moreoveecursors . The Lckecursors . Moreovefeedback . Therefoignaling . Taken tignaling ,69.Y319 showing a higher intrinsic phosphorylation in Jurkat cells, we document a higher phosphorylation of STAT3 in MOLT4. The constitutive phosphorylation of STAT3 in all the T-ALL cell lines analyzed is consistent with previous studies showing high levels of pSTAT3 in T-ALL [In contrast to the phosphorylation pattern described for Erk1/2, Lck, and ZAP-70in T-ALL  could bein T-ALL .Rb is constitutively phosphorylated in CEM and MOLT4, with CEM showing the highest levels of pRb. The tumor suppressor Rb is a negative regulator of cell cycle and Rb inactivation by deletion or hyperphosphorylation has been shown to induce cell-cycle progression in various cancers . HyperphPTEN inactivating mutations [PTEN mutations documented in the analyzed T-ALL cell lines [Akt and JNK are constitutively phosphorylated in CEM and Jurkat cells, with Jurkat showing the higher intrinsic Akt phosphorylation. Moreover, in the presence of tyrosine phosphatase inhibition, pAkt levels are increased in CEM and MOLT4. The PI3K/Akt signaling pathway is frequently constitutively activated in T-ALL, mainly due to utations ,61, whicll lines . In normll lines . In T-ALll lines ,61,76 anll lines . PI3K inll lines . Moreovell lines .JNK, a member of MAPK superfamily, plays an oncogenic role in several cancers, especially in malignant lymphocytes ,81,82. ABasal pSTAT5 and pp38 are both undetectable in the studied T-ALL cell lines, also in the presence of tyrosine-phosphatase inhibition and following treatment with PMA, CXCL12, or IL7.All the analyzed T-ALL cell lines are unresponsive to IL7, probably due to the very low levels of CD127 expressed on the cell surface of these cells. Our data are in accordance with results from Bonaccorso et al., showing that basal STAT5 activation was low and nonresponsive to IL7 in PTEN-mutated T-ALL .CXCL12 stimulation evokes a clear-cut increase of pAkt in CEM and MOLT4, and pJNK in MOLT4. Interestingly, Akt in CEM and MOLT4, and JNK in MOLT4 show low intrinsic phosphorylation levels. In contrast, Akt in Jurkat and JNK in CEM and Jurkat are highly phosphorylated in the basal condition. This phosphorylation pattern suggests that CXCL12 evokes Akt and JNK phosphorylation responses when these phosphoproteins are intrinsically inactive, whereas it is unable to further induce phosphorylation of Akt and JNK when they display high intrinsic activation status.Hierarchical clustering analysis documents that higher intrinsic phosphorylation of Erk1/2, Lck, ZAP70, and Akt, together with ZAP70 phosphorylation in the presence of tyrosine-phosphatase inhibition, identifies Jurkat. Moreover, Jurkat cells exhibit higher phosphorylation levels of these nodes compared with normal T cells. In contrast, CEM are characterized by higher intrinsic phosphorylation of JNK and Rb and higher responsiveness of Akt to external stimuli, which are significantly higher in comparison with normal T cells. MOLT4 cells are characterized by higher STAT3 phosphorylation in the basal condition.Our study originally shows that phosphospecific flow cytometry allows us to capture signaling network variability across different T-ALL cell lines. Although the use of cell lines does not allow us to relate signaling properties with disease characteristics, the ability to detect signaling heterogeneity among T-ALL cell lines—which can reflect biological heterogeneity of primary cells from which they derive—provides an important “proof of concept” for future studies aimed at characterizing signaling heterogeneity across primary leukemic cell samples. Moreover, these results corroborate genetic landscape data, suggesting that distinct signaling pathways are activated in subtypes of T-ALL .Overall, the T-ALL cell populations we characterize in this study exhibit homogeneous phenotypes and monomodal distributions of the analyzed phosphoproteins. Therefore, herein we could not characterize heterogeneity at the single cell level within cell-line populations. However, phospho-specific flow cytometry is an especially useful tool for interrogating the physiology of signaling pathways by measuring network properties at the single-cell level, thus understanding the biology of heterogeneous populations of cells, such as those found in tumor samples from patients. Indeed, the signaling profile of any given leukemic cell is the sum of numerous influences—epigenetic, genetic and microenvironmental—and the heterogeneity of cancer cell behavior can be thought of reflecting the signaling differences that have arisen during the evolution of leukemic cell population. Therefore, integration of genomic and epigenetic studies with functional proteomic approaches at the single-cell level is fundamental to identify potential drug targets or biomarkers.The functional flow cytometry approach we describe in this study provides a powerful tool for characterizing T-ALL heterogeneity across samples and has the potentiality to characterize signaling heterogeneity at the single level within primary leukemic cell populations. Therefore, it could represent an important tool for future studies on T-ALL primary cells aimed at identifying signaling elements as potential precision drug targets or biomarkers.In this study, we demonstrated that phospho-specific flow cytometry can identify intrinsic and extrinsic signaling pathway activities and capture signaling network variability across different T-ALL cell lines. Overall, our study can contribute to a deeper understanding of the complex signaling network altered in T-ALL. Moreover, our data can form the basis for future studies on primary T-ALL cells connecting signaling properties with clinical behaviors and aimed at developing precision medicine tools, particularly for resistant and high-risk T-ALL patients.


Cell lines of monocyte/macrophage origin are often used as model systems to study monocyte/macrophage biology. A relevant question is how similar these cell lines are to their in vivo counterparts? To address this issue, we performed a detailed analysis of the transcriptome of two commonly used human monocyte/macrophage cell lines, Mono Mac 6 and THP-1. Both of these cell lines originate from leukemic cells with myelo-monocytic characteristics. We found that both Mono Mac 6 and THP-1 represent cells of very immature origin. Their transcriptomes show more similarities to immature neutrophils than cells of the monocyte/macrophage lineage. They express significant levels of N-elastase, proteinase 3, cathepsin G, and azurocidin but very low levels of CD14, ficolin, and complement factor P. All major MHC class II genes are also expressed at low levels. They show high levels of lysozyme and low levels of one of the immunoglobulin Fc receptors, FCGRIIA, which is characteristic of both neutrophils and monocytes. THP-1, but not Mono Mac 6, also expresses the high-affinity receptor for IgG, FCGRIA. Both cell lines lack the expression of the connective tissue components fibronectin, proteoglycan 4, and syndecan 3, which are characteristics of tissue macrophages but are absent in blood monocytes, indicating that they originate from bone marrow precursors and not yolk sac-derived hematopoietic cells. Both of these cell lines seem, therefore, to represent cells arrested during early myelo-monocytic development, at a branch point between neutrophil and monocyte differentiation. Their very immature phenotype indicates that great care should be taken when using these cell lines as models for normal monocyte/macrophage biology. Cell lines have been instrumental for many areas of cell biology and also an essential tool for the production of properly folded mammalian proteins for structural and functional analysis as well as for therapeutic use. Cell lines often originate from tumor cells selected for in vitro growth since their normal counterparts are often short lived and change phenotype quickly after being put in culture. Cell lines originating from different hematopoietic lineages have been of major importance for the analysis of signaling pathways regulating cell activation and development. However, the question is always, how similar are they to their normal in vivo counterparts?The two most frequently used in vitro models for studies of human monocytes/macrophage biology are the cell lines Mono Mac 6 and THP-1. Both of them originate from leukemias with myelo-monocytic characteristics. Mono Mac 6 is derived from the peripheral blood of a 64-year-old male with acute monocytic leukemia (AMoL), while THP-1 comes from the peripheral blood of a 1-year-old boy with AMoL, both with monocytic features . These cAlmost 30 years ago, we performed a Northern blot analysis of 