Alteromonas infernus bacterium. These derivatives had no significant effect on the cell cycle neither a pro-apoptotic effect on osteosarcoma cells. Based on this observation, these EPSs could be employed as new drug delivery systems for therapeutic uses. A theranostic approach, i.e., combination of a predictive biomarker with a therapeutic agent, has been developed notably by combining with true pair of theranostic radionuclides, such as scandium 47Sc/44Sc. However, it is crucial to ensure that, once complexation is done, the biological properties of the vector remain intact, allowing the molecular tropism of the ligand to recognize its molecular target. It is important to assess if the biological properties of EPS evidenced on osteosarcoma cell lines remain when scandium is complexed to the polymers and can be extended to other cancer cell types. Scandium-EPS complexes were thus tested in vitro on human cell lines: MNNG/HOS osteosarcoma, A375 melanoma, A549 lung adenocarcinoma, U251 glioma, MDA231 breast cancer, and Caco2 colon cancer cells. An xCELLigence Real Cell Time Analysis (RTCA) technology assay was used to monitor for 160 h, the proliferation kinetics of the different cell lines. The tested complexes exhibited an anti-proliferative effect, this effect was more effective compared to EPS alone. This increase of the antiproliferative properties was explained by a change in conformation of EPS complexes due to their polyelectrolyte nature that was induced by complexation. Alterations of both growth factor-receptor signaling, and transmembrane protein interactions could be the principal cause of the antiproliferative effect. These results are very promising and reveal that EPS can be coupled to scandium for improving its biological effects and also suggesting that no major structural modification occurs on the ligand.Antimetastatic properties on both murine and human osteosarcoma cell lines (POS-1 and KHOS) have been evidenced using exopolysaccharide (EPS) derivatives, produced by  The key role of glycosaminoglycans (GAGs), natural polysaccharides found in mammalian tissue, has been recognized in the regulation of cell properties and functions. They are now considered as pharmacological targets to treat several diseases and notably metastatic cancers. GAGs play a central role in the organization of the extracellular matrix (ECM) through their binding to various molecules . Nowadays, several therapeutic strategies are developed with the aim of restoring cell function due to a degradation of endogenous GAGs either in protecting endogenous GAGs or in using exogenous GAGs or GAG-mimetics ,2,3,4.Unfractionated heparin and its low-molecular-weight (LMW) derivatives are widely used to prevent or treat venous thromboembolism, which is a common complication in cancer patients. Clinical trials have shown that cancer patients treated with heparins have improved, with a decrease in cancer progression and a longer survival time ,6, whichAlteromonas infernus. A monosulfated nonasaccharide composed of six neutral hexoses and three hexuronic acids, including one galacturonic acid unit bearing one sulfate group, makes up the repeating unit of this EPS. It presents a high molecular weight (>106 g mol−1) and a low sulfate content (<10%) [Among these marine polysaccharides, an exopolysaccharide (EPS) of interest was isolated from the culture broth of a hydrothermal bacterium called t (<10%) ,13,14. Wt (<10%) ,16. In tt (<10%) . The antt (<10%) ,16,17.In vitro studies on bone remodeling have displayed different levels of bone resorption regulation by EPS-DRS, most of them leading to pro-resorptive effects . These r44Sc/47Sc could be both employed for Positron Emission Tomography (PET) imaging to detect primary and secondary tumors, as well as for Targeted RadioTherapy in order to bombard tumor tissues with gamma radiations [w = 27 kDa) and a lower-sulfated one, called DR (with Mw = 16 kDa). The complexation properties of these two EPS with scandium were higher than the ones on glucuronic and galacturonic acids, even though Sc-EPS complexes appear to be less stable  than scandium complexes with 1,4,7,10-Tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) and diethylenetriaminepentaacetic acid (DTPA), classically used in radiopharmaceutical drugs.Since their natural tropism in targeting malignant tumor and their metastasis, EPS have been recently considered as vector to be combined with theranostic radionuclide pairs. Through the possibility of doing therapy and diagnostic at the same time, theranostic compounds represent new innovative clinical tools to be employed in a perspective of “personalized/precision” medicine . More prdiations . Complexdiations . The EPS® technology. XCELLigence measures the electric impedance of cell assessed. This impedance is directly related to the cell adhesion process and, consequently, to cell proliferation and/or cell death. Moreover, the effect of the metal-to-ligand ration of the EPS-Sc complexes was assessed and, a heparin was used as a reference system for these EPS.For further use as a therapeutic vector, it is important to ensure that EPS may keep the ability of targeting cancer cells even when complexed with scandium. For this reason, in vitro studies concerning both cell proliferation and cell viability of various tumor cell lines were realized here to evaluate if there is a synergetic effect of Sc-EPS complexes on different cancer cell lines, compared to the EPS alone and scandium alone. To this aim, the cell index of human osteosarcoma (MNNG/HOS), human melanoma (A375), human lung cancer (A549), human glioblastoma (U251), and human breast cancer (MDA231) were monitored over a week using XCELLigence-1 showed a plateau that reflected no more MNNG/HOS cells proliferation but the detachment from the well was not observed yet. For the scandium concentration at 200 µg mL-1, the detachment of cells slightly began at 140 h. Unfortunately, the time frame of 160 h was not sufficient to see if the decrease was definitive or kept oscillating. The MTT assay confirmed that the concentration of 200 µg mL−1 displayed a reduction of cell viability of around 25% after 160 h. For lower concentrations , an increase of cell viability was noticed.The NCI plot shown in −1 of EPS-DR, the lowest sulfated EPS of this study, 100% viability was reached compared to the control.The effect of both EPS-DR and -DRS and heparin on cell proliferation and cell viability is showed in −1, there was no effect on cell proliferation. After 100 h of contact, for the same metal-to-ligand ratio but with higher concentrations of heparin , there was a significant decrease of the cell proliferation rates.With a metal-to-ligand ratio Hep:Sc of 1:1 b, the de−1; at these conditions, there is no more viable cells present in the well after 160 of contact.If those results are compared to the ones obtained on cell viability, the metal-to-ligand ratio ratios of 1:0.5 and 1:4 exhibited higher percentages of cell viability. In contrast, for a constant metal-to-ligand ratio of 1:1, a significant decrease of almost 40% is observed when increasing the total concentration of heparin. The only drop on cell viability is observed for a metal-to-ligand ratio of 1:2 and for a concentration of heparin of 200 µg mL−1, the cell proliferation decreases suddenly after 150 h, after than a plateau has been reached. This has been noticed for the four metal-to-ligand ratios considered. This seems stopping the oscillating kinetics to very low and stable NCI values, comparable to those of heparin complexes , that is a solid compound, on a commercial human osteoblast-like cell line (TE85 HOS). Scandium oxide showed a lower cell proliferation after 3 weeks of contact compared to the control. TE85 HOS represent the parental cells from which the MNNG/HOS cells were derivated after chemical transformation with 0.01 µg mL−1 MNNG (a carcinogenic nitrosamine). TE85 HOS parental cell line is not tumorigenic in mouse in contrast to MNNG/HOS. Herath and Evans [2O3 did not affect cell viability.In our study, a biological effect has been highlighted on the different cancer cell lines . The literature found on the evaluation of scandium has described an absorption and prolonged retention of scandium in some tissues , especia46Sc3+  has been46Sc3+  describend Evans  also demO-sulfated and N-sulfated groups, resulting in GAGs with different chemical and biological properties [It is already known that GAG participate in the regulation of several cellular events, such as cell adhesion, proliferation and migration . Actualloperties . Among Goperties ,32,33. Soperties . The degoperties . Nikitovoperties  have prooperties  and osteoperties .It has been shown that high concentrations of GAGs interrupted growth factor-receptor signaling in osteoblasts-like Saos-2 cells by sequestering growth factors  and preventing their interaction with cell-surface receptors . This biCell viability after 160 h of contact with the polysaccharides alone has been also assessed using MTT assays (results are not showed in this paper), and it resulted high for the polysaccharide concentrations considered. Considering the fact that the cell viability measured through the MTT assays, reflects mitochondrial metabolism as biochemical marker of cell viability, it could be reasonable to think that the slowdown or drop of cell proliferation is not imputable to the blockade of this biochemical process. EPS, as GAG mimetics, interact with cell transmembrane proteins such as selectins and integrins, responsible of cell adhesion and cell-cell interactions . Since t−1 in In the literature there have been evidences on the role of heparin in lung cancer ,38. Low LMWH have also been tested in the past on human melanoma cells displaying valid anti-proliferative effects . AdditioBy contrast, the role of certain GAGs, such as chondroitin sulfate, is controversial in the pathogenesis of glioblastoma and breast cancer, since they seem enhance the tumor invasion ,43. The Polysaccharide-scandium complexes  are more efficient than their respective polysaccharides alone in reducing the cell proliferation. That was the case for all the cell lines tested, thus revealing that the complexation with scandium does not prevent the biological activity of these polysaccharides. There is a synergetic effect by combining these polysaccharides with scandium, since the anti-proliferative effect has risen.On MNNG/HOS cell line, as shown in For the highest concentration for all the complexes tested on MNNG/HOS cells, the profile of their proliferation kinetics was quite different. The complexation with scandium and in particular, the different ratios seem to induce different effects. This could reside in a different polymer conformation assumed by heparin at different ratios of metal. From our previous work, the effect of added counterions on polysaccharide conformation has been assessed revealing how hydrodynamic volume and gyration radius of this polyelectrolyte is influenced by the ionic strength, so by the opposite charges provided by the presence of metal ions. If the conformation of the polysaccharide changes, the way it interacts with its macromolecular targets would change as well, resulting in a different modulation of the biological effect .