Osteosarcoma (OS) is an aggressive mesenchymal cell tumor that carries a poor long-term prognosis. Despite definitive surgery for the primary tumor and adjuvant chemotherapy, pulmonary metastasis is common and is the primary cause of morbidity. To improve outcomes for patients, we have developed and optimized a phenotypic screen for drugs that may target OS disseminated tumor cells (DTCs) and inhibit their metastatic outbreak rather than merely screening for cytotoxic activity against proliferating cells, as is commonly conducted in conventional drug discovery approaches. We report on the validation of a previously described 3D reconstituted basement membrane extract (3D BME) model system for tumor dormancy and metastatic outgrowth adapted to clonal pairs of high and low metastatic OS cells. A post-hoc validation of the assay was possible by comparing the activity of a drug in our assay with early evidence of activity in human OS clinical trials (regorafenib and saracatinib). In this validation, we found concordance between our assay and human clinical trial experience We then explored an approved veterinary small molecule inhibitor of Janus kinase-1 (oclacitinib) as a potential drug candidate to take advantage of the high prevalence of OS in pet dogs and its translational value to humans. Despite the biological rationale, we found no evidence to support the use of oclacitinib as an antimetastatic agent in OS. The findings support our 3D BME assay as a highly efficient method to examine drugs for activity in targeting OS DTCs. Osteosarcoma (OS) is a neoplasia of mesenchymal cell origin. The aggressive nature of OS results in poor prognoses, with metastasis to the lungs being the most common cause of death. Despite complete resection of the primary tumor and adjuvant chemotherapy for patients with localized disease, patients ~35%) develop recurrent metastases most commonly to the lungs within a few years and the five-year survival rate is less than 30% ,2. Accum5% develoThe current approaches to cancer drug development prioritize drugs that kill rapidly dividing cells or induce apoptosis in cancer cells. Although these targets are characteristics of cancer, they do not adequately represent the biology of DTC metastatic progression. DTCs undergo adaptation, survival, and enter a phase of dormancy and eventually outgrowth at their metastatic site. This multistep process which we coined metastatic endurance (ME)  is just Here, we describe the adaptation of a previously reported 3D reconstituted basement membrane extract (3D BME) system modeling breast cancer tumor dormancy and outgrowth ,6 to mod®), would exert a desired antimetastatic effect in 3D BME. Our hypothesis was that oclacitinib would suppress ME within OS cell lines.We first identified exposures of each drug that had minimal cytotoxic effect on two highly metastatic OS cell lines in conventional 2D conditions . We then used exposures of these drugs well below a cytotoxic threshold in the 3D BME, to identify drugs that may have selective activity against OS ME. Our primary objective was to determine if the 3D BME assay would recapitulate early human clinical trial results. Our secondary objective was to determine if oclacitinib, a currently FDA-approved veterinary drug  was active in the adapted 3D BME system, and the selected agent without measurable activity in human OS trials (saracatinib) was inactive in the adapted 3D BME system. The FDA-approved veterinary drug (oclacitinib) was not found to be active despite a hypothesized biological rationale in cancer metastasis. While our data suggests against prioritizing this drug for further development in OS, we believe the 3D BME system and other assays that model metastatic progression will uncover new therapeutics with activity against ME that are amenable to the proposed cross-species approach to metastasis drug development in OS.p < 0.005) compared to the MG63 cell line  and MG63.3 (high metastatic) cells  were culp ≤ 0.05; # p < 0.005).Similar results were found using a distinct and previously validated pair of murine OS cells, K12 (low metastatic) and K7M2 (high metastatic) cell lines . K12 celMTT assays were used to assess the influence of oclacitinib, saracatinib, and regorafenib on cell number in 2D monolayer conditions to determine the maximum physiological concentration that will not have cytotoxic activity on highly metastatic OS cells. This concentration will be further tested for inhibition of ME of highly metastatic OS cell lines in the 3D BME system. Collectively, exposures up to 1 μM were associated with minimal, consistent, and dose-dependent reductions in cell number across drugs and cell lines . Oclacitp ≤ 0.005) compared to vehicle treated cells. Significant inhibition was not seen at 100 nM. Conversely, saracatinib did not significantly inhibit the proliferative outgrowth of both cell lines in the 3D BME system at or below 1 μM. Oclacitinib also had no significant effect in the 3D BME assay on the proliferative outgrowth in either cell line tested . Two small molecular kinase inhibitors, regorafenib and saracatinib were chosen as suspected positive and negative controls respectively given their previous clinical results in early human clinical trials. The results with these two “control drugs” provide a post hoc validation of the 3D BME assay as a predictive screen for drugs with antimetastatic activity in this adaptation of the 3D BME assay. A third small kinase inhibitor was then assessed for efficacy against ME in OS cells.The experiments performed here included OS cell lines that have been rigorously evaluated for biologic and metastatic behavior ,11. The One additional challenge associated with our 3D BME assay approach is the exclusion of the innate/adaptive immune system’s influence on the metastatic niche . In lighRegorafenib, our positive control, significantly suppressed outgrowth of cells in 3D BME at clinically relevant doses, suggestive of potential antimetastatic activity. Regorafenib is already used across a multitude of cancer indications including hepatocellular carcinoma, renal cell carcinoma, and gastrointestinal stromal cell tumors ,15,16. ISaracatinib was used as a negative control for this study given its poor performance in a clinical trial in humans with progressive OS. While the clinical trial showed an increase in progression free survival there was no apparent benefit to long term overall survival . It is fOclacitinib, our drug of interest, is a Janus kinase inhibitor approved for use in controlling allergy, inflammation, and pruritus in dogs. The drug binds to JAK1/2/3 and TYK2  with itsAn additional consideration in deprioritizing oclacitinib as a therapeutic candidate is concern for its immunosuppressive properties. In initial safety studies, concern was raised about emergence of diseases commonly seen in immunosuppressed dogs  . SubsequDespite oclacitinib’s lack of efficacy reported here, we believe the described 3D BME assay approach has value as part of a phenotypic screen of cancer metastasis, that in this case could be rapidly applied to in vivo validation studies in canine OS as a model for human OS. The switch from a dormant cell to an active metastatic cell involves multiple intracellular pathways ,25 and sTwo previously described clonally related and biologically validated high and low metastatic OS cell lines MG63.3 , MG63 , K7M2 (highly metastatic murine OS), and K12 (low metastatic murine OS),  were use2.Cells were maintained and passaged in Dulbecco’s modified Eagle medium (DMEM) +4.5 g/L D-Glucose and 2 mM L-glutamine supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin (P/S) , and incubated at 37 °C in 5% humidified COOclacitinib, saracatinib, and regorafenib  were each dissolved in DMSO at a stock concentration of 10 mM.The number of viable cells present in 2D culture was determined by MTT as previously reported . BrieflyThe resulting absorbance was measured at 490 nm via plate reader . DMSO (0.1%) was used as vehicle control, while 20 μM rapamycin was used as a positive cell-kill control ,28. The The influence of drug exposure on the transition from tumor dormancy to metastatic growth of OS cells was assessed in a 3D BME system as described before ,29.® PathClear, Reduced Growth Factor Basement Membrane Extract  was added (50 μL/well) to a 96-well plate and allowed to gel by incubating at 37 °C for ≥30 min. Cells were harvested, washed with DMEM , and centrifuged at 1500× g 5 min. The media was aspirated, and the remaining cell pellet flicked for ~10 s to ensure disaggregation.CultrexCells were diluted in assay media containing 2% BME, resuspended vigorously, and 150 μL of the cell suspension was added on top of the gelled 3D BME layer. For comparison, matched low-metastatic cell lines (MG63 and K12) were grown under the same conditions as the highly metastatic cells (MG63.3 and K7M2) and did not exhibit the same growth rate or kinetic profile, highlighting the differences in their metastatic potential. Proliferation was determined in the control vehicle (cells in media + 0.1% DMSO) or treated wells at indicated days by the addition of 20 μL/well of MTS reagent , followed by incubation at 37 °C for 2 h and absorbance measurements at 490 nm via plate reader .All conditions were conducted in quadruplicate and each experiment was carried out at least 3 separate times. All comparisons were made between treatment and vehicle control at concordant time points. Statistical analyses and non-linear regressions were derived using GraphPad Prism statistical software.The results reported here validate a modified 3D BME assay system for use with OS cells to assess efficacy of drugs or therapeutic agents against ME satisfying our primary objective. We showed that the assay system was effective at mirroring previously known clinical results with two drugs: regorafenib and saracatinib. In 3D BME, regorafenib suppressed metastatic outgrowth of two highly metastatic OS cell lines while saracatinib was ineffective. Oclacitinib also proved ineffective at suppressing ME in two highly metastatic cell lines, refuting our working hypothesis. Next steps include expansion of the number of cell lines used in 3D BME testing, and continued examination of drugs with potential for translation to anti-metastatic agents. Once identified, these agents will be further evaluated for efficacy against ME using the ex vivo pulmonary metastasis assay to better model an in vivo metastatic niche. Drugs found to show continued efficacy against ME in the PuMA may be brought forward for clinical trials, specifically trials in the canine OS population as a model for human OS.