18 different marker genes in Mono Mac 6, THP-1, and a few other hematopoietic cell lines ,1211,12]The expression pattern of these two cell lines shows a very high degree of similarity in their transcriptomes, indicating that they originate from cells of a similar stage of differentiation at the branch point between monocyte and neutrophil development . HoweverThe now increased detail in the analysis of the transcriptome of these two cell lines confirms the previous indications based on Northern blot analysis of a limited set of marker genes . We can THP-1 and Mono Mac 6 cells were maintained in RPMI1640 medium  supplemented with 10% fetal calf serum . The cells were expanded in standard culture flasks in the above medium without any addition of cytokines or other stimuli such as LPS and no serum starvation. Approximately 4 million cells were harvested and washed once with phosphate-buffered saline (standard PBS) before isolation of total RNA using the RNeasy Plus mini kit from , according to the manufacturer’s recommendations. The RNA was eluted with 30 μL of DEPC-treated water, and the concentration of RNA was determined by using a Nanodrop ND-1000 . Later the integrity of the RNA was confirmed by visualization on 1.2% agarose gel using ethidium bromide staining.The THP-1 and Mono Mac 6 cells were analyzed for their total transcriptome by the Thermo-Fisher chip-based Ampliseq transcriptomic platform at the SciLife lab National Genomics Infrastructure (NGI) facility in Uppsala, Sweden . The sequence results were delivered in Excel files for an easy comparison of expression levels between samples.g for 10 min. The cells were resuspended in PBS (pH 7.4) with 2% heat-inactivated fetal bovine serum , followed by incubation with the following fluorescent-labeled antibodies: F4/80 (BM8), CD11b (M1/70), CD19 (1D3), CD117 (2B8) and FcεRI (MAR-1). The antibodies were obtained from BD Biosciences  or eBioscience . FACS-isolated peritoneal macrophages were collected for RNA isolation. The flow cytometry-based cell sorting was performed on a FACSAria III (BD Biosciences), and data were analyzed with FlowJo software .Information from two previous studies of freshly isolated mouse peritoneal macrophages and human blood monocytes was included as reference material ,12. For + cells was performed through magnetic cell separation. Subsequently, CD14 cells were stained with antihuman CD14 PE antibody , and the purity was verified (average of 95%) by flow cytometry.The human peripheral blood monocytes were purified from buffy coats from the blood center at the Akademiska hospital in Uppsala. Peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll–Paque Plus  and standard density gradient centrifugation. PBMCs were further washed with PBS containing 2 mM of EDTA and incubated with anti-CD14-coated magnetic beads . Positive selection of CD14


HSA) was prepared, and the effect of the position of the hydroxyl group along the chain on a panel of human cancer cell lines was investigated. Among the various regioisomers, those carrying the hydroxyl at positions 5, 7, and 9 had growth inhibitor activity against various human tumor cell lines, including CaCo-2, HT29, HeLa, MCF7, PC3, and NLF cells. 10-HSA and 11-HSA showed a very weak effect. 8-HSA did not show inhibitory activity in all cell lines. The biological role of 7-HSA and 9-HSA is widely recognized, while little is known about the effects of 5-HSA. Therefore, the biological effects of 5-HSA in HeLa, HT29, MCF7, and NLF cell lines were investigated using the Livecyte’s ptychography technology, which allows correlating changes in proliferation, motility, and morphology as a function of treatment at the same time. 5-HSA not only reduces cell proliferation but also induces changes in cell displacement, directionality, and speed. It is important to characterize the biological effects of 5-HSA, this molecule being an important component of fatty acyl esters of hydroxy fatty acids (FAHFA), a class of endogenous mammalian lipids with noticeable anti-diabetic and anti-inflammatory effects.A series of regioisomers of the hydroxystearic acid ( Hydroxy fatty acids and their derivatives are compounds of growing interest in many scientific areas. In industry, they are widely used as lubricants ,2,3, surFAHFA), a class of endogenous mammalian lipids with important effects on metabolism  , 323 [M + Na+], 339 [M + K+].Methyl 5-hydroxyoctadecanoate (°C (Lit. : 81 °C),6, 0.165 g, 1.06 mmol), oleic acid , and Grubbs Catalyst® M51  dissolved in 1 mL of CH2Cl2 were added. The temperature was set to 63 °C, and the mixture was irradiated for 30 min under stirring. The brown crude reaction mixture was immediately purified by flash chromatography , compound 8  was isolated in 20% yield. All spectroscopic data are in accordance with the literature +, 323 [M + Na]+, [M + K]+.11-Hydroxystearic acid , human colorectal adenocarcinoma (HT29), human cervical cancer (HeLa), human breast cancer (MCF7), human caucasian prostate adenocarcinoma (PC3), and human neuroblastoma (NLF) cell lines were purchased from American Type Culture Collection . Cells were cultured in RPMI 1640 medium , supplemented with 10% FCS  and 2 mM L-glutamine , at 37 °C, and a 5% CO4 cells/well in a 96-well culture plastic plate , and after 24 h growth they were exposed to increasing concentrations of 5-HSA (5a), or 7-HSA (5b), or 8-HSA (5c), or 10-HSA (5e), or 11-HSA (5f) (from 0.010 μM to 500 μM) solubilized in RPMI 1640 medium. MTT assay was performed according to Ref [50 μM.Cells were seeded at 1.5 × 10g to Ref . In briePtychography uses multiple diffraction patterns collected from spatially overlapping regions of the samples to form QPI images. QPI is a label-free technique based on different methods, which allows obtaining the phase information of light passing through the cell. QPI techniques quantify the extent of phase delay introduced by the sample and record it as pixel values within the generated image. Its intensity is due to the thickness and the refraction index of the cell, which depends on the biomolecule composition and organization within the cell ,57,58,593 per well. After 24 h, cells were treated with 5-HSA 50 μM in six replicates, and the images were acquired every 60 min for 3 days using a 10× objective lens (0.25 NA), at 37 °C and 5% CO2. Data were analyzed using the Cell Analysis Toolbox software  to evaluate cell growth, doubling times, and motility.Quantitative phase image (QPI) microscopy assay was performed using the Livecyte microscope  according to the manufacturer’s indications. In brief, HT29, HeLa, MCF7, and NLF were seeded in a 96-well plate  at 4 × 10t-test was used between 2 different independent groups. A p-value below 0.05 was considered significant.All experiments were performed in triplicate and repeated at least three times. Results were averaged, and the standard deviation was calculated. To determine statistical significance, unpaired two-tailed Student’s 5-HSA, 7-HSA, 8-HSA, 9-HSA, 10-HSA, and 11-HSA were synthesized in good yield through simple multistep synthetic procedures, and their growth inhibitory effects were evaluated on a panel of cancer cell lines.The regioisomeric series of hydroxystearic acids, namely 5-HSA, 7-HSA, and 9-HSA) showed significant inhibitory potency, while 10-HSA and 11-HSA exhibited a very weak effect, and 8-HSA showed no inhibitory activity in all cell lines. Since little is known about its effects, we focused particular attention on 5-HSA, demonstrating that it shows cell growth inhibitory properties. Quantum phase imaging allowed us to investigate 5-HSA effects more deeply on the four cell lines tested, since treatment influences important cellular parameters, such as dry biomass, morphology, and motility.In general, isomers with the hydroxy group bound to odd carbon atoms  is lower than in MCF7 (IC50 46.4 μM), HT29 (IC50 51.3 μM) and NLF (IC50 38.5 μM) indicates how the potency of the molecule is affected by the characteristics of the individual cell lines.Furthermore, the observation that half maximal inhibitory concentration of An important final consideration is that this is a preliminary work, performed using the racemic mixture of the considered HSA derivatives. As demonstrated by Kokotou M. et al. , it is i