−1, the increase of the metal to ligand ratio had an important effect also on human melanoma and colon cancer cell line. After 95 h of contact for melanoma cells (140 h for colon cancer cells), the ratio 1:2 seems to exhibit the most pronounced anti-proliferative effect, whereas the decrease in cell proliferation is clearly dependent on the L:M ratio. The change in conformation mediated by the complexation would positively modulate the biological effect. By contrast, the proliferation kinetic on human lung cancer, glioblastoma and breast cancer cells did not seem to be impacted by the complex. This behavior indicates that heparin is not efficient as anti-proliferative agent on primary tumors generated from these three human cancer cell lines.Considering a fixed concentration of Hep:Sc complexes of 100 µg mLConsidering the effect of EPS-DR:Sc complexes on human osteosarcoma, melanoma cells, and colon cancer cells, the metal-to-ligand ratio of 1:1 and 1:2 revealed to have a significant impact on cell proliferation. One explanation of these two different kinetics may be related to the fact that complexation with scandium at these ratio lead into a change of conformation, this may inhibit cell proliferation for contact time superior to 90 h and at high concentrations of EPS. For metal-to-ligand ratio of 1:0.5 and 1:4, that are comparable, they show a very similar effect compared to EPS DR alone.The same EPS-DR:Sc complexes did not display the same effect on human lung cancer, glioblastoma and breast cancer cell lines meaning that the slowdown in cell proliferation is not related to this specific kind of tumor. This seems in agreement with the fact that the biological effect normally influences only the formation of metastasis.Glioblastoma appears to be not much sensitive to these GAG mimetics since even EPS-DRS:Sc did not show significant results in decreasing the proliferation kinetics. By the contrary, EPS-DRS:Sc revealed a great slowdown in the cell proliferation of human lung cancer, breast cancer, melanoma and colon cancer cell lines that was ratio dependent. The higher sulfate content of DRS may enhance the anticoagulant properties, resulting in a much stronger interaction with P- and L-selectins due the possibility to allow more electrostatic interactions with those proteins. Moreover, the possible change in conformation would act in a synergic way allowing a positive modulation of the anti-proliferative effect.From cell assays, EPS-DRS:Sc with a 1:2 metal-to-ligand ratio, came out to be the best polysaccharide-scandium complex in reducing the cell proliferation of human osteosarcoma, human melanoma, human lung cancer, human breast cancer and colon cancer cells. These results confirm the importance of the degree of sulfation which positively enhances the cytostatic effect of these exopolysaccharides alone or complexed.Mw = 18,320 Da, S% = 10%) from porcine intestinal mucosa H4784 and scandium chloride hexahydrate were purchased from Sigma-Aldrich . EPS derivatives DR  and DRS  were kindly provided by LEMMMB laboratory of IFREMER (Nantes). Polysaccharide solutions  were prepared at high concentration stock solutions (2 × 10−4 mol L−1) in Milli-Q water; scandium alone solution was prepared at high concentration stock solution (10−1 mol L−1) in Milli-Q water. For each of the three polysaccharides, a set of complexes with four stoichiometric metal-to-ligand ratio  has been prepared by mixing different aliquots of the previous prepared stock solutions. Higher stoichiometric ratios have been excluded to avoid cytotoxic effects related to high ionic strengths.Heparin sodium salt .Complexation between scandium and polysaccharides has been previously checked and displayed using Free-Ion Selective Radiotracer Extraction (FISRE) as described somewhere else . FISRE wM:L ratio has been established by preparing solutions where the concentration in mol/L of the metal was, from 0.5 to 4 times the one of ligand. After preparation, all 16 compounds  were filtered with a 13 μm syringe filter before being frozen to prevent bacteria contamination. Fresh dilutions at 200, 100 and 50 μg/mL of each compound were obtained right before the experiments adding cell culture medium to aliquots from stock solutions.Human MNNG/HOS osteosarcoma cell line, A375 melanoma, A549 lung cancer, U251 glioblastoma, MDA231 breast cancer and Caco2 colon cancer cell lines were purchased from the American Tissue Cell Collection . Human MNNG/HOS osteosarcoma cell line (ATCC) was cultured with DMEM high glucose, pyruvate, non-glutamine from Gibco (Thermo-Fisher), supplemented with glutamine (Thermo-Fisher) and 10% of fetal bovine serum . Human A375 melanoma cell line (ATCC) was cultured with DMEM 4.5 g/L high glucose, pyruvate, non-glutamine from Gibco (Thermo-Fisher), supplemented with glutamine (Thermo-Fisher) and 5% of fetal bovine serum . Human A549 lung cancer cell line (ATCC) was cultured with DEMEM/F12 (Sigma Aldrich), supplemented with glutamine (Thermo-Fisher) and 5% of fetal bovine serum . Human U251 glioblastoma cell line (ATCC) was cultured with DMEM 4.5 high glucose, pyruvate, non-glutamine from Gibco (Thermo-Fisher), supplemented with glutamine (Thermo-Fisher) and 5% of fetal bovine serum . Human MDA231 breast cancer cell line (ATCC) was cultured with L-15 media from Gibco (Thermo-Fisher), supplemented with glutamine (Thermo-Fisher) and 5% of fetal bovine serum . Human Caco2 colon cancer cell line (ATCC) was cultured with DMEM 4.5 g/L high glucose, pyruvate, non-glutamine from Gibco (Thermo-Fisher), supplemented with glutamine (Thermo-Fisher) and 5% of fetal bovine serum .2. At confluence, cells are detached using Trypsin (Thermo-Fisher) and washed to neutralize the enzyme. MNNG/HOS cells were seeded in triplicate at 5000 cells per well (50 µL) with 50 µL of culture medium (to measure the background) for 4 h, the time necessary for cell seeding, in an E-Plate view 96  before adding 100 µL of compound at the three concentrations . The choice of these concentrations provided a wide range to study a possible dose dependent response. Proliferation curves are normalized respect the time point of drug incorporation. The plate was monitored for 160 h (MNNG/HOS cells), 140 h (Caco2 cells) and 95 h  using a RTCA instruments . Experiments have been done in triplicates and repeated twice. Statistical tests have been conducted using Regression Data Analysis Tool in Excel®.Cells were incubated at 37 °C with humidity saturated controlled atmosphere and 5% CO−1). The choice of these concentrations provided a wide range to study a possible dose dependent response. The plate was left 160 h: the volume of each well was reduced to 100 µL before adding 10 µL of 5 mg/mL MTT (Sigma-Aldrich) and incubating for at least 3 h at 37 °C and 5% CO2. After this time, the liquid was removed and 200 µL of DMSO was added to each well to dissolve the formed formazan crystals before proceeding to the colorimetric quantification with a multi-well spectrophotometer  at the wavelength of 500–600 nm.MNNG/HOS cells were seeded in triplicate at 3000 cells par well (50 µL) with 50 µL of culture medium for 4 h in an E-Plate view 96 (Chem Agilent) before adding 100 µL of compound at the three concentrations . EPS derivatives can slowdown cell proliferation kinetic, confirming their anti-proliferative effect on human osteosarcoma cells without significantly influencing the cell viability. Close results have been found also on human melanoma cells, while human glioblastoma cells have not been affected by those EPS derivatives. The anti-proliferative effect on other cell lines  is effective with polysaccharides but even more enhanced when EPS derivatives, and heparin, are complexed to scandium, in which the impact on the NCI is dose–response. The degree of complexation is represented by the different metal-to-ligand ratio. Different behaviors in the decrease of the proliferation kinetic have been evidenced depending on the metal-to-ligand ratio, that could be related to a conformation change of the polysaccharide when complexed with scandium. EPS-DRS:Sc with a 1:2 metal-to-ligand ratio has been shown to be the most effective compound in displaying a cytostatic effect. Our group has evidenced in a previous work  that the44m/44Sc.These results are very encouraging in a theranostic perspective because of the intrinsic tropism of these EPS derivatives for cancer cells is maintained, even after the complexation. Additionally, the anti-proliferative effect on certain cell lines could be modulated by the degree of complexation. In the near future, the influence of the degree of complexation will be performed to investigate which are the best metal-to-ligand ratio in terms of efficacy. Besides, xCELLigence assay with EPS derivatives could be extended to other cell lines known to be sensible to GAG properties, while testing new assays to target other cellular mechanisms involved in cellular toxicity. Moreover, the biological effects of these EPS will be tested on normal cells as well in order to guarantee no cytostatic effect on healthy cells. The efficacy of these compounds will be evaluated with the PET tracer of