Colorectal cancer (CRC) is the fourth most lethal disease worldwide. Despite an urgent need for therapeutic advance, selective target identification in a preclinical phase is hampered by molecular and metabolic variations between cellular models. To foster optimal model selection from a translational perspective, we performed untargeted ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry-based polar metabolomics and lipidomics to non-transformed (CCD841-CON and FHC) and transformed  colon cell lines as well as tissue samples from ten colorectal cancer patients. This unveiled metabolic signatures discriminating the transformed from the non-transformed state. Metabolites involved in glutaminolysis, tryptophan catabolism, pyrimidine, lipid and carnitine synthesis were elevated in transformed cells and cancerous tissue, whereas those involved in the glycerol-3-phosphate shuttle, urea cycle and redox reactions were lowered. The degree of glutaminolysis and lipid synthesis was specific to the colon cancer cell line at hand. Thus, our study exposed pathways that are specifically associated with the transformation state and revealed differences between colon cancer cell lines that should be considered when targeting cancer-associated pathways. Although the discovery of CRC-biomarkers and associated pathways has contributed to enhanced screening and improved treatment, the 5-year survival rate of metastatic CRC is still only 10%, emphasizing the need for better therapeutic strategies3.Colorectal cancer (CRC) is the second and third most diagnosed cancer in females and males, respectively, and the fourth leading cause of cancer-related mortality worldwide. The incidence rates are strongly variable throughout the world, whereby developed regions have more CRC patients than less developed countries. Disease risk is primarily associated with lifestyle factors, in particular Western habits such as the consumption of diets rich in fat and sugar, high in red and processed meat and limited physical activitye.g., azoxymethane) or genetically  induced cancer rodent models6. In keeping with the 3R principles , a framework that aims at minimizing animal use and suffering, researchers continuously seek bona fide alternatives wherever possible7. Therefore, many in vitro models, especially cell culture models, have been established to study CRC and therapeutic strategies8. Evidently, cell lines have less ethical constraints in comparison to animal models, and at the same time they are much easier in use, inexpensive and provide sufficient material, enabling rapid experimental progress9.In cancer research, animal models have been of great value for discovering novel biochemical pathways and drug testing. The most common models are xenograft and chemically  cell lines are both immortalized, but non-transformed (NT) colon cell lines obtained from fetal normal colon mucosa13. On the other side of the spectrum, transformed (T) cell lines isolated from (adeno)carcinomas may serve as representative samples for studying different cancer stages. For example, HT29, SW480 and SW948 were established from colorectal adenocarcinomas, whilst Caco2 and HCT116 were established from colorectal carcinomas14. These cell lines have been extensively used to study regulatory mechanisms in CRC as well as for the purpose of identifying chemopreventive and -therapeutic agents15. Preclinical data using in vivo and in vitro models revealed that only 5% of candidate therapies demonstrate clinical efficacy in phase III trials. This high attrition rate can be attributed to some extent to the use of models that are not fully representative for the type of cancer of interest16. For example, it has been demonstrated that many cancer cell lines used to model various types of cancers were in fact derived from the HeLa cervical cancer cell line and not the corresponding tumor17. Moreover, it was also observed that long-term passaging of cells can lead to a reduced resemblance to the tumor of origin14. Therefore, several efforts have been made to reduce cell line misidentification, primarily by molecular phenotyping19. Although metabolites are the end-products in the gene-protein-metabolite cascade and thus key reporters of cellular activity, metabolic profiling or fingerprinting studies of cancer cells and tissue are scarce20. Pioneering studies have revealed a conspicuous metabolic rewiring in CRC. For example, both CRC tissue and cell lines have been found to display higher levels of phospholipids, of which certain  correlate with metastatic propensity22. Also, specific nucleotides and carbohydrates have been found upregulated in CRC patient tissue23. The latter may represent metabolic evidence for a Warburg phenotype21, although higher rates of oxidative phosphorylation have been reported in CRC cell lines as well23. Thus, our understanding of the exact metabolic status of CRC cells is far from complete. And, to the best of our knowledge, no studies are available that assessed whether metabolic differences between healthy and cancerous colon tissue can be extrapolated to cell cultures and vice versa. We have previously optimized and validated an untargeted ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry-based (UHPLC-HRMS) metabolomics and lipidomics method for human colon tissue and colon cell lines24. In this work, we exploited the approach to unveil pathways linked to the CRC transformation processes in vivo and in vitro and to provide novel insights into the metabolic phenotype of different cell lines that could foster the selection of the optimal one(s) towards specific targets in cancer drug research.Whilst primary cell cultures from healthy patients may be considered to resemble the native state more closely, they are less practical to culture and have limited life span. Immortalized cell lines make a valuable alternative since they are still representative for the tissue of origin and are easier to handle and to maintain in culture25, all cell lines of one passage were harvested at the same day, extracted and analyzed for metabolomics directly after. The remaining cell pellets were stored at − 80 °C and subjected for joint lipidomic extraction and analysis . One sample of the FHC line (P3) was excluded for having too limited starting material. Chromatographic peak processing resulted in the detection of in total 879 metabolite and 17,432 lipid components, after removing those with a coefficient of variation (CV) > 30% in quality control (QC) samples composed of cell line or tissue matrix extracts. Valid orthogonal partial least squares discriminant analysis (OPLS-DA) models , which implies they had a significant contribution to the resulting model (as indicated by a Variable Importance parameter (VIP) value > 1.0). Thus, the untargeted UHPLC-HRMS approach revealed a specific set of metabolomic components that typify the transformation state (NT vs T) of cells and tissue.Untargeted UHPLC-HRMS metabolomics and lipidomics profiling was performed on samples of two NT (FHC and CCD 841 CON) and five T  colon cell lines as well as on cancerous (T) and flanking non-cancerous (NT) tissue biopsy samples from 10 CRC patients. Of each cell line, cellular extracts were obtained from three culture flasks  at three different passages (P1-3), separated by one-week intervals . To avoid metabolic shifts during storage from introducing bias between cell lines, whereof 35 and 52 were discriminative  for the transformation state in colon tissue and cell lines, respectively  for colon tissue and cell lines confirmed their potential to discriminate transformation states and differentiate HT29 and HCT116 from SW480 and HCT116 cell lines (Table The accurate mass of 1626 discriminative components was queried in the HMDB and LIPIDMAPS for putative identifiers (IDs). 377 IDs could be linked to components that were discriminative for the NT or T state in colon cell lines or tissue and were subsequently subjected to fragmentation experiments. This resulted in the identification of 55 metabolites with the highest levels of confidenceP value < 0.05) different between the cancer cell lines  affected pathways for the colon cell line and tissue matrix, respectively  lower in the SW480 than in the HCT116 cell line samples, implying more conversion of L-glutamine to L-glutamic acid in the SW480 cell line. The HT29 and Caco2 cell lines demonstrated similar conversion rates, whilst the ratio was less consistent in the SW948 cell line, as indicated by the high standard deviation  and the SW480 cell line , aligning with the higher L-glutamine/L-glutamic acid ratio and the lower conversion of L-glutamine to L-glutamic acid in the HCT116 cell line , who observed higher L-glutamic acid and lower L-glutamine levels in colon tumor tissue, respectively26. Amongst the T cell lines, the SW480 cell line showed the highest rate of glutaminolysis as represented by higher conversion of L-glutamine towards L-glutamic acid. Also, qPCR results showed higher expression of glutaminase in this cell line indicating more enzyme availability promoting this metabolite conversion. Compared to HCT116 cells, SW480 cells displayed significantly lower L-glutamine and L-glutamic acid levels, which could indicate lower absorption of L-glutamine, but higher efficiency in utilizing this metabolite as an energy source for the latter. Interestingly, it has been shown that the removal of L-glutamine from media results in increased sensitivity of the SW480 as opposed to HCT116 and HT29 cell cultures, hence supporting the function of this metabolite as a more convenient energy source in the former27. L-leucine was more abundant in this cell line compared to the other cancer cell lines, which may hint towards increased activation of glutamate hydrogenase. Glutamate hydrogenase converts L-glutamic acid into α-ketoglutaric acid, an intermediate of the TCA cycle, thus promoting glutaminolytic activity28. Targeting glutaminolysis by inhibiting involved enzymes in cancer has gained interest and has already been proven to reduce the proliferation of cancer cells29.L-glutamic acid was characteristic for colon cancer cell lines and tumor tissue and is generated during glutaminolysis, 30. This is also in concordance with the higher presence of pyrimidines  and lipids (mainly PCs) in T samples31. In our study, especially saturated, mono-unsaturated and low-polyunsaturated PCs were discriminative for transformation, which aligns with a study that performed lipidomics on breast cancer and normal tissue and cell line samples32. Especially the HT29 and Caco2 cell line displayed relatively high PC levels. The HCT116 cell line was characterized by a lower abundance of lipid species, which may be linked to its decreased metastatic potential. Carcinogenesis has been linked with de novo lipogenesis and increased saturation of membrane lipids. The latter results in decreased membrane fluidity and permeability, which make cancer cells less prone to lipid peroxidation and chemotherapy in comparison to normal cells22. Based on this, it can be speculated that strategies to modulate membrane fluidity could improve the treatment of cancer33. Glycerol-3-phosphate, an intermediate in lipid synthesis, was detected at lower concentrations in the T state as opposed to the NT state, which further supports increased phospholipid biosynthesis in the T state34.Only recently, it has been shown that carbon and nitrogen of L-glutamine is incorporated into building blocks of cells, especially during high cell proliferation37. It is a common feature that ROS are elevated in cancer cells, where they are responsible for activating signaling pathways involved in cell proliferation38. However, when ROS are present in excessive amounts, they cause oxidative damage to macromolecules. To prevent this, cancer cells overproduce antioxidants30. Indeed, Kibi et al. (2019) observed higher taurine levels in CRC as opposed to adjacent tissue samples derived from 10 patients39. Therefore, one would intuitively expect that increased antioxidants would be linked to the T state, which was not the case in our work. Nevertheless, Hernandez-Lopez (2018) demonstrated that antioxidant responses between non-tumor adjacent tissue samples retrieved from patients with different stages of CRC differed, where non-tumor adjacent tissue of stage IV showed increased antioxidant levels in comparison to those of stage III40. In our work, 3 CRC patients had classification of tumors below stage II, whereas the tumors of the other 7 patients were classified in stage III and IV  were determined to be discriminative for NT samples in vitro and in vivo and are considered to be important in mitigating the damaging effects of ROS. These compounds act as a coenzyme in redox reactions, improve mitochondrial