A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at   •Pan-cancer proteomic map of 949 human cancer cell lines across over 40 cancer types•There are 8,498 proteins with evidence of cell types and broad post-transcriptional regulation•Deep learning-based pipeline finds biomarkers of drug response and gene essentiality•Random downsampling reveals highly connected and co-regulated protein networks Gonçalves et al. generate a comprehensive proteomic map of 949 human cancer cell lines across more than 40 cancer types. Proteomic data are integrated with multi-omic, drug response, and CRISPR-Cas9 gene essentiality datasets. Deep learning is used to identify biomarkers of cancer vulnerabilities, providing evidence for highly connected protein networks. Precision medicine relies on the identification of specific molecular alterations that can stratify patients and guide the choice of effective therapeutic options. Cancer vulnerabilities, such as synthetic lethalities, can be systematically studied using functional genetic and small molecule screens. To circumvent the limitations of using patient tissue samples for this type of approach, biomarkers of cancer vulnerabilities have been analyzed using cancer cell lines, together with deep molecular characterization, functional genetic and pharmacological screens . The dirTechnological and methodological advances have enabled the standardized quantification of thousands of proteins across dozens to hundreds of cell lines  and the Cell lines have been invaluable models for our understanding of cellular processes and the molecular drivers of carcinogenesis , and forThis study reports a pan-cancer cell line proteomic map quantifying 8,498 proteins across 949 cell lines. The generation and analysis of this rich resource involved the development of a workflow with rapid sample processing and minimal complexity, followed by the application of a deep neural network-based computational pipeline to uncover cancer targets. Integration of our proteomic data (referred to as the ProCan-DepMapSanger dataset) with existing molecular and phenotypic datasets from the Cancer Dependency Map , showed PXD030304. Raw DIA-MS data were processed with DIA-NN (50) experimentally measured.To construct a pan-cancer proteomic map, proteomes of 949 human cancer cell lines from 28 tissues and more than 40 genetically and histologically diverse cancer types were quantified . The proh DIA-NN , using rh DIA-NN  was thenh DIA-NN C, with ah DIA-NN A. The Prh DIA-NN B. Pharmah DIA-NN C, with ar) of 0.92  . VisualiWe next sought to identify the key drivers of the distinct protein expression patterns observed across the cell line panel and to investigate how these integrate with other molecular and phenotypic measurements. Multi-omics factor analysis (MOFA) , 2020 war = 0.42, While EMT markers were largely concordant between transcriptomics and proteomics, more broadly, a modest and variable association was observed between protein and transcript measurements . This is consistent with the known increase in stability of many mutant P53 proteins, which results from a decreased rate of proteasome-mediated degradation . We systematically assessed this enrichment using multiple resources for protein interactions: protein complex interactions from the Comprehensive Resource of Mammalian Protein Complexes (CORUM)  ; functio (CORUM) ; physica (CORUM) ; and the (CORUM) . For all (CORUM) G, indicaThe correlation p value score (Fisher’s combined probability test) for BioGRID and HuRI databases was improved slightly by merging different datasets (AUC = 0.54–0.82), suggesting that different types of interactions are captured across multi-omic layers. Proteins with higher numbers of positive protein-protein correlations were more essential for cancer cell survival, as observed in the CRISPR-Cas9 gene essentiality dataset E. This ir greater than 0.8 that are not reported in any of the protein network resources analyzed here. For example, there were strong correlations between protein profiles for EEF2-EIF3I, RPSA-SERBP1 and CCT6A-EEF2  that we0 values . Among t0 values A. We obs0 values A. This a0 values  and in h0 values . For ano0 values A, indica0 values B. Non-se0 values A.Figure r between the observed and predicted IC50 values, consistently outperformed other machine learning approaches such as elastic net and Random Forest, across a range of multi-omic datasets used in previous studies  C. DeePro studies   in cell lines derived from bone   A. This iroteins) B.Figure To investigate protein networks, DeeProM analyses of drug-protein and CRISPR-Cas9-protein associations were examined in the context of protein networks C and 7D.r = 0.463 for 1,500 proteins, or r = 0.435 for 250 proteins) when compared against less frequently observed proteins  , 20%–90%  or less than 20%  of the cell lines, respectively F. Downsaroteins) E. Categoroteins)   F. It is http://cellmodelpassports.sanger.ac.uk /MS run time, enabling the analysis of large numbers of very small cancer samples, with high throughput and minimal instrument downtime. This will facilitate future validation of proteomic predictions from cell line data in clinical sample cohorts for which outcome of treatment is documented and proteomic data are obtained. The CCLE proteomic dataset was generated using higher peptide loads and longer LC/MS run time to measure more proteins . Despite the different depths of protein coverage, the CCLE and ProCan-DepMapSanger proteomic datasets had equivalent power for predicting cancer dependencies. Similarly, the ProCan-DepMapSanger proteomic dataset had similar predictive power to cancer cell line transcriptomic data. Taken together, this demonstrates that a high-throughput sample workflow, as used in this study, produces data with power to inform predictions of cancer dependencies and indicates the potential of proteomics for clinical applications using small biopsies of human cancer tissue in diverse molecular contexts. Subsequent application of this proteomics sample workflow, and integration of this cell line dataset with proteomic data from cancer tissue samples, is likely to provide numerous potential clinical applications, such as the proteomic molecular identification and stratification of cancer subtypes.Measuring even a fraction of the proteome, as small as 1,500 randomly selected proteins, provided power to predict drug responses that were similar to the full proteome that we report. This suggests that random subsets of protein data comprising a relatively small number of proteins would be sufficient to represent many fundamental cellular processes. This is consistent with an omnigenic model , wherebyIn conclusion, this dataset represents a major resource for the scientific community, for biomarker discovery and for the study of fundamental aspects of protein regulation that are not evident from existing molecular datasets. This will enable the identification of targets (including cell surface proteins) and treatments for validation in cancer tissue cohorts, with applications in precision oncology.rreddel@cmri.org.au.Further information and requests for resources and reagents should be directed to Roger Reddel, This study did not generate new unique reagents.https://cellmodelpassports.sanger.ac.uk/) (The 949 human cancer cell lines used in this study have been obtained from public repositories and private collections and are cultured in DMEM/F12 or RPMI 1640 . More de.ac.uk/) . All celTo prevent cross-contamination or misidentification, all banked cryovials of cell lines were analyzed using a panel of 94 single nucleotide polymorphisms (SNPs)  . To do so, STR loci were amplified using the PowerPlex® 16HS System (Promega) and the data were analyzed using GeneMapper™ ID software (ThermoFisher). Only cell lines that passed this quality control metric were retained for analysis (n = 949).2 in the appropriate medium and then harvested. Suspension cells were centrifuged at 200 g for 5 min at 4°C and the supernatant was removed. The cells were then washed twice by resuspension in ice-cold Dulbecco's phosphate buffered saline containing no calcium or magnesium (DPBS) and centrifugation at 200 g for 5 min at 4°C. For adherent cells, the culture medium was removed before washing with ice-cold DPBS and the cells were removed by mechanical scraping into fresh ice-cold DPBS. The harvested cells were then centrifuged as before, washed twice in ice-cold DPBS, transferred to 1.5 mL centrifuge tubes  and centrifuged at 600 g for 5 min at 4°C. The DPBS was removed and the tubes containing the cell pellets were snap frozen on dry ice, then stored at −80°C.For each cell line, distinct cell pellets from a single cell culture were produced as technical replicates. Cells were cultured to semi-confluence at 37°C and 5% COThree cell pellets were analyzed for each of the 949 cell lines. The cell pellets were processed using Accelerated Barocycler Lysis and Extraction (ABLE) protocol with minor modifications . In brieWe used a workflow that enables high throughput and minimal instrument downtime; 2 μg of peptide was loaded for each replicate with 90-min acquisitions. Three technical replicates of