Serum prostate-specific antigen (PSA) is currently the most used biomarker in clinical practice for prostate cancer (PCa) detection. However, this biomarker has several drawbacks. In this work, an untargeted gas chromatography-mass spectrometry (GC-MS)-based metabolomic profiling of PCa cells was performed to prove the concept that metabolic alterations might differentiate PCa cell lines from normal prostate cell line. For that, we assessed the differences in volatile organic compounds (VOCs) profile in the extracellular medium (exometabolome) of four PCa cell lines and one normal prostate cell line at two pH values (pH 2 and 7) by GC-MS. Multivariate analysis revealed a panel of volatile metabolites that discriminated cancerous from normal prostate cells. The most altered metabolites included ketones, aldehydes and organic acids. Among these, we highlight pentadecane-2-one and decanoic acid, which were significantly increased in PCa compared to normal cells, and cyclohexanone, 4-methylheptan-2-one, 2-methylpentane-1,3-diol, 4-methylbenzaldehyde, 1-ethanone, methyl benzoate and nonanoic acid, which were significantly decreased in PCa cells. The PCa volatilome was markedly influenced by the VOCs extraction pH, though the discriminant capability was similar. Overall, our data suggest that VOCs monitoring has the potential to be used as a PCa screening methodology. Prostate-specific antigen (PSA) is currently the most used biomarker for PCa detection, in combination with digital rectal examination (DRE)3. However, the role of serum PSA levels as a screening tool for PCa meets with important limitations. Although raised PSA levels  are undoubtedly associated with the presence of PCa, benign conditions, such as prostatitis and benign prostatic hyperplasia (BPH), which are common in the elderly, also cause elevated serum PSA. Consequently, there is a relatively high frequency of unnecessary prostate biopsies, an invasive procedure which is expensive and uncomfortable for the cancer suspects4. Moreover, a significant proportion of men with PSA levels within the normal range harbor PCa (up to 22%) and a significant number of these show pathologic features of tumor aggressiveness8. Indeed, 21% of men with positive end of study biopsies enrolled in the Prostate Cancer Prevention Trial (PCPT) had serum PSA levels between 2.6 and 3.9 ng/mL and 15.4% of the tumors found in men with PSA levels < 2.5 ng/mL were high grade cancers9. The PSA test for PCa diagnosis has a area under the curve (AUC) of 0.682 and considering a cut-off of 4.1 ng/ml this biomarker shows a specificity of 93.8% and a sensitivity of 20.5%1. Furthermore, PSA is unable to differentiate aggressive from indolent PCa, which may lead to overtreatment11. Indeed, a large US-based trial found no benefit of PSA screening in reducing PCa-related mortality12 and even the large European trial that found a moderate benefit (approx. 20%) acknowledged that 1410 men would have to be screened and additional 48 cases of PCa would need to be treated to avoid a single death from PCa13. Besides the human cost, implementing widespread PSA screening for PCa might also lead to double of the total financial costs associated with PCa management14. Due to these limitations, the use of PSA for populational screening has been challenged15.Prostate cancer (PCa) is the second leading cause of cancer-related death in men in most western countries16, accurate early detection of PCa remains an unmet need. Thus, discovery and validation of novel, more specific and cost-effective biomarkers that might improve early PCa diagnosis and more precisely forecast its clinical behavior in an individual basis is an important research aim.Although several molecular tests have been developed over the years and some have already obtained FDA approval18. A metabolomic approach may allow for the discovery of biochemical signatures, and, consequently, of differences between cancer and healthy metabolic phenotypes17 in non-invasive samples. An early intervention is possible using metabolomics, since it is believed that metabolic alterations precede neoplastic proliferation11.Metabolomics is a powerful analytical tool in oncology, endowing novel biomarkers and therapeutic targets, as cancer cells have the capacity to modify many homeostatic systems within the body and, consequently, change the production, use and levels of many metabolites17. In fact, the use of cell lines in preliminary studies have important advantages, as cell lines allow to circumvent several important confounding factors, like age, diet, drugs, chronobiological variations, among others21. In addition, cell lines have a perfectly defined cell state which allows for the analysis of a target metabolic status. So, we believe that cell lines are the ideal matrix for hypothesis generation and to unveil unchanged metabolic signature originated directly from cells, metabolic alterations that do not appear in studies using animal models or human subjects, due to sample biological complexity, may be revealed21. Notwithstanding, this in vitro model presents some limitations, particularly the fact that cultured cells fail to reproduce the complex cell–cell and cell–matrix interactions in the tumour microenvironment and these interactions are very important for metabolic alterations occurring with tumor progression22. For these reasons, the findings obtained in in vitro studies need further confirmation and validation in biofluids from PCa patients.Several different matrices may be used in PCa metabolomics studies, but the most common are biofluids , tissues and cell lines. Generally, when the chosen matrix is a biofluid and/or a tissue it is very important to be aware that metabolic profile can be altered by factors not related to cancer cell metabolism, like age, diet, drugs, chronobiological variations, among others, which are very important to control to obtain reliable results. On the other hand, experiments performed in cultured cell lines have several advantages compared to the use of urine or plasma, overcoming these problems23. VOCs correspond to a carbon-based chemical group, with low molecular weight and high volatility. These compounds are excreted (exometabolome) from the human body in several tissues, accumulated and/or eliminated and can reflect the metabolic condition of an individual25 and, therefore, have the potential to provide new biomarkers for cancer detection. Since VOCs are the end products of cell metabolism, the volatilome can provide information about intracellular metabolic status. Alterations in VOCs profile may be related to modifications in gene activation, gene expression, proteins and activity of enzymes involved in metabolic pathways27.Volatilome is defined as the analysis of the volatile profile of a biological system, being accomplished by the evaluation of the volatile organic compounds (VOCs)28. The most commonly used method to analyse VOCs is the Headspace-Solid Phase Microextraction/Gas Chromatography-Mass Spectrometry (HS-SPME/GC-MS), which involves simple and rapid sample preparation, allowing high-throughput screening, has a good sensitivity and does not require a concentration step before analysis30. Moreover, VOCs can be detected in several different matrices, including exhalated breath and urine, particularly interesting for clinical practice use due to their non-invasive nature31.Importantly, the volatilome analysis does not require cell disruption, which allows the use of the same cells at different times32, lung31, head and neck33, esophago-gastric34, colorectal skin35, skin36, liver37 and renal23, proving to be able to discriminate cancer from normal samples. With great interest to this study, some reports determining VOCs in urine of PCa patients showed significantly differences from urines of control individuals39.Some reports have already focused on the application of VOCs analysis in different cancers, namely breast40. This strategy allows to obtain a more comprehensive evaluation of PCa volatile profile and to infer which pH is optimum for VOCs extraction and detection. VOCs significantly altered as a consequence of PCa metabolism will integrate a panel of candidate biomarkers for early detection of PCa that may, in the future, be translated and validated in other biological matrices (e.g. urine) and used in clinical practice. Furthermore, cell lines included in this study exhibit different hormone-dependency and potential for invasiveness, which allowed for extending our investigation on the metabolites responsible for discriminating PCa cell lines according to tumour androgen-deprivation sensitivity and aggressiveness. To the best of our knowledge, this work is the first to analyze VOCs profile in human PCa cell lines.The main objective of this study is to perform the volatile profiling of four different PCa cell lines  and one normal prostate epithelial cell line (PNT2) to prove the concept that VOCs emanated into the extracellular medium may discriminate PCa cells from normal prostate cells. VOCs extraction is performed at two different pH values, pH 7  and pH 2 because it is well established that tumor microenvironment is acidic to promote tumor progression and metastasisIn this study, we performed a metabolomic untargeted approach, at two different pHs, pH 7 and pH 2, to evaluate alterations in the volatilome of PCa cell lines  when compared with normal prostate cell line (PNT2). A multivariate analysis (MVA) approach was applied to evaluate the ability of volatilome to discriminate the different cell lines used in this study. Figure The reproducibility of the analytical method is evidenced by the QCs cluster in PCA scores plot , namely 22RV1 Y-observations, 2 components). The results of this validation showed that all created models were robust for the discrimination between PCa cell lines and the normal prostate cell line  values higher than 1 were considered potentially relevant for the separation among cell lines. Hence, at pH 7 a total of 23 VOCs were considered relevant to differentiate 22RV1 from PNT2 and 32 at pH 2; at pH 7, 16 VOCs were considered relevant to differentiate PC3 from PNT2 and 25 at pH 2; at pH 7, 27 VOCs were considered relevant to differentiate DU145 from PNT2 and 32 at pH 2; and, finally, at pH 7, 21 were considered relevant to differentiate LNCaP from PNT2 and 24 at pH 2 , 2-methylundecanal, , 2,7-dimethyloctan-1-ol (PC3 vs PNT2), 2-(4-methylcyclohex-3-en-1-yl)propan-2-ol (PC3 vs PNT2), decan-1-ol (DU145 vs PNT2), 1-methoxypropan-2-yl acetate (LNCaP vs PNT2), and 1,4-xylene (LNCaP vs PNT2) . A total of 8 VOCs proved to be relevant to differentiate 22RV1 from PNT2 in the results obtained at pH 7 and 11 at pH 2; 8 VOCs proved to be relevant to differentiate PC3 from PNT2 at pH 7 and 19 at pH 2; 7 VOCs proved to be relevant