function resulting in less superoxide generation and scavenge superoxide radicals, respectivelyet al. 209 observe41.The urea cycle is a metabolic pathway that is responsible for converting excess nitrogen from L-aspartic acid and ammonia, which results from amino acid catabolism, into urea. The urea cycle metabolites L-arginine and citrulline were downregulated in T cell lines as opposed to NT cell lines. These results are in accordance with Nagamani et al. (2016), who demonstrated that metabolic wiring accompanying carcinogenesis includes enhanced flux of L-aspartic acid towards pyrimidine synthesis and hence, decreased synthesis of L-arginine42. L-kynurenine, which was elevated in the T state in colon tissue and cell lines, is associated with tumor immune escape and growth and hence, is considered as a hallmark of carcinogenesis43.Tryptophan metabolism involves the conversion of L-tryptophan into N’-formylkynurenine and L-kynurenine and this is depending primarily on indoleamine-2,3-oxygenase (IDO1), which is highly expressed in the colon. IDO-1 is one of the most upregulated in CRC and is strongly induced by inflammatory cytokines such as IFNγ38. L-lysine is a precursor of N6,N6,N6-trimethyllysine, which can be converted to L-carnitine and further transformed to L-ascorbic acid. L-Carnitine is responsible for transporting fatty acids into the mitochondria or peroxisomes by conjugation with the acyl-CoA moiety to form acylcarnitines. Inside these cell organelles, acyl-CoA is released and enters the β-oxidation resulting in the formation of acetyl-CoA, that can in turn enter the TCA cycle37. In this study, only L-carnitine showed a clear association with the T state in both matrices. L-lysine, L-ascorbic acid and N6,N6,N6-trimethyllysine on the other hand were significantly elevated in the NT as opposed to the T state in cell line samples, but showed an opposite trend in the T colon tissue samples. Nevertheless, this observation was not significant for L-lysine and N6,N6,N6-trimethyllysine. Based on these findings, it can be concluded that L-carnitine biosynthesis and lysine degradation are higher in the T state. However, at the tissue level, such metabolic fluxes were less clearly observed, which can be due to genetic heterogeneity of samples. In concordance with these findings, elevated carnitine levels in plasma and tissue have been associated with breast, bladder and colon cancer, respectively46.It has been reported that L-carnitine is elevated in cancer cells and associated with enhanced fatty acid uptake, oxidation and formation of ROS47. Conversely, cells in tumor tissue are subjected to harsh conditions, including hypoxia and limited nutrient availability48. In this study, it was observed that L-ascorbic acid was significantly upregulated in the T as opposed to the NT tissue samples while a higher abundance was detected in NT as compared to the T cell line samples. In vivo trials have demonstrated that L-ascorbic acid is more abundant in cancer cells than normal cells through facilitated transport by glucose transporters, explaining the higher concentrations in colon cancer tissue. Nevertheless, this metabolite acts as a pro-oxidant in tumor cells, since these lack the enzyme catalase that converts L-ascorbic acid-induced ROS (H2O2) into water and oxygen49. Thus, cancer cells are more vulnerable than normal cells to the effects of this metabolite and can eventually stop growing or undergo apoptosis51. Hence, it could be hypothesized that cell lines have adapted to culture conditions by rewiring their utilized energy sources, for example through the absorption and use of L-glutamine, which is highly abundant in commercial cell culture media. This way, cancer cell lines are more protected against the detrimental effects of L-ascorbic acid. Indeed, recent research has shown that increased extracellular availability of glucose does not influence the rate of glucose utilization by cell line models. It was also demonstrated that higher glutamine concentrations in media resulted in higher glutamine fermentation in cell culture52.It is important to note that discrepancies were observed in metabolic profiles between the cell line and tissue samples. This may be due to the cell culture methodology that differs from the in vivo situation. For example, cell lines do not have a physiological extracellular matrix, 3D organization, and are much more homogeneous than tissue. Also, cell lines are adapted to their culture conditions, whereby media are composed in such a way that cell growth and viability are optimally supportedIn conclusion, despite the fact that in vitro cell culture models do not fully represent the in vivo situation, we discovered shared metabolites and corresponding pathways between colon tissue and cell lines involved in carcinogenesis. Glutaminolysis, associated pyrimidine and lipid synthesis, tryptophan catabolism, glycerol-3-phosphate shuttle and carnitine biosynthesis were enhanced in the malignant as opposed to the non-malignant state. In the NT state, urea cycle and antioxidant levels were more prominent. Moreover, it was clear that glutaminolysis, characterized by high expression of glutaminase and high conversion of L-glutamine to L-glutamic acid, was most abundant in the SW480 cell line as opposed to the other T cell lines. In addition, the HT29 and Caco2 cell lines contained the most saturated and one or double bonds-containing phospholipids, essential constituents of cell membranes and thus affecting membrane permeability towards cancer drugs. Hence, this work provides insights in pathways associated with colon carcinogenesis as well as valuable information to select the most appropriate cancer cell lines in the testing of novel chemotherapeutic agents.2/95% air. FHC cells were cultured in DMEM:F12 supplemented with 10 mM HEPES, 0.005 mg mL-1 insulin, 0.005 mg mL-1 transferrin, 100 ng mL-1 hydrocortison, 10% FBS and 1% P/S. All cell reagents were purchased from Life Technologies .The human colorectal cell lines HT29, Caco2, HCT116, SW480 and SW948 (T state) and the immortalized colon cell lines FHC and CCD841-CON (NT state) were obtained from ATCC . Dulbecco’s Modified Eagle’s Medium (DMEM), supplemented with 10% Fetal Bovine Serum (FBS) and 1% penicillin/streptomycin (P/S), was used to culture the cancer cell lines and CCD841-CON in a humidified incubator at 37 °C and 5% COCancerous colon material (100 mg) and corresponding healthy tissue (100 mg) from 10 individuals were provided by Biobank@UZA . The use of human colon tissue samples in this study was ethically approved by the University Hospital of Antwerp  and patient details are listed in Supplementary Table . (2019)24. For each matrix , quality control (QC) samples were made from a pool of the matrix-specific samples and run in duplicate after every 10 samples.In recent work, the applied two-step metabolomics and lipidomics extraction protocols for colon (cancer) cell lines and tissue were successfully optimized and validated. More information about the optimized parameters and validation experiments can be consulted in Rombouts et al54. Solvents were of LC–MS grade for extraction purposes and obtained from Fisher Scientific  and VWR International . Ultrapure water was obtained by usage of a purified-water system .Analytical standards were purchased from Sigma-Aldrich , ICN Biomedicals Inc. , TLC PharmChem  or Cambridge Isotope Laboratories Inc. . Detailed information about chromatographic and mass spectrometric features of the reference metabolite and lipid compounds can be consulted in De Paepe et al. (2018) and Van Meulebroek et al. (2017), respectively53. For fragmentation experiments, the following MS/MS settings were applied during parallel reaction monitoring (PRM): resolution of 17,500 Full Width at Half maximum (FWHM), Automatic Gain Control (AGC) target of 2 × e4 ions, maximum injection time of 40 ms and an isolation window of 2.0 m/z.An Ultimate 3000 XRS UHPLC system  and a Q-Exactive™ stand-alone bench top Orbitrap mass spectrometer (MS) , equipped with a heated electrospray ionization source (HESI II) operating in polarity switching mode, were used for polar metabolomics and lipidomics analysis as described by De Paepe et al. (2018) and Van Meulebroek et al. (2017), respectively24. In brief, components were identified using the following criteria: a minimum peak intensity of 500,000 au, minimum signal-to-noise ratio of 10, minimum number of isotopes of 2, retention time window of 0.75 min, maximum retention time shift of 0.25 min and maximum mass shift at 5 ppm. This resulted in the construction of one metabolomics dataset (two ionization modes combined) and two lipidomics datasets (positive and negative ionization modes separately). Next, unsupervised hierarchical clustering  and multivariate statistical analysis  were performed, whereby OPLS-DA models were built to discriminate the NT from the T state in tissue and cell lines separately. For modeling, data were log-transformed and Pareto-scaling was applied55. Validity of the obtained OPLS-DA models was evaluated based on several parameters including R2Y (> 0.5), Q2 (> 0.5), CV-ANOVA (p < 0.05), as well as permutation testing (n = 100)56. Selection of components that discriminated between the NT and T state in cell lines and tissue was based on a Variable Importance in Projection (VIP) score > 1.0 and a Jack Knife confidence interval that did not include 057. Screening of components of interest was established based on accurate mass (Δ ppm < 6 ppm) in the Human Metabolome Database (HMDB) and LIPIDMAPS59. For those components that could be linked to putative IDs, fragmentation experiments were conducted, and MS/MS profiles were scored in CSI FingerID or compared with these of in-house analytical standards60. Components were then classified into different levels of assignment according to the recommendations for standard metabolite identification from the Chemical Analysis Working Group61. Based on this, retained compounds were described as identified compound  or putatively annotated compound .All cellular LC–MS data, together with tissue LC–MS data were processed simultaneously using Compound Discoverer™ 2.1 or Sieve™ 2.2 software  for the metabolomics and lipidomics experiment, respectively. Herein, chromatographic peak alignment and component extraction was performed as described earlierP value  < 0.05) in MetaboAnalyst 4.0, thereby using the independent two-sample or paired sample Wilcoxon test for, data from the colon cell lines and tissue samples, respectively. To this end, metabolites obtained from the tissue matrix were also statistically evaluated in the cell line matrix and vice versa. This approach allowed pinpointing metabolites relevant for discrimination between NT and T state in tissue or cell lines as well as those relevant in both matrices making extrapolation and comparison easier. Significantly different metabolites between the transformed cell line types were assessed using One-Way Anova and Tukey-adjusted post hoc testing for all pairwise comparisons, whereby P value s < 0.05 were considered significant.Identified and putatively annotated metabolites were statistically evaluated  were constructed and hierarchical clustering (Pearson correlation coefficient and Ward linkage) was performed of the colon cell line and tissue samples in MetaboAnalyst 4.0 . This software program was also used for quantitative enrichment analysis using biologically relevant human metabolic pathways of the Small Molecular Pathway Database (SMDB)62. Pathways with a false discovery rate (FRD) < 0.05 were considered significantly altered. Next to this, the KEGG Compound Database (www.genome.jp/kegg/compound) and scientific literature was consulted to provide a broader overview of transformation-associated pathways in colon tissue and cell lines.Heat maps presenting the abundances of the discriminative metabolites  according to the manufacturer’s instructions. Primers were used for GLS1  and reference genes GADPH , ACTB  and TUB 63. qPCR technical replicates (n = 4) that deviated more than 0.3 Cq units and genes with inconsistent melting curves were removed from downstream analysis. Relative quantification was performed using the 2−ΔΔCT method, whereby the HT29 samples were used as reference64. One-way ANOVA together with Tukey-adjusted post hoc testing for all pairwise comparisons was performed in RStudio 2.1, whereby P value s < 0.05 were considered significant.RNA was extracted using the Nucleospin Triprep kit  and quality control was performed with the NanoDrop 2000 . Next, 1 μg of RNA was converted to cDNA synthesis with Superscript® II Reverse Transcriptase (RT)  . qPCR reactions were performed on a CFX Connect™ system  using SsoAdvanced™ Universal SYBRSupplementary Information.