Fridericia platyphylla is a shrub found in Brazilian cerrado biome which has cytotoxic, anti-inflammatory, and analgesic properties. The aim of this study was to investigate the antiproliferative potential of the crude hydroethanolic extract, subfraction , as well as Brachydin E and Brachydin F isolated from F. platyphylla roots. The cytotoxic activity was evaluated in glioblastoma, lung, prostate, and colorectal human tumor cell lines. The crude hydroethanolic extract did not present cytotoxic activity, but its subfraction presented lower IC50 values for glioblastoma (U-251) and prostate adenocarcinoma (PC-3) cell lines. Brachydins E and F significantly reduced cell viability, proliferation, and clonogenic potential of PC-3, inducing them to the process of regulated cell death. In silico studies have indicated nuclear receptors as targets for Brachydins E and F, and molecular docking has pointed out their binding into glucocorticoid receptor (GR) ligand pocket. Targeting GR pathway has been described as a therapeutic strategy, especially for prostate cancer. These results suggest that Brachydin E and Brachydin F are promising compounds to be further explored for their antitumor effects.Despite the breakthrough in the development of anticancer therapies, plant-derived chemotherapeutics continue to be the basis of treatment for most types of cancers.  Cancer is among the leading causes of death in the world, and according to the World Health Organization (WHO), 21.4 million new cases and 13.2 million deaths caused by cancer are expected by 2030 because of population growth and aging +, respectively. The high-resolution- (HR-) ESIMS of Brachydin E and Brachydin F revealed a [M-H]- ion at m/z 859.2457 and 889.2586, respectively. All these data are in accordance with those described by Rocha et al. [The high performance liquid chromatography-photo diode array-mass spectrometry (HPLC-PDA-MS) analysis of the hydromethanolic fraction revealed the presence of two compounds with ultraviolet-photo diode array (UV-PDA) spectra closely related to the dimeric flavonoids Brachydin E and Brachydin F . Accordia et al. . As show1H NMR spectrum of Brachydin E revealed 13 aromatic protons, 2 ethylene units, 2 oxygenated methines, 2 aliphatic methines, and 2 methoxy groups. The different aromatic signals belonged to 4 independent rings named A, B, C, and D. A pair of meta-coupled protons at δH 6.41 and 6.28 (J = 2.2 Hz) was assigned to the protons H-2 and H-4 in the A ring. Similar to ring A, ring B exhibited another pair of meta-coupled protons at δH 6.38 and 6.30 (J = 2.1 Hz) attributed to H-9 and H-11. Two pairs of ortho-coupled protons at δH 7.25 (H-2′ and H-6′) and 7.39  were assigned to ring C, while the remaining five aromatic protons corresponded to the unsubstituted ring D at δH 7.22 (H-3″ and H-5″) and 7.23 (H-2″ and H-6″). The presence of two β-glucuronic acids was revealed by their anomeric protons at δH 4.94  and 4.97 . The NMR showed the presence of two free hydroxyl groups in Brachydin F [The μg/mL to 47.7 and 43.5 μg/mL compared to the crude extract, respectively. The dimeric flavonoid Brachydins E and F isolated from this subfraction maintained the cytotoxicity for glioblastoma (U-251) and presented greater cytotoxic effects towards lung (NCI-H460) and prostate (PC-3) compared to the subfraction, and Brachydin E was more potent than Brachydin F for these cell lines as shown in The crude hydroethanolic extract did not present cytotoxic effect over none of the cell lines evaluated, but its subfraction reduced the IC50 values for glioblastoma (U-251) and prostate (PC-3) cell lines from 95.8 and 100 Moreover, the selectivity index (SI) values reveal that Brachydin E was 4 times more selective to prostate (PC-3) in comparison to HaCaT  than doxorubicin, a well-established chemotherapeutic agent . BrachydAs shown in Data from the colony formation assay showed that Brachydins E and F were able to reduce the number of PC-3 cell colonies by over 68.9% and 60.1%, respectively, when compared to DMSO treatment over 21 days of experiment Figures . These rThe phosphatidylserine externalization assay  showed tBrachydins E and F were evaluated for their pharmacokinetics properties . It was α and β subtypes), progesterone, and glucocorticoid receptors , estrogen , although with high blinding energy in some cases, did not occur in the pocket, with the binding taking place on the outside of the protein, in a region without biological significance. Natural products represent a rich source for the discovery and development of cancer preventive and anticancer drugs . DespiteFridericia platyphylla (Cham.) L.G.Lohmann  is a native plant from Brazil, widely distributed in different biomes, but specially in cerrado, and the traditional use of teas prepared from the roots of this plant is already known for the treatment of kidney stone and arthritis [Arrabidaea are sources of many compounds, including flavonoids [The Brazilian Cerrado is one of the major biogeographic regions of the world with more than 7000 native species of vascular plant. Many of these plants are commonly used as natural remedies by people living in this biome to treat several illnesses . Friderirthritis . Phytochavonoids .The literature has shown that flavonoids are capable of inhibiting cell proliferation, tumor growth, and carcinogenesis. It has been widely reported that flavonoids interfere with the initiation, promotion, and progression of cancer by modulating different enzymes and receptors responsible for cell proliferation, differentiation, apoptosis, inflammation, angiogenesis, metastasis, and reversal of resistance to multiple drugs . Current″-di-O-methylchamaejasmin isolated from the stem bark and roots of the Kenyan medicinal plant Ormocarpum kirkii S. Moore (Fabaceae) presented low values of IC50 for breast, colon, glioblastoma, human, and murine liver cancer cell lines, significantly elevating the number of cells in apoptosis in the sub-G0/G1 cell cycle phase and also causing cell cycle arrest in the G0/G1 phase. This compound altered the mitochondrial membrane potential (MMP) in acute lymphoblastic leukemia cells and caused an increase in reactive oxygen species generation. Therefore, these studies suggest that biflavonoids constitute an interesting class of compounds to be explored in cancer drug discovery and development.Data on the antiproliferative activity of biflavonoids have been reported in the literature; however, it is still very limited. A study recently published has shown that the biflavonoid hinociflavone suppressed proliferation of colorectal tumor cells, induced apoptosis via the mitochondrial pathway mediated by the production of reactive oxygen species, and inhibited the migration/and invasion of tumor cells, proving that this biflavonoid can be used as an antitumor agent against colorectal cancer . AccordiF. platyphylla roots belong to the special group of natural compounds called dimeric flavonoids. The presence of chalcones in the Brachydin structures adds great pharmacological potential, since this group of substances has several pharmacological properties, such as antioxidant, cytotoxic, anticancer, antimicrobial, antiprotozoal, antiulcer, antihistamine, and anti-inflammatory activity. Some leading compounds with various pharmacological properties have been developed based on the chalcone skeleton [The flavonoids Brachydin E and Brachydin F isolated from the crude hydroethanolic extract of skeleton .F. platyphylla roots and without any previous reports for their anticancer activities. Previous study published by our group has indicated that the flavonoids Brachydins A, B, and C, obtained from the root extract of F. platyphylla (Cham.) L.G. Lohmann, induce cytotoxicity in the human prostate tumor cell line PC-3 [β-glucuronic acids on the last two molecules.In fact, our study describes the antiproliferative effect of two rare dimeric flavonoids, Brachydins E and F, isolated from the crude hydroethanolic extract of ine PC-3 . The difHerein, we show that Brachydin E and Brachydin F possess cytotoxic activity, with high selective index for the human prostate adenocarcinoma cell line PC-3. These results prompted us to select PC-3 for further studies. Taken altogether, the results obtained from the cytotoxicity assessment, wound healing, and clonogenic assay confirm the potent antiproliferative activity of Brachydins E and F for PC-3 cells, once these experiments revealed that these dimeric flavonoids are cytotoxic and inhibit cell proliferation. In agreement, the Annexin V-PE/7-AAD assay suggests that Brachydins E and F are leading prostate cells to death. Annexin V is a phospholipid binding protein with high affinity for phosphatidylserine that is located on the inner face of the plasma membrane of viable cells. In the early phase of some regulated cell death process, phosphatidylserine molecules are translocated to the outer face and free to bind to Annexin V, thereby labeling cells through PE. 7-AAD binds to cell DNA and acts as an indicator of membrane structural integrity since it is not able to label cells that are viable and in initial process of cell death . BrachydIn an attempt to outline a possible molecular target for Brachydins, these molecules were subjected to molecular docking. This methodology is aimed at predicting the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimization and in virtual screening studies to find novel biologically active molecules . In siliAlthough Brachydins E and F differ among themselves only at the aromatic ring C (by the presence of a hydroxyl group in Brachydin E while Brachydin F presents a methoxyl group), results obtained in vitro have pointed out that Brachydin E has better potency in antiproliferative activity. To check their similarities and differences, an in silico absorption, distribution, metabolism, excretion (ADME) study was applied, which is an important tool for rational drug design . BrachydMoreover, the ADME study also suggested that Brachydins E and F are not able to inhibit any metabolism enzyme, suggesting a lower risk for drug interaction .The results herein described are very promising, considering that prostate cancer is the second most common neoplasm in the world among men, and the chemotherapy commonly used in the clinic for its treatment has extensive side effects such as neutropenia, thrombocytopenia, liver toxicity, fatigue, hypertension, and even leukemia induction –35. TherThe collective reported data for dimeric flavonoids and the results obtained in our studies suggest that Brachydins E and F are promising compounds to be further explored for their antitumor effects. The results presented in this study for the dimeric flavonoids Brachydins E and F are unprecedented and reinforce the need for further studies on their mechanism of action, in view of the relevant antiproliferative property evidenced herein and the already reported antiproliferative properties of dimeric flavonoids.