to differentiate DU145 from PNT2 at pH 7 and 13 at pH 2; and 7 proved to be relevant to differentiate LNCaP from PNT2 at pH7 and 13 at pH 2 , 5-methylheptan-2-one , phenylethanol , 4-methylnonanoic acid (22RV1 vs PNT2 and LNCaP vs PNT2), benzyl acetate (22RV1 vs PNT2 and LNCaP vs PNT2), 5-methyl-2-propan-2-ylcyclohexan-1-ol (PC3 vs PNT2 and LNCaP vs PNT2), 1-ethoxypentane (DU145 vs PNT2 and LNCaP vs PNT2), methyl nonanoate (22RV1 vs PNT2), 2-ethoxy-2-methylbutane (PC3 vs PNT2), hexanoic acid (PC3 vs PNT2), phenylmethanol (PC3 vs PNT2), 2,4-dimethylheptan-1-ol (PC3 vs. PNT2), benzoic acid (PC3 vs PNT2), 6-pentyloxan-2-one (PC3 vs PNT2) propan-2-ol were found significantly decreased in the culture medium of PC3 cells, which have high metastatic potential 38, as well as in other cancer cell lines46. This finding may be explained by the increased fatty acid β-oxidation and protein metabolism, used to produce energy, which lead to ketones production47. In particular, the metabolism of C15:0 long-chain fatty acids may originate pentadecan-2-one in PCa cell lines48. Furthermore, ketones can also be produced from the respective secondary alcohols37. Alteration in the activity of alcohol dehydrogenase49 and cytochrome P450 (CYP 450) activities30 can also be related to alteration in ketone and aldehydes levels, since hydrocarbons can be metabolized to aldehydes or ketones in the human body via these enzymes50. In our study, alterations in the aldehydes and hydrocarbons levels were also observed, which reinforce the connection between the observed changes and alterations in the activity of these two enzymes.The increase in pentadecan-2-one observed in all PCa cell lines is in accordance with previously reported increase in ketone levels in urine from PCa patients38 and in cancer cell lines from colon and lung51. This altered profile may be associated with an alteration in lipid peroxidation, induced by the increased ROS levels, characteristic of cancer cell metabolism and inflammation52. Beyond lipid peroxidation, aldehydes can also result from amino acid and carbohydrate catabolism54. An alternative explanation for the aldehyde levels variation can be related to alterations in aldehyde dehydrogenases (ALDHs) activity, responsible for the aldehydes oxidation to carboxylic acids54. Furthermore, ALDHs are involved in cellular proliferation, differentiation and survival, and in the cellular response to oxidative stress. The alteration in ALDHs was associated with PCa and other cancers progression54.The alteration in aldehydes  profiling is common in urine of PCa patients55 and in urines from head and neck cancer patients56. The presence of these molecules was previously related with the presence of ROS and oxidative stress58. So, the increase in aromatic hydrocarbons in cancer cell lines media can be an indicator of an increase in ROS activity, which could lead to lipid peroxidation and consequently to cell membrane damage59.A significant alteration in aromatic hydrocarbons, such as naphthalene and 1,4-xylene, in the culture media of cancer cell lines was also observed. Importantly, the alteration in 1,4-xylene levels observed in our study is in concordance with previously reported alteration in 1,4-xylene in exhaled breath from PCa patients58. The β-oxidation used by cancer cells to produce acetyl-CoA, that can be incorporated in Krebs cycle, may be other important source of alcohols. Thus, one may hypothesize that decan-1-ol can be associated with the synthesis of a medium-chain fatty acid, namely capric acid (C10:0), for energy production44. Other well described characteristic of cancer cells is their rapid growth, which implies an increase of cellular membrane synthesis. The alcohols may be metabolized to carboxylic acids and these acids further used to the synthesis of cellular membrane precursors60. Another possible explanation for the increased levels of some alcohols is the induction of the cytochrome P450 enzymes that occurs during carcinogenesis56. CYP 450 can hydroxylate several VOCs, including the alkanes produced during lipid peroxidation of polyunsaturated fatty acids, which leads to the production of corresponding alcohols56. Alcohols can also be produced by enzymatic reduction of aldehydes by alcohol dehydrogenases37.In our study, several alcohols, such as 3-methylbut-3-en-2-ol, 2,7-dimethyloctan-1-ol, 2-(4-methylcyclohex-3-en-1-yl)propan-2-ol, 3,7-dimethyloct-7-en-1-ol and decan-1-ol were altered in PCa cell lines; this alteration in alcohols levels may be due to the alteration in hydrocarbon metabolism, being alcohols end-products of this metabolism40, to promote tumor progression and metastasis. The biochemical mechanisms related with acidic pH include hypoxia, excessive glycolysis, hyperexpression of carbonic anhydrase and poor perfusion40. The important role of acidic pH in tumor microenvironment highlights the interest of studying the volatilome at pH 2.It is well established that tumor microenvironment is acidic2X = 0.526; R2Y = 0.457; Q2 = 0.437 for pH 2 vs R2X = 0.445; R2Y = 0.463; Q2 = 0.446 for pH 7). Although the discriminant capability of these two models is similar, the discriminant metabolites were not the same at the two medium pH, showing that acidification of the samples markedly influences the prostate cell volatilome (e.g. detection of volatile organic acids). As described by other authors30, the detected volatilome was markedly different in the two pHs in study, which highlights the importance of the study of volatilome at different pH, to obtain a comprehensive picture of cells volatilome.As for physiological pH, the volatilome obtained at pH 2 was able to differentiate PCa cell lines from normal prostate cell line, taking into account the quality of the two PLS-DA models ethanone, methyl benzoate, nonanoic acid, cyclohexanone, 4-methylbenzaldehyde , hence, to have a more comprehensive analysis of cell volatilome it is advantageous to use both pH.The integration of VOCs in specific metabolic pathways is still very difficult, needing more studies to completely accomplish this goal. Nevertheless, our results indicate that PCa cell lines have lipid metabolism altered, mainly in lipid peroxidation, lipid hydrolysis, synthesis of fatty acid and fatty acid β-oxidation. The obtained results also suggest the modification in the activity of some important enzymes, namely, alcohol dehydrogenase, cytochrome P450 and aldehyde dehydrogenase.in vivo samples, preferentially urine. Further studies are still needed to confirm our results, and find out a non-invasive, sensible and specific biomarker (or set of biomarkers) for prostate cancer.It is important to emphasize that for a possible use in the clinical practice it is mandatory to prove the translatability of these results to All chemicals and reagents were of analytical grade. RPMI-1640 medium, 4-fluorobenzaldeyde  and all GC-MS standard compounds were purchased from Sigma-Aldrich Co. . The antibiotic mixture penicillin/streptomycin (10000 U/mL/10000 µg/mL), heat inactivated fetal bovine serum (FBS) and trypsin 0.25%-EDTA were purchased from GIBCO Invitrogen . Hydrochloric acid (HCl) and sodium hydrogen carbonate were obtained from Merck . Sodium chloride (NaCl) was from VWR .64.PCa human immortalized cell lines  and human normal prostate epithelium immortalized cell line (PNT2) were provided by Portuguese Oncology Institute-Porto (IPO-Porto) and their respective characteristics are shown in Supplementary Table 2 in 75 cm2 culture flasks and grown to 80% confluency, before each passage. For all cell lines the same passages were used. All cell lines were routinely tested for Mycoplasma spp. contamination .All cell lines were grown in RPMI-1640 supplemented with 10% of FBS and 1% of penicillin/streptomycin, maintained at 37 °C and 5% CO2 for 48 h, together with three blanks (cellular medium without cells treated exactly as the study samples). After 48 h, the extracellular medium from flasks with cells and blanks were collected, centrifuged (2000 × g for 10 min at 4 °C), the supernatant separated from the pellet, and divided into two equal aliquots . To prevent the loss of VOCs, all collection steps were performed on ice and immediately frozen at −80 °C until analysis.The experiments were carried out during 4 consecutive passages (passages 3 to 6) with triplicates for each passage, after an adaptation stage of at least 3 passages for all cell cultures. After the cells had reached near confluence, the medium was discarded and replaced with 15 mL of fresh RMPI-1640 medium, and incubated at 37 °C and 5% CO65. All samples were analyzed at the pH of the medium culture around pH 7 (pH = 7.675 ± 0.28) and at pH 2 (pH = 2.156 ± 0.31). For acidification, a fixed volume of 5 M HCl was added. For GC-MS analysis, 2 mL of sample were placed into a 10 mL glass vial with the internal standard ) and NaCl (≈0.59 g). The extraction was performed according to the conditions previously determined65. Simultaneously, quality control (QCs) samples were also prepared using the same protocol. QCs samples were a pool of all samples and blanks used in the study. To avoid the frequent freezing and thawing, that could change the VOCs fingerprint, the QCs samples were divided in several aliquots and each aliquot was defrost prior to GC-MS analysis.Stored samples were thawed slowly at low temperatures, in order to minimize the loss of VOCs65. The carrier gas used was helium C-60  (flow of 1 mL/min) and the injector port was heated to 230 °C. The analysis was performed in Full Scan mode. The oven temperature was fixed at 40 °C for 1 min, then increasing to 250 °C (rate 5 °C/min), held for 5 min, then increasing to 300 °C (rate 5 °C/min) and held for 1 min. The transfer line temperature was 280 °C, manifold temperature was 50 °C and the trap temperature was 180 °C. The mass range was 40–350 m/z, with a scan rate of 6 scan/s. All samples were injected randomly65.The data acquisition was performed in a Scion-436 gas chromatograph coupled to a Bruker SQ (single quadrupole) equipped with a SCION SQ ion trap mass detector and a Bruker Daltonics MS workstation software version 6.8, with a Rxi-5Sil MS (30 m × 0.25 mm × 0.25 µm) column from RESTEK. A CombiPAL automatic autosampler  was used and experimental conditions were previously describedper day)66.To ensure reproducibility, QCs samples were injected at the same conditions on every 6 samples  data base spectra library and by comparing experimental Kovats index and Kovats index from literature  coming from the column, the fiber, among others were manually removed from the matrix. The obtained data was normalized for the total area of the chromatograms.Prior to statistical analysis of results, all chromatograms were pre-processed. This pre-processing includes: baseline correction, peak detection, chromatogram deconvolution and alignment, performed using the program MZminep value. In addition, the percentage of variation, uncertainty of the percentage of variation, and effect size and the standard error were calculated69. Bonferroni correction was used to adjust p-values. For all significantly  altered metabolites, receiver operating characteristic10 curves were computed and AUC (area under the curve) was also calculated .The statistical treatment includes unsupervised (PCA) and supervised analyzes (PLS-DA). All VOCs with VIP values greater than one were considered potential relevant for the separation among cell lines. For these relevant compounds, univariate analysis was performed, using Shapiro-Wilk test, to determine normality distribution of data, and unpaired Student’s t-test with Welch correction test, for normal distribution, or unpaired Mann-Whitney test, for non-normal distribution, to calculate the Y-observations, 2 components). The sensitivity and specificity of the discriminant sets of metabolites obtained from all comparison pairs at pH 7 and pH 2 were also calculated .Finally, to confirm the robustness of the PLS-DA models, all PLS-DA models were validated through permutation test (200 random permutations of Supplemetary figures and tables