peptide preparations were generated. Each replicate was injected on two of six different SCIEX™ 6600 TripleTOF® mass spectrometers coupled to Eksigent nanoLC 425 high-performance liquid chromatography (HPLC) systems, housed in a single laboratory, ProCan in Westmead, Australia B. In eacThe peptide digests (2 μg) were spiked with retention time standards and injected onto a C18 trap column (SGE TRAPCOL C18 G203 300 μm × 100 mm) and desalted for 5 min at 10 μL/min with solvent A (0.1% [v/v] formic acid). The trap column was switched in-line with a reversed-phase capillary column (SGE C18 G203 250 mm × 300 μm ID 3 μm 200 Å), maintained at a temperature of 40°C. The flow rate was 5 μL/min. The gradient started at 2% solvent B  and increased to 10% over 5 min. This was followed by an increase of solvent B to 25% over 60 min, then a further increase to 40% for 5 min. The column was washed with a 4 min linear gradient to 95% solvent B held for 5 min, followed by a 9 min column equilibration step with 98% solvent A. The TripleTOF® 6600 system was equipped with a DuoSpray source and 50 μm internal diameter electrode and controlled by Analyst 1.7.1 software. The following parameters were used: 5500 V ion spray voltage; 25 nitrogen curtain gas; 100°C TEM, 20 source gas 1, 20 source gas 2.in silico spectral library was created using DIA-NN (version 1.8)  quantification strategy was used, and precursors were filtered at a q-value of 0.01. Protein grouping was done at the canonical protein sequence level rather than gene level. The final spectral library contained a total of 12,487 proteins and 144,578 precursors. DIA-NN (version 1.8) (PXD030304.An ion 1.8)  for the ion 1.8) , and allion 1.8) . To reduion 1.8)  was usedion 1.8)  with idehttps://github.com/vdemichev/diann-rpackage) and with default parameters. Data were then log2 - transformed. 117 files were discarded from downstream analyses  to systematically fit drug response curves and derive half-maximal inhibitory concentration (IC50) measurements for each drug across the cell lines measured  transcriptomics and Infinium HumanMethylation450 methylation measurements for the same set of cancer cell lines were assembled from previous analyses, for which the acquisition and processing are described in detail . Mutatio2 fold-changes corrected for copy number bias   was calcMulti-omics decomposition by factor analysis was performed using the mofapy2 Python module (v0.5.6) , 2020. Dr correlations between their protein, gene expression and essentiality measurements. A minimum of 15 complete observations was required to calculate the correlation, yielding 16,580,952 pairwise combinations. Protein-protein correlations were annotated using multiple sources of protein interactions: Comprehensive Resource of Mammalian Protein Complexes (CORUM) . For all pairwise protein combinations, we calculated Pearson’s  (CORUM) ; Search  (CORUM) ; Biologi (CORUM) ; and Hum (CORUM) . For Bio (CORUM) .We developed a multistep computational workflow, Deep Proteomic Marker (DeeProM), to identify protein biomarkers of cancer vulnerabilities. The analysis steps in DeeProM are fourfold. First, it prioritizes drug responses and CRISPR-Cas9 gene essentialities that can be confidently predicted using proteomic profiles. Second, it prioritizes strong drug responses and gene essentialities that are specific to small subsets of cancer cell lines. Third, it prioritizes protein biomarkers that show significant associations with drug responses or gene essentialities. Fourth, it prioritizes protein biomarkers that are present in specific tissues. The source code is provided in the GitHub repository see .DeeProM is powered by a deep neural network architecture, DeepOmicNet, to predict drug responses and CRISPR-Cas9 gene essentialities. DeepOmicNet ranks drugs and gene essentialities based on predicted cellular responses using the ProCan-DepMapSanger dataset as the input. Both the proteomic and the drug response datasets contain missing values, while the gene essentiality dataset provides a complete data matrix. DeepOmicNet models these missing values accordingly. Multilayer perceptron (MLP) is a classic neural network architecture that has been used by default for deep learning in numerous biomedical studies Z. . To enhaW ∈ R(k x k) containing k2 parameters with grouped bottlenecks can not only reduce the number of parameters, but also provide better predictive performance. Instead of connecting all pairs of neurons, neurons can be broken into groups, and only neurons within the same group are connected between layers , the value of xi is calculated with skip connections as:f is the activation function, i-2 is fed into the hidden layer i by skipping the hidden layer i-1, resulting in skip connections  and n. The label ym,n is missing if a particular cell line m was not screened with drug n.DeepOmicNet is trained with mini-batches using a customized mean squared error (MSE) as the loss function. DeepOmicNet is applied to predict both drug responses and CRISPR-Cas9 gene essentialities. For the cell line In addition to the three major improvements, other characteristics of DeepOmicNet include the following:One specific challenge of DIA-MS based proteomics is the missing values in the data matrix . Imputatr between true and predicted values is used as the evaluation metric. Hyperparameters that resulted in the highest performance in five-fold cross-validation of the 80% training data were chosen for the final evaluation on the 20% independent test set. The chosen hyperparameters can be found in the configuration files in the source code  or viral transformation, we approximated this task by finding drug responses and gene essentialities that were selective for only a small fraction of the cell lines, which indicates that the drug response or gene essentiality is less likely to be toxic to normal cells. Ranking of strongly selective drug responses and gene essentialities was performed using Python package scipy (v1.5.2) skew function, which calculates the Fisher-Pearson’s coefficient of skewness. Skewness values of −1 and −2 were used for drug responses and gene essentialities, respectively.50 across all drugs. Discrete covariates were represented as dummy binary variables. Second, to identify associations that were exclusively found at the protein level, we added the following gene expression covariates to the model: (i) the first ten gene expression principal components using the Python package scikit-learn (v0.24.2) (d represents an n x 1 vector of the drug response IC50 (n = 710 drugs) for 947 cell lines or CRISPR-Cas9 gene essentiality log2 fold changes  for 534 cell lines; M is the n x k matrix of covariates (k = 11 covariates); E is an n x m matrix containing the first (m = 10) principal components of the gene expression dataset; e is a vector of size n x 1 containing the transcriptomics measurements of the corresponding protein p; p is a vector of size n x 1 containing the protein measurements; and n x 1. For each protein, cell lines with missing values were dropped from the modeling. For drug response, missing values were replaced by the drug mean IC50. The number of cell lines with complete information per fit was provided. The model was fitted by minimizing the residual sum of squares to estimate the parameters Associations between protein and phenotypic measurements, drug responses and gene essentialities were performed using linear regression models (sklearn v0.24.2 class LinearRegression). Several technical and biological covariates were added to the model to remove potentially spurious associations. First, we built the following technical covariates into the model: (i) the growth rate of the cell lines; (ii) cell culture medium, D/F12  or R ; (iii) cell line growth properties , ploidy, and if they are hematopoietic and lymphoid cell lines; (iv) sample mean protein replicates Pearson’s correlation; (v) for the CRISPR-Cas9 gene essentiality only, we considered the institute of origin of the CRISPR-Cas9 screen, i.e., Wellcome Sanger or Broad Institute; and (vi) for the drug response models only, we considered the cell line mean ICv0.24.2) ; and tissue for a target-protein association in a given tissue type. Here, protein indicates a protein of interest, target indicates either a drug response or CRISPR-Cas9 gene essentiality and tissue refers to the tissue type under investigation. We then calculated the Pearson’s r for the gene that encodes each protein, and we defined this value as rtissue. To prioritize associations that were uniquely identified at the protein level, the difference d for a given tissue type between target-protein and target-RNA associations was set to be larger than 0.15, where d = |rtissue|-|rtissue |. This prioritizes associations that have either strong positive or strong negative correlations at the protein level but have weak correlations around zero at the RNA level. Rare cases where rtissue and rtissue have opposite signs and d is close to 0 were not considered.For each significant association identified from 7,698 drug-protein and 5,823 CRISPR-Cas9-protein associations, Pearson’s d alone is insufficient to select candidate associations, because a drug may be entirely ineffective for all the cell lines in a particular tissue type. Therefore, we applied an additional filter to ensure that a drug is effective on the cell lines for which the protein abundance is high. That is, for a drug-protein association to be included for a given tissue type, the median IC50 of the 20% of cell lines with the highest corresponding protein abundance must be lower than the maximal concentration for that drug.For drug-protein associations, a large value of d.The remaining associations were ranked in descending order according to DeepOmicNet was compared against traditional machine learning models, including elastic net and Random Forest. A total of 947 cell lines were randomly separated into a training set comprising 80% of the cell lines, and a test set with the remaining cell lines for unbiased evaluation. Grid search was used to find the best hyperparameters for elastic net and Random Forest in the training set by cross-validation. Hyperparameter tuning for DeepOmicNet was performed manually due to the limit of graphics processing unit (GPU) memory. For each model, missing values were imputed using the method that gave the best prediction based on cross-validation. Specifically, four imputation methods were considered, including imputation by minimum, first percentile of the whole input matrix, mean and zero. Based on the predictive performance of models in cross-validation, imputation by one percentile of the whole matrix was chosen for the elastic net and Random Forest, and imputation by zero was used for DeepOmicNet. This strategy yielded the best prediction accuracy in comparison with other imputation and normalization methods, such as imputation with k-nearest-neighbor, mean values of proteins and zeros. A cut-off was set at a minimum of 300 screened cell lines for the drug response dataset to filter out drugs without sufficient data. A simplified version of DeepOmicNet without grouped bottlenecks was used for omics data other than proteomic data due to the large input dimension. The Python package scikit-learn (v.0.22.1)  was usedDue to the limit of GPU memory, elastic net, Random Forest and DeepOmicNet were applied only to transcriptomic and proteomic data to predict CRISPR-Cas9 gene essentialities. The same computational strategy for drug response prediction was used to predict CRISPR-Cas9 gene essentialities.https://depmap.org/portal/). DeepOmicNet was used to compare the predictive power of models for CRISPR-Cas9 gene essentialities, and Random Forest was used for drug response prediction due to the limited number of cell lines for certain drugs. AUC instead of IC50 was used for the drug response (PRISM) dataset due to a large proportion of drugs having no IC50 values provided.The predictive power of machine learning models for two proteomic datasets (ProCan-DepMapSanger and the Cancer Cell Line Encyclopedia (CCLE) ) were coFor downsampling analysis, the full set of 8,498 proteins were randomly downsampled using a step decrease of 500 proteins A. Each sFigures were generated using Pandas , Seabornr. Statistical tests were adjusted for multiple hypotheses correction using the Benjamini-Hochberg False Discovery Rate (FDR), and statistical significance was considered when FDR < 5%, except when otherwise specified (such as when multiple thresholds were compared). Quantification methods and statistical analyses for the proteomics, drug response and multi-omics datasets are described in the respective sections of the DIA-NN (version 1.8)  was used


Quinolones, broad-spectrum antibiotics, are frequently prescribed by urologists for many urological disorders. The mechanism of their bactericidal activity is based on the inhibition of topoisomerase II or IV complex with DNA, which consequently leads to cell death. It has been observed that these antibiotics also act against the analogous enzymes present in eukaryotic cells. Due to their higher accumulation in urine and prostate tissue than in serum, these drugs seem to be ideal candidates for application in genitourinary cancer treatment. In this study, an extensive literature review has been performed to collect information about concentrations achievable in urine and prostate tissue together with information about anticancer properties of 15 quinolones. Special attention was paid to the application of cytotoxic properties of quinolones for bladder and prostate cancer cell lines. Data available in the literature showed promising properties of quinolones, especially in the case of urinary bladder cancer treatment. In the case of prostate cancer, due to low concentrations of quinolones achievable in prostate tissue, combination therapy with other chemotherapeutics or another method of drug administration is necessary. Quinolones are chemotherapeutics discovered in 1962 by Lescher et al. who have synthetized nalidixic acid . It has in vitro, indicating their potential usage for cancer patients. Induction of apoptosis, cell cycle arrest, and disruption of mitochondrial membrane potential are examples of quinolones’ mechanism of action against cancer cells  is the most frequent type of cancer among men in Europe and second around the globe. According to the recent incidences in 2020, almost 1,400,000 new PCa patients with 375,000 deaths were noticed . Over thBladder cancer (BCa) is a much less common type of cancer than PCa. Worldwide, it is the 10th most common type of cancer, with 573,000 new cases and 213,000 deaths. It is more common in men than in women (4-times) . The incE. coli has dramatically emerged due to fluoroquinolone overuse and has become a challenge in the medical therapy of patients with urinary tract infection (UTI) (Escherichia coli (ESBL-EC)  . FluoroqESBL-EC) .Escherichia coli. However, in light of rising resistance, current prescribing guidelines recommend fluoroquinolones as second-line drugs  for normal and cancer cells other than those originating from the genitourinary system were also included. Additionally, the PubMed database was searched for information about concentrations achievable in urine and prostate tissue after oral or intravenous administration of different quinolones. The databases were reviewed until the end of June 2021.Extracted data included the following elements: type of quinolones and their derivatives, quinolone concentrations, type of tested cells, incubation time, influence on cell viability, changes induced in cells, measurement results, the way of assessing results, and the effectiveness of tested compounds. Among the 17 quinolones described, antitumor activity was examined for 15 of them, and no cytotoxic effects of grepafloxacin and finafloxacin have been analyzed so far in the literature. For other compounds, 54 full-text articles were included for the analysis of anticancer properties of quinolones. Detailed data extraction for each quinolone is presented in Most of the analyzed quinolones reach high concentrations in the urine Table 1.50 presented in 50 value after 72 h of incubation. Ciprofloxacin has a stronger activity than levofloxacin, ofloxacin, and fleroxacin due to the lower IC50 values obtained for three  or one (T24) tested cell line, respectively. Levofloxacin has better cytotoxic properties against the T24 cell line and similar to the BOY cell line compared to ofloxacin. The calculated IC50 values of ciprofloxacin, norfloxacin, enoxacin, and levofloxacin are achievable in the urine  show a stronger cytotoxic effect in acidic pH, and such conditions are present in urine. The results discussed above indicate that all quinolones presented in 50 values calculated for all tested cell lines. Low IC50 values were also calculated for norfloxacin, levofloxacin, and enoxacin, but compared to ciprofloxacin, a small number of data available in literature do not allow to draw reliable conclusions about their effectiveness. In our recent study, we additionally, for the first time, analyzed the influence of ciprofloxacin and levofloxacin on bladder cancer cells in 3D culture (spheroids). We showed that IC50 values calculated after drug incubation with cells in standard 2D culture were not effective in 3D culture. Only the concentration reducing 90% of cell viability was effective in cell viability