Disparities between risk, treatment outcomes and survival rates in cancer patients across the world may be attributed to socioeconomic factors. In addition, the role of ancestry is frequently discussed. In preclinical studies, high-throughput drug screens in cancer cell lines have empowered the identification of clinically relevant molecular biomarkers of drug sensitivity; however, the genetic ancestry from tissue donors has been largely neglected in this setting. In order to address this, here, we show that the inferred ancestry of cancer cell lines is conserved and may impact drug response in patients as a predictive covariate in high-throughput drug screens. We found that there are differential drug responses between European and East Asian ancestries, especially when treated with PI3K/mTOR inhibitors. Our finding emphasizes a new angle in precision medicine, as cancer intervention strategies should consider the germline landscape, thereby reducing the failure rate of clinical trials. Pre-clinical studies in drug development can help to refine the target population and thus increase the success of clinical trials . To thisIn the last two decades, the Cancer Genome Atlas (TCGA)  and InteIn contrast, genetic ancestry is mostly neglected in HTSs, although it is an established factor of risk, progression and response to treatment in several cancer types in the clinic ,13. IncoIn this study, we leveraged the drug sensitivity profiles of >1000 molecularly characterised CCLs across >400 drugs obtained from the GDSC project for revealing ancestry-dependent pharmacogenetic interactions. First, we inferred the ancestry of the cell line panel using a Bayesian method, using a list of 100 predictive single-nucleotide polymorphisms   for infeUsing our processing pipeline a, the anThe CCLs were classified in 25 subpopulations a, which A principal component analysis using the genotype matrix showed that the cells were clustered into three distinct groups, which correspond to AFR, EAS and EUR/AMR ancestries c, thus hIn order to examine the validity of our method, we compared the results with a similar dataset from the CCLE project. We found 644 cell lines overlapping between the two datasets, using the shared COSMIC and DepMap cell IDs. To make our data compatible with the CCLE data, we summarised both AMR and EUR cell lines as Caucasian and EAS and SAS cell lines as Asian in the annotated GDSC set. As expected, the inferred ancestry showed very high concordance with the referenced ancestry by the CCLE. Particularly, 634 out of 644 cell lines were consistently annotated (98.4%). From the remaining ten cells, nine were CCLE Caucasian that were classified as SAS (eight cells) or AFR (1 cell) and one was an African cell line that was classified as AMR in GDSC, set by our pipeline d. AlthouFurthermore, using the inferred ancestry information, we managed to predict the HLA genotypes of the CCLs using the HiBAG method  with higNext, we investigated CCL ancestries which confer drug sensitivity, which may be leveraged for selecting target cohorts based on demographics. The drug response data from the GDSC project served as our discovery cohort, and CTRP was used for validation. We performed one-way ANOVA tests for investigating imbalances in drug responses across two pairwise ancestries. Comparing the largest two populations, i.e., Asian and Caucasian .For validation, we investigated the overlapping screens between GDSC and CTRP b. Four od = −1.73, adj. p = 0.06), GSK1059615 , torin 2  and WYE-125132  . Noticeably, the majority of these inhibitors (three out of four) target only mTOR or a combination of mTOR and PI3K, whereas GSK1059615 only targets PI3K.Among the top significant associations, Asian cell lines showed higher sensitivity to PI3K/mTOR inhibitors, especially in glioblastoma (GBM). Specifically, out of all 12 associations in GBM, the cell lines were significantly sensitive to apitolisib (Cohen’s  = 0.04) c. Furthep = 0.09).Associations in which Caucasian CCLs were found to be more sensitive accounted for 47 out of 59 total significant associations . In factd = 1.71, adj. p = 0.14) and CI-1033   and PF-00299804  showed similar trends but did not pass our set FDR threshold of 20%. Interestingly, all CCLs with a copy number gain of EGFR showed sensitivity to TKIs independent of ancestry, but here we can reveal that other Caucasian CCLs with wild-type EGFR respond better than their Asian CCL wild-type counterparts.Furthermore, we identified that Caucasian CCLs in low-grade glioma (LGG) were more sensitive to irreversible tyrosine kinase inhibitors (TKI) targeting EGFR, ERBB2 or ERBB4, such as AST-1306 (Cohen’s  = 0.17) e. Remarkp = 0.05; p = 0.26 and adj. p = 0.21, respectively; Somatic driver mutations are commonly investigated as potential drug response biomarkers. It is likely that the frequency by which somatic mutations are observed in patients can be dependent on their ancestry. Consequently, we screened for enrichments of high-confidence cancer genes in CCLs in either Asian or Caucasian ancestry CCLs (Methods). We only found a handful of enriched cancer genes, namely for Asian CCLs; NF1 mutations are more abundant for GBM . For inferring ancestry, we leveraged the set of 100 ancestral SNPs from Sampson et al. [https://www.internationalgenome.org/, accessed on 20 February 2020) [CSV files from the Genomics of Drug Sensitivity in Cancer (GDSC) database were transformed into text (PED and MAP) and binary file sets  for each chromosome using PLINK . First, ry 2020)  and mismThe quality-controlled binary file sets were converted to VCF files using PLINK. A reference genome was downloaded from the 1000G database in the form of phased VCF files and converted to BCF using BCFtools  and M3VCiY that has the genotype iG of the ancestral SNPs independently occurring, the probability P(iG) that iY belongs to a population k (k corresponds to one of 25 subpopulations in the 1000 G) is:The imputed genotypes were then combined with the typed set to assemble a complete ancestral list to infer the ancestral origin of the cell lines. We calculated the probability that a cell belongs to a population given its observed ancestral genotype, the population’s genotype frequency and the population weight in the 1000G, based on Bayesian inference. The population genotype frequencies were obtained using the Ensembl API. Particularly, for a cell Then, the subpopulation was assigned to the cell line which had the highest corresponding probability: The imputed genotypes and the inferred ancestry of cancer cell lines (CCLs) were used to predict the genotypes of seven human leukocyte antigen (HLA) loci, including HLA-A, HLA-B, HLA-C, HLA-DPB1, HLA-DQA1, HLA-DQB1 and HLA-DRB1, at 4-digit resolution using the HIBAG algorithm .50 values (considering the maximum screening concentrations) in more than 50% of the screened CCLs. Then, we performed one-way ANOVAs across the remaining drugs and for each cancer type using ancestry as a predictor and drug response (log10(IC50)) as the dependent variable, adjusting for cell characteristics and growth properties as covariates. The unclassified cell lines and the cancer types that contained only one population were filtered out of the analysis. We also removed the drugs that were treated in less than 10 cell lines. To simplify the subsequent analysis and comparison to the Cancer Cell Line Encyclopedia (CCLE) dataset, we re-encoded the ancestry variable as follows: American (AMR) and European (EUR) were combined into Caucasian; East Asian (EAS) and South Asian (SAS) were combined into Asian. ANOVAs were performed in a pairwise manner between two out of the three populations. Populations that had less than three cells were not tested. The effect size was calculated as Cohen’s d. The p-values of the ANOVA tests for each cancer type were adjusted using the Benjamini–Hochberg correction.First, we combined GDSC1 and GDSC2 datasets and generated a unique drug identifier (including drug ID and dataset). Next, we removed the drugs that had extrapolated ICp-values for multiplicity using the Bonferroni adjustment method.We identified enriched drug targets in drugs with differential drug response for each cancer type independently. We extracted the putative drug targets from the GDSC manifest files and for each drug target that had at least 2 significant drugs per cancer type. We subsequently tested for enrichment using a hypergeometric test for nine drug targets in total, and we adjusted the enrichment https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources/Home.html, accessed on 13 March 2020), which contains a curated set of cancer somatic driver genes observed in both CCLs and primary tumours, from which the binary mutational status is given. We used a two-sided Fisher’s test for performing the enrichment tests. We only tested cancer types with at least one drug displaying a differential drug response, and only mutations with at least six mutated CCLs. In total, we performed 88 statistical tests, and adjusted the enrichment p-values for multiplicity with the Bonferroni adjustment method for each cancer type independently.We downloaded the Binary Event Matrices (BEM) from the GDSC portal (


Ras-specific proteases to degrade RAS within cancer cells are under active development as an innovative strategy to treat tumorigenesis. The naturally occurring biological toxin effector called RAS/RAP1-specific endopeptidase (RRSP) is known to cleave all RAS within a cell, including HRAS, KRAS, NRAS and mutant KRAS G13D. Yet, our understanding of the mechanisms by which RRSP drives growth inhibition are unknown. Here, we demonstrate, using isogenic mouse fibroblasts expressing a single isoform of RAS or mutant KRAS, that RRSP equally inactivates all isoforms of RAS as well as the major oncogenic KRAS mutants. To investigate how RAS processing might lead to varying outcomes in cell fate within cancer cells, we tested RRSP against four colorectal cancer cell lines with a range of cell fates. While cell lines highly susceptible to RRSP (HCT116 and SW1463) undergo apoptosis, RRSP treatment of GP5d and SW620 cells induces G1 cell cycle arrest. In some cell lines, growth effects were dictated by rescued expression of the tumor suppressor protein p27 (Kip1). The ability of RRSP to irreversibly inhibit cancer cell growth highlights the antitumor potential of RRSP, and further warrants investigation as a potential anti-tumor therapeutic. This process is highly reliant on GTPase activating proteins (GAPs) and guanine exchange factors (GEFs) for hydrolysis of GTP and nucleotide exchange of GDP to GTP, respectively4. Upon growth receptor stimulation, activated RAS recruits downstream effectors, including Rapidly Accelerated Fibrosarcoma (RAF) kinase and phosphatidylinositol-3-kinase (PI3K). These effectors subsequently activate signaling pathways responsible for cell growth and survival, including the mitogen-activated kinase to extracellular signal-regulated kinase (ERK)5 signaling pathway and the protein kinase B  to mammalian target of rapamycin (mTOR) pathway, respectively7.The oncoprotein Rat sarcoma GTPase (RAS) cycles between GTP-bound (active) and GDP-bound (inactive) states for activation of downstream effectors, each playing key roles in cell proliferation and survivalRAS8. Mutant RAS, paired with loss of function in tumor suppressor genes such as TP53 and APC, are sufficient to fully transform cells and drive tumorigenesis8. Nearly all RAS mutations occur as point mutations at Gly-12, Gly-13 or Gln-61, resulting in constitutive activation of RAS2. Among the major RAS isoforms , KRAS is the most frequently mutated isoform among all cancers (85%) followed by NRAS (11%) and HRAS (4%)8. RAS mutations are highly enriched specifically in three of the four most lethal cancers in the United States, including pancreatic adenocarcinoma (98%), colorectal adenocarcinoma (52%), and lung adenocarcinoma (32%)8.Thirty percent of all human cancers contain mutations in 13. Recent studies have taken advantage of biochemical properties of specific RAS mutants to develop selective small molecule inhibitors specific for highly oncogenic mutant forms of RAS. In particular, small molecules targeting KRAS G12C have been developed and are undergoing clinical trials16. Many of these agents have shown clinical success with one molecule receiving FDA accelerated approval earlier this year for treatment of KRAS G12C tumors in non-small cell lung carcinoma17. Despite this success, the strategy of selective inhibition has problems of being applicable to only a limited range of cancers integrated with personalized medicine and cannot be used to treat cancers that lack the specific mutation. To address this gap, new approaches are being developed to more broadly target RAS either with proteases that directly cleave RAS19 or with linkers that target RAS for cellular degradation23.Although numerous studies support the advantages of targeting RAS to treat cancer, it remains an unsolved challenge in the clinicVibrio vulnificus during host infection. V. vulnificus delivers RRSP into intestinal epithelial cells during host infection, where it targets all RAS isoforms and close homolog Ras-related protein 1 (RAP1). Through RAS inactivation, this bacterium suppresses the host immune response, thereby aiding systemic dissemination of the bacterium25. Detailed structural and biochemical studies have shown that RRSP attacks the peptide bond between Tyr-32 and Asp-33 in the