Testicular germ cell tumors (TGCTs) are the most common malignancies in men between the age of 15 and 35. Although cisplatin-based chemotherapy is highly effective in advanced disease, approximately 20% of patients have an unfavorable prognosis due to primary or acquired cisplatin resistance. For these patients, new therapeutic options are urgently needed. In numerous tumor entities, combinations of monoclonal antibodies or kinase inhibitors with chemotherapy exerted promising preclinical or clinical results, which have led to new treatment concepts. This prompted us to investigate the activity of different targeted agents alone or in combination with cisplatin in a panel of TGCT cell lines.in vitro activity of kinase inhibitors targeting mTOR (RAD001), EGFR, HER2/neu, VEGFR (AEE788) and IGF-1R (AEW541) alone or in combination with cisplatin was tested in the cisplatin sensitive TGCT cell lines H12.1 and GCT72 as well as in the resistant cell lines H12.1RA, H12.1D, 1411HP and 1777NRpmet using the sulforhodamin-B-(SRB)-cytotoxicity-assay. To evaluate the activity of the kinase inhibitors, western blot analysis of the targeted receptors and their phosphorylated state was performed before and after exposure to each substance.The The different kinase inhibitors demonstrated significant cell growth inhibition in both cisplatin sensitive and resistant cell lines. The examined cell lines showed different protein expression levels of the targeted receptors. However there was no correlation between the targeted receptor expression and phosphorylation level and the antiproliferative effect of the respective agent. Furthermore, the combination of cisplatin and the kinase inhibitors exerted both additive and antagonistic effects in the studied cell lines.Our data suggest potential activity of the investigated kinase inhibitors in both cisplatin sensitive and resistant TGCT cell lines as a single agent. However, when combined with cisplatin they did not demonstrate any promising ability to overcome cisplatin resistance in TGCTs. Testicular germ cell tumors (TGCTs) are the most common malignant solid tumors in young adult men between the age of 15 and 35 with a rising incidence within the last 40 years . TGCTs aAs a heterogeneous group of tumors, GCTs account for 90–95% of all testicular malignancies and are classified into seminomatous and nonseminomatous germ cell tumors based on their histology –9. NonseDuring the last decade, the development of new therapeutic agents in the field of oncology focused on the wide spectrum of targeted drugs, both as monoclonal antibodies and so-called “small molecules”, directly inferring into the regulatory mechanisms of the cell by influencing extra- and intracellular signaling cascades. Due to this different mode of action and therefore different side effect profile compared to conventional cytotoxic agents, they are considered to be promising combination partners for the well-known chemotherapeutic substances. In various tumor entities, both the overexpression of receptors and tyrosine kinases such as EGFR, HER2/neu and IGF-1R, the inhibition of tumor cell growth by kinase inhibitors and synergistic effects of combining these inhibitors with conventional chemotherapy were shown –25.RAD001 is an inhibitor of the serin/threoninkinase mTOR  which is integrated in many intracellular signaling pathways. Although no mutations of mTOR itself are known, alterations of mTOR dependent processes have been described in various tumor entities ,27. AgenAEW541 is a selective, orally applicable inhibitor of IGF-1R (insulin-like growth factor-1 receptor), showing a 27fold higher affinity to this receptor compared to the structurally similar insulin receptor . AlthougAEE788 is a tyrosine kinase inhibitor with multiple target proteins, inhibiting EGFR , HER2/neu  and VEGFR  . OverexpThe promising preclinical or clinical results of combination therapy in numerous tumor entities prompted us to investigate the activity of different targeted agents in a panel of TGCT cell lines with different cisplatin sensitivity. The kinase inhibitors RAD001, AEW541 and AEE788 were chosen due to their broad spectrum of targeted kinases—also possibly inhibiting escape pathways, especially when combined with each other—and the significance of the inhibited kinases in interfering with apoptosis enhancing mechanisms essential to the efficacy of cisplatin. The aim was to prove a possible ability of these agents to overcome cisplatin resistance.The following nonseminomatous TGCT cell lines were used: H12.1 , H12.1D 2.The cell lines were maintained as monolayer cultures in RPMI 1640  supplemented with 10% fetal bovine serum  and 1% streptomycin/penicillin (Sigma) and cultures were grown in a 37°C humidified atmosphere containing 5% COCytotoxicity of cisplatin (Sigma), RAD001, AEW541 and AEE788 (Novartis Pharma GmbH) was determined using the sulforhodamin-B-(SRB)-cytotoxicity-assay. In growth kinetic curves previously defined cell line specific cell numbers were plated in 96-well plates overnight. Subsequently they were treated either with a vehicle control (RPMI 1640) or increasing concentrations of cisplatin  or one of the above enumerated agents  diluted in RPMI 1640 for 96 hours respectively. The SRB assay was carried out as described previously . Trichlocontrol: IC50combination) in both curves. Q = 1 corresponds to an additive, Q < 1 to an antagonistic and Q > 1 to a synergistic effect.To evaluate the effect of combining cisplatin with the kinase inhibitors, cells were seeded into 96-well plates with two plates necessary for every cell line and combination . Leaving the first row as a vehicle control in every plate, the kinase inhibitor with a concentration corresponding to its IC30 value was added to plate 1 from the second row on. Simultaneously, cisplatin in a dilution series was added starting at row 3, leaving row 2 on plate 1 with only the kinase inhibitor as a control sample to determine growth inhibition caused by the kinase inhibitor alone. Cells in plate 2 were only treated with a dilution series of cisplatin  as described above. After 96 hours of incubation, the SRB assay was carried out. To evaluate the effect of combining two agents, a hypothetical curve calculated by normalizing the dose response data points of plate 1 to the inhibitor control  was compared to the dose-response curve for cisplatin alone (plate 2). Synergy can be assumed if the hypothetical curve runs below the dose-response curve for cisplatin and antagonism is indicated if the hypothetical curve runs above the cisplatin curve. In case of additivity both curves are superimposed. The effect of the combination treatment was assessed using the quotient Q of the IC50 values , AEW541 (IGF-1R) and AEE788  was examined in all cell lines using western blotting . The TGCIGF-1R was found in all cell lines with the strongest expression in H12.1, H12.1RA and H12.1D, therefore showing no clear correlation between receptor expression and sensitivity to AEW541. The comparison of the expression was complicated by the formation of multiple bands. The phosphorylated form of IGF-1R could not be detected sufficiently in our western blot analyses. This was in contrast to the good response of all cell lines to treatment with AEW541.Both mTOR and its activated form, investigated using two antibodies against both phosphorylation sites S2448 und S2481, were detected consistently in all examined cell lines. This corresponds at least in part to the observed activity of RAD001 in all cell lines at a very low concentration.Due to the growth inhibition caused by RAD001 in all cell lines and the central role of mTOR in intracellular signaling pathways regulating cell growth downstream of IGF-1R, EGFR, HER2/neu and VEGFR, we then concentrated on the effects of the tyrosine inhibitors in mTOR expression and phosphorylation. Treatment with RAD001 did not influence the expression of mTOR but led to an inhibition of phosphorylation on both phosphorylation sites S2481 und S2448 in all investigated cell lines. Interestingly, treatment with AEE788 also led to a decreased activation of mTOR in some cell lines .Cisplatin resistance remains a major problem in the treatment of patients with nonseminomatous germ cell tumors. At this point, neither the mechanisms underlying this resistance nor promising therapeutic options have been identified. The introduction of targeted drugs such as monoclonal antibodies and kinase inhibitors has led to encouraging results for many other tumor entities. Due to their differing mechanism of action and side effect profile, a combination with traditional chemotherapeutic agents is both feasible and reasonable, showing additive and synergistic effects in both preclinical and clinical studies ,25. TherThe first goal was to characterize the TGCT cell lines according to their sensitivity towards cisplatin and the examined kinase inhibitors. The IC values acquired for cisplatin were consistent with previously published data . H12.1RAin vitro tests in other malignancies, the investigated TGCT cell lines showed a relative resistance towards RAD001. While the most sensitive TGCT cell line H12.1 exhibited an IC50 of 170 nM and the H12.1D did not reach IC50, in a trial on 48 different tumor cell lines 71% of the treated cell lines had an IC50 < 0,1 μM and in sensitive renal cell cancer cell lines an IC50 of 0,78±0,23 nM has been described [Cytotoxicity assays for RAD001 showed a similar sensitivity of all cell lines with dose-response curves showing a plateau spanning a