Interrogation of cellular metabolism with high-throughput screening approaches can unravel contextual biology and identify cancer-specific metabolic vulnerabilities. To systematically study the consequences of distinct metabolic perturbations, we assemble a comprehensive metabolic drug library  covering 243 compounds. We, next, characterize it phenotypically in a diverse panel of myeloid leukemia cell lines and primary patient cells. Analysis of the drug response profiles reveals that 77 drugs affect cell viability, with the top effective compounds targeting nucleic acid synthesis, oxidative stress, and the PI3K/mTOR pathway. Clustering of individual drug response profiles stratifies the cell lines into five functional groups, which link to specific molecular and metabolic features. Mechanistic characterization of selective responses to the PI3K inhibitor pictilisib, the fatty acid synthase inhibitor GSK2194069, and the SLC16A1 inhibitor AZD3965, bring forth biomarkers of drug response. Phenotypic screening using CLIMET represents a valuable tool to probe cellular metabolism and identify metabolic dependencies at large. Metabolic reprogramming contributes to cancer development and progression. Here, the authors show the utility of a metabolic drug library to uncover metabolic vulnerabilities and obtain functional insights into myeloid leukemia biology. The first observations that transformed cells exhibit a distinct metabolic program came from Otto Warburg almost hundred years ago. The Warburg effect depicts the phenomenon of cancer cells preferentially undergoing glycolysis and converting carbon to lactate even in high oxygen conditions3. In cancer, genetic events activate signaling pathways that subsequently modulate cellular metabolism to satisfy the increased bioenergetic, biosynthetic and redox demands6. Moreover, this supports cancer initiation and progression and is typically accompanied with changes in expression of metabolic enzymes and transporters, which has implications for nutrient uptake, distribution of nutrients to pathways for biomass generation ultimately affecting therapy response8. Therefore, cancer-specific metabolic changes confer a selective advantage for survival, but also introduce metabolic liabilities that provide a unique opportunity for therapeutic targeting.Cellular metabolism represents a dynamic network of regulated pathways that is frequently reprogrammed in cancer and has been recognized as an emerging hallmark10. Hence, despite glycolysis many more metabolic dependencies are altered to support cancer cell proliferation8. A greater insight in the metabolic differences between cancer and nonmalignant cells may lead to development of improved cancer therapies and ultimately patient outcomes. However, normal cells often physiologically activate metabolic pathways that are upregulated in cancer. Thus, identifying metabolic processes that offer sufficient therapeutic window and patients most likely to respond to a given therapy remains a challenge11.Considering the matter closer, it becomes apparent that there are different individual metabolic signatures of cancer cells. Transformed cells display greater flexibility in catabolite utilization, contingent on nutrient accessibility in the environment, cell lineage or tissue of origin, and genetic events13. While the hematologists toolbox has increased, the survival of AML patients remains poor14. Moreover, prior studies have illustrated metabolic pe3culiarities in myeloid leukemias that can be exploited for either the development of novel therapies or for particular stratification rationales21. For instance, the development and clinical utility of IDH1/2 inhibitors for IDH1/2 mutant AML25 as wells as BCL-2 inhibitors26 illustrates that untangling metabolic changes could provide therapeutic avenues in AML. Here, we profiled the metabolic drug library phenotypically in a panel of 15 diverse myeloid leukemia cell lines for drug-induced effects on cell growth and survival.Obtaining a collection of chemical agents affecting different aspects of cancer metabolism would represent a toolbox allowing to interrogate cell lines, primary samples and animal models in a versatile mode and may suggest therapeutic strategies. Here, we assembled a custom metabolic drug library covering 243 compounds allowing to systematically identify metabolic dependencies in high-throughput phenotypic screens. We focused on myeloid leukemias as a model disease, as they are heterogeneous clonal malignancies for which there is a need for comprehensive understanding of disease-specific molecular mechanisms. Even though, there has been substantial advancement in mapping the genetic landscapes of AML, the first approvals for targeted agents came only in the last few yearsWe functionally grouped the cell lines and drugs based on metabolic drug efficacy patterns and associated them with distinct genomic and metabolic attributes. Moreover, we identified a number of differential vulnerabilities, such as the sensitivity to the monocarboxylate transporter (MCT) SLC16A1  inhibitor AZD3965, that could be rationalized by differential expression as well as target essentiality. Our data provides a primer for the use of a focused metabolic drug library in phenotypic screening platforms, thereby identifying metabolic vulnerabilities and pinpointing that targeted metabolic perturbations may provide promising new therapeutic strategies for myeloid leukemias and beyond.27. CLIMET was compiled in a stepwise fashion, starting from 8000 candidate compounds, after a survey of public drug-target databases, and ending with 243 highly curated compounds after extensive crosschecking for approval status, structural information, compound’s potency and selectivity for the intended target, pathway/target redundancy, and commercial availability . While probing cellular metabolism could also be achieved with genetic screening technologies such as CRISPR, compound libraries are more readily applicable to challenging cell and tissue samples including primary patient cells and provide rapid result outputs. Furthermore, they can easily be combined with other interventions  for higher-order perturbationsity Fig. .Fig. 1Won = 84), protein and amino acid (n = 43), and glycolysis/sugar metabolism (n = 39)  compounds are clinically approved for various disease indications allowing for rapid translation, roughly half  are chemical probes, 10 in clinical trials and 25 either withdrawn or their clinical development discontinued Fig. . CLIMET 39) Fig. .To assess the potency of CLIMET compounds, we screened the full collection against a panel of myeloid leukemia cell lines . Each compound was tested for its effect on cell growth and survival over a 10,000-fold concentration range (1 nM to 10 µM) enabling the generation of dose–response curves and area under the curve (AUC) calculation Fig. . From thr = 0.78), with the exception of CML cells being more sensitive to Torin1 (mTOR inhibitor), cytarabine , and rotenone (complex I inhibitor)  was selectively sensitive to a cluster of compounds targeting processes and enzymes capable of producing reactive oxygen species such as cytochrome P450 oxidases (econazole), NADPH oxidases (GKT-136901), mitochondrial complex II (3-nitropropionic acid), and phosphodiesterases (PF-02545920)  and extracellular acidification rate (ECAR) as proxies for mitochondrial respiration and glycolysis, respectively, by performing the Seahorse mitochondrial stress test  approach was applied on 225 metabolites in 928 cell lines from more than 20 different cancer types. Data were available for 13 of the 15 cell lines and the levels of 57 metabolites were found to be significantly different between at least two groups using a two-way ANOVA analysis  in comparison to largely glycolytic cell lines (Groups III and V) , revealed statistically significant novel and previously known correlations between FLT3 mutations and sensitivity to 5-fluorouracil, lestaurtinib32, and PF-02545920. Moreover, CREBBP mutations linked to sphingosine kinase inhibitor  sensitivity, whereas RAS and TP53 mutations conferred a resistance phenotype especially to energy and lipid metabolism inhibitors such as PI3K/mTOR and mitochondrial respiration inhibitors  and dorsomorphin (AMPK inhibitor). These findings imply that the activity of these compounds in the setting of myeloid malignancies could be functionally related.Co-occurring and mutually exclusive drug sensitivities were detected by pair-wise Spearman’s correlation analysis of the 77 active drugs Fig. . InhibitTo investigate the identified drug interactions further we performed drug combinatorial screens in four different cell lines  that best captured the significant drug–drug correlations. The results showed that in majority of cases combinatorial effects were observed as hypothesized  Fig. . For insC2A Fig. . This isers Fig. . Thus, wPIK3CA, PIK3CB or PIK3CD in comparison to HL-60 cells lentivirally transduced with an empty vector 29, and drug sensitivity data36 in HL-60 cells and cluster I cell lines (MV4-11 and MOLM-13) revealed that HL-60 cells exhibit higher dependency and activity on the MAPK pathway and mTORC2 complex signaling resulting in reduced sensitivity to AKT and mTOR inhibitors . Varied gene expression analysis identified 49 most differently expressed genes in relation to the gene expression profile of the remaining 13 cell lines  and SLC16A3 (low/no) may serve as an actionable biomarker for AZD3965 response in hematological malignancies. To explore the potential clinical value of this biomarker, we turned to The Cancer Genome Atlas (TCGA) gene expression dataset accessed via the UCSF Xena project45. Utilizing the gene expression profiling interactive analysis server (GEPIA2)46, we assessed the expression pattern of SLC16A1 and SLC16A3 in cancer patients in comparison to normal samples (TCGA-matching and the Genotype-Tissue Expression project47)  and downregulated in AML, while the opposite is true for SLC16A3. Moreover, the expression of SLC16A1 and SLC16A3 is negatively correlated on a pan-cancer and AML-specific level  Fig. . For insvel Fig. , indicatTo determine whether metabolic vulnerabilities can be influenced by drug exposure we re-tested the entire drug library in HL-60 cells in the presence of vehicle (DMSO) or 100 nM AZD3965, pictilisib, or GSK2194069 for 72 h Fig. . Even thn = 15; Supplementary Table r = 0.5716) with ~64% of compounds not exhibiting a differential response  were predominantly glycolytic and displayed lack of sensitivity to mTOR and mitochondrial respiration inhibitors in line with previous reports that RAS-mutated cancers display a Warburg effect phenotype exemplified by increased glucose utilization and lactate production, and upregulation of the glucose transporter SLC2A154. While some genotype to phenotype links were identified, for the majority of drug responses no clear genotype associations could be established, in accordance to prior studies showing a better association between multiple molecular datasets and drug sensitivities56.The tested cell lines were functionally grouped in five taxonomic groups that could further be associated with somatic mutational patterns as well as cellular metabolic profiles. For instance, cell lines harboring activating mutations in the 42. A recent study found that signaling models based on multiplex single-cell mass cytometry data were more predictive of pictilisib sensitivity than gene expression alone in breast cancer cell lines57. We, further, detected a significant positive correlation between the FASN inhibitor, GSK2194069, and pictilisib, which also showed a combinatorial effect. This finding suggests a functional link between PI3K signaling and de novo FA synthesis. In breast cancer cells synergistic responses between rapamycin and FASN inhibition have been reported58. The PI3K/AKT pathway could stimulate the expression of enzymes necessary for FA synthesis and promote activation of ATP-citrate lyase, an enzyme that catalyzes the production of acetyl-CoA from cytoplasmic citrate60. PI3K signaling in known to induce lipogenic enzymes, including FASN61. Cancer cells with upregulated PI3K signaling have been linked to increased FASN expression levels and enhanced glucose uptake thus fulfilling the increased need for membrane synthesis of rapidly dividing cells63.The sensitivity of the PI3K inhibitor pictilisib could be associated with lower