Switch I region of both RAS and RAP126. As a result, RAS and RAP1 are unable to undergo GTP-GDP exchange or bind to their downstream effectors28. Recently, RRSP engineered as a chimeric toxin for in vivo delivery was shown to significantly reduce breast and colon tumor growth in xenograft mouse models18.In line with this alternative strategy, our lab has identified a potent protease that cleaves RAS called the Ras/Rap1-specific endopeptidase (RRSP). RRSP is a small domain of a large toxin secreted by the bacterium 29. Fourteen of 60 cell lines were classified as highly susceptible with cells undergoing cytotoxic effects. However, 38/60 of cell lines showed growth inhibition, but not cytotoxicity. Only 8/60 showed low or no susceptibility with cell growing near normal rates, possibly due to lack of the receptor for the engineered chimeric toxin18. The observed wide range of cell fates highlights that the cellular responses to total RAS cleavage has the potential to be quite variable across cancer cell lines. Here, we investigate how RRSP processing affects cell signaling and demonstrate that cleaving total RAS can have a variable impact on cancer cell growth and survival. Specifically, we demonstrate that RRSP can disrupt colorectal cancer (CRC) cell growth through multiple mechanisms, including loss of cell viability, cell cycle arrest, and senescence.The potential applicability of RRSP to a broader range of cancers was screened using the standardized National Cancer Institute (NCI) cancer cell panel26. To get an even broader sense of RRSP effectiveness across different isoforms and mutants of RAS, we tested RRSP against the ‘RAS-less’ mouse embryonic fibroblast (MEF) cell line panel developed by Drosten et al.7. These isogenic cell lines have endogenous RAS genetically deleted from their genome and a single allele of a human RAS gene is integrated to rescue growth. For delivery of RRSP into mouse cells, we used the anthrax toxin-based delivery system wherein the anthrax toxin lethal factor N-terminus was fused with RRSP (LFNRRSP) or LFNRRSP with a catalytically inactivating H4030A amino acid substitution (here after referred to as LFNRRSP*). Intracellular delivery of RRSP (previously known as DUF5) by anthrax toxin protective antigen (PA) was previously demonstrated in several mammalian and mouse cell lines31.RRSP was previously shown to specifically cleave HRAS, NRAS, and KRAS when the proteins were ectopically expressed in HeLa cells and recombinant RRSP was shown to process purified KRAS G12D, G13D, and Q61R in biochemical assaysNRRSP dramatically decreased intact full-length RAS levels with increased detection of cleaved RAS. For each isoform, LFNRRSP was found to cleave at least 80% of RAS after 24 h 18. Similar to the anthrax toxin system, RRSP-DTB binds to a human receptor ), is endocytosed, and translocated into the cytosol across the vacuolar membrane. Expression of HB-EGF receptor was found to be similar between the selected CRC cell lines  and growth inhibition was monitored. HCT-116 cells showed the greatest impact of RRSP-DTB on cell confluency and the lowest IC50  and high (10 nM) concentrations of RRSP-DT34. However, phosphorylation of Thr-60 has no effect on its kinase activity or its cellular localization35. Because RRSP decreases AKT activation  and SW620 (less sensitive) were incubated overnight with nitrocellulose membranes containing capture antibodies towards 43 different phosphorylated proteins  Fig. B. While B. The treatment increased p27 protein levels in HCT-116, SW620, and SW1463 cells, while in GP5d cells levels remained unchanged  do not correlate with pERK activity in KRAS and BRAF mutant CRC cell lines45. Further investigation of RRSP in the context of KRAS mutants expressing low levels of pERK would elucidate mechanisms regulating cell fate signals between cell lines.We next examined RRSP effectiveness in four CRC cell lines, which displayed variations in susceptibility to RRSP. Two of the cell lines with the greatest RRSP growth sensitivity, HCT-116 and SW1463, had dramatically lower metabolic activity and induction of apoptosis compared to controls. Interestingly, GP5d and SW620 retained normal metabolic activity, yet showed significant inability to form colonies following RRSP treatment, mimicking a senescent-like phenotype. This observation is consistent with prior data for SW620 showing a reduction in cellular material stained with sulforhodamine B after 48 h of treatment at 13.5 nM RRSP, but RRSP was not cytotoxic42. Upon mitogen stimulation, RAS activation suppresses p27 protein expression through post-translational modifications that signal for its ubiquitin-mediated degradation39. In RAS-driven human cancers, low levels of p27 are frequently observed. We demonstrated that the growth inhibition in HCT-116, SW1463, and SW620 is the result of G1 cell cycle arrest through the upregulation of p27. These data suggest that RAS cleavage in certain CRC cell lines induces p27 upregulation, leading to a cell cycle arrest state that can induce apoptosis at later timepoints. Transient overexpression of p27 is known to then induce cell cycle arrest and later apoptosis47. Although in our studies, only the highly susceptible cell lines showed decreases in viability and induction of apoptosis, whereas SW620 retained metabolic activity and undergoes cell cycle arrest. It is important to note that a prolonged cell cycle arrest through p27 induces a persistent cell cycle arrest through induce senescence48. We observe this phenotype in SW620 cells in which RRSP treatment induced a senescent-like phenotype.Mechanisms that link RAS and the cell cycle have been well examined. In quiescent cells, p27 is highly expressed in order to inhibit CDKs activity and to suppress RB phosphorylationPhotorhabdus asymbiotica, which is identical to RRSP, also cleaves RAS26 and was recently reported to induce G1 cell cycle arrest49. The proposed mechanism did not depend on RAS processing, and instead involved RRSP directly binding to CDK1 when it is overexpressed, essentially functioning as a protein trap. Thus, the multi-domain RRSP may possess at least two mechanisms to inhibit the cell cycle, one by restoring p27 downstream of RAS processing and another by directly binding to CDK1. Notably, because low p27 expression levels have been correlated with poor survival in patients with different types of cancer including colon, the ability of RRSP to restore p27 expression and to initiate cell cycle arrest could have important implications for the treatment of tumors with aberrant RAS signaling.Unexpectedly, GP5d cells did not show upregulation of p27 or p21, although RB was hypo-phosphorylated and cells initiated G1 cell cycle arrest. These data reveal there must be other pathways that drive growth inhibition following RAS cleavage. In fact, RRSP from the insect pathogen KRAS oncogenic mutation as all mutant forms of KRAS were susceptible to RRSP-driven RAS processing in an isogenic system and RAS was processed in all the CRC cell lines. The impact of RAS processing is thus linked to cancer cell differences in signal pathways downstream of RAS. A limitation of this study is that only four lines were assessed, suggesting that the array of variable outcomes could increase as additional cell lines are considered in the future. However, the key finding here is that, despite differences in the mechanisms underlying RRSP susceptibility, all cells tested were ultimately susceptible and all led to reduced cell viability, growth, and/or proliferation. Thus, RAS processing or degradation has great promise as a potential broadly applicable therapy against colon cancer.In summary, a critical finding of this study was that processing of RAS does not result in a single easily tractable cell fate in all cancer cells, but drives a suite of alternative overlapping outcomes that can include cytotoxicity, inhibition of cell cycle and senescence. The differences are not driven by the nature of ‘RAS-less’ mouse embryonic fibroblast (MEF) cells were provided by the NCI RAS Initiative at Frederick National Laboratory for Cancer Research (FNLCR). HCT-116 cells were purchased from the American Type Culture Collection. SW1463, GP5d, and SW620 were provided by the NCI. Each cell line was validated by the Northwestern University Sequencing Core by Short Tandem Repeat profiling.2 atmosphere. HCT-116, SW1463, GP5d, SW620 cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM)-F12 with Glutamax (Gibco) containing 10% fetal bovine serum  and 1% penicillin/streptomycin . All MEF cells, except for HRAS RAS-less MEFs, were cultured in DMEM (Gibco) with 10% FBS,1% P/S, and 4 µg/ml of blasticidin (ThermoFisher Scientific). HRAS RAS-less MEFs was cultured in 2.5 µg/ml of puromycin (ThermoFisher Scientific).All cells were cultured at 37 °C and 5% CO18. Other commercially available primary antibodies used were: anti–Phospho-p44/42 MAPK , anti-p44/42 MAPK , anti-HB-EGF , anti-p27Kip1 XP , Phospho-RB , anti-p21WAF/Cip1 , anti-α-Tubulin , and anti-vinculin . Fluorescently-labeled secondary antibodies obtained from LI-COR Biosciences and used at 1:10,000 dilution were: IRDye 680RD goat anti-mouse (926-68070), IRDye 800CW goat anti-rabbit (925-322211), and IRDye 800CW donkey anti-goat (925-32214). Western blot images were acquired using an Odyssey Infrared Imaging System (LI-COR Biosciences) and quantified by densitometry using NIH ImageJ software.Anti-RAS monoclonal antibody recognizing G-domain of all major RAS isoforms was purified from a hybridoma cell line provided by FNLCR and used at 1:2000 dilution as previously describedPlasmid RB-GFP FL for expression of GFP-tagged RB was obtained from Addgene . For ectopic gene expression, cell lines were transfected using FuGene HD (Promega) according to the manufacturer’s protocols. GFP fluorescence was analyzed using EVOS XL Core imaging system.g at 4 °C for 15 min. The concentration of protein in the collected supernatant fluid was determined using the bicinchoninic acid (BCA) assay . Samples were boiled at 95 °C in Laemmli SDS loading buffer for 10 min and protein was separated on either 15 or 18% SDS–polyacrylamide gels. Proteins were transferred to nitrocellulose membranes and blocked in Tris-buffered saline (TBS) [10 mM Tris (pH 7.4) and 150 mM NaCl] with 5% (w/v) milk for 1 h. Membranes were washed with TBS and then incubated in indicated primary antibodies in TBS with 5% (w/v) Fraction V bovine serum album (Fisher BioReagents #194850) overnight at 4 °C. Total percentage RAS was calculated using the following equation: % Total RAS = uncleaved RAS band / (RAS uncleaved band / RAS cleaved band) X 100.Cells were washed in PBS and then resuspended in lysis buffer . Lysates were incubated for 15 min on ice and centrifuged at 20,000 × NRRSP and LFNRRSP* were expressed in Escherichia coli BL21(DE3) and purified over a HisTrap FF nickel affinity column followed by Superdex 75 size exclusion chromatography using the ÄKTA protein purifier purification system , as previously described30. For intoxication, MEFs were seeded in 6-well plates at 3 × 105 cells per well for 1 h, after which medium was replaced with fresh medium containing with 7 nM Protective antigen (PA) alone  or in the presence of 3 nM LFNRRSP/ LFNRRSPH4030A and incubated at indicated timepoints at 37 °C in the presence of 5% CO2.Recombinant LFB and RRSP*-DTB were expressed in E. coli BL21(DE3) and purified over a HisTrap FF nickel affinity column as previously described18. Eluted fractions were loaded onto a gravity column containing Strep-Tactin Superflow high capacity resin, followed by SUMO-tag removal and size exclusion purification over a Superdex 75 column using ÄKTA protein purifier purification system as previously described18. For intoxication, CRC cell lines were seeded in 6-well plates (~ 70% confluency) overnight, after which medium was replaced with fresh medium containing either RRSP-DTB or RRSP*-DTB and incubated at indicated timepoints at 37 °C in the presence of 5% CO2.Recombinant RRSP-DT3 cells per well) were cultured in 96-well clear bottom white plates in corresponding complete growth medium and treated after 4 h with RRSP-DTB or RRSP*-DTB. Colorectal cancer cell lines were plated at ~ 80% confluency and cultured in 96-well clear bottom white plates. Complete growth medium with RRSP-DTB or RRSP*-DTB was added after overnight cell attachment. All cells were cultured were in Nikon Biostation CT and images were taken at indicated timepoints. Cell confluency was quantified using Nikon Elements software. IC50 concentrations were calculated using log(inhibitor) vs. response variable slope (four parameters) function in Graphpad Prism 8.For RAS-less MEFs  reagent was added to each well and luminescence was detected using Tecan Safire2 plate reader. For apoptosis assay, CRC cell lines were seeded at 10,000 cells per well in a 96-well clear bottom white plate. Complete growth medium with RRSP-DTB or RRSP*-DTB was added overnight after cell attachment. After 48 h, Caspase-Glo 3/7 Assay (Promega) regent was added to each well and luminescence was detected using Tecan Safire2 plate reader. For crystal violet assays, cells were treated as described above and were incubated for 48 h. Following incubation cells were harvested and reseeded at low seeding densities in 6-well plates. Colony formation was monitored over 14 days, during which media was replaced every three days. On day 14 colonies were fixed in crystal violet fixing/staining solution (0.05% (g/vol) crystal violet, 1% formaldehyde, 1% (v/v) methanol in PBS. Open source ColonyArea ImageJ plug-in was used for quantitative analysis of the area % covered by the stained colonies50. Due to high background from crystal violet staining in SW620 cells, stained wells were dissolved in 10% acetic acid and destained on rocker for 30 min. Absorbance was measured at 590 nm using Tecan Safire2 plate reader.For cell viability assay CRC cell lines were seeded in 96-well clear bottom white plates at ~ 80% confluency. Complete growth medium with RRSP-DTg and supernatant was collected. Concentration of protein in the collected supernatant fluid determined using the BCA assay . 