concentration range which included the IC50 starting around a concentration of 0,001 μM. This characteristic curve progression has been described for many other tumor entities [escribed . Yet, asThe overall small differences in sensitivity towards RAD001 in the investigated cell lines are reflected in the equal expression and activation of mTOR. Furthermore, there was neither a difference in sensitivity nor in expression and activation of mTOR between cisplatin sensitive and resistant cell lines. While the inhibition of mTOR expression and phosphorylation at S2448 by RAD001 has been proven in multiple cancer cell lines –54, an iin vitro [et al. [pathway might be a promising target to overcome cisplatin resistance. In their studies of lung cancer cells, Beuvink et al. could show that RAD001 inhibits the cisplatin induced induction of p21 by p53. In case of a p53-wildtype both the p21-induced cell cycle arrest and the inhibition of pro-apoptotic caspases  by p21 are averted [Another goal of our study was to examine the influence of kinase inhibitors on cisplatin sensitivity, especially in cisplatin resistant TGCT cell lines. The synergistic effect of combining RAD001 and cisplatin has been shown in many different tumor entities, both in vitro ,57 and iin vitro . Further [et al. . In our  averted . Due to  averted .in vitro analyses, e.g. in Ewing’s sarcoma, multiple myeloma and gastrointestinal stromal tumors and we could not find any data concerning IGR-1R expression in TGCTs [Although at this point no IGF-1R inhibiting substance has been approved for clinical use due to severe side effects, we also investigated the IGF-1R inhibitor AEW541 since its cytotoxic activity has been proven in several in TGCTs ,61,62. Iin TGCTs ,64.et al. and Moser et al. could only detect both the phosphorylated form of IGF-1R and its inhibition by AEW541 after stimulation with IGF-1 [et al. could show that while expression of IGF-1R remains uninfluenced by AEW541, phosphorylation of the receptor is inhibited. They could further show that a change in serum concentration (10% vs. 2%) led to an increased activity of AEW541 in the lower concentrated serum. The authors attributed this finding to the growth factors contained in fetal bovine serum, decreasing the efficacy of AEW541 by stimulating antiapoptotic signaling pathways [The lack of detection of the phosphorylated IGF-1R in our western blot analyses despite expression of the receptor and efficacy of AEW541 may have had methodological reasons. In their studies on gastrointestinal stromal tumors and pancreatic cancer cell lines, both Tarn th IGF-1 ,65. Besipathways .et al. described a relative resistance towards AEW541 in breast cancer cell lines with increased expression and phosphorylation of IGF-1R [et al. could demonstrate that in their investigated breast cancer cell lines the expression of IRS-1 (insulin receptor substrate 1) correlates with the response to AEW541, identifying IRS-1 expression of a marker for AEW541 efficacy independent of IGF-1R expression [IGF-1R = 0,09 μM, IC50IR = 2,30 μM), part of the cytotoxic potential may also be caused by inhibition of the IR [Concerning the correlation between expression and phosphorylation of IGF-1R and the efficacy of AEW541 contradictory data have been published. While Browne f IGF-1R , this cof IGF-1R . Yet Mukpression . This mif the IR ,69.in vitro studies, different IGF-1R inhibiting substances have led to additive and synergistic effects when combined with cisplatin, e.g. the IGF-1R antibodies MK-0646 in endometrial carcinoma and R1507 in small cell lung cancer [In g cancer ,71. The g cancer ,63. In oin vivo studies in mice using Lapatinib [et al. found IC50-values between 0,12 μM and 0,96 μM in squamous cell carcinoma [The efficacy of an inhibition of EGFR and HER2/neu in TGCTs has been demonstrated in apatinib . The duaarcinoma .et al., showing that HER2/neu expression was limited to the teratoma and choriocarcinoma components of mixed TGCTs [et al. could detect expression of EGFR and HER2/neu in all components of TGCTs independent of their differentiation [There is conflicting data concerning the expression and activity of EGFR, HER2/neu and VEGFR in TGCTs with no clear proof of an overexpression of any of the investigated receptors ,39. The ed TGCTs . In contntiation .et al., showing that an inhibition of EGFR and HER2/neu by Lapatinib did not influence the efficacy of cisplatin in TGCTs, regardless of cisplatin sensitivity [et al. on non small cell lung cancer. They could prove that an inhibition of the EGFR pathway by Gefitinib also inhibits caspase independent cell death, which can be induced by cisplatin. Since they also detected an activation of EGFR and its downstream targets AKT and ERK by cisplatin, they postulated that cisplatin might induce caspase independent cell death via EGFR associated pathways—a mechanism that is repressed by EGFR inhibition [Because numerous studies have shown increased cisplatin sensitivity after inhibition of EGFR, HER2/neu or VEGFR in different tumor entities, we performed combination assays despite the relative resistance of our investigated cell lines towards AEE788 ,75. Evensitivity . An explhibition .pathway, since an inhibition of IGF-1R by AEW541 leads to decreased phosphorylation of AKT [pathway. Combining different kinase inhibitors could be a strategy to overcome chemotherapy resistance but it may not only lead to an increased efficacy but also to severe side effects. For example, a simultaneous application of RAD001 and AEE788 in glioblastoma patients was reported to cause significant thrombocytopenia [Finally, we also tested the effects of a combination of two kinase inhibitors, thereby causing a vertical blockade of the involved signaling pathways with mTOR being an important downstream target of the inhibited growth factor receptors IGF-1R, EGFR, HER2/neu and VEGFR. Furthermore, a combined blockade of multiple kinases at different levels within the signaling pathways eliminates possible compensating mechanisms such as an increased activation of upstream targets. In vitro, a simultaneous application of RAD001 and AEW541 has shown additive and synergistic effects in both multiple myeloma and hepatocellular carcinoma ,81. In on of AKT ,82. An an of AKT –85. Addiytopenia .In sum, our data show that the investigated kinase inhibitors RAD001, AEW541 and AEE788 are effective in TGCT cell lines independent of their cisplatin sensitivity. However, when combined with cisplatin they do not demonstrate promising ability to overcome cisplatin resistance in these cell lines. No correlation between the receptor expression and phosphorylation and the effectiveness of the agents was observed. Therefore, these targeted drugs do not show potential in the treatment of cisplatin refractory nonseminomatous germ cell tumors.


MPNST), a rare soft tissue cancer associated with loss of the tumor suppressor neurofibromin (NF1), have poor prognosis and typically respond poorly to adjuvant therapy. We evaluated the effect of 299 clinical and investigational compounds on seven MPNST cell lines, two primary cultures of human Schwann cells, and five normal bone marrow aspirates, to identify potent drugs for MPNST treatment with few side effects. Top hits included Polo‐like kinase 1 (PLK1) inhibitors (volasertib and BI2536) and the fluoronucleoside gemcitabine, which were validated in orthogonal assays measuring viability, cytotoxicity, and apoptosis. DNA copy number, gene expression, and protein expression were determined for the cell lines to assess pharmacogenomic relationships. MPNST cells were more sensitive to BI2536 and gemcitabine compared to a reference set of 94 cancer cell lines. PLK1,RRM1, and RRM2 mRNA levels were increased in MPNST compared to benign neurofibroma tissue, and the protein level of PLK1 was increased in the MPNST cell lines compared to normal Schwann cells, indicating an increased dependence on these drug targets in malignant cells. Furthermore, we observed an association between increased mRNA expression of PLK1,RRM1, and RRM2 in patient samples and worse disease outcome, suggesting a selective benefit from inhibition of these genes in the most aggressive tumors.Patients with malignant peripheral nerve sheath tumor ( AKTAKTserine/threonine kinaseBRAFB‐Raf proto‐oncogene, serine/threonine kinaseCIconfidence intervalCTG®, viability assayCellTiter‐GloCTX®, cytotoxicity assayCellTox‐GreenDMEMDulbecco's modified Eagle mediumDMSOdimethyl sulfoxideDSRTdrug sensitivity and resistance testingDSSdrug sensitivity scoreERKmitogen‐activated protein kinaseHRhazard ratioHSChuman Schwann cells HS‐PSSMPNST cell lineHS‐Sch‐2MPNST cell line50IChalf maximal inhibitory concentrationKRASV‐Ki‐ras2 Kirsten rat sarcoma viral oncogene homologMEKmitogen‐activated protein kinase kinaseMIMMendelian inheritance in ManMPNSTmalignant peripheral nerve sheath tumormTORmammalian target of rapamycinNAENEDD8‐activating enzymeNEDD8neural precursor cell expressed, developmentally down‐regulated 8NF1neurofibromatosis type 1 (disease)NF1neurofibromin 1 (gene/protein)PI3Kphosphatidylinositol‐4,5‐bisphosphate 3‐kinasePLK1polo‐like kinase 1QCquality controlRAFsee BRAFRASsee KRASRPPAreverse‐phase protein arrayRRM1ribonucleotide reductase catalytic subunit M1RRM2ribonucleotide reductase catalytic subunit M2S1507‐2MPNST cell lineS462MPNST cell lineβTrCPSCFSkp, Cullin, F‐box‐containing complex, E3 ligaseSCGFSchwann cell growth supplementSCMSchwann cell mediumSSMDstrictly standardized mean differenceST8814MPNST cell lineSTRshort tandem repeatsSTS26TMPNST cell lineTOP2Atopoisomerase (DNA) II alphaTP53tumor suppressor protein p53YST‐1MPNST cell lineet al., et al., Malignant peripheral nerve sheath tumors (MPNST) are rare and aggressive soft tissue cancers that arise from cells of neuroectodermal origin in the peripheral nervous system. MPNST often strikes young adults and adolescents, and nearly half of all cases are associated with the genetic syndrome neurofibromatosis type 1 . The median age for the NF1‐associated cases is around 25 years, while the sporadic cases have a median in the forties  termed HSC1 and HSC2 that were isolated from human spinal nerves  and four MPNST cell lines, STS26T . The HSC were maintained in Schwann cell medium  supplemented with Schwann cell growth supplement  according to the suppliers’ recommendations.All cancer cell lines were maintained in DMEM‐F12 medium supplemented with 10% fetal bovine serum, 2 mwww.expasy.org/cellosaurus), and for STS26T and ST8814, identical STR profiles were obtained by N. Ratner . All the cell lines were tested and found negative for mycoplasma contamination using the MycoAlert detection kit .The identity of the cell lines was validated by genotyping of the isolated DNA Table  accordinet al., Fresh‐frozen tumor material was available