activation of the mTORC1 complex, as mTORC1 activation has been described to be a crucial event in the resistance to PI3K inhibitors in many tumor typesSLC16A1 in sensitive cell lines due to near or complete absent expression of the functionally related transporter SLC16A3. Hence, inhibition of SLC16A1 in cells lacking SLC16A3 led to cell death due to the synthetic lethal relationship of these transporters and functional redundancy64. Similar observations have been made in small cell lung cancer, DLBCL, and Burkitt lymphoma66. The MCTs are bidirectional and execute proton-linked transport of lactate, pyruvate, and ketone bodies across the plasma membrane thereby playing a crucial role in preventing intracellular acidification68. Importantly, several cancers use lactate as a metabolic energy source70 and MCTs have been reported to impact cancer cell growth and survival71. Thus, as our data also show, MCTs represent attractive potential therapeutic cancer targets. Several SLC16A1-specific inhibitors have been developed72 with AZD3965 being in phase I clinical trials for advanced cancers (NCT01791595). The structural basis for its specificity and mode of action was also recently identified73. Our study illustrates that a relatively small percentage of myeloid malignancies (10–13%) would be susceptible to SLC16A1 inhibition, but highlights phenotypic drug screening as attractive means of identifying responding cells/patients harboring metabolic dependencies.We also observed a differential sensitivity pattern to AZD3965, which could be rationalized by dependency on 74, that could guide the exploitation of novel metabolic vulnerabilities by alternative chemical agents. Nonetheless, any therapy success of metabolic inhibitors would be contingent on an adequate integrative patient stratification. Several limitations of our study should be contemplated for future metabolic drug profiling efforts. First, the inactivity of roughly 70% of CLIMET compounds might be due to the concentration range used as some compounds are known to have activity in higher concentrations. Second, we may have missed out on interesting compounds that affect cellular metabolism by not impacting cell viability. Third, we envisage a wider collection of metabolic inhibitors would create insights into larger array of metabolic pathways/processes. Lastly, the composition of cell culture media is likely distinct from in vivo settings and in future, this parameter would need to be assessed more carefully. Nonetheless we uncovered functional and clinically relevant determinants of metabolic drug response. In-depth characterization of cancer metabolic dependencies and differences between normal and cancer cells will be a crucial step toward translating metabolic inhibitors to the clinic.While the genetic diversity of cancers is substantial, changes in numerous oncogenes and tumor suppressor genes stimulate a limited number of metabolic changes converging on a metabolic phenotype. In fact, previous studies have linked lack of drug response or the presence of drug resistance to specific intrinsic molecular and/or metabolic cellular propertiesMOLM-13, MV4-11, Mono-Mac-6, KG-1, SHI-1, ML-2, NOMO-1, HEL, LAMA-84, KU-812, KCL-22, and BV-173 were obtained from DSMZ, K-562 and HL-60 from NCI 60 panel and THP-1 from ATCC. All cell lines were maintained in RPMI 1640 medium (Gibco) with 10% fetal bovine serum (Gibco), penicillin (100 U/mL), and streptomycin (100 μg/mL). Cell lines were authenticated with STR profiling and mycoplasma tested by PCR prior to the study.D or IC50) together with PubChem commercial chemical vendor records, which led to initial 8000 compound candidates  information. The target annotation was performed by collecting information from publicly available databases  and additional manual curation. The compounds were ordered via the Sigma-Aldrich Marker Select service and sourced at 10 mM concentration in DMSO directly on Echo 384-well plates . In addition, we obtained 1 mL of 10 mM stock in vials for future use. The detailed description of the compounds is depicted in Supplementary Data Information on metabolic enzymes and drug-target annotations were integrated from publicly available databases such as Kyoto Encyclopedia of Genes and Genomes (KEGG), the Small Molecule Pathway Database (SMPDB) and ChEMBL target database  after 72 h incubation at 37 °C according to the manufacturer’s instructions. The highest cell number at which the growth curve was in linear range was chosen for drug testing. The compounds were printed on tissue culture-treated 384-well plates (Corning) using Echo 550 (Labcyte Inc.) in five different concentrations in duplicate in tenfold dilutions encompassing a 10,000-fold concentration range . Twenty-five microliters of single-cell suspension were seeded to each well with a Multidrop Combi peristaltic dispenser (Thermo Scientific). The plates were incubated at 37 °C and 5% CO2 for 72 h after which cell viability was measured using CellTiter-Glo in a multilabel plate reader . Data were normalized to DMSO wells serving as negative controls and 10  μM bortezomib wells  serving as a positive control. A four-parameter log-logistic model was fit for each compound’s dose–response data using the drc75 R package and corresponding AUC values calculated for each compound using the compute AUC function of the PharmacoGx76 R package. Unsupervised hierarchical clustering of the active compounds  was performed with Gene Cluster 3.0 software (complete linkage method and Spearman (drugs) and Euclidean (cell lines) distance measures). The resulting heatmap was visualized with Java Tree View software Version 1.1.6r4. Reproducibility of the clustering was assessed with the bootstrap resampling method using the Pvclust R package77. Similarity of drug sensitivity was evaluated by SD of AUC values for a particular compound within a cluster with SD ≤ 0.05 indicating concordance. The results were visualized with Venn diagrams with the Venn webtool from the University of Gent (http://bioinformatics.psb.ugent.be/webtools/Venn/). For detecting selective drug sensitivities in each cell line, the average AUC per drug was subtracted from the AUC value of the drug in each individual cell line and visualized with a spider plot using R statistical environment.The optimal cell number per well for each cell line was assessed for cell viability analysis in 384-well plates (Corning 3764). Briefly, cells were suspended in RPMI 1640 and seeded in increasing concentrations in triplicate  and cell viability was assessed with CellTiter-GloEvaluation if metabolic inhibitors can alter metabolic vulnerabilities was performed by rescreening the entire drug library as described above (each drug tested in five different concentrations in duplicate) in HL-60 cells in the presence of either DMSO, 100 nM AZD3965, 100 nM pictilisib, or 100 nM GSK2194069. Data analysis was the same as for the original screen with overall drug response profiles being compared with a Spearman correlation analysis performed in GraphPad Prism 9.To assess basal cellular metabolic activity, we utilized the Seahorse XF Cell Mito Stress Test Kit (Agilent) according to the manufacturer’s instructions. Briefly, cells suspended in Seahorse XF base medium, supplemented with glucose (10 mM), sodium pyruvate (1 mM) and L-glutamine (2 mM), were plated at 150,000 cells per well in XF 96-well cell culture microplates (Agilent). After 1 h of incubation at 37 °C the plate was loaded into the XF96 Extracellular Flux Analyzer (Agilent) and the Seahorse XF Cell Mito Stress Test was performed using oligomycin (1 µM), FCCP (1 µM) and rotenone (0.5 µM).28 was utilized for assessment of the metabolic profiles of the cell lines included in this study. A LC–MS approach was used to measure 225 metabolites in 928 cell lines from more than 20 cancer types. For analysis the imputed batch-corrected dataset with log10-transformed values (clean data) was used. To determine metabolite levels that were significantly different between at least two groups, a two-way ANOVA analysis and multiple testing correction with false discovery rate (FDR) of 10% was performed with GraphPad Prism version 9. The results were normalized with a Z score for a heatmap visualization. Metabolite enrichment and pathway analysis was performed with MetaboAnalyst79.Publicly available targeted metabolomics dataset from the CCLE80. For each recurrent mutation in at least three cell lines an unpaired two-sided parametric Student’s t test was performed to identify significant associations between mutations and drug response. FDR was computed using the Benjamini–Hochberg method over all drugs .Somatic mutation data for the cell lines included in this study was obtained from The Cancer Dependency Map portal 81 R package. The analysis was applied for the 77 active drugs and the Spearman’s correlation coefficient of each drug was plotted onto a clustered heatmap visualized with a correlogram. Positive correlations are showed in red and negative in blue. The intensity of the color and size of the circles are proportional to the correlation coefficients. The significant drug–drug correlations were identified after correcting for multiple testing with Benjamini–Hochberg at FDR of 10% and highlighted with thick black border.Co-occurring and mutually exclusive drug sensitivities were identified by pair-wise Spearman’s correlation analysis using the corplot83 web portal (https://synergyfinder.fimm.fi) using the Bliss independent model.The significant drug–drug interactions identified in Fig. 80 as log2 (TPM+1). Data were available for 14 of the 15 cell lines included in this study. Genes that were not expressed in any of the cell lines of interest (expression level log2 (TPM) < 1) were excluded from further analysis. Most variedly expressed genes in HL-60 cells were identified by assessing the SD between gene expression levels in HL-60 in relation to the average gene expression across the remaining 13 cell lines. Moreover, genes for which the SD was above 1.7 were considered and additionally filtered for unique expression pattern in HL-60 cells. Pathway and process gene set enrichment analysis was performed with Metascape84.Gene expression data for the cell lines included in this study were obtained from The Cancer Dependency Map portal t-test for each expressed gene. The resulting P values were adjusted with a Benjamini–Hochberg correction with FDR of 5% deemed significant. A volcano plot was generated with Prism 8. Biological process and molecular function gene annotation was retrieved from the gene ontology resource86. An SLC16A1/SLC16A3 expression ratio of 3 or higher was considered predictive of AZD3965 sensitivity, as K-562 cells were tested to be sensitive in this study but had the lowest SLC16A1/SLC16A3 expression ration of 3.1 among the 44 cell lines deemed sensitive to AZD3965. Thus, the threshold cutoff was placed at SLC16A1/SLC16A3 expression ratio of 3. Lineage and disease subtype of the cell lines was retrieved from the DepMap portal (20Q2 data release).Differentially expressed genes in AZD3965 sensitive and nonsensitive cell lines were identified by performing an unpaired two-sample, two-sided SLC16A1 and SLC16A3 deficient cells were generated by CRISPR/Cas9 technology as previously described87 using the pLentiCRISPRv2 vector (Addgene plasmid #52961)88. In brief, guide RNAs (sgRNA) were designed using CHOPCHOP89 and sgRNA designer tools90, oligonucleotides harboring BsmBI restriction site-compatible overhangs were annealed, phosphorylated and ligated into pLentiCRISPRv2 by standard cloning methods and sequences were confirmed by Sanger sequencing. An sgRNA targeting the Renilla luciferase coding sequence:Ren.208  was used as a negative control. Three different sgRNAs per gene were used and the sequences are provided bellow:sgSLC16A1.530 sgSLC16A1.777 sgSLC16A1.1291 sgSLC16A3.381 sgSLC16A3.384 sgSLC16A3.1059 .sgSLC16A1, SLC16A3, PIK3CA, PIK3CB, PIK3CD, and PIK3CG was performed by lentiviral cDNA delivery experiments. SLC-encoding cDNAs were obtained as codon-optimized versions from Genscript and transferred into pDONR221 entry vector using BP reaction gateway cloning (Invitrogen). PI3K isoform cDNAs were ordered from Addgene .Generation of cells overexpressing 87 or LE3FgwIP (pRRL-EF1a-3xFLAG-gw-IRES-PuroR) using LR reaction-based gateway cloning (Invitrogen). Cells infected with a corresponding empty lentiviral expression