reduction and caspase 3/7 activation in 3D culture. These concentrations were 1,500 and 245 µg/ml for ciprofloxacin and 4,670 and 730 µg/ml for levofloxacin, respectively, after 24 and 48 h of incubation with drugs. The concentration obtained after 48 h of incubation with ciprofloxacin is achievable in urine, which suggests the potential of this drug in clinical application. Additionally, we tested both drugs on a non-malignant uroepithelial cell line. Our results showed that both tested drugs were more effective against cancer cell lines which were more pronounced in 3D culture ; however, the majority of the compounds mentioned above (except enoxacin and norfloxacin) were tested only on the T24 cell line Table 3. culture .There is a lack of data in the literature about the concentration of some quinolones in prostate tissue. This concentration has not been determined for nalidixic and pipemidic acids, grepafloxacin, sparfloxacin, gemifloxacin, and finafloxacin. The use of other quinolones, in most cases, allows reaching a higher concentration in the prostate tissue than in the serum Table 2.50 values  can be obtained  have been so far studied on prostate cancer cell lines . In six studies, the use of ciprofloxacin was especially focused on prostate cancer, and the other two studies analyzed modified quinolones on the panel of various cell lines; therefore, information about prostate cancer is very limited Table 3.0 values , 76. In obtained . Ninety his drug , 72. In o et al. . Levofloo et al. . In the e tissue . In the e tissue . The IC5TOP2A and TOP2B genes, two isomers of topoisomerase II, were observed after ciprofloxacin and levofloxacin treatment . Three quinolones have been tested on an animal model so far. After oral administration of fleroxacin, a reduction in chemically induced bladder cancer was observed only when tested fluoroquinolone was used together with 5-FU  reach very high concentrations in urine both after oral or intravenous administration. Additionally, long-time administration of quinolones in high doses is safe, and if the risk of drug resistance is not taken into account, such therapy doses do not cause side effects. That is why quinolones are frequently used by urologists . AnotherTK: study design, data analysis, data collection, writing; SF: data collection, writing; JA: writing; KS: writing; MR: writing; TD: review and editing, supervision; MP: review and editing, supervision. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The protein expression of IKK-α and IKK-β, as inhibitors of NF-KB which exhibit a critical function in the regulation of genes involved in chronic inflammatory disorders, were studied in the tested cancerous cell lines. In this study, the anticancer activity of household-processed fenugreek leaves and seeds against HepG2, HCT-116, MCF-7, and VERO cell lines was measured using an MTT assay. DNA fragmentation of both HepG2 and MCF-7 was investigated by using gel electrophoresis. RT-PCR was used to evaluate the relative expression of each p53, caspase-3, Bax, and Bcl-2 genes, whereas ELISA assay determined the expression of caspase-3, TNF-α, and 8-OHDG genes. Western blotting analyzed the protein-expressing levels of IKK-α and IKK-β proteins in each studied cell line. Data showed that at 500 µg mL−1, ADFL had the highest cytotoxicity against the HepG2 and HCT-116 cell lines. Although, each UFS and GFS sample had a more inhibitory effect on MCF-7 cells than ADFL. Gel electrophoresis demonstrated that the IC50 of each ADFL, UFS, and GFS sample induced DNA fragmentation in HepG2 and MCF-7, contrary to untreated cell lines. Gene expression using RT-PCR showed that IC50 doses of each sample induced apoptosis through the up-regulation of the p53, caspase-3, and Bax genes and the down-regulation of the Bcl-2 gene in each studied cell line. The relative expression of TNF-α, 8-OHDG, and caspase-3 genes of each HepG2 and MCF-7 cell line using ELISA assays demonstrated that ADFL, UFS, and GFS samples reduced the expression of TNF-α and 8-OHDG genes but increased the expression of the caspase-3 gene. Protein-expressing levels of IKK-α and IKK-β proteins in each studied cell line, determined using Western blotting, indicated that household treatments decreased IKK-α expression compared to the UFS sample. Moreover, the ADFL and SFS samples had the most activity in the IKK-β expression levels. Among all studied samples, air-dried fenugreek leaves and unprocessed and germinated fenugreek seeds had the most anti-proliferative and apoptotic-inducing properties against human HepG2, MCF-7, and HCT-116 cell lines, as compared to the VERO cell line. So, these crude extracts can be used in the future for developing new effective natural drugs for the treatment of hepatocellular, breast, and colon carcinomas.Household processing of fenugreek seeds and leaves, including soaking, germination, and boiling of the seeds, and air-drying of the leaves, has improved the levels of human consumption of the bitter seeds and increased the shelf life of fresh leaves, respectively. The potential anticancer activity of either unprocessed or processed fenugreek seeds or leaves and the relative expression of pro-apoptotic and anti-apoptotic genes of the studied cancerous cell lines exposed to IC Cancer remains an aggressive cause of death on a global scale despite significant efforts. The chemotherapeutic agents currently in use have negative side effects and have not performed as expected. Medicinal herbs have been used for ages to cure a variety of illnesses and are also known to have anticancer properties .Tumor chemoprevention is a procedure used to decelerate, degenerate, or prevent the cross-mechanism of tumorigenesis, including blocking its important morphogenetic stages, such as preneoplasia, neoplasia, and metastasis. Alternatives such as naturally occurring phytochemicals found in food are increasingly preferred over manufactured medications for a number of reasons, including less (or no) toxicity or side effects, improved availability, and safety. Programmed cell death (PCD)-type I (apoptosis), PCD-type II (autophagic cell death), and necrosis are some of the mechanisms involved in cell death produced by plant products . A complex biological process called apoptosis is crucial for removing damaged cells from living things. A type of controlled cell death known as apoptosis is characterized by distinctive morphological and biochemical alterations in the cells. The apoptotic cascade is fueled by a variety of proteins. They can be assessed utilizing common analytical procedures based on protein detection. Caspases are enzymes , and their activity can be used to determine the stage and rate of apoptosis, particularly when they are cleaving the peptide bonds of the right substrate. In humans, there are eleven isoenzymes of caspases, seven of which  are involved in apoptosis .The I kappa B protein kinase complex, often known as IKK, is a 700 kDa protein that can phosphorylate the conserved N-terminal serine residues of IκB-α and IκB-β and is triggered by inflammatory signals  . The IKKTrigonella foenum-graecum, is a traditional herbal remedy used for the treatment of diabetes, high cholesterol, wounds, inflammation, digestive problems, etc. It is also thought to have anti-tumor characteristics; however, the exact mechanisms for this activity are still unclear [Fenugreek, or  unclear . Numerou unclear .50 doses of ethanolic crude extracts of each tested sample. Finally, a Western blot assay was used to measure the protein-expressing levels of both IKK-α and IKK-β protein kinases in each cancerous and normal cell line exposed to the IC50 doses of the studied samples.The aim of this work is to evaluate the anti-proliferative activity of air-dried fenugreek leaves (ADFL), unprocessed fenugreek seeds (UFS), germinated fenugreek seeds (GFS), soaked fenugreek seeds (SFS), and boiled fenugreek seeds (BFS) against three cancerous cells from hepatic (HepG2), colon (HCT-116), and breast (MCF-7) cancer cell lines in comparison to normal kidney cell lines (VERO) using MTT assays. AGE, comet assay, RT-PCR, and ELISA methods were used to study the ability of each studied sample on DNA damage and regulation of pro- and anti-apoptotic genes, by exposing p53- caspase-3, Bax, and Bcl-2 of HepG2, HCT-116, MCF-7, and VERO cells to ICAll RPMI 1640 culture media, glutamine, FBS, penicillin, streptomycin, trypsin, primers for p53, caspase-3, Bcl-2, Bax, GAPDH, solvents, dyes, and chemicals were purchased from Sigma-Aldrich, St. Louis, MO, USA. Primary antibodies included TNF-α, 8-OHDG, Cas-3, GAPDH, IKK-α, and IKK-β, and horseradish peroxidase-conjugated secondary antibodies were obtained from Santa Cruz Biotechnology, . Western