200 μg of sample lysate was applied to nitrocellulose membranes kinase arrays and incubated overnight at 4 °C. Provided detection antibodies were incubated with specified concentrations as suggested by the supplier. Membrane arrays were acquired using Odyssey Infrared Imaging System (LI-COR Biosciences) and quantified by densitometry using NIH ImageJ software. Values from densitometry analysis were normalized to HSP60 control. Normalized value was then converted to Log2 fold change and plotted on heatmap using Graphpad Prism 8.CRC cell lines were treated as described and washed in 1X PBS. Cells were solubilized using lysis buffer provided by the vendor (R&D Systems) and rocked for 30 min at 4 °C. Suspension was spun for 5 min at 14,000 × g for 5 min. Cells were washed twice in PBS and spun down at 700 × g for 5 min. PBS was removed and cells were resuspended in 600 μL of ice-cold PBS. Cell were permeabilized with addition of 1.4 mL of ice-cold ethanol slowly and incubated overnight at -20 °C. Following two washes with PBS (centrifuged at 700 × g for 5 min), cells were stained in 200 μL PI staining solution , 0.2 mg/mL RNase) for 30 min. Samples were analyzed on BD LSR Fortessa 1 Analyzer. At least 10,000 events were collected for each sample. Single cell populations were viewed and gated on cyanine-3 area (Cy3-A) versus cyanine-3 width (Cy3-W) channels, to eliminate doublet events. ModFit LT Software (Version 5) was used for cell cycle analysis.CRC cell lines were treated as described above. After 24 h of treatment, cells were collected from medium, washed with 1X PBS, and released from well with Trypsin–EDTA (0.25%), phenol red (Invitrogen). Harvested cells were centrifuged at 700 × Supplementary Information.


Computational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays, combined with profiling of cancer cell lines and drugs become increasingly available for training such models. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Importantly, multi-task models require a tailored approach to model interpretability. In this work, we develop DEERS, a neural network recommender system for kinase inhibitor sensitivity prediction. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project cell line and drug features into 10-dimensional hidden representations and a feed-forward neural network to combine them into response prediction. We propose a novel interpretability approach, which in addition to the set of modeled features considers also the genes and processes outside of this set. Our approach outperforms simpler matrix factorization models, achieving R  Drug sensitivity data from cancer models are frequently generated to provide the basis for the discovery of molecular markers to predict drug efficacy. To predict the response of a specific cell line to a specific drug, there is a need of computational models that can leverage the abundance of information about drugs and cancer cell lines.Matching the optimal drugs for individual cancer patients remains a crucial problem of precision medicine6. Despite their drawbacks11, multi-omics cell line data can provide important insights about the molecular mechanisms underlying susceptibility to distinct drugs. Arguably, the subclass of kinase inhibitor drugs is best characterized by their kinase inhibition profiles, which, apart from the intended on-targets, manifest also off-target effects. Despite their frequent use during the early phases of drug development, when inhibitory profiles of kinase inhibitors are optimized, to our knowledge such data has not been used for modelling of drug response.Strongly parallelized assay formats provide a variety of data that can be used to comprehensively describe the characteristics of both cancer cell lines and drugs14, ranging from traditional algorithms18 to models based on neural networks and deep learning24. Recently, the problem has also been addressed using generative modeling, including variational autoencoders27, as well as using the reinforcement learning framework28.Computational drug sensitivity prediction has been approached by many machine learning methodologies31. Recently, this approach has been modified by introducing non-linearity in the projection step, where the projections are computed by neural networks or autoencoders, but the corresponding hidden representations are still connected via a dot product in the linear fashion. Dot product, however, as a simple linear function, has a limited ability to capture the complex user-item interactions in the hidden space. To address this issue, deep neural networks have been proposed to replace the dot product for modeling the user-item interactions in the latent space33. Since neural networks are known as the universal approximators34, they are expected to be more suitable to learn complex relationships between the hidden representations of the users and items and the response variable.The problem of drug sensitivity prediction can be stated as a recommendation problem, where cancer cell lines and drugs are analogous to users and items, respectively. The goal is to recommend the best drug for a given cell line. One of the most popular recommender system techniques is matrix factorization (MF), where the user-item interaction matrix is decomposed into a product of two lower-dimensional rectangular matrices. The problem of so called matrix factorization with side information incorporates features of users and items in the factorization process. The simplest approach to such MF problems involves linear projection of the features to lower-dimensional hidden space, followed by computing the dot product between corresponding user and item hidden representations in order to obtain user-item interaction prediction35. Moreover, deep neural networks have a reputation of being difficult to interpret due to their non-linearity and complex structure. The majority of so called explainable artificial intelligence methods focus on finding attributions between specific neurons in the network by analyzing the underlying gradient flow39. Although useful, these methods provide rather standard utilities (e.g. feature importances), often available also for traditional machine learning models. Moreover, the insights derived from such interpretability approaches are limited by the features chosen for training the model.While the neural-network based models are more expressive, previous analyses point out that the deep learning models do not necessarily outperform simpler models when the latter are finely tuned, and that some published neural network model results are hard to reproduceWe argue that a desired recommender system for the problem of drug sensitivity prediction should satisfy several objectives. First, it should solve a multi-task learning problem, i.e. model multiple drugs and cell lines simultaneously. This allows to capture general mechanisms driving the drug-cell lines interactions. Second, it should achieve state-of-the art predictive performance, especially in the task of predicting drug sensitivities for new cell lines. This is due to the fact that in this setting, the new cell line mimics a new patient, and the recommendation problem corresponds to identifying the best therapy for that patient. Finally, the model should be interpretable. Specifically, the model should explain the rationale behind its predictions and provide biological and pharmacological insights regarding the mechanism underlying the drugs-cell lines interactions. The emphasis on model interpretability is crucial in the context of its potential clinical applications.To address these objectives, we develop a recommender system model for drug sensitivity prediction, called Drug Efficacy Estimation Recommender System (DEERS). DEERS incorporates two autoencoders to project the drug and cell line features, respectively, into lower dimensional representations, and uses a feed forward network to predict the sensitivities of the cell lines to the drugs based on their hidden representations. The proposed framework brings several advantages. First, the model solves a multi-drug and multi-cell line sensitivity learning problem and utilizes cell lines biological data and drugs inhibition profiles as side information Fig. a,b. SecoThe crucial aspect of the proposed interpretability approach is that it offers the widest possible assessment of the specific genes and biological processes that underlie the action of the drugs on the cell lines. The novelty of this approach stems from the fact that it considers also such genes and processes that were not included in the set of modeled features. Using the interpretablity approach, we demonstrate that the low-dimensional representations of the model capture the high dimensional features of drugs or cell lines, specifically the molecular patterns of cell lines and drug inhibition profiles that govern the response of distinct cell lines to drugs Fig. c. FinallDEERS was developed with two aims in mind. One, to achieve state-of-the art predictive performance in predicting the response of cancer cell lines to kinase inhibitor drugs. Second, to identify the biological mechanisms that drive this response. Below, we evaluate the performance of DEERS in comparison to other models and conduct its interpretability analysis.3 database. GDSC provides two sensitivity measurements, summarizing the dose-response curve: area under the curve (AUC) and log half maximal inhibitory concentration (IC50), defined as a drug concentration needed to reduce cell viability by 50%.The drug sensitivity measurements were acquired from the Genomics of Drug Sensitivity in Cancer (GDSC)The predictive performance of DEERS is compared with four other methods. Two of those, Elastic net and XGBoost, are traditional, frequently used machine learning algorithms. Remaining two, referred to as Lin MF and Autoen MF, are versions of matrix factorization with side information see “ and AUC In general, IC50 as a prediction target is easier to learn than AUC. Indeed, in terms of correlation between predicted and true response values, better results are obtained by all models for IC50 than for AUC.With IC50 as the response variable, the DEERS model mostly outperforms or at least performs similarly well as the other two matrix factorization-based models with regard to all of the six performance metrics , and the CCLE dataset in terms of cell lines and drugs, along with corresponding, min–max-scaled CCLE IC50 values. The data regarding the intersection between GDSC and CCLE, as well as CCLE IC50 values were extracted using the PharmacoDB package41. The resulting dataset contained 351 common cell lines and 5 common drugs , constituting 1747 pairs in total. The cell lines and drugs were described by the same features as in the original GDSC dataset. We next used the GDSC data corresponding to the remaining 571 cell lines that are not present in the CCLE-GDSC intersection dataset and all 74 drugs to train DEERS. From those 571 cell lines of GDSC, 50 were randomly chosen to construct the validation dataset for tuning the learning and dropout rates tes see “” sectionTaken together, these results demonstrate that thanks to its deep neural network-based recommender system architecture and utilization of informative drug features, DEERS obtains state-of-the art performance in predicting cell lines sensitivity to drugs in a multitask setup. In contrast to the other well performing model, XGBoost, however, DEERS obtains highly informative reduced-dimension representations of the cell line and drug features, respectively. This aspect of the model is discussed below.As the first step of the DEERS model interpretability analysis, we computed the attributions between the input features and the hidden dimensions using Integrated Gradients see “” sectionBLK, BRSK1, BTK, CSK, DDR1, EGFR, EPHA2, FGFR2, GAK, LCK, MET, NLK, NUAK1, NUAK2, PIM2, PLK2, RIOK1 and ZAP70, as well as one-hot encoded tissue indication features corresponding to leukemia, lung NSCLC, lymphoma, myeloma, pancreas and urogenital system.The exact groups of features corresponding to hidden dimensions of the drug autoencoder and to hidden dimensions of the cell line autoencoder are listed in Tables In the next step of the interpretability analysis, we associate each hidden dimension of the cell line autoencoder with a biological process. To this end, for each hidden dimension and each gene, we correlate the values of the hidden dimension with the expression values of the gene across cell lines. For a given hidden dimension, the obtained correlations are then ranked and we apply gene set enrichment analysis (GSEA) to identify biological processes positively or negatively correlated with that dimension Fig. c. ImportWhen inspecting the heatmap Fig. , we iden39. The attributions are first computed