vector LEIB (pRRL-EF1a-IRES-BlastR) or LEIP (pRRL-EF1a-IRES-PuroR) served as negative control.In the case of Addgene plasmid #81843 the stop codon was removed by site directed mutagenesis to enable C-terminal tagging . Constructs were subsequently transferred into lentiviral expression vectors LEgwSHIB (pRRL-EF1a-gwSH-IRES-BlastR)For lentivirus production, HEK293T cells were transiently transfected with psPAX2 and pMD2.G packaging plasmids (Addgene) and corresponding expression vectors. Media were changed 24 h post transfection and replaced with the target cell line-specific media. After 48 h the supernatant (containing virus) was collected, filtered through a 0.45 μm filter supplemented with 8 μg/mL protamine sulfate (Sigma-Aldrich) and added to subconfluent target cells. Cells were then spinfected at 2000 rpm for 45 min at room temperature. After 24 h the media were exchanged with fresh media and 24 h later the media were complemented with selection antibiotic for 5–7 days to select infected target cells. Knockout and overexpression efficiency was evaluated by immunoblot analysis.Cells were centrifuged, washed once with cold PBS and subsequently lysed in Nonidet-40 lysis buffer  per 50 mL) for 10 min on ice. Lysates were then centrifuged  and proteins were quantified with the Bradford assay using γ-globin as a standard (Bio Rad). Cell lysates were separated by SDS-PAGE and transferred to nitrocellulose membranes Protran BA85 . The membranes were incubated with α-SLC16A1 , α-SLC16A3 , α-FLAG , α-HSP90  or α-AKT  and visualized with goat-anti-mouse IgG (115-035-003) or goat-anti-rabbit IgG (111-035-003) horseradish peroxidase-conjugated secondary antibodies  utilizing the ECL western blotting system (Thermo Fisher Scientific).n = 13) and CML (n = 3) after written informed consent were used for this study. Ethical approval was granted by the Ethics Commission of the Medical University of Vienna (Ethik Kommission 1676/2016). Patient characteristics are described in Supplementary Table Vitaly frozen mononuclear cells (MNCs) from patients with AML n = 3 and CMLFASN mRNA in response to pictilisib treatment and GADPH expression in HL-60 cells was performed using high-capacity RNA-to-cDNA kit and SYBR qPCR Green PCR Master Mix (Applied Biosystems). The following primers were used:Total RNA was extracted using Trizol according to the manufacturer’s protocol (Gibco). Quantification of Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting summary


Evidence bearing on the role of statins in the prevention and treatment of cancer is confounded by the diversity of statins, chemotherapeutic agents and cancer types included in the numerous published studies; consequently, the adjunctive value of statins with chemotherapy remains uncertain.Saccharomyces cerevisiae. Cell density (OD600) data were analyzed for synergism and antagonism using the Loewe additivity model implemented with the Combenefit software.We assayed lovastatin in combination with each of ten commonly prescribed chemotherapy drugs in highly reproducible in vitro assays, using a neutral cellular substrate, Four of the ten chemotherapy drugs – tamoxifen, doxorubicin, methotrexate and rapamycin – exhibited net synergism with lovastatin. The remaining six agents  compiled neutral or antagonistic scores. Distinctive patterns of synergism and antagonism, often coexisting within the same concentration space, were documented with the various combinations, including those with net synergism scores. Two drug pairs, lovastatin combined with tamoxifen or cisplatin, were also assayed in human cell lines as proof of principle.The synergistic interactions of tamoxifen, doxorubicin, methotrexate and rapamycin with lovastatin – because they suggest the possibility of clinical utility - merit further exploration and validation in cell lines and animal models. No less importantly, strong antagonistic interactions between certain agents and lovastatin argue for a cautious, data-driven approach before adding a statin to any chemotherapeutic regimen. We also urge awareness of adventitious statin usage by patients entering cancer treatment protocols.The online version contains supplementary material available at 10.1186/s12885-021-07963-w. From antiquityIn 1971 Akira Endo isolated the first statin progenitor, citrinin . Soon itWithin a few years of the introduction of statins, however, concerns regarding their safety emerged, notably an associated increase in non-cardiac mortality . Animal Several lines of evidence have suggested that statins might also have value in the treatment of cancer: statins modulate the mevalonate pathway , which ualone have not proven effective as anticancer therapy; however, there is evidence that statins might potentiate the effects of anti-cancer drugs [Disappointingly, statins er drugs . A recener drugs  , in comSaccharomyces cerevisiae [S. cerevisiae has been utilized in numerous pharmacologic studies and high throughput screens [S. cerevisiae essential genes. Barcoded pools [Combenefit dual-drug interaction software [Combenefit package.Because our objective was to compare the ten drug pairings with each other, it was important to use a neutral cellular substrate, rather than a particular human cancer cell line which because of its inherent cellular origin and genetic mutations might favor or disadvantage a particular drug pair. We therefore chose as the cellular substrate the exceptionally well-studied model organism, revisiae . There irevisiae –28. Manyrevisiae  and many screens –40, incl screens ). In anted pools  were emped pools , 34, 36 ed pools .  Wesoftware ; cell cosoftware  which isS. cerevisiae but with variations in the patterns of synergism and antagonism between individual cell lines, even of the same cancer type.Our data demonstrate that combining lovastatin with conventional chemotherapeutic agents results in drastically different interactions, ranging from strong synergism to profound antagonism, sometimes within the same concentration space. Of the ten chemotherapeutic drugs, four  exhibited net synergism with lovastatin; two drugs (gemcitabine and 5-fluorouracil) had neutral scores; and four  displayed net antagonism. As proof of principle, two of the drug combinations, tamoxifen/lovastatin and cisplatin/lovastatin, were further evaluated in human cancer cell lines. The results in cell lines were generally accordant with the data obtained with S. cerevisiae heterozygous diploid essential gene deletion strains was obtained from ThermoScientific. A balanced pool was created, aliquoted and frozen : cisplatin (cis-Diamminedichloroplatinum), cyclophosphamide (hydrate), epothilone B, etoposide, 5-fluorouracil, lovastatin , rapamycin, and tamoxifen. Compounds obtained from Millipore Sigma included: doxorubicin hydrochloride, gemcitabine hydrochloride, and methotrexate.S. cerevisiae from the NetwoRx data base [In order to determine suitable concentration ranges for the dual drug dilution assays, dose response curves were compiled for each of the 11 drugs, using a range of concentrations based upon experimental data in ata base  and/or pata base , 47, 48 600), after two and 3 days of incubation. Each experiment was conducted a minimum of 3 times; the numbers of biologic replicates are given in the figure legends.Dual drug assays were performed in 96-well tissue culture plates using a uniform format compatible with the input requirements of the Combenefit software package (see later). In each assay, lovastatin was paired with a single chemotherapeutic drug. A schematic diagram of the plate configuration is shown in Additional file Crosswise, dual-dilution format assays were also performed using human cancer cell lines as substrate. The cell lines, obtained from ATCC, included: A549 , HCC827 , HT-29 , MCF7 , MDA-MB-231 , and SK-BR-3 . The cells were cultured according to the corresponding ATCC protocols.S. cerevisiae experiments and likewise included a plate blank, individual drug dose responses, and cells-only positive controls , according to the manufacturer’s protocol.The dual drug crosswise dilution matrix assays were performed in a similar format as the S. cerevisiae assays, each plate was scrutinized for outlier values by the Grubb’s test, implemented with the XLSTAT software package. When an outlier was identified, it was removed and replaced, as described in Additional file In the http://sourceforge.net/projects/combenefit/) calculates and displays the synergism-antagonism distributions and computes a variety of metrics from the distributions [experimental concentration space to a reference concentration space. The latter is calculated from the individual dose-response curves for the two drugs in accordance with the assumptions of the Loewe model; thus, these must be included in each assay  to a given cell, the cell is colored appropriately to highlight its synergy or antagonism  doxorubicin (+ 5.43), methotrexate + 2.93) and rapamycin + 2.16) each achieved net synergism when paired with lovastatin , yet a further 0.70 dilution of lovastatin (108 μM) at the same concentration of tamoxifen resulted in significant antagonism , Figs. Tamoxifen, which earned the highest composite score (+ 9.36) on day two Fig. a, also eThe interactions of doxorubicin with lovastatin were statistically neutral over more than 80% of the concentration space Fig. d; howeveThe SUM_SYN_ANT score for the methotrexate-lovastatin pairing was a modest, perhaps misleading, + 2.93 Fig. a: statis50 for rapamycin increased by about 3.5-fold on day three  positive, MCF7, and two ER negative, MDA-MB-231 and SK-BR-3 cell lines (see Methods). The results obtained with the ER positive MCF7 cell line are shown in Fig. When tamoxifen and lovastatin were assayed in the lung adenocarcinoma cell line, HCC827  SUM_SYN_ANT scores, with weak maximum synergy and stronger maximum antagonism .Together, the results with lovastatin in combination with cisplatin in three human cell lines and tamoxifen with MCF7 breast cancer cells are concordant with the findings in the Saccharomyces cerevisiae; a balanced pool of heterozygous deletions of all essential genes was created for this study. Highly reproducible dilution assays were performed in microplates; cell densities were compiled with a plate reader. The data from the assays were submitted to rigorous statistical analysis to identify synergistic and antagonistic interactions, according to the Loewe additivity model, implemented with the Combenefit software package [The fundamental question we sought to answer in this study is whether statin-chemotherapy interactions are a manifestation of a global effect of statins or are, as we hypothesized, specific to individual chemotherapeutic drugs. To that end, we assayed a single statin, lovastatin, paired with each of ten commonly prescribed chemotherapeutic agents, with various mechanisms of action Table . The cel package .The Combenefit program compiles the data from the plate replicates for a given drug pair and computes various global metrics according to assumptions dictated by the chosen model. The metric “SUM_SYN_ANT”, which is as summation of all interactions within the concentration space, proved most useful. In accordance with our hypothesis, the ten chemotherapeutic agents exhibited a spectrum of global interactions, ranging from synergism to neutrality to antagonism Fig. a. Four cS. cerevisiae, which is devoid of estrogen receptors.  