from 30 MPNSTs (17 NF1‐associated and 13 non‐NF1 cases) and eight benign neurofibromas  from Oslo University Hospital, Oslo, Norway, and Skåne University Hospital, Lund Sweden, as previously described , ovarian (n = 30), and acute myeloid leukemia (n = 28) . The preprinted plates were kept in pressurized StoragePods  under inert nitrogen gas until needed. Five microlitre of CellTox‐Green (CTX) Cytotoxicity Assay Reagent , 1 : 200 dilution in growth media, was added to each 384‐well plate prior to seeding of cells to achieve a final concentration of 1 : 1000. The CTX assay is based on quantification of fluorescence‐labeled DNA released from disrupted cells. Plates were subsequently centrifuged briefly and put on an orbital shaker for 10 min. Twenty microliters of single‐cell suspension (750–1000 cells) was transferred to each well using a Multidrop Combi Reagent Dispenser (Thermo Fisher Scientific). Proliferation rates and growth patterns of the cell lines were evaluated prior to the experiments in order to determine the optimal cell seeding density to assure logarithmic growth throughout the 72‐h incubation period. The plates were incubated in a humidified environment at 37 °C and 5% CO2, and after 72 h, cell cytotoxicity and cell viability (assessed using CellTiter‐Glo (CTG) Luminescent Cell Viability Assay, Promega) were measured according to the manufacturer's instructions with a PHERAstar FS microplate reader . The CTG assay generates luminescence proportional to the amount of ATP that is extracted from living cells in the culture. The data were normalized to negative control (0.1% DMSO) and positive control wells . Quality control (QC) metrics, Z′, and strictly standardized mean difference  for each plate were calculated as described  and the CTX assay (DSSCTX) by fitting of the dose–response curves on the basis of a four‐parameter logistic fit function defined by the top and bottom asymptote, the slope, and the inflection point (IC50). In the curve fitting, the bottom asymptote of the curve was fixed to 0% inhibition (=100% viability), whereas the top asymptote was allowed to float above 10% inhibition , and the slope was allowed to float between 0 and 2.5 . The Triplex assay allows for luminescence measurements of caspase 3/7 activity as a measure of apoptosis levels in addition to viability and cytotoxicity in response to drug treatment. Cells treated with 0.1% DMSO were used as negative controls, 10 μm staurosporin was used as positive control for apoptosis, while 100 μm benzethonium chloride was used as positive control for viability and cytotoxicity.Drug sensitivity and resistance testing (DSRT) was performed as described earlier (Pemovska Z′) for all cell lines in the viability assay was 0.72 ± 0.04 and average SSMD was 14.1 ± 2.4 for each drug plate  and to a lesser extent between MPNST and bone marrow (Pearson's correlation r = 0.752)  at the MD Anderson RPPA core facility  in the four MPNST cell lines, S1507‐2, S462, ST8814, and STS26T, and normal HSC1 according to the published protocol   as described in the Affymetrix Cytogenetics Copy Number Assay User Guide (P/N 702607 Rev. 2) (see Appendix TP53 (exon 2–11) and BRAF (exon 15) were sequenced using DNA extracts of the four MPNST cell lines S1507‐2, S462, ST8814, and STS26T by Sanger sequencing using in‐laboratory‐established protocols .Association between gene expression and disease‐specific survival was analyzed using Cox proportional hazards regression modeling with Wald test to provide univariate hazard ratios (HR) and confidence intervals (CI) and visualized by Kaplan–Meier plots. Comparison of gene expression differences between MPNST cell lines and other cell lines, and between MPNST patient samples and neurofibromas, was assessed by two‐tailed Student's Four MPNST cell lines S1507‐2, S462, ST8814, and STS26T and two HSC primary cultures were subjected to high‐throughput DSRT with 309 emerging and clinical oncology compounds. Three additional MPNST cell lines HS‐PSS, HS‐Sch‐2, and YST‐1 were screened using an updated compound library of 527 compounds (with 303 compounds overlapping between both libraries). Data for 299 of these drugs were also available from normal bone marrow aspirates from healthy donors. As drug sensitivity readout, we used two chemically different assays measuring cell viability  in our assay, and they also showed differential response in MPNST cells as compared to bone marrow cells (missing data for ganetespib and carboplatin in bone marrow)  Fig. B. Only tow) Fig. . StrikinCTG values were filtered according to MPNST specificity and off‐target toxicity , and of these, 49 drugs showed differential sensitivity in MPNST cells with five or more DSSCTG units higher in MPNST cells as compared to normal bone marrow. Nine of these drugs also showed higher selectivity for MPNST cells as compared to HSC (ΔDSSCTG(MPNST vs. HSC) ≥ 5). These included the polo‐like kinase 1 (PLK1) inhibitor BI2536, three tubulin/kinesin inhibitors , two nucleoside analogs (floxuridine and thioguanine), two folate analogs (methotrexate and pemetrexed), as well as one proteasome‐ubiquitin inhibitor [NEDD8‐activating enzyme (NAE) inhibitor pevonedistat]. Four of these nine compounds were selected for validation: BI2536 was selected as a targeted kinase inhibitor, floxuridine was selected due to its association with thymidine kinase 1 (TK1), previously identified as a prognostic biomarker  in the MPNST cells, while methotrexate, pemetrexed, and floxuridine showed limited or no cytotoxicity in the CTX assay  . All authors analyzed and interpreted the data. MK and JB drafted the manuscript. All authors reviewed and revised the manuscript. OK and RAL provided administrative, technical, or material support. RAL supervised the study.Fig. S1. Overview of drugs tested in MPNST patients.Click here for additional data file.Fig. S2. Correlation between CellTiter‐Glo (CTG) viability experiments.Click here for additional data file.Fig. S3. Drug cytotoxicity response profiles of MPNST cell lines and normal HSCs.Click here for additional data file.Fig. S4. Dose–response curves.Click here for additional data file.Fig. S5. Gene expression of drug targets in cell lines and patient tumors, and association to patient survival.Click here for additional data file.Fig. S6. Correlation with public dataset.Click here for additional data file.Table S1. List of drugs in current or previous clinical testing against sarcoma, including MPNST .Click here for additional data file.Table S2. STR profiles of MPNST cell lines*.Table S3. List of tested compounds.Table S4. (A) QC‐scores from viability assay (CTG). (B) QC‐scores from cytotoxicity assay (CTX).Table S5. Cell viability assay (CTG) data.Table S6. Cytotoxicity assay (CTX) data.Table S7. Protein expression data from reverse phase protein lysate microarray (RPPA)a.Table S8. Primer sequences.Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.Appendix S1. Supplementary methods.Click here for additional data file. Click here for additional data file.


Immunohistochemical detection of these markers was performed on formalin-fixed paraffin-embedded tissue of the matching primary tumors. The results revealed substantial divergences between the primary tumor tissues and matching cell lines in the immunoreactivity pattern of medulloblastoma-subgroup-specific protein markers. Regardless of the molecular subgroup of the primary tumor, all six patient-derived medulloblastoma cell lines exhibited a uniform phenotype: immunofluorescence showed the nuclear localization of YAP1, accompanied by strong cytoplasmic positivity for β-catenin and filamin A, as well as weak positivity for GAB1. The same immunoreactivity pattern was also found in both DAOY and D283 Med reference medulloblastoma cell lines. Therefore, we can conclude that various medulloblastoma cell lines tend to exhibit the same characteristics of protein marker expression under standard in vitro conditions. Such a finding emphasizes the importance of the analyses of primary tumors in clinically oriented medulloblastoma research and the urgent need to develop in vitro models of improved clinical relevance, such as 3D cultures and organotypic slice cultures.Medulloblastoma comprises four main subgroups  originally defined by transcriptional profiling. In primary medulloblastoma tissues, these groups are thought to be distinguishable using the immunohistochemical detection of β-catenin, filamin A, GAB1 and YAP1 protein markers. To investigate the utility of these markers for  Approximately 10% of all pediatric cancer-related deaths are caused by medulloblastoma, an embryonal neuroectodermal tumor of the cerebellum ,3. ComprFrom a clinical point of view, the amount of genomic and molecular data gained over the last few years encourages optimism that improved risk stratification based on biological prognostic markers and new molecular targets will improve the outcomes in medulloblastoma. However, the transition from bench to bedside and from knowledge to its applications is hampered by the lack of robust and reliable tests that could be easily used in routine practice.The highly complex technologies that generated the progress of "medulloblastomics" are not widely used for diagnostic purposes at this moment. Diagnostic tests for routine subgrouping will be probably based on methylation profiling employing formalin-fixed tissues ,12. For The basic biological models for medulloblastoma translational research are genetically engineered mouse models and cell culture . Howeverin vitro conditions might differ.Here, we employed a unique panel of six medulloblastoma cell lines derived in our laboratory from the primary tumor tissues of known molecular subgroups to determine whether the immunodetection pattern of markers suggested for diagnostic IHC methods is also suitable for group assignment in cell lines. The expression of β-catenin, filamin A, GAB1 and YAP1 was evaluated in these cell lines and in formalin-fixed paraffin-embedded (FFPE) tissue samples of the corresponding tumors from which these cell lines were derived. We hypothesized that the relevant phenotypes of medulloblastoma cells under Six tumor samples were included in this study. These samples were taken from the patients  surgically treated for medulloblastoma. The tumor tissue was subdivided into several parts. Fresh tumor samples were processed for primary cultures as described below, and FFPE samples were used for immunohistochemical (IHC) analyses. A description of the cohort of patients included in this study is provided in Cell lines were derived from the respective tumor tissues according to a previously described protocol . All of 2.The DAOY cell line was maintained in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal calf serum (FCS), 1% non-essential amino acids, 2 mM