blot kits were purchased from Amersham, Buckinghamshire, UK.All cell lines were obtained from the American Type Culture Collection . Anti-proliferative activity was investigated in vitro against three human cancerous cell lines, including the human hepatic (HepG2), breast (MCF-7), and colon (HCT-116) carcinoma cell lines, and normal human kidney cell line (VERO), and determined by the Bioassay—Cell Culture Laboratory, National Research Centre. All cell lines were cultured in RPMI 1640 medium supplemented with 2 mM L-glutamine, 10% FBS, and 1% penicillin/streptomycin. Then, sub-confluent cultures (70–80%) were trypsinized (trypsin 0.05%/0.53 mM EDTA) and split depending on the seeding ratio.Trigonella foenum-graecum Linn.) were purchased from the local market in Egypt in the month of December 2019 and identified at the Faculty of Science, Cairo University. The leaves were washed thoroughly with tap water, and the surface water was removed by air-drying under shade for 15 days. Leaves were subsequently dried in a hot air oven at 50 °C for 4 h, homogenized to a fine powder, and then stored at 4 °C.Fresh and healthy leaves of fenugreek ((a)Untreated Fenugreek Seeds (UFS)Two kilograms of fenugreek seeds were purchased from the local market in Egypt, identified at the Faculty of Science, Cairo University, and divided into four groups as follows:Five hundred grams of seeds were cleaned manually to remove dust and foreign particles, crushed into a fine powder flour with the help of Moulinex blender LM 241, and sieved through a 0.5 mm mesh size. The powdered flour was kept at 4 °C to prevent changes until further analysis .(b)Soaked Fenugreek Seeds (SFS)w/v) for 12 h at room temperature. After removing the soaking water, the seeds were air-dried in shade for 5 days, followed by hot air oven-drying at 50 °C for 4 h in a conventional oven, and stored at 4 °C [Five hundred grams of untreated seeds were soaked in tap water at the ratio of 1:5 ((c)Germinated Fenugreek Seeds (GFS)w/v) for 12 h at room temperature. After removing the soaking water, seeds were kept in the dark for germination (tied in a cotton cloth) at 20 °C for 60 h in darkness. After harvesting the sprouts, they were air-dried for 5 days, followed by hot air oven-drying at 50 °C for 4 h in a conventional oven, and stored at 4 °C [Five hundred grams of untreated seeds were soaked in tap water at the ratio of 1:5 ((d)Boiled Fenugreek Seeds (BFS)2O (1:5 w/v). The sample was boiled on a hot plate for 10 min, and then, after the water was discarded, the boiled seeds were air-dried in shade for 5 days, followed by hot air oven-drying at 50 °C for 4 h in a conventional oven, and stored at 4 °C [Five hundred grams of untreated seeds were put in 2 L beaker containing 1250 mL tap H at 4 °C .w/v) 80% ethanol in distilled H2O through sonication for 60 min. Extraction was repeated three times. After filtration, an extract of each sample was condensed until dry (resulting in crude extracts) by use of a rotary evaporator at 40 °C. A known weight of each crude extract was dissolved in appropriate volume of dimethyl sulfoxide (DMSO) and stored at −80 °C until further analysis [One hundred grams of each sample were extracted with 1 L -2,5-diphenyltetrazolium bromide (MTT) assay, according to Mosmann .The qualitative method for assessing cell death by detecting DNA fragments using AGE was performed according to Kasibhatla et al. .50 of each studied sample. The method of Kent et al.  was foll50 doses of the tested samples for 24 h, according to Robert [®cycler software 2.1  to calculate the threshold cycle (Ct) using the second derivative maximum. The fold change value for each gene was determined after normalization to the expression levels of GAPDH as an HK gene, which was calculated using the equation 2−∆∆Ct.Gene expression of pro-apoptotic genes  and Bax, an anti-apoptotic gene, were tested against HepG2, HCT-116, MCF-7, and VERO cells exposed to ICo Robert . Raw dat50 of crude extracts of each studied sample was seeded at a density of 2 × 104/well in a 96-well plate and incubated for 24 h. Absorbance was measured using an ELISA reader . Determination was performed in triplicate, and standard deviation was determined.Quantitative analysis of TNF-α, 8-OHDG, and Cas-3 antibodies using ELISA technique was investigated according to the method of Khalil . Each ceThe Western blot for IKK-α and IKK-β primary antibodies was performed according to the method of Morgia . The exp50 was calculated by using GraphPad prism 7. One-way analysis of variance (ANOVA) was used to analyze the difference between groups by applying the least significant difference (LSD) test with a 5% level of significance (p < 0.05).All assays used in this work were evaluated in triplicate, and the data obtained were represented by the mean ± standard deviation (SD). Statistical Analysis Software (SAS 9.1) was applied for the statistical analysis of data, and IC−1. In contrast, the household processing of fenugreek seeds at 1000 µg mL−1 caused a significant decrease in the cell proliferation of the HepG2 cells, and BFS had the lowest cytotoxicity (56%). The IC50 values were 220 µg mL−1 for ADFL and 320 µg mL−1 for UFS, although these values increased after household treatments. The impacts of household-processed fenugreek leaves and seeds on the cell proliferation of HepG2, HCT-116, MCF-7, and VERO cell lines exposed to serial concentrations of the tested samples are presented in −1 also had the highest anti-proliferative activity (95%) against the HCT-116 cell line, and the germinated (GFS), soaked (SFS), and boiled (BFS) fenugreek seeds reduced the activity compared to the untreated seeds (UFS). The IC50 values were 240 µg mL−1 for ADFL and 275 µg mL−1 for UFS, although these values increased after household treatments. Furthermore, the impact of the tested samples on the human colon cancer cell line (HCT-116) is represented in −1, both the untreated and germinated fenugreek seeds (UFS and GFS) had the highest potential cytotoxicity against the MCF-7 (95%), compared to the other tested samples. ADFL had 92% cytotoxicity at 1000 µg mL−1, SFS had 90% at 2000 µg mL−1, and BFS had 95% at 2000 µg mL−1. The IC50 values of both UFS and GFS were 500 and 620 µg mL−1, respectively. In addition, the soaking and boiling treatments of seeds at 1000 µg mL−1 caused a significant decline  in the cell proliferation of MCF-7. Protodioscin, a steroidal saponin of fenugreek, had an inhibitory impact on the leukemic cell line (HL-60) and a weak inhibitory effect on the growth of the gastric cell line (KATO-III) [On the contrary, as shown in 50 (µg mL−1) values of ADFL, UFS, and GFS were 1660, 1840, and 1940 µg mL−1, respectively. However, the soaking and boiling treatments had no effects on the normal VERO cell line.Finally, the effect of the tested samples on the cell proliferation of the normal kidney cell line (VERO) can be observed in 50 doses of each tested sample can be observed in Agarose gel electrophoresis (AGE) was used to evaluate the DNA fragmentation activity of the tested samples to indicate whether or not the action of the extract is associated with apoptosis. DNA fragmentation of the HepG2 and MCF-7 cancer cell lines treated with the IC50 doses of each studied sample was observed. The treatment of the HepG2 and MCF-7 cell lines with the IC50 doses of the household-processed fenugreek leaves and seeds  caused the fragmentation of DNA in each cancer cell line compared to the untreated control cell lines and the cells exposed to the IC50 of the BFS sample. Therefore, the air-dried fenugreek leaves (ADFL) and unprocessed and germinated fenugreek seeds  induced DNA fragmentation and, thus, apoptosis in both HepG2 and MCF-7 cancer cell lines.As shown in The DNA damage of the HepG2 and MCF-7 cancer cell lines was observed by using single-cell gel electrophoresis (SCGE) (Comet assay), which is able to detect DNA strand breaks (SBs) and oxidative DNA damage in cultured cell lines.50 doses of ADFL, UFS, GFS, SFS, and BFS, which were 13.01, 12.91, 11.41, 10.00, and 9.31%, respectively, for HepG2 cell lines, and were 12.27, 11.10, 9.89, 9.66, and 8.77%, respectively, for MCF-7 cell lines. As presented in 50 doses of the studied samples. The head diameters of the untreated HepG2 and MCF-7 cell lines were 17.61 and 17.15 px, respectively. The values of head diameters are also observable in 50 doses of the tested samples had a decrease in DNA % in the head , except in the BFS sample (84.01%), which was not significantly different compared to the control sample. The values were 76.01, 78.00, 79.00, 81.04, and 81.48% for the MCF-7 cell lines treated with ADFL, UFS, GFS, SFS, and BFS, respectively.The percentages of DNA values in the heads of the untreated HepG2 and MCF-7 cell lines (control) were 84.00 and 81.51%, respectively. In contrast, the HepG2 cell lines exposed to the IC50 of ADFL (4.00 px), UFS (3.94 px), and GFS (3.91 px), but was not significantly changed after treatment with either SFS (3.85) or BFS (3.81) samples. On the other hand, the tail length value of the untreated control cell line of MCF-7 was 3.80 px, and increased to 4.01, 3.98, 3.90, and 3.85 px after exposure to ADFL, UFS, GFS, and SFS, respectively, with the exception of the BFS sample (3.81 px).The data in Furthermore, the percentages of DNA in the head were found to be 22.17% for ADFL, 21.00% for UFS, 19.41% for GFS, 18.12% for SFS, and, lastly, 18.00% for BFS relative to the untreated control HepG2 cell lines. In addition, the untreated MCF-7 cell line (control) had a DNA% in the head of 18.20%, which was also raised to 23.21, 22.11, 20.00, and 18.41% for ADFL, UFS, GFS, and SFS, respectively, but did not vary with the BFS sample (18.21%). 