The efng yeast , 53–58, S. cerevisiae, devoid of estrogen receptors (ERs), yet displayed a similar pattern of strong synergism only in a breast cancer cell line, MCF7, which possesses ERs. 50 of lovastatin showed substantially greater variability between breast cancer cell lines than did the EC50 of tamoxifen. A possible resolution of these seeming contradictions is suggested by the reports of Radin and Patel [Curiously, the ECnd Patel  and Tan nd Patel ; higher nd Patel .S. cerevisiae-based experimental design by testing the combination of cisplatin and lovastatin in three human cell lines: A549 (lung adenocarcinoma); HT29 (colon adenocarcinoma); and MCF7 (breast carcinoma). This combination was chosen as a surrogate for the six neutral or antagonistic pairings because it so commonly prescribed for a variety of malignancies. In the yeast model, the combination exhibited strong antagonism  The balanced pool of heterozygous deletion strains of all essential genes, which we created in order to have a cellular substrate with consistent genetic diversity, proved to be only modestly more sensitive to the various drugs than the cognate wildtype strain. However, because each deletion is bar-coded, the pool provides a useful resource for the analysis of genetic targets and resistance mechanisms of drug treatments, studies beyond the scope of this report.Two features of the experimental design merit further discussion. Because our intent was to compare the ten statin-chemotherapeutic drug pairs with each other, an important consideration was that the cellular target be a neutral one, free of the biologic and genetic biases inherent in every cancer cell line; choosing any one human cancer cell line for the assays risked biasing the assays for or against one or another drug pair. In the Background we set forth our rationale for choosing the model organism  studies , 34, 36  studies , which iA second important consideration in experimental design was the choice of synergy model. The Combenefit software package  renders Undue reliance upon global metrics, as for example in high throughput studies, risks overlooking potentially useful synergistic interactions which are confined to portions of the concentration space, often with coexisting antagonism . In Additional file 1.Additional file 2: Legends Figure S1. Schematic diagram of the 96-well plate for the dual-drug assays (see Methods). The dose-responses (Columns 9 and 10), which are required for the computation of Loewe additive synergy, are performed at the same starting concentrations and with the same dilutions as in the dual-dilution portion of the plate. The format shown is compatible with the Combenefit software. BL = Plate blank (synthetic complete media + dextrose only). Figure S2. Tamoxifen plus lovastatin data on day 3. The comparable data for day two are depicted and described in Figure Figure S3. Doxorubicin and lovastatin interactions on day three. The data are quite similar to the results on day two, shown in Figure Figure S4. Methotrexate and lovastatin on day three. The patterns are quite similar to those recorded on day two   and MCF7 (breast cancer) , exhibited significant antagonism (and no significant synergism) on day two; the antagonism was more pronounced on day three. The HT29 colon adenocarcinoma cell line exhibited synergism and antagonism, both statistically significant, on day two (B) and to a lesser degree, on day three (E). The data shown here are comparable to that obtained with the S. cerevisiae assays (see Table


A combination of the lactones with glucocorticoids confirmed their synergy in anti-tumor activity in vitro. Conclusions: Methoxy-substituted flavanone-derived lactones effectively kill canine lymphoma/leukemia cells in vitro and, thanks to their synergistic action with glucocorticoids, may potentially be applied in the treatment of hematopoietic cancers.Background: The study investigated four flavanone-derived γ-oxa-ε-lactones: a parent unsubstituted compound and its three derivatives with the methoxy group in positions 2′, 4′ and 8. Our objective was to find out if the introduction of the methoxy group into the aromatic ring affects in vitro anti-tumor potency of the investigated lactones. Methods: Cytotoxic and pro-apoptotic effects were assessed with cytometric tests with propidium iodide, annexin V, and Western blot techniques. We also investigated potential synergistic potency of the tested lactones and glucocorticoids in canine lymphoma/leukemia cell lines. Results: The tested flavanone-derived lactones showed anti-cancer activity in vitro. Depending on its location, the methoxy group either increased or decreased cytotoxicity of the derivatives as compared with the parent compound. The most potent lactone was the one with the methoxy group at position 4′ of the B ring (compound  Compounds with a lactone ring in their structure are the subject of numerous studies due to their common occurrence in nature as well as a broad spectrum of biological activity. Lactones are secondary metabolites of various plants, insects, microorganisms and marine organisms and exhibit biologically important properties such as antifeedant, antibacterial, antifungal and cytostatic properties . Lactone1 (lactone with unsubstituted phenyl rings) and its three derivatives  with methoxy groups in positions 2′, 4′ and 8, respectively , and on the other hand, the dogs are excellent animal models for studying human non-Hodgkin’s lymphomas  . Researc1 by an addition of methoxy substituents increased or decreased the cytotoxic activity of the new derivative, depending on the position of the group. Excluding the resistant CNK-89 cell line, the introduction of the methoxy group into position 8 of the A ring significantly lowered the activity in comparison with the parent compound, and γ-oxa-ε-lactone 4 was also less effective than other derivatives. However, the presence of the methoxy group in the B ring increased the cytotoxic activity of the resulting compounds, which was particularly noticeable in the case of lactone with the methoxy group at position 4′ (3) when tested on the CLB-70 cell line.The study began with checking the cytotoxic activity of the tested compounds by means of propidium iodide staining and flow cytometry analysis. All tested compounds at the concentration range up to 50 µg/mL exhibited cytotoxic activity. The differences in their strength were visible for individual derivatives  and indi2 in the highest concentration induced marked cell death in this cell line. Detailed results are shown in In addition, the cytotoxic effect in the noncancerous canine cell line was evaluated and it was found that in this cell line none of the compounds was able to kill 50% of the cells and only compound 50 for this cell line, except for the most potent derivative—lactone 3. Detailed differences in the sensitivity of each cell line to individual compounds are shown in Not only tested γ-oxa-ε-lactones differed in their activity but the cell lines also showed different sensitivity to their action. While the cell lines representing typical B-cell proliferation turned out to be highly sensitive to the anti-proliferative effect of the examined lactones, the GL-1 cell line representing B/T-cell leukemia seemed slightly less sensitive, and the cell line formed by neoplastic NK cells was clearly resistant. Within the range of concentrations tested, it was not possible to determine ICSince the assay with propidium iodide alone showed the cytotoxic effect of the tested compounds, we decided to check the type of cell death (apoptosis or necrosis) induced by the tested compounds. To distinguish apoptosis from necrosis the cells were additionally stained with annexin V, which binds with phosphatidylserine externalized in apoptotic cells.3, but we observed no differences in the type of cell death induced by the tested compounds. Similarly, in the two less sensitive cell lines, GL-1 and CNK-89, the populations of necrotic and late apoptotic cells were of comparable size. Detailed results along with representative dot-plots showing apoptotic and necrotic cells are presented in The experiment revealed both types of cell death ensuing from the treatment with the tested compounds. A particularly high percentage of necrotic cells was found in CLB70 and CLBL-1 cell lines, which were the most sensitive to the tested lactones. The population of necrotic cells was accompanied by the population of cells in late apoptosis also at the lowest (12.5 µg/mL) concentration of the tested substances. The strongest effect was again shown for lactone Weak apoptosis induction was also confirmed by the Western blot, which showed no statistically significant changes in the expression level of the most important anti-apoptotic protein, i.e., Bcl-2. As the imbalance between pro- and anti-apoptotic proteins occurs only in living cells, relatively low concentrations of tested lactones were used in the study: 3.125 and 6.25 µg/mL for the sensitive cell lines (CLBL-1 and CLB70) and 6.25 and 12.5 µg/mL for the resistant ones (CNK-89 and GL-1). Detailed results are shown in 2 and 3, were selected for these tests with two cell lines (CLBL-1 and CLB70) most sensitive to the lactone action. The experiments clearly showed that the combination of glucocorticosteroids (both dexamethasone and prednisolone) with the flavonoid derivatives induced stronger cell death than either of the substances alone. Synergy evaluation confirmed the synergistic effect for the combinations of dexamethasone and prednisolone with lactones 2 and 3 in both cell lines (synergistic effect was identified by combination index (CI) values less than 1). The effect was most pronounced at low concentrations of the tested lactones (3.125 µg/mL) in combination with 2.5 µg/mL of steroid drugs. In these concentrations, in the CLB70 cell line, CI was about 0.4 for combinations of lactone 2 and both glucocorticosteroids while for lactone 3 it was about 0.62 and 0.76 for dexamethasone and prednisolone, respectively. In the CLBL-1 cell line, the CI for lactone 2 in combination with dexamethasone or prednisolone was about 0.5, while for lactone 3 it was about 0.75. At the higher concentration the lactones were so effective in inducing cell death that the effect of their combination with steroids could not be observed. Detailed results are shown in As our research demonstrated the anti-tumor potential of the tested compounds, we decided to check whether the obtained derivatives could be used in the treatment of lymphomas/leukemias in dogs. To this end, we resolved to verify whether the tested compounds act synergistically with common anti-cancer drugs used in canine therapy. As each therapeutic regimen used in the treatment of hematopoietic neoplasms in dogs contains glucocorticosteroids, we investigated the lactones in combinations with dexamethasone and prednisolone. The two most potent methoxy substituted lactones, Despite years of intensive research into the causes and treatment of cancer, it still remains a huge and unsolved problem for mankind. According to the data provided by the World Health Organization (WHO) in 2018, the global annual number of new cancer cases is estimated at about 18.1 million. The same source states the specific cancer-related mortality at about 10 million yearly. Moreover, the WHO estimates that the global cancer incidence could increase by more than 63% in 2040 as compared with 2018 . Such a The search for new compounds with potential anti-cancer activity among agents of natural origin is still a popular field of research. In fact, the treatment of cancer has started with the use of natural substances. An example here could be basic cytostatic drugs such as irinotecan, vincristine, etoposide and paclitaxel sourced from plants, actinomycin D and mitomycin C produced by microorganisms or marine-derived bleomycin . FlavonoConsidering the above, we decided to combine our knowledge of the anti-cancer properties of flavonoids and natural compounds containing lactone moiety to create new compounds with the advantages of both these groups. Since poor bioavailability penetration into the cells through membranes may significantly limit the activity of the flavanone-derived lactones, we decided to modify the structure of our compounds by synthesis of methoxy derivatives. We also resolved to determine if the location of the methoxy group in the tested compounds would affect their activity.Our previous research demonstrated that introduction of lactone function increased anti-microbial activity of a tested compound as compared with its flavonoid precursors . Studies4) with lower activity than that of the parent compound 1  [2 and/or 4′-methoxylactone 3 on the balance between pro- and anti-apoptotic proteins in the cell that would facilitate the course of apoptosis  ,22 than ctively) , or NK cctively) . The rempoptosis . It can Artocarpus heterophyllus heartwoods on the anti-cancer activity of cisplatin against h460 and mcf-7 cell lines [At the final stage of the study, we assessed the potential use and role of the obtained lactones in the treatment of hematopoietic cancers. A combination treatment strategy, which serves as an important direction for the development of natural products in cancer therapy, seems an interesting research direction. Adjuvant treatment with natural products supplementing current treatment regimens could be beneficial in multiple aspects, including mitigation of adverse effects, overcoming