separately for each cell line and IC50 as the response variable, and next summarized by averaging over all cell lines. Second, for each compound we display the cell lines in two chosen dimensions of the hidden space of the cell line autoencoder, and color them by their IC50 response to the compound. In this way, we identify such regions in this space that are correlated with sensitivity to the compound. Finally, we explore in detail how well one chosen hidden dimension correlates with the true response and we list the biological processes that are associated with that dimension  are inhibited by PHA-79388746. Interestingly, cell line features corresponding to the CDK family do not obtain top attribution values. Instead, the highest average attributions are associated with the expression of BTK, PIM2 and TEC genes, suggesting that their activity in the cell lines is important for PHA-793887 action  identifies a region corresponding to a good response of PHA-793887  with activity against CDK2, CDK1 and CDK4BTK, PIM2 and TEC genes), indicating some similarity between these two drugs. Hidden dimensions 3 and 4 allow to visualize regions in the cell line hidden space with distinctive responses In the final step of the interpretability analysis, we associate biological processes to all of the analyzed drugs. This analysis is based on the idea behind the bottom panels of Fig. entclass1pt{minimaFive of the drugs targeting the RTK signaling pathway Fig. a are posDrugs targeting the PI3K/MTOR signaling pathways generally share very similar profiles of association with biological processes Fig. b. All buIn general, these results can serve as a validation and explanation of the model predictions, as well as provide insights regarding the drugs mechanisms of action and drivers of the cell lines response. Considering drug-biological process associations within a certain drug category enables insights into drugs action on a more general level than putative targets or target pathway information alone.In this work, we propose a deep neural network recommender system-based approach to the problem of kinase inhibitor sensitivity prediction based on side information about drugs and cancer cell lines. The proposed model, DEERS, combines dimensionality reduction of the cell line and drug features using autoencoders and neural network-based prediction based on the obtained hidden representations. The modeled drug features are the strengths of inhibition of kinases by the drugs. The cell line features include expression and mutation calls for the same kinases in cancer cell lines, complemented by primary tissue type of origin for the cell lines. To our knowledge, this type of modeling using these types of input data has not been applied before to predict sensitivity to kinase inhibitors.Our focus on modeling kinase inhibitors is motivated by the fact that binding profiles across kinases represent exquisite data to characterize such drugs. Alternative information about drugs could be the list of specific known drug targets. In contrast to continuous and rich data about kinase inhibition, however, annotations of known targets are relatively incomplete. The quality of the kinase binding data that we used is assured by a standardized assay platform interrogating a large number of kinases. Therefore, off-target inhibition effects are most likely captured completely. An alternative could be to use information on which signaling pathways are affected by a drug since this information is often provided in drug databases. However, clearly the information about target pathways is only high-level, less detailed than using kinase binding data, and suffers from incomplete understanding about the complete set of pathways that a drug effects in different cellular contexts.Despite its advantages, the data used to describe the drugs can also be seen as a source of limitation of this study. While usage of continuous inhibition profiles provides more information regarding drugs’ action, these types of data are available only for a subset of drugs. In comparison, utilizing raw data (e.g. SMILES) would lead to more labelled samples for training, which could improve the predictive performance. However, using continuous inhibition profiles sheds a light on drugs’ mechanisms of action w.r.t. their response on cell lines and provide more interpretable drugs’ representations. The ablation study regarding inhibition profiles and putative targets shows that drugs’ interactions across a large enough panel of kinases can provide more informative profile of drugs, even in the absence of their putative targets. This provides valuable insight on which properties of the drugs are useful to model in these kind of prediction tasks.The DEERs model aims at two goals: 1) high predictive performance and 2) outstanding model interpretability. Our analysis constitutes a thorough comparative assessment of model performance, evaluating both traditional and variants of matrix factorization-based methods. Out of the two traditional models, XGBoost achieves better results than Elastic net, indicating that accounting for non-linear interactions among features is crucial for prediction performance. DEERS outperforms the other two matrix factorization-based approaches, Lin MF (basic matrix factorization model) and Autoen MF . We observe little difference in performance between the Lin MF model and Autoen MF  were used to train and assess the performance of the presented models. Drug sensitivity of a cell line is the prediction target of our modeling approach.55. The features set of these drugs consisted of binding strength across a panel of 294 protein kinases  and binary mutation calls 21 features) were extracted for all cell lines. Furthermore, the dummy-encoded tissue type was added, producing additional 18 binary features, yielding the final set of 241 biological features for 922 cell lines  activation function, and the 10-dimensional hidden representation layer. The subsequent feed-forward network consists of a 20-dimensional input layer, followed by two hidden layers of length 512 and 256 with the ReLU activation. The regularization of the system is incorporated via the dropout with 0.5 probability, applied in the first, 512-dimensional hidden layer of the feed-forward network.DEERS is a deep neural network-based recommender system. It consists of three major parts: drug autoencoder, cell line autoencoder and the subsequent feed-forward neural network. Fig. b58,59. Ti and cell line j feature vector representations along with the corresponding response value, Consider a training data point consisting of original drug mentclass2pt{minimmentclass2pt{minimJ is the cost function, MSE denotes mean squared error, d is a weight of the dependence penalty, m, nth entry of matrix DEERS has three outputs and three main optimization goals: minimizing the differences between d control the contribution of the particular optimization task in the general optimization goal of the system. Setting all of these weights to zero would result in a network without decoding tasks and no dependence restrictions on the hidden dimensions of the drugs and cell lines.Intuitively, the cost function weights We compare the proposed model to four other methods; two of which are based on traditional machine learning algorithms, while the other two are forms of matrix factorization.60 and XGBoost61. The former is a linear model and the latter is a more complex, nonlinear model.In order to evaluate the traditional methods in a multi-task setting, where the data for all drugs and all cell lines are modeled at once, the traditional methods are used to predict drug response for the union of drugs and cell lines features. To this end, for every data point (30.The compared matrix factorization models aim at solving a similar matrix-factorization type of problem Fig. a and can62.The second of the compared matrix factorization-based models is an non-linear extension of the basic model, where the dimensionality reduction is performed via one-layered autoencoders and data reconstruction is also taken into consideration Fig. b. SimilaIn order to assess the performance of the considered models on the unseen cell lines, we construct the validation and test sets by first randomly selecting two sets of 100 unique cell lines each. We then extract the data points containing selected cell lines, producing the validation and test sets with Before the training, the input cell line data were preprocessed by standard scaling of the continuous gene expression data so that every feature has zero mean and unit standard deviation, while binary coding variants and dummy encoded tissue types were unmodified. For the input drug data, all features were standardized in the same way as the gene expression. Since the GDSC AUC values are in the range of , they were not scaled, while the log IC50 values were linearly preprocessed with min-max scaler to the  range. Notably, all values necessary to perform each of the applied preprocessing schemes were calculated only on the training set and applied to the validation and test sets.d, regularization type and learning rate. We establish the DEERS architecture as consisting of two-layer autoencoders, with 10-dimensional hidden representations (Fig. We use the training and validation sets in order to find the optimal set of hyperparameters, consisting of: network architecture, cost function weights mentclass2pt{minimmentclass2pt{minimAfter all parameters are found, we use them to train the model using the union of training and validation sets, and apply the resulting model to the test set in order to assess the performance. We repeat this procedure ten times with different cell lines in training, validation and test sets in order to improve the robustness of the results.We adopt the similar methodology for the compared models, where we first tune the hyperparameters using training and validation sets, and then apply the final retrained model to the test set, using the same data splits for training, validation and testing for all models. For the compared models, we perform this experimental procedure five times. In addition, we incorporate a simple data augmentation scheme, where we add a random gaussian noise with zero mean to the cell lines gene expression data and the corresponding AUC or IC50 values. The standard deviations of cell lines and response noise were 0.6 and 0.15, respectively. The augmentation was performed iteratively in every batch during training, tripling the original batch size. This data augmentation scheme was added for the two models involving autoencoders, i.e. both the Autoen MF and the DEERS model.This analysis aims at an explanation of the model predictions from the biological standpoint. In order to incorporate all available data for model interpretation, we first re-train the model with all available 922 cell lines and 74 drugs, without excluding any cell lines, and using IC50 as a drug response metric.The interpretation of the hidden dimensions concerns assigning a biological meaning to the individual dimensions of the hidden space. To this end, we first pass the input drugs and cell lines input representations into their corresponding, already trained autoencoders, producing a 10-dimensional representation for each 294-dimensional input data vector corresponding to a drug and a 10-dimensional representation for each 241-dimensional input data vector corresponding to a cell line, respectively.39, by computing the attributions between input features and the ten neurons constituting the hidden representation layers. This is performed separately for the drug and the cell line autoencoders, and the attributions are averaged across the drugs and cell lines, respectively. As a result, we obtain drugs and cell lines feature-representation attribution matrices of size To compute the association of each input feature with each hidden dimension, we utilize the Integrated Gradients methodj corresponds to a single cell line hidden representation, and every column c represents the values of a given hidden variable across all cell lines. Next, we examine the full genome-wide gene expression data of the full set 17,419 genes extracted from GDSC. In this way, this analysis goes beyond the restricted set of the modeled input 241 cell line features. Using this data, we construct a j corresponds to a single cell line gene expression profile, and every column g represents the expression values of a given gene across the examined cell lines. We then compute a gth column of cth column of In this interpretability analysis, we exploit the fact that the cell line autoencoder in DEERS is trained to reconstruct the data and to find low-dimensional representations that reflect the true properties of the analyzed cell lines. The produced hidden representations of the cell lines are organized into a mentclass2pt{minim63 against gene sets that are involved in specific biological processes as defined by the Biological Process GO Terms (Fig. 65. We then extract the top 15 enriched terms with the smallest FDR value for every hidden dimension, which indicates the general biological mechanisms are most related to that dimension. Finally, we eliminate the redundant gene ontology terms using the Revigo tool66, assigning the set of biological mechanisms to every dimension of the cell lines hidden space.Given such correlation matrix mentclass2pt{minimSupplementary Figures.Supplementary Table S1.Supplementary Table S2.