glutamine, and antibiotics: 100 IU/ml penicillin and 100 μg/ml streptomycin . All of the other cell lines were maintained in DMEM supplemented with 20% FCS, 2 mM glutamine, and antibiotics as specified above. All of the cell lines were cultivated under standard conditions at 37°C in an atmosphere of 95% air and 5% CO2O2 in methanol for 10 min, the samples were then rinsed with distilled water, and antigen retrieval was performed (incubation in citrate buffer of pH 6.0 at 98°C for 20 min and then cooling for 20 min and washing in PBS for 3× 5 min) followed by incubation with primary antibodies. The incubations were performed in a wet chamber for 1 h at room temperature followed by rinsing with PBS for 3× 5 min. The EnVision+System streptavidin-biotin peroxidase detection system  was used in accordance with the manufacturer's instructions in the wet chamber at room temperature for 45 min, followed by rinsing with PBS and then visualization using 3,3'diaminobenzidine as a substrate . Nuclei were counterstained using Gill's hematoxylin for 1 min followed by bluing in water for 2–3 min for optimal results. Following dehydration in a series of up-concentrated ethanol baths and clearing in xylene, the preparations were mounted onto Entelan™ . All of the antibodies used in this protocol are described in Representative sections from archival FFPE tumor samples were analyzed by immunohistochemistry (IHC). Formalin-fixed, paraffin-embedded 4-μm-thick sections from both tissue microarray blocks were cut, deparaffinized with pure xylene for 3× 5 min, washed in 96% alcohol for 3× 5 min and finally rinsed with distilled water. Endogenous peroxidase was inactivated by 3% HThe cell suspensions were seeded onto glass coverslips and were grown under standard conditions for 24 h. The cells were then washed in phosphate-buffered saline (PBS), fixed with 3% para-formaldehyde (Sigma) in PBS at room temperature for 20 min, and permeabilized with 0.2% Triton X-100  in PBS at room temperature for 1 min. The cells were subsequently washed in PBS and incubated with 2% bovine serum albumin (PAA) for 10 min to block nonspecific binding of the secondary antibodies. In the next step, the cells were treated with primary antibodies at 37°C for 1 h and then were washed three times in PBS. The corresponding secondary antibody conjugated with Alexa Fluor 488 was applied under the same conditions. All of the antibodies used in this protocol are described in Whole-cell extracts were loaded onto polyacrylamide (Sigma) gels– 6% for the detection of Filamin A and 8% for others–electrophoresed, and blotted onto polyvinylidene difluoride membranes . The membranes were blocked with 5% nonfat milk in PBS with 0.1% Tween 20 (Sigma) and then were incubated primary in blocking solution at 4°C overnight. After rinsing with PBS-T, the membranes were incubated with the corresponding secondary antibody at room temperature for 60 min. All of the antibodies used in this protocol are also described in IHC detection of markers using anti-β-catenin, anti-filamin A, anti-GAB1 and anti-YAP1 antibodies was employed in FFPE tissue samples , Table 4After unblinding the molecular subgroup , assignmTwo methods–immunofluorescence and immunoblotting–were used for the detection of four molecular markers  in patient-derived cell lines to compare the results with those obtained from the corresponding tumor samples on the protein level. Furthermore, two reference medulloblastoma cell lines, DAOY and D283 Med, were included in these analyses.The results from immunofluorescence detection are summarized in In all of the examined cell lines, the almost uniform expression of β-catenin, filamin A and YAP1 was verified by immunoblotting as summarized in in vitro conditions regardless of the original medulloblastoma subgroup classification of the respective tumor tissue. Furthermore, these results fully corresponded to the observed phenotypes of both reference medulloblastoma cell lines, DAOY and D283 Med.Both immunofluorescence and immunoblotting clearly showed that all six patient-derived cell lines acquired the uniform phenotype under in vitro studies have presumed–despite in vitro selection and heterogeneity–that a cancer cell line preserves the important biological features of cancer cells in the original tumor from which the cell line was derived. This issue was recently discussed for various human solid tumors, including ovarian carcinoma [Although cell lines have been successfully used in cancer research for decades and the limitations of such biological models are widely recognized and accepted , many inarcinoma ,24, breaarcinoma , lung caarcinoma  or thyroarcinoma .in vitro conditions. To compare the phenotype of medulloblastoma cells both in situ and after transfer into cell culture, six samples of medulloblastoma tissues together with six corresponding cell lines derived in our laboratory from these tumors were used for this study. DAOY and D283 Med established medulloblastoma cell lines served as reference cell lines for these experiments. To determine the medulloblastoma subgroups in tumor tissue samples, the IHC detection of the four protein markers mentioned above was performed and the achieved results were compared to those obtained by gene expression profiling on the same tumor samples [In this study, we investigated the possibilities of using the intracellular protein markers GAB1, β-catenin, filamin A, and YAP1 for the determination of defined molecular subgroups of medulloblastoma under  samples . In cellOur results revealed substantial divergence in the immunoreactivity pattern of the four medulloblastoma-subgroup-specific protein markers between the primary tumor tissue and matching cell line. Regardless of the molecular subgroup of the primary tumor, all six patient-derived medulloblastoma cell lines exhibited a uniform phenotype: immunofluorescence showed the nuclear localization of YAP1 accompanied by strong cytoplasmic positivity for β-catenin and filamin A, as well as weak positivity for GAB1 , Fig 2. These discrepancies in the expression pattern of protein markers between the primary tumor tissues and derived cell lines can be explained from several viewpoints. One of them is the lower sensitivity of the IHC method used for the detection of these markers in tumor tissue than that with immunofluorescence and immunoblotting used in the analysis of the cell lines. The other explanation is based on the specific functions of the marker proteins within the cell and on the influence of the cell culture conditions on these functions.in vitro conditions. Filamins are large actin-binding proteins that stabilize three-dimensional actin network and link the actin filaments to cellular membranes [in vitro conditions, an increase in filamin A expression is not surprising in such a situation and fully corresponds to the previously published studies on the role of filamin A in cultured cells [The cultivation of cells in a monolayer has an apparently strong impact on the cell morphology, organization of the cytoskeleton and on the proteins participating in the formation of cell-cell and cell-substrate adhesions . Such chembranes , and filembranes . Becauseed cells –33. Veryed cells .in vitro conditions may be associated with changed cell morphology and polarity. Nevertheless, the increased levels of Gab1 protein were associated with decreased oncogenic potential and with phenotypic changes accompanying monocytic differentiation of murine myeloid cells: increased attachment to the substrate and filopodia formation [Similarly, members of the Gab family interact with specific membrane lipids and with signaling proteins encompassing the SH2 and SH3 domains. They typically act as downstream effectors of receptor tyrosine kinase-triggered signal transduction that controls many processes, including the regulation of cell polarity , and a dormation .in vivo may induce the overexpression of proteins involved in the response to oxidative stress. Although YAP1 is known primarily as the oncogene involved in the Hippo tumor suppressor pathway, the induction of YAP1 expression caused by an increase in the intracellular ROS concentration was described in glioblastoma cells [Another mechanism is probably responsible for the apparent increase in the expression of YAP1 protein: a higher concentration of oxygen in cell culture than in the tumor bulk ma cells .in vitro conditions, as defined by the described protein markers [In all of the analyzed cell lines, we found no discrepancy in the expression or localization of β-catenin. β-catenin is known as one of the proteins participating in adherens junctions, and it also plays a key role in the WNT signaling pathway. Thus, the nuclear accumulation of β-catenin is routinely used as a biomarker of the WNT pathway and is typical for the WNT medulloblastoma subgroup . This co markers .in vitro conditions.A uniform pattern of protein marker expression was observed in all patient-derived cell lines included in this study as well as in two reference cell lines, although the respective primary tumors showed diverse expression patterns belonging to different medulloblastoma subgroups. This phenotype uniformity of our patient-derived cell lines with the DAOY and D283 Med reference cell lines is another very interesting aspect of our results. Because both of these cell lines were previously analyzed using expression profiling, their transcriptomic molecular classification is available but is partly controversial: the DAOY cell line was described consistently as belonging to the SHH group, and the results for the D283 Med cell line were contradictory and varied between groups 3 and 4 ,38,39. NThis finding is in accordance with other recently published studies: substantial discrepancies between primary tumors and related cancer cell lines have already been described using genomic ,41, tranin vitro conditions. Because clinically relevant medulloblastoma subgroups are defined exactly based on transcriptional profiles, such a finding emphasizes the importance of analyzing primary tumors rather than cell line model systems in clinically oriented medulloblastoma research. Moreover, the urgent need for the development of in vitro models of improved clinical relevance, such as 3D cultures and organotypic slice cultures, is also evident.Achieved results clearly showed that various medulloblastoma cell lines tend to exhibit the same characteristics of protein marker expression under standard S1 FigExpression of β-catenin in liver tissue, expression of filamin A and YAP1 in endothelial cells within the medulloblastoma tissue, and expression of GAB1 in breast carcinoma tissue as detected by immunohistochemistry. Bars, 100 μm.(TIF)Click here for additional data file.S2 FigBars, 50 μm.(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.