drug resistance and improving therapeutic response. The idea of a combination therapy with flavonoids is already being researched. Firstly, numerous studies confirmed that flavonoids show better anti-tumor activity when used in complex preparations containing a mixture of several compounds from this group ,29,30,31ll lines , synergill lines , or querll lines . More anll lines  or immunll lines .2 and 3. We found that non-toxic concentrations of both glucocorticosteroids (2.5 µg/mL) markedly potentiated the killing effect of these derivatives. The effect of the combination was so strong that cell viability could only be assessed at the lowest concentration of lactones used in the study (3.125 µg/mL). The scope and strength of the cytotoxic effect of the proposed combination as well as its mechanism of action require further research but we established the obtained compounds as useful adjuvants in standard therapeutic protocols and possibly as an interesting alternative for classic chemotherapy.To initially assess the usefulness of the lactones in the adjuvant therapy of lymphomas and leukemias, we examined their synergistic action with basic drugs used in the treatment of hematopoietic neoplasms, i.e., glucocorticosteroids. This preliminary study, involving two canine cancer cell lines, confirmed the synergistic effect of both dexamethasone and prednisolone with the two lactones with the highest cytotoxic activity, 1–4) obtained in a three-step synthesis from corresponding flavanones as described earlier [The study investigated four flavanone-derived γ-oxa-ε-lactones ( earlier . The strAll tested compounds were dissolved in dimethyl sulfoxide (DMSO) to prepare 10 mg/mL stock solution. The stock solutions were stored at room temperature. Tested dilutions were obtained by dissolving the stock solution in a complete culture medium.The study involved a panel of canine cancer cell lines: CLBL-1 (B-cell lymphoma), GL-1 (B/T-cell leukemia), CLB70 (B-cell chronic lymphocytic leukemia) and CNK-89 (NK-cell lymphoma), and a normal canine cell line (MDCK). CLBL-1 was obtained from Barbara C. Ruetgen from the Institute of Immunology, Department of Pathobiology, University of Veterinary Medicine, Vienna, Austria ; GL-1 frThe cell lines were maintained in RPMI 1640   or Advanced RPMI  (CLB70 and CNK-89) culture medium supplemented with 2 mM L-glutamine , 100 U/mL penicillin, 100 μg/mL streptomycin  and 10–20% heat-inactivated fetal bovine serum—FBS .4 cells per well in a 96-well plate . The cells were incubated in the medium alone or in the medium containing either the vehicle control or increasing concentration of the tested substance for 48 h. After the incubation, the cells were harvested and washed twice in PBS, transferred into cytometric tubes and stained with propidium iodide . Flow cytometry analysis was performed immediately, using a flow cytometer FACS Calibur . CellQuest 3.lf. software  was used for data analysis on the basis of histograms of FL2-H showing the population of alive (PI negative) and dead (PI positive) cells. The values presented as means with standard deviations were obtained from three independent experiments and shown as a concentration-dependent curve. IC50 for the tested compounds was calculated as a mean concentration inhibiting cell viability by 50% in three independent experiments.To evaluate and compare the effects exerted by γ-oxa-ε-lactones 1–4 on the cultures of canine lymphoma  and leukemia  cell lines, the cells were treated with increasing concentrations of each compound  for 48 h. This incubation time was chosen after preliminary studies showed that 48 h of treatment of the cells with the tested compounds allows both the evaluation of the cytotoxic effect and the observation of late signs of apoptosis. The cells were seeded at a concentration of 1 × 102 and 3, and two cell lines (CLBL-1 and CLB70) most sensitive to the activity of the tested lactones, were selected. Prednisolone and dexamethasone, frequently used in the treatment of lymphomas and leukemias in dogs, were tested as glucocorticoid compounds. The cells were incubated with individual compounds or their combinations for 48 h. The range of the tested concentrations for a single treatment was 3.125, 6.25 and 12.5 µg/mL for lactones and 2.5, 5 and 10 µg/mL for glucocorticoids. Dexamethasone and prednisolone concentrations of 2.5 µg/mL were chosen to evaluate the synergistic effect, and these drugs were added together with the lactones in the entire range of concentrations tested . Synergy was estimated using CompuSyn software and the Chou-Talalay method [1.The method of staining cells with propidium iodide was also used to evaluate a synergistic effect of the tested derivatives with glucocorticosteroids. For this study, the two most potent derivatives y method . The syn6 cells per 25 cm2 cell culture flasks and the compounds were added at two selected concentrations (3.125 and 6.25 µg/mL for the resistant cell lines and 6.25 and 12.5 µg/mL for the sensitive cell lines). After 48 h incubation the cells were harvested, rinsed with cold PBS, suspended in a lysis buffer  and incubated for 20 min on ice. The suspensions were centrifuged at 10,000 rpm at 4 °C for 12 min. Then, a sodium dodecyl sulfate (SDS) sample buffer was added to clear supernatants, and the samples were boiled at 95 °C for 5 min and subjected to SDS-PAGE on 12% gel. The resolved proteins were transferred to a PVDF membrane , using Semidry Transfer Cell . After the transfer, the membrane was blocked for 1 h with 3% bovine serum albumin (BSA) in TBS at room temperature, and then incubated with primary antibody (dilution 1:1000)  at 4 °C overnight, followed by secondary horseradish peroxidase-labeled antibody  for 1 h at room temperature. The bound antibodies were visualized using ChemiDoc Touch Instruments . The anti-β actin (C-4) and anti-Bcl-2 (C-2) antibodies from Santa Cruz Biotechnology  were used in this study. For Bcl-2 expression quantification, Western blot normalization using a single protein  was performed using Image LabTM software .For Western blot analysis the cells were seeded in a total of 5 × 10Early apoptotic events were detected by means of annexin V FITC/PI staining. Briefly, the cells were treated with the tested compounds at 12.5 and 25 µg/mL for 48 h. The concentrations of the compounds were the same as for determination of cell viability. After harvesting the cells and washing them twice with cold PBS, they were suspended in a binding buffer and stained with annexin V-FITC for 10 min at room temperature or PI . Immediately after, the incubation flow cytometric analysis was performed using a FACS Calibur analyzer . CellQuest 3.lf. software was used for data analysis.p < 0.05.All data are shown as means with standard deviations (SD). Statistical differences were analyzed using one-way ANOVA followed by Tukey’s multiple comparison test. Statistical analysis was performed with STATISTICA version 13.3 software . The results were considered significant at 1. The most potent lactone was obtained by the introduction of the methoxy group at position 4′ of the B ring (compound 3), whereas the presence of this group at C-8 in the A ring resulted in its lower activity. The substitution pattern of methoxy-substituted flavanone-derived lactones did not affect the mechanism of the cytotoxic action of the investigated compounds but influenced the strength of their anti-cancer activity. In vitro examination of the potential synergistic anti-tumor activity of the tested lactones with glucocorticoids showed that this combination was more potent than either of the compounds alone. This may indicate a potential for the application of the lactones in the adjuvant treatment of hematopoietic neoplasms in the future. However, further studies on the mechanism of action and possible in vivo use of the obtained flavanone-derived lactones are required.The tested flavanone-derived lactones show anti-cancer activity in vitro. Depending on its location, the presence of the methoxy group in the molecules can both increase or decrease their cytotoxicity in comparison with the parent compound


Cardiovascular disease (CVD) and cancer are two major causes of death worldwide. The question is, “Could there be a link between these two pathologies in addition to their shared, common risk factors?” To find some answers, we studied the effect of oxidized low-density lipoproteins (oxLDL) on head and neck cancer (HNC) cell lines, since oxLDL is a major contributor to atherosclerosis and the principal cause of CVD. In this study, we exposed three HNC cell lines  to oxLDL. We investigated two oxLDL receptors, CD36 and Lox-1, using immunofluorescence. Cancer cell migration was evaluated using Boyden chambers and the Wnt/β-catenin pathway was investigated using Western blotting. We demonstrated that the expression of CD36 and Lox-1 significantly increases after exposure to oxLDL. Moreover, we found that oxLDL reduces the migration of HNC cell lines, an observation that is in line with an increased degradation of β-catenin under oxLDL. Finally, the inhibition of CD36 with sulfosuccinimidyl oleate (SSO) reverses the inhibition of cell migration. In conclusion, we report that oxLDL seems to induce an increase in CD36 expression on HNC cell lines, enhancing the uptake of these lipids in cells to finally decrease cancer cell migration via the CD36/β-catenin pathway. Cardiovascular disease (CVD) and cancer are the two main causes of death worldwide. Indeed, the World Health Organization (WHO) has estimated that the global burden of cancer rose to 18.1 million new cases and 9.6 million deaths in 2018, with an estimated 5-year prevalence of 43.8 million, placing cancer as the second leading cause of death worldwide  vs. 0.58 × 106 µm2 [oxLDL], t-test, p = 0.001), -B , -C ). To understand this result, we explored the Wnt/β-catenin pathway, which is involved in cell motility and migration [In order to assess whether oxLDL impacts HNC cell migration, we used Boyden chambers and showed that oxLDL decreased cell migration in all HNC cell lines (igration . Western6 µm2 (oxLDL) vs. 4.7 × 106 µm2 (oxLDL + SSO), t-test, p = 0.04). The involvement of CD36 in the decreasing cell migration induced by oxLDL is reflected in the significant increase in the surface area covered by cells under oxLDL and SSO treatment.To investigate the mechanisms involved in the cell migration decline after oxLDL exposure, we explored the implication of CD36 in Detroit 562 cells after its inhibition with 1 µM of SSO. No change in cell migration appeared between the control cells (NT) and the SSO-treated cells, but oxLDL exposure led to a decrease in cell migration that was significantly overcome by the SSO treatment  (another oxLDL receptor) in macrophages, where the oxLDL uptake by CD36, but not by SR-BI, is dependent on dynamin . Howeverbright cells were described in oral cancer as initiating metastasis. They did not overexpress the mesenchymal genes, were slow cycling and expressed high levels of the fatty acid receptor CD36 and lipid metabolism genes [Regarding CD36, we have demonstrated that its expression increases under oxLDL exposure, which is not demonstrated elsewhere in cancer. In ovarian and cervical cancer, it was observed that CD36, as Lox-1, leads to cancer progression and metastasis ,30. Indesm genes . They alsm genes . Howeversm genes . In factsm genes . This reRegarding the Wnt/β-catenin pathway, it is well known that without the stimulation of this pathway, β-catenin is phosphorylated by the destruction complex, which is composed of adenomatous polyposis coli, Axin 1/2, casein kinase I and glycogen synthase kinase 3β . Wnt ligOLR1 gene coding for the Lox-1 receptor [Another aspect of this study is the use of HPV-negative HNC cell lines, which represent the major HNC population. However, HPV infection is estimated to cause 22% of oropharyngeal cancer and 47% of tonsillar cancer cases . A studyreceptor . In thisIn conclusion, oxLDL seems to induce an increased expression of both the Lox-1 and CD36 receptors on HNC cell lines, enhancing their uptake of oxidized LDL and decreasing cancer cell migration via the CD36/β-catenin pathway. However, more clarifications are needed to verify the exact contribution of CD36 and Lox-1 in cancer progression. Indeed, studies demonstrated that the upregulation of CD36 is associated either with tumor progression or tumor repression ,32. Fina