50 of ADFL (1.10), UFS (1.07), GFS (1.01), and SFS (0.96), but not with the BFS sample (0.94). Lastly, the tail moment values of DNA of the untreated (control) HepG2 and MCF-7 cell lines were 1.02 and 0.93, respectively. These values were higher after the exposure of HepG2 cell lines to ADFL (1.16), UFS (1.14), GFS (1.10), and SFS (1.07), but did not differ from the BFS sample (1.01). The untreated control MCF-7 cell lines had a tail moment of 0.93 and increased after treatment with the IC50 doses of the selected sample, is summarized in The gene expression of the pro-apoptotic genes involved either p53, Caspase-3 (Cas-3), or Bax, as well as the anti-apoptotic Bcl-2 gene, against HepG2, HCT-116, MCF-7, and VERO cell lines treated with IC50 doses of ADFL, UFS, GFS, SFS, and BFS are compared to the untreated cell lines (control). The results showed that air-dried fenugreek leaves (ADFL) were the most potent sample, which significantly up-regulated p53, Cas-3, and Bax  and down-regulated Bcl-2 (at a 0.52 fold change) compared to untreated cell lines. However, the standard drug (doxorubicin) caused an up-regulation of the studied pro-apoptotic genes, as follows: 1.87 for p53, 3.82 for Cas-3, and 3.11 fold change for Bax genes, and a down-regulation of the Bcl-2 gene (at a 0.32 fold change). In addition, each of the UFS, GFS, SFS, and BFS samples significantly increased the up-regulation of p53 , Cas-3 , and Bax  genes relative to the untreated control cell lines. According to 50 doses of ADFL, UFS, GFS, SFS, and BFS compared to both the control cell line and the other samples that were treated with doxorubicin were also used to study the apoptosis-inducing property of the tested samples  exposed to IC samples B. Data s50 in each studied sample were compared to the untreated cell lines, and the other samples treated with doxorubicin were also used to evaluate the apoptotic-inducing activity of the tested samples  treated with IC samples C. Findin50 concentration of each studied sample was also used to study the apoptosis-inducing properties of the tested samples  exposed to the IC samples D. The da50 doses of household-processed fenugreek leaves and seeds involved ADFL, UFS, GFS, SFS, and BFS, which enhanced the up-regulation of the studied pro-apoptotic p53, Cas-3, and Bax genes and down-regulation of anti-apoptotic Bcl-2 genes, which induced apoptosis of the studied cell lines, then decreased the tumor growth and cell proliferation.According to these findings, we summarized that the treatment of hepatic, colon, and breast cancer cell lines exposed to IC−1 protein, respectively, for each HepG2 and MCF-7 cell lines treated with IC50 doses of ADFL, UFS, GFS, SFS, and BFS. This caused a significant decline up to 51 and 57%, 61 and 61%, 72 and 81%, 89 and 92%, and finally, 96 and 99%, respectively for the same tested samples. The control (untreated cell line) showed 25.03 and 23.00 pg mg−1 protein for each untreated HepG2 and MCF-7 cell line.Tumor necrosis factor alpha (TNF-α) is an inflammatory cytokine which is involved in immune function and is also proposed to play a role in metabolic disorders. The results presented in An 8-Hydroxy-2′-deoxyguanosine (8-OHDG) biomarker was evaluated to measure DNA damage. The results presented in The caspase-3 (Cas-3) factor is usually used to evaluate the apoptotic cascade. This biomarker factor was activated and increased after exposure to the HepG2 and MCF-7 cell lines to the studied samples, with the exception of the boiled fenugreek seeds (BFS). According to the presented data in To further confirm the results of the IKK-α and IKK-β expressions, the signaling pathway was further investigated through a Western blotting assay . 50 doses . Meanwhile, IKK-α expression levels in VERO cells were not detected in all studied samples, except for UFS (60.78%) and GFS (64.83%).As observable in 50 doses . Meanwhile, IKK-β expression levels were been high in HCT-116 cells treated with SFS (46.90%), GFS (45.57%), and ADFL (44.56%), respectively.In contrast, the results shown in The fenugreek plant has had an important role for a long time in the treatment and prevention of diseases. Various studies have also proved many of these conventional applications and have observed the beneficial value of this plant and its capabilities in traditional medicine. However, the scientific information about the evaluation of the mechanism of action of this plant is not enough .Finding naturally occurring chemo-preventive compounds has received more attention in recent years, especially those found in dietary and medicinal plants due to their bioactive components ,23. These findings are in agreement with Raju  and ThirThe majority of these bioactive chemicals inhibit cell cycle progression and cause apoptotic cell death to carry out their cancer-chemotherapeutic activities. As a result, the induction of apoptosis in tumor cells has evolved into a marker of the tumor treatment response when using a bioactive chemical derived from plants to lower and manage cancer-related mortality in humans ,27. TherIn this work, household processes were been involved in the air-drying of fenugreek leaves for preserving the leaves from spoilage resulting from the moisture content of fresh leaves, and soaking, germination, and boiling of raw fenugreek seeds to reduce the bitter taste, due to the presence of saponins, and specific smell, due to the presence of alkaloids and volatile oils, which limit their usability and acceptability in food industries. This study aimed to evaluate the anti-proliferative activity of air-dried fenugreek leaves (ADFL), unprocessed fenugreek seeds (UFS), germinated fenugreek seeds (GFS), soaked fenugreek seeds (SFS), and boiled fenugreek seeds (BFS) against three cancerous cells involved hepatic (HepG2), colon (HCT-116), and breast (MCF-7) cells in comparison to normal kidney cell lines by using an MTT assay.A typical genetic change linked to cancer is the mutation of the tumor suppressor p53, which inactivates 50% of human malignancies . A complDNA damage is one of the most well-known effects of oxidative stress. Biomarkers for nucleic acid oxidation are frequently utilized to evaluate this damage . The mosThrough the inhibition of Akt activity, diosgenin reduced TNF-induced NF-κB activation, IκB-α kinase activation, IκB-α phosphorylation, IκB degradation, p65 (RELA) phosphorylation, and p65 nuclear translocation. Diosgenin also inhibited the TNF-induced production of genes related to cell proliferation , anti-apoptosis , and invasion -9). Additionally, TNF and chemotherapeutic medicines such as paclitaxel and doxorubicin that promote apoptosis were potentiated by diosgenin .The primary public health issue across the globe is cancer. The identification of naturally occurring chemo-preventive compounds has recently received increased interest, particularly those found in dietary and medicinal plants because of their bioactive components. The majority of these bioactive chemicals inhibit cell cycle progression and cause apoptotic cell death to carry out their cancer-chemotherapeutic activities. As a result, the induction of apoptosis in tumor cells has evolved into a marker of the tumor treatment response when using a bioactive substance produced from plants to lower and manage cancer-related mortality in humans. When compared to the VERO cell line, air-dried fenugreek leaves, raw fenugreek seeds, and germinated fenugreek seeds had the most anti-proliferative and apoptosis-inducing effects on each of the human HepG2, MCF-7, and HCT-116 cell lines. So, these crude extracts can be used in the future for developing new effective natural drugs for the treatment of hepatocellular, breast, and colon carcinomas.