ComBat software and re-processed the entire gene expression and SNP chip data. Cell line names and clinical information were standardized using Cellosaurus from ExPASy. Our user interface supports cell line search, gene search, browsing for specific molecular characteristics and complex queries-based on Boolean logic rules. We also implemented many interactive features and user-friendly visualizations. Providing molecular characteristics and clinical information, we believe that GEMiCCL would be a valuable resource for biomedical research for functional or screening studies.Cancer cell lines are essential components for biomedical research. However, proper choice of cell lines for experimental purposes is often difficult because genotype and/or expression data are missing or scattered in diverse resources. Here, we report Gene Expression and Mutations in Cancer Cell Lines (GEMiCCL), an online database of human cancer cell lines that provides genotype and expression information. We have collected mutation, gene expression and copy number variation (CNV) data from three representative databases on cell lines—Cancer Cell Line Encyclopedia , Catalogue of Somatic Mutations in Cancer and NCI60. In total, GEMiCCL includes 1406 cell lines from 185 cancer types and 29 tissues. Gene expression, mutation and CNV information are available for 1304, 1334 and 1365 cell lines, respectively. We removed batch effects due to different microarray platforms using the Database URL: GEMiCCL is available at https://www.kobic.kr/GEMICCL/ Thus, cell lines are firmly established as an essential component of drug development.Cell lines are widely used in a variety of biomedical studies because of their unlimited supply, homogeneous characteristics, convenience and cost-effectiveness. Thousands of cell lines are commercially available from public and private resources such as ATCC and Sigma-Aldrich. They are routinely used for various purposes ranging from testing compound toxicity to production of eukaryotic proteins Many immortalized cell lines originate from cancer cells. Both clinical and molecular characteristics are important in selecting cell lines suitable for a study design. Several attempts have been made to gather clinical information on frequently used cell lines , 2. CellIn line with the increasing importance of molecular characteristics of cell lines, several groups have performed large-scale genomic projects on cancer cell lines. Two notable examples are the Cancer Cell Line Encyclopedia (CCLE) project  by BroadPrior knowledge of molecular and clinical characteristics including genetic abnormalities would allow more informed choice of cell lines in biological experiments and drug testing. Moreover, molecular information may be helpful in authentication and quality control of cell lines . Therefoftp://ftp.expasy.org/databases/cellosaurus) database , CCLE  and NCI60 .Since several databases did not provide raw sequencing data, we collected mutation information as provided from the original databases—COSMIC , CCLE  and NCI60 . We used the Affy R package  and NCI60  websites. COSMIC only provided processed results; thus, we downloaded the final results from the website (cell_lines_copy_number.csv file at http://cancer.sanger.ac.uk/cell_lines/download). Raw CEL files were processed using the PennCNV-Affy package  in a tabular format, our web interface supports elaborate searches/browsing with molecular output characteristics visualized in intuitive graphics. For example, smart searches like ‘melanoma cell lines with BRAF mutation’ or ‘lung cancer cell lines with high EGFR expression’ can be easily achieved by Boolean logic queries.Each resource has varying platforms (e.g. microarray versions) and analysis methods. This leads to source-dependent batch effects, making direct comparison across different resources almost impossible. Whenever possible, we analyzed the raw data ourselves using our own pipelines and applied a statistical method to remove batch effects as indicated in the Materials and methods section.Affy R package). ComBat program. Note that CCLE and NCI60 data showed higher correlation probably because they were obtained from the same microarray version (hg U133 plus 2.0). Clustering of cell lines based on gene expression data yielded the same conclusion. As shown in ComBat program. Thus, direct comparison was possible only after removing batch effects.Gene expression microarray data were available from all three resources. They were processed using a standard protocol for Affymetrix chips . CNV plots were produced for the NCI-H460 cell line, which is frequently used in lung cancer research . Visual GEMiCCL supports cell line search, gene search, browsing with histological characteristics and complex queries based on the Boolean logic rules. Cell line search shows summary information, copy number, gene expression and mutation data displayed in separate tab pages.The copy number tab shows CNV plots from available data sources with a table of CNV values as shown in The expression tab shows a table of gene expression values again with sorting and searching functions . Here thThe mutation tab displays a collection of mutations in the selected cell line. Exome sequencing usually identifies hundreds or thousands of mutations, making it difficult to see the gene of interest. Again, users may use the built-in search function of the table to locate specific genes . In an eIt is often the case that users have a gene of particular interest. Gene searches rather than cell line searches show CNV, expression and mutation information for the user-specified gene across many cell lines, in a similar fashion to the cell line search. The role of cell lines and genes is just interchanged in the output pages.We also support a browsing function that combines tissue, histology and cancer types in a sequential order. Lastly, we support a smart search scheme based on the combination of Boolean logic rules. Users may enter query terms in any combination of histological characteristics and gene names. We also support diverse types of query tags including CNV type, mutation, expression and DB sources. A search of cell lines from lung adenocarcinoma with BRAF mutation can be easily accomplished using this Boolean logic search. A query builder was implemented to help users compose complex queries in accordance with our query tags.Clinical and molecular characteristics are two most important factors in selecting cancer cell lines for functional or screening experiments. GEMiCCL is the most comprehensive database of cancer cell lines to date, covering gene expression, mutation and copy number information, integrated carefully from three representative resources of CCLE, COSMIC and NCI60. It also supports users to compare such properties from different resources objectively without batch effect. Furthermore, experimental scientists should be able to look up molecular characteristics easily with our user-friendly web interface and interactive features.


Malignant transformation is often accompanied by significant metabolic changes. To identify drivers underlying these changes, we calculated metabolic flux states for the NCI60 cell line collection and correlated the variance between metabolic states of these lines with their other properties. The analysis revealed a remarkably consistent structure underlying high flux metabolism. The three primary uptake pathways, glucose, glutamine and serine, are each characterized by three features: (1) metabolite uptake sufficient for the stoichiometric requirement to sustain observed growth, (2) overflow metabolism, which scales with excess nutrient uptake over the basal growth requirement, and (3) redox production, which also scales with nutrient uptake but greatly exceeds the requirement for growth. We discovered that resistance to chemotherapeutic drugs in these lines broadly correlates with the amount of glucose uptake. These results support an interpretation of the Warburg effect and glutamine addiction as features of a growth state that provides resistance to metabolic stress through excess redox and energy production. Furthermore, overflow metabolism observed may indicate that mitochondrial catabolic capacity is a key constraint setting an upper limit on the rate of cofactor production possible. These results provide a greater context within which the metabolic alterations in cancer can be understood. Over the past decade there has been a revival of metabolic research in oncology345613The NCI60 cell line collection consists of 60 cancer cell lines that have been extensively used as a model to study characteristics of cancer cells over the past quarter of a century101112131920First, we calculated metabolic reaction fluxes for each cell line in the NCI60 collection using FBA on a core cancer metabolic model constrained by measured cell line-specific uptake and secretion rates for 23 metabolites2413C tracing data  (The flux states computed with the core model showed an excellent correlation with  > 0.95)  as well  > 0.95) . As the 2, suggesting that the second component can be interpreted as a total substrate intake component. Although there is no clear separation between distinct metabolic states apparent, examination of the states by tissue type shows some trends. For example, the leukemia-derived lines in the NCI60 tend to be characteristically fast growing with high substrate uptakes, while the colon-derived lines tend to be characteristically fast growing with low substrate uptakes.Principle component analysis (PCA) revealed that the metabolic states of the NCI60 lines are structurally simple , with laConsistent with the dominance of the metabolic state by growth rate, we find that uptake of many amino acids are determined largely by growth requirements . Growth We identified a similar dominance by growth demands among the related amino acids serine and glycine . We founLooking then at the other major group of non-essential amino acids, which are linked through glutamate , we founTo determine whether the high glutamine uptake is tied to the biosynthetic requirement for nitrogen groups as a whole, we looked at the amine balance with and without glutamine uptake considered . It was We then examined the metabolic fate of the excessive glutamine taken up by NCI60 lines. We found that glutamate dehydrogenase (GLUD) metabolizes an average 70% of glutamine taken up . However12Next, we sought to apply a similar analysis to glucose metabolism, which is typically considered to be the primary source of energy in the cancer metabolic phenotype. Calculating the balance of NTP production and consumption , we deteWe find that net glycolytic ATP production is set at a level that mostly (80%) satisfies the known growth and maintenance ATP requirements of the cell, with little apparent bias related to the cell line tissue of origin . HoweverSeeking to identify some relationship through which to understand this excess redox production, we correlated the glucose uptake with a large set of available data on the NCI60 lines, including gene expression, drug response, metabolomics, and proteomics. Remarkably, we found that higher glucose uptake broadly correlated with resistance to chemotherapeutic compounds within the NCI Developmental Therapeutics Program database, which contains sensitivity data for 20500 compounds 10. This Finally, looking at the question of what limits are placed upon the cancer metabolic phenotype, we make special note of a trend that was discovered among the three highest uptake metabolites: glucose, glutamine, and serine . We founTaken together, the results of this structured analysis of disparate data types suggest a fundamental structure to cancer cell metabolism across multiple tissue types where biosynthetic demands are primary determinants of much of the metabolic phenotype. However, glucose and glutamine uptake rates scale above the biomass requirement resulting in an excess of redox and energy cofactor production beyond biosynthetic and energy requirements. These results suggest an interpretation of the Warburg effect and glutamine addiction as drivers of metabolic stress resistance. The role of these high uptake pathways on sustaining stress resistance has been demonstrated before in individual casesA number of other ideas have been put forth on the drivers underlying the Warburg effect and glutamine addiction. We used the abundance of available data on the NCI60 cell line panel with the rigorous metabolic network analysis framework to evaluate whether these alternate hypotheses may also be promising in explaining the origin of the Warburg effect and glutamine addiction. First, we looked at explanations put forth for origin of the Warburg effect. We examined the hypothesis that the shift to aerobic glycolysis occurs to supply the cell with biosynthetic precursorsThen, we examined a recently proposed hypothesis that the Warburg effect serves to reduce spatial constraints due to macromolecule crowding by shifting from high protein-requiring oxidative phosphorylation to primarily glycolysis4−1, a cell mass of 0.3 ng, and a cell volume of 2 pL, which are approximate averages for the NCI60 lines, each TCA metabolite would need to be replenished at a rate of 0.13 uM/gDW/hr. However, glutamine uptake is an average of 0.19 mM/gDW/hr for the NCI60 cell lines, three orders of magnitude higher. Thus, replenishing the TCA metabolite