E. coli harboring this biosynthetic gene cluster cause DNA damage and tumorigenesis in cell lines and pre-clinical models, yet fundamental knowledge regarding colibactin function is lacking. To accurately assess the role of pks+ E. coli in cancer etiology, the biological mechanisms governing production and delivery of colibactin by these bacteria must be elucidated. In this review, we will focus on recent advances in our understanding of colibactin’s structural mode-of-action and mutagenic potential with consideration for how this activity may be regulated by physiologic conditions within the intestine. Colibactin is a secondary metabolite encoded by the  Escherichia coli (E. coli) are a group of Gram-negative facultative anaerobic bacteria isolated from multiple body sites of almost every individual, paradoxically implicated in both intestinal homeostasis and various pathologies depending on species and subspecies classification . Thi. Thipks ug motif . Precoliug motif  21]. Co. Copks gug motif  . Pks. Pkspks+AX sites , suggestein Ku80 , a pathwein Ku80 . Alternause DSBs  expectedpks+ infection in vitro, as separation of mammalian cells and bacteria by cell-impermeable membranes attenuates colibactin’s cytotoxic effects  tha thapks oks genes . Transcrnditions . These pene clbA , which iene clbA . The regnditions . As expenhibited . Additiohimurium . Thus, ppks+ E. coli or the host diet similarly alter transcription of clb genes. Spermidine, a polyamine produced during bacterial metabolism or scavenged from the environment is necessary for the production of colibactin [clbA transcription and pks+ genotoxicity [pks transcription can be attenuated by inhibitory factors , suggesh anemia  or otherE. coli prevalence. For example, chemically-induced inflammation following dextran sodium sulfate (DSS) treatment reduces populations of anaerobic Bacteroidetes by approximately 70% while increasing levels of various aerobic species by approximately 25% [E. coli strain doubled in DSS treated mice, and colonization in E. coli-naïve mice corresponded with a decrease in mucosal Bacteroidales colonization [pks+ E. coli, accounting for as much as 13% (IBD) or 26% (CRC) of all E. coli strains isolated from these patients [pks+ strains within the colonic mucosa. This hypothesis is borne out by studies in murine models of CAC utilizing azoxymethane treated −/−Il10 mice, showing that the presence of pks+ E. coli promotes DNA damage and neoplastic transformation under inflammatory conditions [−/− mice with no inflammatory response [pks+ E. coli colonization during inflammation by inhibiting nitrate reductase activity abrogates pks-associated tumorigenesis [E. coli populations in colitis patients renders this an unlikely preventative treatment option to limit tumor progression in IBD patient.Inflammatory bowel disease (IBD) creates a pro-neoplastic environment within the colonic epithelium  that protely 25% . In thisnization . Severalpatients ,42,43. Tresponse . In simiigenesis . Howeverpks expression in inflamed tissue may be attenuated as a byproduct of IBD treatments which dampen inflammatory cascades or directly inhibit clb gene transcription. In murine CAC models gavaged with pks+ E. coli, inflammation promotes transcription of multiple clb genes involved in colibactin biosynthesis during tumor initiation [Min/+Apc) and spontaneous (−/− ApcMin/+Il10) CAC models by modulating microbiota composition and transcription, while co-housing anti-TNF treated with control mice prevented these microbial change and inhibited the anti-tumor effects of TNF neutralization [pks+ E. coli colonization level, pointing to a change in microbial activity [in vitro [+ E. colipks, and the evidence suggests mesalamine directly inhibits colibactin production and ICL formation in vitro [pks+ E. coli  to avoid perturbations from host defenses or the intestinal environment . Biofilmal cells . In two in-situ hybridization and 16S rRNA gene sequencing to investigate the composition of mucosal biofilms in CRC patients, frequently identifying Proteobacteria in mucosal biofilms from both tumor and normal adjacent tissue, with a high prevalence of pks+ E. coli (68%) in proximal CRC biofilms [pks+ E. coli seems likely given their ubiquitous distribution throughout biofilms isolated from CRC patients. Co-infection with the biofilm initiating strain enterotoxigenic Bacteroides fragilis (ETBF) and pks+ E. coli in specific pathogen free (SPF) mice demonstrate that biofilm association promotes mucosal invasion, expansion of pks+ E. coli populations, and a concomitant increase in γH2AX histological staining and tumor burden relative to monoassociation [pks+ E. coli and host epithelial cells by degrading the protective mucus layer through an undetermined mechanism  or colibactin (E. coli) diminished tumor burdens, suggesting the existence of a cooperative network between these toxins [Several studies have used a combination of biofilms ,59. Biofbiofilms , suggestociation . ETBF mammentary ), minimie toxins . Howeverpks+ E. coli association with microbial biofilms enhances colibactin’s carcinogenic activity, possibly by increasing proximity to host epithelial cells and enhancing total abundance of these bacteria within the mucosa. In these scenarios, normally innocuous genotoxins secreted by the bacteria may occur in elevated doses near replicating host cells resulting in carcinogenic activity and CRC initiation or progression.Altogether, these findings suggest that pks-induced DNA damage, cells may undergo apoptosis, transformation, or senescence. Adoption of a senescent state induces a senescence-associated secretory phenotype (SASP) characterized by increased secretion of various growth factors that promote proliferation of nearby cells , that has a long history of use as a beneficial probiotic [pks biosynthetic gene cluster [pks peptidase ClbP is actually essential for the antibacterial activity of EcN, contributing to the maturation of siderophores that enable this probiotic to outcompete pathogenic bacteria [clbA mutation [Overrepresentation of robiotic  despite  cluster . Counterbacteria . Whetherbacteria , others mutation . Thus, mPks+ bacteria may have relevance to cancer outside of the intestine. These bacteria frequently colonize extraintestinal sites and colibactin mutational signatures have been reported in a subset of patients with head and neck, urinary tract, neuroendocrine tumors, and ovarian cancer [+ E. colipks causes DNA damage within the regenerative compartment of the bladder after transurethral infection in mice, suggesting a potential role for these microbes in bladder cancer [pks+ E. coli in association with DNA damage in biopsies from prostate cancer patients [pks+ Klebsiella pneumoniae in a subset of hospital patients with liver abscess [pks+ E. coli may be influenced by many factors altering colibactin production and localization within the intestinal tract. Colibactin’s instability suggests it may only exhibit oncogenic activity while in close association with host epithelial cells, and limits mechanistic studies aimed at studying the effects of the toxin without influence from other aspects of bacterial infection or unrelated virulence factors. Because all colibactin-related studies utilize pks or clb mutants  ,6,13 Pkss  inhabiting the intestine is unclear at best. It is intriguing to note that certain secondary metabolites generated from biosynthetic gene clusters are implicated in bacteria anti-phage defense response [The most evident question is related to colibactin’s function within the microbiota. What biological advantage is gained by response , therebyresponse . There ipks-derived mutational signatures are acquired during childhood [E. coli in the human microbiome, especially at early age [pks+ E. coli, the need for childhood screening is nonexistent. If this moment ever arises, the challenge would be to design precision microbiome interventions since antibiotic approach would likely do more harm than good in this young population. Engineered probiotics [pks+ microbes or produce metabolites which directly interrupt colibactin activity. Such small molecule inhibitors may come from a self-resistance component of the pks island itself, the cyclopropane hydrolase ClbS, which disrupts active cyclopropane sites in colibactin [pks+ isolates from individuals against phage libraries may allow for the creation of personalized phage cocktails with high activity for patient-specific strains. Such an approach has been used in pre-clinical model of CRC driven by pks+ E. coli [Bdellovibrio bacteriovorus could represent another means to selectively deplete pks+ E.coli from a complex community [pks/colibactin’s discovery to molecular structure characterization and potential in vivo function. Yet, much remains to be addressed regarding this fascinating molecule and the years to come promise to be exciting for this field of research. Evidence suggests that hildhood ,31 and tarly age , raise tobiotics  may be dlibactin ,84. Phaglibactin , and scr E. coli . In addiommunity . In conc