pool only demands a small fraction of glutamine uptake. Although glutamine enters the TCA cycle and provides TCA metabolites, this does not sufficient support that this is the primary reason for the high glutamine uptake, since the majority of glutamine cannot stay within the TCA cycle.We then examined existing hypothesis for drivers of glutamine addiction. First, we examined the idea that the role of glutamine’s high uptake is to provide the pool of TCA metabolites2 production4 can do very little to serve as a pH buffer for two reasons. First, neither of the enzymes glutaminase and glutamate dehydrogenase consume a proton directly as a reactant. Second, these reactions produce NH4 with the proton already bound, preventing the molecule from scavenging further protons produced from other processes. In line with this, we observe no correlation between glutamine uptake and any acid production metrics, including secreted protein, secreted lactate, and secreted CO2. However, this argument is flux-based and says nothing about a potential regulatory role that ammonium production may have.Second, it has been proposed that the cleavage of ammonia from glutamine and glutamate by glutaminase and glutamate dehydrogenase can serve as a pH buffer to mitigate the acid production due to lactate and CO2 = 0.39). Thus, it may be conjectured that the mesenchymal nature of the cell could be the underlying origin of the observed drug resistance in high glucose uptake cell lines. However, while we did verify a correlation of vimentin with cell volume (Pearson R2 = 0.22), we found that mesenchymality as estimated by this marker does not correlate with drug resistance in these lines, with a median Pearson correlation coefficient r of −0.04, indicating a non-significant relationship opposite of the hypothesized direction of correlation.One surprising result of this study was the broad correlation of glucose uptake with drug resistance in the NCI60 cell line panel. We note that this correlation was found between glucose uptake in per mass units as opposed to per cell units, which are the original units reportedInvestigating into the relationship between glucose uptake and drug resistance, we hypothesize that the excess redox and energy cofactor production by high glucose uptake lines provides a measure of resistance to metabolic stress imposed by chemotherapeutics as a part of their mechanism of action. The role of metabolic stress in the mechanism of action of chemotherapeutics is becoming increasingly well established in the literature. We take a look at two such metabolic stresses: ATP depletion and oxidative stress. ATP depletion has long been targeted as a possible adjutant strategy for increasing the effectiveness of chemotherapy37We note that stress resistance has been associated with the appearance of aerobic glycolysis in other contexts than malignant transformation. Yeast has been shown to shift toward anaerobic glycolysis during normal growth in order to minimize energy demands and stresses associated with aerobic metabolism4749E. coliThis work presents a cohesive framework underlying high flux metabolism in cancer cell lines. Several natural questions arise regarding the universality of the presented metabolic structure. First, whether these results apply to non-cancer rapidly growing cells is unknown; however, it has been shown that the Warburg effect appears in many physiological contexts, and thus it appears likely that some of the structure may be consistent between cancer and healthy growing cells. The overflow metabolism we identify within the serine and glutamine groups has been shown in stem cellsin vivo cancer metabolism has been called into question. For example, certain cancers do not exhibit the Warburg effect in vivo, and other cancer subtypes show an importance of de novo serine synthesis54Second, the relevance of cancer metabolic hallmarks identified in cell culture to Another area requiring further study is the role of lipid metabolism in the cancer metabolic phenotype as described in this work. There was very little data on lipid uptake in the exometabolomic study used to constrain metabolite uptakesThis study attempted to integrate various qualitative and quantitative data sources to identify general principles underlying the metabolic phenotype in cancer cell lines. A key issue in such a study is the quality of data used. In the course of the investigation, we identified several data sets that we found to be of insufficient quality to include for analysis. Other data, such as the exometabolomic dataAnother technical area of a data analysis study such as this is determining the desired complexity of the relationships investigated. In the case of this study, we largely looked at simple correlations between variables in an effort to maximize the interpretability of any findings. However, more complex grouping of variables and non-linear relationships could be found with more advanced statistical and machine learning methods. We attempted this approach initially, but found that showing the interpretability and statistical significance of complex signatures was a challenging task, especially faced with the difficulty of multiple hypothesis testing in cases where hundreds of thousands of data points from various data types are available, as are for the NCI60 cell lines. However, it is quite likely that more complex relationships are yet to be discovered in the data and this may prove a useful area of investigation.13C tracing studies of which we were aware. Still, there are a number of pathways, such as lipid catabolism as mentioned, for which we did not have any ‘functional’ (i.e. flux) data. Thus, the conclusions in this work are subject to be modified if pathways carrying a large amount of flux are later characterized, for example lipid metabolic, protein glycosylation, and epigenetic methylation and acetylation are relatively under-characterized from a quantitative flux perspective.This work involved the use of a simplified ‘core’ model of metabolism derived from human Recon 2. This model consisted of pathways involved in core energy metabolism and production of known biomass precursors. The use such a model is always at risk of ‘missing’ a key pathway that could affect model predictions. In the case of this study, our calculations were predominantly based on measured growth and metabolite uptake and secretion data. We attempted to include any pathways that had been measured to be active in prior The results of this study suggest two avenues of inquiry. First, while the NCI60 cell line panel is historically important due to its high degree of characterization, it will be important to determine whether the metabolic structure described in this study is conserved in primary tumors. Second, as the overflow metabolites  coupled to high uptake metabolites  occur at branch points upstream of catabolic pathways in the mitochondria, we hypothesize that mitochondrial catabolic capacity may be a limiting constraint preventing complete substrate catabolism in high uptake metabolism in cancer cells. Investigation of the role of mitochondrial catabolic capacity in regulating the cancer metabolic phenotype may thus be a fruitful approach. The broad range of important metabolic pathways that lie in the mitochondria is becoming increasingly appreciatedTo construct a minimal model, we included reactions necessary for biomass formation and primary metabolite catabolic pathways, while excluding anabolic pathways not associated with core biomass precursor production as well as secondary catabolic pathways. The primary distinction to be made was thus determining which reactions belong to primary and secondary catabolic pathways. This classification was performed algorithmically with manual justification based on (1) literature evidence1229The following pathways were included: (1) pathways required for synthesis of biomass precursors, (2) core energy metabolism , (3) pathways previously shown to be active in cancer cell lines under normal conditions12In general, we found that the addition of further pathways beyond those defined above would have to be accompanied by additional assumptions on uptake rates or cellular demands related to these pathways that would have made the modeling useless for practical differentiation between metabolic function across cell lines. As an example, we look at the possibility of including the methionine pathway. We found that modeling this pathway is difficult because the pathway is adjacent to cysteine metabolism, and the uptake of cysteine was not measured. Cysteine requires methionine to synthesize unless cysteine is directly taken up. However, not enough methionine is taken up to satisfy the biomass requirement for both amino acids. Thus, when we included this pathway along with biomass demands on methionine and cysteine, none of the cell lines were able to meet biosynthetic demands. The possibility of including this pathway was originally examined when we were defining the scope of the core model, and we discovered that it was not possible to model without making up an artificial cysteine uptake, which was problematic.Other pathways are difficult to include because their composition in biomass is unknown. Specifically, the glycosylation pathway might be very interesting to study across the NCI60 cell lines; however, without glyco-profiling these NCI60 cell lines, we have no basis for any differential production across the cell lines, analogous to differential DNA production based on karyotype for example, and so any modeling we could do of these pathways is extremely limited. Thus, we only are able to include an approximation of the carbohydrate macromolecule content of the cell dry weight in the biomass without any meaningful specification of the particular macromolecule form of those carbohydrates.Based on this approach, a core metabolic network of 382 reactions  was extrCell biomass is composed of protein, lipids, DNA, RNA and small molecules, in weight fractions determined by cell composition studies24592459We chose to set the macromolecule weight fractions to be constant between lines. Previous studies show minimal variance between macromolecule weight fractions for particular types of cells, such as hybridoma cells and Chinese hamster ovary cells24592 = 0.23) and we observed certain spurious data points causing concern. For example, the SR line was reported to have a protein content of 0.021 ng/cell, which given the reported cell volume and average protein density would result in a dry weight fraction of protein of approximately 0.08, which is substantially lower than measured values around 0.7. Volume measurements were based in microscopy, and thus were seen as less error prone than protein content measurements which require cell count estimation, which can be a significant source of error. When sustainable protein was greater than measured protein, the measured value was used to correct the sustainable protein, using the formula mestimate = mmeasured + 0.25*(msustainable − mmeasured). This formula was chosen based on resulting agreement with cell volume data. The correlation of estimated protein content with cell volume (Pearson R2 = 0.60) was higher than either measured protein (Pearson R2 = 0.23) or sustainable protein (Pearson R2 = 0.52).To determine the cell-specific dry weights, we integrated cell volume data with the uptake rates as follows. First, the amount of biomass sustainable by each cell was determined by maximizing the growth through each line using FBA while constrained by measured uptakes in per cell units. Then, this sustainable biomass was corrected using measured protein content data as follows. If the sustainable protein, taking protein as 0.70 of total cell dry mass, is less than the measured protein, a value of 95% of the sustainable protein measurement was used as the estimate of cellular protein. This was done because the measured protein could not be sustained by the measured uptake rates, which we assumed was due to error in the measured protein. Measured protein was assumed to be the greater source of error because the measured uptake rates are highly correlated and there was no general bias of sustainable protein being greater or less than measured protein. Also the measured protein showed a relatively low agreement with cell volumes (Pearson RPublished metabolite uptake and release (exometabolomic) data on the NCI60 lines was re-processed in a semi-automated process. The original dataset was processed by correcting for drift in the peak area standardization across runs by a linear L1 regression of blank media standards. However, upon detailed inspection of the drift for different metabolites, it was apparent that the drift was highly non-linear for some metabolites. The effect of applying a linear approximation in these non-linear cases was that metabolite uptakes were significantly mis-represented, and in some cases, metabolites were actually not exchanged substantially at all, once a non-linear drift correction was applied. We manually created non-linear approximations of drift for each metabolite in Mathematica based on the media standards for each metabolite. We applied these non-linear corrections to the raw data to recalculate the metabolite uptake and release profiles.13C tracing studies performed on the NCI60 lines .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.